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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vs_trawl.R
\name{vs_trawl}
\alias{vs_trawl}
\title{Variance signature values}
\usage{
vs_trawl(object, h, method = "sample", ...)
}
\arguments{
\item{object}{object containing all the specifications for the process, see details}
\item{h}{vector containing the observation frequencies at
which to compute the variance signature values}
\item{method}{"sample" for the empirical values, "vs_C" or "vs_SY" for theoretical values when
estimated by these respective mtehods with fit_trawl}
\item{\dots}{any other passthrough parameters}
}
\description{
compute empirical and theoretical variance signature values
}
\details{
the object is either an output object from sim_trawl for the empirical variance signature
values, or an output object from fit_trawl for the theoretical ones. When computing the sample
variance signature plot, it is possible to add the argument 'multi' for reducing the
estimation variance. See Shephard and Yang (2017) for a definition of the variance
signature values.
}
\examples{
# simulate a trawl process
sim <- sim_trawl(list("levy_seed" = "Skellam", levy_par = c(0.13, 0.11), b = 0.3))
h <- exp(seq(log(1e-2), log(60), length.out = 51))
# empirical variance signature values
vsS <- vs_trawl(sim, h)
# theoretical values when estimated with "vs_C"
sim$method <- "vs_C" # add fitting method
sim$h <- h # add grid on which to fit
ft <- fit_trawl(sim) # fit the trawl process
vsC <- vs_trawl(ft, h, method = "vs_C") # compute fitted variance signature values
# theoretical values when estimated with "vs_SY"
sim$method <- "vs_SY" # add fitting method
sim$h <- h # add grid on which to fit
ft <- fit_trawl(sim) # fit the trawl process
vsSY <- vs_trawl(ft, h, method = "vs_SY") # compute fitted variance signature values
# plot resulting fits
plot(log(h), vsS)
lines(log(h), vsC, col = "blue")
lines(log(h), vsSY, col = "red")
legend("topright", c("vs_C", "vs_SY"), lwd = c(2, 2), col = c("blue", "red"))
# trawl process estimated by fitting the autocorrelation function
sim <- sim_trawl(list())
sim$h <- 0.5
sim$trawl <- "exp"
sim$lag_max <- 3
sim$method <- "acf"
ft <- fit_trawl(sim)
h <- exp(seq(log(1e-2), log(60), length.out = 51))
vsS <- vs_trawl(sim, h)
vsACF <- vs_trawl(ft, h, method = "acf")
plot(log(h), vsS)
lines(log(h), vsACF, col = "blue")
}
\references{
Shephard, N., & Yang, J. J. (2017).
Continuous time analysis of fleeting discrete price moves.
Journal of the American Statistical Association, 112(519), 1090-1106.
}
\seealso{
\code{\link{fit_trawl}}, \code{\link{sim_trawl}}
}
\author{
Dries Cornilly
}
\concept{trawl}
|
/man/vs_trawl.Rd
|
no_license
|
cdries/rTrawl
|
R
| false | true | 2,700 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vs_trawl.R
\name{vs_trawl}
\alias{vs_trawl}
\title{Variance signature values}
\usage{
vs_trawl(object, h, method = "sample", ...)
}
\arguments{
\item{object}{object containing all the specifications for the process, see details}
\item{h}{vector containing the observation frequencies at
which to compute the variance signature values}
\item{method}{"sample" for the empirical values, "vs_C" or "vs_SY" for theoretical values when
estimated by these respective mtehods with fit_trawl}
\item{\dots}{any other passthrough parameters}
}
\description{
compute empirical and theoretical variance signature values
}
\details{
the object is either an output object from sim_trawl for the empirical variance signature
values, or an output object from fit_trawl for the theoretical ones. When computing the sample
variance signature plot, it is possible to add the argument 'multi' for reducing the
estimation variance. See Shephard and Yang (2017) for a definition of the variance
signature values.
}
\examples{
# simulate a trawl process
sim <- sim_trawl(list("levy_seed" = "Skellam", levy_par = c(0.13, 0.11), b = 0.3))
h <- exp(seq(log(1e-2), log(60), length.out = 51))
# empirical variance signature values
vsS <- vs_trawl(sim, h)
# theoretical values when estimated with "vs_C"
sim$method <- "vs_C" # add fitting method
sim$h <- h # add grid on which to fit
ft <- fit_trawl(sim) # fit the trawl process
vsC <- vs_trawl(ft, h, method = "vs_C") # compute fitted variance signature values
# theoretical values when estimated with "vs_SY"
sim$method <- "vs_SY" # add fitting method
sim$h <- h # add grid on which to fit
ft <- fit_trawl(sim) # fit the trawl process
vsSY <- vs_trawl(ft, h, method = "vs_SY") # compute fitted variance signature values
# plot resulting fits
plot(log(h), vsS)
lines(log(h), vsC, col = "blue")
lines(log(h), vsSY, col = "red")
legend("topright", c("vs_C", "vs_SY"), lwd = c(2, 2), col = c("blue", "red"))
# trawl process estimated by fitting the autocorrelation function
sim <- sim_trawl(list())
sim$h <- 0.5
sim$trawl <- "exp"
sim$lag_max <- 3
sim$method <- "acf"
ft <- fit_trawl(sim)
h <- exp(seq(log(1e-2), log(60), length.out = 51))
vsS <- vs_trawl(sim, h)
vsACF <- vs_trawl(ft, h, method = "acf")
plot(log(h), vsS)
lines(log(h), vsACF, col = "blue")
}
\references{
Shephard, N., & Yang, J. J. (2017).
Continuous time analysis of fleeting discrete price moves.
Journal of the American Statistical Association, 112(519), 1090-1106.
}
\seealso{
\code{\link{fit_trawl}}, \code{\link{sim_trawl}}
}
\author{
Dries Cornilly
}
\concept{trawl}
|
context("Parse clarion file")
test_that("file is parsed correctly", {
object <- parser(file = "wiki_example.clarion", dec = ",")
expect_is(object, "Clarion")
expect_equal(object$get_delimiter(), "|")
expect_equal(object$get_factors(), object$metadata[, c("key", "factor1", "factor2")])
expect_equal(object$get_id(), "id")
expect_equal(object$get_name(), "name")
expect_equal(object$is_delimited(names(object$data)), c(rep(FALSE, 3), TRUE, rep(FALSE, 8)))
})
|
/tests/testthat/test-parser.R
|
permissive
|
loosolab/wilson
|
R
| false | false | 474 |
r
|
context("Parse clarion file")
test_that("file is parsed correctly", {
object <- parser(file = "wiki_example.clarion", dec = ",")
expect_is(object, "Clarion")
expect_equal(object$get_delimiter(), "|")
expect_equal(object$get_factors(), object$metadata[, c("key", "factor1", "factor2")])
expect_equal(object$get_id(), "id")
expect_equal(object$get_name(), "name")
expect_equal(object$is_delimited(names(object$data)), c(rep(FALSE, 3), TRUE, rep(FALSE, 8)))
})
|
add2 <- function(x,y){
x+ y
}
above10 <- function(x){
use <- x > 10
x[use]
}
above <- function(x,n=10){
use <- x > n
x[use]
}
columnmean <- function(y){
nc <- nol(y)
means <- numeric(nc)
for(i in 1:nc){
means[i] <- mean(y[,i])
}
means
}
cube <- function(x, n) {
x^3
}
f <- function(x) {
g <- function(y) {
y + z
}
z <- 4
x + g(x)
}
|
/Function.R
|
no_license
|
kennethchung/HopkinsDataScience
|
R
| false | false | 356 |
r
|
add2 <- function(x,y){
x+ y
}
above10 <- function(x){
use <- x > 10
x[use]
}
above <- function(x,n=10){
use <- x > n
x[use]
}
columnmean <- function(y){
nc <- nol(y)
means <- numeric(nc)
for(i in 1:nc){
means[i] <- mean(y[,i])
}
means
}
cube <- function(x, n) {
x^3
}
f <- function(x) {
g <- function(y) {
y + z
}
z <- 4
x + g(x)
}
|
#'
#' Return C++ code generated by \pkg{rstan}
#'
#' @param object a \code{bdm} class object
#' @param ... additional arguments to generic function
#'
#' @export
getcpp <- function(object, ...) UseMethod("getcpp")
#'
#' @rdname getcpp
#' @export
getcpp.bdm <- function(object, ...) {
if (length(object@model_cpp) == 0) {
stanc_list_return <- stanc(model_code = object@model_code, model_name = object@model_name)
cpp <- stanc_list_return$cppcode
} else {
cpp <- object@model_cpp$model_cppcode
}
return(cpp)
}
|
/R/getcpp.R
|
no_license
|
cttedwards/bdm
|
R
| false | false | 561 |
r
|
#'
#' Return C++ code generated by \pkg{rstan}
#'
#' @param object a \code{bdm} class object
#' @param ... additional arguments to generic function
#'
#' @export
getcpp <- function(object, ...) UseMethod("getcpp")
#'
#' @rdname getcpp
#' @export
getcpp.bdm <- function(object, ...) {
if (length(object@model_cpp) == 0) {
stanc_list_return <- stanc(model_code = object@model_code, model_name = object@model_name)
cpp <- stanc_list_return$cppcode
} else {
cpp <- object@model_cpp$model_cppcode
}
return(cpp)
}
|
library(sp)
library(raster)
library(rgdal)
library(rgeos)
library(caTools)
library(icesTAF)
install.packages("rgdal")
install.packages("rgeos")
install.packages("caTools")
install.packages("icesTAF")
install.packages("")
cy0.data.path <- "cors.csv"
cy0.data <- read.csv(cy0.data.path, check.names=FALSE)
start.year <-1999
end.year<-2017
year=2012
c=6
plot(mapp.cp)
for (year in c(start.year:end.year)){
sleuth.data.path<-sprintf("/Users/kaso/Downloads/R/LandScan/lspop%s.tif",year)
mapp <- raster(sleuth.data.path)
column.blank1 <- rep.int(0, 143)
for (c in c(1:143)){
mapRange <- 0.0435*(189-c)**0.5
cy <- cy0.data["city"][[1]][c]
coy <- cy0.data["corY"][[1]][c]
cox <- cy0.data["corX"][[1]][c]
urban.map.ext <-extent(cox-mapRange,cox+mapRange,coy-mapRange,coy+mapRange)
mapp.cp <- crop(mapp, urban.map.ext)
values(mapp.cp)[values(mapp.cp)<1000]=0
values(mapp.cp)[values(mapp.cp)>=1000]=1
alal=sum(na.omit(values(mapp.cp)))
column.blank1[c] <- alal
}
clm.name1 <- sprintf("%s_ls",year)
cy0.data[, clm.name1] <- column.blank1
}
write.csv(cy0.data,"ls_hist.csv")
|
/python/.ipynb_checkpoints/pick-checkpoint.R
|
no_license
|
kamomehz/graduate-git
|
R
| false | false | 1,134 |
r
|
library(sp)
library(raster)
library(rgdal)
library(rgeos)
library(caTools)
library(icesTAF)
install.packages("rgdal")
install.packages("rgeos")
install.packages("caTools")
install.packages("icesTAF")
install.packages("")
cy0.data.path <- "cors.csv"
cy0.data <- read.csv(cy0.data.path, check.names=FALSE)
start.year <-1999
end.year<-2017
year=2012
c=6
plot(mapp.cp)
for (year in c(start.year:end.year)){
sleuth.data.path<-sprintf("/Users/kaso/Downloads/R/LandScan/lspop%s.tif",year)
mapp <- raster(sleuth.data.path)
column.blank1 <- rep.int(0, 143)
for (c in c(1:143)){
mapRange <- 0.0435*(189-c)**0.5
cy <- cy0.data["city"][[1]][c]
coy <- cy0.data["corY"][[1]][c]
cox <- cy0.data["corX"][[1]][c]
urban.map.ext <-extent(cox-mapRange,cox+mapRange,coy-mapRange,coy+mapRange)
mapp.cp <- crop(mapp, urban.map.ext)
values(mapp.cp)[values(mapp.cp)<1000]=0
values(mapp.cp)[values(mapp.cp)>=1000]=1
alal=sum(na.omit(values(mapp.cp)))
column.blank1[c] <- alal
}
clm.name1 <- sprintf("%s_ls",year)
cy0.data[, clm.name1] <- column.blank1
}
write.csv(cy0.data,"ls_hist.csv")
|
library(httr)
library(jsonlite)
library(dplyr)
library(plotly)
source("api.R")
scatter_plot <- function(dataset, year_start, year_end, genre, rating_low,
rating_high) {
# create new column called "release_year", get rid of NA for "release_year"
# column
dataset$release_year <- as.numeric(format(as.Date(
dataset$release_date,
"%Y-%m-%d"
), "%Y"))
complete_vec <- complete.cases(dataset[, "release_year"])
dataset <- dataset[complete_vec, ]
# get genre list
response <-
GET(paste0(
"https://api.themoviedb.org/3/genre/movie/list?api_key=",
api_key
))
response_content <- content(response, type = "text")
genre_list <- fromJSON(response_content)$genres
# get language list
response_new <-
GET(paste0(
"https://api.themoviedb.org/3/configuration/languages?api_key=",
api_key
))
response_content_new <- content(response_new, type = "text")
language_list <- fromJSON(response_content_new)
# join dataset with language list
language_list$original_language <- language_list$iso_639_1
language <- select(language_list, original_language, english_name)
dataset <- full_join(dataset, language)
# max for x-axis and y-axis
xmax <- max(dataset$release_year)
ymax <- max(dataset$vote_average)
# filter dataset
if (genre != "") {
genre <- as.character(genre_list[genre_list$name == genre, ]$id)
}
new_data <- dataset %>%
filter(release_year >= year_start & release_year <= year_end) %>%
filter(grepl(genre, genre_ids)) %>%
filter(vote_average >= rating_low & vote_average <= rating_high)
# scatter plot
plot_ly(new_data,
x = ~ release_year, y = ~ vote_average, hoverinfo = "text",
text = ~ paste(
"Movie: ", title, " (", original_title, ")", "<br>Year: ", release_date,
"<br>Rating: ", vote_average, "<br>Language: ",
english_name
),
color = ~ english_name,
mode = "markers", marker = list(opacity = .7, size = 10)
) %>%
layout(
title = "Movie Release Year vs Vote Average",
xaxis = list(range = c(1920, xmax), title = "Release Year"),
yaxis = list(range = c(0, ymax), title = "Vote Average")
) %>%
return()
}
|
/scripts/scatter_plot.R
|
permissive
|
katiechen00/Final-Project
|
R
| false | false | 2,217 |
r
|
library(httr)
library(jsonlite)
library(dplyr)
library(plotly)
source("api.R")
scatter_plot <- function(dataset, year_start, year_end, genre, rating_low,
rating_high) {
# create new column called "release_year", get rid of NA for "release_year"
# column
dataset$release_year <- as.numeric(format(as.Date(
dataset$release_date,
"%Y-%m-%d"
), "%Y"))
complete_vec <- complete.cases(dataset[, "release_year"])
dataset <- dataset[complete_vec, ]
# get genre list
response <-
GET(paste0(
"https://api.themoviedb.org/3/genre/movie/list?api_key=",
api_key
))
response_content <- content(response, type = "text")
genre_list <- fromJSON(response_content)$genres
# get language list
response_new <-
GET(paste0(
"https://api.themoviedb.org/3/configuration/languages?api_key=",
api_key
))
response_content_new <- content(response_new, type = "text")
language_list <- fromJSON(response_content_new)
# join dataset with language list
language_list$original_language <- language_list$iso_639_1
language <- select(language_list, original_language, english_name)
dataset <- full_join(dataset, language)
# max for x-axis and y-axis
xmax <- max(dataset$release_year)
ymax <- max(dataset$vote_average)
# filter dataset
if (genre != "") {
genre <- as.character(genre_list[genre_list$name == genre, ]$id)
}
new_data <- dataset %>%
filter(release_year >= year_start & release_year <= year_end) %>%
filter(grepl(genre, genre_ids)) %>%
filter(vote_average >= rating_low & vote_average <= rating_high)
# scatter plot
plot_ly(new_data,
x = ~ release_year, y = ~ vote_average, hoverinfo = "text",
text = ~ paste(
"Movie: ", title, " (", original_title, ")", "<br>Year: ", release_date,
"<br>Rating: ", vote_average, "<br>Language: ",
english_name
),
color = ~ english_name,
mode = "markers", marker = list(opacity = .7, size = 10)
) %>%
layout(
title = "Movie Release Year vs Vote Average",
xaxis = list(range = c(1920, xmax), title = "Release Year"),
yaxis = list(range = c(0, ymax), title = "Vote Average")
) %>%
return()
}
|
#Input dataset
ExpDegree <- read.csv("ExpectedDegree.csv", header=FALSE)
ActualDegreeADR <- read.csv("ActualDegreeADR.csv", header=FALSE)
ActualDegreeTRPW <- read.csv("ActualDegreeTRPW.csv", header=FALSE)
ExpTriangle <- read.csv("ExpectedTriangleDegree.csv", header=FALSE)
ActualTriangleADR <- read.csv("ActualTriangleDegreeADR.csv", header=FALSE)
ActualTriangleTRPW <- read.csv("ActualTriangleDegreeTRPW.csv", header=FALSE)
ExpCoefficient <- read.csv("ExpectedCoefficient.csv", header=FALSE)
ActualCoefficientADR <- read.csv("ActualCoefficientADR.csv", header=FALSE)
ActualCoefficientTRPW <- read.csv("ActualCoefficientTRPW.csv", header=FALSE)
#Rename
names(ExpDegree) <- c("ExpDegree")
names(ActualDegreeADR) <- c("ActualDegreeADR")
names(ActualDegreeTRPW) <- c("ActualDegreeTRPW")
names(ExpTriangle) <- c("ExpTriangle")
names(ActualTriangleADR) <- c("ActualTriangleADR")
names(ActualTriangleTRPW) <- c("ActualTriangleTRPW")
names(ExpCoefficient) <- c("ExpCoefficient")
names(ActualCoefficientADR) <- c("ActualCoefficientADR")
names(ActualCoefficientTRPW) <- c("ActualCoefficientTRPW")
#Round
ExpDegreeRound <- round(ExpDegree,0)
ExpTriangleRound <- round(ExpTriangle,0)
ExpCoefficientRound <- round(ExpCoefficient,1)
ActualCoefficientADRRound <- round(ActualCoefficientADR,1)
ActualCoefficientTRPWRound <- round(ActualCoefficientTRPW,1)
#Sort
ExpDegreeSorted <- data.frame(sort(ExpDegreeRound$ExpDegree))
ActualDegreeADRSorted <- data.frame(sort(ActualDegreeADR$ActualDegreeADR))
ActualDegreeTRPWSorted <- data.frame(sort(ActualDegreeTRPW$ActualDegreeTRPW))
ExpTriangleSorted <- data.frame(sort(ExpTriangleRound$ExpTriangle))
ActualTriangleADRSorted <- data.frame(sort(ActualTriangleADR$ActualTriangleADR))
ActualTriangleTRPWSorted <- data.frame(sort(ActualTriangleTRPW$ActualTriangleTRPW))
ExpCoefficientSorted <- data.frame(sort(ExpCoefficientRound$ExpCoefficient))
ActualCoefficientADRSorted <- data.frame(sort(ActualCoefficientADRRound$ActualCoefficientADR))
ActualCoefficientTRPWSorted <- data.frame(sort(ActualCoefficientTRPWRound$ActualCoefficientTRPW))
#Find times of exisits
ExpDegreeCount <- data.frame(table(ExpDegreeSorted$sort.ExpDegreeRound.ExpDegree.))
names(ExpDegreeCount) <- c("Degree", "Count")
ActualDegreeADRCount <- data.frame(table(ActualDegreeADRSorted$sort.ActualDegreeADR.ActualDegreeADR.))
names(ActualDegreeADRCount) <- c("Degree", "Count")
ActualDegreeTRPWCount <- data.frame(table(ActualDegreeTRPWSorted$sort.ActualDegreeTRPW.ActualDegreeTRPW.))
names(ActualDegreeTRPWCount) <- c("Degree", "Count")
ExpTriangleCount <- data.frame(table(ExpTriangleSorted$sort.ExpTriangleRound.ExpTriangle.))
names(ExpTriangleCount) <- c("TriangleDegree", "Count")
ActualTriangleADRCount <- data.frame(table(ActualTriangleADRSorted$sort.ActualTriangleADR.ActualTriangleADR.))
names(ActualTriangleADRCount) <- c("TriangleDegree", "Count")
ActualTriangleTRPWCount <- data.frame(table(ActualTriangleTRPWSorted$sort.ActualTriangleTRPW.ActualTriangleTRPW.))
names(ActualTriangleTRPWCount) <- c("TriangleDegree", "Count")
ExpCoefficientCount <- data.frame(table(ExpCoefficientSorted$sort.ExpCoefficientRound.ExpCoefficient.))
names(ExpCoefficientCount) <- c("Coefficient", "Count")
ActualCoefficientADRCount <- data.frame(table(ActualCoefficientADRSorted$sort.ActualCoefficientADRRound.ActualCoefficientADR.))
names(ActualCoefficientADRCount) <- c("Coefficient", "Count")
ActualCoefficientTRPWCount <- data.frame(table(ActualCoefficientTRPWSorted$sort.ActualCoefficientTRPWRound.ActualCoefficientTRPW.))
names(ActualCoefficientTRPWCount) <- c("Coefficient", "Count")
#Calculate Percentages
ExpDegreeFinal <- within(ExpDegreeCount, {
percentage = Count / nrow(ExpDegreeSorted)
group="Expected"
})
ActualDegreeADRFinal <- within(ActualDegreeADRCount, {
percentage = Count / nrow(ActualDegreeADRSorted)
group="ADR"
})
ActualDegreeTRPWFinal <- within(ActualDegreeTRPWCount, {
percentage = Count / nrow(ActualDegreeTRPWSorted)
group="TRPW"
})
ExpTriangleFinal <- within(ExpTriangleCount, {
percentage = Count / nrow(ExpTriangleSorted)
group="Expected"
})
ActualTriangleADRFinal <- within(ActualTriangleADRCount, {
percentage = Count / nrow(ActualTriangleADRSorted)
group="ADR"
})
ActualTriangleTRPWFinal <- within(ActualTriangleTRPWCount, {
percentage = Count / nrow(ActualTriangleTRPWSorted)
group="TRPW"
})
ExpCoefficientFinal <- within(ExpCoefficientCount, {
percentage = Count / nrow(ExpCoefficientSorted)
group="Expected"
})
ActualCoefficientADRFinal <- within(ActualCoefficientADRCount, {
percentage = Count / nrow(ActualCoefficientADRSorted)
group="ADR"
})
ActualCoefficientTRPWFinal <- within(ActualCoefficientTRPWCount, {
percentage = Count / nrow(ActualCoefficientTRPWSorted)
group="TRPW"
})
#Plot
library(ggplot2)
#Bind data
DegreeFinal <- rbind(ExpDegreeFinal, ActualDegreeADRFinal, ActualDegreeTRPWFinal)
TriangleFinal <- rbind(ExpTriangleFinal, ActualTriangleADRFinal, ActualTriangleTRPWFinal)
CoefficientFinal <- rbind(ExpCoefficientFinal, ActualCoefficientADRFinal, ActualCoefficientTRPWFinal)
#Vertex Degree
ggplot(DegreeFinal, aes(x=Degree,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10(limits=c(1e-3,1))+
# scale_y_log10()+
coord_cartesian(xlim=c(0,20))+
# scale_x_discrete(breaks=seq(0,20,by=1))+
# coord_cartesian(xlim=c(0,100))+
# scale_x_discrete(breaks=seq(0,100,by=5))+
labs(title="Vertex Degree Distribution AsSkitter", x="Vertex Degree", y="Percentage Vertices")
#Triangle Degree
ggplot(TriangleFinal, aes(x=TriangleDegree,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10(limits=c(1e-4,1))+
# scale_y_log10()+
coord_cartesian(xlim=c(0,20))+
scale_x_discrete(breaks=seq(0, 20, by=1))+
labs(title="Triangle Degree Distribution AsSkitter", x="Triangle Degree", y="Percentage Vertices")
#Clustering Coefficient
ggplot(CoefficientFinal, aes(x=Coefficient,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10()+
coord_cartesian(xlim=c(0,8))+
scale_x_discrete(breaks=seq(0, 1, by=0.1))+
labs(title="Clustering Coefficient Distribution AsSkitter", x="Clustering Coefficient Degree", y="Percentage Vertices")
#Triangle count
ExpTriangleCount <- data.frame(sum(as.numeric(ExpTriangleFinal$TriangleDegree)*as.numeric(ExpTriangleFinal$Count)))
names(ExpTriangleCount) <- c("TriangleDegree")
ADRTriangleCount <- data.frame(sum(as.numeric(ActualTriangleADRFinal$TriangleDegree)*as.numeric(ActualTriangleADRFinal$Count)))
names(ADRTriangleCount) <- c("TriangleDegree")
TRPWTriangleCount <- data.frame(sum(as.numeric(ActualTriangleTRPWFinal$TriangleDegree)*as.numeric(ActualTriangleTRPWFinal$Count)))
names(TRPWTriangleCount) <- c("TriangleDegree")
ExpTriangleCountFinal <- within(ExpTriangleCount, {
group = "Expected"
})
ADRTriangleCountFinal <- within(ADRTriangleCount, {
group = "ADR"
})
TRPWTriangleCountFinal <- within(TRPWTriangleCount, {
group = "TRPW"
})
TriangleCount <- rbind(ExpTriangleCountFinal, ADRTriangleCountFinal, TRPWTriangleCountFinal)
ggplot(TriangleCount, aes(x=group,y=TriangleDegree,group=group,fill=group))+
geom_bar(stat="identity")+
labs(title="Tiangle Count AsSkitter",x="Algorithm",y="Number of Triangles")+
guides(fill=guide_legend(title="Algorithm"))
|
/Experiments/AsSkitter/AsSkitter.r
|
no_license
|
t-liu93/2IMW20-Project
|
R
| false | false | 7,335 |
r
|
#Input dataset
ExpDegree <- read.csv("ExpectedDegree.csv", header=FALSE)
ActualDegreeADR <- read.csv("ActualDegreeADR.csv", header=FALSE)
ActualDegreeTRPW <- read.csv("ActualDegreeTRPW.csv", header=FALSE)
ExpTriangle <- read.csv("ExpectedTriangleDegree.csv", header=FALSE)
ActualTriangleADR <- read.csv("ActualTriangleDegreeADR.csv", header=FALSE)
ActualTriangleTRPW <- read.csv("ActualTriangleDegreeTRPW.csv", header=FALSE)
ExpCoefficient <- read.csv("ExpectedCoefficient.csv", header=FALSE)
ActualCoefficientADR <- read.csv("ActualCoefficientADR.csv", header=FALSE)
ActualCoefficientTRPW <- read.csv("ActualCoefficientTRPW.csv", header=FALSE)
#Rename
names(ExpDegree) <- c("ExpDegree")
names(ActualDegreeADR) <- c("ActualDegreeADR")
names(ActualDegreeTRPW) <- c("ActualDegreeTRPW")
names(ExpTriangle) <- c("ExpTriangle")
names(ActualTriangleADR) <- c("ActualTriangleADR")
names(ActualTriangleTRPW) <- c("ActualTriangleTRPW")
names(ExpCoefficient) <- c("ExpCoefficient")
names(ActualCoefficientADR) <- c("ActualCoefficientADR")
names(ActualCoefficientTRPW) <- c("ActualCoefficientTRPW")
#Round
ExpDegreeRound <- round(ExpDegree,0)
ExpTriangleRound <- round(ExpTriangle,0)
ExpCoefficientRound <- round(ExpCoefficient,1)
ActualCoefficientADRRound <- round(ActualCoefficientADR,1)
ActualCoefficientTRPWRound <- round(ActualCoefficientTRPW,1)
#Sort
ExpDegreeSorted <- data.frame(sort(ExpDegreeRound$ExpDegree))
ActualDegreeADRSorted <- data.frame(sort(ActualDegreeADR$ActualDegreeADR))
ActualDegreeTRPWSorted <- data.frame(sort(ActualDegreeTRPW$ActualDegreeTRPW))
ExpTriangleSorted <- data.frame(sort(ExpTriangleRound$ExpTriangle))
ActualTriangleADRSorted <- data.frame(sort(ActualTriangleADR$ActualTriangleADR))
ActualTriangleTRPWSorted <- data.frame(sort(ActualTriangleTRPW$ActualTriangleTRPW))
ExpCoefficientSorted <- data.frame(sort(ExpCoefficientRound$ExpCoefficient))
ActualCoefficientADRSorted <- data.frame(sort(ActualCoefficientADRRound$ActualCoefficientADR))
ActualCoefficientTRPWSorted <- data.frame(sort(ActualCoefficientTRPWRound$ActualCoefficientTRPW))
#Find times of exisits
ExpDegreeCount <- data.frame(table(ExpDegreeSorted$sort.ExpDegreeRound.ExpDegree.))
names(ExpDegreeCount) <- c("Degree", "Count")
ActualDegreeADRCount <- data.frame(table(ActualDegreeADRSorted$sort.ActualDegreeADR.ActualDegreeADR.))
names(ActualDegreeADRCount) <- c("Degree", "Count")
ActualDegreeTRPWCount <- data.frame(table(ActualDegreeTRPWSorted$sort.ActualDegreeTRPW.ActualDegreeTRPW.))
names(ActualDegreeTRPWCount) <- c("Degree", "Count")
ExpTriangleCount <- data.frame(table(ExpTriangleSorted$sort.ExpTriangleRound.ExpTriangle.))
names(ExpTriangleCount) <- c("TriangleDegree", "Count")
ActualTriangleADRCount <- data.frame(table(ActualTriangleADRSorted$sort.ActualTriangleADR.ActualTriangleADR.))
names(ActualTriangleADRCount) <- c("TriangleDegree", "Count")
ActualTriangleTRPWCount <- data.frame(table(ActualTriangleTRPWSorted$sort.ActualTriangleTRPW.ActualTriangleTRPW.))
names(ActualTriangleTRPWCount) <- c("TriangleDegree", "Count")
ExpCoefficientCount <- data.frame(table(ExpCoefficientSorted$sort.ExpCoefficientRound.ExpCoefficient.))
names(ExpCoefficientCount) <- c("Coefficient", "Count")
ActualCoefficientADRCount <- data.frame(table(ActualCoefficientADRSorted$sort.ActualCoefficientADRRound.ActualCoefficientADR.))
names(ActualCoefficientADRCount) <- c("Coefficient", "Count")
ActualCoefficientTRPWCount <- data.frame(table(ActualCoefficientTRPWSorted$sort.ActualCoefficientTRPWRound.ActualCoefficientTRPW.))
names(ActualCoefficientTRPWCount) <- c("Coefficient", "Count")
#Calculate Percentages
ExpDegreeFinal <- within(ExpDegreeCount, {
percentage = Count / nrow(ExpDegreeSorted)
group="Expected"
})
ActualDegreeADRFinal <- within(ActualDegreeADRCount, {
percentage = Count / nrow(ActualDegreeADRSorted)
group="ADR"
})
ActualDegreeTRPWFinal <- within(ActualDegreeTRPWCount, {
percentage = Count / nrow(ActualDegreeTRPWSorted)
group="TRPW"
})
ExpTriangleFinal <- within(ExpTriangleCount, {
percentage = Count / nrow(ExpTriangleSorted)
group="Expected"
})
ActualTriangleADRFinal <- within(ActualTriangleADRCount, {
percentage = Count / nrow(ActualTriangleADRSorted)
group="ADR"
})
ActualTriangleTRPWFinal <- within(ActualTriangleTRPWCount, {
percentage = Count / nrow(ActualTriangleTRPWSorted)
group="TRPW"
})
ExpCoefficientFinal <- within(ExpCoefficientCount, {
percentage = Count / nrow(ExpCoefficientSorted)
group="Expected"
})
ActualCoefficientADRFinal <- within(ActualCoefficientADRCount, {
percentage = Count / nrow(ActualCoefficientADRSorted)
group="ADR"
})
ActualCoefficientTRPWFinal <- within(ActualCoefficientTRPWCount, {
percentage = Count / nrow(ActualCoefficientTRPWSorted)
group="TRPW"
})
#Plot
library(ggplot2)
#Bind data
DegreeFinal <- rbind(ExpDegreeFinal, ActualDegreeADRFinal, ActualDegreeTRPWFinal)
TriangleFinal <- rbind(ExpTriangleFinal, ActualTriangleADRFinal, ActualTriangleTRPWFinal)
CoefficientFinal <- rbind(ExpCoefficientFinal, ActualCoefficientADRFinal, ActualCoefficientTRPWFinal)
#Vertex Degree
ggplot(DegreeFinal, aes(x=Degree,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10(limits=c(1e-3,1))+
# scale_y_log10()+
coord_cartesian(xlim=c(0,20))+
# scale_x_discrete(breaks=seq(0,20,by=1))+
# coord_cartesian(xlim=c(0,100))+
# scale_x_discrete(breaks=seq(0,100,by=5))+
labs(title="Vertex Degree Distribution AsSkitter", x="Vertex Degree", y="Percentage Vertices")
#Triangle Degree
ggplot(TriangleFinal, aes(x=TriangleDegree,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10(limits=c(1e-4,1))+
# scale_y_log10()+
coord_cartesian(xlim=c(0,20))+
scale_x_discrete(breaks=seq(0, 20, by=1))+
labs(title="Triangle Degree Distribution AsSkitter", x="Triangle Degree", y="Percentage Vertices")
#Clustering Coefficient
ggplot(CoefficientFinal, aes(x=Coefficient,y=percentage,group=group,color=group,shape=group))+
geom_point()+
geom_line()+
scale_y_log10()+
coord_cartesian(xlim=c(0,8))+
scale_x_discrete(breaks=seq(0, 1, by=0.1))+
labs(title="Clustering Coefficient Distribution AsSkitter", x="Clustering Coefficient Degree", y="Percentage Vertices")
#Triangle count
ExpTriangleCount <- data.frame(sum(as.numeric(ExpTriangleFinal$TriangleDegree)*as.numeric(ExpTriangleFinal$Count)))
names(ExpTriangleCount) <- c("TriangleDegree")
ADRTriangleCount <- data.frame(sum(as.numeric(ActualTriangleADRFinal$TriangleDegree)*as.numeric(ActualTriangleADRFinal$Count)))
names(ADRTriangleCount) <- c("TriangleDegree")
TRPWTriangleCount <- data.frame(sum(as.numeric(ActualTriangleTRPWFinal$TriangleDegree)*as.numeric(ActualTriangleTRPWFinal$Count)))
names(TRPWTriangleCount) <- c("TriangleDegree")
ExpTriangleCountFinal <- within(ExpTriangleCount, {
group = "Expected"
})
ADRTriangleCountFinal <- within(ADRTriangleCount, {
group = "ADR"
})
TRPWTriangleCountFinal <- within(TRPWTriangleCount, {
group = "TRPW"
})
TriangleCount <- rbind(ExpTriangleCountFinal, ADRTriangleCountFinal, TRPWTriangleCountFinal)
ggplot(TriangleCount, aes(x=group,y=TriangleDegree,group=group,fill=group))+
geom_bar(stat="identity")+
labs(title="Tiangle Count AsSkitter",x="Algorithm",y="Number of Triangles")+
guides(fill=guide_legend(title="Algorithm"))
|
\name{coefOther}
\alias{coefOther}
\title{Exogenous Coefficients of the Fitted Model in the \code{ivmodel} Object}
\description{This \code{coefOther} returns the point estimates, standard errors, test statistics and p values for the exogenous covariates associated with the outcome. It returns a list of matrices where each matrix is one of the k-Class estimates from an \code{ivmodel} object.}
\usage{
coefOther(ivmodel)
}
\arguments{
\item{ivmodel}{\code{ivmodel} object.}
}
\value{
A list of matrices swhere each matrix summarizes the estimated coefficients from one of hte k-Class estimates.
}
\author{Hyunseung Kang}
\seealso{See also \code{\link{ivmodel}} for details on the instrumental variables model.}
\examples{
data(card.data)
Y=card.data[,"lwage"]
D=card.data[,"educ"]
Z=card.data[,"nearc4"]
Xname=c("exper", "expersq", "black", "south", "smsa", "reg661",
"reg662", "reg663", "reg664", "reg665", "reg666", "reg667",
"reg668", "smsa66")
X=card.data[,Xname]
foo = ivmodel(Y=Y,D=D,Z=Z,X=X)
coefOther(foo)
}
|
/man/coefOther.Rd
|
permissive
|
hyunseungkang/ivmodel
|
R
| false | false | 1,030 |
rd
|
\name{coefOther}
\alias{coefOther}
\title{Exogenous Coefficients of the Fitted Model in the \code{ivmodel} Object}
\description{This \code{coefOther} returns the point estimates, standard errors, test statistics and p values for the exogenous covariates associated with the outcome. It returns a list of matrices where each matrix is one of the k-Class estimates from an \code{ivmodel} object.}
\usage{
coefOther(ivmodel)
}
\arguments{
\item{ivmodel}{\code{ivmodel} object.}
}
\value{
A list of matrices swhere each matrix summarizes the estimated coefficients from one of hte k-Class estimates.
}
\author{Hyunseung Kang}
\seealso{See also \code{\link{ivmodel}} for details on the instrumental variables model.}
\examples{
data(card.data)
Y=card.data[,"lwage"]
D=card.data[,"educ"]
Z=card.data[,"nearc4"]
Xname=c("exper", "expersq", "black", "south", "smsa", "reg661",
"reg662", "reg663", "reg664", "reg665", "reg666", "reg667",
"reg668", "smsa66")
X=card.data[,Xname]
foo = ivmodel(Y=Y,D=D,Z=Z,X=X)
coefOther(foo)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GauPro_S3.R
\name{summary.GauPro}
\alias{summary.GauPro}
\title{if (F) {
# Plot is automatically dispatched, same with print and format
#' Plot for class GauPro
#'
#' @param x Object of class GauPro
#' @param ... Additional parameters
#'
#' @return Nothing
#' @export
#'
#' @examples
#' n <- 12
#' x <- matrix(seq(0,1,length.out = n), ncol=1)
#' y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#' gp <- GauPro(X=x, Z=y, parallel=FALSE)
#' if (requireNamespace("MASS", quietly = TRUE)) {
#' plot(gp)
#' }
#'
plot.GauPro <- function(x, ...) {
x$plot(...)
# if (x$D == 1) {
# x$cool1Dplot(...)
# } else if (x$D == 2) {
# x$plot2D(...)
# } else {
# # stop("No plot method for higher than 2 dimension")
# x$plotmarginal()
# }
}
}
Summary for GauPro object}
\usage{
\method{summary}{GauPro}(object, ...)
}
\arguments{
\item{object}{GauPro R6 object}
\item{...}{Additional arguments passed to summary}
}
\value{
Summary
}
\description{
if (F) {
# Plot is automatically dispatched, same with print and format
#' Plot for class GauPro
#'
#' @param x Object of class GauPro
#' @param ... Additional parameters
#'
#' @return Nothing
#' @export
#'
#' @examples
#' n <- 12
#' x <- matrix(seq(0,1,length.out = n), ncol=1)
#' y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#' gp <- GauPro(X=x, Z=y, parallel=FALSE)
#' if (requireNamespace("MASS", quietly = TRUE)) {
#' plot(gp)
#' }
#'
plot.GauPro <- function(x, ...) {
x$plot(...)
# if (x$D == 1) {
# x$cool1Dplot(...)
# } else if (x$D == 2) {
# x$plot2D(...)
# } else {
# # stop("No plot method for higher than 2 dimension")
# x$plotmarginal()
# }
}
}
Summary for GauPro object
}
|
/man/summary.GauPro.Rd
|
no_license
|
CollinErickson/GauPro
|
R
| false | true | 1,845 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GauPro_S3.R
\name{summary.GauPro}
\alias{summary.GauPro}
\title{if (F) {
# Plot is automatically dispatched, same with print and format
#' Plot for class GauPro
#'
#' @param x Object of class GauPro
#' @param ... Additional parameters
#'
#' @return Nothing
#' @export
#'
#' @examples
#' n <- 12
#' x <- matrix(seq(0,1,length.out = n), ncol=1)
#' y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#' gp <- GauPro(X=x, Z=y, parallel=FALSE)
#' if (requireNamespace("MASS", quietly = TRUE)) {
#' plot(gp)
#' }
#'
plot.GauPro <- function(x, ...) {
x$plot(...)
# if (x$D == 1) {
# x$cool1Dplot(...)
# } else if (x$D == 2) {
# x$plot2D(...)
# } else {
# # stop("No plot method for higher than 2 dimension")
# x$plotmarginal()
# }
}
}
Summary for GauPro object}
\usage{
\method{summary}{GauPro}(object, ...)
}
\arguments{
\item{object}{GauPro R6 object}
\item{...}{Additional arguments passed to summary}
}
\value{
Summary
}
\description{
if (F) {
# Plot is automatically dispatched, same with print and format
#' Plot for class GauPro
#'
#' @param x Object of class GauPro
#' @param ... Additional parameters
#'
#' @return Nothing
#' @export
#'
#' @examples
#' n <- 12
#' x <- matrix(seq(0,1,length.out = n), ncol=1)
#' y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#' gp <- GauPro(X=x, Z=y, parallel=FALSE)
#' if (requireNamespace("MASS", quietly = TRUE)) {
#' plot(gp)
#' }
#'
plot.GauPro <- function(x, ...) {
x$plot(...)
# if (x$D == 1) {
# x$cool1Dplot(...)
# } else if (x$D == 2) {
# x$plot2D(...)
# } else {
# # stop("No plot method for higher than 2 dimension")
# x$plotmarginal()
# }
}
}
Summary for GauPro object
}
|
rm(list = ls())
home = '~/Google Drive/projects/TFbenchmark/'
setwd(home)
# Load expression
exp = get(load('~/data/GTEx_expressionatlas/raw/GTex_v6_atlas.genes.voom.batchcor.merged.rdata'))
fpkms = get(load('~/data/GTEx_expressionatlas/raw/GTex_v6_atlas.genes.fpkms.merged.rdata'))
# Load samples annotation
load('~/data/GTEx_expressionatlas/annot/E-MTAB-5214.sdrf.rdata')
# Load TFs
TFs = read.delim(file = 'data/TF_census/vaquerizas/TF_census.txt', header = F, stringsAsFactors = F)[,1]
# Load gene annot and convert gene names
exp = exp[ rownames(exp) %in% ensemblgenes_annot$ensembl_gene_id, ]
rownames(exp) = ensemblgenes_annot$hgnc_symbol[ match(rownames(exp), ensemblgenes_annot$ensembl_gene_id) ]
exp = exp[ ! duplicated(rownames(exp)), ]
fpkms = fpkms[ rownames(fpkms) %in% ensemblgenes_annot$ensembl_gene_id, ]
rownames(fpkms) = ensemblgenes_annot$hgnc_symbol[ match(rownames(fpkms), ensemblgenes_annot$ensembl_gene_id) ]
fpkms = fpkms[ ! duplicated(rownames(fpkms)), ]
# Filter exp for TFs
table(rownames(exp) %in% TFs)
table(TFs %in% rownames(exp))
exp = exp[ rownames(exp) %in% TFs, ]
fpkms = fpkms[ rownames(fpkms) %in% TFs, ]
# plot profiles
tissues = sample_annotation$Comment.histological.type.[ match(colnames(exp), sample_annotation$Source.Name)]
idx = sample(1:ncol(exp), 1000)
tissue_cluster = tissues[idx]
library(Rtsne)
library(RColorBrewer)
colors = sample(c(brewer.pal(n = 10, name = "Paired"), brewer.pal(n = 8, name = "Accent"), brewer.pal(n = 8, name = "Dark2"), brewer.pal(n = 4, name = "Set1")[3:4])[1:length(unique(tissue_cluster))])
names(colors) = unique(tissue_cluster)
tsne = Rtsne(scale(t(exp[, idx ])), dims = 2, perplexity=50, verbose=TRUE, max_iter = 1000)
plot(tsne$Y, t='n', main="tSNE")
text(tsne$Y, labels=tissue_cluster, col=colors[tissue_cluster], cex = .7)
tissues = sample_annotation$Comment.histological.type.[ sample_annotation$Source.Name %in% colnames(exp) ]
##########################################################################################################################################################################################################################################
# differential expression per tissue type
##########################################################################################################################################################################################################################################
ggplot( sample_annotation[ sample_annotation$Source.Name %in% colnames(exp) , ], aes (x = Comment.histological.type.) ) + geom_bar() + theme_classic(18) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) + xlab('tissue')
table(tissues)
results = list()
for (ti in sort(unique(tissues))){
message(ti)
design = model.matrix(~factor((sample_annotation$Comment.histological.type.[ match(colnames(exp), sample_annotation$Source.Name) ] == ti) + 0))
## fit the same linear model now to the enrichment scores
fit = lmFit(exp, design)
## estimate moderated t-statistics
fit = eBayes(fit, robust=TRUE, trend=TRUE)
## set1 is differentially expressed
results[[ti]] = topTable(fit, coef = 2, p.value = 1, number = nrow(exp))
results[[ti]]$TF = rownames(results[[ti]])
}
df = melt(results, id.vars = names(results[[1]]))
df$adj.P.Val_global = p.adjust(df$P.Value, method = 'fdr')
save(df, file = 'data/TF_target_sources/reverse_engenieered_networks/tf_differential_expression.rdata')
# pvalue heatmap
df$value = df$adj.P.Val +1E-322
M = -log10(acast(df, formula = L1~TF))
df$value = df$logFC
M = M * sign(acast(df, formula = L1~TF))
pheatmap::pheatmap(M)
# Counts
df$veredict = 'neutral'
df$veredict[ df$adj.P.Val < 0.01 & df$logFC < 0 ] = '-'
df$veredict[ df$adj.P.Val < 0.01 & df$logFC > 0 ] = '+'
ggplot(df, aes(x= veredict, fill = veredict)) + geom_bar() +
theme_bw(18) + xlab('TF status in tissue') +
scale_fill_manual(values = c('steelblue3', 'coral3', 'grey')) + theme(legend.position = 'none')
ddf = melt(ddply(df, 'TF', function(x) table(x$veredict) ), id.vars = 'TF')
ddf$value = as.factor(ddf$value)
ggplot(ddf[ ddf$variable != 'neutral', ], aes(x= value, fill = variable) ) + geom_bar(position = 'dodge') +
scale_fill_manual(values = c('steelblue3', 'coral3'), name = 'TF status in tissue') +
theme_bw(18) + ylab('number of TFs') + xlab('number of tissues') + ggtitle('TF expression in 30 tissues')
##########################################################################################################################################################################################################################################
##########################################################################################################################################################################################################################################
# 25th > 5 fpkms
##########################################################################################################################################################################################################################################
results = list()
for (ti in sort(unique(tissues))){
message(ti)
results[[ti]]$active = names(which(apply(fpkms[, colnames(fpkms) %in% sample_annotation$Source.Name[ sample_annotation$Comment.histological.type. == ti ] ], 1, quantile, 0.25) > 1))
results[[ti]]$inactive = names(which(apply(fpkms[, colnames(fpkms) %in% sample_annotation$Source.Name[ sample_annotation$Comment.histological.type. == ti ] ], 1, quantile, 0.75) < 1))
}
df = melt(results)
ddf = as.data.frame(rbind( cbind(table(df$value[ df$L2 == 'active']), 'active'), cbind(table(df$value[ df$L2 == 'inactive']), 'inactive')), stringsAsFactors = F)
ddf$V1 = as.factor(as.integer(ddf$V1))
ggplot(ddf, aes(x= V1, fill = V2) ) + geom_bar(position = 'dodge') +
scale_fill_manual(values = c('steelblue3', 'coral3'), name = 'TF status in tissue') +
theme_bw(18) + ylab('number of TFs') + xlab('number of tissues') + ggtitle('TF expression in 30 tissues')
save(df, file = 'data/TF_target_sources/reverse_engenieered_networks/tf_tissues_call_percentile.rdata')
|
/code/TF_targets/inferred/old/research/differentialy_expressed_TFs.r
|
no_license
|
Ran485/TFbenchmark
|
R
| false | false | 6,172 |
r
|
rm(list = ls())
home = '~/Google Drive/projects/TFbenchmark/'
setwd(home)
# Load expression
exp = get(load('~/data/GTEx_expressionatlas/raw/GTex_v6_atlas.genes.voom.batchcor.merged.rdata'))
fpkms = get(load('~/data/GTEx_expressionatlas/raw/GTex_v6_atlas.genes.fpkms.merged.rdata'))
# Load samples annotation
load('~/data/GTEx_expressionatlas/annot/E-MTAB-5214.sdrf.rdata')
# Load TFs
TFs = read.delim(file = 'data/TF_census/vaquerizas/TF_census.txt', header = F, stringsAsFactors = F)[,1]
# Load gene annot and convert gene names
exp = exp[ rownames(exp) %in% ensemblgenes_annot$ensembl_gene_id, ]
rownames(exp) = ensemblgenes_annot$hgnc_symbol[ match(rownames(exp), ensemblgenes_annot$ensembl_gene_id) ]
exp = exp[ ! duplicated(rownames(exp)), ]
fpkms = fpkms[ rownames(fpkms) %in% ensemblgenes_annot$ensembl_gene_id, ]
rownames(fpkms) = ensemblgenes_annot$hgnc_symbol[ match(rownames(fpkms), ensemblgenes_annot$ensembl_gene_id) ]
fpkms = fpkms[ ! duplicated(rownames(fpkms)), ]
# Filter exp for TFs
table(rownames(exp) %in% TFs)
table(TFs %in% rownames(exp))
exp = exp[ rownames(exp) %in% TFs, ]
fpkms = fpkms[ rownames(fpkms) %in% TFs, ]
# plot profiles
tissues = sample_annotation$Comment.histological.type.[ match(colnames(exp), sample_annotation$Source.Name)]
idx = sample(1:ncol(exp), 1000)
tissue_cluster = tissues[idx]
library(Rtsne)
library(RColorBrewer)
colors = sample(c(brewer.pal(n = 10, name = "Paired"), brewer.pal(n = 8, name = "Accent"), brewer.pal(n = 8, name = "Dark2"), brewer.pal(n = 4, name = "Set1")[3:4])[1:length(unique(tissue_cluster))])
names(colors) = unique(tissue_cluster)
tsne = Rtsne(scale(t(exp[, idx ])), dims = 2, perplexity=50, verbose=TRUE, max_iter = 1000)
plot(tsne$Y, t='n', main="tSNE")
text(tsne$Y, labels=tissue_cluster, col=colors[tissue_cluster], cex = .7)
tissues = sample_annotation$Comment.histological.type.[ sample_annotation$Source.Name %in% colnames(exp) ]
##########################################################################################################################################################################################################################################
# differential expression per tissue type
##########################################################################################################################################################################################################################################
ggplot( sample_annotation[ sample_annotation$Source.Name %in% colnames(exp) , ], aes (x = Comment.histological.type.) ) + geom_bar() + theme_classic(18) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5)) + xlab('tissue')
table(tissues)
results = list()
for (ti in sort(unique(tissues))){
message(ti)
design = model.matrix(~factor((sample_annotation$Comment.histological.type.[ match(colnames(exp), sample_annotation$Source.Name) ] == ti) + 0))
## fit the same linear model now to the enrichment scores
fit = lmFit(exp, design)
## estimate moderated t-statistics
fit = eBayes(fit, robust=TRUE, trend=TRUE)
## set1 is differentially expressed
results[[ti]] = topTable(fit, coef = 2, p.value = 1, number = nrow(exp))
results[[ti]]$TF = rownames(results[[ti]])
}
df = melt(results, id.vars = names(results[[1]]))
df$adj.P.Val_global = p.adjust(df$P.Value, method = 'fdr')
save(df, file = 'data/TF_target_sources/reverse_engenieered_networks/tf_differential_expression.rdata')
# pvalue heatmap
df$value = df$adj.P.Val +1E-322
M = -log10(acast(df, formula = L1~TF))
df$value = df$logFC
M = M * sign(acast(df, formula = L1~TF))
pheatmap::pheatmap(M)
# Counts
df$veredict = 'neutral'
df$veredict[ df$adj.P.Val < 0.01 & df$logFC < 0 ] = '-'
df$veredict[ df$adj.P.Val < 0.01 & df$logFC > 0 ] = '+'
ggplot(df, aes(x= veredict, fill = veredict)) + geom_bar() +
theme_bw(18) + xlab('TF status in tissue') +
scale_fill_manual(values = c('steelblue3', 'coral3', 'grey')) + theme(legend.position = 'none')
ddf = melt(ddply(df, 'TF', function(x) table(x$veredict) ), id.vars = 'TF')
ddf$value = as.factor(ddf$value)
ggplot(ddf[ ddf$variable != 'neutral', ], aes(x= value, fill = variable) ) + geom_bar(position = 'dodge') +
scale_fill_manual(values = c('steelblue3', 'coral3'), name = 'TF status in tissue') +
theme_bw(18) + ylab('number of TFs') + xlab('number of tissues') + ggtitle('TF expression in 30 tissues')
##########################################################################################################################################################################################################################################
##########################################################################################################################################################################################################################################
# 25th > 5 fpkms
##########################################################################################################################################################################################################################################
results = list()
for (ti in sort(unique(tissues))){
message(ti)
results[[ti]]$active = names(which(apply(fpkms[, colnames(fpkms) %in% sample_annotation$Source.Name[ sample_annotation$Comment.histological.type. == ti ] ], 1, quantile, 0.25) > 1))
results[[ti]]$inactive = names(which(apply(fpkms[, colnames(fpkms) %in% sample_annotation$Source.Name[ sample_annotation$Comment.histological.type. == ti ] ], 1, quantile, 0.75) < 1))
}
df = melt(results)
ddf = as.data.frame(rbind( cbind(table(df$value[ df$L2 == 'active']), 'active'), cbind(table(df$value[ df$L2 == 'inactive']), 'inactive')), stringsAsFactors = F)
ddf$V1 = as.factor(as.integer(ddf$V1))
ggplot(ddf, aes(x= V1, fill = V2) ) + geom_bar(position = 'dodge') +
scale_fill_manual(values = c('steelblue3', 'coral3'), name = 'TF status in tissue') +
theme_bw(18) + ylab('number of TFs') + xlab('number of tissues') + ggtitle('TF expression in 30 tissues')
save(df, file = 'data/TF_target_sources/reverse_engenieered_networks/tf_tissues_call_percentile.rdata')
|
createPlot3 <- function()
{
require(data.table)
plotData<-fread("exdata_data_household_power_consumption\\household_power_consumption.txt",
colClasses=c("Date", "time", "character","character","character","character","character","character","character" ))
#str(plot1Data)
plotData$Global_active_power[plotData$Global_active_power == "?"] <- NA
plotData$Date <- as.Date(plotData$Date, "%d/%m/%Y")
plotData <- plotData[plotData$Date >="2007-02-01" & plotData$Date <= "2007-02-02"]
png("plot3.png", width=480, height=480)
g_range <- range(0, plotData$Sub_metering_1)
plot(as.numeric(plotData$Sub_metering_1) , type="l", axes=FALSE, xlab="", ylab="Energy Sub Metering")
lines(as.numeric(plotData$Sub_metering_2), type="l", col="red")
lines(as.numeric(plotData$Sub_metering_3), type="l", col="blue")
axis(1, at=1, lab=c("Thu"), xlab="NULL")
axis(1, at=nrow(plotData)/2, lab=c("Fri"),xlab="NULL")
axis(1, at=nrow(plotData), lab=c("Sat"),xlab="NULL")
axis(2, las=1, at=10*0:g_range[2])
box()
legend("topright", c("Sub Metering 1" ,"Sub Metering 2","Sub Metering 3"),
col=c("black","red", "blue"), lty=1);
dev.off()
}
|
/plot3.R
|
no_license
|
richbenmintz/ExData_Plotting1
|
R
| false | false | 1,285 |
r
|
createPlot3 <- function()
{
require(data.table)
plotData<-fread("exdata_data_household_power_consumption\\household_power_consumption.txt",
colClasses=c("Date", "time", "character","character","character","character","character","character","character" ))
#str(plot1Data)
plotData$Global_active_power[plotData$Global_active_power == "?"] <- NA
plotData$Date <- as.Date(plotData$Date, "%d/%m/%Y")
plotData <- plotData[plotData$Date >="2007-02-01" & plotData$Date <= "2007-02-02"]
png("plot3.png", width=480, height=480)
g_range <- range(0, plotData$Sub_metering_1)
plot(as.numeric(plotData$Sub_metering_1) , type="l", axes=FALSE, xlab="", ylab="Energy Sub Metering")
lines(as.numeric(plotData$Sub_metering_2), type="l", col="red")
lines(as.numeric(plotData$Sub_metering_3), type="l", col="blue")
axis(1, at=1, lab=c("Thu"), xlab="NULL")
axis(1, at=nrow(plotData)/2, lab=c("Fri"),xlab="NULL")
axis(1, at=nrow(plotData), lab=c("Sat"),xlab="NULL")
axis(2, las=1, at=10*0:g_range[2])
box()
legend("topright", c("Sub Metering 1" ,"Sub Metering 2","Sub Metering 3"),
col=c("black","red", "blue"), lty=1);
dev.off()
}
|
geodistance <- function(longvar,latvar,lotarget,latarget,dcoor=FALSE) {
latvar <- 2*pi*latvar/360
longvar <- 2*pi*longvar/360
lotarget <- 2*pi*lotarget/360
latarget <- 2*pi*latarget/360
dnorth <- NULL
deast <- NULL
dist <- pmin(sin(latvar)*sin(latarget) + cos(latvar)*cos(latarget)*cos(lotarget-longvar), 1)
dist <- acos(dist)*3958
if (dcoor==TRUE) {
dnorth <- pmin(sin(latvar)*sin(latarget) + cos(latvar)*cos(latarget)*cos(0), 1)
dnorth <- acos(dnorth)*3958
dnorth <- ifelse(latvar<latarget, -dnorth, dnorth)
}
if (dcoor==TRUE) {
deast <- pmin(sin(latvar)^2 + (cos(latvar)^2)*cos(lotarget-longvar), 1)
deast <- acos(deast)*3958
deast <- ifelse(longvar<lotarget, -deast, deast)
}
out <- list(dist,dnorth,deast)
names(out) <- c("dist","dnorth","deast")
return(out)
}
|
/McSpatial/R/geodistance.R
|
no_license
|
albrizre/spatstat.revdep
|
R
| false | false | 827 |
r
|
geodistance <- function(longvar,latvar,lotarget,latarget,dcoor=FALSE) {
latvar <- 2*pi*latvar/360
longvar <- 2*pi*longvar/360
lotarget <- 2*pi*lotarget/360
latarget <- 2*pi*latarget/360
dnorth <- NULL
deast <- NULL
dist <- pmin(sin(latvar)*sin(latarget) + cos(latvar)*cos(latarget)*cos(lotarget-longvar), 1)
dist <- acos(dist)*3958
if (dcoor==TRUE) {
dnorth <- pmin(sin(latvar)*sin(latarget) + cos(latvar)*cos(latarget)*cos(0), 1)
dnorth <- acos(dnorth)*3958
dnorth <- ifelse(latvar<latarget, -dnorth, dnorth)
}
if (dcoor==TRUE) {
deast <- pmin(sin(latvar)^2 + (cos(latvar)^2)*cos(lotarget-longvar), 1)
deast <- acos(deast)*3958
deast <- ifelse(longvar<lotarget, -deast, deast)
}
out <- list(dist,dnorth,deast)
names(out) <- c("dist","dnorth","deast")
return(out)
}
|
######################
# AUXILLIARY FUNCTIONS
######################
generate_png <- function(origin_file_name, output_file_name) {
system(paste0("sips -s formatOptions best -s format png ",
origin_file_name, " --out ", output_file_name))
file.info(output_file_name)
}
trim_png_read <- function(file_path) {
png_file <- png::readPNG(file_path)
trim_png_blanks(png_file)
}
are_all_one <- function(x) {
all(x == 1)
}
trim_png_blanks <- function(png_mat) {
chosen_rows <- !apply(png_mat[, , 1], 1, are_all_one)
chosen_cols <- !apply(png_mat[, , 1], 2, are_all_one)
png_mat[chosen_rows, chosen_cols, ]
}
make_grob_png <- function(...) {
grid::rasterGrob(trim_png_read(...), interpolate = TRUE)
}
# from stackoverflow
# question 22488563
# ggplot2-annotate-layer-position-in-r
annotate_textp <- function(label, x, y, facets = NULL, hjust = 0,
vjust = 0, color = "black", alpha = NA,
family = thm$text$family,
size = thm$text$size, fontface = 1,
line_height = 1.0,
box_just = ifelse(c(x, y) < 0.5, 0, 1),
margin = grid::unit(size / 2, "pt"),
thm = theme_get()) {
x <- scales::squish_infinite(x)
y <- scales::squish_infinite(y)
tg <- grid::textGrob(label, x = 0, y = 0, hjust = hjust, vjust = vjust,
gp = grid::gpar(col = alpha(color, alpha),
fontsize = size, fontfamily = family,
fontface = fontface,
lineheight = line_height))
ts <- grid::unit.c(grid::grobWidth(tg), grid::grobHeight(tg))
vp <- grid::viewport(x = x, y = y, width = ts[1], height = ts[2],
just = box_just)
tg <- grid::editGrob(tg, x = ts[1] * hjust, y = ts[2] * vjust, vp = vp)
unt <- grid::unit(1, "npc") - margin * 2
inr <- grid::grobTree(tg, vp = grid::viewport(width = unt, height = unt))
layer(data = NULL, stat = StatIdentity, position = PositionIdentity,
geom = GeomCustomAnn, inherit.aes = TRUE,
params = list(grob = grid::grobTree(inr),
xmin = -Inf,
xmax = Inf,
ymin = -Inf,
ymax = Inf))
}
clip_vec_hdi <- function(data, probs = c(0.005, 0.995)) {
lim_x <- quantile_hdi(data$param, probs = probs)
data %>%
dplyr::filter(param >= lim_x[[1]] & param <= lim_x[[2]])
}
empty_panel_w_txt <- function(color, label) {
grid::grobTree(grid::rectGrob(width = grid::unit(0.915, "npc"),
height = grid::unit(0.9, "npc"),
just = 0.45,
gp = grid::gpar(fill = color)),
grid::textGrob(label,
gp = grid::gpar(fontsize = 15,
col = "black"),
hjust = 0.4))
}
sim_pp_mean <- function(data, model, type) {
posteriors <- as.data.frame(model)
data <- dplyr::filter(data, sample == tidyselect::all_of(type))
y_rep <- matrix(0, nrow(posteriors), nrow(data))
for (j in seq_len(ncol(y_rep))) {
target_phi <- paste0("r_phi[", data$pond[j], "]")
target_ka <- paste0("r_ka[", data$pond[j], "]")
target_ke <- paste0("r_ke[", data$pond[j], "]")
phi <- posteriors[, target_phi]
ka <- posteriors[, target_ka]
ke <- posteriors[, target_ke]
sg <- posteriors[, "sigma"]
mu <- calc_eqn_1(data$day[j], ka, ke, phi)
y_rep[, j] <- rnorm(rep(1, nrow(y_rep)), mu, sg)
}
y_rep
}
###############
# PAPER FIGURES
###############
make_fig_1 <- function(dest, fig_out_folder, ...) {
pdf(dest, width = 5.819004, height = 2.950226)
p <- fig_1(...)
print(p)
grid::grid.text("a", x = grid::unit(0.11, "npc"), y = grid::unit(0.82, "npc"),
gp = grid::gpar("fontsize" = 13, "fontface" = "bold"))
grid::grid.text("b", x = grid::unit(0.58, "npc"), y = grid::unit(0.82, "npc"),
gp = grid::gpar("fontsize" = 13, "fontface" = "bold"))
dev.off()
}
fig_1 <- function(post_pred_means, my_cols_treatment, phy_png, zoo_png) {
polygons <- post_pred_means$polygons
linesd <- post_pred_means$lines
tp <- polygons %>%
dplyr::distinct(sample, treatment) %>%
dplyr::mutate(x = 1)
ggplot(data = polygons, mapping = aes(x = x, y = y_cred)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf,
ymax = Inf, inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_polygon(data = polygons,
mapping = aes(x = x, y = y_cred, fill = treatment)) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean, group = treatment),
col = "black", lty = 1, size = 0.5) +
geom_polygon(data = polygons,
mapping = aes(x = x, y = y_cred, fill = treatment),
alpha = 0.7,
inherit.aes = FALSE) +
scale_fill_manual(labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.4, 0.1, 0.1, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 14),
legend.position = c(0.5, 1.2),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab(substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
xlab("Time (days)") +
layer(data = polygons %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.5,
ymax = 0.65,
xmin = 43,
xmax = 63)) +
layer(data = polygons %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.3,
ymax = 0.45,
xmin = 45,
xmax = 65))
}
make_fig_2 <- function(dest, fig_out_folder, ...) {
p <- fig_2(...)
pdf(dest, width = 5.737556, height = 6.950226)
print(p)
grid.text(substitute(kappa["a"] * " (d"^-1 * ")"),
x = grid::unit(0.56, "npc"),
y = grid::unit(0.775, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute(kappa["e"] * " (d"^-1 * ")"),
x = grid::unit(0.56, "npc"),
y = grid::unit(0.54, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute(italic(phi) * " (% d)"),
x = grid::unit(0.565, "npc"),
y = grid::unit(0.31, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute("Efficiency of N transfer (" * bar(epsilon) * ")"),
x = grid::unit(0.54, "npc"),
y = grid::unit(0.075, "npc"),
gp = grid::gpar("fontsize" = 10))
# legends
grid.text("a",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.985, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("b",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.76, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("c",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.525, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("d",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.295, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
dev.off()
}
fig_2 <- function(data_eff, data_ka, data_ke, data_phi,
my_cols_treatment, phy_png, zoo_png) {
nreps <- nrow(data_eff)
plot_data <- do.call("rbind.data.frame",
list(data_ka, data_ke, data_phi, data_eff)) %>%
dplyr::mutate(name = rep(c("a_kas", "b_kes", "c_phi", "d_eff"),
each = nreps)) %>%
plyr::ddply(.(name, sample, treatment), clip_vec_hdi)
tp <- plot_data %>%
dplyr::distinct(sample, name) %>%
dplyr::mutate(param = 1)
ggplot(data = plot_data, mapping = aes(x = param)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf,
inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_density(data = plot_data, adjust = 2, trim = TRUE,
mapping = aes(x = param, fill = treatment),
colour = NA) +
geom_density(adjust = 2, alpha = 0.7, trim = TRUE,
mapping = aes(fill = treatment, colour = treatment),
show.legend = FALSE) +
scale_fill_manual(values = rev(my_cols_treatment),
labels = c("Ambient", "Warmed")) +
facet_wrap(name~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(-0.3, 0.1, 0.6, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.y = grid::unit(-1.2, "lines"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 14),
legend.position = c(0.5, -0.1),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab("Posterior density (99% C.I.)\n") +
scale_color_manual(values = rev(my_cols_treatment)) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
coord_cartesian(clip = "off") +
layer(data = plot_data %>%
dplyr::filter(sample == "phytoplankton" & name == "a_kas"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 2.2,
ymax = 3,
xmin = 1,
xmax = 1.25)) +
layer(data = plot_data %>%
dplyr::filter(sample == "zooplankton" & name == "a_kas"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 6,
ymax = 10.5,
xmin = 0.62,
xmax = 0.74))
}
make_fig_3 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, fig_3(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
fig_3 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
data <- data %>%
dplyr::group_by(sample, pond, treatment) %>%
dplyr::summarise(av_C_ug_L = mean(av_C_ug_L), ln_av_C_ug_L = log(av_C_ug_L))
a <- ggplot(data = data, mapping = aes(y = ln_av_C_ug_L, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 1.8, ymax = 1.8 + (10.7 - 1.8) / 2),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 1.8 + (10.7 - 1.8) / 2, ymax = 10.7),
fill = "#CCECE6") +
geom_boxplot(mapping = aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3) +
geom_point(mapping = aes(fill = interaction(treatment, sample),
shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = substitute("Total Biomass (" * mu * "g C L"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed")) +
scale_y_continuous(limits = c(1.8, 10.7), expand = c(0, 0),
breaks = c(log(8), log(50), log(400),
log(3e3), log(22e3)),
labels = c(8, 50, 400, 3e3, 22e3))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = 6.6,
ymax = 7.4,
xmin = 0.3,
xmax = 1.3)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = 4.5,
ymax = 6,
xmin = 2,
xmax = 2.5))
}
#######################
# EXTENDED DATA FIGURES
#######################
make_internal_ed_fig_1d <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, internal_ed_fig_1d(...), device = "pdf", width = 8.5,
height = 3.5, units = "in", onefile = FALSE)
}
internal_ed_fig_1d <- function(physchem_data, my_cols_treatment) {
make_bef_aft_data <- function(data, vrb) {
data$y_var <- data[[vrb]]
data <- data %>%
dplyr::select(Date, pairs, treatment, pond, y_var) %>%
tidyr::drop_na()
bef <- data %>%
dplyr::filter(Date < as.Date("2013-07-16")) %>%
dplyr::group_by(pairs, treatment, pond) %>%
dplyr::summarise(mean = mean(y_var)) %>%
dplyr::mutate(period = "before") %>%
data.frame()
aft <- data %>%
dplyr::filter(Date > as.Date("2013-07-16") &
Date < as.Date("2013-07-20")) %>%
dplyr::group_by(pairs, treatment, pond) %>%
dplyr::summarise(mean = mean(y_var)) %>%
dplyr::mutate(period = "after") %>%
data.frame()
rbind(bef, aft)
}
plot_bef_aft <- function(data, vrb, ylab, my_cols_treatment) {
data$vrb <- data[[vrb]]
ggplot(data = data, mapping = aes(y = vrb, x = period,
fill = treatment,
shape = treatment)) +
geom_boxplot(colour = "black",
show.legend = FALSE,
width = 0.6,
size = 0.3,
outlier.shape = NA) +
geom_point(position = position_dodge(0.4),
size = 2.5) +
labs(x = "Treatment",
y = ylab) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(labels = c("Before", "After")) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25))
}
pdosat <- make_bef_aft_data(physchem_data, "DOsat") %>%
plot_bef_aft("mean", "DO saturation (%)", my_cols_treatment) +
scale_y_continuous(trans = "log10") +
theme(legend.position = "none")
pph <- make_bef_aft_data(physchem_data, "pH") %>%
plot_bef_aft("mean", "pH", my_cols_treatment) +
theme(legend.position = "none")
gridExtra::grid.arrange(pdosat, pph, ncol = 2)
}
make_ed_fig_2 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_2(...), device = "pdf", width = 12,
height = 4, units = "in", onefile = FALSE)
}
ed_fig_2 <- function(data, my_cols_treatment) {
my_theme <- function(leg = FALSE) {
theme_bw() +
theme(axis.title.y = element_text(size = 14,
margin = margin(t = 0,
r = 10,
b = 0,
l = 10)),
axis.title.x = element_text(size = 14,
margin = margin(t = 10,
r = 0,
b = 10,
l = 0)),
axis.text.x = element_text(size = 12,
margin = margin(t = 4,
r = 0,
b = 0,
l = 0)),
axis.text.y = element_text(size = 12,
margin = margin(t = 0,
r = 4,
b = 0,
l = 0)),
axis.ticks.length = grid::unit(5, "pt"),
strip.background = element_blank(),
legend.position = if (leg) c(0.8, 0.8) else "none")
}
unique_ggplot <- function(data, vrb, my_cols_treatment,
my_ylab, leg = FALSE) {
data <- data %>%
dplyr::mutate(sample_date = as.Date(sample_date,
format = "%Y-%m-%d")) %>%
dplyr::select(vrb = tidyselect::all_of(vrb),
"sample_date", "treatment") %>%
tidyr::drop_na() %>%
dplyr::group_by(sample_date, treatment) %>%
dplyr::summarise(mean = mean(vrb),
sd = sd(vrb),
se = sd / sqrt(n()),
conf = se * 1.96)
ggplot(data = data, mapping = aes(x = sample_date,
y = mean,
fill = treatment,
shape = treatment)) +
geom_errorbar(mapping = aes(ymax = mean + conf,
ymin = ifelse(mean - conf < 0,
0,
mean - conf)),
width = 0.2,
color = "black",
lwd = 0.3,
position = position_dodge(width = 1)) +
geom_point(size = 2,
position = position_dodge(width = 1)) +
geom_vline(mapping = aes(xintercept = as.Date("2013-07-16")),
color = "black",
size = 0.5,
lty = 2) +
scale_fill_manual(values = my_cols_treatment,
labels = c("Ambient", "Control", "Warmed"),
name = "Treatment") +
scale_shape_manual(values = c(24, 23, 25),
labels = c("Ambient", "Control", "Warmed"),
name = "Treatment") +
ylab(my_ylab) +
xlab("Sampling date (2013)") +
my_theme(leg)
}
my_cols_treatment <- c(my_cols_treatment, "white")
names(my_cols_treatment) <- c("H", "A", "C")
no2 <- unique_ggplot(data, "NO2_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NO2-"))),
leg = TRUE)
no3 <- unique_ggplot(data, "NO3_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NO3-"))))
nh4 <- unique_ggplot(data, "NH3_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NH4+"))))
gridExtra::grid.arrange(no2, no3, nh4, ncol = 3)
}
make_ed_fig_3 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_3(...), device = "pdf", width = 7,
height = 6, units = "in", onefile = FALSE)
}
ed_fig_3 <- function(co2_data, my_cols_treatment) {
ylims <- co2_data %>%
dplyr::group_by(treatment, period) %>%
dplyr::summarise(lims = boxplot.stats(influx)$stats[c(1, 5)]) %>%
data.frame()
ylims <- range(ylims$lims)
###################
## OULIERS EXCLUDED
###################
ggplot(data = co2_data, mapping = aes(y = influx,
x = period,
fill = treatment,
shape = treatment)) +
geom_boxplot(colour = "black",
show.legend = FALSE,
width = 0.6,
size = 0.3,
outlier.shape = NA) +
geom_point(position = position_jitterdodge(dodge.width = 0.6,
jitter.width = 0.1),
size = 2.5) +
labs(x = "Period",
y = substitute("Daytime CO"[2] *
" influx (" *
mu *
"mol m"^-2 * " d"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 20, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 20, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 17, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 17),
panel.grid = element_blank(),
legend.position = c(0.85, 0.12),
legend.background = element_blank(),
legend.title = element_text(size = 20),
legend.text = element_text(size = 16)) +
scale_x_discrete(labels = c("Before", "After"),
limits = rev(sort(unique(co2_data$period)))) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25)) +
coord_cartesian(ylim = ylims * 1.03)
}
make_ed_fig_5 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_5(...), device = "pdf", width = 10.5,
height = 7.86, units = "in", onefile = FALSE)
}
ed_fig_5 <- function(data, posterior_predictions,
my_cols_treatment, my_cols_group) {
empty_void <- function() {
ggplot() +
theme_void()
}
ambient_panel <- empty_panel_w_txt(color = my_cols_treatment[2],
label = "Ambient")
warmed_panel <- empty_panel_w_txt(color = my_cols_treatment[1],
label = "Warmed")
ponds <- posterior_predictions$ponds
preds <- posterior_predictions$preds
out <- vector(mode = "list", length = nrow(ponds) + 4)
out[[1]] <- ambient_panel
out[[2]] <- warmed_panel
out[[19]] <- empty_void()
out[[20]] <- empty_void()
for (i in ponds$pond) {
grob1 <- grid::grobTree(grid::textGrob(paste0("Pond #", ponds$pond[i]),
x = 0.95, y = 0.9, hjust = 1,
gp = grid::gpar(col = "grey30",
fontsize = 10)))
p <- ggplot(data = preds[[i]], mapping = aes(x = x, y = y_conf,
fill = sample)) +
geom_polygon(alpha = 0.2) +
labs(x = "", y = "") +
scale_color_manual(values = c(my_cols_group[1], my_cols_group[2]),
aesthetics = "fill") +
theme_bw() +
theme(plot.margin = margin(-0.2, 0, 0, 0, "in"),
axis.text.y = element_text(size = 9),
legend.position = "none") +
geom_line(mapping = aes(x = x, y = y_mean, colour = sample),
data = preds[[i]][complete.cases(preds[[i]]), ],
linetype = 2,
size = 0.6,
inherit.aes = FALSE) +
scale_color_manual(values = c("seagreen4", my_cols_group[2]),
aesthetics = "colour") +
geom_point(mapping = aes(x = day, y = add15N_perc,
shape = sample, fill = sample),
size = 2, data = data[data$pond == ponds$pond[i], ],
inherit.aes = FALSE) +
scale_shape_manual(values = c(21, 22)) +
scale_fill_manual(values = c(my_cols_group[1], my_cols_group[2])) +
annotation_custom(grob1)
if (i %in% seq(4, 16, 4)) {
out[[i + 2]] <- p +
theme(axis.text.x = element_text(size = 9))
} else {
out[[i + 2]] <- p +
theme(axis.text.x = element_text(size = 9)) +
scale_x_continuous(labels = rep("", 4), breaks = seq(0, 60, 20))
}
if (i == 8) {
out[[i + 2]] <- out[[i + 2]] +
scale_y_continuous(labels = seq(0, 0.2, 0.1), breaks = seq(0, 0.2, 0.1))
}
}
lay_mat <- rbind(matrix(c(1, 1, 2, 2), 1, 4, byrow = TRUE),
matrix(NA, 1, 4, byrow = TRUE),
matrix(rep(3:18, each = 4), 16, 4),
matrix(c(19, 19, 20, 20), 1, 4, byrow = TRUE))
x <- gridExtra::arrangeGrob(grobs = out, layout_matrix = lay_mat)
ggpubr::annotate_figure(x,
bottom = ggpubr::text_grob("Time (days)",
hjust = 0.5,
vjust = -1,
size = 25,
lineheight = grid::unit(2, "in")),
left = ggpubr::text_grob(substitute("Excess "^15 *
"N"["%"] *
", " * chi),
hjust = 0.25,
vjust = 0.5,
size = 25,
rot = 90)) +
annotation_custom(grid::grid.points(x = grid::unit(1.4, "in"),
y = grid::unit(0.7, "in"),
size = grid::unit(0.15, "in"),
pch = 21,
gp = grid::gpar(col = "black",
fill = my_cols_group[1]))) +
annotation_custom(grid::grid.text("Phytoplankton",
x = grid::unit(1.5, "in"),
y = grid::unit(0.7, "in"),
hjust = 0,
vjust = 0.5,
gp = grid::gpar(cex = 1.2))) +
annotation_custom(grid::grid.points(x = grid::unit(1.4, "in"),
y = grid::unit(0.4, "in"),
size = grid::unit(0.15, "in"),
pch = 22,
gp = grid::gpar(col = "black",
fill = my_cols_group[2]))) +
annotation_custom(grid::grid.text("Zooplankton",
x = grid::unit(1.5, "in"),
y = grid::unit(0.4, "in"),
hjust = 0,
vjust = 0.5,
gp = grid::gpar(cex = 1.2)))
}
make_ed_fig_6 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_6(...), device = "pdf", width = 8.2,
height = 3.8, units = "in", onefile = FALSE)
}
ed_fig_6 <- function(post_effs, diff_biomass, phy_png, zoo_png) {
diff_effs <- tapply(post_effs$param,
list(post_effs$iter,
post_effs$sample),
function(x) 1 - (x[2] / x[1])) %>%
as.data.frame %>%
dplyr::mutate(iter = seq_len(nrow(.))) %>%
tidyr::pivot_longer(c(phytoplankton, zooplankton),
names_to = "sample",
values_to = "diff") %>%
dplyr::mutate(variable = "efficiency") %>%
dplyr::arrange(sample, iter) %>%
dplyr::select(sample, iter, diff, variable) %>%
as.data.frame
plot_data <- rbind(diff_effs, diff_biomass) %>%
dplyr::rename(param = diff) %>%
dplyr::mutate(param = param * 1e2) %>%
plyr::ddply(c("sample", "variable"), clip_vec_hdi)
tp <- plot_data %>%
dplyr::distinct(sample) %>%
dplyr::mutate(param = 1)
ggplot(data = plot_data, mapping = aes(x = param)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf,
inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_density(data = plot_data, adjust = 2, trim = TRUE,
mapping = aes(x = param, fill = variable),
colour = NA) +
geom_density(adjust = 2, alpha = 0.7, trim = TRUE,
mapping = aes(fill = variable, colour = variable),
show.legend = FALSE) +
scale_fill_manual(values = c("black", "grey60"),
labels = c("Biomass", "Efficiency")) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.3, 0.1, 0.3, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.x = element_text(size = 14,
margin = grid::unit(c(0.15,
0,
0,
0),
"in")),
axis.title.y = element_text(size = 14),
legend.position = c(0.5, 1.1),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab("Posterior density (99% C.I.)\n") +
xlab("Percentage decline relative to ambient") +
scale_color_manual(values = c("black", "grey60")) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
coord_cartesian(clip = "off") +
layer(data = plot_data %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.028,
ymax = 0.033,
xmin = -10,
xmax = 10)) +
layer(data = plot_data %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.017,
ymax = 0.024,
xmin = -40,
xmax = -15))
}
make_ed_fig_7 <- function(dest, fig_out_folder, ...) {
pdf(dest, width = 9, height = 2.8)
ed_fig_7(...)
dev.off()
}
ed_fig_7 <- function(control_data, my_cols_group) {
ponds <- unique(control_data$pond)
par(mfrow = c(1, 3),
omi = c(0, 0.5, 0, 0),
mai = c(1.02, 0.72, 0.2, 0.1),
cex = 1,
cex.lab = 1.2,
cex.axis = 0.8,
cex.main = 1.2,
xpd = NA)
for (i in seq_along(ponds)) {
x <- control_data %>%
dplyr::filter(pond == ponds[i])
if (i == 1) {
ylab <- substitute("Excess "^15 * "N"["%"] * ", " * chi)
} else {
ylab <- ""
}
plot(NA, xlim = c(0, 60), ylim = c(0, 0.1),
xlab = ifelse(i == 2, "Time (days)", ""),
ylab = ylab, las = 1)
x %>%
dplyr::filter(sample == "phytoplankton") %>%
{
points(.$day, .$add15N_perc, pch = 21, bg = my_cols_group[1], cex = 1.3)
}
x %>%
dplyr::filter(sample == "zooplankton") %>%
{
points(.$day, .$add15N_perc, pch = 22, bg = my_cols_group[2], cex = 1.3)
}
LoLinR::proportionalLabel(0.95, 0.9, paste0("Control pond ", i),
adj = c(1, 0.5), cex = 0.9)
}
}
make_ed_fig_8 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_8(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
ed_fig_8 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
data <- data %>%
dplyr::group_by(sample, pond, treatment) %>%
dplyr::summarise(av_N_ug_L = mean(av_N_ug_L), ln_av_N_ug_L = log(av_N_ug_L))
a <- ggplot(data = data, mapping = aes(y = ln_av_N_ug_L, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 0.5, ymax = 0.5 + (8.5 - 0.5) / 2),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 0.5 + (8.5 - 0.5) / 2, ymax = 8.5),
fill = "#CCECE6") +
geom_boxplot(aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = interaction(treatment, sample),
shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = substitute("Total Biomass (" * mu * "g N L"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed")) +
scale_y_continuous(limits = c(0.5, 8.5), expand = c(0, 0),
breaks = c(log(8), log(50), log(400),
log(3e3)),
labels = c(8, 50, 400, 3e3))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = 7.4,
ymax = 8.2,
xmin = 0.3,
xmax = 1.3)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = 3,
ymax = 4.3,
xmin = 2,
xmax = 2.5))
}
make_ed_fig_9 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_9(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
ed_fig_9 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
a <- ggplot(data = data, mapping = aes(y = mean, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 3, ymax = 7.5),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 7.5, ymax = 25),
fill = "#CCECE6") +
geom_boxplot(mapping = aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = interaction(treatment, sample), shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = "C:N ratio") +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_y_continuous(trans = "log10", limits = c(3, 25), expand = c(0, 0)) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed"))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = log10(20),
ymax = log10(24),
xmin = 2,
xmax = 2.5)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = log10(3.2),
ymax = log10(4.8),
xmin = 0.5,
xmax = 1))
}
#######################
# SUPPLEMENTARY FIGURES
#######################
make_sp_fig_inorganic <- function(dest, fig_out_folder, model_list, vrb, ...) {
mod <- model_list[[vrb]]
ggplot2::ggsave(dest, make_ba_plots(mod, ...), device = "pdf", width = 14,
height = 7, units = "in", onefile = FALSE)
}
make_sp_fig_4 <- function(dest, fig_out_folder, mod, ...) {
names(mod$data)[1] <- "y"
ggplot2::ggsave(dest, make_ba_plots(mod, ...), device = "pdf", width = 14,
height = 7, units = "in", onefile = FALSE)
}
make_ba_plots <- function(mod, x_lab, my_cols_treatment) {
a <- mod$data %>%
dplyr::select(period, treatment) %>%
dplyr::distinct() %>%
tidybayes::add_fitted_draws(mod, dpar = c("mu", "sigma"),
re_formula = ~ 0) %>%
tidybayes::sample_draws(200) %>%
ggplot(aes(y = interaction(period, treatment))) +
ggdist::stat_dist_slab(mapping = aes(dist = "norm",
arg1 = mu,
arg2 = sigma),
slab_color = "gray65",
alpha = 5e-2,
fill = NA,
size = 0.2) +
geom_point(data = mod$data,
inherit.aes = FALSE,
mapping = aes(x = y,
y = interaction(period, treatment),
fill = treatment,
colour = treatment,
shape = treatment),
size = 3,
alpha = 0.3) +
labs(x = x_lab,
y = "") +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 20, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 20, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 17, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 17),
panel.grid = element_blank(),
legend.position = c(0.85, 0.9),
legend.background = element_blank(),
legend.title = element_text(size = 20),
legend.text = element_text(size = 16)) +
scale_y_discrete(labels = c("After", "Before", "After", "Before")) +
scale_colour_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25))
b <- plot(mod, N = 6, plot = FALSE)
gridExtra::grid.arrange(a, b[[1]], ncol = 2)
}
make_sp_fig_5 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_5(...), device = "pdf", width = 10.2,
height = 4.8, units = "in", onefile = FALSE)
}
sp_fig_5 <- function(cn_ratios, phy_png, zoo_png, my_cols_treatment) {
targets <- names(cn_ratios)
both <- plyr::llply(targets, function(x, models, my_cols_treatment) {
my_cs <- my_cols_treatment
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
data <- models[[x]]$data
names(my_cs) <- c("H", "A")
type <- c("mod_phy" = "phytoplankton", "mod_zoo" = "zooplankton")
s_dat <- data.frame(y = data$CN_ratio,
y_rep = colMeans(brms::posterior_epred(models[[x]])),
group = data$treatment,
stringsAsFactors = FALSE) %>%
dplyr::mutate(cols = my_cs[group])
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
a <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y, fill = group)) +
geom_point(shape = my_shape[type[x]], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
list(data = s_dat, plot = a)
}, models = cn_ratios, my_cols_treatment)
a <- both[[1]]$plot +
layer(data = both[[1]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 27,
ymax = 30,
xmin = 6,
xmax = 11))
b <- both[[2]]$plot +
layer(data = both[[2]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 9,
ymax = 12,
xmin = 2,
xmax = 4))
gridExtra::grid.arrange(a, b, ncol = 2)
}
make_sp_fig_6 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_6(...), device = "pdf", width = 14,
height = 6, units = "in", onefile = FALSE)
}
sp_fig_6 <- function(...) {
pp_checks_set <- function(type, data,
model_list,
my_cols_treatment,
my_shape) {
fit <- model_list[[type]]$model
y_rep <- sim_pp_mean(data, fit, type)
sdat <- data %>%
dplyr::filter(sample == type)
y <- sdat$add15N_perc
x <- sdat$day
bayesplot::color_scheme_set("gray")
# preamble
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
mean_y <- function(data, group) {
mean(data %>%
dplyr::filter(treatment == group) %>%
dplyr::select(add15N_perc) %>%
unlist)
}
# for p_b
h_dat <- plyr::adply(y_rep, 1, function(x, treat) {
data.frame(group = c("A", "H"),
y_rep = tapply(x, treat, mean),
stringsAsFactors = FALSE)
}, treat = sdat$treatment)
# for p_c
my_cs <- my_cols_treatment
names(my_cs) <- c("H", "A")
s_dat <- data.frame(y = y,
y_rep = colMeans(y_rep),
group = sdat$treatment,
cols = my_cs[sdat$treatment],
stringsAsFactors = FALSE)
p_a <- bayesplot::ppc_dens_overlay(y, y_rep[1:200, ]) +
labs(x = substitute("Excess "^15 * "N"["%"] * ", " * chi),
y = "Density") +
my_theme() +
theme(legend.text = element_blank(),
legend.position = c(0.58, 0.79),
legend.background = element_blank()) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
annotate_textp("Observed", x = 0.9, y = 0.85, hjust = 0) +
annotate_textp("Predicted", x = 0.9, y = 0.74, hjust = 0)
p_b <- ggplot(h_dat, aes(x = y_rep)) +
geom_histogram(data = dplyr::filter(h_dat, group == "A"),
colour = "black",
fill = my_cols_treatment[2],
size = 0.2) +
geom_vline(mapping = aes(xintercept = mean_y(sdat, "A")),
color = "black",
size = 0.5,
lty = 2) +
geom_histogram(data = dplyr::filter(h_dat, group == "H"),
colour = "black",
fill = my_cols_treatment[1],
size = 0.2) +
geom_vline(mapping = aes(xintercept = mean_y(sdat, "H")),
color = "black",
size = 0.5,
lty = 2) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05)),
breaks = seq(0, 6000, 2000),
labels = c("0.0", "2.0", "4.0", "6.0")) +
labs(x = substitute("Mean " * chi * " (%)"),
y = "Frequency (thousands)") +
my_theme() +
theme(legend.title = element_blank(),
legend.text = element_text(size = 18, hjust = 0),
legend.position = c(0.8, 0.8),
legend.background = element_blank())
p_c <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y,
fill = group)) +
geom_point(shape = my_shape[type], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
p_d <- bayesplot::ppc_intervals(y = y, yrep = y_rep, x = x, prob = 0.5) +
labs(x = "Time (days)",
y = substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
my_theme() +
theme(legend.text = element_blank(),
legend.position = c(0.58, 0.79),
legend.background = element_blank()) +
annotate_textp("Observed", x = 0.92, y = 0.85, hjust = 0) +
annotate_textp("Predicted", x = 0.92, y = 0.74, hjust = 0)
list(p_a = p_a, p_b = p_b, p_c = p_c, p_d = p_d)
}
out <- vector(mode = "list", length = 2)
names(out) <- c("phy", "zoo")
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
for (i in seq_along(out)) {
out[[i]] <- pp_checks_set(names(my_shape)[i],
my_shape = my_shape,
...)
names(out[[i]]) <- paste0(names(out)[i], "_", names(out[[i]]))
}
p <- append(out[["phy"]], out[["zoo"]])
p <- gridExtra::grid.arrange(p$phy_p_a, p$phy_p_b,
p$phy_p_c, p$phy_p_d,
p$zoo_p_a, p$zoo_p_b,
p$zoo_p_c, p$zoo_p_d,
ncol = 4)
ggpubr::annotate_figure(p,
left = ggpubr::text_grob("",
hjust = 0.2,
vjust = 0.4,
size = 40,
rot = 90)) +
annotation_custom(grid::grid.text("Phytoplankton",
x = grid::unit(0.015, "npc"),
y = grid::unit(0.8, "npc"),
gp = grid::gpar("fontsize" = 20),
hjust = 0.5,
vjust = 0.5,
rot = 90)) +
annotation_custom(grid::grid.text("Zooplankton",
x = grid::unit(0.015, "npc"),
y = grid::unit(0.3, "npc"),
gp = grid::gpar("fontsize" = 20),
hjust = 0.5,
vjust = 0.5,
rot = 90))
}
make_sp_fig_7 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_7(...), device = "pdf", width = 10.2,
height = 4.8, units = "in", onefile = FALSE)
}
sp_fig_7 <- function(hierarchical_biomass, phy_png,
zoo_png, my_cols_treatment) {
targets <- names(hierarchical_biomass)
both <- plyr::llply(targets, function(x, models, my_cols_treatment) {
my_cs <- my_cols_treatment
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
names(my_cs) <- c("H", "A")
data <- models[[x]]$data
s_dat <- data.frame(y = data$ln_av_C_ug_L,
y_rep = colMeans(brms::posterior_epred(models[[x]])),
group = data$treatment,
stringsAsFactors = FALSE) %>%
dplyr::mutate(cols = my_cs[group])
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
a <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y,
fill = group)) +
geom_point(shape = my_shape[x], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
list(data = s_dat, plot = a)
}, models = hierarchical_biomass, my_cols_treatment)
a <- both[[1]]$plot +
layer(data = both[[1]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 10,
ymax = 10.6,
xmin = 6.2,
xmax = 7))
b <- both[[2]]$plot +
layer(data = both[[2]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 4.5,
ymax = 6.5,
xmin = -1.3,
xmax = 0.2))
gridExtra::grid.arrange(a, b, ncol = 2)
}
make_sp_fig_8 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_8(...), device = "pdf", width = 7,
height = 7, units = "in", onefile = FALSE)
}
sp_fig_8 <- function(community_data_2012, community_data_2016,
size_data_2012, size_data_2016,
my_cols_treatment) {
sp_fig_8_bxpl <- function(data, my_cols_treatment, title) {
cols <- rev(my_cols_treatment)
ggplot(data = data, mapping = aes(y = log_av_C_ug, x = treatment)) +
geom_boxplot(mapping = aes(colour = treatment),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 0.7,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = treatment,
shape = treatment),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(24, 25)) +
labs(x = "Treatment",
y = expression(log[10]~Organism~mass~(µg~C))) +
theme_classic() +
theme(plot.title = element_text(face = "bold")) +
ggtitle(title) +
scale_x_discrete(labels = c("Ambient", "Warmed"))
}
sp_fig_8_nmds <- function(data, my_cols_treatment, tlab) {
treat_rest <- data$treatment
data <- data %>%
dplyr::select(-pond, -treatment)
bc_mds <- vegan::metaMDS(data, distance = "bray",
trace = FALSE, autotransform = FALSE,
k = 2)
ponds <- data.frame(bc_mds$points) %>%
dplyr::mutate(treat = treat_rest,
shape = ifelse(treat == "H", 25, 24))
species <- data.frame(bc_mds$species) %>%
dplyr::arrange(MDS1) %>%
tidyr::drop_na()
spp_h <- head(species, 4)
spp_a <- tail(species, 4)
spp <- rbind(spp_h, spp_a) %>%
dplyr::mutate(treat = rep(c("H", "A"), each = 4),
shape = rep(c(25, 24), each = 4))
treat_a <- ponds[ponds$treat == "A", ][chull(ponds[ponds$treat == "A",
c("MDS1", "MDS2")]), ]
treat_h <- ponds[ponds$treat == "H", ][chull(ponds[ponds$treat == "H",
c("MDS1", "MDS2")]), ]
hull_data <- rbind(treat_a, treat_h)
ggplot() +
geom_point(data = ponds,
mapping = aes(x = MDS1,
y = MDS2,
fill = treat),
size = 3,
shape = ponds$shape) +
geom_point(data = species,
mapping = aes(x = MDS1,
y = MDS2,
alpha = 0.001),
size = 1.5) +
geom_point(data = spp,
mapping = aes(x = MDS1,
y = MDS2,
alpha = 0.001),
size = 5,
shape = spp$shape) +
geom_polygon(data = hull_data,
mapping = aes(x = MDS1,
y = MDS2,
fill = treat,
group = treat),
alpha = 0.30) +
scale_colour_manual(values = rev(my_cols_treatment)) +
scale_fill_manual(values = rev(my_cols_treatment)) +
scale_shape_manual(values = c(21, 21)) +
labs(x = "NMDS1", y = "NMDS2") +
theme_classic() +
ggtitle(tlab) +
theme(legend.position = "none",
plot.title = element_text(face = "bold"))
}
nmds_2012 <- sp_fig_8_nmds(community_data_2012,
my_cols_treatment,
"a (2012)") +
geom_text(mapping = aes(x = -1, y = -1,
label = "Stress = 0.12",
size = 5), hjust = 0) +
scale_y_continuous(limits = c(-1, 1),
breaks = c(-1, -0.5, 0, 0.5, 1))
nmds_2016 <- sp_fig_8_nmds(community_data_2016,
my_cols_treatment,
"b (2016)") +
geom_text(mapping = aes(x = -1.1, y = -1.8,
label = "Stress = 0.05",
size = 5), hjust = 0) +
scale_y_continuous(limits = c(-1.8, 2),
breaks = c(-1.8, -0.9, 0, 0.9, 1.8))
bxpl_2012 <- sp_fig_8_bxpl(size_data_2012, my_cols_treatment, "c (2012)") +
scale_y_continuous(limits = c(-4.8, -2.4),
breaks = c(-4.8, -3.8, -2.8))
bxpl_2016 <- sp_fig_8_bxpl(size_data_2016, my_cols_treatment, "d (2016)") +
scale_y_continuous(limits = c(-4.8, -3.5),
breaks = c(-4.8, -4.2, -3.8))
gridExtra::grid.arrange(nmds_2012, nmds_2016,
bxpl_2012, bxpl_2016, ncol = 2)
}
make_sp_fig_9 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_9(...), device = "pdf", width = 6.3,
height = 3.76, units = "in", onefile = FALSE)
}
sp_fig_9 <- function(data, my_cols_treatment) {
data <- data %>%
dplyr::mutate(shape = ifelse(treat == "H", 25, 24))
ggplot(data = data) +
geom_line(mapping = aes(x = doy,
y = ratio_fits,
colour = treat,
linetype = treat)) +
scale_colour_manual(values = rev(my_cols_treatment)) +
scale_fill_manual(values = rev(my_cols_treatment)) +
geom_point(mapping = aes(x = doy,
y = ER.GPP,
colour = treat,
fill = treat),
alpha = 0.3,
shape = data$shape) +
cowplot::theme_cowplot(font_size = 12) +
theme(legend.position = "none") +
facet_wrap(~ year, scales = "fixed") +
theme(strip.background = element_blank()) +
xlab("Day of the Year") +
ylab(expression(R[eco] / GPP))
}
make_sp_fig_10 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_10(...), device = "pdf", width = 5.82,
height = 2.95, units = "in", onefile = FALSE)
}
sp_fig_10 <- function(pp_means, phy_png, zoo_png, my_cols_treatment) {
make_curve <- function(days, ln_ka, logit_ke, logit_phi) {
ka <- exp(ln_ka)
ke <- 1 / (1 + exp(-logit_ke))
phi <- (1 / ke) / (1 + exp(-logit_phi))
calc_eqn_1(days, ka, ke, phi)
}
curve_df <- function(data, days) {
c_amb <- make_curve(days, data$ln_ka_amb,
data$logit_ke_amb,
data$logit_phi_amb)
c_war <- make_curve(days, data$ln_ka_war,
data$logit_ke_war,
data$logit_phi_war)
# cap samples that yield predictions
# that are within the reasonable range of
# data
if (max(c_amb) < 1 && max(c_war) < 1) {
data.frame(sample = data$sample,
days = days,
values = c(c_amb, c_war),
treatment = rep(c("amb", "war"),
each = length(c_amb)))
}
}
make_prior_lines <- function(ln_ka_amb, ln_ka_war, logit_ke_amb,
logit_ke_war, logit_phi_amb, logit_phi_war,
sample, iter) {
plot_data <- data.frame(iter = seq_len(iter)) %>%
dplyr::mutate(ln_ka_amb = rnorm(iter, ln_ka_amb, 1),
logit_ke_amb = rnorm(iter, logit_ke_amb, 1),
logit_phi_amb = rnorm(iter, logit_phi_amb, 1),
ln_ka_war = rnorm(iter, ln_ka_war, 1),
logit_ke_war = rnorm(iter, logit_ke_war, 1),
logit_phi_war = rnorm(iter, logit_phi_war, 1),
sample = sample)
days <- seq(0.01, 60, length.out = 60)
plyr::ddply(plot_data, .(iter), curve_df, days)
}
prior_bkg <- data.frame(sample = c("phytoplankton", "zooplankton")) %>%
dplyr::mutate(ln_ka_amb = c(0, 0),
ln_ka_war = c(0, 0),
logit_ke_amb = c(0, 0),
logit_ke_war = c(0, 0),
logit_phi_amb = c(0, 0),
logit_phi_war = c(0, 0))
set.seed(1)
prior_lines <- plyr::ddply(prior_bkg, .(sample), function(data) {
make_prior_lines(data$ln_ka_amb, data$ln_ka_war, data$logit_ke_amb,
data$logit_ke_war, data$logit_phi_amb, data$logit_phi_war,
data$sample, iter = 1000)
})
chosen <- dplyr::distinct(prior_lines, iter, sample) %>%
dplyr::group_by(sample) %>%
dplyr::summarise(chosen = sample(iter, 300)) %>%
data.frame()
prior_lines <- prior_lines %>%
dplyr::group_by(sample) %>%
dplyr::filter(iter %in% chosen$chosen[chosen$sample %in% unique(sample)]) %>%
data.frame()
prior_means <- plyr::ddply(prior_bkg, .(sample), curve_df,
days = seq(0.01, 60, length.out = 60)) %>%
dplyr::filter(treatment == "amb") %>%
dplyr::select(sample, days, values)
linesd <- pp_means$lines %>%
dplyr::mutate(col = ifelse(treatment == "amb",
my_cols_treatment[2],
my_cols_treatment[1]))
tp <- linesd %>%
dplyr::distinct(sample, treatment) %>%
dplyr::mutate(x = 1)
ggplot() +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf,
ymax = Inf, inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_line(data = prior_lines,
mapping = aes(x = days, y = values,
group = paste(iter, treatment)),
colour = "grey40", lty = 1, size = 0.5, alpha = 0.07,
show.legend = FALSE) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean,
group = treatment,
linetype = treatment),
col = "black", size = 1, show.legend = FALSE) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean,
group = treatment,
linetype = treatment,
colour = treatment),
size = 1) +
scale_colour_manual(labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_linetype_manual(labels = c("Ambient", "Warmed"),
values = c(1, 3)) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.4, 0.1, 0.1, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 14),
legend.position = c(0.5, 1.2),
legend.direction = "horizontal",
legend.background = element_blank(),
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab(substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
xlab("Time (days)") +
layer(data = linesd %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.55,
ymax = 0.75,
xmin = 50,
xmax = 60)) +
layer(data = linesd %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.45,
ymax = 0.65,
xmin = 50,
xmax = 63))
}
make_sp_fig_11_13 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, supp_resid_figure(...), device = "pdf", width = 10.5,
height = 7.86, units = "in", onefile = FALSE)
}
supp_resid_figure <- function(data, pred_tag, my_cols_group, lab) {
names(my_cols_group) <- c("phytoplankton", "zooplankton")
data <- data %>%
dplyr::filter(predictor == pred_tag)
ggplot(data = data) +
geom_polygon(mapping = aes(x = x_conf, y = y_conf,
fill = sample),
alpha = 0.5,
show.legend = FALSE) +
geom_line(mapping = aes(x = x, y = y,
linetype = sample),
colour = "black",
show.legend = FALSE) +
geom_point(mapping = aes(x = raw_x,
y = raw_y,
shape = sample,
fill = sample),
show.legend = FALSE,
size = 2) +
labs(x = lab,
y = "Model residuals") +
scale_shape_manual(values = c("phytoplankton" = 21,
"zooplankton" = 22)) +
scale_fill_manual(values = my_cols_group) +
theme_bw() +
facet_wrap(~ pond, scales = "free", dir = "v") +
theme(axis.title = element_text(size = 15))
}
make_resid_plot_data <- function(resid_brm_model, data) {
plot_data <- plyr::ldply(resid_brm_model, function(model) {
df <- model$data %>%
tidyr::pivot_longer(cols = c(NO2_uM, NO3_uM, NH3_uM),
names_to = "predictor",
values_to = "value")
plyr::ddply(df, .(pond), function(df_i, ...) {
means <- tapply(df_i$value, df_i$predictor, mean)
plyr::ddply(df_i, .(predictor), function(dd, model, means) {
pred_name <- unique(dd$predictor)
means <- means[names(means) != pred_name]
nd <- data.frame(x = seq(min(dd$value),
max(dd$value),
length.out = 50),
pond = unique(dd$pond),
pred_1 = means[[1]],
pred_2 = means[[2]])
names(nd) <- c(pred_name, "pond", names(means))
fits <- fitted(model, newdata = nd) %>%
cbind(nd)
fits %>% {
data.frame(y = NA,
x = NA,
x_conf = c(.[[pred_name]], rev(.[[pred_name]])),
y_conf = c(.$Q2.5, rev(.$Q97.5)),
raw_x = NA,
raw_y = NA)
} %>%
rbind(fits %>% {
data.frame(y = .$Estimate,
x = .[[pred_name]],
x_conf = NA,
y_conf = NA,
raw_x = NA,
raw_y = NA)
}) %>%
rbind(dd %>% {
data.frame(y = NA,
x = NA,
x_conf = NA,
y_conf = NA,
raw_x = .$value,
raw_y = .$residual_15N_perc)
})
}, means = means, ...)
}, model = model)
})
plot_data$treatment <- data$treatment[match(plot_data$pond, data$pond)]
plot_data$pond <- paste0("Pond = ", formatC(plot_data$pond, width = 2,
format = "d", flag = "0"),
"; Treatment = ",
ifelse(plot_data$treatment == "H",
"W", plot_data$treatment))
plot_data
}
|
/R/figures.R
|
permissive
|
dbarneche/nature20200508666
|
R
| false | false | 71,886 |
r
|
######################
# AUXILLIARY FUNCTIONS
######################
generate_png <- function(origin_file_name, output_file_name) {
system(paste0("sips -s formatOptions best -s format png ",
origin_file_name, " --out ", output_file_name))
file.info(output_file_name)
}
trim_png_read <- function(file_path) {
png_file <- png::readPNG(file_path)
trim_png_blanks(png_file)
}
are_all_one <- function(x) {
all(x == 1)
}
trim_png_blanks <- function(png_mat) {
chosen_rows <- !apply(png_mat[, , 1], 1, are_all_one)
chosen_cols <- !apply(png_mat[, , 1], 2, are_all_one)
png_mat[chosen_rows, chosen_cols, ]
}
make_grob_png <- function(...) {
grid::rasterGrob(trim_png_read(...), interpolate = TRUE)
}
# from stackoverflow
# question 22488563
# ggplot2-annotate-layer-position-in-r
annotate_textp <- function(label, x, y, facets = NULL, hjust = 0,
vjust = 0, color = "black", alpha = NA,
family = thm$text$family,
size = thm$text$size, fontface = 1,
line_height = 1.0,
box_just = ifelse(c(x, y) < 0.5, 0, 1),
margin = grid::unit(size / 2, "pt"),
thm = theme_get()) {
x <- scales::squish_infinite(x)
y <- scales::squish_infinite(y)
tg <- grid::textGrob(label, x = 0, y = 0, hjust = hjust, vjust = vjust,
gp = grid::gpar(col = alpha(color, alpha),
fontsize = size, fontfamily = family,
fontface = fontface,
lineheight = line_height))
ts <- grid::unit.c(grid::grobWidth(tg), grid::grobHeight(tg))
vp <- grid::viewport(x = x, y = y, width = ts[1], height = ts[2],
just = box_just)
tg <- grid::editGrob(tg, x = ts[1] * hjust, y = ts[2] * vjust, vp = vp)
unt <- grid::unit(1, "npc") - margin * 2
inr <- grid::grobTree(tg, vp = grid::viewport(width = unt, height = unt))
layer(data = NULL, stat = StatIdentity, position = PositionIdentity,
geom = GeomCustomAnn, inherit.aes = TRUE,
params = list(grob = grid::grobTree(inr),
xmin = -Inf,
xmax = Inf,
ymin = -Inf,
ymax = Inf))
}
clip_vec_hdi <- function(data, probs = c(0.005, 0.995)) {
lim_x <- quantile_hdi(data$param, probs = probs)
data %>%
dplyr::filter(param >= lim_x[[1]] & param <= lim_x[[2]])
}
empty_panel_w_txt <- function(color, label) {
grid::grobTree(grid::rectGrob(width = grid::unit(0.915, "npc"),
height = grid::unit(0.9, "npc"),
just = 0.45,
gp = grid::gpar(fill = color)),
grid::textGrob(label,
gp = grid::gpar(fontsize = 15,
col = "black"),
hjust = 0.4))
}
sim_pp_mean <- function(data, model, type) {
posteriors <- as.data.frame(model)
data <- dplyr::filter(data, sample == tidyselect::all_of(type))
y_rep <- matrix(0, nrow(posteriors), nrow(data))
for (j in seq_len(ncol(y_rep))) {
target_phi <- paste0("r_phi[", data$pond[j], "]")
target_ka <- paste0("r_ka[", data$pond[j], "]")
target_ke <- paste0("r_ke[", data$pond[j], "]")
phi <- posteriors[, target_phi]
ka <- posteriors[, target_ka]
ke <- posteriors[, target_ke]
sg <- posteriors[, "sigma"]
mu <- calc_eqn_1(data$day[j], ka, ke, phi)
y_rep[, j] <- rnorm(rep(1, nrow(y_rep)), mu, sg)
}
y_rep
}
###############
# PAPER FIGURES
###############
make_fig_1 <- function(dest, fig_out_folder, ...) {
pdf(dest, width = 5.819004, height = 2.950226)
p <- fig_1(...)
print(p)
grid::grid.text("a", x = grid::unit(0.11, "npc"), y = grid::unit(0.82, "npc"),
gp = grid::gpar("fontsize" = 13, "fontface" = "bold"))
grid::grid.text("b", x = grid::unit(0.58, "npc"), y = grid::unit(0.82, "npc"),
gp = grid::gpar("fontsize" = 13, "fontface" = "bold"))
dev.off()
}
fig_1 <- function(post_pred_means, my_cols_treatment, phy_png, zoo_png) {
polygons <- post_pred_means$polygons
linesd <- post_pred_means$lines
tp <- polygons %>%
dplyr::distinct(sample, treatment) %>%
dplyr::mutate(x = 1)
ggplot(data = polygons, mapping = aes(x = x, y = y_cred)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf,
ymax = Inf, inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_polygon(data = polygons,
mapping = aes(x = x, y = y_cred, fill = treatment)) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean, group = treatment),
col = "black", lty = 1, size = 0.5) +
geom_polygon(data = polygons,
mapping = aes(x = x, y = y_cred, fill = treatment),
alpha = 0.7,
inherit.aes = FALSE) +
scale_fill_manual(labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.4, 0.1, 0.1, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 14),
legend.position = c(0.5, 1.2),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab(substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
xlab("Time (days)") +
layer(data = polygons %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.5,
ymax = 0.65,
xmin = 43,
xmax = 63)) +
layer(data = polygons %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.3,
ymax = 0.45,
xmin = 45,
xmax = 65))
}
make_fig_2 <- function(dest, fig_out_folder, ...) {
p <- fig_2(...)
pdf(dest, width = 5.737556, height = 6.950226)
print(p)
grid.text(substitute(kappa["a"] * " (d"^-1 * ")"),
x = grid::unit(0.56, "npc"),
y = grid::unit(0.775, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute(kappa["e"] * " (d"^-1 * ")"),
x = grid::unit(0.56, "npc"),
y = grid::unit(0.54, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute(italic(phi) * " (% d)"),
x = grid::unit(0.565, "npc"),
y = grid::unit(0.31, "npc"),
gp = grid::gpar("fontsize" = 10))
grid.text(substitute("Efficiency of N transfer (" * bar(epsilon) * ")"),
x = grid::unit(0.54, "npc"),
y = grid::unit(0.075, "npc"),
gp = grid::gpar("fontsize" = 10))
# legends
grid.text("a",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.985, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("b",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.76, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("c",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.525, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
grid.text("d",
x = grid::unit(0.13, "npc"),
y = grid::unit(0.295, "npc"),
gp = grid::gpar("fontsize" = 11, "fontface" = "bold"))
dev.off()
}
fig_2 <- function(data_eff, data_ka, data_ke, data_phi,
my_cols_treatment, phy_png, zoo_png) {
nreps <- nrow(data_eff)
plot_data <- do.call("rbind.data.frame",
list(data_ka, data_ke, data_phi, data_eff)) %>%
dplyr::mutate(name = rep(c("a_kas", "b_kes", "c_phi", "d_eff"),
each = nreps)) %>%
plyr::ddply(.(name, sample, treatment), clip_vec_hdi)
tp <- plot_data %>%
dplyr::distinct(sample, name) %>%
dplyr::mutate(param = 1)
ggplot(data = plot_data, mapping = aes(x = param)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf,
inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_density(data = plot_data, adjust = 2, trim = TRUE,
mapping = aes(x = param, fill = treatment),
colour = NA) +
geom_density(adjust = 2, alpha = 0.7, trim = TRUE,
mapping = aes(fill = treatment, colour = treatment),
show.legend = FALSE) +
scale_fill_manual(values = rev(my_cols_treatment),
labels = c("Ambient", "Warmed")) +
facet_wrap(name~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(-0.3, 0.1, 0.6, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.y = grid::unit(-1.2, "lines"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 14),
legend.position = c(0.5, -0.1),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab("Posterior density (99% C.I.)\n") +
scale_color_manual(values = rev(my_cols_treatment)) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
coord_cartesian(clip = "off") +
layer(data = plot_data %>%
dplyr::filter(sample == "phytoplankton" & name == "a_kas"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 2.2,
ymax = 3,
xmin = 1,
xmax = 1.25)) +
layer(data = plot_data %>%
dplyr::filter(sample == "zooplankton" & name == "a_kas"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 6,
ymax = 10.5,
xmin = 0.62,
xmax = 0.74))
}
make_fig_3 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, fig_3(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
fig_3 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
data <- data %>%
dplyr::group_by(sample, pond, treatment) %>%
dplyr::summarise(av_C_ug_L = mean(av_C_ug_L), ln_av_C_ug_L = log(av_C_ug_L))
a <- ggplot(data = data, mapping = aes(y = ln_av_C_ug_L, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 1.8, ymax = 1.8 + (10.7 - 1.8) / 2),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 1.8 + (10.7 - 1.8) / 2, ymax = 10.7),
fill = "#CCECE6") +
geom_boxplot(mapping = aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3) +
geom_point(mapping = aes(fill = interaction(treatment, sample),
shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = substitute("Total Biomass (" * mu * "g C L"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed")) +
scale_y_continuous(limits = c(1.8, 10.7), expand = c(0, 0),
breaks = c(log(8), log(50), log(400),
log(3e3), log(22e3)),
labels = c(8, 50, 400, 3e3, 22e3))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = 6.6,
ymax = 7.4,
xmin = 0.3,
xmax = 1.3)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = 4.5,
ymax = 6,
xmin = 2,
xmax = 2.5))
}
#######################
# EXTENDED DATA FIGURES
#######################
make_internal_ed_fig_1d <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, internal_ed_fig_1d(...), device = "pdf", width = 8.5,
height = 3.5, units = "in", onefile = FALSE)
}
internal_ed_fig_1d <- function(physchem_data, my_cols_treatment) {
make_bef_aft_data <- function(data, vrb) {
data$y_var <- data[[vrb]]
data <- data %>%
dplyr::select(Date, pairs, treatment, pond, y_var) %>%
tidyr::drop_na()
bef <- data %>%
dplyr::filter(Date < as.Date("2013-07-16")) %>%
dplyr::group_by(pairs, treatment, pond) %>%
dplyr::summarise(mean = mean(y_var)) %>%
dplyr::mutate(period = "before") %>%
data.frame()
aft <- data %>%
dplyr::filter(Date > as.Date("2013-07-16") &
Date < as.Date("2013-07-20")) %>%
dplyr::group_by(pairs, treatment, pond) %>%
dplyr::summarise(mean = mean(y_var)) %>%
dplyr::mutate(period = "after") %>%
data.frame()
rbind(bef, aft)
}
plot_bef_aft <- function(data, vrb, ylab, my_cols_treatment) {
data$vrb <- data[[vrb]]
ggplot(data = data, mapping = aes(y = vrb, x = period,
fill = treatment,
shape = treatment)) +
geom_boxplot(colour = "black",
show.legend = FALSE,
width = 0.6,
size = 0.3,
outlier.shape = NA) +
geom_point(position = position_dodge(0.4),
size = 2.5) +
labs(x = "Treatment",
y = ylab) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(labels = c("Before", "After")) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25))
}
pdosat <- make_bef_aft_data(physchem_data, "DOsat") %>%
plot_bef_aft("mean", "DO saturation (%)", my_cols_treatment) +
scale_y_continuous(trans = "log10") +
theme(legend.position = "none")
pph <- make_bef_aft_data(physchem_data, "pH") %>%
plot_bef_aft("mean", "pH", my_cols_treatment) +
theme(legend.position = "none")
gridExtra::grid.arrange(pdosat, pph, ncol = 2)
}
make_ed_fig_2 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_2(...), device = "pdf", width = 12,
height = 4, units = "in", onefile = FALSE)
}
ed_fig_2 <- function(data, my_cols_treatment) {
my_theme <- function(leg = FALSE) {
theme_bw() +
theme(axis.title.y = element_text(size = 14,
margin = margin(t = 0,
r = 10,
b = 0,
l = 10)),
axis.title.x = element_text(size = 14,
margin = margin(t = 10,
r = 0,
b = 10,
l = 0)),
axis.text.x = element_text(size = 12,
margin = margin(t = 4,
r = 0,
b = 0,
l = 0)),
axis.text.y = element_text(size = 12,
margin = margin(t = 0,
r = 4,
b = 0,
l = 0)),
axis.ticks.length = grid::unit(5, "pt"),
strip.background = element_blank(),
legend.position = if (leg) c(0.8, 0.8) else "none")
}
unique_ggplot <- function(data, vrb, my_cols_treatment,
my_ylab, leg = FALSE) {
data <- data %>%
dplyr::mutate(sample_date = as.Date(sample_date,
format = "%Y-%m-%d")) %>%
dplyr::select(vrb = tidyselect::all_of(vrb),
"sample_date", "treatment") %>%
tidyr::drop_na() %>%
dplyr::group_by(sample_date, treatment) %>%
dplyr::summarise(mean = mean(vrb),
sd = sd(vrb),
se = sd / sqrt(n()),
conf = se * 1.96)
ggplot(data = data, mapping = aes(x = sample_date,
y = mean,
fill = treatment,
shape = treatment)) +
geom_errorbar(mapping = aes(ymax = mean + conf,
ymin = ifelse(mean - conf < 0,
0,
mean - conf)),
width = 0.2,
color = "black",
lwd = 0.3,
position = position_dodge(width = 1)) +
geom_point(size = 2,
position = position_dodge(width = 1)) +
geom_vline(mapping = aes(xintercept = as.Date("2013-07-16")),
color = "black",
size = 0.5,
lty = 2) +
scale_fill_manual(values = my_cols_treatment,
labels = c("Ambient", "Control", "Warmed"),
name = "Treatment") +
scale_shape_manual(values = c(24, 23, 25),
labels = c("Ambient", "Control", "Warmed"),
name = "Treatment") +
ylab(my_ylab) +
xlab("Sampling date (2013)") +
my_theme(leg)
}
my_cols_treatment <- c(my_cols_treatment, "white")
names(my_cols_treatment) <- c("H", "A", "C")
no2 <- unique_ggplot(data, "NO2_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NO2-"))),
leg = TRUE)
no3 <- unique_ggplot(data, "NO3_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NO3-"))))
nh4 <- unique_ggplot(data, "NH3_uM", my_cols_treatment,
substitute(mu * "mol " * a * " L"^-1,
list(a = chemf("NH4+"))))
gridExtra::grid.arrange(no2, no3, nh4, ncol = 3)
}
make_ed_fig_3 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_3(...), device = "pdf", width = 7,
height = 6, units = "in", onefile = FALSE)
}
ed_fig_3 <- function(co2_data, my_cols_treatment) {
ylims <- co2_data %>%
dplyr::group_by(treatment, period) %>%
dplyr::summarise(lims = boxplot.stats(influx)$stats[c(1, 5)]) %>%
data.frame()
ylims <- range(ylims$lims)
###################
## OULIERS EXCLUDED
###################
ggplot(data = co2_data, mapping = aes(y = influx,
x = period,
fill = treatment,
shape = treatment)) +
geom_boxplot(colour = "black",
show.legend = FALSE,
width = 0.6,
size = 0.3,
outlier.shape = NA) +
geom_point(position = position_jitterdodge(dodge.width = 0.6,
jitter.width = 0.1),
size = 2.5) +
labs(x = "Period",
y = substitute("Daytime CO"[2] *
" influx (" *
mu *
"mol m"^-2 * " d"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 20, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 20, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 17, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 17),
panel.grid = element_blank(),
legend.position = c(0.85, 0.12),
legend.background = element_blank(),
legend.title = element_text(size = 20),
legend.text = element_text(size = 16)) +
scale_x_discrete(labels = c("Before", "After"),
limits = rev(sort(unique(co2_data$period)))) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25)) +
coord_cartesian(ylim = ylims * 1.03)
}
make_ed_fig_5 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_5(...), device = "pdf", width = 10.5,
height = 7.86, units = "in", onefile = FALSE)
}
ed_fig_5 <- function(data, posterior_predictions,
my_cols_treatment, my_cols_group) {
empty_void <- function() {
ggplot() +
theme_void()
}
ambient_panel <- empty_panel_w_txt(color = my_cols_treatment[2],
label = "Ambient")
warmed_panel <- empty_panel_w_txt(color = my_cols_treatment[1],
label = "Warmed")
ponds <- posterior_predictions$ponds
preds <- posterior_predictions$preds
out <- vector(mode = "list", length = nrow(ponds) + 4)
out[[1]] <- ambient_panel
out[[2]] <- warmed_panel
out[[19]] <- empty_void()
out[[20]] <- empty_void()
for (i in ponds$pond) {
grob1 <- grid::grobTree(grid::textGrob(paste0("Pond #", ponds$pond[i]),
x = 0.95, y = 0.9, hjust = 1,
gp = grid::gpar(col = "grey30",
fontsize = 10)))
p <- ggplot(data = preds[[i]], mapping = aes(x = x, y = y_conf,
fill = sample)) +
geom_polygon(alpha = 0.2) +
labs(x = "", y = "") +
scale_color_manual(values = c(my_cols_group[1], my_cols_group[2]),
aesthetics = "fill") +
theme_bw() +
theme(plot.margin = margin(-0.2, 0, 0, 0, "in"),
axis.text.y = element_text(size = 9),
legend.position = "none") +
geom_line(mapping = aes(x = x, y = y_mean, colour = sample),
data = preds[[i]][complete.cases(preds[[i]]), ],
linetype = 2,
size = 0.6,
inherit.aes = FALSE) +
scale_color_manual(values = c("seagreen4", my_cols_group[2]),
aesthetics = "colour") +
geom_point(mapping = aes(x = day, y = add15N_perc,
shape = sample, fill = sample),
size = 2, data = data[data$pond == ponds$pond[i], ],
inherit.aes = FALSE) +
scale_shape_manual(values = c(21, 22)) +
scale_fill_manual(values = c(my_cols_group[1], my_cols_group[2])) +
annotation_custom(grob1)
if (i %in% seq(4, 16, 4)) {
out[[i + 2]] <- p +
theme(axis.text.x = element_text(size = 9))
} else {
out[[i + 2]] <- p +
theme(axis.text.x = element_text(size = 9)) +
scale_x_continuous(labels = rep("", 4), breaks = seq(0, 60, 20))
}
if (i == 8) {
out[[i + 2]] <- out[[i + 2]] +
scale_y_continuous(labels = seq(0, 0.2, 0.1), breaks = seq(0, 0.2, 0.1))
}
}
lay_mat <- rbind(matrix(c(1, 1, 2, 2), 1, 4, byrow = TRUE),
matrix(NA, 1, 4, byrow = TRUE),
matrix(rep(3:18, each = 4), 16, 4),
matrix(c(19, 19, 20, 20), 1, 4, byrow = TRUE))
x <- gridExtra::arrangeGrob(grobs = out, layout_matrix = lay_mat)
ggpubr::annotate_figure(x,
bottom = ggpubr::text_grob("Time (days)",
hjust = 0.5,
vjust = -1,
size = 25,
lineheight = grid::unit(2, "in")),
left = ggpubr::text_grob(substitute("Excess "^15 *
"N"["%"] *
", " * chi),
hjust = 0.25,
vjust = 0.5,
size = 25,
rot = 90)) +
annotation_custom(grid::grid.points(x = grid::unit(1.4, "in"),
y = grid::unit(0.7, "in"),
size = grid::unit(0.15, "in"),
pch = 21,
gp = grid::gpar(col = "black",
fill = my_cols_group[1]))) +
annotation_custom(grid::grid.text("Phytoplankton",
x = grid::unit(1.5, "in"),
y = grid::unit(0.7, "in"),
hjust = 0,
vjust = 0.5,
gp = grid::gpar(cex = 1.2))) +
annotation_custom(grid::grid.points(x = grid::unit(1.4, "in"),
y = grid::unit(0.4, "in"),
size = grid::unit(0.15, "in"),
pch = 22,
gp = grid::gpar(col = "black",
fill = my_cols_group[2]))) +
annotation_custom(grid::grid.text("Zooplankton",
x = grid::unit(1.5, "in"),
y = grid::unit(0.4, "in"),
hjust = 0,
vjust = 0.5,
gp = grid::gpar(cex = 1.2)))
}
make_ed_fig_6 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_6(...), device = "pdf", width = 8.2,
height = 3.8, units = "in", onefile = FALSE)
}
ed_fig_6 <- function(post_effs, diff_biomass, phy_png, zoo_png) {
diff_effs <- tapply(post_effs$param,
list(post_effs$iter,
post_effs$sample),
function(x) 1 - (x[2] / x[1])) %>%
as.data.frame %>%
dplyr::mutate(iter = seq_len(nrow(.))) %>%
tidyr::pivot_longer(c(phytoplankton, zooplankton),
names_to = "sample",
values_to = "diff") %>%
dplyr::mutate(variable = "efficiency") %>%
dplyr::arrange(sample, iter) %>%
dplyr::select(sample, iter, diff, variable) %>%
as.data.frame
plot_data <- rbind(diff_effs, diff_biomass) %>%
dplyr::rename(param = diff) %>%
dplyr::mutate(param = param * 1e2) %>%
plyr::ddply(c("sample", "variable"), clip_vec_hdi)
tp <- plot_data %>%
dplyr::distinct(sample) %>%
dplyr::mutate(param = 1)
ggplot(data = plot_data, mapping = aes(x = param)) +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf,
inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_density(data = plot_data, adjust = 2, trim = TRUE,
mapping = aes(x = param, fill = variable),
colour = NA) +
geom_density(adjust = 2, alpha = 0.7, trim = TRUE,
mapping = aes(fill = variable, colour = variable),
show.legend = FALSE) +
scale_fill_manual(values = c("black", "grey60"),
labels = c("Biomass", "Efficiency")) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.3, 0.1, 0.3, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.x = element_text(size = 14,
margin = grid::unit(c(0.15,
0,
0,
0),
"in")),
axis.title.y = element_text(size = 14),
legend.position = c(0.5, 1.1),
legend.direction = "horizontal",
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab("Posterior density (99% C.I.)\n") +
xlab("Percentage decline relative to ambient") +
scale_color_manual(values = c("black", "grey60")) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
coord_cartesian(clip = "off") +
layer(data = plot_data %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.028,
ymax = 0.033,
xmin = -10,
xmax = 10)) +
layer(data = plot_data %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.017,
ymax = 0.024,
xmin = -40,
xmax = -15))
}
make_ed_fig_7 <- function(dest, fig_out_folder, ...) {
pdf(dest, width = 9, height = 2.8)
ed_fig_7(...)
dev.off()
}
ed_fig_7 <- function(control_data, my_cols_group) {
ponds <- unique(control_data$pond)
par(mfrow = c(1, 3),
omi = c(0, 0.5, 0, 0),
mai = c(1.02, 0.72, 0.2, 0.1),
cex = 1,
cex.lab = 1.2,
cex.axis = 0.8,
cex.main = 1.2,
xpd = NA)
for (i in seq_along(ponds)) {
x <- control_data %>%
dplyr::filter(pond == ponds[i])
if (i == 1) {
ylab <- substitute("Excess "^15 * "N"["%"] * ", " * chi)
} else {
ylab <- ""
}
plot(NA, xlim = c(0, 60), ylim = c(0, 0.1),
xlab = ifelse(i == 2, "Time (days)", ""),
ylab = ylab, las = 1)
x %>%
dplyr::filter(sample == "phytoplankton") %>%
{
points(.$day, .$add15N_perc, pch = 21, bg = my_cols_group[1], cex = 1.3)
}
x %>%
dplyr::filter(sample == "zooplankton") %>%
{
points(.$day, .$add15N_perc, pch = 22, bg = my_cols_group[2], cex = 1.3)
}
LoLinR::proportionalLabel(0.95, 0.9, paste0("Control pond ", i),
adj = c(1, 0.5), cex = 0.9)
}
}
make_ed_fig_8 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_8(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
ed_fig_8 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
data <- data %>%
dplyr::group_by(sample, pond, treatment) %>%
dplyr::summarise(av_N_ug_L = mean(av_N_ug_L), ln_av_N_ug_L = log(av_N_ug_L))
a <- ggplot(data = data, mapping = aes(y = ln_av_N_ug_L, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 0.5, ymax = 0.5 + (8.5 - 0.5) / 2),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 0.5 + (8.5 - 0.5) / 2, ymax = 8.5),
fill = "#CCECE6") +
geom_boxplot(aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = interaction(treatment, sample),
shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = substitute("Total Biomass (" * mu * "g N L"^-1 * ")")) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed")) +
scale_y_continuous(limits = c(0.5, 8.5), expand = c(0, 0),
breaks = c(log(8), log(50), log(400),
log(3e3)),
labels = c(8, 50, 400, 3e3))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = 7.4,
ymax = 8.2,
xmin = 0.3,
xmax = 1.3)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = 3,
ymax = 4.3,
xmin = 2,
xmax = 2.5))
}
make_ed_fig_9 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, ed_fig_9(...), device = "pdf", width = 3.87,
height = 5.46, units = "in", onefile = FALSE)
}
ed_fig_9 <- function(data, phy_png, zoo_png, my_cols_treatment) {
cols <- rep(c(my_cols_treatment[2], my_cols_treatment[1]), 2)
a <- ggplot(data = data, mapping = aes(y = mean, x = treatment)) +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 3, ymax = 7.5),
fill = "#F6F1EB") +
geom_rect(mapping = aes(xmin = -Inf, xmax = Inf,
ymin = 7.5, ymax = 25),
fill = "#CCECE6") +
geom_boxplot(mapping = aes(colour = interaction(treatment, sample)),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 1,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = interaction(treatment, sample), shape = sample),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(21, 22)) +
labs(x = "Treatment",
y = "C:N ratio") +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10),
panel.grid = element_blank()) +
scale_y_continuous(trans = "log10", limits = c(3, 25), expand = c(0, 0)) +
scale_x_discrete(expand = c(1, -0.5), labels = c("Ambient", "Warmed"))
a$coordinates$clip <- "off"
a +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = phy_png,
ymin = log10(20),
ymax = log10(24),
xmin = 2,
xmax = 2.5)) +
layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = zoo_png,
ymin = log10(3.2),
ymax = log10(4.8),
xmin = 0.5,
xmax = 1))
}
#######################
# SUPPLEMENTARY FIGURES
#######################
make_sp_fig_inorganic <- function(dest, fig_out_folder, model_list, vrb, ...) {
mod <- model_list[[vrb]]
ggplot2::ggsave(dest, make_ba_plots(mod, ...), device = "pdf", width = 14,
height = 7, units = "in", onefile = FALSE)
}
make_sp_fig_4 <- function(dest, fig_out_folder, mod, ...) {
names(mod$data)[1] <- "y"
ggplot2::ggsave(dest, make_ba_plots(mod, ...), device = "pdf", width = 14,
height = 7, units = "in", onefile = FALSE)
}
make_ba_plots <- function(mod, x_lab, my_cols_treatment) {
a <- mod$data %>%
dplyr::select(period, treatment) %>%
dplyr::distinct() %>%
tidybayes::add_fitted_draws(mod, dpar = c("mu", "sigma"),
re_formula = ~ 0) %>%
tidybayes::sample_draws(200) %>%
ggplot(aes(y = interaction(period, treatment))) +
ggdist::stat_dist_slab(mapping = aes(dist = "norm",
arg1 = mu,
arg2 = sigma),
slab_color = "gray65",
alpha = 5e-2,
fill = NA,
size = 0.2) +
geom_point(data = mod$data,
inherit.aes = FALSE,
mapping = aes(x = y,
y = interaction(period, treatment),
fill = treatment,
colour = treatment,
shape = treatment),
size = 3,
alpha = 0.3) +
labs(x = x_lab,
y = "") +
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.3, 0.2, 0.4), "in"),
axis.title.x = element_text(size = 20, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 20, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 17, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 17),
panel.grid = element_blank(),
legend.position = c(0.85, 0.9),
legend.background = element_blank(),
legend.title = element_text(size = 20),
legend.text = element_text(size = 16)) +
scale_y_discrete(labels = c("After", "Before", "After", "Before")) +
scale_colour_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_fill_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_shape_manual(name = "Treatment",
labels = c("Ambient", "Warmed"),
values = c(24, 25))
b <- plot(mod, N = 6, plot = FALSE)
gridExtra::grid.arrange(a, b[[1]], ncol = 2)
}
make_sp_fig_5 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_5(...), device = "pdf", width = 10.2,
height = 4.8, units = "in", onefile = FALSE)
}
sp_fig_5 <- function(cn_ratios, phy_png, zoo_png, my_cols_treatment) {
targets <- names(cn_ratios)
both <- plyr::llply(targets, function(x, models, my_cols_treatment) {
my_cs <- my_cols_treatment
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
data <- models[[x]]$data
names(my_cs) <- c("H", "A")
type <- c("mod_phy" = "phytoplankton", "mod_zoo" = "zooplankton")
s_dat <- data.frame(y = data$CN_ratio,
y_rep = colMeans(brms::posterior_epred(models[[x]])),
group = data$treatment,
stringsAsFactors = FALSE) %>%
dplyr::mutate(cols = my_cs[group])
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
a <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y, fill = group)) +
geom_point(shape = my_shape[type[x]], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
list(data = s_dat, plot = a)
}, models = cn_ratios, my_cols_treatment)
a <- both[[1]]$plot +
layer(data = both[[1]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 27,
ymax = 30,
xmin = 6,
xmax = 11))
b <- both[[2]]$plot +
layer(data = both[[2]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 9,
ymax = 12,
xmin = 2,
xmax = 4))
gridExtra::grid.arrange(a, b, ncol = 2)
}
make_sp_fig_6 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_6(...), device = "pdf", width = 14,
height = 6, units = "in", onefile = FALSE)
}
sp_fig_6 <- function(...) {
pp_checks_set <- function(type, data,
model_list,
my_cols_treatment,
my_shape) {
fit <- model_list[[type]]$model
y_rep <- sim_pp_mean(data, fit, type)
sdat <- data %>%
dplyr::filter(sample == type)
y <- sdat$add15N_perc
x <- sdat$day
bayesplot::color_scheme_set("gray")
# preamble
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
mean_y <- function(data, group) {
mean(data %>%
dplyr::filter(treatment == group) %>%
dplyr::select(add15N_perc) %>%
unlist)
}
# for p_b
h_dat <- plyr::adply(y_rep, 1, function(x, treat) {
data.frame(group = c("A", "H"),
y_rep = tapply(x, treat, mean),
stringsAsFactors = FALSE)
}, treat = sdat$treatment)
# for p_c
my_cs <- my_cols_treatment
names(my_cs) <- c("H", "A")
s_dat <- data.frame(y = y,
y_rep = colMeans(y_rep),
group = sdat$treatment,
cols = my_cs[sdat$treatment],
stringsAsFactors = FALSE)
p_a <- bayesplot::ppc_dens_overlay(y, y_rep[1:200, ]) +
labs(x = substitute("Excess "^15 * "N"["%"] * ", " * chi),
y = "Density") +
my_theme() +
theme(legend.text = element_blank(),
legend.position = c(0.58, 0.79),
legend.background = element_blank()) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
annotate_textp("Observed", x = 0.9, y = 0.85, hjust = 0) +
annotate_textp("Predicted", x = 0.9, y = 0.74, hjust = 0)
p_b <- ggplot(h_dat, aes(x = y_rep)) +
geom_histogram(data = dplyr::filter(h_dat, group == "A"),
colour = "black",
fill = my_cols_treatment[2],
size = 0.2) +
geom_vline(mapping = aes(xintercept = mean_y(sdat, "A")),
color = "black",
size = 0.5,
lty = 2) +
geom_histogram(data = dplyr::filter(h_dat, group == "H"),
colour = "black",
fill = my_cols_treatment[1],
size = 0.2) +
geom_vline(mapping = aes(xintercept = mean_y(sdat, "H")),
color = "black",
size = 0.5,
lty = 2) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05)),
breaks = seq(0, 6000, 2000),
labels = c("0.0", "2.0", "4.0", "6.0")) +
labs(x = substitute("Mean " * chi * " (%)"),
y = "Frequency (thousands)") +
my_theme() +
theme(legend.title = element_blank(),
legend.text = element_text(size = 18, hjust = 0),
legend.position = c(0.8, 0.8),
legend.background = element_blank())
p_c <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y,
fill = group)) +
geom_point(shape = my_shape[type], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
p_d <- bayesplot::ppc_intervals(y = y, yrep = y_rep, x = x, prob = 0.5) +
labs(x = "Time (days)",
y = substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
my_theme() +
theme(legend.text = element_blank(),
legend.position = c(0.58, 0.79),
legend.background = element_blank()) +
annotate_textp("Observed", x = 0.92, y = 0.85, hjust = 0) +
annotate_textp("Predicted", x = 0.92, y = 0.74, hjust = 0)
list(p_a = p_a, p_b = p_b, p_c = p_c, p_d = p_d)
}
out <- vector(mode = "list", length = 2)
names(out) <- c("phy", "zoo")
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
for (i in seq_along(out)) {
out[[i]] <- pp_checks_set(names(my_shape)[i],
my_shape = my_shape,
...)
names(out[[i]]) <- paste0(names(out)[i], "_", names(out[[i]]))
}
p <- append(out[["phy"]], out[["zoo"]])
p <- gridExtra::grid.arrange(p$phy_p_a, p$phy_p_b,
p$phy_p_c, p$phy_p_d,
p$zoo_p_a, p$zoo_p_b,
p$zoo_p_c, p$zoo_p_d,
ncol = 4)
ggpubr::annotate_figure(p,
left = ggpubr::text_grob("",
hjust = 0.2,
vjust = 0.4,
size = 40,
rot = 90)) +
annotation_custom(grid::grid.text("Phytoplankton",
x = grid::unit(0.015, "npc"),
y = grid::unit(0.8, "npc"),
gp = grid::gpar("fontsize" = 20),
hjust = 0.5,
vjust = 0.5,
rot = 90)) +
annotation_custom(grid::grid.text("Zooplankton",
x = grid::unit(0.015, "npc"),
y = grid::unit(0.3, "npc"),
gp = grid::gpar("fontsize" = 20),
hjust = 0.5,
vjust = 0.5,
rot = 90))
}
make_sp_fig_7 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_7(...), device = "pdf", width = 10.2,
height = 4.8, units = "in", onefile = FALSE)
}
sp_fig_7 <- function(hierarchical_biomass, phy_png,
zoo_png, my_cols_treatment) {
targets <- names(hierarchical_biomass)
both <- plyr::llply(targets, function(x, models, my_cols_treatment) {
my_cs <- my_cols_treatment
my_shape <- c("phytoplankton" = 21, "zooplankton" = 22)
names(my_cs) <- c("H", "A")
data <- models[[x]]$data
s_dat <- data.frame(y = data$ln_av_C_ug_L,
y_rep = colMeans(brms::posterior_epred(models[[x]])),
group = data$treatment,
stringsAsFactors = FALSE) %>%
dplyr::mutate(cols = my_cs[group])
my_theme <- function() {
theme_bw() +
theme(plot.margin = grid::unit(c(0.2, 0.1, 0.4, 0.2), "in"),
panel.grid = element_blank(),
axis.title.x = element_text(size = 15, vjust = -1, hjust = 0.5),
axis.title.y = element_text(size = 15, vjust = 4, hjust = 0.5),
axis.text.x = element_text(size = 10, vjust = -1, hjust = 0.5),
axis.text.y = element_text(size = 10))
}
a <- ggplot(data = s_dat, mapping = aes(x = y_rep, y = y,
fill = group)) +
geom_point(shape = my_shape[x], size = 2, data = s_dat) +
geom_smooth(mapping = aes(x = y_rep, y = y),
method = "lm",
se = FALSE,
lty = 2,
colour = "grey60",
size = 0.5,
inherit.aes = FALSE) +
geom_smooth(mapping = aes(x = y, y = y),
method = "lm",
se = FALSE,
lty = 1,
colour = "black",
size = 0.5,
inherit.aes = FALSE) +
scale_fill_manual(values = my_cs) +
labs(x = "Predicted", y = "Observed") +
my_theme() +
theme(legend.position = "none")
list(data = s_dat, plot = a)
}, models = hierarchical_biomass, my_cols_treatment)
a <- both[[1]]$plot +
layer(data = both[[1]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 10,
ymax = 10.6,
xmin = 6.2,
xmax = 7))
b <- both[[2]]$plot +
layer(data = both[[2]]$data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 4.5,
ymax = 6.5,
xmin = -1.3,
xmax = 0.2))
gridExtra::grid.arrange(a, b, ncol = 2)
}
make_sp_fig_8 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_8(...), device = "pdf", width = 7,
height = 7, units = "in", onefile = FALSE)
}
sp_fig_8 <- function(community_data_2012, community_data_2016,
size_data_2012, size_data_2016,
my_cols_treatment) {
sp_fig_8_bxpl <- function(data, my_cols_treatment, title) {
cols <- rev(my_cols_treatment)
ggplot(data = data, mapping = aes(y = log_av_C_ug, x = treatment)) +
geom_boxplot(mapping = aes(colour = treatment),
position = position_dodge(0),
fill = cols,
show.legend = FALSE,
width = 0.7,
size = 0.3,
outlier.shape = NA) +
geom_point(mapping = aes(fill = treatment,
shape = treatment),
colour = "black",
position = position_jitterdodge(jitter.width = 0.4,
dodge.width = 0,
seed = 1),
size = 2.5,
show.legend = FALSE) +
scale_colour_manual(values = rep("black", 4)) +
scale_fill_manual(values = cols) +
scale_shape_manual(values = c(24, 25)) +
labs(x = "Treatment",
y = expression(log[10]~Organism~mass~(µg~C))) +
theme_classic() +
theme(plot.title = element_text(face = "bold")) +
ggtitle(title) +
scale_x_discrete(labels = c("Ambient", "Warmed"))
}
sp_fig_8_nmds <- function(data, my_cols_treatment, tlab) {
treat_rest <- data$treatment
data <- data %>%
dplyr::select(-pond, -treatment)
bc_mds <- vegan::metaMDS(data, distance = "bray",
trace = FALSE, autotransform = FALSE,
k = 2)
ponds <- data.frame(bc_mds$points) %>%
dplyr::mutate(treat = treat_rest,
shape = ifelse(treat == "H", 25, 24))
species <- data.frame(bc_mds$species) %>%
dplyr::arrange(MDS1) %>%
tidyr::drop_na()
spp_h <- head(species, 4)
spp_a <- tail(species, 4)
spp <- rbind(spp_h, spp_a) %>%
dplyr::mutate(treat = rep(c("H", "A"), each = 4),
shape = rep(c(25, 24), each = 4))
treat_a <- ponds[ponds$treat == "A", ][chull(ponds[ponds$treat == "A",
c("MDS1", "MDS2")]), ]
treat_h <- ponds[ponds$treat == "H", ][chull(ponds[ponds$treat == "H",
c("MDS1", "MDS2")]), ]
hull_data <- rbind(treat_a, treat_h)
ggplot() +
geom_point(data = ponds,
mapping = aes(x = MDS1,
y = MDS2,
fill = treat),
size = 3,
shape = ponds$shape) +
geom_point(data = species,
mapping = aes(x = MDS1,
y = MDS2,
alpha = 0.001),
size = 1.5) +
geom_point(data = spp,
mapping = aes(x = MDS1,
y = MDS2,
alpha = 0.001),
size = 5,
shape = spp$shape) +
geom_polygon(data = hull_data,
mapping = aes(x = MDS1,
y = MDS2,
fill = treat,
group = treat),
alpha = 0.30) +
scale_colour_manual(values = rev(my_cols_treatment)) +
scale_fill_manual(values = rev(my_cols_treatment)) +
scale_shape_manual(values = c(21, 21)) +
labs(x = "NMDS1", y = "NMDS2") +
theme_classic() +
ggtitle(tlab) +
theme(legend.position = "none",
plot.title = element_text(face = "bold"))
}
nmds_2012 <- sp_fig_8_nmds(community_data_2012,
my_cols_treatment,
"a (2012)") +
geom_text(mapping = aes(x = -1, y = -1,
label = "Stress = 0.12",
size = 5), hjust = 0) +
scale_y_continuous(limits = c(-1, 1),
breaks = c(-1, -0.5, 0, 0.5, 1))
nmds_2016 <- sp_fig_8_nmds(community_data_2016,
my_cols_treatment,
"b (2016)") +
geom_text(mapping = aes(x = -1.1, y = -1.8,
label = "Stress = 0.05",
size = 5), hjust = 0) +
scale_y_continuous(limits = c(-1.8, 2),
breaks = c(-1.8, -0.9, 0, 0.9, 1.8))
bxpl_2012 <- sp_fig_8_bxpl(size_data_2012, my_cols_treatment, "c (2012)") +
scale_y_continuous(limits = c(-4.8, -2.4),
breaks = c(-4.8, -3.8, -2.8))
bxpl_2016 <- sp_fig_8_bxpl(size_data_2016, my_cols_treatment, "d (2016)") +
scale_y_continuous(limits = c(-4.8, -3.5),
breaks = c(-4.8, -4.2, -3.8))
gridExtra::grid.arrange(nmds_2012, nmds_2016,
bxpl_2012, bxpl_2016, ncol = 2)
}
make_sp_fig_9 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_9(...), device = "pdf", width = 6.3,
height = 3.76, units = "in", onefile = FALSE)
}
sp_fig_9 <- function(data, my_cols_treatment) {
data <- data %>%
dplyr::mutate(shape = ifelse(treat == "H", 25, 24))
ggplot(data = data) +
geom_line(mapping = aes(x = doy,
y = ratio_fits,
colour = treat,
linetype = treat)) +
scale_colour_manual(values = rev(my_cols_treatment)) +
scale_fill_manual(values = rev(my_cols_treatment)) +
geom_point(mapping = aes(x = doy,
y = ER.GPP,
colour = treat,
fill = treat),
alpha = 0.3,
shape = data$shape) +
cowplot::theme_cowplot(font_size = 12) +
theme(legend.position = "none") +
facet_wrap(~ year, scales = "fixed") +
theme(strip.background = element_blank()) +
xlab("Day of the Year") +
ylab(expression(R[eco] / GPP))
}
make_sp_fig_10 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, sp_fig_10(...), device = "pdf", width = 5.82,
height = 2.95, units = "in", onefile = FALSE)
}
sp_fig_10 <- function(pp_means, phy_png, zoo_png, my_cols_treatment) {
make_curve <- function(days, ln_ka, logit_ke, logit_phi) {
ka <- exp(ln_ka)
ke <- 1 / (1 + exp(-logit_ke))
phi <- (1 / ke) / (1 + exp(-logit_phi))
calc_eqn_1(days, ka, ke, phi)
}
curve_df <- function(data, days) {
c_amb <- make_curve(days, data$ln_ka_amb,
data$logit_ke_amb,
data$logit_phi_amb)
c_war <- make_curve(days, data$ln_ka_war,
data$logit_ke_war,
data$logit_phi_war)
# cap samples that yield predictions
# that are within the reasonable range of
# data
if (max(c_amb) < 1 && max(c_war) < 1) {
data.frame(sample = data$sample,
days = days,
values = c(c_amb, c_war),
treatment = rep(c("amb", "war"),
each = length(c_amb)))
}
}
make_prior_lines <- function(ln_ka_amb, ln_ka_war, logit_ke_amb,
logit_ke_war, logit_phi_amb, logit_phi_war,
sample, iter) {
plot_data <- data.frame(iter = seq_len(iter)) %>%
dplyr::mutate(ln_ka_amb = rnorm(iter, ln_ka_amb, 1),
logit_ke_amb = rnorm(iter, logit_ke_amb, 1),
logit_phi_amb = rnorm(iter, logit_phi_amb, 1),
ln_ka_war = rnorm(iter, ln_ka_war, 1),
logit_ke_war = rnorm(iter, logit_ke_war, 1),
logit_phi_war = rnorm(iter, logit_phi_war, 1),
sample = sample)
days <- seq(0.01, 60, length.out = 60)
plyr::ddply(plot_data, .(iter), curve_df, days)
}
prior_bkg <- data.frame(sample = c("phytoplankton", "zooplankton")) %>%
dplyr::mutate(ln_ka_amb = c(0, 0),
ln_ka_war = c(0, 0),
logit_ke_amb = c(0, 0),
logit_ke_war = c(0, 0),
logit_phi_amb = c(0, 0),
logit_phi_war = c(0, 0))
set.seed(1)
prior_lines <- plyr::ddply(prior_bkg, .(sample), function(data) {
make_prior_lines(data$ln_ka_amb, data$ln_ka_war, data$logit_ke_amb,
data$logit_ke_war, data$logit_phi_amb, data$logit_phi_war,
data$sample, iter = 1000)
})
chosen <- dplyr::distinct(prior_lines, iter, sample) %>%
dplyr::group_by(sample) %>%
dplyr::summarise(chosen = sample(iter, 300)) %>%
data.frame()
prior_lines <- prior_lines %>%
dplyr::group_by(sample) %>%
dplyr::filter(iter %in% chosen$chosen[chosen$sample %in% unique(sample)]) %>%
data.frame()
prior_means <- plyr::ddply(prior_bkg, .(sample), curve_df,
days = seq(0.01, 60, length.out = 60)) %>%
dplyr::filter(treatment == "amb") %>%
dplyr::select(sample, days, values)
linesd <- pp_means$lines %>%
dplyr::mutate(col = ifelse(treatment == "amb",
my_cols_treatment[2],
my_cols_treatment[1]))
tp <- linesd %>%
dplyr::distinct(sample, treatment) %>%
dplyr::mutate(x = 1)
ggplot() +
geom_rect(data = tp, mapping = aes(fill = sample),
xmin = -Inf, xmax = Inf, ymin = -Inf,
ymax = Inf, inherit.aes = FALSE, show.legend = FALSE) +
scale_fill_manual(values = c("#CCECE6", "#F6F1EB")) +
ggnewscale::new_scale("fill") +
geom_line(data = prior_lines,
mapping = aes(x = days, y = values,
group = paste(iter, treatment)),
colour = "grey40", lty = 1, size = 0.5, alpha = 0.07,
show.legend = FALSE) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean,
group = treatment,
linetype = treatment),
col = "black", size = 1, show.legend = FALSE) +
geom_line(data = linesd,
mapping = aes(x = x, y = mean,
group = treatment,
linetype = treatment,
colour = treatment),
size = 1) +
scale_colour_manual(labels = c("Ambient", "Warmed"),
values = rev(my_cols_treatment)) +
scale_linetype_manual(labels = c("Ambient", "Warmed"),
values = c(1, 3)) +
facet_wrap(~sample, scales = "free", ncol = 2) +
theme_bw() +
theme(plot.margin = grid::unit(c(0.4, 0.1, 0.1, 0.2), "in"),
strip.background = element_blank(),
strip.text = element_text(color = "transparent"),
panel.spacing.x = grid::unit(1.2, "lines"),
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 14),
legend.position = c(0.5, 1.2),
legend.direction = "horizontal",
legend.background = element_blank(),
legend.text = element_text(size = 12),
legend.title = element_blank()) +
ylab(substitute("Excess "^15 * "N"["%"] * ", " * chi)) +
xlab("Time (days)") +
layer(data = linesd %>%
dplyr::filter(sample == "phytoplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = phy_png,
ymin = 0.55,
ymax = 0.75,
xmin = 50,
xmax = 60)) +
layer(data = linesd %>%
dplyr::filter(sample == "zooplankton"),
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = FALSE,
params = list(grob = zoo_png,
ymin = 0.45,
ymax = 0.65,
xmin = 50,
xmax = 63))
}
make_sp_fig_11_13 <- function(dest, fig_out_folder, ...) {
ggplot2::ggsave(dest, supp_resid_figure(...), device = "pdf", width = 10.5,
height = 7.86, units = "in", onefile = FALSE)
}
supp_resid_figure <- function(data, pred_tag, my_cols_group, lab) {
names(my_cols_group) <- c("phytoplankton", "zooplankton")
data <- data %>%
dplyr::filter(predictor == pred_tag)
ggplot(data = data) +
geom_polygon(mapping = aes(x = x_conf, y = y_conf,
fill = sample),
alpha = 0.5,
show.legend = FALSE) +
geom_line(mapping = aes(x = x, y = y,
linetype = sample),
colour = "black",
show.legend = FALSE) +
geom_point(mapping = aes(x = raw_x,
y = raw_y,
shape = sample,
fill = sample),
show.legend = FALSE,
size = 2) +
labs(x = lab,
y = "Model residuals") +
scale_shape_manual(values = c("phytoplankton" = 21,
"zooplankton" = 22)) +
scale_fill_manual(values = my_cols_group) +
theme_bw() +
facet_wrap(~ pond, scales = "free", dir = "v") +
theme(axis.title = element_text(size = 15))
}
make_resid_plot_data <- function(resid_brm_model, data) {
plot_data <- plyr::ldply(resid_brm_model, function(model) {
df <- model$data %>%
tidyr::pivot_longer(cols = c(NO2_uM, NO3_uM, NH3_uM),
names_to = "predictor",
values_to = "value")
plyr::ddply(df, .(pond), function(df_i, ...) {
means <- tapply(df_i$value, df_i$predictor, mean)
plyr::ddply(df_i, .(predictor), function(dd, model, means) {
pred_name <- unique(dd$predictor)
means <- means[names(means) != pred_name]
nd <- data.frame(x = seq(min(dd$value),
max(dd$value),
length.out = 50),
pond = unique(dd$pond),
pred_1 = means[[1]],
pred_2 = means[[2]])
names(nd) <- c(pred_name, "pond", names(means))
fits <- fitted(model, newdata = nd) %>%
cbind(nd)
fits %>% {
data.frame(y = NA,
x = NA,
x_conf = c(.[[pred_name]], rev(.[[pred_name]])),
y_conf = c(.$Q2.5, rev(.$Q97.5)),
raw_x = NA,
raw_y = NA)
} %>%
rbind(fits %>% {
data.frame(y = .$Estimate,
x = .[[pred_name]],
x_conf = NA,
y_conf = NA,
raw_x = NA,
raw_y = NA)
}) %>%
rbind(dd %>% {
data.frame(y = NA,
x = NA,
x_conf = NA,
y_conf = NA,
raw_x = .$value,
raw_y = .$residual_15N_perc)
})
}, means = means, ...)
}, model = model)
})
plot_data$treatment <- data$treatment[match(plot_data$pond, data$pond)]
plot_data$pond <- paste0("Pond = ", formatC(plot_data$pond, width = 2,
format = "d", flag = "0"),
"; Treatment = ",
ifelse(plot_data$treatment == "H",
"W", plot_data$treatment))
plot_data
}
|
#' Lemmatization List
#'
#' A dataset based on Mechura's (2016) English lemmatization list. This
#' data set can be useful for join style lemma replacement of inflected token
#' forms to their root lemmas. While this is not a true morphological analysis
#' this style of lemma replacement is fast and typically still robust.
#'
#' @details
#' \itemize{
#' \item token. An inflected token with affixes
#' \item lemma. A base form
#' }
#'
#' ## ODC Open Database License (ODbL)
#'
#' ### Preamble
#'
#' The Open Database License (ODbL) is a license agreement intended to
#' allow users to freely share, modify, and use this Database while
#' maintaining this same freedom for others. Many databases are covered by
#' copyright, and therefore this document licenses these rights. Some
#' jurisdictions, mainly in the European Union, have specific rights that
#' cover databases, and so the ODbL addresses these rights, too. Finally,
#' the ODbL is also an agreement in contract for users of this Database to
#' act in certain ways in return for accessing this Database.
#'
#' Databases can contain a wide variety of types of content (images,
#' audiovisual material, and sounds all in the same database, for example),
#' and so the ODbL only governs the rights over the Database, and not the
#' contents of the Database individually. Licensors should use the ODbL
#' together with another license for the contents, if the contents have a
#' single set of rights that uniformly covers all of the contents. If the
#' contents have multiple sets of different rights, Licensors should
#' describe what rights govern what contents together in the individual
#' record or in some other way that clarifies what rights apply.
#'
#' Sometimes the contents of a database, or the database itself, can be
#' covered by other rights not addressed here (such as private contracts,
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#' still applies to this Database or a Derivative Database as a part of
#' the Collective Database;
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#' b. Using this Database, a Derivative Database, or this Database as
#' part of a Collective Database to create a Produced Work does not
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#' c. Use of a Derivative Database internally within an organisation is
#' not to the public and therefore does not fall under the requirements
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#' 4.6 Access to Derivative Databases. If You Publicly Use a Derivative
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#' automatically terminates this License with immediate effect and without
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#' Database, the whole or a Substantial part of the Contents, Derivative
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#' the Database under different license terms or to stop distributing or
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#' license terms or stopping the distribution of the Database will not
#' withdraw this License (or any other license that has been, or is
#' required to be, granted under the terms of this License), and this
#' License will continue in full force and effect unless terminated as
#' stated above.
#'
#' ### 10.0 General
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#' 10.1 If any provision of this License is held to be invalid or
#' unenforceable, that must not affect the validity or enforceability of
#' the remainder of the terms and conditions of this License and each
#' remaining provision of this License shall be valid and enforced to the
#' fullest extent permitted by law.
#'
#' 10.2 This License is the entire agreement between the parties with
#' respect to the rights granted here over the Database. It replaces any
#' earlier understandings, agreements or representations with respect to
#' the Database.
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#' 10.3 If You are in breach of the terms of this License, You will not be
#' entitled to rely on the terms of this License or to complain of any
#' breach by the Licensor.
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#' 10.4 Choice of law. This License takes effect in and will be governed by
#' the laws of the relevant jurisdiction in which the License terms are
#' sought to be enforced. If the standard suite of rights granted under
#' applicable copyright law and Database Rights in the relevant
#' jurisdiction includes additional rights not granted under this License,
#' these additional rights are granted in this License in order to meet the
#' terms of this License.
#' @docType data
#' @keywords datasets
#' @name hash_lemmas
#' @usage data(hash_lemmas)
#' @format A data frame with 41,531 rows and 2 variables
#' @references Mechura, M. B. (2016). \emph{Lemmatization list: English (en)} [Data file]. Retrieved from \url{http://www.lexiconista.com}
NULL
|
/trinker-lexicon-4c5e22b/R/hash_lemmas.R
|
no_license
|
pratyushaj/abusive-language-online
|
R
| false | false | 27,633 |
r
|
#' Lemmatization List
#'
#' A dataset based on Mechura's (2016) English lemmatization list. This
#' data set can be useful for join style lemma replacement of inflected token
#' forms to their root lemmas. While this is not a true morphological analysis
#' this style of lemma replacement is fast and typically still robust.
#'
#' @details
#' \itemize{
#' \item token. An inflected token with affixes
#' \item lemma. A base form
#' }
#'
#' ## ODC Open Database License (ODbL)
#'
#' ### Preamble
#'
#' The Open Database License (ODbL) is a license agreement intended to
#' allow users to freely share, modify, and use this Database while
#' maintaining this same freedom for others. Many databases are covered by
#' copyright, and therefore this document licenses these rights. Some
#' jurisdictions, mainly in the European Union, have specific rights that
#' cover databases, and so the ODbL addresses these rights, too. Finally,
#' the ODbL is also an agreement in contract for users of this Database to
#' act in certain ways in return for accessing this Database.
#'
#' Databases can contain a wide variety of types of content (images,
#' audiovisual material, and sounds all in the same database, for example),
#' and so the ODbL only governs the rights over the Database, and not the
#' contents of the Database individually. Licensors should use the ODbL
#' together with another license for the contents, if the contents have a
#' single set of rights that uniformly covers all of the contents. If the
#' contents have multiple sets of different rights, Licensors should
#' describe what rights govern what contents together in the individual
#' record or in some other way that clarifies what rights apply.
#'
#' Sometimes the contents of a database, or the database itself, can be
#' covered by other rights not addressed here (such as private contracts,
#' trade mark over the name, or privacy rights / data protection rights
#' over information in the contents), and so you are advised that you may
#' have to consult other documents or clear other rights before doing
#' activities not covered by this License.
#'
#' ------
#'
#' The Licensor (as defined below)
#'
#' and
#'
#' You (as defined below)
#'
#' agree as follows:
#'
#' ### 1.0 Definitions of Capitalised Words
#'
#' "Collective Database" - Means this Database in unmodified form as part
#' of a collection of independent databases in themselves that together are
#' assembled into a collective whole. A work that constitutes a Collective
#' Database will not be considered a Derivative Database.
#'
#' "Convey" - As a verb, means Using the Database, a Derivative Database,
#' or the Database as part of a Collective Database in any way that enables
#' a Person to make or receive copies of the Database or a Derivative
#' Database. Conveying does not include interaction with a user through a
#' computer network, or creating and Using a Produced Work, where no
#' transfer of a copy of the Database or a Derivative Database occurs.
#' "Contents" - The contents of this Database, which includes the
#' information, independent works, or other material collected into the
#' Database. For example, the contents of the Database could be factual
#' data or works such as images, audiovisual material, text, or sounds.
#'
#' "Database" - A collection of material (the Contents) arranged in a
#' systematic or methodical way and individually accessible by electronic
#' or other means offered under the terms of this License.
#'
#' "Database Directive" - Means Directive 96/9/EC of the European
#' Parliament and of the Council of 11 March 1996 on the legal protection
#' of databases, as amended or succeeded.
#'
#' "Database Right" - Means rights resulting from the Chapter III ("sui
#' generis") rights in the Database Directive (as amended and as transposed
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#' Contents, or any Derivative Database to anyone else in any way, the
#' Licensor offers to the recipient a license to the Database on the same
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#' solely responsible for any modifications of a Derivative Database made
#' by You or another Person at Your direction. You may not impose any
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#' from non-electronic Databases for private purposes, Extraction for
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#' Contents, see Section 2.4). The repeated and systematic Extraction or
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#' direct financial loss to the extent it is caused by proved negligence on
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#' automatically terminates this License with immediate effect and without
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#' Database, the whole or a Substantial part of the Contents, Derivative
#' Databases, or the Database as part of a Collective Database from You
#' under this License will not have their licenses terminated provided
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#' under Section 4.8 of this License. Sections 1, 2, 7, 8, 9 and 10 will
#' survive any termination of this License.
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#' will not terminate Your rights under it.
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#' for the duration of applicable rights in the Database.
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#' 9.4 Reinstatement of rights. If you cease any breach of the terms and
#' conditions of this License, then your full rights under this License
#' will be reinstated:
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#' day after cessation of breach;
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#' otherwise reasonably notified by the Licensor; or
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#' c. Permanently if reasonably notified by the Licensor of the
#' violation, this is the first time You have received notice of
#' violation of this License from the Licensor, and You cure the
#' violation prior to 30 days after your receipt of the notice.
#'
#' Persons subject to permanent termination of rights are not eligible to
#' be a recipient and receive a license under Section 4.8.
#'
#' 9.5 Notwithstanding the above, Licensor reserves the right to release
#' the Database under different license terms or to stop distributing or
#' making available the Database. Releasing the Database under different
#' license terms or stopping the distribution of the Database will not
#' withdraw this License (or any other license that has been, or is
#' required to be, granted under the terms of this License), and this
#' License will continue in full force and effect unless terminated as
#' stated above.
#'
#' ### 10.0 General
#'
#' 10.1 If any provision of this License is held to be invalid or
#' unenforceable, that must not affect the validity or enforceability of
#' the remainder of the terms and conditions of this License and each
#' remaining provision of this License shall be valid and enforced to the
#' fullest extent permitted by law.
#'
#' 10.2 This License is the entire agreement between the parties with
#' respect to the rights granted here over the Database. It replaces any
#' earlier understandings, agreements or representations with respect to
#' the Database.
#'
#' 10.3 If You are in breach of the terms of this License, You will not be
#' entitled to rely on the terms of this License or to complain of any
#' breach by the Licensor.
#'
#' 10.4 Choice of law. This License takes effect in and will be governed by
#' the laws of the relevant jurisdiction in which the License terms are
#' sought to be enforced. If the standard suite of rights granted under
#' applicable copyright law and Database Rights in the relevant
#' jurisdiction includes additional rights not granted under this License,
#' these additional rights are granted in this License in order to meet the
#' terms of this License.
#' @docType data
#' @keywords datasets
#' @name hash_lemmas
#' @usage data(hash_lemmas)
#' @format A data frame with 41,531 rows and 2 variables
#' @references Mechura, M. B. (2016). \emph{Lemmatization list: English (en)} [Data file]. Retrieved from \url{http://www.lexiconista.com}
NULL
|
#' Example phenotype data file from Prefrontal Cortex (PFC) Methylation Data of Alzheimer's Disease subjects
#'
#' @description Subset of phenotype information for Alzheimer's methylation dataset.
#'
#' @format A data frame containing variables for Braak stage (stage), subject.id, Batch (slide), Sex,
#' Sample, age of brain sample (age.brain)
#'
#' @source GEO accession: GSE59685
"pheno_df"
|
/R/data_pheno_df.R
|
no_license
|
lissettegomez/coMethDMR
|
R
| false | false | 394 |
r
|
#' Example phenotype data file from Prefrontal Cortex (PFC) Methylation Data of Alzheimer's Disease subjects
#'
#' @description Subset of phenotype information for Alzheimer's methylation dataset.
#'
#' @format A data frame containing variables for Braak stage (stage), subject.id, Batch (slide), Sex,
#' Sample, age of brain sample (age.brain)
#'
#' @source GEO accession: GSE59685
"pheno_df"
|
#' Elastic Net Weigthing Method
#'
#' @param pred_train A list that contain multi-study datasets (can be generated from the simulation function)
#' @param dat_train A dataframe that combines all multi-study datasets
#' @return The coeficient for each study using stacking regression with intercept weighting method
#' @seealso [MultiStudySim()]
#' @seealso [get_input_data()]
#' @examples
#' lst_ori <- MultiStudySim(k,nk,p,m,mu_x,SIG,pii,mu_beta,sigma_beta)
#' a <- get_input_data(lst_ori)[[1]]
#' b <- get_input_data(lst_ori)[[2]]
#' fit <- lapply(a, function(a) lm(y~.))
#' b1 <- lapply(fit, function(fit)
#' predict(fit,b[,-which(names(b) %in% c('y'))]))
#' elnet_weight_int(b1,b)
elnet_weight_int <- function(pred_train,dat_train){
train.data <- as.matrix.data.frame(cbind(do.call(cbind,pred_train),dat_train$y))
colnames(train.data) <- c(1:(ncol(train.data)-1),'y')
model <- caret::train(
y ~., data = train.data, method = "glmnet",
trControl = trainControl("cv", number = 5),
tuneLength = 10
)
nl_elnet_int <- coef(model$finalModel, model$bestTune$lambda)
return(nl_elnet_int)
}
|
/MultiStudyPack/mssp/R/elnet_weight_int.R
|
no_license
|
xutao-wang/MultiStudySim
|
R
| false | false | 1,134 |
r
|
#' Elastic Net Weigthing Method
#'
#' @param pred_train A list that contain multi-study datasets (can be generated from the simulation function)
#' @param dat_train A dataframe that combines all multi-study datasets
#' @return The coeficient for each study using stacking regression with intercept weighting method
#' @seealso [MultiStudySim()]
#' @seealso [get_input_data()]
#' @examples
#' lst_ori <- MultiStudySim(k,nk,p,m,mu_x,SIG,pii,mu_beta,sigma_beta)
#' a <- get_input_data(lst_ori)[[1]]
#' b <- get_input_data(lst_ori)[[2]]
#' fit <- lapply(a, function(a) lm(y~.))
#' b1 <- lapply(fit, function(fit)
#' predict(fit,b[,-which(names(b) %in% c('y'))]))
#' elnet_weight_int(b1,b)
elnet_weight_int <- function(pred_train,dat_train){
train.data <- as.matrix.data.frame(cbind(do.call(cbind,pred_train),dat_train$y))
colnames(train.data) <- c(1:(ncol(train.data)-1),'y')
model <- caret::train(
y ~., data = train.data, method = "glmnet",
trControl = trainControl("cv", number = 5),
tuneLength = 10
)
nl_elnet_int <- coef(model$finalModel, model$bestTune$lambda)
return(nl_elnet_int)
}
|
#' classify_layer
#'
#' @param rna_counts count matrix
#' @param cells_to_use vector indicating which cells to classify in layer
#' @param imputation whether to apply MAGIC imputation - recommended
#' @param genes_in_model genes used in the model
#' @param model model
#' @param ref_based whether to use ref based - currently depreceated
#' @param mc.cores number of cores
#' @param unimodal_nsd number of sd for setting threshold in unimodal distributions
#' @param bimodal_nsd number of sd for setting threshold in bimodal distributions
#' @param layer which layer is being classified
#'
#' @return returns a prediction matrix for a layer - used in run_scATOMIC
#' @export
classify_layer <- function(rna_counts, cells_to_use, imputation = TRUE, genes_in_model, model,
ref_based = F, mc.cores = (parallel::detectCores()-1), unimodal_nsd = 3, bimodal_nsd = 2,
layer, normalized_counts){
if(.Platform$OS.type == "windows"){
mc.cores = 1
}
layer_predictions <- classify_cells_RNA_no_scale(rna_counts = rna_counts, imputation = imputation,
genes_in_model = genes_in_model,
model = model, cells_to_use = cells_to_use,
ref_based = ref_based, normalized_counts=normalized_counts, mc.cores = mc.cores)
layer_predictions <- add_class_per_layer(layer_predictions = layer_predictions,
layer = layer)
predicted_class <- unlist(parallel::mclapply(row.names(layer_predictions),
score_class, predictions = layer_predictions,
layer = layer, mc.cores = mc.cores), use.names = F)
layer_predictions <- cbind(layer_predictions, predicted_class)
layer_predictions$predicted_class <- as.character(predicted_class)
threshold_per_class <- get_auto_threshold(layer_predictions, mc.cores=mc.cores)
predicted_tissue_with_cutoff <- unlist(parallel::mclapply(row.names(layer_predictions),
add_unclassified_automatic_threshold, predictions = layer_predictions,
layer = layer, threshold_use = threshold_per_class,
mc.cores = mc.cores), use.names = F)
layer_predictions <- cbind(layer_predictions, predicted_tissue_with_cutoff)
return(layer_predictions)
}
|
/R/classify_layer.R
|
permissive
|
abelson-lab/scATOMIC
|
R
| false | false | 2,657 |
r
|
#' classify_layer
#'
#' @param rna_counts count matrix
#' @param cells_to_use vector indicating which cells to classify in layer
#' @param imputation whether to apply MAGIC imputation - recommended
#' @param genes_in_model genes used in the model
#' @param model model
#' @param ref_based whether to use ref based - currently depreceated
#' @param mc.cores number of cores
#' @param unimodal_nsd number of sd for setting threshold in unimodal distributions
#' @param bimodal_nsd number of sd for setting threshold in bimodal distributions
#' @param layer which layer is being classified
#'
#' @return returns a prediction matrix for a layer - used in run_scATOMIC
#' @export
classify_layer <- function(rna_counts, cells_to_use, imputation = TRUE, genes_in_model, model,
ref_based = F, mc.cores = (parallel::detectCores()-1), unimodal_nsd = 3, bimodal_nsd = 2,
layer, normalized_counts){
if(.Platform$OS.type == "windows"){
mc.cores = 1
}
layer_predictions <- classify_cells_RNA_no_scale(rna_counts = rna_counts, imputation = imputation,
genes_in_model = genes_in_model,
model = model, cells_to_use = cells_to_use,
ref_based = ref_based, normalized_counts=normalized_counts, mc.cores = mc.cores)
layer_predictions <- add_class_per_layer(layer_predictions = layer_predictions,
layer = layer)
predicted_class <- unlist(parallel::mclapply(row.names(layer_predictions),
score_class, predictions = layer_predictions,
layer = layer, mc.cores = mc.cores), use.names = F)
layer_predictions <- cbind(layer_predictions, predicted_class)
layer_predictions$predicted_class <- as.character(predicted_class)
threshold_per_class <- get_auto_threshold(layer_predictions, mc.cores=mc.cores)
predicted_tissue_with_cutoff <- unlist(parallel::mclapply(row.names(layer_predictions),
add_unclassified_automatic_threshold, predictions = layer_predictions,
layer = layer, threshold_use = threshold_per_class,
mc.cores = mc.cores), use.names = F)
layer_predictions <- cbind(layer_predictions, predicted_tissue_with_cutoff)
return(layer_predictions)
}
|
\name{ghq}
\docType{data}
\alias{ghq}
\title{Psychiatric diagnosis based on GHQ}
\description{
These data were published by Silvapulle, and come from a
psychiatric study of the relation between psychiatric diagnosis (as
case or non-case) and the value of the score on a
12-item General Health Questionnaire (\acronym{GHQ}), for 120 patients
attending a general practitioner's surgery.
Each patient was administered the \acronym{GHQ}, resulting in a score
between 0 and 12, (however there were no cases or non-cases with \acronym{GHQ} scores
of 11 or 12) and was subsequently given a full psychiatric
examination by a psychiatrist who did not know the patient's
\acronym{GHQ} score.
The patient was classified by the psychiatrist as either a ``case'',
requiring psychiatric treatment, or a ``non-case''.
}
\usage{data(ghq)}
\format{
\tabular{rll}{
[,1]\tab sex\tab Factor w/ 2 levels "men","women"\cr
[,2] \tab ghq\tab integer, score from 0,...,12\cr
[,3] \tab c \tab integer, number of patients considered a
"case"\cr
[,4] \tab nc \tab integer, number of patients considered a "non-case"\cr
}
}
\source{Silvapulle, M. J. (1981), "On the existence of maximum
likelihood estimators for the binomial response model",
\emph{J. Roy. Statist. Soc. B.}, \bold{43}, 310--13.
}
\note{See p.235 in SMIR}
\keyword{datasets}
|
/man/ghq.Rd
|
no_license
|
cran/SMIR
|
R
| false | false | 1,375 |
rd
|
\name{ghq}
\docType{data}
\alias{ghq}
\title{Psychiatric diagnosis based on GHQ}
\description{
These data were published by Silvapulle, and come from a
psychiatric study of the relation between psychiatric diagnosis (as
case or non-case) and the value of the score on a
12-item General Health Questionnaire (\acronym{GHQ}), for 120 patients
attending a general practitioner's surgery.
Each patient was administered the \acronym{GHQ}, resulting in a score
between 0 and 12, (however there were no cases or non-cases with \acronym{GHQ} scores
of 11 or 12) and was subsequently given a full psychiatric
examination by a psychiatrist who did not know the patient's
\acronym{GHQ} score.
The patient was classified by the psychiatrist as either a ``case'',
requiring psychiatric treatment, or a ``non-case''.
}
\usage{data(ghq)}
\format{
\tabular{rll}{
[,1]\tab sex\tab Factor w/ 2 levels "men","women"\cr
[,2] \tab ghq\tab integer, score from 0,...,12\cr
[,3] \tab c \tab integer, number of patients considered a
"case"\cr
[,4] \tab nc \tab integer, number of patients considered a "non-case"\cr
}
}
\source{Silvapulle, M. J. (1981), "On the existence of maximum
likelihood estimators for the binomial response model",
\emph{J. Roy. Statist. Soc. B.}, \bold{43}, 310--13.
}
\note{See p.235 in SMIR}
\keyword{datasets}
|
##----------------------------------------------------------------
##
## fire.polygon.pcvalues.R
##
## Purpose: append princiipal component values (mean within fire perimeter)
## to fire polygon shp to use in analysis of high severity metrics
##
## Author: S. Haire, @HaireLab
##
##
## Date: 3 july 2020
##
##--------------------------------------------------------------
##
## Notes:
## Before running this script, download the fire perimeters
## < https://doi.org/10.5066/P9BB5TIO >
##
## PC1 and PC2 rasters are available in the respository data folder
##
##---------------------------------------------------------------
library(raster)
library(rgdal)
library(landscapemetrics)
library(plyr)
library(dplyr)
library(geosphere)
## Read in the data and project the fire perimeter polygons to match the principal component layers
## paths to input data
perimpath<-'./data/sp'# fire perimeters...put the shp with new attributes here too
pcpath<-'./data/PCA/' ## PCA rasters
## data
## pc's have bioclim data projection
bioclim.prj<-"+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs" # from metadata lambert conformal conic
## read in pc's and assign prj
pc1<-raster(paste(pcpath, "PC1b.rot.tif", sep="")); crs(pc1)<-bioclim.prj
pc2<-raster(paste(pcpath, "PC2b.rot.tif", sep="")); crs(pc2)<-bioclim.prj
## read in perimeters/sp polys and project the perimeters to match the pc's
perims<-readOGR(perimpath, "Sky_Island_Fire_Polys_1985_2017")
perims.lcc<-spTransform(perims, bioclim.prj)
## Extract pc 1 & pc2 values and output the mean w/in polygon (fire perimeter). Save appended shp.
## stack the pc's and extract values within the polygons
s<-stack(pc1,pc2)
pc.ex<-extract(s, perims.lcc, method="bilinear",df=TRUE)
## calulate the mean values within the fire perimeters
mean.pc<-ddply(pc.ex,~ID, summarise, mean.pc1 = mean(PC1b.rot), mean.pc2=mean(PC2b.rot))
## add pc mean values to the spatial polygons
perims$pc1<-mean.pc[,2]; perims$pc2<-mean.pc[,3]
perims<-perims[,c(1:5,15,16)] ## just save the year, name & id, country, sky island and pc values
## save the perim shp
writeOGR(perims, "./data/sp", "fire.polys.pcvalues", driver="ESRI Shapefile", overwrite=TRUE)
|
/fire.pcvalues.R
|
no_license
|
HaireLab/Fuego-en-la-Frontera
|
R
| false | false | 2,293 |
r
|
##----------------------------------------------------------------
##
## fire.polygon.pcvalues.R
##
## Purpose: append princiipal component values (mean within fire perimeter)
## to fire polygon shp to use in analysis of high severity metrics
##
## Author: S. Haire, @HaireLab
##
##
## Date: 3 july 2020
##
##--------------------------------------------------------------
##
## Notes:
## Before running this script, download the fire perimeters
## < https://doi.org/10.5066/P9BB5TIO >
##
## PC1 and PC2 rasters are available in the respository data folder
##
##---------------------------------------------------------------
library(raster)
library(rgdal)
library(landscapemetrics)
library(plyr)
library(dplyr)
library(geosphere)
## Read in the data and project the fire perimeter polygons to match the principal component layers
## paths to input data
perimpath<-'./data/sp'# fire perimeters...put the shp with new attributes here too
pcpath<-'./data/PCA/' ## PCA rasters
## data
## pc's have bioclim data projection
bioclim.prj<-"+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs" # from metadata lambert conformal conic
## read in pc's and assign prj
pc1<-raster(paste(pcpath, "PC1b.rot.tif", sep="")); crs(pc1)<-bioclim.prj
pc2<-raster(paste(pcpath, "PC2b.rot.tif", sep="")); crs(pc2)<-bioclim.prj
## read in perimeters/sp polys and project the perimeters to match the pc's
perims<-readOGR(perimpath, "Sky_Island_Fire_Polys_1985_2017")
perims.lcc<-spTransform(perims, bioclim.prj)
## Extract pc 1 & pc2 values and output the mean w/in polygon (fire perimeter). Save appended shp.
## stack the pc's and extract values within the polygons
s<-stack(pc1,pc2)
pc.ex<-extract(s, perims.lcc, method="bilinear",df=TRUE)
## calulate the mean values within the fire perimeters
mean.pc<-ddply(pc.ex,~ID, summarise, mean.pc1 = mean(PC1b.rot), mean.pc2=mean(PC2b.rot))
## add pc mean values to the spatial polygons
perims$pc1<-mean.pc[,2]; perims$pc2<-mean.pc[,3]
perims<-perims[,c(1:5,15,16)] ## just save the year, name & id, country, sky island and pc values
## save the perim shp
writeOGR(perims, "./data/sp", "fire.polys.pcvalues", driver="ESRI Shapefile", overwrite=TRUE)
|
# Example 5.3
n<- 10
nrep<-1000
xbar<- 1.4 # the only value needed from the data
set.seed(6)
x<- matrix(rpois(nrep*n,xbar),ncol=10)
xmean <- x %*% rep(1,n)/n
xmean<- c(xmean)
par(mfrow=c(1,1))
hist(exp(-xmean),
nclass=20,
xlab=expression(paste(exp,'(',-bar(x),'*)')),
main='')
title('Bootstrap distribution of exp(-xbar)',cex=.4)
|
/R/LKPACK/EX5-3.R
|
permissive
|
yanliangs/in-all-likelihood
|
R
| false | false | 359 |
r
|
# Example 5.3
n<- 10
nrep<-1000
xbar<- 1.4 # the only value needed from the data
set.seed(6)
x<- matrix(rpois(nrep*n,xbar),ncol=10)
xmean <- x %*% rep(1,n)/n
xmean<- c(xmean)
par(mfrow=c(1,1))
hist(exp(-xmean),
nclass=20,
xlab=expression(paste(exp,'(',-bar(x),'*)')),
main='')
title('Bootstrap distribution of exp(-xbar)',cex=.4)
|
\name{fractionation}
\alias{fractionation}
\docType{data}
\title{British Institute of Radiology Fractionation Studies}
\description{
The British Institute of Radiology (BIR) conducted two large-scale
randomized clinical trials to assess the effectiveness of different
radiotherapy treatment schedules for cancer of the larynx and pharynx.
The cambined data come from 858 subjects with laryngeal squamous cell
carcinomas and no involvement of asjacent organs. These data have been
described and analyzed by Rezvani, Fowler, Hopewell, and Alcock (1993).
}
\usage{data(fractionation)}
\format{
A data frame with 858 observations on the following 6 variables.
\describe{
\item{\code{response}}{Three-year local control 1 (0 no control).}
\item{\code{dose}}{Total dose (grays).}
\item{\code{df}}{Total dose x dose/fraction.}
\item{\code{time}}{Total time of treatment (days).}
\item{\code{kt2}}{Tumor status (indicators for 2nd level of factor).}
\item{\code{kt3}}{Tumor status (indicators for 3rd level of factor).}
}
}
\details{
Three-year local control - meaning no detection of laryngeal carcinoma
within three years after treatment - is the binary response, coded as 1
if local control is achieved and 0 otherwise. For this data set, three-year
local control is achieved for 69% of the cases. In a treatment schedule,
a total dose of radiation is administered in fractions over a treatment
period. The dose per fraction df is measured in grays (Gy), the length
of treatment period time is measured in days, and the number of fractions
of the dose is nf. Tumors are classified by stage (i.e., the extent of
invasion), into three groups. This categorical covariate is coded by two
indicator variables kt2 and kt3, which are defined by kt2=1 (kt3=1) is the
tumor is stage II (stage III) and zero otherwise.
Chappell, Nondahl and Fowler (1995) argued that the tumor stage, the total dose, the
total time, and the interaction of the total dose per fraction are the
relevant explanantory variables affecting probability of local control.
}
\references{
Chappell, R., Nondahl, D.M., and Fowler, J.F. (1995) Modelling Dose and
Local Control in Radiotheraphy. Journal of the American Statistical Asso-
ciation, 90: 829 - 838.
Newton, M.A., Czado, C., and Chappell, R. (1996) Bayesian inference
for semiparametric binary regression. Journal of the American Statistical
Association, 91, 142-153.
Rezvani, M., Fowler, J., Hopewell, J., and Alcock, C. (1993)
Sensitivity of Human Squamous Cell Carcinoma of the Larynx to Fractionated
Radiotherapy. British Journal of Radiology, 66: 245 - 255.
}
\examples{
data(fractionation)
## maybe str(fractionation) ; plot(fractionation) ...
}
\keyword{datasets}
|
/man/fractionation.Rd
|
no_license
|
cran/DPpackage
|
R
| false | false | 2,853 |
rd
|
\name{fractionation}
\alias{fractionation}
\docType{data}
\title{British Institute of Radiology Fractionation Studies}
\description{
The British Institute of Radiology (BIR) conducted two large-scale
randomized clinical trials to assess the effectiveness of different
radiotherapy treatment schedules for cancer of the larynx and pharynx.
The cambined data come from 858 subjects with laryngeal squamous cell
carcinomas and no involvement of asjacent organs. These data have been
described and analyzed by Rezvani, Fowler, Hopewell, and Alcock (1993).
}
\usage{data(fractionation)}
\format{
A data frame with 858 observations on the following 6 variables.
\describe{
\item{\code{response}}{Three-year local control 1 (0 no control).}
\item{\code{dose}}{Total dose (grays).}
\item{\code{df}}{Total dose x dose/fraction.}
\item{\code{time}}{Total time of treatment (days).}
\item{\code{kt2}}{Tumor status (indicators for 2nd level of factor).}
\item{\code{kt3}}{Tumor status (indicators for 3rd level of factor).}
}
}
\details{
Three-year local control - meaning no detection of laryngeal carcinoma
within three years after treatment - is the binary response, coded as 1
if local control is achieved and 0 otherwise. For this data set, three-year
local control is achieved for 69% of the cases. In a treatment schedule,
a total dose of radiation is administered in fractions over a treatment
period. The dose per fraction df is measured in grays (Gy), the length
of treatment period time is measured in days, and the number of fractions
of the dose is nf. Tumors are classified by stage (i.e., the extent of
invasion), into three groups. This categorical covariate is coded by two
indicator variables kt2 and kt3, which are defined by kt2=1 (kt3=1) is the
tumor is stage II (stage III) and zero otherwise.
Chappell, Nondahl and Fowler (1995) argued that the tumor stage, the total dose, the
total time, and the interaction of the total dose per fraction are the
relevant explanantory variables affecting probability of local control.
}
\references{
Chappell, R., Nondahl, D.M., and Fowler, J.F. (1995) Modelling Dose and
Local Control in Radiotheraphy. Journal of the American Statistical Asso-
ciation, 90: 829 - 838.
Newton, M.A., Czado, C., and Chappell, R. (1996) Bayesian inference
for semiparametric binary regression. Journal of the American Statistical
Association, 91, 142-153.
Rezvani, M., Fowler, J., Hopewell, J., and Alcock, C. (1993)
Sensitivity of Human Squamous Cell Carcinoma of the Larynx to Fractionated
Radiotherapy. British Journal of Radiology, 66: 245 - 255.
}
\examples{
data(fractionation)
## maybe str(fractionation) ; plot(fractionation) ...
}
\keyword{datasets}
|
" This function takes a specific data frame with numeric items in the
'rate' column and character items in the 'hospital' column. It creates
a data frame that is ordered first by the rate column and second by
the hospital name column.
"
orderData <- function(info) {
orderedIndexes <- order(info$rate, info$hospital)
rates <- info$rate[orderedIndexes]
names <- info$hospital[orderedIndexes]
return(data.frame(hospital=names, rate=rates))
}
|
/RCourse/assignments/assignment3/baseRWay/orderdata.R
|
no_license
|
statisticallyfit/R
|
R
| false | false | 463 |
r
|
" This function takes a specific data frame with numeric items in the
'rate' column and character items in the 'hospital' column. It creates
a data frame that is ordered first by the rate column and second by
the hospital name column.
"
orderData <- function(info) {
orderedIndexes <- order(info$rate, info$hospital)
rates <- info$rate[orderedIndexes]
names <- info$hospital[orderedIndexes]
return(data.frame(hospital=names, rate=rates))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/room.R
\name{hipchat_delete_room}
\alias{hipchat_delete_room}
\title{Delete a Hipchat room.}
\usage{
hipchat_delete_room(room_name_or_id, confirm = TRUE)
}
\arguments{
\item{room_name_or_id}{character or integer.}
\item{confirm}{logical. Whether or not to ask for a confirmation message
before deleting the room. By default, \code{TRUE}. (Deleting rooms
is dangerous!)}
}
\value{
\code{TRUE} or \code{FALSE} according as the room was deleted.
}
\description{
Delete a Hipchat room.
}
\examples{
\dontrun{
hipchat_create_room('Example room')
hipchat_delete_room('Example room') # Will ask a confirmation message.
hipchat_delete_room('Example room', confirm = FALSE) # Dangerous! No confirmation.
}
}
|
/man/hipchat_delete_room.Rd
|
no_license
|
davidboren/hipchat
|
R
| false | true | 788 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/room.R
\name{hipchat_delete_room}
\alias{hipchat_delete_room}
\title{Delete a Hipchat room.}
\usage{
hipchat_delete_room(room_name_or_id, confirm = TRUE)
}
\arguments{
\item{room_name_or_id}{character or integer.}
\item{confirm}{logical. Whether or not to ask for a confirmation message
before deleting the room. By default, \code{TRUE}. (Deleting rooms
is dangerous!)}
}
\value{
\code{TRUE} or \code{FALSE} according as the room was deleted.
}
\description{
Delete a Hipchat room.
}
\examples{
\dontrun{
hipchat_create_room('Example room')
hipchat_delete_room('Example room') # Will ask a confirmation message.
hipchat_delete_room('Example room', confirm = FALSE) # Dangerous! No confirmation.
}
}
|
testlist <- list(x = c(1.90986016056392e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(diceR:::indicator_matrix,testlist)
str(result)
|
/diceR/inst/testfiles/indicator_matrix/libFuzzer_indicator_matrix/indicator_matrix_valgrind_files/1609959638-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false | false | 282 |
r
|
testlist <- list(x = c(1.90986016056392e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(diceR:::indicator_matrix,testlist)
str(result)
|
#topics ----
#factors, env, import/export. package install
#rep, recode, split, partition, subset, loops, cast & melt
#missing values. duplicates, apply
#graphs - bar, multiple line, pie, box, corrgram
fit = lm(weight ~ height,data = women)
summary(fit)
(ndata = data.frame(height = c(60.5, 75.5)))
(predictedwt = predict(fit, newdata = ndata))
cbind(ndata, predictedwt)
resid(fit)
fitted(fit)
|
/Simple Linear Regression - Women.R
|
no_license
|
Nirgun32/analytics
|
R
| false | false | 396 |
r
|
#topics ----
#factors, env, import/export. package install
#rep, recode, split, partition, subset, loops, cast & melt
#missing values. duplicates, apply
#graphs - bar, multiple line, pie, box, corrgram
fit = lm(weight ~ height,data = women)
summary(fit)
(ndata = data.frame(height = c(60.5, 75.5)))
(predictedwt = predict(fit, newdata = ndata))
cbind(ndata, predictedwt)
resid(fit)
fitted(fit)
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Classifier/NSCLC.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0,family="gaussian",standardize=TRUE)
sink('./NSCLC_003.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/Classifier/NSCLC/NSCLC_003.R
|
no_license
|
esbgkannan/QSMART
|
R
| false | false | 344 |
r
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Classifier/NSCLC.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0,family="gaussian",standardize=TRUE)
sink('./NSCLC_003.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
library(magrittr)
#TODO: update for auth with webAPi
stop(1)
overwrite <- TRUE
atlasCohorts <- data.frame(atlasName = c("[EPI_851] New users of beta-blockers",
"[EPI_851] New users of ACE-inhibitors",
"[EPI_851] New users of thiazides or thiazide-like diuretics",
"[EPI_851] Angioedema"),
entityType = c("C", "T", "C", "O"))
tryCatch({
ROhdsiWebApi:::getWebApiVersion(baseUrl = Sys.getenv("WEBAPI_BASE_URL"))
},
error = function(e) {
print(sprintf("Could not interface with WebAPI:\n\t%s", e))
stop(1)
})
replaceExpr <- function(x, extras = NULL, extrasReplace = NULL) {
x <- gsub("^\\[.*\\]\\s+", "", x)
if(!is.null(extras) && !is.null(extrasReplace) && length(extras) == length(extrasReplace)) {
for(i in 1:length(extras)) {
x <- gsub(extras[i], extrasReplace[i], x)
}
}
return(x)
}
cohortsToCreate <- NULL
for (id in 1:nrow(atlasCohorts)) {
atlasCohort <- atlasCohorts[id, ]
print(sprintf('checking if cohort %s exists', atlasCohort$atlasName))
t <- ROhdsiWebApi::existsCohortName(baseUrl = Sys.getenv("WEBAPI_BASE_URL"), cohortName = atlasCohort$atlasName)
if (tibble::is_tibble(t) && overwrite) {
cohortDef <- ROhdsiWebApi::getCohortDefinition(baseUrl = Sys.getenv("WEBAPI_BASE_URL"),
cohortId = t$id)
json <- RJSONIO::toJSON(cohortDef$expression, pretty = TRUE)
normalizedName <- stringr::str_to_title(gsub("-", " ", t$name))
normalizedName <- gsub("^\\[.*\\]\\s+|\\s+", "", normalizedName)
if(grepl("Or", normalizedName)) {
normalizedName <- strsplit(normalizedName, "Or")[[1]][1]
}
#normalizedName <- replaceExpr(stringr::str_to_title(replaceExpr(t$name)), extras = c("-", " "), extrasReplace = c(" ", ""))
SqlRender::writeSql(json, file.path("inst", "cohorts", paste0(normalizedName, ".json")))
if(is.null(cohortsToCreate)) {
cohortsToCreate <- data.frame(atlasId = t$id,
atlasName = t$name,
cohortId = t$id,
name = normalizedName,
entityType = atlasCohort$entityType)
next
}
cohortsToCreate <- rbind(cohortsToCreate, c(t$id, t$name, t$id, normalizedName, atlasCohort$entityType))
} else {
print(sprintf('cohort %s does not exist??', atlasCohort$atlasName))
next
}
}
readr::write_csv(cohortsToCreate, file.path("inst", "settings", "CohortsToCreate.csv"))
# something up with generating sql?
# ROhdsiWebApi::insertCohortDefinitionSetInPackage(baseUrl = Sys.getenv("WEBAPI_BASE_URL"), insertTableSql = FALSE, insertCohortCreationR = FALSE)
jsonFiles <- list.files(file.path("inst", "cohorts"), pattern = "\\.json$", full.names=TRUE)
options <- CirceR::createGenerateOptions()
for(file in jsonFiles) {
print(file)
fileData <- readChar(file, file.info(file)$size)
j <- CirceR::cohortExpressionFromJson(fileData)
sql <- CirceR::buildCohortQuery(j, options)
print(sprintf("writing %s to disc", basename(file)))
SqlRender::writeSql(sql, file.path("inst", "sql", "sql_server", paste0(gsub("json", "sql", basename(file)))))
}
targetIds <- cohortsToCreate %>%
dplyr::filter(entityType == "T") %>%
dplyr::pull(cohortId)
comparatorIds <- cohortsToCreate %>%
dplyr::filter(entityType == "C") %>%
dplyr::pull(cohortId)
outcomeIds <- cohortsToCreate %>%
dplyr::filter(entityType == "O") %>%
dplyr::pull(cohortId)
print("Creating TCOs of interest")
tcosOfInterest <- expand.grid(targetIds, comparatorIds)
colnames(tcosOfInterest) <- c("targetId", "comparatorId")
tcosOfInterest[, "outcomeIds"] = paste0(outcomeIds, collapse = ";")
readr::write_csv(tcosOfInterest, file.path("inst", "settings", "TcosOfInterest.csv"))
print("Harmonizing negative controls")
negativeControlInit <- readr::read_csv(file.path("inst", "settings", "NegativeControlsInit.csv"), col_types = readr::cols())
negativeControlConcepts <- unique(negativeControlInit$outcomeId)
# ncs <- expand.grid(combn(cohortsToCreate$cohortId, 2, simplify=FALSE), negativeControlConcepts)
# ncs <- cbind(do.call(rbind, ncs$Var1), ncs$Var2)
ncs <- expand.grid(targetIds, comparatorIds, negativeControlConcepts)
colnames(ncs) <- c("targetId", "comparatorId", "outcomeId")
ncs <- merge(ncs, unique(negativeControlInit[, c("outcomeId", "outcomeName", "type")]))
ncs <- ncs[, c(2, 3, 1, 4, 5)]
ncs <- ncs[order(ncs[, 1], ncs[, 2], ncs[, 3]),]
readr::write_csv(ncs, file.path("inst", "settings", "NegativeControls.csv"))
source(file.path("extras", "CreateAnalyses.R"))
|
/extras/Setup.R
|
no_license
|
ohdsi-studies/CovariateImbalanceDiagnosticsEvaluation
|
R
| false | false | 4,641 |
r
|
library(magrittr)
#TODO: update for auth with webAPi
stop(1)
overwrite <- TRUE
atlasCohorts <- data.frame(atlasName = c("[EPI_851] New users of beta-blockers",
"[EPI_851] New users of ACE-inhibitors",
"[EPI_851] New users of thiazides or thiazide-like diuretics",
"[EPI_851] Angioedema"),
entityType = c("C", "T", "C", "O"))
tryCatch({
ROhdsiWebApi:::getWebApiVersion(baseUrl = Sys.getenv("WEBAPI_BASE_URL"))
},
error = function(e) {
print(sprintf("Could not interface with WebAPI:\n\t%s", e))
stop(1)
})
replaceExpr <- function(x, extras = NULL, extrasReplace = NULL) {
x <- gsub("^\\[.*\\]\\s+", "", x)
if(!is.null(extras) && !is.null(extrasReplace) && length(extras) == length(extrasReplace)) {
for(i in 1:length(extras)) {
x <- gsub(extras[i], extrasReplace[i], x)
}
}
return(x)
}
cohortsToCreate <- NULL
for (id in 1:nrow(atlasCohorts)) {
atlasCohort <- atlasCohorts[id, ]
print(sprintf('checking if cohort %s exists', atlasCohort$atlasName))
t <- ROhdsiWebApi::existsCohortName(baseUrl = Sys.getenv("WEBAPI_BASE_URL"), cohortName = atlasCohort$atlasName)
if (tibble::is_tibble(t) && overwrite) {
cohortDef <- ROhdsiWebApi::getCohortDefinition(baseUrl = Sys.getenv("WEBAPI_BASE_URL"),
cohortId = t$id)
json <- RJSONIO::toJSON(cohortDef$expression, pretty = TRUE)
normalizedName <- stringr::str_to_title(gsub("-", " ", t$name))
normalizedName <- gsub("^\\[.*\\]\\s+|\\s+", "", normalizedName)
if(grepl("Or", normalizedName)) {
normalizedName <- strsplit(normalizedName, "Or")[[1]][1]
}
#normalizedName <- replaceExpr(stringr::str_to_title(replaceExpr(t$name)), extras = c("-", " "), extrasReplace = c(" ", ""))
SqlRender::writeSql(json, file.path("inst", "cohorts", paste0(normalizedName, ".json")))
if(is.null(cohortsToCreate)) {
cohortsToCreate <- data.frame(atlasId = t$id,
atlasName = t$name,
cohortId = t$id,
name = normalizedName,
entityType = atlasCohort$entityType)
next
}
cohortsToCreate <- rbind(cohortsToCreate, c(t$id, t$name, t$id, normalizedName, atlasCohort$entityType))
} else {
print(sprintf('cohort %s does not exist??', atlasCohort$atlasName))
next
}
}
readr::write_csv(cohortsToCreate, file.path("inst", "settings", "CohortsToCreate.csv"))
# something up with generating sql?
# ROhdsiWebApi::insertCohortDefinitionSetInPackage(baseUrl = Sys.getenv("WEBAPI_BASE_URL"), insertTableSql = FALSE, insertCohortCreationR = FALSE)
jsonFiles <- list.files(file.path("inst", "cohorts"), pattern = "\\.json$", full.names=TRUE)
options <- CirceR::createGenerateOptions()
for(file in jsonFiles) {
print(file)
fileData <- readChar(file, file.info(file)$size)
j <- CirceR::cohortExpressionFromJson(fileData)
sql <- CirceR::buildCohortQuery(j, options)
print(sprintf("writing %s to disc", basename(file)))
SqlRender::writeSql(sql, file.path("inst", "sql", "sql_server", paste0(gsub("json", "sql", basename(file)))))
}
targetIds <- cohortsToCreate %>%
dplyr::filter(entityType == "T") %>%
dplyr::pull(cohortId)
comparatorIds <- cohortsToCreate %>%
dplyr::filter(entityType == "C") %>%
dplyr::pull(cohortId)
outcomeIds <- cohortsToCreate %>%
dplyr::filter(entityType == "O") %>%
dplyr::pull(cohortId)
print("Creating TCOs of interest")
tcosOfInterest <- expand.grid(targetIds, comparatorIds)
colnames(tcosOfInterest) <- c("targetId", "comparatorId")
tcosOfInterest[, "outcomeIds"] = paste0(outcomeIds, collapse = ";")
readr::write_csv(tcosOfInterest, file.path("inst", "settings", "TcosOfInterest.csv"))
print("Harmonizing negative controls")
negativeControlInit <- readr::read_csv(file.path("inst", "settings", "NegativeControlsInit.csv"), col_types = readr::cols())
negativeControlConcepts <- unique(negativeControlInit$outcomeId)
# ncs <- expand.grid(combn(cohortsToCreate$cohortId, 2, simplify=FALSE), negativeControlConcepts)
# ncs <- cbind(do.call(rbind, ncs$Var1), ncs$Var2)
ncs <- expand.grid(targetIds, comparatorIds, negativeControlConcepts)
colnames(ncs) <- c("targetId", "comparatorId", "outcomeId")
ncs <- merge(ncs, unique(negativeControlInit[, c("outcomeId", "outcomeName", "type")]))
ncs <- ncs[, c(2, 3, 1, 4, 5)]
ncs <- ncs[order(ncs[, 1], ncs[, 2], ncs[, 3]),]
readr::write_csv(ncs, file.path("inst", "settings", "NegativeControls.csv"))
source(file.path("extras", "CreateAnalyses.R"))
|
library(e1071)
library(mlbench)
library(caret)
library(Amelia)
# uploading data
data<-read.table(file.choose(),header=T,sep=",")
# To Check
is.na(data)
missmap(data)
# To Remove
na.omit(data)
|
/1. Missing.R
|
no_license
|
zeeshan-ramzan/Cervical-Cancer-Risk-Assessment
|
R
| false | false | 210 |
r
|
library(e1071)
library(mlbench)
library(caret)
library(Amelia)
# uploading data
data<-read.table(file.choose(),header=T,sep=",")
# To Check
is.na(data)
missmap(data)
# To Remove
na.omit(data)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Basic_EndoPathExtractors.R
\name{HistolTypeAndSite}
\alias{HistolTypeAndSite}
\title{HistolTypeAndSite}
\usage{
HistolTypeAndSite(inputString1, inputString2, procedureString)
}
\arguments{
\item{inputString1}{The first column to look in}
\item{inputString2}{The second column to look in}
\item{procedureString}{The column with the procedure in it}
}
\value{
a list with two columns, one is the type and site and the other
is the index to be used for OPCS4 coding later if needed.
}
\description{
This needs some blurb to be written. Used in the OPCS4 coding
}
\examples{
Myendo2<-Endomerge2(Myendo,'Dateofprocedure','HospitalNumber',
Mypath,'Dateofprocedure','HospitalNumber')
PathSiteAndType <- HistolTypeAndSite(Myendo2$PathReportWhole,
Myendo2$Macroscopicdescription, Myendo2$ProcedurePerformed)
}
\keyword{Find}
\keyword{and}
\keyword{replace}
|
/man/HistolTypeAndSite.Rd
|
no_license
|
camcaan/enDo
|
R
| false | true | 928 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Basic_EndoPathExtractors.R
\name{HistolTypeAndSite}
\alias{HistolTypeAndSite}
\title{HistolTypeAndSite}
\usage{
HistolTypeAndSite(inputString1, inputString2, procedureString)
}
\arguments{
\item{inputString1}{The first column to look in}
\item{inputString2}{The second column to look in}
\item{procedureString}{The column with the procedure in it}
}
\value{
a list with two columns, one is the type and site and the other
is the index to be used for OPCS4 coding later if needed.
}
\description{
This needs some blurb to be written. Used in the OPCS4 coding
}
\examples{
Myendo2<-Endomerge2(Myendo,'Dateofprocedure','HospitalNumber',
Mypath,'Dateofprocedure','HospitalNumber')
PathSiteAndType <- HistolTypeAndSite(Myendo2$PathReportWhole,
Myendo2$Macroscopicdescription, Myendo2$ProcedurePerformed)
}
\keyword{Find}
\keyword{and}
\keyword{replace}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generated_client.R
\name{users_delete_2fa}
\alias{users_delete_2fa}
\title{Wipes the user's current 2FA settings so that they must reset them upon next login}
\usage{
users_delete_2fa(id)
}
\arguments{
\item{id}{integer required. The ID of this user.}
}
\value{
A list containing the following elements:
\item{id}{integer, The ID of this user.}
\item{user}{string, The username of this user.}
\item{name}{string, The name of this user.}
\item{email}{string, The email of this user.}
\item{active}{boolean, Whether this user account is active or deactivated.}
\item{primaryGroupId}{integer, The ID of the primary group of this user.}
\item{groups}{array, An array containing the following fields:
\itemize{
\item id integer, The ID of this group.
\item name string, The name of this group.
\item slug string, The slug of this group.
\item organizationId integer, The ID of the organization associated with this group.
\item organizationName string, The name of the organization associated with this group.
}}
\item{city}{string, The city of this user.}
\item{state}{string, The state of this user.}
\item{timeZone}{string, The time zone of this user.}
\item{initials}{string, The initials of this user.}
\item{department}{string, The department of this user.}
\item{title}{string, The title of this user.}
\item{githubUsername}{string, The GitHub username of this user.}
\item{prefersSmsOtp}{boolean, The preference for phone authorization of this user}
\item{vpnEnabled}{boolean, The availability of vpn for this user.}
\item{ssoDisabled}{boolean, The availability of SSO for this user.}
\item{otpRequiredForLogin}{boolean, The two factor authentication requirement for this user.}
\item{exemptFromOrgSmsOtpDisabled}{boolean, Whether the user has SMS OTP enabled on an individual level. This field does not matter if the org does not have SMS OTP disabled.}
\item{smsOtpAllowed}{boolean, Whether the user is allowed to receive two factor authentication codes via SMS.}
\item{robot}{boolean, Whether the user is a robot.}
\item{phone}{string, The phone number of this user.}
\item{organizationSlug}{string, The slug of the organization the user belongs to.}
\item{organizationSSODisableCapable}{boolean, The user's organization's ability to disable sso for their users.}
\item{organizationLoginType}{string, The user's organization's login type.}
\item{organizationSmsOtpDisabled}{boolean, Whether the user's organization has SMS OTP disabled.}
\item{myPermissionLevel}{string, Your permission level on the object. One of "read", "write", or "manage".}
\item{createdAt}{string, The date and time when the user was created.}
\item{updatedAt}{string, The date and time when the user was last updated.}
\item{lastSeenAt}{string, The date and time when the user last visited Platform.}
\item{suspended}{boolean, Whether the user is suspended due to inactivity.}
\item{createdById}{integer, The ID of the user who created this user.}
\item{lastUpdatedById}{integer, The ID of the user who last updated this user.}
\item{unconfirmedEmail}{string, The new email address awaiting confirmation from the user.}
\item{accountStatus}{string, Account status of this user. One of: "Active", "Deactivated", "Suspended", "Unsuspended"}
}
\description{
Wipes the user's current 2FA settings so that they must reset them upon next login
}
|
/man/users_delete_2fa.Rd
|
no_license
|
cran/civis
|
R
| false | true | 3,399 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generated_client.R
\name{users_delete_2fa}
\alias{users_delete_2fa}
\title{Wipes the user's current 2FA settings so that they must reset them upon next login}
\usage{
users_delete_2fa(id)
}
\arguments{
\item{id}{integer required. The ID of this user.}
}
\value{
A list containing the following elements:
\item{id}{integer, The ID of this user.}
\item{user}{string, The username of this user.}
\item{name}{string, The name of this user.}
\item{email}{string, The email of this user.}
\item{active}{boolean, Whether this user account is active or deactivated.}
\item{primaryGroupId}{integer, The ID of the primary group of this user.}
\item{groups}{array, An array containing the following fields:
\itemize{
\item id integer, The ID of this group.
\item name string, The name of this group.
\item slug string, The slug of this group.
\item organizationId integer, The ID of the organization associated with this group.
\item organizationName string, The name of the organization associated with this group.
}}
\item{city}{string, The city of this user.}
\item{state}{string, The state of this user.}
\item{timeZone}{string, The time zone of this user.}
\item{initials}{string, The initials of this user.}
\item{department}{string, The department of this user.}
\item{title}{string, The title of this user.}
\item{githubUsername}{string, The GitHub username of this user.}
\item{prefersSmsOtp}{boolean, The preference for phone authorization of this user}
\item{vpnEnabled}{boolean, The availability of vpn for this user.}
\item{ssoDisabled}{boolean, The availability of SSO for this user.}
\item{otpRequiredForLogin}{boolean, The two factor authentication requirement for this user.}
\item{exemptFromOrgSmsOtpDisabled}{boolean, Whether the user has SMS OTP enabled on an individual level. This field does not matter if the org does not have SMS OTP disabled.}
\item{smsOtpAllowed}{boolean, Whether the user is allowed to receive two factor authentication codes via SMS.}
\item{robot}{boolean, Whether the user is a robot.}
\item{phone}{string, The phone number of this user.}
\item{organizationSlug}{string, The slug of the organization the user belongs to.}
\item{organizationSSODisableCapable}{boolean, The user's organization's ability to disable sso for their users.}
\item{organizationLoginType}{string, The user's organization's login type.}
\item{organizationSmsOtpDisabled}{boolean, Whether the user's organization has SMS OTP disabled.}
\item{myPermissionLevel}{string, Your permission level on the object. One of "read", "write", or "manage".}
\item{createdAt}{string, The date and time when the user was created.}
\item{updatedAt}{string, The date and time when the user was last updated.}
\item{lastSeenAt}{string, The date and time when the user last visited Platform.}
\item{suspended}{boolean, Whether the user is suspended due to inactivity.}
\item{createdById}{integer, The ID of the user who created this user.}
\item{lastUpdatedById}{integer, The ID of the user who last updated this user.}
\item{unconfirmedEmail}{string, The new email address awaiting confirmation from the user.}
\item{accountStatus}{string, Account status of this user. One of: "Active", "Deactivated", "Suspended", "Unsuspended"}
}
\description{
Wipes the user's current 2FA settings so that they must reset them upon next login
}
|
get_exclusions <- function(wbd_gdb) {
if("HUC12" %in% st_layers(wbd_gdb)$name) {
wbd <- read_sf(wbd_gdb, "HUC12")
wbd <- rename(wbd, HUC12 = HUC_12, TOHUC = HU_12_DS)
} else {
wbd <- read_sf(wbd_gdb, "WBDHU12")
}
wbd_type <- st_set_geometry(wbd, NULL)
if("HUTYPE" %in% names(wbd_type)) {
wbd_type <- distinct(select(wbd_type, HUC12, HUTYPE))
} else {
wbd_type <- distinct(select(wbd_type, HUC12, HUTYPE = HU_12_TYPE))
}
wbd <- group_by(wbd, HUC12) %>%
summarise(TOHUC = TOHUC[1])
# Exclusions where river-flow does not apply:
exclude_type <- wbd_type$HUC12[wbd_type$HUTYPE %in% c("F", "I", "C", "U")] # frontal closed or island
exclude_first_order_toHUC <- wbd$HUC12[wbd$TOHUC %in% c("OCEAN", "CANADA", "GEATLAKES", "UNKNOWN") &
!wbd$HUC12 %in% wbd$TOHUC] # Unless it has something flowing to it.
exclude <- unique(c(exclude_type, exclude_first_order_toHUC))
return(exclude)
}
|
/R/5_find_outlets.R
|
permissive
|
jsta/HU12_NHD
|
R
| false | false | 992 |
r
|
get_exclusions <- function(wbd_gdb) {
if("HUC12" %in% st_layers(wbd_gdb)$name) {
wbd <- read_sf(wbd_gdb, "HUC12")
wbd <- rename(wbd, HUC12 = HUC_12, TOHUC = HU_12_DS)
} else {
wbd <- read_sf(wbd_gdb, "WBDHU12")
}
wbd_type <- st_set_geometry(wbd, NULL)
if("HUTYPE" %in% names(wbd_type)) {
wbd_type <- distinct(select(wbd_type, HUC12, HUTYPE))
} else {
wbd_type <- distinct(select(wbd_type, HUC12, HUTYPE = HU_12_TYPE))
}
wbd <- group_by(wbd, HUC12) %>%
summarise(TOHUC = TOHUC[1])
# Exclusions where river-flow does not apply:
exclude_type <- wbd_type$HUC12[wbd_type$HUTYPE %in% c("F", "I", "C", "U")] # frontal closed or island
exclude_first_order_toHUC <- wbd$HUC12[wbd$TOHUC %in% c("OCEAN", "CANADA", "GEATLAKES", "UNKNOWN") &
!wbd$HUC12 %in% wbd$TOHUC] # Unless it has something flowing to it.
exclude <- unique(c(exclude_type, exclude_first_order_toHUC))
return(exclude)
}
|
#' specify a general vecchia approximation
#'
#' specify the vecchia approximation for later use in likelihood evaluation or prediction.
#' This function does not depend on parameter values, and only has to be run once before
#' repeated likelihood evaluations.
#' @param locs nxd matrix of observed locs
#' @param m Number of nearby points to condition on
#' @param ordering options are 'coord' or 'maxmin'
#' @param cond.yz options are 'y', 'z', 'SGV', 'SGVT', 'RVP', 'LK', and 'zy'
#' @param locs.pred nxd matrix of locations at which to make predictions
#' @param ordering.pred options are 'obspred' or 'general'
#' @param pred.cond prediction conditioning, options are 'general' or 'independent'
#' @param conditioning conditioning on 'NN' (nearest neighbor) or 'firstm' (fixed set for low rank)
#' or 'mra'
#' @param mra.options Settings for number of levels and neighbors per level
#' @param verbose Provide more detail when using MRA calculations. Default is false.
#'
#' @return An object that specifies the vecchia approximation for later use in likelihood
#' evaluation or prediction.
#' @examples
#' locs=matrix(1:5,ncol=1); vecchia_specify(locs,m=2)
#' @export
# specify the vecchia approximation, prepare U
# this fct does not depend on data or parameter values
# only has to be run once before repeated likelihood evals
vecchia_specify=function(locs,m=-1,ordering,cond.yz,locs.pred,ordering.pred,pred.cond,
conditioning, mra.options=NULL, ic0=FALSE, verbose=FALSE) {
if(!is.matrix(locs)) {
warning("Locations must be in matrix form")
return(NA)
}
if(m==-1 || is.null(m)) {
if(conditioning=='mra' && !is.null(mra.options) && !is.null(mra.options$J) && !is.null(mra.options$r) && !is.null(mra.options$J))
warning("m not defined; using MRA parameters")
else if(is.null(mra.options$r)) stop("neither m nor r defined!")
}
spatial.dim=ncol(locs)
n=nrow(locs)
# check that locs.preds does not contain any locations in locs
if(!missing(locs.pred)){
locs.all = rbind(locs, locs.pred)
if(anyDuplicated(locs.all)>0)
stop("Prediction locations contain observed location(s), remove redundancies.")
}
if(m>n){
warning("Conditioning set size m chosen to be larger than n. Changing to m=n-1")
m=n-1
}
# The fully independent case with no conditioning
if(m==0){
if(!missing(locs.pred)) warning("Attempting to make predictions with m=0. Prediction ignored")
ord = 1:n
ord.z=ord
locsord=locs[ord,,drop=FALSE]
NNarray= matrix(cbind(ord, rep(NA, nrow(locs))), ncol=2)
Cond=matrix(NA,n,2); Cond[,1]=T
obs=rep(TRUE,n)
### determine the sparsity structure of U
U.prep=U_sparsity( locsord, NNarray, obs, Cond )
### object that specifies the vecchia approximation
vecchia.approx=list(locsord=locsord, obs=obs, ord=ord, ord.z=ord.z, ord.pred='general',
U.prep=U.prep,cond.yz='false',conditioning='NN')
return(vecchia.approx)
}
# subsume firstm into mra
if(!missing(conditioning) && conditioning == 'firstm'){
conditioning = 'mra'
mra.options = list(r=c(m,1))
}
# default options
if(missing(ordering)){
if(spatial.dim==1) {ordering = 'coord'} else ordering = 'maxmin'
}
if(missing(pred.cond)) pred.cond='general'
if(missing(conditioning)) conditioning='NN'
if(conditioning %in% c('mra', 'firstm')){
if(ordering!='maxmin') warning("ordering for the selected conditioning scheme changed to required 'maxmin'")
ordering='maxmin'
}
if(missing(cond.yz)){
if (conditioning %in% c('mra', 'firstm')) { cond.yz='y'
} else if(missing(locs.pred) | spatial.dim==1 ){ cond.yz = 'SGV'
} else cond.yz = 'zy'
}
### order locs and z
if(missing(locs.pred)){ # no prediction
if(ordering=='coord') { ord=order_coordinate(locs)
} else if(ordering=='maxmin'){
ord = order_maxmin_exact(locs)
cut = min(n, 9)
ord = c(ord[1], ord[-seq(1,cut)], ord[2:cut])
} else if(ordering=='outsidein'){
ord = order_outsidein(locs)
} else if(ordering=='none'){
ord = seq(n)
}
ord.z=ord
locsord=locs[ord,,drop=FALSE]
obs=rep(TRUE,n)
ordering.pred='general'
} else { # prediction is desired
n.p=nrow(locs.pred)
locs.all=rbind(locs,locs.pred)
observed.obspred=c(rep(TRUE,n),rep(FALSE,n.p))
if(missing(ordering.pred))
if(spatial.dim==1 & ordering=='coord') ordering.pred='general' else
ordering.pred='obspred'
if(ordering.pred=='general'){
if(ordering=='coord') ord=order_coordinate(locs.all) else {
ord = order_maxmin_exact(locs.all)
}
ord.obs=ord[ord<=n]
} else {
if(ordering=='coord') {
ord.obs=order_coordinate(locs)
ord.pred=order_coordinate(locs.pred)
} else if(ordering=='none') {
ord.obs = seq(n)
ord.pred = seq(n.p)
} else {
temp=order_maxmin_exact_obs_pred(locs,locs.pred)
ord.obs=temp$ord
ord.pred=temp$ord_pred
}
ord=c(ord.obs,ord.pred+n)
}
ord.z=ord.obs
locsord=locs.all[ord,,drop=FALSE]
obs=observed.obspred[ord]
}
### obtain conditioning sets
if( conditioning == 'mra' ){
NNarray = findOrderedNN_mra(locsord, mra.options, m, verbose)
if(!methods::hasArg(m)) m = ncol(NNarray)-1
} else if( conditioning %in% c('firstm', 'NN')){
if(spatial.dim==1) {
NNarray=findOrderedNN_kdtree2(locsord,m)
} else NNarray <- GpGp::find_ordered_nn(locsord,m)
if(conditioning == 'firstm'){
first_m = NNarray[m+1,2:(m+1)]
n.all=nrow(NNarray)
if (m < n.all-1){ # if m=n-1, nothing to replace
NNarray[(m+2):n.all, 2:(m+1)] = matrix(rep(first_m, n.all-m-1), byrow = TRUE, ncol = m)
}
}
} else stop(paste0("conditioning='",conditioning,"' not defined"))
if(!missing(locs.pred) & pred.cond=='independent'){
if(ordering.pred=='obspred'){
NNarray.pred <- array(dim=c(n.p,m+1))
for(j in 1:n.p){
dists=fields::rdist(locsord[n+j,,drop=FALSE],locsord[1:n,,drop=FALSE])
m.nearest.obs=sort(order(dists)[1:m],decreasing=TRUE)
NNarray.pred[j,]=c(n+j,m.nearest.obs)
}
NNarray[n+(1:n.p),]=NNarray.pred
} else warning('indep. conditioning currently only implemented for obspred ordering')
}
### conditioning on y or z?
if(cond.yz=='SGV'){
Cond=whichCondOnLatent(NNarray,firstind.pred=n+1)
} else if(cond.yz=='SGVT'){
Cond=rbind(whichCondOnLatent(NNarray[1:n,]),matrix(TRUE,nrow=n.p,ncol=m+1))
} else if(cond.yz=='y'){
Cond=matrix(NA,nrow(NNarray),ncol(NNarray)); Cond[!is.na(NNarray)]=TRUE
#Cond=matrix(NA,nrow(NNarray),m+1); Cond[!is.na(NNarray)]=FALSE; Cond[,1]=TRUE
} else if(cond.yz=='z'){
Cond=matrix(NA,nrow(NNarray),m+1); Cond[!is.na(NNarray)]=FALSE; Cond[,1]=TRUE
} else if(cond.yz %in% c('RVP','LK','zy')){
### "trick" code into response-latent ('zy') ordering
## reset variables
obs=c(rep(TRUE,n),rep(FALSE,nrow(locsord)))
locsord=rbind(locsord[1:n,,drop=FALSE],locsord)
## specify neighbors
NNs=FNN::get.knn(locsord[1:n,,drop=FALSE],m-1)$nn.index
if(cond.yz %in% c('RVP','zy')){
prev=(NNs<matrix(rep(1:n,m-1),nrow=n))
NNs[prev]=NNs[prev]+n # condition on latent y.obs if possible
}
## create NN array
NNarray.z=cbind(1:n,matrix(nrow=n,ncol=m))
NNarray.y=cbind((1:n)+n,1:n,NNs)
if(missing(locs.pred)){
NNarray.yp=matrix(nrow=0,ncol=m+1)
ordering.pred='obspred'
} else {
if(ordering.pred!='obspred') warning('ZY only implemented for obspred ordering')
if(cond.yz=='zy'){
NNarray.yp=NNarray[n+(1:n.p),]+n
} else {
NNarray.yp=NNarray[n+(1:n.p),]
NNarray.yp[NNarray.yp>n]=NNarray.yp[NNarray.yp>n]+n
}
}
NNarray=rbind(NNarray.z,NNarray.y,NNarray.yp)
## conditioning
Cond=(NNarray>n); Cond[,1]=TRUE
cond.yz='zy'
} else stop(paste0("cond.yz='",cond.yz,"' not defined"))
### determine the sparsity structure of U
U.prep=U_sparsity( locsord, NNarray, obs, Cond )
### object that specifies the vecchia approximation
vecchia.approx=list(locsord=locsord,obs=obs,ord=ord,ord.z=ord.z,ord.pred=ordering.pred,
U.prep=U.prep,cond.yz=cond.yz, ic0=ic0, conditioning=conditioning)
# U.prep has attributes: revNNarray,revCond,n.cores,size,rowpointers,colindices,y.ind)
return(vecchia.approx)
}
|
/R/vecchia_specify.R
|
no_license
|
Sadra1999/GPvecchia
|
R
| false | false | 8,451 |
r
|
#' specify a general vecchia approximation
#'
#' specify the vecchia approximation for later use in likelihood evaluation or prediction.
#' This function does not depend on parameter values, and only has to be run once before
#' repeated likelihood evaluations.
#' @param locs nxd matrix of observed locs
#' @param m Number of nearby points to condition on
#' @param ordering options are 'coord' or 'maxmin'
#' @param cond.yz options are 'y', 'z', 'SGV', 'SGVT', 'RVP', 'LK', and 'zy'
#' @param locs.pred nxd matrix of locations at which to make predictions
#' @param ordering.pred options are 'obspred' or 'general'
#' @param pred.cond prediction conditioning, options are 'general' or 'independent'
#' @param conditioning conditioning on 'NN' (nearest neighbor) or 'firstm' (fixed set for low rank)
#' or 'mra'
#' @param mra.options Settings for number of levels and neighbors per level
#' @param verbose Provide more detail when using MRA calculations. Default is false.
#'
#' @return An object that specifies the vecchia approximation for later use in likelihood
#' evaluation or prediction.
#' @examples
#' locs=matrix(1:5,ncol=1); vecchia_specify(locs,m=2)
#' @export
# specify the vecchia approximation, prepare U
# this fct does not depend on data or parameter values
# only has to be run once before repeated likelihood evals
vecchia_specify=function(locs,m=-1,ordering,cond.yz,locs.pred,ordering.pred,pred.cond,
conditioning, mra.options=NULL, ic0=FALSE, verbose=FALSE) {
if(!is.matrix(locs)) {
warning("Locations must be in matrix form")
return(NA)
}
if(m==-1 || is.null(m)) {
if(conditioning=='mra' && !is.null(mra.options) && !is.null(mra.options$J) && !is.null(mra.options$r) && !is.null(mra.options$J))
warning("m not defined; using MRA parameters")
else if(is.null(mra.options$r)) stop("neither m nor r defined!")
}
spatial.dim=ncol(locs)
n=nrow(locs)
# check that locs.preds does not contain any locations in locs
if(!missing(locs.pred)){
locs.all = rbind(locs, locs.pred)
if(anyDuplicated(locs.all)>0)
stop("Prediction locations contain observed location(s), remove redundancies.")
}
if(m>n){
warning("Conditioning set size m chosen to be larger than n. Changing to m=n-1")
m=n-1
}
# The fully independent case with no conditioning
if(m==0){
if(!missing(locs.pred)) warning("Attempting to make predictions with m=0. Prediction ignored")
ord = 1:n
ord.z=ord
locsord=locs[ord,,drop=FALSE]
NNarray= matrix(cbind(ord, rep(NA, nrow(locs))), ncol=2)
Cond=matrix(NA,n,2); Cond[,1]=T
obs=rep(TRUE,n)
### determine the sparsity structure of U
U.prep=U_sparsity( locsord, NNarray, obs, Cond )
### object that specifies the vecchia approximation
vecchia.approx=list(locsord=locsord, obs=obs, ord=ord, ord.z=ord.z, ord.pred='general',
U.prep=U.prep,cond.yz='false',conditioning='NN')
return(vecchia.approx)
}
# subsume firstm into mra
if(!missing(conditioning) && conditioning == 'firstm'){
conditioning = 'mra'
mra.options = list(r=c(m,1))
}
# default options
if(missing(ordering)){
if(spatial.dim==1) {ordering = 'coord'} else ordering = 'maxmin'
}
if(missing(pred.cond)) pred.cond='general'
if(missing(conditioning)) conditioning='NN'
if(conditioning %in% c('mra', 'firstm')){
if(ordering!='maxmin') warning("ordering for the selected conditioning scheme changed to required 'maxmin'")
ordering='maxmin'
}
if(missing(cond.yz)){
if (conditioning %in% c('mra', 'firstm')) { cond.yz='y'
} else if(missing(locs.pred) | spatial.dim==1 ){ cond.yz = 'SGV'
} else cond.yz = 'zy'
}
### order locs and z
if(missing(locs.pred)){ # no prediction
if(ordering=='coord') { ord=order_coordinate(locs)
} else if(ordering=='maxmin'){
ord = order_maxmin_exact(locs)
cut = min(n, 9)
ord = c(ord[1], ord[-seq(1,cut)], ord[2:cut])
} else if(ordering=='outsidein'){
ord = order_outsidein(locs)
} else if(ordering=='none'){
ord = seq(n)
}
ord.z=ord
locsord=locs[ord,,drop=FALSE]
obs=rep(TRUE,n)
ordering.pred='general'
} else { # prediction is desired
n.p=nrow(locs.pred)
locs.all=rbind(locs,locs.pred)
observed.obspred=c(rep(TRUE,n),rep(FALSE,n.p))
if(missing(ordering.pred))
if(spatial.dim==1 & ordering=='coord') ordering.pred='general' else
ordering.pred='obspred'
if(ordering.pred=='general'){
if(ordering=='coord') ord=order_coordinate(locs.all) else {
ord = order_maxmin_exact(locs.all)
}
ord.obs=ord[ord<=n]
} else {
if(ordering=='coord') {
ord.obs=order_coordinate(locs)
ord.pred=order_coordinate(locs.pred)
} else if(ordering=='none') {
ord.obs = seq(n)
ord.pred = seq(n.p)
} else {
temp=order_maxmin_exact_obs_pred(locs,locs.pred)
ord.obs=temp$ord
ord.pred=temp$ord_pred
}
ord=c(ord.obs,ord.pred+n)
}
ord.z=ord.obs
locsord=locs.all[ord,,drop=FALSE]
obs=observed.obspred[ord]
}
### obtain conditioning sets
if( conditioning == 'mra' ){
NNarray = findOrderedNN_mra(locsord, mra.options, m, verbose)
if(!methods::hasArg(m)) m = ncol(NNarray)-1
} else if( conditioning %in% c('firstm', 'NN')){
if(spatial.dim==1) {
NNarray=findOrderedNN_kdtree2(locsord,m)
} else NNarray <- GpGp::find_ordered_nn(locsord,m)
if(conditioning == 'firstm'){
first_m = NNarray[m+1,2:(m+1)]
n.all=nrow(NNarray)
if (m < n.all-1){ # if m=n-1, nothing to replace
NNarray[(m+2):n.all, 2:(m+1)] = matrix(rep(first_m, n.all-m-1), byrow = TRUE, ncol = m)
}
}
} else stop(paste0("conditioning='",conditioning,"' not defined"))
if(!missing(locs.pred) & pred.cond=='independent'){
if(ordering.pred=='obspred'){
NNarray.pred <- array(dim=c(n.p,m+1))
for(j in 1:n.p){
dists=fields::rdist(locsord[n+j,,drop=FALSE],locsord[1:n,,drop=FALSE])
m.nearest.obs=sort(order(dists)[1:m],decreasing=TRUE)
NNarray.pred[j,]=c(n+j,m.nearest.obs)
}
NNarray[n+(1:n.p),]=NNarray.pred
} else warning('indep. conditioning currently only implemented for obspred ordering')
}
### conditioning on y or z?
if(cond.yz=='SGV'){
Cond=whichCondOnLatent(NNarray,firstind.pred=n+1)
} else if(cond.yz=='SGVT'){
Cond=rbind(whichCondOnLatent(NNarray[1:n,]),matrix(TRUE,nrow=n.p,ncol=m+1))
} else if(cond.yz=='y'){
Cond=matrix(NA,nrow(NNarray),ncol(NNarray)); Cond[!is.na(NNarray)]=TRUE
#Cond=matrix(NA,nrow(NNarray),m+1); Cond[!is.na(NNarray)]=FALSE; Cond[,1]=TRUE
} else if(cond.yz=='z'){
Cond=matrix(NA,nrow(NNarray),m+1); Cond[!is.na(NNarray)]=FALSE; Cond[,1]=TRUE
} else if(cond.yz %in% c('RVP','LK','zy')){
### "trick" code into response-latent ('zy') ordering
## reset variables
obs=c(rep(TRUE,n),rep(FALSE,nrow(locsord)))
locsord=rbind(locsord[1:n,,drop=FALSE],locsord)
## specify neighbors
NNs=FNN::get.knn(locsord[1:n,,drop=FALSE],m-1)$nn.index
if(cond.yz %in% c('RVP','zy')){
prev=(NNs<matrix(rep(1:n,m-1),nrow=n))
NNs[prev]=NNs[prev]+n # condition on latent y.obs if possible
}
## create NN array
NNarray.z=cbind(1:n,matrix(nrow=n,ncol=m))
NNarray.y=cbind((1:n)+n,1:n,NNs)
if(missing(locs.pred)){
NNarray.yp=matrix(nrow=0,ncol=m+1)
ordering.pred='obspred'
} else {
if(ordering.pred!='obspred') warning('ZY only implemented for obspred ordering')
if(cond.yz=='zy'){
NNarray.yp=NNarray[n+(1:n.p),]+n
} else {
NNarray.yp=NNarray[n+(1:n.p),]
NNarray.yp[NNarray.yp>n]=NNarray.yp[NNarray.yp>n]+n
}
}
NNarray=rbind(NNarray.z,NNarray.y,NNarray.yp)
## conditioning
Cond=(NNarray>n); Cond[,1]=TRUE
cond.yz='zy'
} else stop(paste0("cond.yz='",cond.yz,"' not defined"))
### determine the sparsity structure of U
U.prep=U_sparsity( locsord, NNarray, obs, Cond )
### object that specifies the vecchia approximation
vecchia.approx=list(locsord=locsord,obs=obs,ord=ord,ord.z=ord.z,ord.pred=ordering.pred,
U.prep=U.prep,cond.yz=cond.yz, ic0=ic0, conditioning=conditioning)
# U.prep has attributes: revNNarray,revCond,n.cores,size,rowpointers,colindices,y.ind)
return(vecchia.approx)
}
|
# Introduction to webpage
Intro <- function() {
return("Using a dataset from FiveThirtyEight that has information on comic book characters,
we were interested in the diversity of comic book characters. As the dataset contains information such as
a character's physical attributes, the year of their first appearance, gender orientation, and intentions,
we focused on comparing different variables and if minority groups are accurately represented for comic characters."
)
}
|
/script/introduction.R
|
no_license
|
aysiab/final-test
|
R
| false | false | 490 |
r
|
# Introduction to webpage
Intro <- function() {
return("Using a dataset from FiveThirtyEight that has information on comic book characters,
we were interested in the diversity of comic book characters. As the dataset contains information such as
a character's physical attributes, the year of their first appearance, gender orientation, and intentions,
we focused on comparing different variables and if minority groups are accurately represented for comic characters."
)
}
|
## Put comments here that give an overall description of what #your functions do
## creates a special "vector", which is a list containing a function to
## 1.set the value of the matrix
## 2.get the value of the matrix
## 3.set the value of the inverse
## 4.get the value of the inverse
makeCacheMatrix <- function(x = matrix()) {
m<-NULL
set<-function(y){
x<<-y
m<<-NULL
}
get<-function() x
setinverse<-function(solve) m<<-solve
getinverse<-function() m
list(set=set, get=get,
setinverse=setinverse,
getinverse=getinverse)
}
## This function first checks to see if the inverse has already been calculated. If so, it gets the inverse
## from the cache and skips the computation.Otherwise, it calculates the inverse of the matrix and sets the
## value of the matrix in the cache via the setinverse function.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m<-x$getinverse
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinverse(m)
m
}
|
/cachematrix.R
|
no_license
|
malikrocks/ProgrammingAssignment2
|
R
| false | false | 1,104 |
r
|
## Put comments here that give an overall description of what #your functions do
## creates a special "vector", which is a list containing a function to
## 1.set the value of the matrix
## 2.get the value of the matrix
## 3.set the value of the inverse
## 4.get the value of the inverse
makeCacheMatrix <- function(x = matrix()) {
m<-NULL
set<-function(y){
x<<-y
m<<-NULL
}
get<-function() x
setinverse<-function(solve) m<<-solve
getinverse<-function() m
list(set=set, get=get,
setinverse=setinverse,
getinverse=getinverse)
}
## This function first checks to see if the inverse has already been calculated. If so, it gets the inverse
## from the cache and skips the computation.Otherwise, it calculates the inverse of the matrix and sets the
## value of the matrix in the cache via the setinverse function.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m<-x$getinverse
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinverse(m)
m
}
|
#' plotExternalCostTotalCost
#'
#' Plots the welfare loss measures for the voters of interest, in the round the proposal passed, for each of the kMajority rules.
#' @param outputDataList The output data list of summaries, that is generated by the iterations() function
#' @param plotMeanEC if TRUE plot the costs for the Mean Group
#' @param plotBestEC if TRUE plot the costs for the Best Group
#' @param plotWorstEC if TRUE plot the costs for the Worst Group
#' @return A plot of the mean, across all iterations, of the external cost incurred for each k-majority rule.
#' @export
plotExternalCostTotalCost <- function(outputDataList,plotMeanEC,plotBestEC,plotWorstEC){
# Set the margins so the labels in the Top Margin will display.
# par(mar=c(5.1, 4.1, 5.1, 2.1))
#Extract External Cost
meanExternalCost <- outputDataList$externalCost$meanOfMeanVotersExternalCostEachIteration
bestExternalCost <- outputDataList$externalCost$meanOfbestOffGroupsMeanExternalCostEachIteration
worstExternalCost <- outputDataList$externalCost$meanOfworstOffGroupsMeanExternalCostEachIteration
# Calculate Decision Cost
decisionCost <- outputDataList$rounds$meanNumberOfProposalsConsideredEachIteration * outputDataList$theInputParameters$perProposalDecisionCost
# Calculate Total Cost for Mean, Highest and Lowest groups
meanTotalCost <- meanExternalCost + decisionCost
bestTotalCost <- bestExternalCost + decisionCost
worstTotalCost <- worstExternalCost + decisionCost
# Store a few parameters for use in plots
parameters <- outputDataList$theInputParameters
numK <- nrow(outputDataList$externalCost)
K <- seq(1:numK)
# PLOTS
xRange <- c(0,numK)
yRange <- c(0,1)
## full yaxis ## yRange <- (0,max(tc.best))
x_axislabels <- seq(0,numK,by=10)
y_axislabels <- seq(0,1,by=.1)
## full yaxis ## y_axislabels <- seq(0,max(tc.best),by=.1)
plot(xRange, yRange, type="n", main="", xlab="K-Majority Rule", ylab="Expected Costs", ylim=c(0,1), font=2, axes=FALSE, frame=TRUE)
## full yaxis ## plot(K,ec.worst,type="n", main="", xlab="K-Majority Rule", ylab="Expected Costs", ylim=c(0,max(tc.best)), font=2, axes=FALSE, frame=TRUE)
axis(side=1, at=x_axislabels)
axis(side=2, at=y_axislabels)
lines(K,decisionCost,lty=1,lwd=3,col='black')
lineNames <- c("decision costs") # For the Legend
lineLty <- c(1) # For the Legend
lineLwd <- c(3) # For the Legend
lineCol <- c("black")
if (plotMeanEC==TRUE){
lines(K,meanExternalCost,lty=1,lwd=1,col='black')
lines(K,meanTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "mean group e.c.", "mean group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
if (plotBestEC==TRUE){
lines(K,bestExternalCost,lty=1,lwd=1,col='black')
lines(K,bestTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "best group e.c.", "best group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
if (plotWorstEC==TRUE){
lines(K,worstExternalCost,lty=1,lwd=1,col='black')
lines(K,worstTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "worst group e.c.", "worst group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
mtext(text=paste("Group Size:", paste(outputDataList$theInputParameters$groupSize, collapse=", ")),
side=3,
line=1.50,
adj=0,
cex=1.1,
font=2)
mtext(text=paste("Initial Utility:", paste(
toupper(
substring(
outputDataList$allGroups[[1]]$utilityDistribution,1,1)),
"(",
sub('^(-)?0[.]', '\\1.', outputDataList$allGroups[[1]]$utilityDistributionParam1),
",",
sub('^(-)?0[.]', '\\1.',outputDataList$allGroups[[1]]$utilityDistributionParam2),
")",
collapse=", ", sep="")),
side=3,
line=1.50,
adj=1,
cex=1.1,
font=2)
mtext(text=paste("Group Error:", paste(
toupper(
substring(
outputDataList$allGroups[[1]]$errorDistribution,1,1)),
"(",
sub('^(-)?0[.]', '\\1.', outputDataList$allGroups[[1]]$errorDistributionParam1),
",",
sub('^(-)?0[.]', '\\1.',outputDataList$allGroups[[1]]$errorDistributionParam2),
")",
collapse=", ", sep="")),
side=3,
line=.25,
adj=0,
cex=1.1,
font=2)
mtext(text=paste("Change Mean Utility:", paste(outputDataList$theInputParameters$groupPostFailingProposalMeanUiIncrease, collapse=", ")),
side=3,
line=.25,
adj=1,
cex=1.1,
font=2)
mtext(text=paste("Per Round Decision Cost:", paste(outputDataList$theInputParameters$perProposalDecisionCost, collapse=", ")),
side=1,
line=2.00,
adj=0,
cex=1.1,
font=2)
legend(5,1, # places a legend at the appropriate place
lineNames, # puts text in the legend
lty=lineLty, # gives the legend appropriate symbols (lines)
lwd=lineLwd, # gives the legend the appropriate weight
col=lineCol # gives the legend the appropriate color
) #
}
|
/R/plotExternalCostTotalCost.R
|
no_license
|
codeForReviewer/kMajorityRule
|
R
| false | false | 5,224 |
r
|
#' plotExternalCostTotalCost
#'
#' Plots the welfare loss measures for the voters of interest, in the round the proposal passed, for each of the kMajority rules.
#' @param outputDataList The output data list of summaries, that is generated by the iterations() function
#' @param plotMeanEC if TRUE plot the costs for the Mean Group
#' @param plotBestEC if TRUE plot the costs for the Best Group
#' @param plotWorstEC if TRUE plot the costs for the Worst Group
#' @return A plot of the mean, across all iterations, of the external cost incurred for each k-majority rule.
#' @export
plotExternalCostTotalCost <- function(outputDataList,plotMeanEC,plotBestEC,plotWorstEC){
# Set the margins so the labels in the Top Margin will display.
# par(mar=c(5.1, 4.1, 5.1, 2.1))
#Extract External Cost
meanExternalCost <- outputDataList$externalCost$meanOfMeanVotersExternalCostEachIteration
bestExternalCost <- outputDataList$externalCost$meanOfbestOffGroupsMeanExternalCostEachIteration
worstExternalCost <- outputDataList$externalCost$meanOfworstOffGroupsMeanExternalCostEachIteration
# Calculate Decision Cost
decisionCost <- outputDataList$rounds$meanNumberOfProposalsConsideredEachIteration * outputDataList$theInputParameters$perProposalDecisionCost
# Calculate Total Cost for Mean, Highest and Lowest groups
meanTotalCost <- meanExternalCost + decisionCost
bestTotalCost <- bestExternalCost + decisionCost
worstTotalCost <- worstExternalCost + decisionCost
# Store a few parameters for use in plots
parameters <- outputDataList$theInputParameters
numK <- nrow(outputDataList$externalCost)
K <- seq(1:numK)
# PLOTS
xRange <- c(0,numK)
yRange <- c(0,1)
## full yaxis ## yRange <- (0,max(tc.best))
x_axislabels <- seq(0,numK,by=10)
y_axislabels <- seq(0,1,by=.1)
## full yaxis ## y_axislabels <- seq(0,max(tc.best),by=.1)
plot(xRange, yRange, type="n", main="", xlab="K-Majority Rule", ylab="Expected Costs", ylim=c(0,1), font=2, axes=FALSE, frame=TRUE)
## full yaxis ## plot(K,ec.worst,type="n", main="", xlab="K-Majority Rule", ylab="Expected Costs", ylim=c(0,max(tc.best)), font=2, axes=FALSE, frame=TRUE)
axis(side=1, at=x_axislabels)
axis(side=2, at=y_axislabels)
lines(K,decisionCost,lty=1,lwd=3,col='black')
lineNames <- c("decision costs") # For the Legend
lineLty <- c(1) # For the Legend
lineLwd <- c(3) # For the Legend
lineCol <- c("black")
if (plotMeanEC==TRUE){
lines(K,meanExternalCost,lty=1,lwd=1,col='black')
lines(K,meanTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "mean group e.c.", "mean group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
if (plotBestEC==TRUE){
lines(K,bestExternalCost,lty=1,lwd=1,col='black')
lines(K,bestTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "best group e.c.", "best group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
if (plotWorstEC==TRUE){
lines(K,worstExternalCost,lty=1,lwd=1,col='black')
lines(K,worstTotalCost,lty=2,lwd=3,col='black')
lineNames <- c(lineNames, "worst group e.c.", "worst group t.c.")
lineLty <- c(lineLty,1,2) # For the Legend
lineLwd <- c(lineLwd,1,3) # For the Legend
lineCol <- c(lineCol,"black")
}
mtext(text=paste("Group Size:", paste(outputDataList$theInputParameters$groupSize, collapse=", ")),
side=3,
line=1.50,
adj=0,
cex=1.1,
font=2)
mtext(text=paste("Initial Utility:", paste(
toupper(
substring(
outputDataList$allGroups[[1]]$utilityDistribution,1,1)),
"(",
sub('^(-)?0[.]', '\\1.', outputDataList$allGroups[[1]]$utilityDistributionParam1),
",",
sub('^(-)?0[.]', '\\1.',outputDataList$allGroups[[1]]$utilityDistributionParam2),
")",
collapse=", ", sep="")),
side=3,
line=1.50,
adj=1,
cex=1.1,
font=2)
mtext(text=paste("Group Error:", paste(
toupper(
substring(
outputDataList$allGroups[[1]]$errorDistribution,1,1)),
"(",
sub('^(-)?0[.]', '\\1.', outputDataList$allGroups[[1]]$errorDistributionParam1),
",",
sub('^(-)?0[.]', '\\1.',outputDataList$allGroups[[1]]$errorDistributionParam2),
")",
collapse=", ", sep="")),
side=3,
line=.25,
adj=0,
cex=1.1,
font=2)
mtext(text=paste("Change Mean Utility:", paste(outputDataList$theInputParameters$groupPostFailingProposalMeanUiIncrease, collapse=", ")),
side=3,
line=.25,
adj=1,
cex=1.1,
font=2)
mtext(text=paste("Per Round Decision Cost:", paste(outputDataList$theInputParameters$perProposalDecisionCost, collapse=", ")),
side=1,
line=2.00,
adj=0,
cex=1.1,
font=2)
legend(5,1, # places a legend at the appropriate place
lineNames, # puts text in the legend
lty=lineLty, # gives the legend appropriate symbols (lines)
lwd=lineLwd, # gives the legend the appropriate weight
col=lineCol # gives the legend the appropriate color
) #
}
|
# SOMnn topology-based classifier
# Copyright (C) 2017 Andreas Dominik
# THM University of Applied Sciences
# Gießen, Germany
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#' Plot method for S4 class \code{SOMnn}
#'
#' Creates a plot of the hexagonal som in the model of type \code{SOMnn}.
#'
#' In addition to the required parameters, many options can be
#' specified to plot predicted samples and to modify colours, legend and scaling.
#'
#'
#' @rdname plot-methods
#' @aliases plot,SOMnn-method
#'
#' @param x trained som of type \code{SOMnn}.
#' @param title \code{logical}; if TRUE, slots name and date are used as main title.
#' @param col defines colours for the classes of the dataset. Possible values include:
#' \code{NA}: default value; colours are generated with \code{rainbow},
#' a \code{vector} of colour definitions or a
#' \code{data.frame} with categories in the first and respective colours in the second column.
#' @param onlyDefCols \code{logical}; if TRUE, only categories are plotted, for which colours are defined.
#' Default: FALSE.
#' @param edit.cols \code{logical}; if TRUE, colour definitions can be edited interactively before plotting.
#' Default: FALSE.
#' @param show.legend \code{logical}; if TRUE, a legend is displayed,. Default: TRUE.
#' @param legend.loc Legend position as specified for \code{\link{legend}}. Default is \code{"bottomright"}.
#' @param legend.width size of the legend.
#' @param window.width Manual setting of window width. Default is NA.
#' @param window.height Manual setting of window height. Default is NA.
#' @param show.box Show frame around the plot . Default is TRUE.
#' @param show.counter.border Percentile as limit for the display of labels in the pie charts. Default is 0.98.
#' Higher counts are displayed as numbers in the neuron.
#' @param predict \code{data.frame} as returned by the \code{som.nn::predict} function
#' or a \code{data.frame} or matrix that follows the specification:
#' If columns \code{x} and \code{y} exist, these are used as coordinates
#' for the traget neuron; otherwise the first two columns are used.
#' Default: NULL.
#' @param add \code{logical}; if TRUE, points are plotted on an existing plot. This can be used to
#' stepwise plot
#' points of different classes with different colours or symbols.
#' @param pch.col Colour of the markers for predicted samples.
#' @param pch Symbol of the markers for predicted samples.
#' @param ... More parameters as well as general
#' plot parameters are allowed; see \code{\link{par}}.
#'
#'
#' @import hexbin
#'
#' @example examples/example.train.R
#'
#' @export
setMethod( f = "plot", signature = "SOMnn",
definition = function(x, title = TRUE,
col = NA, onlyDefCols = FALSE, edit.cols = FALSE,
show.legend = TRUE, legend.loc = "bottomright", legend.width = 4,
window.width = NA, window.height = NA, show.box = TRUE,
show.counter.border = 0.98,
predict=NULL, add = FALSE, pch.col = "black", pch = 19,
...){
# make vis from prediction (cave: in somplot, indices start at 0):
som <- x
classes <- som@classes
grid <- make.codes.grid(som@xdim, som@ydim, topo = "hexagonal")
counts <- som@class.counts
counts$i <- grid$i - 1
counts$x <- grid$ix - 1
counts$y <- grid$iy -1
# count class matches:
vis <- data.frame(x=numeric(0), y=numeric(0), kat=character(0), stringsAsFactors = FALSE)
for (code in seq_along(counts[,1])) {
for (class in classes) {
for (i.count in seq_len( counts[code,class])){
vis <- rbind(vis, data.frame(x=counts[code,"x"], y=counts[code,"y"], kat=class,
stringsAsFactors = FALSE))
}
}
}
# print(vis)
if (!add) {
makehexbinplot(data = vis, col = col,
show.legend = show.legend, legend.loc = legend.loc, legend.width = legend.width,
window.width = window.width, window.height = window.height,
onlyDefCols = onlyDefCols,
show.box = show.box, edit.cols = edit.cols, show.counter.border = show.counter.border,
...)
if (title) {title(paste(som@name, "-", som@date))}
}
# plot samples, if arg predict is given:
if (!is.null(predict)){
# make data.frame with columns i, x, y:
if (("x" %in% names(predict) && ("y" %in% names(predict)))){
predict <- data.frame(x = predict[,"x"], y = predict[,"y"])
} else {
predict <- data.frame(x = predict[,1], y = predict[,2])
}
predict$i <- (predict$y-1) * som@xdim + predict$x
predict <- data.frame(i = predict$i, x = predict$x, y = predict$y)
plot.predictions(grid, predict, pch.col = pch.col, pch = pch, ...)
}
})
#' Plots the hexagonals and pi charts.
#' Adapted code from package somplot.
#'
#' @keywords internal
hexbinpie <- function(x, y, kat, xbnds=range(x), ybnds=range(y), hbc = NA, pal = NA, hex = "gray", circ = "gray50", cnt = "black", show.counter.border, ...)
{
hb <- hexbin(shape = (diff(ybnds) + 1) / (diff(xbnds) + 1), x, y, xbnds = xbnds, ybnds = ybnds, IDs = TRUE, xbins = diff(xbnds)*2)
rx <- 0.5 -> ry
hexC <- hexcoords(dx = rx, dy = ry / sqrt(3), n = 1)
nl <- length(levels(as.factor(kat)))
zbnds <- stats::quantile(hb@count, prob = c(0.05, 0.98, show.counter.border), na.rm = TRUE ) # quantile borders for circle diameter and display counter
zz <- pmax(pmin(sqrt(hb@count / zbnds[2]), 0.85), 0.2) # circle diameter from 20 to 85% of hexgon diameter
tt <- unclass(table(kat, hb@cID))
for (i in seq(along=zz)) # loop neurons
{
if (!is.na(hex))
{
graphics::polygon(hbc$x[i] + hexC$x, hbc$y[i] + hexC$y, col = NA, border = hex)
}
tp <- pi / 2 - 2 * pi * c(0, cumsum(tt[,i]) / sum(tt[,i]))
used = FALSE
for (j in 1:nl) # loop categories
{
if (tp[j+1] == tp[j])
{
next
}
if (j >= 2)
{
used = TRUE
pp <- seq(tp[j], tp[j+1], length = floor((tp[j] - tp[j + 1]) * 4) + 2)
xi <- hbc$x[i] + c(0, zz[i] * rx * cos(pp))
yi <- hbc$y[i] + c(0, zz[i] * ry * sin(pp))
graphics::polygon(xi, yi, col = pal[j], border = NA, ...)
}
#print(j)
}
if (!is.na(circ) & used)
{
graphics::polygon(hbc$x[i] + rx * zz[i] * cos((1:18) * pi / 9), hbc$y[i] + ry * zz[i] * sin((1:18) * pi / 9), col = NA, border = circ)
}
}
for (i in seq(along = zz))
{
if ((!is.na(cnt)) & (hb@count[i] > zbnds[3]))
{
graphics::text(hbc$x[i], hbc$y[i], hb@count[i], col = cnt, cex = 0.5)
}
}
}
#' makes the actual heagonal plot.
#' Adapted code from package somplot.
#'
#' @keywords internal
makehexbinplot <-function(data, col = NA, show.legend = TRUE, legend.loc = "bottomright", legend.width = 4, window.width = NA, window.height = NA,
onlyDefCols = FALSE,
show.box = TRUE, edit.cols = FALSE, show.counter.border = 0.98, ...)
{
if (!show.legend)
{
legend.width = 0
}
# calc hbc an fill up empty coordinates with an "empty" element
pos = 1
range.x = max(data$x) - min(data$x) + 1
range.y = max(data$y) - min(data$y) + 1
hbc = data.frame(x = seq(1,(range.x) * (range.y),1), y = NA)
for (y in c(min(data$y) : max(data$y)))
{
for (x in c(min(data$x):max(data$x)))
{
hbc$x[pos] = ifelse(y %% 2 == 1, x - 0.5, x)
hbc$y[pos] = y * 0.866
pos = pos + 1
if (nrow(data[data$x == x & data$y == y,]) == 0)
{
data = rbind(data, data.frame(x = x, y = y, kat = ""))
}
}
}
lvls = levels(as.factor(data$kat))
lvls = lvls[lvls != ""]
pal = grDevices::rainbow(length(lvls))
if (!is.na(col[1]))
{
if (onlyDefCols)
{
tmp.pal = rep("white", length(lvls))
}
else
{
tmp.pal = vector("character", length = length(lvls))
}
if (is.data.frame(col))
{
for (i in c(1 : nrow(col)))
{
tmp.pal[lvls == col[i,1]] = as.character(col[i,2])
}
}
else
{
tmp.pal[c(1:length(col))] = col
}
# convert color names into hex values and fill up colors
if(!onlyDefCols)
{
dbl.pal = sprintf("#%02X%02X%02XFF",
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[1,],
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[2,],
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[3,])
pal = setdiff(pal, dbl.pal)
for (i in c(1 : length(lvls)))
{
if (is.na(tmp.pal[i]) | tmp.pal[i] == "")
{
tmp.pal[i] = pal[1]
pal = pal[-1]
}
}
}
pal = tmp.pal
}
if(edit.cols)
{
pal = as.vector(utils::edit(data.frame(kat = lvls, col = pal))[,2])
}
lvls = append("empty", lvls)
pal = c("white", pal)
if(!is.na(window.width))
{
window.height = ifelse(is.na(window.height), window.width * (max(hbc$y) - min(hbc$y) - 1 + (range.x / range.y * 2)) / (max(hbc$x) - min(hbc$x) + legend.width), window.height)
grDevices::dev.new(width = window.width, height = window.height)
}
graphics::plot.new()
graphics::plot.window(c(min(hbc$x) - 0.5, max(hbc$x) + 0.5 + legend.width), c(min(hbc$y) - 0.5, max(hbc$y) + 1), asp=0.866)
if(show.box)
{
graphics::box()
}
if (show.legend)
{
graphics::legend(legend.loc, lvls[-1], fill=pal[-1], x.intersp=0.2)
}
hexbinpie(data$x, data$y, kat=data$kat, hbc = hbc, pal = pal, show.counter.border = show.counter.border, ...)
}
#' Plots predicted samples as points into a plotted som.
#'
#' @keywords internal
plot.predictions <- function(grid, predict, pch.col, pch, ...){
# fit grid to plot coordinates (left-bootom is (0,0) in plot, but (1,5,0.8660254) in somgrid:
grid$x <- grid$x - grid$x[1]
grid$y <- grid$y - grid$y[1]
# map indices to coors:
coors <- grid[predict$i,c("x","y")]
# function get.pattern
# returns a pattern of points with relative coors-
# n : number of points to be organised
# sep : separation between points
#
get.pattern <- function(n, sep = 0.2){
sml <- sep * 0.65
big <- sep * 1.2
if (n == 1){
return(data.frame(x=0, y=0))
} else if (n == 2) {
return(data.frame(x=c(-sml, sml), y=c(sml, -sml)))
} else if (n == 3) {
return(data.frame(x=c(-sml, 0, sml), y=c(-sml*0.87*2/3, sml*0.87*4/3, -sml*0.87*2/3)))
} else if (n == 4) {
return(data.frame(x=c(-sml, -sml, sml, sml), y=c(sml,-sml, -sml, sml)))
} else if (n == 5) {
return(data.frame(x=c(-sep, -sep, sep, sep, 0), y=c(sep,-sep, -sep, sep, 0)))
} else if (n == 6) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep), y=c(sep, 0, -sep, -sep, 0, sep)))
} else if (n == 7) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0), y=c(sep, 0, -sep, -sep, 0, sep, 0)))
} else if (n == 8) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0, 0), y=c(sep, 0, -sep, -sep, 0, sep, sep, -sep)))
} else if (n == 9) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0, 0, 0), y=c(sep, 0, -sep, -sep, 0, sep, sep, -sep, 0)))
} else {
return(data.frame(x=stats::runif(n, min=-big, max=big), y=stats::runif(n, min=-big, max=big)))
}
}
# group points in the same neuron:
nums <- by(predict, predict$i, function(x){ # process all points in same neuron as one group
n <- nrow(x)
coors <- grid[x$i,c("x","y")] + get.pattern(n)
graphics::points(coors, pch = pch, col = pch.col)
})
}
|
/R/plot.SOMnn.R
|
no_license
|
cran/som.nn
|
R
| false | false | 12,955 |
r
|
# SOMnn topology-based classifier
# Copyright (C) 2017 Andreas Dominik
# THM University of Applied Sciences
# Gießen, Germany
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#' Plot method for S4 class \code{SOMnn}
#'
#' Creates a plot of the hexagonal som in the model of type \code{SOMnn}.
#'
#' In addition to the required parameters, many options can be
#' specified to plot predicted samples and to modify colours, legend and scaling.
#'
#'
#' @rdname plot-methods
#' @aliases plot,SOMnn-method
#'
#' @param x trained som of type \code{SOMnn}.
#' @param title \code{logical}; if TRUE, slots name and date are used as main title.
#' @param col defines colours for the classes of the dataset. Possible values include:
#' \code{NA}: default value; colours are generated with \code{rainbow},
#' a \code{vector} of colour definitions or a
#' \code{data.frame} with categories in the first and respective colours in the second column.
#' @param onlyDefCols \code{logical}; if TRUE, only categories are plotted, for which colours are defined.
#' Default: FALSE.
#' @param edit.cols \code{logical}; if TRUE, colour definitions can be edited interactively before plotting.
#' Default: FALSE.
#' @param show.legend \code{logical}; if TRUE, a legend is displayed,. Default: TRUE.
#' @param legend.loc Legend position as specified for \code{\link{legend}}. Default is \code{"bottomright"}.
#' @param legend.width size of the legend.
#' @param window.width Manual setting of window width. Default is NA.
#' @param window.height Manual setting of window height. Default is NA.
#' @param show.box Show frame around the plot . Default is TRUE.
#' @param show.counter.border Percentile as limit for the display of labels in the pie charts. Default is 0.98.
#' Higher counts are displayed as numbers in the neuron.
#' @param predict \code{data.frame} as returned by the \code{som.nn::predict} function
#' or a \code{data.frame} or matrix that follows the specification:
#' If columns \code{x} and \code{y} exist, these are used as coordinates
#' for the traget neuron; otherwise the first two columns are used.
#' Default: NULL.
#' @param add \code{logical}; if TRUE, points are plotted on an existing plot. This can be used to
#' stepwise plot
#' points of different classes with different colours or symbols.
#' @param pch.col Colour of the markers for predicted samples.
#' @param pch Symbol of the markers for predicted samples.
#' @param ... More parameters as well as general
#' plot parameters are allowed; see \code{\link{par}}.
#'
#'
#' @import hexbin
#'
#' @example examples/example.train.R
#'
#' @export
setMethod( f = "plot", signature = "SOMnn",
definition = function(x, title = TRUE,
col = NA, onlyDefCols = FALSE, edit.cols = FALSE,
show.legend = TRUE, legend.loc = "bottomright", legend.width = 4,
window.width = NA, window.height = NA, show.box = TRUE,
show.counter.border = 0.98,
predict=NULL, add = FALSE, pch.col = "black", pch = 19,
...){
# make vis from prediction (cave: in somplot, indices start at 0):
som <- x
classes <- som@classes
grid <- make.codes.grid(som@xdim, som@ydim, topo = "hexagonal")
counts <- som@class.counts
counts$i <- grid$i - 1
counts$x <- grid$ix - 1
counts$y <- grid$iy -1
# count class matches:
vis <- data.frame(x=numeric(0), y=numeric(0), kat=character(0), stringsAsFactors = FALSE)
for (code in seq_along(counts[,1])) {
for (class in classes) {
for (i.count in seq_len( counts[code,class])){
vis <- rbind(vis, data.frame(x=counts[code,"x"], y=counts[code,"y"], kat=class,
stringsAsFactors = FALSE))
}
}
}
# print(vis)
if (!add) {
makehexbinplot(data = vis, col = col,
show.legend = show.legend, legend.loc = legend.loc, legend.width = legend.width,
window.width = window.width, window.height = window.height,
onlyDefCols = onlyDefCols,
show.box = show.box, edit.cols = edit.cols, show.counter.border = show.counter.border,
...)
if (title) {title(paste(som@name, "-", som@date))}
}
# plot samples, if arg predict is given:
if (!is.null(predict)){
# make data.frame with columns i, x, y:
if (("x" %in% names(predict) && ("y" %in% names(predict)))){
predict <- data.frame(x = predict[,"x"], y = predict[,"y"])
} else {
predict <- data.frame(x = predict[,1], y = predict[,2])
}
predict$i <- (predict$y-1) * som@xdim + predict$x
predict <- data.frame(i = predict$i, x = predict$x, y = predict$y)
plot.predictions(grid, predict, pch.col = pch.col, pch = pch, ...)
}
})
#' Plots the hexagonals and pi charts.
#' Adapted code from package somplot.
#'
#' @keywords internal
hexbinpie <- function(x, y, kat, xbnds=range(x), ybnds=range(y), hbc = NA, pal = NA, hex = "gray", circ = "gray50", cnt = "black", show.counter.border, ...)
{
hb <- hexbin(shape = (diff(ybnds) + 1) / (diff(xbnds) + 1), x, y, xbnds = xbnds, ybnds = ybnds, IDs = TRUE, xbins = diff(xbnds)*2)
rx <- 0.5 -> ry
hexC <- hexcoords(dx = rx, dy = ry / sqrt(3), n = 1)
nl <- length(levels(as.factor(kat)))
zbnds <- stats::quantile(hb@count, prob = c(0.05, 0.98, show.counter.border), na.rm = TRUE ) # quantile borders for circle diameter and display counter
zz <- pmax(pmin(sqrt(hb@count / zbnds[2]), 0.85), 0.2) # circle diameter from 20 to 85% of hexgon diameter
tt <- unclass(table(kat, hb@cID))
for (i in seq(along=zz)) # loop neurons
{
if (!is.na(hex))
{
graphics::polygon(hbc$x[i] + hexC$x, hbc$y[i] + hexC$y, col = NA, border = hex)
}
tp <- pi / 2 - 2 * pi * c(0, cumsum(tt[,i]) / sum(tt[,i]))
used = FALSE
for (j in 1:nl) # loop categories
{
if (tp[j+1] == tp[j])
{
next
}
if (j >= 2)
{
used = TRUE
pp <- seq(tp[j], tp[j+1], length = floor((tp[j] - tp[j + 1]) * 4) + 2)
xi <- hbc$x[i] + c(0, zz[i] * rx * cos(pp))
yi <- hbc$y[i] + c(0, zz[i] * ry * sin(pp))
graphics::polygon(xi, yi, col = pal[j], border = NA, ...)
}
#print(j)
}
if (!is.na(circ) & used)
{
graphics::polygon(hbc$x[i] + rx * zz[i] * cos((1:18) * pi / 9), hbc$y[i] + ry * zz[i] * sin((1:18) * pi / 9), col = NA, border = circ)
}
}
for (i in seq(along = zz))
{
if ((!is.na(cnt)) & (hb@count[i] > zbnds[3]))
{
graphics::text(hbc$x[i], hbc$y[i], hb@count[i], col = cnt, cex = 0.5)
}
}
}
#' makes the actual heagonal plot.
#' Adapted code from package somplot.
#'
#' @keywords internal
makehexbinplot <-function(data, col = NA, show.legend = TRUE, legend.loc = "bottomright", legend.width = 4, window.width = NA, window.height = NA,
onlyDefCols = FALSE,
show.box = TRUE, edit.cols = FALSE, show.counter.border = 0.98, ...)
{
if (!show.legend)
{
legend.width = 0
}
# calc hbc an fill up empty coordinates with an "empty" element
pos = 1
range.x = max(data$x) - min(data$x) + 1
range.y = max(data$y) - min(data$y) + 1
hbc = data.frame(x = seq(1,(range.x) * (range.y),1), y = NA)
for (y in c(min(data$y) : max(data$y)))
{
for (x in c(min(data$x):max(data$x)))
{
hbc$x[pos] = ifelse(y %% 2 == 1, x - 0.5, x)
hbc$y[pos] = y * 0.866
pos = pos + 1
if (nrow(data[data$x == x & data$y == y,]) == 0)
{
data = rbind(data, data.frame(x = x, y = y, kat = ""))
}
}
}
lvls = levels(as.factor(data$kat))
lvls = lvls[lvls != ""]
pal = grDevices::rainbow(length(lvls))
if (!is.na(col[1]))
{
if (onlyDefCols)
{
tmp.pal = rep("white", length(lvls))
}
else
{
tmp.pal = vector("character", length = length(lvls))
}
if (is.data.frame(col))
{
for (i in c(1 : nrow(col)))
{
tmp.pal[lvls == col[i,1]] = as.character(col[i,2])
}
}
else
{
tmp.pal[c(1:length(col))] = col
}
# convert color names into hex values and fill up colors
if(!onlyDefCols)
{
dbl.pal = sprintf("#%02X%02X%02XFF",
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[1,],
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[2,],
grDevices::col2rgb(tmp.pal[tmp.pal != ""])[3,])
pal = setdiff(pal, dbl.pal)
for (i in c(1 : length(lvls)))
{
if (is.na(tmp.pal[i]) | tmp.pal[i] == "")
{
tmp.pal[i] = pal[1]
pal = pal[-1]
}
}
}
pal = tmp.pal
}
if(edit.cols)
{
pal = as.vector(utils::edit(data.frame(kat = lvls, col = pal))[,2])
}
lvls = append("empty", lvls)
pal = c("white", pal)
if(!is.na(window.width))
{
window.height = ifelse(is.na(window.height), window.width * (max(hbc$y) - min(hbc$y) - 1 + (range.x / range.y * 2)) / (max(hbc$x) - min(hbc$x) + legend.width), window.height)
grDevices::dev.new(width = window.width, height = window.height)
}
graphics::plot.new()
graphics::plot.window(c(min(hbc$x) - 0.5, max(hbc$x) + 0.5 + legend.width), c(min(hbc$y) - 0.5, max(hbc$y) + 1), asp=0.866)
if(show.box)
{
graphics::box()
}
if (show.legend)
{
graphics::legend(legend.loc, lvls[-1], fill=pal[-1], x.intersp=0.2)
}
hexbinpie(data$x, data$y, kat=data$kat, hbc = hbc, pal = pal, show.counter.border = show.counter.border, ...)
}
#' Plots predicted samples as points into a plotted som.
#'
#' @keywords internal
plot.predictions <- function(grid, predict, pch.col, pch, ...){
# fit grid to plot coordinates (left-bootom is (0,0) in plot, but (1,5,0.8660254) in somgrid:
grid$x <- grid$x - grid$x[1]
grid$y <- grid$y - grid$y[1]
# map indices to coors:
coors <- grid[predict$i,c("x","y")]
# function get.pattern
# returns a pattern of points with relative coors-
# n : number of points to be organised
# sep : separation between points
#
get.pattern <- function(n, sep = 0.2){
sml <- sep * 0.65
big <- sep * 1.2
if (n == 1){
return(data.frame(x=0, y=0))
} else if (n == 2) {
return(data.frame(x=c(-sml, sml), y=c(sml, -sml)))
} else if (n == 3) {
return(data.frame(x=c(-sml, 0, sml), y=c(-sml*0.87*2/3, sml*0.87*4/3, -sml*0.87*2/3)))
} else if (n == 4) {
return(data.frame(x=c(-sml, -sml, sml, sml), y=c(sml,-sml, -sml, sml)))
} else if (n == 5) {
return(data.frame(x=c(-sep, -sep, sep, sep, 0), y=c(sep,-sep, -sep, sep, 0)))
} else if (n == 6) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep), y=c(sep, 0, -sep, -sep, 0, sep)))
} else if (n == 7) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0), y=c(sep, 0, -sep, -sep, 0, sep, 0)))
} else if (n == 8) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0, 0), y=c(sep, 0, -sep, -sep, 0, sep, sep, -sep)))
} else if (n == 9) {
return(data.frame(x=c(-sep, -sep, -sep, sep, sep, sep, 0, 0, 0), y=c(sep, 0, -sep, -sep, 0, sep, sep, -sep, 0)))
} else {
return(data.frame(x=stats::runif(n, min=-big, max=big), y=stats::runif(n, min=-big, max=big)))
}
}
# group points in the same neuron:
nums <- by(predict, predict$i, function(x){ # process all points in same neuron as one group
n <- nrow(x)
coors <- grid[x$i,c("x","y")] + get.pattern(n)
graphics::points(coors, pch = pch, col = pch.col)
})
}
|
norm = readr::read_csv("./outdir/2020/FIA_2020_no_mlbs_full.csv")
norm = sf::st_as_sf(norm, coords=c("LON", "LAT"), crs = 4326)
ecoregions = sf::read_sf("/Users/sergiomarconi/Documents/Data/Data_products/Chapter3_product/atlantic/atlantic_outer.shp")
ecoregions = sf::st_transform(ecoregions, st_crs(norm))
norm = sf::st_join(norm, ecoregions)
norm = norm %>% filter(!is.na(ECO_US_))
yep= norm #%>% #group_by(statecd, countycd, unitcd, plot) %>%
filter(taxonID == "QULA3")
yep$N_LMA = yep$nitrogenPercent.Estimate/yep$leafMassPerArea.Estimate
crds = st_coordinates(yep)
yep = cbind.data.frame(crds, yep)
yep = yep %>% group_by(Y, taxonID) %>% summarize_if(is.numeric, mean)
# ggplot(data = world) +
# xlim(-95,-65)+ ylim(24,50)+
# theme_bw() +
# geom_point(data = yep, aes(x = LON, y = LAT,
# fill = cut(yep$N_LMA,quantile(yep$N_LMA))), alpha = 0.6,
# size = 0.7, shape = 23, stroke = 0) + scale_fill_viridis_d()+
# geom_sf(alpha = 0)+
# geom_point(data = ACSA, aes(x = LON_site, y = LAT_site, color = "red", alpha = 0.9),
# size = 1, shape =4, stroke = 1)
# #scale_colour_gradient("red")
model_95 = quantile(yep$N_LMA,probs=c(.025,.975))
#plot(yep$LAT, yep$N_LMA)
nLm=ggplot(yep, aes(x = Y, y = N_LMA))+ geom_density2d() + geom_smooth(method = "loess") +
theme_bw()+ facet_wrap(.~taxonID, scales = "free")
ggsave(nLm, "n_lma_ratio.png")
|
/src/plots/Figure S17.R
|
permissive
|
MarconiS/Disentangling-the-role-of-phylogeny-and-climate-on-joint-leaf-trait-distributions-across-Eastern-Uni
|
R
| false | false | 1,416 |
r
|
norm = readr::read_csv("./outdir/2020/FIA_2020_no_mlbs_full.csv")
norm = sf::st_as_sf(norm, coords=c("LON", "LAT"), crs = 4326)
ecoregions = sf::read_sf("/Users/sergiomarconi/Documents/Data/Data_products/Chapter3_product/atlantic/atlantic_outer.shp")
ecoregions = sf::st_transform(ecoregions, st_crs(norm))
norm = sf::st_join(norm, ecoregions)
norm = norm %>% filter(!is.na(ECO_US_))
yep= norm #%>% #group_by(statecd, countycd, unitcd, plot) %>%
filter(taxonID == "QULA3")
yep$N_LMA = yep$nitrogenPercent.Estimate/yep$leafMassPerArea.Estimate
crds = st_coordinates(yep)
yep = cbind.data.frame(crds, yep)
yep = yep %>% group_by(Y, taxonID) %>% summarize_if(is.numeric, mean)
# ggplot(data = world) +
# xlim(-95,-65)+ ylim(24,50)+
# theme_bw() +
# geom_point(data = yep, aes(x = LON, y = LAT,
# fill = cut(yep$N_LMA,quantile(yep$N_LMA))), alpha = 0.6,
# size = 0.7, shape = 23, stroke = 0) + scale_fill_viridis_d()+
# geom_sf(alpha = 0)+
# geom_point(data = ACSA, aes(x = LON_site, y = LAT_site, color = "red", alpha = 0.9),
# size = 1, shape =4, stroke = 1)
# #scale_colour_gradient("red")
model_95 = quantile(yep$N_LMA,probs=c(.025,.975))
#plot(yep$LAT, yep$N_LMA)
nLm=ggplot(yep, aes(x = Y, y = N_LMA))+ geom_density2d() + geom_smooth(method = "loess") +
theme_bw()+ facet_wrap(.~taxonID, scales = "free")
ggsave(nLm, "n_lma_ratio.png")
|
# load library
library(seqinr)
library(Biostrings)
# get parameters
get_param <- commandArgs(trailingOnly = TRUE)
# read RPF counts per nucleotide
rpf <- read.table(get_param[1], sep = "\t", header = F, stringsAsFactors = F)
# get genes
u_genes <- unique(rpf[,1:6])
# cutoff for selection of candidates: (FT at period 3) / (FT between 1.5 and 3)
cutoff <- 3
## function for fourier transform input counts
# counts: input count
# it will only take a vector which is of length 3 of dividable by 3, others are skipped
# output(in default mode): 4 values (FT at 1.5, FT at 3, mean of FT between 1.5 and 3, mean > 3)
# normalze: per default it normalizes the transformed values by length (which we have to do)
# full_output: if TRUE will output list with all data, e.g. to plot
get_FT_signal = function(counts, normalize = T, full_output = F){
# check if length diviable by 3
if( length(counts)%%3 == 0){
# get frequency (x-axis) for FT transform
# this is a bit complicated to explain and understand (we can try later)
freq <- length(counts)/(0:(length(counts)-1))
# perform fast FT
ft <- abs(fft(counts))
# normalize by length
if(normalize){
ft <- ft / length(counts)
}
# get identity of period 3 and 1.5 together with mean of inter-regions
idx3 <- which(freq == 3)
idx15 <- which(freq == 1.5)
res <- c(
ft[idx15],
ft[idx3],
mean(ft[(idx3+1):(idx15-1)]),
mean(ft[(idx15+1):(length(ft))])
)
# return
if(!full_output){
res
} else {
list(
res = res,
ft = ft,
freq = freq
)
}
# else skip with message
} else {
cat('skipped\n')
NULL
}
}
### calculate FT for RPFs
# store FT results
storeFT <- matrix(NA, nrow=nrow(u_genes),ncol=4)
# for each unique genes
for(i in 1:nrow(u_genes)){
# status print status every 100 genes / ORFs
if(i == 1){
cat('Start obtaining FT signals\n')
}
if(i %% 100 == 0){
cat(paste0(i, ' genes/ORFs processed \n'))
}
# get counts for selected gene only
rpf_sel <- rpf[rpf[,4]==u_genes[i,4],]
# check if dividable by 3
if(nrow(rpf_sel)%%3 == 0){
# do FT transform (remove 50nt at start and end)
ft <- get_FT_signal(rpf_sel[,8])
storeFT[i,1:4] <- ft
} else {
# if not dividable by 3 print skipped
cat(paste0(' ',u_genes[i,4],' skipped\n'))
}
}
cat('DONE\n')
# select candidates with high FT at period 3
idx <- which(storeFT[,2]/storeFT[,3] > cutoff) # this is the genes we would be interested
# create table
verified <- cbind(u_genes[idx,],round(storeFT[idx,2]/storeFT[idx,3], digits=6))
colnames(verified)[7] <- 'FT_ratio'
# write candidates
write.table(verified,paste0(get_param[2],'/output/RPF_3nt_translated.txt'), sep = "\t", col.names = F, row.names = F, quote = F)
|
/modulB_RPF_analysis/5_FT_RPF/FT_RPF.R
|
no_license
|
AlexanderBartholomaeus/smORFer
|
R
| false | false | 2,817 |
r
|
# load library
library(seqinr)
library(Biostrings)
# get parameters
get_param <- commandArgs(trailingOnly = TRUE)
# read RPF counts per nucleotide
rpf <- read.table(get_param[1], sep = "\t", header = F, stringsAsFactors = F)
# get genes
u_genes <- unique(rpf[,1:6])
# cutoff for selection of candidates: (FT at period 3) / (FT between 1.5 and 3)
cutoff <- 3
## function for fourier transform input counts
# counts: input count
# it will only take a vector which is of length 3 of dividable by 3, others are skipped
# output(in default mode): 4 values (FT at 1.5, FT at 3, mean of FT between 1.5 and 3, mean > 3)
# normalze: per default it normalizes the transformed values by length (which we have to do)
# full_output: if TRUE will output list with all data, e.g. to plot
get_FT_signal = function(counts, normalize = T, full_output = F){
# check if length diviable by 3
if( length(counts)%%3 == 0){
# get frequency (x-axis) for FT transform
# this is a bit complicated to explain and understand (we can try later)
freq <- length(counts)/(0:(length(counts)-1))
# perform fast FT
ft <- abs(fft(counts))
# normalize by length
if(normalize){
ft <- ft / length(counts)
}
# get identity of period 3 and 1.5 together with mean of inter-regions
idx3 <- which(freq == 3)
idx15 <- which(freq == 1.5)
res <- c(
ft[idx15],
ft[idx3],
mean(ft[(idx3+1):(idx15-1)]),
mean(ft[(idx15+1):(length(ft))])
)
# return
if(!full_output){
res
} else {
list(
res = res,
ft = ft,
freq = freq
)
}
# else skip with message
} else {
cat('skipped\n')
NULL
}
}
### calculate FT for RPFs
# store FT results
storeFT <- matrix(NA, nrow=nrow(u_genes),ncol=4)
# for each unique genes
for(i in 1:nrow(u_genes)){
# status print status every 100 genes / ORFs
if(i == 1){
cat('Start obtaining FT signals\n')
}
if(i %% 100 == 0){
cat(paste0(i, ' genes/ORFs processed \n'))
}
# get counts for selected gene only
rpf_sel <- rpf[rpf[,4]==u_genes[i,4],]
# check if dividable by 3
if(nrow(rpf_sel)%%3 == 0){
# do FT transform (remove 50nt at start and end)
ft <- get_FT_signal(rpf_sel[,8])
storeFT[i,1:4] <- ft
} else {
# if not dividable by 3 print skipped
cat(paste0(' ',u_genes[i,4],' skipped\n'))
}
}
cat('DONE\n')
# select candidates with high FT at period 3
idx <- which(storeFT[,2]/storeFT[,3] > cutoff) # this is the genes we would be interested
# create table
verified <- cbind(u_genes[idx,],round(storeFT[idx,2]/storeFT[idx,3], digits=6))
colnames(verified)[7] <- 'FT_ratio'
# write candidates
write.table(verified,paste0(get_param[2],'/output/RPF_3nt_translated.txt'), sep = "\t", col.names = F, row.names = F, quote = F)
|
library(glmnet)
mydata = read.table("./TrainingSet/Correlation/central_nervous_system.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.8,family="gaussian",standardize=TRUE)
sink('./Model/EN/Correlation/central_nervous_system/central_nervous_system_081.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/Correlation/central_nervous_system/central_nervous_system_081.R
|
no_license
|
leon1003/QSMART
|
R
| false | false | 407 |
r
|
library(glmnet)
mydata = read.table("./TrainingSet/Correlation/central_nervous_system.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.8,family="gaussian",standardize=TRUE)
sink('./Model/EN/Correlation/central_nervous_system/central_nervous_system_081.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
source("./tirSettings.R")
setwd(dir.tif) ## very important tips for use rLandsat8
l8.lst <- lapply(dir(dir.tif), ReadLandsat8)
bandnames <-c("tirs1", "tirs2")
sceneList <- list.files(dir.toaTbk, full.names = TRUE)
for (i in sceneList) {
bandList <- list.files(sceneList, full.names = TRUE)
emiName <- paste0(basename(i), ".tif")
pngName <- paste0(emiName,".png")
Tb10 <- raster::raster(bandList[1])
Tb11 <- raster::raster(bandList[2])
TbS <- raster::stack(Tb10, Tb11)
a <- 1.438*10^-2
L10 <- 10.9
L11 <- 12.0
TbE <- exp((a*(Tb10 - Tb11))/(Tb10*Tb11*(L10-L11)))
Ts10 <- Tb10/(1 + (L10*Tb10/a)*log(TbE)
writeRaster(TbE, filename = file.path(dir.toaTe,emiName), overwrite = T)
png(file.path(dir.toaTe,pngName))
plot(TbE)
dev.off()
raster::removeTmpFiles(h = 1) ## Improtant tips for save hardisk
}
}
|
/demo/tir14_mergeTOABrightTemperature.R
|
permissive
|
bwtian/TIR
|
R
| false | false | 939 |
r
|
source("./tirSettings.R")
setwd(dir.tif) ## very important tips for use rLandsat8
l8.lst <- lapply(dir(dir.tif), ReadLandsat8)
bandnames <-c("tirs1", "tirs2")
sceneList <- list.files(dir.toaTbk, full.names = TRUE)
for (i in sceneList) {
bandList <- list.files(sceneList, full.names = TRUE)
emiName <- paste0(basename(i), ".tif")
pngName <- paste0(emiName,".png")
Tb10 <- raster::raster(bandList[1])
Tb11 <- raster::raster(bandList[2])
TbS <- raster::stack(Tb10, Tb11)
a <- 1.438*10^-2
L10 <- 10.9
L11 <- 12.0
TbE <- exp((a*(Tb10 - Tb11))/(Tb10*Tb11*(L10-L11)))
Ts10 <- Tb10/(1 + (L10*Tb10/a)*log(TbE)
writeRaster(TbE, filename = file.path(dir.toaTe,emiName), overwrite = T)
png(file.path(dir.toaTe,pngName))
plot(TbE)
dev.off()
raster::removeTmpFiles(h = 1) ## Improtant tips for save hardisk
}
}
|
# load utility functions
source("UtilityFunctions.R")
library(dplyr)
# reduce data function
# keep data that their value >= threshold
reduceData<- function(df, threshold=1)
{
df<- df %>%
group_by(name) %>%
summarise(value= sum(value)) %>%
arrange(desc(value)) %>%
filter(value >= threshold)
}
# compute number of lines to be sampled
samplePct<- 1 # 1% sample rate
nIter<- 100/ samplePct
# output file name
fout<- "./sampledData/AllData.RData"
filect<- 0 # file read counter
ng1df<- data.frame()
ng2df<- data.frame()
ng3df<- data.frame()
ng4df<- data.frame()
for (i in c(35,75))#seq(1,nIter))
{
# file name
fname<- sprintf('./sampledData/sampleData%d.RData',i)
if (!file.exists(fname))
next
sout<- sprintf('Reading Iter %d of %d...',i,nIter)
print(sout)
# get sampling now
filect<- filect+1
load(fname)
# remove non english and number character
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng1))
ng1a<- ng1[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng2))
ng2a<- ng2[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng3))
ng3a<- ng3[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng4))
ng4a<- ng4[-ind]
# combine data now
df1<- data.frame(name= names(ng1a), value= ng1a)
df2<- data.frame(name= names(ng2a), value= ng2a)
df3<- data.frame(name= names(ng3a), value= ng3a)
df4<- data.frame(name= names(ng4a), value= ng4a)
ng1df<- rbind(ng1df,df1)
ng2df<- rbind(ng2df,df2)
ng3df<- rbind(ng3df,df3)
ng4df<- rbind(ng4df,df4)
# make names unique
if ((filect%%5==0) || (filect==98))
{
sout<- sprintf('Combine data now...')
print(sout)
th<- round(filect/5)
ng1df<- reduceData(ng1df,th)
print("unigram...")
ng2df<- reduceData(ng2df,th)
print("2-gram...")
ng3df<- reduceData(ng3df,th)
print("3-gram...")
ng4df<- reduceData(ng4df,th)
print("4-gram...")
# ======== save data to files ==========
print("save file now...")
save(ng1df,ng2df,ng3df,ng4df, file= fout)
}
}
|
/NGramModel_CollectData.R
|
no_license
|
AndresSotoA/DataScienceCapstone
|
R
| false | false | 2,230 |
r
|
# load utility functions
source("UtilityFunctions.R")
library(dplyr)
# reduce data function
# keep data that their value >= threshold
reduceData<- function(df, threshold=1)
{
df<- df %>%
group_by(name) %>%
summarise(value= sum(value)) %>%
arrange(desc(value)) %>%
filter(value >= threshold)
}
# compute number of lines to be sampled
samplePct<- 1 # 1% sample rate
nIter<- 100/ samplePct
# output file name
fout<- "./sampledData/AllData.RData"
filect<- 0 # file read counter
ng1df<- data.frame()
ng2df<- data.frame()
ng3df<- data.frame()
ng4df<- data.frame()
for (i in c(35,75))#seq(1,nIter))
{
# file name
fname<- sprintf('./sampledData/sampleData%d.RData',i)
if (!file.exists(fname))
next
sout<- sprintf('Reading Iter %d of %d...',i,nIter)
print(sout)
# get sampling now
filect<- filect+1
load(fname)
# remove non english and number character
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng1))
ng1a<- ng1[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng2))
ng2a<- ng2[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng3))
ng3a<- ng3[-ind]
ind<- grep("[^a-zA-Z0-9[:punct:][:space:]]", names(ng4))
ng4a<- ng4[-ind]
# combine data now
df1<- data.frame(name= names(ng1a), value= ng1a)
df2<- data.frame(name= names(ng2a), value= ng2a)
df3<- data.frame(name= names(ng3a), value= ng3a)
df4<- data.frame(name= names(ng4a), value= ng4a)
ng1df<- rbind(ng1df,df1)
ng2df<- rbind(ng2df,df2)
ng3df<- rbind(ng3df,df3)
ng4df<- rbind(ng4df,df4)
# make names unique
if ((filect%%5==0) || (filect==98))
{
sout<- sprintf('Combine data now...')
print(sout)
th<- round(filect/5)
ng1df<- reduceData(ng1df,th)
print("unigram...")
ng2df<- reduceData(ng2df,th)
print("2-gram...")
ng3df<- reduceData(ng3df,th)
print("3-gram...")
ng4df<- reduceData(ng4df,th)
print("4-gram...")
# ======== save data to files ==========
print("save file now...")
save(ng1df,ng2df,ng3df,ng4df, file= fout)
}
}
|
#' DatasetsList
#'
#' List datasets
#'
#' @param page integer, Page number. Defaults to 1. Retrieve datasets via
#' page, search, or (ownerSlug and datasetSlug)
#' @param search string, Search terms. Defaults to . Retrieve datasets via
#' page, search, or (ownerSlug and datasetSlug)
#' @param owner_dataset Alternative to page/search. The owner and dataset
#' slug as it appears in the URL, i.e.,
#' \code{"mathan/fifa-2018-match-statistics"}.
#' @export
kgl_datasets_list <- function(page = 1, search = "",
owner_dataset = NULL) {
if (!is.null(owner_dataset)) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue("datasets/list/{ownerSlug}/{datasetSlug}"))
} else {
kgl_api_get("datasets/list", page = page, search = search)
}
}
#' DatasetsView
#'
#' Show details about a dataset
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @export
kgl_datasets_view <- function(owner_dataset) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue("datasets/view/{ownerSlug}/{datasetSlug}"))
}
#' DatasetsDownloadFile
#'
#' Download dataset file
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @param fileName string, File name. Required: TRUE.
#' @param datasetVersionNumber string, Dataset version number. Required: FALSE.
#' @param type string, Forcing datacet type. Required: FALSE.
#' @export
kgl_datasets_download <- function(owner_dataset, fileName,
datasetVersionNumber = NULL,
type = NULL) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue(
"datasets/download/{ownerSlug}/{datasetSlug}/{fileName}"),
datasetVersionNumber = datasetVersionNumber,
type = type)
}
#' DatasetsUploadFile
#'
#' Get URL and token to start uploading a data file
#'
#' @param fileName string, Dataset file name. Required: TRUE.
#' @param contentLength integer, Content length of file in bytes. Required: TRUE.
#' @param lastModifiedDateUtc integer, Last modified date of file in milliseconds
#' since epoch in UTC. Required: TRUE.
#' @export
kgl_datasets_upload_file <- function(fileName, contentLength,
lastModifiedDateUtc) {
contentLength <- file.size(fileName)
lastModifiedDateUtc <- format(file.info(fileName)$mtime,
format = "%Y-%m-%d %H-%M-%S", tz = "UTC")
kgl_api_post(glue::glue(
"datasets/upload/file/{contentLength}/{lastModifiedDateUtc}"),
fileName = fileName)
}
#' DatasetsCreateVersion
#'
#' Create a new dataset version
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @param datasetNewVersionRequest Information for creating a new dataset version.
#' Required: TRUE.
#' @export
kgl_datasets_create_version <- function(owner_dataset,
datasetNewVersionRequest) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_post(glue::glue(
"datasets/create/version/{ownerSlug}/{datasetSlug}"),
datasetNewVersionRequest = datasetNewVersionRequest)
}
#' DatasetsCreateNew
#'
#' Create a new dataset
#'
#' @param datasetNewRequest Information for creating a new dataset. Required: TRUE.
#' @export
kgl_datasets_create_new <- function(datasetNewRequest) {
kgl_api_post("datasets/create/new", datasetNewRequest = datasetNewRequest)
}
|
/R/datasets.R
|
permissive
|
bernardo-dauria/kaggler
|
R
| false | false | 3,889 |
r
|
#' DatasetsList
#'
#' List datasets
#'
#' @param page integer, Page number. Defaults to 1. Retrieve datasets via
#' page, search, or (ownerSlug and datasetSlug)
#' @param search string, Search terms. Defaults to . Retrieve datasets via
#' page, search, or (ownerSlug and datasetSlug)
#' @param owner_dataset Alternative to page/search. The owner and dataset
#' slug as it appears in the URL, i.e.,
#' \code{"mathan/fifa-2018-match-statistics"}.
#' @export
kgl_datasets_list <- function(page = 1, search = "",
owner_dataset = NULL) {
if (!is.null(owner_dataset)) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue("datasets/list/{ownerSlug}/{datasetSlug}"))
} else {
kgl_api_get("datasets/list", page = page, search = search)
}
}
#' DatasetsView
#'
#' Show details about a dataset
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @export
kgl_datasets_view <- function(owner_dataset) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue("datasets/view/{ownerSlug}/{datasetSlug}"))
}
#' DatasetsDownloadFile
#'
#' Download dataset file
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @param fileName string, File name. Required: TRUE.
#' @param datasetVersionNumber string, Dataset version number. Required: FALSE.
#' @param type string, Forcing datacet type. Required: FALSE.
#' @export
kgl_datasets_download <- function(owner_dataset, fileName,
datasetVersionNumber = NULL,
type = NULL) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_get(glue::glue(
"datasets/download/{ownerSlug}/{datasetSlug}/{fileName}"),
datasetVersionNumber = datasetVersionNumber,
type = type)
}
#' DatasetsUploadFile
#'
#' Get URL and token to start uploading a data file
#'
#' @param fileName string, Dataset file name. Required: TRUE.
#' @param contentLength integer, Content length of file in bytes. Required: TRUE.
#' @param lastModifiedDateUtc integer, Last modified date of file in milliseconds
#' since epoch in UTC. Required: TRUE.
#' @export
kgl_datasets_upload_file <- function(fileName, contentLength,
lastModifiedDateUtc) {
contentLength <- file.size(fileName)
lastModifiedDateUtc <- format(file.info(fileName)$mtime,
format = "%Y-%m-%d %H-%M-%S", tz = "UTC")
kgl_api_post(glue::glue(
"datasets/upload/file/{contentLength}/{lastModifiedDateUtc}"),
fileName = fileName)
}
#' DatasetsCreateVersion
#'
#' Create a new dataset version
#'
#' @param owner_dataset The owner and data set slug as it appears in the URL,
#' i.e., \code{"mathan/fifa-2018-match-statistics"}.
#' @param datasetNewVersionRequest Information for creating a new dataset version.
#' Required: TRUE.
#' @export
kgl_datasets_create_version <- function(owner_dataset,
datasetNewVersionRequest) {
owner_dataset <- strsplit(owner_dataset, "/")[[1]]
ownerSlug <- owner_dataset[1]
datasetSlug <- owner_dataset[2]
kgl_api_post(glue::glue(
"datasets/create/version/{ownerSlug}/{datasetSlug}"),
datasetNewVersionRequest = datasetNewVersionRequest)
}
#' DatasetsCreateNew
#'
#' Create a new dataset
#'
#' @param datasetNewRequest Information for creating a new dataset. Required: TRUE.
#' @export
kgl_datasets_create_new <- function(datasetNewRequest) {
kgl_api_post("datasets/create/new", datasetNewRequest = datasetNewRequest)
}
|
#' @examples
#'
#' ## Example data
#' sliFile <- system.file("external/vegSpec.sli", package="RStoolbox")
#' sliTmpFile <- paste0(tempdir(),"/vegetationSpectra.sli")
#'
#' ## Read spectral library
#' sli <- readSLI(sliFile)
#' head(sli)
#' plot(sli[,1:2], col = "orange", type = "l")
#' lines(sli[,c(1,3)], col = "green")
#'
#' ## Write to binary spectral library
#' writeSLI(sli, path = sliTmpFile)
|
/man-roxygen/examples_SLI.R
|
no_license
|
bleutner/RStoolbox
|
R
| false | false | 406 |
r
|
#' @examples
#'
#' ## Example data
#' sliFile <- system.file("external/vegSpec.sli", package="RStoolbox")
#' sliTmpFile <- paste0(tempdir(),"/vegetationSpectra.sli")
#'
#' ## Read spectral library
#' sli <- readSLI(sliFile)
#' head(sli)
#' plot(sli[,1:2], col = "orange", type = "l")
#' lines(sli[,c(1,3)], col = "green")
#'
#' ## Write to binary spectral library
#' writeSLI(sli, path = sliTmpFile)
|
/plugins/MacAU/UltrasonicMed/UltrasonicMed.r
|
permissive
|
themucha/airwindows
|
R
| false | false | 3,270 |
r
| ||
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/libraryDepth.R
\name{estimateDepthFactors}
\alias{estimateDepthFactors}
\title{estimate library size correction factors}
\usage{
estimateDepthFactors(
obj,
lib.factor = NULL,
which.lib = "both",
depth.estimator = "uq"
)
}
\arguments{
\item{obj}{the MpraObject}
\item{lib.factor}{the factor associating each sample to a library. Can be a
factor or the name of a column in the object's colAnnot. If not provided, the
data is assumed to have been generated from a single library, and constant
library depth is set.}
\item{which.lib}{which library to compute the depth factors for. Options are
"both" (default), "dna" or "rna". If the DNA and RNA counts have different
library factors, this function should be called twice: once with "dna" and
once with "rna"}
\item{depth.estimator}{a character indicating which depth estimation to use,
or a function to perform the estimation. Currently supported values are "uq"
for upper quantile of non-zero values (default), "rle" for RLE (uses
geometric mean, and is therefore not recommended if libraries have 0 counts),
or "totsum" for total sum.
For a function input: function should take a numeric vector and return a
single numeric, and preferably handle NA values. See examples.}
}
\value{
the MpraObject with estimated values for sequencing depth factors
}
\description{
estimate library size correction factors
}
\note{
since in most MPRA experiments multiple barcodes exist within a single
library, each column in the matrix is usually not a separate library. For
this reason, it is recommended to supply this function with the appropriate
partitioning of the data matrix columns into libraries, see lib.factor
}
\examples{
data <- simulateMPRA(tr = rep(2,10), da=NULL, nbatch=2, nbc=20)
obj <- MpraObject(dnaCounts = data$obs.dna,
rnaCounts = data$obs.rna,
colAnnot = data$annot)
obj <- estimateDepthFactors(obj, lib.factor = "batch", which.lib = "both")
## Upper quantile, using a higher quantile than 0.75:
obj <- estimateDepthFactors(obj, lib.factor = "batch", which.lib = "both",
depth.estimator = function(x) quantile(x, .95,
na.rm=TRUE))
}
|
/man/estimateDepthFactors.Rd
|
no_license
|
YosefLab/MPRAnalyze
|
R
| false | true | 2,286 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/libraryDepth.R
\name{estimateDepthFactors}
\alias{estimateDepthFactors}
\title{estimate library size correction factors}
\usage{
estimateDepthFactors(
obj,
lib.factor = NULL,
which.lib = "both",
depth.estimator = "uq"
)
}
\arguments{
\item{obj}{the MpraObject}
\item{lib.factor}{the factor associating each sample to a library. Can be a
factor or the name of a column in the object's colAnnot. If not provided, the
data is assumed to have been generated from a single library, and constant
library depth is set.}
\item{which.lib}{which library to compute the depth factors for. Options are
"both" (default), "dna" or "rna". If the DNA and RNA counts have different
library factors, this function should be called twice: once with "dna" and
once with "rna"}
\item{depth.estimator}{a character indicating which depth estimation to use,
or a function to perform the estimation. Currently supported values are "uq"
for upper quantile of non-zero values (default), "rle" for RLE (uses
geometric mean, and is therefore not recommended if libraries have 0 counts),
or "totsum" for total sum.
For a function input: function should take a numeric vector and return a
single numeric, and preferably handle NA values. See examples.}
}
\value{
the MpraObject with estimated values for sequencing depth factors
}
\description{
estimate library size correction factors
}
\note{
since in most MPRA experiments multiple barcodes exist within a single
library, each column in the matrix is usually not a separate library. For
this reason, it is recommended to supply this function with the appropriate
partitioning of the data matrix columns into libraries, see lib.factor
}
\examples{
data <- simulateMPRA(tr = rep(2,10), da=NULL, nbatch=2, nbc=20)
obj <- MpraObject(dnaCounts = data$obs.dna,
rnaCounts = data$obs.rna,
colAnnot = data$annot)
obj <- estimateDepthFactors(obj, lib.factor = "batch", which.lib = "both")
## Upper quantile, using a higher quantile than 0.75:
obj <- estimateDepthFactors(obj, lib.factor = "batch", which.lib = "both",
depth.estimator = function(x) quantile(x, .95,
na.rm=TRUE))
}
|
source("get_dataset.R")
make_plot3 <- function() {
# read dataset, prefiltering so only early february dates are imported
# get_dataset downloads and unpacks, then returns name of the raw data file
data <- read.csv(pipe(paste('grep -E \"Date|^[12]/2/2007\"', get_dataset())),
sep = ";", na.strings = "?")
data$Time <- strptime(paste(data$Date, data$Time), "%d/%m/%Y %H:%M:%S")
data$Date <- as.Date(data$Time)
png("plot3.png")
with(data, {
plot(Time, Sub_metering_1, type = "l",
xlab = "", ylab = "Energy sub metering" )
points(Time, Sub_metering_2, type = "l", col = "red")
points(Time, Sub_metering_3, type = "l", col = "blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty = 1, col = c("black", "red", "blue"))
})
dev.off()
}
|
/plot3.R
|
no_license
|
cdcooley/ExData_Plotting1
|
R
| false | false | 889 |
r
|
source("get_dataset.R")
make_plot3 <- function() {
# read dataset, prefiltering so only early february dates are imported
# get_dataset downloads and unpacks, then returns name of the raw data file
data <- read.csv(pipe(paste('grep -E \"Date|^[12]/2/2007\"', get_dataset())),
sep = ";", na.strings = "?")
data$Time <- strptime(paste(data$Date, data$Time), "%d/%m/%Y %H:%M:%S")
data$Date <- as.Date(data$Time)
png("plot3.png")
with(data, {
plot(Time, Sub_metering_1, type = "l",
xlab = "", ylab = "Energy sub metering" )
points(Time, Sub_metering_2, type = "l", col = "red")
points(Time, Sub_metering_3, type = "l", col = "blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty = 1, col = c("black", "red", "blue"))
})
dev.off()
}
|
library('affy')
library('gplots')
library('lattice')
library('biomaRt')
convertEnsemblToEntrez1 = function (ensemblListFile, ensemblToEntrezFile)
{
mitocarta = read.table(ensemblListFile)
ensemblHuman = useMart('ensembl', dataset='hsapiens_gene_ensembl')
ensemblMouse = useMart('ensembl', dataset='mmusculus_gene_ensembl')
mapmitocarta1 = getBM(attributes = c('ensembl_gene_id','hsapiens_homolog_ensembl_gene'), filters = c('ensembl_gene_id','with_homolog_hsap'), values = list( as.vector(mitocarta[,1]),TRUE ), mart = ensemblMouse)
mapmitocarta2 = getBM(attributes = c('ensembl_gene_id','entrezgene'), filters = c('ensembl_gene_id','with_entrezgene'), values = list( as.vector(mapmitocarta1[,2]),TRUE ), mart = ensemblHuman)
matchIdxs1 = match(intersect( as.vector(mapmitocarta1[,2]),as.vector(mapmitocarta2[,1]) ),mapmitocarta1[,2])
matchIdxs2 = match(intersect( as.vector(mapmitocarta1[,2]),as.vector(mapmitocarta2[,1]) ),mapmitocarta2[,1])
mapmitocarta3 = cbind(mapmitocarta1[matchIdxs1,1], mapmitocarta2[matchIdxs2,2])
write.table(mapmitocarta3, file=ensemblToEntrezFile, quote=FALSE, sep=",", row.names=FALSE)
}
convertEnsemblToEntrez2 = function (ensemblListFile, ensemblToEntrezFile, entrezFile)
{
mitocarta = read.table(ensemblListFile)
ensemblMouse = useMart('ensembl', dataset='mmusculus_gene_ensembl')
mapmitocarta = getBM(attributes = c('ensembl_gene_id','entrezgene'), filters = c('ensembl_gene_id','with_entrezgene'), values = list( as.vector(mitocarta[,1]),TRUE ), mart = ensemblMouse)
write.table(mapmitocarta, file=ensemblToEntrezFile, quote=FALSE, sep=",", row.names=FALSE)
write.table(mapmitocarta[,2], file=entrezFile, quote=FALSE, sep=",", row.names=FALSE, col.names=FALSE)
}
convertEnsemblToEntrez1("input/MitoCarta Mouse Ensembl List.txt","input/MitoCarta Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez1("input/MitoMiner Mouse Ensembl List.txt","input/MitoMiner Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez1("input/All Mouse Ensembl List.txt","input/All Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez2("input/MitoCarta Mouse Ensembl List.txt","input/MitoCarta Mouse Ensembl To Mouse Entrez.csv","input/MitoCarta Mouse Entrez List.txt")
convertEnsemblToEntrez2("input/All Mouse Ensembl List.txt","input/All Mouse Ensembl To Mouse Entrez.csv","input/All Mouse Entrez List.txt")
|
/Bioinformatics/biomaRtCommandsOrigHD.R
|
no_license
|
yw595/AllUtils
|
R
| false | false | 2,346 |
r
|
library('affy')
library('gplots')
library('lattice')
library('biomaRt')
convertEnsemblToEntrez1 = function (ensemblListFile, ensemblToEntrezFile)
{
mitocarta = read.table(ensemblListFile)
ensemblHuman = useMart('ensembl', dataset='hsapiens_gene_ensembl')
ensemblMouse = useMart('ensembl', dataset='mmusculus_gene_ensembl')
mapmitocarta1 = getBM(attributes = c('ensembl_gene_id','hsapiens_homolog_ensembl_gene'), filters = c('ensembl_gene_id','with_homolog_hsap'), values = list( as.vector(mitocarta[,1]),TRUE ), mart = ensemblMouse)
mapmitocarta2 = getBM(attributes = c('ensembl_gene_id','entrezgene'), filters = c('ensembl_gene_id','with_entrezgene'), values = list( as.vector(mapmitocarta1[,2]),TRUE ), mart = ensemblHuman)
matchIdxs1 = match(intersect( as.vector(mapmitocarta1[,2]),as.vector(mapmitocarta2[,1]) ),mapmitocarta1[,2])
matchIdxs2 = match(intersect( as.vector(mapmitocarta1[,2]),as.vector(mapmitocarta2[,1]) ),mapmitocarta2[,1])
mapmitocarta3 = cbind(mapmitocarta1[matchIdxs1,1], mapmitocarta2[matchIdxs2,2])
write.table(mapmitocarta3, file=ensemblToEntrezFile, quote=FALSE, sep=",", row.names=FALSE)
}
convertEnsemblToEntrez2 = function (ensemblListFile, ensemblToEntrezFile, entrezFile)
{
mitocarta = read.table(ensemblListFile)
ensemblMouse = useMart('ensembl', dataset='mmusculus_gene_ensembl')
mapmitocarta = getBM(attributes = c('ensembl_gene_id','entrezgene'), filters = c('ensembl_gene_id','with_entrezgene'), values = list( as.vector(mitocarta[,1]),TRUE ), mart = ensemblMouse)
write.table(mapmitocarta, file=ensemblToEntrezFile, quote=FALSE, sep=",", row.names=FALSE)
write.table(mapmitocarta[,2], file=entrezFile, quote=FALSE, sep=",", row.names=FALSE, col.names=FALSE)
}
convertEnsemblToEntrez1("input/MitoCarta Mouse Ensembl List.txt","input/MitoCarta Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez1("input/MitoMiner Mouse Ensembl List.txt","input/MitoMiner Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez1("input/All Mouse Ensembl List.txt","input/All Mouse Ensembl To Human Entrez.csv")
convertEnsemblToEntrez2("input/MitoCarta Mouse Ensembl List.txt","input/MitoCarta Mouse Ensembl To Mouse Entrez.csv","input/MitoCarta Mouse Entrez List.txt")
convertEnsemblToEntrez2("input/All Mouse Ensembl List.txt","input/All Mouse Ensembl To Mouse Entrez.csv","input/All Mouse Entrez List.txt")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catboost.R
\name{catboost.from_data_frame}
\alias{catboost.from_data_frame}
\title{catboost.from_data_frame}
\usage{
catboost.from_data_frame(data, target = NULL, pairs = NULL, weight = NULL,
query_id = NULL, pairs_weight = NULL, baseline = NULL,
feature_names = NULL)
}
\arguments{
\item{data}{A data.frame with features.
The following column types are supported:
\itemize{
\item double
\item factor.
It is assumed that categorical features are given in this type of columns.
A standard CatBoost processing procedure is applied to this type of columns:
\describe{
\item{1.}{The values are converted to strings.}
\item{2.}{The ConvertCatFeatureToFloat function is applied to the resulting string.}
}
}
Default value: Required argument}
\item{target}{The target vector.}
\item{pairs}{A data.frame that contains the pairs descriptions. The shape should be Nx2, where N is the pairs' count.
The first element of pair is the index of winner document in training set. The second element of pair is the index of loser document in training set.}
\item{weight}{The weights of the target vector.}
\item{query_id}{The query_id of the target vector.}
\item{pairs_weight}{The weights of the pairs.}
\item{baseline}{Vector of initial (raw) values of the target function for the object.
Used in the calculation of final values of trees.}
\item{feature_names}{A list of names for each feature in the dataset.}
}
\value{
catboost.Pool
}
\description{
Create a dataset from the given data.frame.
Only numeric features are supported (their type should be double or factor).
Categorical features must be converted to numerical first.
For example, use \code{as.factor()} or the \code{colClasses} argument of the \code{read.table} method. The target type should be double.
}
\examples{
pool_path <- 'train_full3'
cd_vector <- c('numeric', rep('numeric',2), rep('factor',7))
data <- read.table(pool_path, head = F, sep = "\\t", colClasses = cd_vector)
target <- c(1)
learn_size <- floor(0.8 * nrow(data))
learn_ind <- sample(nrow(data), learn_size)
learn <- data[learn_ind,]
test <- data[-learn_ind,]
learn_pool <- catboost.from_data_frame(data = learn[,-target], target = learn[,target])
test_pool <- catboost.from_data_frame(data = test[,-target], target = test[,target])
}
|
/catboost/R-package/man/catboost.from_data_frame.Rd
|
permissive
|
exprmntr/test
|
R
| false | true | 2,386 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catboost.R
\name{catboost.from_data_frame}
\alias{catboost.from_data_frame}
\title{catboost.from_data_frame}
\usage{
catboost.from_data_frame(data, target = NULL, pairs = NULL, weight = NULL,
query_id = NULL, pairs_weight = NULL, baseline = NULL,
feature_names = NULL)
}
\arguments{
\item{data}{A data.frame with features.
The following column types are supported:
\itemize{
\item double
\item factor.
It is assumed that categorical features are given in this type of columns.
A standard CatBoost processing procedure is applied to this type of columns:
\describe{
\item{1.}{The values are converted to strings.}
\item{2.}{The ConvertCatFeatureToFloat function is applied to the resulting string.}
}
}
Default value: Required argument}
\item{target}{The target vector.}
\item{pairs}{A data.frame that contains the pairs descriptions. The shape should be Nx2, where N is the pairs' count.
The first element of pair is the index of winner document in training set. The second element of pair is the index of loser document in training set.}
\item{weight}{The weights of the target vector.}
\item{query_id}{The query_id of the target vector.}
\item{pairs_weight}{The weights of the pairs.}
\item{baseline}{Vector of initial (raw) values of the target function for the object.
Used in the calculation of final values of trees.}
\item{feature_names}{A list of names for each feature in the dataset.}
}
\value{
catboost.Pool
}
\description{
Create a dataset from the given data.frame.
Only numeric features are supported (their type should be double or factor).
Categorical features must be converted to numerical first.
For example, use \code{as.factor()} or the \code{colClasses} argument of the \code{read.table} method. The target type should be double.
}
\examples{
pool_path <- 'train_full3'
cd_vector <- c('numeric', rep('numeric',2), rep('factor',7))
data <- read.table(pool_path, head = F, sep = "\\t", colClasses = cd_vector)
target <- c(1)
learn_size <- floor(0.8 * nrow(data))
learn_ind <- sample(nrow(data), learn_size)
learn <- data[learn_ind,]
test <- data[-learn_ind,]
learn_pool <- catboost.from_data_frame(data = learn[,-target], target = learn[,target])
test_pool <- catboost.from_data_frame(data = test[,-target], target = test[,target])
}
|
## Downloading the file, unzipping and saving it. Commented as reading data is asked in the assignment.
##fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
##temp <- tempfile()
##download.file(fileUrl, destfile=temp)
##unzip(temp)
##unlink(temp)
colnames <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
feb_1_2_Data_temp <- read.table(pipe('grep "^[12]/2/2007" household_power_consumption.txt'),sep=";",col.names=colnames)
## if not Windows machine. Please use the following method.
## x <- read.table("household_power_consumption.txt",sep=";",header=TRUE)
## feb_1_2_Data_temp <- subset(x, Date %in% c("01/02/2007","02/02/2007"))
feb_1_2_Data_temp <- transform(feb_1_2_Data_temp,DateTime = as.POSIXlt(paste(Date,Time, sep=" "),format="%d/%m/%Y %H:%M:%S"))
feb_1_2_Data <- feb_1_2_Data_temp[,c(length(feb_1_2_Data_temp),3:(length(feb_1_2_Data_temp)-1))]
png(filename="plot2.png",width=480,height=480)
with(feb_1_2_Data,plot(DateTime,Global_active_power,type="l",ylab="Global Active Power (kilowatts)",xlab=""))
dev.off()
|
/plot2.R
|
no_license
|
sidj-14/ExData_Plotting1
|
R
| false | false | 1,179 |
r
|
## Downloading the file, unzipping and saving it. Commented as reading data is asked in the assignment.
##fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
##temp <- tempfile()
##download.file(fileUrl, destfile=temp)
##unzip(temp)
##unlink(temp)
colnames <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
feb_1_2_Data_temp <- read.table(pipe('grep "^[12]/2/2007" household_power_consumption.txt'),sep=";",col.names=colnames)
## if not Windows machine. Please use the following method.
## x <- read.table("household_power_consumption.txt",sep=";",header=TRUE)
## feb_1_2_Data_temp <- subset(x, Date %in% c("01/02/2007","02/02/2007"))
feb_1_2_Data_temp <- transform(feb_1_2_Data_temp,DateTime = as.POSIXlt(paste(Date,Time, sep=" "),format="%d/%m/%Y %H:%M:%S"))
feb_1_2_Data <- feb_1_2_Data_temp[,c(length(feb_1_2_Data_temp),3:(length(feb_1_2_Data_temp)-1))]
png(filename="plot2.png",width=480,height=480)
with(feb_1_2_Data,plot(DateTime,Global_active_power,type="l",ylab="Global Active Power (kilowatts)",xlab=""))
dev.off()
|
load("C:/Users/Kevin Caref/Google Drive/RScripts/Functions/unineuralhist.rFunc")
load("C:/Users/Kevin Caref/Google Drive/RScripts/Functions/KC.sigbins_inhib_uni.Rfunc")
side = "ipsi"
prefiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 0
, endt = 2000, binw = 50, psthmin = 5, psthmax = 1, event = 4, cueexonly = T, side)
cueexidx = prefiring[[2]]
prefiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 0
, endt = 2000, binw = 50, psthmin = 5, psthmax = 1, event = 1, cueexonly = F, side)
postfiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 2720
, endt = 4720, binw = 50, psthmin = 5, psthmax = 1, event = 1, cueexonly = F, side)
#cueexidx = prefiring[[2]]
#prefiring = prefiring[[1]]
cuein = KC.sigbins_inhib_uni.Rfunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt=0, endt=2000, event=1, side)
cueinidx = which(colSums(cuein) > 0)
prebaseline = sapply(seq(1, ncol(prefiring)), function(x)
mean(as.numeric(prefiring[1:100,x])))
postbaseline = sapply(seq(1, ncol(postfiring)), function(x)
mean(as.numeric(postfiring[1:100,x])))
#peaksort = order(sapply(seq(1,ncol(prefiring)), function(x) mean(prefiring[c(51:56),x])))
#cueencoding = peaksort[21:53]
otheridx = which(seq(1:length(prebaseline)) %in% c(cueexidx, cueinidx)== F)
unipre = prebaseline
unipost = postbaseline
bipre = prebaseline
bipost = postbaseline
bl.lm = lm(c(unipost, bipost) ~ c(unipre, bipre))
confint(bl.lm, level = .95)
#bl.lm = lm(postbaseline[-16]~prebaseline[-16]) #remove contra outlier
#confint(bl.lm, level = .95)
cueex.lm = lm(postbaseline[cueexidx]~prebaseline[cueexidx])
confint(cueex.lm, level = .95)
cuein.lm = lm(postbaseline[cueinidx]~prebaseline[cueinidx])
confint(cuein.lm, level = .95)
other.lm = lm(postbaseline[otheridx]~prebaseline[otheridx])
confint(other.lm, level = .95)
#remove contra outlier
#cueinidx = cueinidx[-1]
par(pty = "s")
plot.new()
plot.window(xlim = c(0,10), ylim = c(0,10))
#uni points
points(prebaseline[otheridx], postbaseline[otheridx], pch = 19, cex = 2, col = "black")
points(prebaseline[cueexidx], postbaseline[cueexidx], pch = 19, cex = 2, col = "firebrick1")
points(prebaseline[cueinidx], postbaseline[cueinidx], pch = 19, cex = 2, col = "blue")
#bi points
points(prebaseline[otheridx], postbaseline[otheridx], pch = 18, cex = 2, col = "black")
points(prebaseline[cueexidx], postbaseline[cueexidx], pch = 18, cex = 2, col = "firebrick1")
points(prebaseline[cueinidx], postbaseline[cueinidx], pch = 18, cex = 2, col = "blue")
#abline(a = 0, b = 1)
segments(0,0,10,10, lwd = 2,col = "black", lty=2)
#abline(cue.lm, col = "gray", lwd = 2)
#abline(other.lm, col = "black", lwd = 2)
abline(bl.lm, col = "gray", lwd = 2)
axis(1, at = seq(0,10, 2), cex.axis = 1.75)
axis(2, at = seq(0,10, 2), cex.axis = 1.75, las = 2, tcl = -.8)
mtext("Pre-injection Baseline (Hz)", side = 1, cex = 1.75, line=3)
mtext("Post-injection Baseline (Hz)", side = 2, cex = 1.75, line=3)
mtext("Cue-excited", side = 3, cex = 1.5, line = -2, at = 2, col = "firebrick1")
mtext("Cue-inhibited", side = 3, cex = 1.5, line = -3, at = 2, col = "blue")
mtext("Other", side = 3, cex = 1.5, line = -4, at = 2, col = "black")
mtext("Red = unity line, gray = regression for all points", side = 3, cex = 2, line=4)
|
/scripts/baseline firing comparison_pooled uni and bi.R
|
permissive
|
kcaref/neural-analysis
|
R
| false | false | 3,617 |
r
|
load("C:/Users/Kevin Caref/Google Drive/RScripts/Functions/unineuralhist.rFunc")
load("C:/Users/Kevin Caref/Google Drive/RScripts/Functions/KC.sigbins_inhib_uni.Rfunc")
side = "ipsi"
prefiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 0
, endt = 2000, binw = 50, psthmin = 5, psthmax = 1, event = 4, cueexonly = T, side)
cueexidx = prefiring[[2]]
prefiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 0
, endt = 2000, binw = 50, psthmin = 5, psthmax = 1, event = 1, cueexonly = F, side)
postfiring = unineuralhist.rFunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt = 2720
, endt = 4720, binw = 50, psthmin = 5, psthmax = 1, event = 1, cueexonly = F, side)
#cueexidx = prefiring[[2]]
#prefiring = prefiring[[1]]
cuein = KC.sigbins_inhib_uni.Rfunc(path = "C:/Users/Kevin Caref/Google Drive/Opioid Pilot/Neural Data/sorted/unilateral ctap/", startt=0, endt=2000, event=1, side)
cueinidx = which(colSums(cuein) > 0)
prebaseline = sapply(seq(1, ncol(prefiring)), function(x)
mean(as.numeric(prefiring[1:100,x])))
postbaseline = sapply(seq(1, ncol(postfiring)), function(x)
mean(as.numeric(postfiring[1:100,x])))
#peaksort = order(sapply(seq(1,ncol(prefiring)), function(x) mean(prefiring[c(51:56),x])))
#cueencoding = peaksort[21:53]
otheridx = which(seq(1:length(prebaseline)) %in% c(cueexidx, cueinidx)== F)
unipre = prebaseline
unipost = postbaseline
bipre = prebaseline
bipost = postbaseline
bl.lm = lm(c(unipost, bipost) ~ c(unipre, bipre))
confint(bl.lm, level = .95)
#bl.lm = lm(postbaseline[-16]~prebaseline[-16]) #remove contra outlier
#confint(bl.lm, level = .95)
cueex.lm = lm(postbaseline[cueexidx]~prebaseline[cueexidx])
confint(cueex.lm, level = .95)
cuein.lm = lm(postbaseline[cueinidx]~prebaseline[cueinidx])
confint(cuein.lm, level = .95)
other.lm = lm(postbaseline[otheridx]~prebaseline[otheridx])
confint(other.lm, level = .95)
#remove contra outlier
#cueinidx = cueinidx[-1]
par(pty = "s")
plot.new()
plot.window(xlim = c(0,10), ylim = c(0,10))
#uni points
points(prebaseline[otheridx], postbaseline[otheridx], pch = 19, cex = 2, col = "black")
points(prebaseline[cueexidx], postbaseline[cueexidx], pch = 19, cex = 2, col = "firebrick1")
points(prebaseline[cueinidx], postbaseline[cueinidx], pch = 19, cex = 2, col = "blue")
#bi points
points(prebaseline[otheridx], postbaseline[otheridx], pch = 18, cex = 2, col = "black")
points(prebaseline[cueexidx], postbaseline[cueexidx], pch = 18, cex = 2, col = "firebrick1")
points(prebaseline[cueinidx], postbaseline[cueinidx], pch = 18, cex = 2, col = "blue")
#abline(a = 0, b = 1)
segments(0,0,10,10, lwd = 2,col = "black", lty=2)
#abline(cue.lm, col = "gray", lwd = 2)
#abline(other.lm, col = "black", lwd = 2)
abline(bl.lm, col = "gray", lwd = 2)
axis(1, at = seq(0,10, 2), cex.axis = 1.75)
axis(2, at = seq(0,10, 2), cex.axis = 1.75, las = 2, tcl = -.8)
mtext("Pre-injection Baseline (Hz)", side = 1, cex = 1.75, line=3)
mtext("Post-injection Baseline (Hz)", side = 2, cex = 1.75, line=3)
mtext("Cue-excited", side = 3, cex = 1.5, line = -2, at = 2, col = "firebrick1")
mtext("Cue-inhibited", side = 3, cex = 1.5, line = -3, at = 2, col = "blue")
mtext("Other", side = 3, cex = 1.5, line = -4, at = 2, col = "black")
mtext("Red = unity line, gray = regression for all points", side = 3, cex = 2, line=4)
|
## The functions herein create a special "matrix" and calculate its inverse
## The first function, makeCacheMatrix, creates a special "matrix", which is really a list containing a function to
#set the value of the matrix
#get the value of the matrix
#set the inverse of the matrix
#get the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setInverseMatrix <- function(InvMatrix) m <<- solve
getInverseMatrix <- function() m
list(set = set, get = get,
setInverseMatrix = setInverseMatrix,
getInverseMatrix = getInverseMatrix)
}
##cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
#If the inverse has already been calculated (and the matrix has not changed),
#then the cachesolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
m <- x$getInverseMatrix()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- mean(data, ...)
x$setInverseMatrix(m)
m
}
|
/cachematrix.R
|
no_license
|
shumakerro/ProgrammingAssignment2
|
R
| false | false | 1,278 |
r
|
## The functions herein create a special "matrix" and calculate its inverse
## The first function, makeCacheMatrix, creates a special "matrix", which is really a list containing a function to
#set the value of the matrix
#get the value of the matrix
#set the inverse of the matrix
#get the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setInverseMatrix <- function(InvMatrix) m <<- solve
getInverseMatrix <- function() m
list(set = set, get = get,
setInverseMatrix = setInverseMatrix,
getInverseMatrix = getInverseMatrix)
}
##cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
#If the inverse has already been calculated (and the matrix has not changed),
#then the cachesolve should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
m <- x$getInverseMatrix()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- mean(data, ...)
x$setInverseMatrix(m)
m
}
|
## sQTL.
# Maps splice eQTLs.
sQTL <- function () {
### Pre-processing
## Load data.
# Set working directory.
#setwd("~/Dropbox/Erik\ Schutte\ Internship\ 2016/")
cat("Starting splice QTL analysis..\n")
# Load in the data files.
trans.exp.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/gsTcell_TranscriptExpression.csv"
cat("Loading gene bed file..\n")
gene.bed.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genes.bed"
## Order data.
# Ordered genotype file should be compressed and indexed if not done before.
cat("Indexing genotype file..\n")
if ( file.exists("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata") ) {
load("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
} else{
# Load the genotype Information without the samples, they are generated in this script so we are going to do it on the fly.
genoTypeInformation.unfinished = read.table("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeInformation.tsv",header=T,sep="\t")
# There are less snps in the positions file than in my genotype file, find out which are not there and exclude them.
snps_present = which(genoTypeInformation.unfinished[,4] %in% rownames(snps.t))
# Create a tmp variable to store the new matrix in.
tmp = genoTypeInformation.unfinished[snps_present,]
# Save an order for the chromosomes.
chrOrder<-c(paste(1:22,sep=""),"chrX")
# Order the chromosomes factors on the new order.
tmp[,1] <-factor(tmp[,1], levels=chrOrder)
# Order the data on the new chromosome order.
tmp <- tmp[order(tmp[,1],tmp[,2]),]
# We can just cbind here, because the rs from snps.t and tmp are orderd the same.
genoTypeInf <- cbind(tmp, snps.t)
# Remove the Missing ones, or rather replace them with -1.
genoTypeInf[is.na(genoTypeInf)] <- -1
# Change the factor level so we don't get an NA as chr.
levels(genoTypeInf[,1])[23] <- 23
# Change the X chromosome to 23.
genoTypeInf[is.na(genoTypeInf), 1] <- 23
#colnames(genoTypeInf) <- gsub("\"","",colnames(genoTypeInf))
cat("Loading genotype file..\n")
# Load the genotype file, we have to do this, because sQTLseeker's logic only accepts a file path instead of a file/matrix or anythings else.
genotype.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeinf.tsv"
# Write the now finished genotype information matrix to a file, since sqtlseeker only accepts file input.
write.table( genoTypeInf, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeinf.tsv",
sep="\t", row.names = F, quote = F)
# Index the genotype file.
genotype.indexed.f = index.genotype(genotype.f)
# Save an image of the genotype file.
save.image(file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
save(genotype.indexed.f, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
}
## Read data.
# Import transcript expression, cleaned.
if ( file.exists("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata") ) {
load("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
} else{
cat("Reading transcript expression..\n")
te.df = read.table(trans.exp.f, as.is=T, header=T, sep="\t")
cat("Prepare transcript expression..\n")
tre.df = prepare.trans.exp(te.df, min.transcript.exp = 0, min.gene.exp = 0, min.dispersion = 0, verbose = T )
save.image(file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
save(tre.df, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
}
### Test for gene/SNP associations
## Tests.
# Run test with transcript expression and genotype file and gene coordinates.
cat("Read gene bed file...\n")
gene.bed = read.table(gene.bed.f, as.is=T, sep="\t")
# Set column names.
cat("Set column gene bed file..\n")
colnames(gene.bed) = c("chr","start","end","geneId")
# Change col names format for tre.df to _ instead of .
colnames(tre.df) <- sub("\\.","_",colnames(tre.df))
colnames(tre.df) <- sub("\\.","_",colnames(tre.df))
# Filter Tre.df for samples that don't exist in the genotype file.
### DYNAMIC SQTL ###
# Save index for each time point.
#sort(colnames(tre.df[3:length(colnames(tre.df))]))
timepoints = list(t0 = grep("_t0",colnames(tre.df)),
t10 = grep("_t10",colnames(tre.df)),
t30 = grep("_t30",colnames(tre.df)),
t180 = grep("_t180",colnames(tre.df)))
times <- c("t0","t10","t30","t180")
iteration = 1
for ( timepoint in timepoints) {
cat("Do sqtlseeker dynamic..\n")
# For every timepoint create a new data frame, set the first two columns to trId and geneId.
tre.df.tp <- tre.df[,1:2]
# Add the timepoints according to a grepped index to the new table.
tre.df.tp <- cbind(tre.df.tp, tre.df[,timepoint])
# Change the column names, so that they match with the sample names from the genotype file.
colnames(tre.df.tp) <- sub("batch[0-9]+_T","T",colnames(tre.df.tp))
colnames(tre.df.tp) <- sub("_t[0-9]+","",colnames(tre.df.tp))
# Filter the samples from the transcript expression file that don't exist in the genotype file.
noGenoTypeSampleIndex <- which(!colnames(tre.df.tp) %in% colnames(snps.t))
tre.df.tp <- tre.df.tp[,-noGenoTypeSampleIndex[3:length(noGenoTypeSampleIndex)]]
# There is apperantly a sample in batch 2 that is also in batch 4. That only comes to the front if I execute this
# twice, I don't know why this happens but this solves this.
noGenoTypeSampleIndex <- which(!colnames(tre.df.tp) %in% colnames(snps.t))
tre.df.tp <- tre.df.tp[,-noGenoTypeSampleIndex[3:length(noGenoTypeSampleIndex)]]
# Start sqtl.seeker.
res.df.dynamic = sqtl.seeker.modified(tre.df.tp, genotype.indexed.f, gene.bed, svQTL=F, genic.window=250000, verbose=F)
cat("write to table..\n")
# Write the output to a tsv file.
write.table(res.df.dynamic,
file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-dynamic-",times[iteration],"all.tsv",sep=""),
quote=FALSE, row.names=FALSE, sep="\t")
# Save an image so we can work with the data later if we want to.
save.image(file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/res.df.dynamic-",times[iteration],"-.Rdata",sep=""))
save(res.df.dynamic, file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/res.df.dynamic-",times[iteration],"-.Rdata",sep=""))
# Execute sqtls, a function that checks the qvalue of ech sqtl.
sqtls.df = sqtls(res.df = res.df.dynamic, FDR = 1, out.pdf=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-dyanmic-",times[iteration],"-FDR1.pdf", sep="") )
cat("\nProcessed: ",times[iteration])
# Increase iteration time, mainly for name.
iteration <- iteration + 1
}
### STATIC SQTL ###
tre.df.static <- tre.df
cat("Do sqtlseeker static..\n")
colnames(tre.df.static) <- sub("batch[0-9]+_T","T",colnames(tre.df.static))
colnames(tre.df.static) <- sub("_t[0-9]+","",colnames(tre.df.static))
res.df.static = sqtl.seeker.modified(tre.df.static, genotype.indexed.f, gene.bed, svQTL=F, verbose=F, genic.window=250000)
cat("write to table..\n")
write.table(res.df.static,
file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-static-all.tsv",
quote=FALSE, row.names=FALSE, sep="\t")
sqtls.df = sqtls(res.df.static, FDR = 1, out.pdf="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-static-FDR01.pdf")
}
|
/mapping/archive/spliceQTL-Mapping-calculon.R
|
no_license
|
ErikSchutte/QTL-Mapping
|
R
| false | false | 8,367 |
r
|
## sQTL.
# Maps splice eQTLs.
sQTL <- function () {
### Pre-processing
## Load data.
# Set working directory.
#setwd("~/Dropbox/Erik\ Schutte\ Internship\ 2016/")
cat("Starting splice QTL analysis..\n")
# Load in the data files.
trans.exp.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/gsTcell_TranscriptExpression.csv"
cat("Loading gene bed file..\n")
gene.bed.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genes.bed"
## Order data.
# Ordered genotype file should be compressed and indexed if not done before.
cat("Indexing genotype file..\n")
if ( file.exists("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata") ) {
load("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
} else{
# Load the genotype Information without the samples, they are generated in this script so we are going to do it on the fly.
genoTypeInformation.unfinished = read.table("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeInformation.tsv",header=T,sep="\t")
# There are less snps in the positions file than in my genotype file, find out which are not there and exclude them.
snps_present = which(genoTypeInformation.unfinished[,4] %in% rownames(snps.t))
# Create a tmp variable to store the new matrix in.
tmp = genoTypeInformation.unfinished[snps_present,]
# Save an order for the chromosomes.
chrOrder<-c(paste(1:22,sep=""),"chrX")
# Order the chromosomes factors on the new order.
tmp[,1] <-factor(tmp[,1], levels=chrOrder)
# Order the data on the new chromosome order.
tmp <- tmp[order(tmp[,1],tmp[,2]),]
# We can just cbind here, because the rs from snps.t and tmp are orderd the same.
genoTypeInf <- cbind(tmp, snps.t)
# Remove the Missing ones, or rather replace them with -1.
genoTypeInf[is.na(genoTypeInf)] <- -1
# Change the factor level so we don't get an NA as chr.
levels(genoTypeInf[,1])[23] <- 23
# Change the X chromosome to 23.
genoTypeInf[is.na(genoTypeInf), 1] <- 23
#colnames(genoTypeInf) <- gsub("\"","",colnames(genoTypeInf))
cat("Loading genotype file..\n")
# Load the genotype file, we have to do this, because sQTLseeker's logic only accepts a file path instead of a file/matrix or anythings else.
genotype.f = "/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeinf.tsv"
# Write the now finished genotype information matrix to a file, since sqtlseeker only accepts file input.
write.table( genoTypeInf, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genoTypeinf.tsv",
sep="\t", row.names = F, quote = F)
# Index the genotype file.
genotype.indexed.f = index.genotype(genotype.f)
# Save an image of the genotype file.
save.image(file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
save(genotype.indexed.f, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/genotype.indexed.f.Rdata")
}
## Read data.
# Import transcript expression, cleaned.
if ( file.exists("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata") ) {
load("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
} else{
cat("Reading transcript expression..\n")
te.df = read.table(trans.exp.f, as.is=T, header=T, sep="\t")
cat("Prepare transcript expression..\n")
tre.df = prepare.trans.exp(te.df, min.transcript.exp = 0, min.gene.exp = 0, min.dispersion = 0, verbose = T )
save.image(file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
save(tre.df, file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/data/tre.df.Rdata")
}
### Test for gene/SNP associations
## Tests.
# Run test with transcript expression and genotype file and gene coordinates.
cat("Read gene bed file...\n")
gene.bed = read.table(gene.bed.f, as.is=T, sep="\t")
# Set column names.
cat("Set column gene bed file..\n")
colnames(gene.bed) = c("chr","start","end","geneId")
# Change col names format for tre.df to _ instead of .
colnames(tre.df) <- sub("\\.","_",colnames(tre.df))
colnames(tre.df) <- sub("\\.","_",colnames(tre.df))
# Filter Tre.df for samples that don't exist in the genotype file.
### DYNAMIC SQTL ###
# Save index for each time point.
#sort(colnames(tre.df[3:length(colnames(tre.df))]))
timepoints = list(t0 = grep("_t0",colnames(tre.df)),
t10 = grep("_t10",colnames(tre.df)),
t30 = grep("_t30",colnames(tre.df)),
t180 = grep("_t180",colnames(tre.df)))
times <- c("t0","t10","t30","t180")
iteration = 1
for ( timepoint in timepoints) {
cat("Do sqtlseeker dynamic..\n")
# For every timepoint create a new data frame, set the first two columns to trId and geneId.
tre.df.tp <- tre.df[,1:2]
# Add the timepoints according to a grepped index to the new table.
tre.df.tp <- cbind(tre.df.tp, tre.df[,timepoint])
# Change the column names, so that they match with the sample names from the genotype file.
colnames(tre.df.tp) <- sub("batch[0-9]+_T","T",colnames(tre.df.tp))
colnames(tre.df.tp) <- sub("_t[0-9]+","",colnames(tre.df.tp))
# Filter the samples from the transcript expression file that don't exist in the genotype file.
noGenoTypeSampleIndex <- which(!colnames(tre.df.tp) %in% colnames(snps.t))
tre.df.tp <- tre.df.tp[,-noGenoTypeSampleIndex[3:length(noGenoTypeSampleIndex)]]
# There is apperantly a sample in batch 2 that is also in batch 4. That only comes to the front if I execute this
# twice, I don't know why this happens but this solves this.
noGenoTypeSampleIndex <- which(!colnames(tre.df.tp) %in% colnames(snps.t))
tre.df.tp <- tre.df.tp[,-noGenoTypeSampleIndex[3:length(noGenoTypeSampleIndex)]]
# Start sqtl.seeker.
res.df.dynamic = sqtl.seeker.modified(tre.df.tp, genotype.indexed.f, gene.bed, svQTL=F, genic.window=250000, verbose=F)
cat("write to table..\n")
# Write the output to a tsv file.
write.table(res.df.dynamic,
file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-dynamic-",times[iteration],"all.tsv",sep=""),
quote=FALSE, row.names=FALSE, sep="\t")
# Save an image so we can work with the data later if we want to.
save.image(file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/res.df.dynamic-",times[iteration],"-.Rdata",sep=""))
save(res.df.dynamic, file=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/res.df.dynamic-",times[iteration],"-.Rdata",sep=""))
# Execute sqtls, a function that checks the qvalue of ech sqtl.
sqtls.df = sqtls(res.df = res.df.dynamic, FDR = 1, out.pdf=paste("/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-dyanmic-",times[iteration],"-FDR1.pdf", sep="") )
cat("\nProcessed: ",times[iteration])
# Increase iteration time, mainly for name.
iteration <- iteration + 1
}
### STATIC SQTL ###
tre.df.static <- tre.df
cat("Do sqtlseeker static..\n")
colnames(tre.df.static) <- sub("batch[0-9]+_T","T",colnames(tre.df.static))
colnames(tre.df.static) <- sub("_t[0-9]+","",colnames(tre.df.static))
res.df.static = sqtl.seeker.modified(tre.df.static, genotype.indexed.f, gene.bed, svQTL=F, verbose=F, genic.window=250000)
cat("write to table..\n")
write.table(res.df.static,
file="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-static-all.tsv",
quote=FALSE, row.names=FALSE, sep="\t")
sqtls.df = sqtls(res.df.static, FDR = 1, out.pdf="/groups/umcg-wijmenga/tmp04/umcg-eschutte/projects/gluten_specific_Tcells/R/Splice/sQTLs-static-FDR01.pdf")
}
|
#' \pkg{rdhs} DHS database through R
#'
#' Provides a client for (1) querying the DHS API for survey indicators
#' and metadata, (2) identifying surveys and datasets for analysis,
#' (3) downloading survey datasets from the DHS website, (4) loading
#' datasets and associate metadata into R, and (5) extracting variables
#' and combining datasets for pooled analysis.
#'
#' @docType package
#' @name rdhs
#'
#' @importFrom stats setNames
#' @importFrom utils tail type.convert packageVersion unzip str capture.output
#' @importFrom rappdirs user_cache_dir
#' @importFrom R6 R6Class
#' @importFrom storr storr_rds
#'
"_PACKAGE"
globalVariables(c("x"))
|
/R/rdhs.R
|
no_license
|
LucyMcGowan/rdhs
|
R
| false | false | 652 |
r
|
#' \pkg{rdhs} DHS database through R
#'
#' Provides a client for (1) querying the DHS API for survey indicators
#' and metadata, (2) identifying surveys and datasets for analysis,
#' (3) downloading survey datasets from the DHS website, (4) loading
#' datasets and associate metadata into R, and (5) extracting variables
#' and combining datasets for pooled analysis.
#'
#' @docType package
#' @name rdhs
#'
#' @importFrom stats setNames
#' @importFrom utils tail type.convert packageVersion unzip str capture.output
#' @importFrom rappdirs user_cache_dir
#' @importFrom R6 R6Class
#' @importFrom storr storr_rds
#'
"_PACKAGE"
globalVariables(c("x"))
|
#' Gower factor Kernel R6 class
#'
#' For a factor that has been converted to its indices.
#' Each factor will need a separate kernel.
#'
#'
#' @docType class
#' @importFrom R6 R6Class
#' @export
#' @useDynLib GauPro, .registration = TRUE
#' @importFrom Rcpp evalCpp
#' @importFrom stats optim
# @keywords data, kriging, Gaussian process, regression
#' @return Object of \code{\link{R6Class}} with methods for fitting GP model.
#' @format \code{\link{R6Class}} object.
#' @field p Parameter for correlation
#' @field p_est Should p be estimated?
#' @field p_lower Lower bound of p
#' @field p_upper Upper bound of p
#' @field s2 variance
#' @field s2_est Is s2 estimated?
#' @field logs2 Log of s2
#' @field logs2_lower Lower bound of logs2
#' @field logs2_upper Upper bound of logs2
#' @field xindex Index of the factor (which column of X)
#' @field nlevels Number of levels for the factor
#' @field offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
#' @examples
#' kk <- GowerFactorKernel$new(D=1, nlevels=5, xindex=1, p=.2)
#' kmat <- outer(1:5, 1:5, Vectorize(kk$k))
#' kmat
#' kk$plot()
#'
#'
#' # 2D, Gaussian on 1D, index on 2nd dim
#' library(dplyr)
#' n <- 20
#' X <- cbind(matrix(runif(n,2,6), ncol=1),
#' matrix(sample(1:2, size=n, replace=TRUE), ncol=1))
#' X <- rbind(X, c(3.3,3))
#' n <- nrow(X)
#' Z <- X[,1] - (X[,2]-1.8)^2 + rnorm(n,0,.1)
#' tibble(X=X, Z) %>% arrange(X,Z)
#' k2a <- IgnoreIndsKernel$new(k=Gaussian$new(D=1), ignoreinds = 2)
#' k2b <- GowerFactorKernel$new(D=2, nlevels=3, xind=2)
#' k2 <- k2a * k2b
#' k2b$p_upper <- .65*k2b$p_upper
#' gp <- GauPro_kernel_model$new(X=X, Z=Z, kernel = k2, verbose = 5,
#' nug.min=1e-2, restarts=0)
#' gp$kernel$k1$kernel$beta
#' gp$kernel$k2$p
#' gp$kernel$k(x = gp$X)
#' tibble(X=X, Z=Z, pred=gp$predict(X)) %>% arrange(X, Z)
#' tibble(X=X[,2], Z) %>% group_by(X) %>% summarize(n=n(), mean(Z))
#' curve(gp$pred(cbind(matrix(x,ncol=1),1)),2,6, ylim=c(min(Z), max(Z)))
#' points(X[X[,2]==1,1], Z[X[,2]==1])
#' curve(gp$pred(cbind(matrix(x,ncol=1),2)), add=TRUE, col=2)
#' points(X[X[,2]==2,1], Z[X[,2]==2], col=2)
#' curve(gp$pred(cbind(matrix(x,ncol=1),3)), add=TRUE, col=3)
#' points(X[X[,2]==3,1], Z[X[,2]==3], col=3)
#' legend(legend=1:3, fill=1:3, x="topleft")
#' # See which points affect (5.5, 3 themost)
#' data.frame(X, cov=gp$kernel$k(X, c(5.5,3))) %>% arrange(-cov)
#' plot(k2b)
#'
#'
# GowerFactorKernel ----
GowerFactorKernel <- R6::R6Class(
classname = "GauPro_kernel_GowerFactorKernel",
inherit = GauPro_kernel,
public = list(
p = NULL, # vector of correlations
p_est = NULL,
p_lower = NULL,
p_upper = NULL,
s2 = NULL, # variance coefficient to scale correlation matrix to covariance
s2_est = NULL,
logs2 = NULL,
logs2_lower = NULL,
logs2_upper = NULL,
nlevels = NULL,
xindex = NULL,
offdiagequal = NULL,
#' @description Initialize kernel object
#' @param s2 Initial variance
#' @param D Number of input dimensions of data
#' @param p_lower Lower bound for p
#' @param p_upper Upper bound for p
#' @param p_est Should p be estimated?
#' @param p Vector of correlations
#' @param s2_lower Lower bound for s2
#' @param s2_upper Upper bound for s2
#' @param s2_est Should s2 be estimated?
#' @param xindex Index of the factor (which column of X)
#' @param nlevels Number of levels for the factor
#' @param useC Should C code used? Not implemented for FactorKernel yet.
#' @param offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
initialize = function(s2=1, D, nlevels, xindex,
p_lower=0, p_upper=.9, p_est=TRUE,
s2_lower=1e-8, s2_upper=1e8, s2_est=TRUE,
p, useC=TRUE, offdiagequal=1-1e-6
) {
# Must give in D
if (missing(D)) {stop("Must give Index kernel D")}
self$D <- D
self$nlevels <- nlevels
self$xindex <- xindex
if (missing(p)) {
p <- 0
} else {
stopifnot(is.numeric(p), length(p) == 1, p>=0, p<=1)
}
self$p <- p
stopifnot(is.numeric(p_lower), length(p_lower) == 1,
p_lower>=0, p_lower<=1)
self$p_lower <- p_lower
stopifnot(is.numeric(p_upper), length(p_upper) == 1,
p_upper>=0, p_upper<=1,
p_lower <= p_upper)
# Don't give upper 1 since it will give optimization error
self$p_upper <-p_upper
self$p_est <- p_est
self$s2 <- s2
self$logs2 <- log(s2, 10)
self$logs2_lower <- log(s2_lower, 10)
self$logs2_upper <- log(s2_upper, 10)
self$s2_est <- s2_est
self$useC <- useC
self$offdiagequal <- offdiagequal
},
#' @description Calculate covariance between two points
#' @param x vector.
#' @param y vector, optional. If excluded, find correlation
#' of x with itself.
#' @param p Correlation parameters.
#' @param s2 Variance parameter.
#' @param params parameters to use instead of beta and s2.
k = function(x, y=NULL, p=self$p, s2=self$s2, params=NULL) {
if (!is.null(params)) {
lenparams <- length(params)
if (self$p_est) {
p <- params[1]
} else {
p <- self$p
}
# if (self$alpha_est) {
# logalpha <- params[1 + as.integer(self$p_est) * self$p_length]
# } else {
# logalpha <- self$logalpha
# }
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
s2 <- 10^logs2
} else {
if (is.null(p)) {p <- self$p}
if (is.null(s2)) {s2 <- self$s2}
}
if (is.null(y)) {
if (is.matrix(x)) {
val <- outer(1:nrow(x), 1:nrow(x),
Vectorize(function(i,j){
self$kone(x[i,],x[j,],p=p, s2=s2, isdiag=i==j)
}))
return(val)
} else {
return(s2 * 1)
}
}
if (is.matrix(x) & is.matrix(y)) {
outer(1:nrow(x), 1:nrow(y),
Vectorize(function(i,j){self$kone(x[i,],y[j,],p=p, s2=s2)}))
} else if (is.matrix(x) & !is.matrix(y)) {
apply(x, 1, function(xx) {self$kone(xx, y, p=p, s2=s2)})
} else if (is.matrix(y)) {
apply(y, 1, function(yy) {self$kone(yy, x, p=p, s2=s2)})
} else {
self$kone(x, y, p=p, s2=s2)
}
},
#' @description Find covariance of two points
#' @param x vector
#' @param y vector
#' @param p correlation parameters on regular scale
#' @param s2 Variance parameter
#' @param isdiag Is this on the diagonal of the covariance?
#' @param offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
kone = function(x, y, p, s2, isdiag=TRUE, offdiagequal=self$offdiagequal) {
x <- x[self$xindex]
y <- y[self$xindex]
stopifnot(x>=1, y>=1, x<=self$nlevels, y<=self$nlevels,
abs(x-as.integer(x)) < 1e-8, abs(y-as.integer(y)) < 1e-8)
if (x==y) {
# out <- s2 * 1
# Trying to avoid singular values
if (isdiag) {
out <- s2 * 1
} else {
out <- s2 * offdiagequal
}
} else {
out <- s2 * p
}
if (any(is.nan(out))) {stop("Error #9228878341")}
out
},
#' @description Derivative of covariance with respect to parameters
#' @param params Kernel parameters
#' @param X matrix of points in rows
#' @param C_nonug Covariance without nugget added to diagonal
#' @param C Covariance with nugget
#' @param nug Value of nugget
dC_dparams = function(params=NULL, X, C_nonug, C, nug) {
# stop("not implemented, kernel index, dC_dp")
n <- nrow(X)
lenparams <- length(params)
if (lenparams > 0) {
if (self$p_est) {
p <- params[1]
} else {
p <- self$p
}
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
} else {
p <- self$p
logs2 <- self$logs2
}
log10 <- log(10)
s2 <- 10 ^ logs2
if (missing(C_nonug)) { # Assume C missing too, must have nug
C_nonug <- self$k(x=X, params=params)
C <- C_nonug + diag(nug*s2, nrow(C_nonug))
}
lenparams_D <- as.integer(self$p_est + self$s2_est)
dC_dparams <- array(dim=c(lenparams_D, n, n), data=0)
if (self$s2_est) {
dC_dparams[lenparams_D,,] <- C * log10
}
if (self$p_est) {
for (k in 1:length(p)) { # k is index of parameter
for (i in seq(1, n-1, 1)) {
xx <- X[i, self$xindex]
for (j in seq(i+1, n, 1)) {
yy <- X[j, self$xindex]
if (xx == yy) {
# Corr is just 1, parameter has no effect
} else {
dC_dparams[k,i,j] <- 1 * s2
dC_dparams[k,j,i] <- dC_dparams[k,i,j]
}
#
# r2 <- sum(p * (X[i,]-X[j,])^2)
# dC_dparams[k,i,j] <- -C_nonug[i,j] * alpha *
# dC_dparams[k,j,i] <- dC_dparams[k,i,j]
}
}
for (i in seq(1, n, 1)) { # Get diagonal set to zero
dC_dparams[k,i,i] <- 0
}
}
}
return(dC_dparams)
},
#' @description Calculate covariance matrix and its derivative
#' with respect to parameters
#' @param params Kernel parameters
#' @param X matrix of points in rows
#' @param nug Value of nugget
C_dC_dparams = function(params=NULL, X, nug) {
s2 <- self$s2_from_params(params)
C_nonug <- self$k(x=X, params=params)
C <- C_nonug + diag(s2*nug, nrow(X))
dC_dparams <- self$dC_dparams(params=params, X=X, C_nonug=C_nonug,
C=C, nug=nug)
list(C=C, dC_dparams=dC_dparams)
},
#' @description Derivative of covariance with respect to X
#' @param XX matrix of points
#' @param X matrix of points to take derivative with respect to
#' @param ... Additional args, not used
dC_dx = function(XX, X, ...) {
if (!is.matrix(XX)) {stop()}
d <- ncol(XX)
if (ncol(X) != d) {stop()}
n <- nrow(X)
nn <- nrow(XX)
dC_dx <- array(0, dim=c(nn, d, n))
dC_dx[, self$xindex, ] <- NA
dC_dx
},
#' @description Starting point for parameters for optimization
#' @param jitter Should there be a jitter?
#' @param y Output
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_start = function(jitter=F, y, p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {
vec <- min(max(self$p + jitter*rnorm(1, 0, .1),
self$p_lower), self$p_upper)
} else {
vec <- c()
}
if (s2_est) {
vec <- c(vec, self$logs2 + jitter*rnorm(1))
}
vec
},
#' @description Starting point for parameters for optimization
#' @param jitter Should there be a jitter?
#' @param y Output
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_start0 = function(jitter=F, y, p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {
vec <- min(max(0 + jitter*rnorm(1, 0, .1),
self$p_lower), self$p_upper)
} else {
vec <- c()
}
if (s2_est) {
vec <- c(vec, self$logs2 + jitter*rnorm(1))
}
vec
},
#' @description Lower bounds of parameters for optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_lower = function(p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {vec <- c(self$p_lower)} else {vec <- c()}
if (s2_est) {vec <- c(vec, self$logs2_lower)} else {}
vec
},
#' @description Upper bounds of parameters for optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_upper = function(p_est=self$p_est,
# alpha_est=self$alpha_est,
s2_est=self$s2_est) {
if (p_est) {vec <- c(self$p_upper)} else {vec <- c()}
if (s2_est) {vec <- c(vec, self$logs2_upper)} else {}
vec
},
#' @description Set parameters from optimization output
#' @param optim_out Output from optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
set_params_from_optim = function(optim_out, p_est=self$p_est,
s2_est=self$s2_est) {
loo <- length(optim_out)
if (p_est) {
self$p <- optim_out[1]
}
if (s2_est) {
self$logs2 <- optim_out[loo]
self$s2 <- 10 ^ self$logs2
}
},
#' @description Get s2 from params vector
#' @param params parameter vector
#' @param s2_est Is s2 being estimated?
s2_from_params = function(params, s2_est=self$s2_est) {
# 10 ^ params[length(params)]
if (s2_est && !is.null(params)) { # Is last if in params
10 ^ params[length(params)]
} else { # Else it is just using set value, not being estimated
self$s2
}
},
#' @description Print this object
print = function() {
cat('GauPro kernel: Gower Factor\n')
cat('\tD =', self$D, '\n')
cat('\ts2 =', self$s2, '\n')
cat('\ton x-index', self$xindex, 'with', self$nlevels, 'levels\n')
}
)
)
|
/R/kernel_GowerFactor.R
|
no_license
|
CollinErickson/GauPro
|
R
| false | false | 14,246 |
r
|
#' Gower factor Kernel R6 class
#'
#' For a factor that has been converted to its indices.
#' Each factor will need a separate kernel.
#'
#'
#' @docType class
#' @importFrom R6 R6Class
#' @export
#' @useDynLib GauPro, .registration = TRUE
#' @importFrom Rcpp evalCpp
#' @importFrom stats optim
# @keywords data, kriging, Gaussian process, regression
#' @return Object of \code{\link{R6Class}} with methods for fitting GP model.
#' @format \code{\link{R6Class}} object.
#' @field p Parameter for correlation
#' @field p_est Should p be estimated?
#' @field p_lower Lower bound of p
#' @field p_upper Upper bound of p
#' @field s2 variance
#' @field s2_est Is s2 estimated?
#' @field logs2 Log of s2
#' @field logs2_lower Lower bound of logs2
#' @field logs2_upper Upper bound of logs2
#' @field xindex Index of the factor (which column of X)
#' @field nlevels Number of levels for the factor
#' @field offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
#' @examples
#' kk <- GowerFactorKernel$new(D=1, nlevels=5, xindex=1, p=.2)
#' kmat <- outer(1:5, 1:5, Vectorize(kk$k))
#' kmat
#' kk$plot()
#'
#'
#' # 2D, Gaussian on 1D, index on 2nd dim
#' library(dplyr)
#' n <- 20
#' X <- cbind(matrix(runif(n,2,6), ncol=1),
#' matrix(sample(1:2, size=n, replace=TRUE), ncol=1))
#' X <- rbind(X, c(3.3,3))
#' n <- nrow(X)
#' Z <- X[,1] - (X[,2]-1.8)^2 + rnorm(n,0,.1)
#' tibble(X=X, Z) %>% arrange(X,Z)
#' k2a <- IgnoreIndsKernel$new(k=Gaussian$new(D=1), ignoreinds = 2)
#' k2b <- GowerFactorKernel$new(D=2, nlevels=3, xind=2)
#' k2 <- k2a * k2b
#' k2b$p_upper <- .65*k2b$p_upper
#' gp <- GauPro_kernel_model$new(X=X, Z=Z, kernel = k2, verbose = 5,
#' nug.min=1e-2, restarts=0)
#' gp$kernel$k1$kernel$beta
#' gp$kernel$k2$p
#' gp$kernel$k(x = gp$X)
#' tibble(X=X, Z=Z, pred=gp$predict(X)) %>% arrange(X, Z)
#' tibble(X=X[,2], Z) %>% group_by(X) %>% summarize(n=n(), mean(Z))
#' curve(gp$pred(cbind(matrix(x,ncol=1),1)),2,6, ylim=c(min(Z), max(Z)))
#' points(X[X[,2]==1,1], Z[X[,2]==1])
#' curve(gp$pred(cbind(matrix(x,ncol=1),2)), add=TRUE, col=2)
#' points(X[X[,2]==2,1], Z[X[,2]==2], col=2)
#' curve(gp$pred(cbind(matrix(x,ncol=1),3)), add=TRUE, col=3)
#' points(X[X[,2]==3,1], Z[X[,2]==3], col=3)
#' legend(legend=1:3, fill=1:3, x="topleft")
#' # See which points affect (5.5, 3 themost)
#' data.frame(X, cov=gp$kernel$k(X, c(5.5,3))) %>% arrange(-cov)
#' plot(k2b)
#'
#'
# GowerFactorKernel ----
GowerFactorKernel <- R6::R6Class(
classname = "GauPro_kernel_GowerFactorKernel",
inherit = GauPro_kernel,
public = list(
p = NULL, # vector of correlations
p_est = NULL,
p_lower = NULL,
p_upper = NULL,
s2 = NULL, # variance coefficient to scale correlation matrix to covariance
s2_est = NULL,
logs2 = NULL,
logs2_lower = NULL,
logs2_upper = NULL,
nlevels = NULL,
xindex = NULL,
offdiagequal = NULL,
#' @description Initialize kernel object
#' @param s2 Initial variance
#' @param D Number of input dimensions of data
#' @param p_lower Lower bound for p
#' @param p_upper Upper bound for p
#' @param p_est Should p be estimated?
#' @param p Vector of correlations
#' @param s2_lower Lower bound for s2
#' @param s2_upper Upper bound for s2
#' @param s2_est Should s2 be estimated?
#' @param xindex Index of the factor (which column of X)
#' @param nlevels Number of levels for the factor
#' @param useC Should C code used? Not implemented for FactorKernel yet.
#' @param offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
initialize = function(s2=1, D, nlevels, xindex,
p_lower=0, p_upper=.9, p_est=TRUE,
s2_lower=1e-8, s2_upper=1e8, s2_est=TRUE,
p, useC=TRUE, offdiagequal=1-1e-6
) {
# Must give in D
if (missing(D)) {stop("Must give Index kernel D")}
self$D <- D
self$nlevels <- nlevels
self$xindex <- xindex
if (missing(p)) {
p <- 0
} else {
stopifnot(is.numeric(p), length(p) == 1, p>=0, p<=1)
}
self$p <- p
stopifnot(is.numeric(p_lower), length(p_lower) == 1,
p_lower>=0, p_lower<=1)
self$p_lower <- p_lower
stopifnot(is.numeric(p_upper), length(p_upper) == 1,
p_upper>=0, p_upper<=1,
p_lower <= p_upper)
# Don't give upper 1 since it will give optimization error
self$p_upper <-p_upper
self$p_est <- p_est
self$s2 <- s2
self$logs2 <- log(s2, 10)
self$logs2_lower <- log(s2_lower, 10)
self$logs2_upper <- log(s2_upper, 10)
self$s2_est <- s2_est
self$useC <- useC
self$offdiagequal <- offdiagequal
},
#' @description Calculate covariance between two points
#' @param x vector.
#' @param y vector, optional. If excluded, find correlation
#' of x with itself.
#' @param p Correlation parameters.
#' @param s2 Variance parameter.
#' @param params parameters to use instead of beta and s2.
k = function(x, y=NULL, p=self$p, s2=self$s2, params=NULL) {
if (!is.null(params)) {
lenparams <- length(params)
if (self$p_est) {
p <- params[1]
} else {
p <- self$p
}
# if (self$alpha_est) {
# logalpha <- params[1 + as.integer(self$p_est) * self$p_length]
# } else {
# logalpha <- self$logalpha
# }
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
s2 <- 10^logs2
} else {
if (is.null(p)) {p <- self$p}
if (is.null(s2)) {s2 <- self$s2}
}
if (is.null(y)) {
if (is.matrix(x)) {
val <- outer(1:nrow(x), 1:nrow(x),
Vectorize(function(i,j){
self$kone(x[i,],x[j,],p=p, s2=s2, isdiag=i==j)
}))
return(val)
} else {
return(s2 * 1)
}
}
if (is.matrix(x) & is.matrix(y)) {
outer(1:nrow(x), 1:nrow(y),
Vectorize(function(i,j){self$kone(x[i,],y[j,],p=p, s2=s2)}))
} else if (is.matrix(x) & !is.matrix(y)) {
apply(x, 1, function(xx) {self$kone(xx, y, p=p, s2=s2)})
} else if (is.matrix(y)) {
apply(y, 1, function(yy) {self$kone(yy, x, p=p, s2=s2)})
} else {
self$kone(x, y, p=p, s2=s2)
}
},
#' @description Find covariance of two points
#' @param x vector
#' @param y vector
#' @param p correlation parameters on regular scale
#' @param s2 Variance parameter
#' @param isdiag Is this on the diagonal of the covariance?
#' @param offdiagequal What should offdiagonal values be set to when the
#' indices are the same? Use to avoid decomposition errors, similar to
#' adding a nugget.
kone = function(x, y, p, s2, isdiag=TRUE, offdiagequal=self$offdiagequal) {
x <- x[self$xindex]
y <- y[self$xindex]
stopifnot(x>=1, y>=1, x<=self$nlevels, y<=self$nlevels,
abs(x-as.integer(x)) < 1e-8, abs(y-as.integer(y)) < 1e-8)
if (x==y) {
# out <- s2 * 1
# Trying to avoid singular values
if (isdiag) {
out <- s2 * 1
} else {
out <- s2 * offdiagequal
}
} else {
out <- s2 * p
}
if (any(is.nan(out))) {stop("Error #9228878341")}
out
},
#' @description Derivative of covariance with respect to parameters
#' @param params Kernel parameters
#' @param X matrix of points in rows
#' @param C_nonug Covariance without nugget added to diagonal
#' @param C Covariance with nugget
#' @param nug Value of nugget
dC_dparams = function(params=NULL, X, C_nonug, C, nug) {
# stop("not implemented, kernel index, dC_dp")
n <- nrow(X)
lenparams <- length(params)
if (lenparams > 0) {
if (self$p_est) {
p <- params[1]
} else {
p <- self$p
}
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
} else {
p <- self$p
logs2 <- self$logs2
}
log10 <- log(10)
s2 <- 10 ^ logs2
if (missing(C_nonug)) { # Assume C missing too, must have nug
C_nonug <- self$k(x=X, params=params)
C <- C_nonug + diag(nug*s2, nrow(C_nonug))
}
lenparams_D <- as.integer(self$p_est + self$s2_est)
dC_dparams <- array(dim=c(lenparams_D, n, n), data=0)
if (self$s2_est) {
dC_dparams[lenparams_D,,] <- C * log10
}
if (self$p_est) {
for (k in 1:length(p)) { # k is index of parameter
for (i in seq(1, n-1, 1)) {
xx <- X[i, self$xindex]
for (j in seq(i+1, n, 1)) {
yy <- X[j, self$xindex]
if (xx == yy) {
# Corr is just 1, parameter has no effect
} else {
dC_dparams[k,i,j] <- 1 * s2
dC_dparams[k,j,i] <- dC_dparams[k,i,j]
}
#
# r2 <- sum(p * (X[i,]-X[j,])^2)
# dC_dparams[k,i,j] <- -C_nonug[i,j] * alpha *
# dC_dparams[k,j,i] <- dC_dparams[k,i,j]
}
}
for (i in seq(1, n, 1)) { # Get diagonal set to zero
dC_dparams[k,i,i] <- 0
}
}
}
return(dC_dparams)
},
#' @description Calculate covariance matrix and its derivative
#' with respect to parameters
#' @param params Kernel parameters
#' @param X matrix of points in rows
#' @param nug Value of nugget
C_dC_dparams = function(params=NULL, X, nug) {
s2 <- self$s2_from_params(params)
C_nonug <- self$k(x=X, params=params)
C <- C_nonug + diag(s2*nug, nrow(X))
dC_dparams <- self$dC_dparams(params=params, X=X, C_nonug=C_nonug,
C=C, nug=nug)
list(C=C, dC_dparams=dC_dparams)
},
#' @description Derivative of covariance with respect to X
#' @param XX matrix of points
#' @param X matrix of points to take derivative with respect to
#' @param ... Additional args, not used
dC_dx = function(XX, X, ...) {
if (!is.matrix(XX)) {stop()}
d <- ncol(XX)
if (ncol(X) != d) {stop()}
n <- nrow(X)
nn <- nrow(XX)
dC_dx <- array(0, dim=c(nn, d, n))
dC_dx[, self$xindex, ] <- NA
dC_dx
},
#' @description Starting point for parameters for optimization
#' @param jitter Should there be a jitter?
#' @param y Output
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_start = function(jitter=F, y, p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {
vec <- min(max(self$p + jitter*rnorm(1, 0, .1),
self$p_lower), self$p_upper)
} else {
vec <- c()
}
if (s2_est) {
vec <- c(vec, self$logs2 + jitter*rnorm(1))
}
vec
},
#' @description Starting point for parameters for optimization
#' @param jitter Should there be a jitter?
#' @param y Output
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_start0 = function(jitter=F, y, p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {
vec <- min(max(0 + jitter*rnorm(1, 0, .1),
self$p_lower), self$p_upper)
} else {
vec <- c()
}
if (s2_est) {
vec <- c(vec, self$logs2 + jitter*rnorm(1))
}
vec
},
#' @description Lower bounds of parameters for optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_lower = function(p_est=self$p_est,
s2_est=self$s2_est) {
if (p_est) {vec <- c(self$p_lower)} else {vec <- c()}
if (s2_est) {vec <- c(vec, self$logs2_lower)} else {}
vec
},
#' @description Upper bounds of parameters for optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
param_optim_upper = function(p_est=self$p_est,
# alpha_est=self$alpha_est,
s2_est=self$s2_est) {
if (p_est) {vec <- c(self$p_upper)} else {vec <- c()}
if (s2_est) {vec <- c(vec, self$logs2_upper)} else {}
vec
},
#' @description Set parameters from optimization output
#' @param optim_out Output from optimization
#' @param p_est Is p being estimated?
#' @param alpha_est Is alpha being estimated?
#' @param s2_est Is s2 being estimated?
set_params_from_optim = function(optim_out, p_est=self$p_est,
s2_est=self$s2_est) {
loo <- length(optim_out)
if (p_est) {
self$p <- optim_out[1]
}
if (s2_est) {
self$logs2 <- optim_out[loo]
self$s2 <- 10 ^ self$logs2
}
},
#' @description Get s2 from params vector
#' @param params parameter vector
#' @param s2_est Is s2 being estimated?
s2_from_params = function(params, s2_est=self$s2_est) {
# 10 ^ params[length(params)]
if (s2_est && !is.null(params)) { # Is last if in params
10 ^ params[length(params)]
} else { # Else it is just using set value, not being estimated
self$s2
}
},
#' @description Print this object
print = function() {
cat('GauPro kernel: Gower Factor\n')
cat('\tD =', self$D, '\n')
cat('\ts2 =', self$s2, '\n')
cat('\ton x-index', self$xindex, 'with', self$nlevels, 'levels\n')
}
)
)
|
# 03/11/2016
#
#
# Get experiment-level estimates for the Overlap cases
# of the Ribosome pathway.
# The experiments are identified by its GSE id,
# and we get the estimates for every random split of the
# ribosome pathway into gene sets with a predetermined overlap
#
# The job array index is used to select the GSE series with the script,
# and another command line argument to select the overlap case
# RUN ON ODYSSEY (pcxn_over_estimates01.sh) [1-863]
#
# In an improved version of the SLURM script, the
# job array index is used to select the GSE series and the
# argument to select the overlap case is passed through a
# shell script
# (pcxn_over_estimates01a_shell.sh)
# (pcxn_over_estimates01.R)
rm(list=ls())
library(parallel)
library(matrixStats)
options(stringsAsFactors = F)
pcxn_dir = "/net/hsphfs1/srv/export/hsphfs1/share_root/hide_lab/PCxN/"
# ==== PCxN Functions ====
OverlapCoefficient <- function(x,y){
# function to calculate the overlap coefficient between x and y
# which is defined as the size of the intersection divided by the
# size of the smaller set
#
# Args
# x: a vector
# y: a vector
#
# Returns
# the overlap coefficient, a number between 0 and 1
length(intersect(x,y))/min(length(unique(x)),length(unique(y)))
}
GetSummary = function(dat,gs,sum_fun){
# function to calculate the summary statistic for the pathway
#
# Args.
# dat: genes by samples matrix
# gs: vector with the names of the genes in the gene set
# sum_fun: function to calculate the summary
#
# Returns
# a 1 by samples vector with the summary statistic for the pathway
if(length(gs) > 1){
# calculate summary for pathways with more than 1 element
return(sum_fun(dat[rownames(dat) %in% gs,]))
}else{
# return actual value for pathways with a single element
return(dat[rownames(dat) %in% gs,])
}
}
GetCorEstimates = function(x,y,METHOD = "spearman"){
# function to estimate the correlation coefficient between x and y,
# the corresponding t-statistic and p-value
#
# Args
# x: a vector with n observations
# y: a vector with n observations
# method: character to pick either the Pearson or Spearman correlation coefficient
#
# Returns
# a named vector with the correlation estimate, the sample size n, the t-statistic
# and its corresponding p-value
# function to estimate t-statistic
GetStatistic = function(r,n){r*sqrt((n-2)/(1-r^2))}
# get sample size
if(length(x) == length(y)){
n <- length(x)
}else{
cat("x and y have different lengths! >=( \n")
return(NA)
}
# get correlation estimate
estimate <- cor(x,y,method = METHOD)
# get t-statistic
statistic <- GetStatistic(estimate,n)
# get p-value for the two-sided test
p.value <- 2*pt(-abs(statistic),n-2)
res <- c(estimate,n,statistic,p.value)
names(res) <- c("estimate","n","statistic","p.value")
return(res)
}
# ==== GSE annotation ====
gse_annot = readRDS(paste0(pcxn_dir,"data/GSE_annotation.RDS"))
# get sample size per GSE series
gse_count = table(gse_annot$GSE)
gse_count = sort(gse_count,decreasing=T)
# keep series with at least 5 samples
gse_ids = names(gse_count[gse_count >= 15])
# ==== Expression Background ====
exprs_rnk = readRDS(paste0(pcxn_dir,"output/Benchmark/GPL570_Rb_mat.RDS"))
# read command line arguments
args = as.numeric(commandArgs(trailingOnly = T))
# the first argument is the overlap case [1-10]
# the second argument is the GSE series [1-863]
# ==== Ribosome Overlap Case ====
cat("Overlap Case:",args[1],"\n")
gs_lst = readRDS(paste0(pcxn_dir,"output/Benchmark/ribosome_over",args[1],"_gs.RDS"))
# argument to pick GSE series
cat("GSE Series:", gse_ids[args[2]],"\n")
(nc = detectCores())
# loop through all no overlap cases
number_of_pathways = length(gs_lst)
input = 1:number_of_pathways
# select GSE series
gse = gse_ids[args[2]]
gsm_targets = gse_annot$GSM[gse_annot$GSE == gse]
gsm_ind = colnames(exprs_rnk) %in% gsm_targets
# subset expression ranks
exprs_rnk = exprs_rnk[,gsm_ind]
# R garbage collection
gc()
# function to process elements
ProcessElement = function(ic,METHOD,sum_fun){
# pathway gene sets
gsA = gs_lst[[ic]]$gs01
gsB = gs_lst[[ic]]$gs02
# split into three disjoint sets:
# 1. genes shared by pathwatys A and B
gsAB = intersect(gsA,gsB)
# 2. genes unique to pathway A
gsAu = setdiff(gsA,gsB)
# 3. genes unique to pathway B
gsBu = setdiff(gsB,gsA)
# get pathway summaries for unique genes in each pathway
summaryAu = GetSummary(dat=exprs_rnk,gs=gsAu,sum_fun=sum_fun)
summaryBu = GetSummary(dat=exprs_rnk,gs=gsBu,sum_fun=sum_fun)
# get correlation between the summaries for the unique genes
tmp = data.frame(Pathway.A=names(gs_lst)[ic],Pathway.B=names(gs_lst)[ic])
tmp = c(tmp,GetCorEstimates(x=summaryAu,y=summaryBu,METHOD = METHOD))
# get overlap coefficient
tmp$Overlap.Coeff= OverlapCoefficient(gsA,gsB)
setTxtProgressBar(pb,ic)
return(tmp)
}
# ==== PCxN Estimates: Mean/Pearson ====
ProcessElementMnPn = function(ic){ProcessElement(ic,METHOD="pearson",sum_fun=colMeans)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMnPn = mclapply(input,ProcessElementMnPn,mc.cores=nc)
close(pb)
# save results
saveRDS(resMnPn,paste0(pcxn_dir,"output/Benchmark/Overlap/Mean/Pearson/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMnPn)
# ==== PCxN Estimates: Mean/Spearman ====
ProcessElementMnSp = function(ic){ProcessElement(ic,METHOD="spearman",sum_fun=colMeans)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMnSp = mclapply(input,ProcessElementMnSp,mc.cores=nc)
close(pb)
# save results
saveRDS(resMnSp,paste0(pcxn_dir,"output/Benchmark/Overlap/Mean/Spearman/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMnSp)
# ==== PCxN Estimates: Median/Pearson ====
ProcessElementMdPn = function(ic){ProcessElement(ic,METHOD="pearson",sum_fun=colMedians)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMdPn = mclapply(input,ProcessElementMdPn,mc.cores=nc)
close(pb)
# save results
saveRDS(resMdPn,paste0(pcxn_dir,"output/Benchmark/Overlap/Median/Pearson/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMdPn)
# ==== PCxN Estimates: Median/Spearman ====
ProcessElementMdSp = function(ic){ProcessElement(ic,METHOD="spearman",sum_fun=colMedians)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMdSp = mclapply(input,ProcessElementMdSp,mc.cores=nc)
close(pb)
# save results
saveRDS(resMdSp,paste0(pcxn_dir,"output/Benchmark/Overlap/Median/Spearman/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(list=ls())
|
/src/pcxn_over_estimates01.R
|
no_license
|
yeredh/PCxN_benchmark
|
R
| false | false | 6,759 |
r
|
# 03/11/2016
#
#
# Get experiment-level estimates for the Overlap cases
# of the Ribosome pathway.
# The experiments are identified by its GSE id,
# and we get the estimates for every random split of the
# ribosome pathway into gene sets with a predetermined overlap
#
# The job array index is used to select the GSE series with the script,
# and another command line argument to select the overlap case
# RUN ON ODYSSEY (pcxn_over_estimates01.sh) [1-863]
#
# In an improved version of the SLURM script, the
# job array index is used to select the GSE series and the
# argument to select the overlap case is passed through a
# shell script
# (pcxn_over_estimates01a_shell.sh)
# (pcxn_over_estimates01.R)
rm(list=ls())
library(parallel)
library(matrixStats)
options(stringsAsFactors = F)
pcxn_dir = "/net/hsphfs1/srv/export/hsphfs1/share_root/hide_lab/PCxN/"
# ==== PCxN Functions ====
OverlapCoefficient <- function(x,y){
# function to calculate the overlap coefficient between x and y
# which is defined as the size of the intersection divided by the
# size of the smaller set
#
# Args
# x: a vector
# y: a vector
#
# Returns
# the overlap coefficient, a number between 0 and 1
length(intersect(x,y))/min(length(unique(x)),length(unique(y)))
}
GetSummary = function(dat,gs,sum_fun){
# function to calculate the summary statistic for the pathway
#
# Args.
# dat: genes by samples matrix
# gs: vector with the names of the genes in the gene set
# sum_fun: function to calculate the summary
#
# Returns
# a 1 by samples vector with the summary statistic for the pathway
if(length(gs) > 1){
# calculate summary for pathways with more than 1 element
return(sum_fun(dat[rownames(dat) %in% gs,]))
}else{
# return actual value for pathways with a single element
return(dat[rownames(dat) %in% gs,])
}
}
GetCorEstimates = function(x,y,METHOD = "spearman"){
# function to estimate the correlation coefficient between x and y,
# the corresponding t-statistic and p-value
#
# Args
# x: a vector with n observations
# y: a vector with n observations
# method: character to pick either the Pearson or Spearman correlation coefficient
#
# Returns
# a named vector with the correlation estimate, the sample size n, the t-statistic
# and its corresponding p-value
# function to estimate t-statistic
GetStatistic = function(r,n){r*sqrt((n-2)/(1-r^2))}
# get sample size
if(length(x) == length(y)){
n <- length(x)
}else{
cat("x and y have different lengths! >=( \n")
return(NA)
}
# get correlation estimate
estimate <- cor(x,y,method = METHOD)
# get t-statistic
statistic <- GetStatistic(estimate,n)
# get p-value for the two-sided test
p.value <- 2*pt(-abs(statistic),n-2)
res <- c(estimate,n,statistic,p.value)
names(res) <- c("estimate","n","statistic","p.value")
return(res)
}
# ==== GSE annotation ====
gse_annot = readRDS(paste0(pcxn_dir,"data/GSE_annotation.RDS"))
# get sample size per GSE series
gse_count = table(gse_annot$GSE)
gse_count = sort(gse_count,decreasing=T)
# keep series with at least 5 samples
gse_ids = names(gse_count[gse_count >= 15])
# ==== Expression Background ====
exprs_rnk = readRDS(paste0(pcxn_dir,"output/Benchmark/GPL570_Rb_mat.RDS"))
# read command line arguments
args = as.numeric(commandArgs(trailingOnly = T))
# the first argument is the overlap case [1-10]
# the second argument is the GSE series [1-863]
# ==== Ribosome Overlap Case ====
cat("Overlap Case:",args[1],"\n")
gs_lst = readRDS(paste0(pcxn_dir,"output/Benchmark/ribosome_over",args[1],"_gs.RDS"))
# argument to pick GSE series
cat("GSE Series:", gse_ids[args[2]],"\n")
(nc = detectCores())
# loop through all no overlap cases
number_of_pathways = length(gs_lst)
input = 1:number_of_pathways
# select GSE series
gse = gse_ids[args[2]]
gsm_targets = gse_annot$GSM[gse_annot$GSE == gse]
gsm_ind = colnames(exprs_rnk) %in% gsm_targets
# subset expression ranks
exprs_rnk = exprs_rnk[,gsm_ind]
# R garbage collection
gc()
# function to process elements
ProcessElement = function(ic,METHOD,sum_fun){
# pathway gene sets
gsA = gs_lst[[ic]]$gs01
gsB = gs_lst[[ic]]$gs02
# split into three disjoint sets:
# 1. genes shared by pathwatys A and B
gsAB = intersect(gsA,gsB)
# 2. genes unique to pathway A
gsAu = setdiff(gsA,gsB)
# 3. genes unique to pathway B
gsBu = setdiff(gsB,gsA)
# get pathway summaries for unique genes in each pathway
summaryAu = GetSummary(dat=exprs_rnk,gs=gsAu,sum_fun=sum_fun)
summaryBu = GetSummary(dat=exprs_rnk,gs=gsBu,sum_fun=sum_fun)
# get correlation between the summaries for the unique genes
tmp = data.frame(Pathway.A=names(gs_lst)[ic],Pathway.B=names(gs_lst)[ic])
tmp = c(tmp,GetCorEstimates(x=summaryAu,y=summaryBu,METHOD = METHOD))
# get overlap coefficient
tmp$Overlap.Coeff= OverlapCoefficient(gsA,gsB)
setTxtProgressBar(pb,ic)
return(tmp)
}
# ==== PCxN Estimates: Mean/Pearson ====
ProcessElementMnPn = function(ic){ProcessElement(ic,METHOD="pearson",sum_fun=colMeans)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMnPn = mclapply(input,ProcessElementMnPn,mc.cores=nc)
close(pb)
# save results
saveRDS(resMnPn,paste0(pcxn_dir,"output/Benchmark/Overlap/Mean/Pearson/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMnPn)
# ==== PCxN Estimates: Mean/Spearman ====
ProcessElementMnSp = function(ic){ProcessElement(ic,METHOD="spearman",sum_fun=colMeans)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMnSp = mclapply(input,ProcessElementMnSp,mc.cores=nc)
close(pb)
# save results
saveRDS(resMnSp,paste0(pcxn_dir,"output/Benchmark/Overlap/Mean/Spearman/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMnSp)
# ==== PCxN Estimates: Median/Pearson ====
ProcessElementMdPn = function(ic){ProcessElement(ic,METHOD="pearson",sum_fun=colMedians)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMdPn = mclapply(input,ProcessElementMdPn,mc.cores=nc)
close(pb)
# save results
saveRDS(resMdPn,paste0(pcxn_dir,"output/Benchmark/Overlap/Median/Pearson/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(resMdPn)
# ==== PCxN Estimates: Median/Spearman ====
ProcessElementMdSp = function(ic){ProcessElement(ic,METHOD="spearman",sum_fun=colMedians)}
pb = txtProgressBar(min=0,max=number_of_pathways,style=3,initial=0)
cat("\n")
resMdSp = mclapply(input,ProcessElementMdSp,mc.cores=nc)
close(pb)
# save results
saveRDS(resMdSp,paste0(pcxn_dir,"output/Benchmark/Overlap/Median/Spearman/Case",args[1],"/GSE/",gse,"_ribosome_over",args[1],".RDS"))
rm(list=ls())
|
plot.DMFA <- function (x, axes = c(1, 2), choix = "ind", label = "all",
lim.cos2.var = 0., xlim=NULL, ylim=NULL, title = NULL,palette = NULL, new.plot = FALSE,
autoLab = c("auto","yes","no"), ...)
{
res.dmfa = x
autoLab <- match.arg(autoLab,c("auto","yes","no"))
if (autoLab=="yes") autoLab=TRUE
if (autoLab=="no") autoLab=FALSE
class(res.dmfa) <- c("PCA", "list ")
if (choix == "ind"){
if(is.null(title)) titre = "Individuals factor map (PCA)"
else titre = title
plot.PCA(res.dmfa, habillage = 1, axes = axes, label = label, xlim = xlim, ylim = ylim,
autoLab = autoLab, title = titre,...)
}
if (choix == "quali") {
if (length(res.dmfa$call$quali.sup$modalite) == 1)
stop("There is no supplementary qualitative variable")
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
nb.quali = (length(res.dmfa$call$quali.sup$modalite) - 1)/2
xlim = 1.1 * c(min(res.dmfa$quali.sup$coord[, axes[1]]),
max(res.dmfa$quali.sup$coord[, axes[1]]))
ylim = 1.1 * c(min(res.dmfa$quali.sup$coord[, axes[2]]),
max(res.dmfa$quali.sup$coord[, axes[2]]))
lab.x <- paste("Dim ", axes[1], " (", signif(res.dmfa$eig[axes[1],
2], 4), " %)", sep = "")
lab.y <- paste("Dim ", axes[2], " (", signif(res.dmfa$eig[axes[2],
2], 4), " %)", sep = "")
if(is.null(title)) titre = "Qualitative representation"
else titre = title
if ((new.plot)&!nzchar(Sys.getenv("RSTUDIO_USER_IDENTITY"))) dev.new()
if (is.null(palette)) palette(c("black", "red", "green3", "blue", "cyan", "magenta","darkgray", "darkgoldenrod", "darkgreen", "violet","turquoise", "orange", "lightpink", "lavender", "yellow","lightgreen", "lightgrey", "lightblue", "darkkhaki","darkmagenta", "darkolivegreen", "lightcyan", "darkorange","darkorchid", "darkred", "darksalmon", "darkseagreen","darkslateblue", "darkslategray", "darkslategrey","darkturquoise", "darkviolet", "lightgray", "lightsalmon","lightyellow", "maroon"))
plot(0, 0, main = titre, xlab = lab.x, ylab = lab.y,
xlim = xlim, ylim = ylim, col = "white", asp = 1, ...)
abline(v = 0, lty = 2,...)
abline(h = 0, lty = 2,...)
for (i in 1:sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)])) {
points(res.dmfa$quali.sup$coord[i, axes[1]], res.dmfa$quali.sup$coord[i,
axes[2]], pch = 15,...)
text(res.dmfa$quali.sup$coord[i, axes[1]], res.dmfa$quali.sup$coord[i,
axes[2]], rownames(res.dmfa$quali.sup$coord)[i],
pos = 3,...)
for (j in 1:ng) {
points(res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[1]], res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[2]], col = j +
1, pch = 20,...)
lines(c(res.dmfa$quali.sup$coord[i, axes[1]],
res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[1]]),
c(res.dmfa$quali.sup$coord[i, axes[2]], res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[2]]),
col = j + 1,...)
}
}
legend("topleft", legend = rownames(res.dmfa$group$coord),
text.col = 2:(1 + ng), cex = 0.8, bg = "white")
}
if (choix == "var") {
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
if(is.null(title)) titre = "Variables factor map (PCA)"
else titre = title
plot.PCA(res.dmfa, choix = "var", axes = axes, col.var = ng +
1, lim.cos2.var = lim.cos2.var, label = label, autoLab = autoLab, title = titre,...)
for (j in 1:ng) {
cor.partiel = res.dmfa$var.partiel[[j]][, axes]
cor.cos2 = res.dmfa$cor.dim.gr[[j]][axes[1], axes[2]]
for (v in 1:nrow(cor.partiel)) {
qualite = (cor.partiel[v, 1]^2 + cor.partiel[v,
2]^2)/sqrt(cor.partiel[v, 1]^2 + cor.partiel[v,
2]^2 + 2 * cos(cor.cos2) * (cor.partiel[v,
1] * cor.partiel[v, 2]))
arrows(0, 0, cor.partiel[v, 1], cor.partiel[v,
2], length = 0.1 * qualite, angle = 15, code = 2,
col = j,...)
if (abs(cor.partiel[v, 1]) > abs(cor.partiel[v,
2])) {
if (cor.partiel[v, 1] >= 0)
pos <- 4
else pos <- 2
}
else {
if (cor.partiel[v, 2] >= 0)
pos <- 3
else pos <- 1
}
if (label == "all")
text(cor.partiel[v, 1], y = cor.partiel[v,
2], labels = rownames(cor.partiel)[v], pos = pos,
col = j,...)
}
}
legend("bottomleft", legend = c(lev, "var"), text.col = 1:(ng +
1), cex = 0.8, bg = "white")
Xc = res.dmfa$Xc
for (j in 1:ng) {
auxil = res.dmfa$ind$coord[res.dmfa$call$X[, 1] ==
lev[j], axes]
prefpls(cbind(auxil, Xc[[j]][rownames(auxil), ]),
title = paste("Biplot between axes ", axes[1],
" and ", axes[2], " for group ", lev[j], sep = ""))
}
}
if (choix == "group") {
if ((new.plot)&!nzchar(Sys.getenv("RSTUDIO_USER_IDENTITY"))) dev.new()
if (is.null(palette)) palette(c("black", "red", "green3", "blue", "cyan", "magenta","darkgray", "darkgoldenrod", "darkgreen", "violet","turquoise", "orange", "lightpink", "lavender", "yellow","lightgreen", "lightgrey", "lightblue", "darkkhaki","darkmagenta", "darkolivegreen", "lightcyan", "darkorange","darkorchid", "darkred", "darksalmon", "darkseagreen","darkslateblue", "darkslategray", "darkslategrey","darkturquoise", "darkviolet", "lightgray", "lightsalmon","lightyellow", "maroon"))
coord.gr = res.dmfa$group$coord.n
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
xlim = 1.1 * c(0, max(1, max(coord.gr[, axes[1]])))
ylim = 1.1 * c(0, max(1, max(coord.gr[, axes[2]])))
if(is.null(title)) titre = "Projection of the groups"
else titre = title
plot(0, 0, xlab = paste("Dim", axes[1]), ylab = paste("Dim",
axes[2]), xlim = xlim, ylim = ylim, col = "white",
asp = 1, main = titre,...)
for (j in 1:ng) {
points(coord.gr[j, axes[1]], coord.gr[j, axes[2]],
col = j, pch = 15,...)
if (label == "all")
text(coord.gr[j, axes[1]], coord.gr[j, axes[2]],
labels = lev[j], pos = 3,...)
}
abline(v = 0, lty = 2,...)
abline(h = 0, lty = 2,...)
}
}
|
/FactoMineR/R/plot.DMFA.R
|
no_license
|
Etjean/M1stuff
|
R
| false | false | 7,202 |
r
|
plot.DMFA <- function (x, axes = c(1, 2), choix = "ind", label = "all",
lim.cos2.var = 0., xlim=NULL, ylim=NULL, title = NULL,palette = NULL, new.plot = FALSE,
autoLab = c("auto","yes","no"), ...)
{
res.dmfa = x
autoLab <- match.arg(autoLab,c("auto","yes","no"))
if (autoLab=="yes") autoLab=TRUE
if (autoLab=="no") autoLab=FALSE
class(res.dmfa) <- c("PCA", "list ")
if (choix == "ind"){
if(is.null(title)) titre = "Individuals factor map (PCA)"
else titre = title
plot.PCA(res.dmfa, habillage = 1, axes = axes, label = label, xlim = xlim, ylim = ylim,
autoLab = autoLab, title = titre,...)
}
if (choix == "quali") {
if (length(res.dmfa$call$quali.sup$modalite) == 1)
stop("There is no supplementary qualitative variable")
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
nb.quali = (length(res.dmfa$call$quali.sup$modalite) - 1)/2
xlim = 1.1 * c(min(res.dmfa$quali.sup$coord[, axes[1]]),
max(res.dmfa$quali.sup$coord[, axes[1]]))
ylim = 1.1 * c(min(res.dmfa$quali.sup$coord[, axes[2]]),
max(res.dmfa$quali.sup$coord[, axes[2]]))
lab.x <- paste("Dim ", axes[1], " (", signif(res.dmfa$eig[axes[1],
2], 4), " %)", sep = "")
lab.y <- paste("Dim ", axes[2], " (", signif(res.dmfa$eig[axes[2],
2], 4), " %)", sep = "")
if(is.null(title)) titre = "Qualitative representation"
else titre = title
if ((new.plot)&!nzchar(Sys.getenv("RSTUDIO_USER_IDENTITY"))) dev.new()
if (is.null(palette)) palette(c("black", "red", "green3", "blue", "cyan", "magenta","darkgray", "darkgoldenrod", "darkgreen", "violet","turquoise", "orange", "lightpink", "lavender", "yellow","lightgreen", "lightgrey", "lightblue", "darkkhaki","darkmagenta", "darkolivegreen", "lightcyan", "darkorange","darkorchid", "darkred", "darksalmon", "darkseagreen","darkslateblue", "darkslategray", "darkslategrey","darkturquoise", "darkviolet", "lightgray", "lightsalmon","lightyellow", "maroon"))
plot(0, 0, main = titre, xlab = lab.x, ylab = lab.y,
xlim = xlim, ylim = ylim, col = "white", asp = 1, ...)
abline(v = 0, lty = 2,...)
abline(h = 0, lty = 2,...)
for (i in 1:sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)])) {
points(res.dmfa$quali.sup$coord[i, axes[1]], res.dmfa$quali.sup$coord[i,
axes[2]], pch = 15,...)
text(res.dmfa$quali.sup$coord[i, axes[1]], res.dmfa$quali.sup$coord[i,
axes[2]], rownames(res.dmfa$quali.sup$coord)[i],
pos = 3,...)
for (j in 1:ng) {
points(res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[1]], res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[2]], col = j +
1, pch = 20,...)
lines(c(res.dmfa$quali.sup$coord[i, axes[1]],
res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[1]]),
c(res.dmfa$quali.sup$coord[i, axes[2]], res.dmfa$quali.sup$coord[sum(res.dmfa$call$quali.sup$modalite[2:(1 +
nb.quali)]) + ng * (i - 1) + j, axes[2]]),
col = j + 1,...)
}
}
legend("topleft", legend = rownames(res.dmfa$group$coord),
text.col = 2:(1 + ng), cex = 0.8, bg = "white")
}
if (choix == "var") {
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
if(is.null(title)) titre = "Variables factor map (PCA)"
else titre = title
plot.PCA(res.dmfa, choix = "var", axes = axes, col.var = ng +
1, lim.cos2.var = lim.cos2.var, label = label, autoLab = autoLab, title = titre,...)
for (j in 1:ng) {
cor.partiel = res.dmfa$var.partiel[[j]][, axes]
cor.cos2 = res.dmfa$cor.dim.gr[[j]][axes[1], axes[2]]
for (v in 1:nrow(cor.partiel)) {
qualite = (cor.partiel[v, 1]^2 + cor.partiel[v,
2]^2)/sqrt(cor.partiel[v, 1]^2 + cor.partiel[v,
2]^2 + 2 * cos(cor.cos2) * (cor.partiel[v,
1] * cor.partiel[v, 2]))
arrows(0, 0, cor.partiel[v, 1], cor.partiel[v,
2], length = 0.1 * qualite, angle = 15, code = 2,
col = j,...)
if (abs(cor.partiel[v, 1]) > abs(cor.partiel[v,
2])) {
if (cor.partiel[v, 1] >= 0)
pos <- 4
else pos <- 2
}
else {
if (cor.partiel[v, 2] >= 0)
pos <- 3
else pos <- 1
}
if (label == "all")
text(cor.partiel[v, 1], y = cor.partiel[v,
2], labels = rownames(cor.partiel)[v], pos = pos,
col = j,...)
}
}
legend("bottomleft", legend = c(lev, "var"), text.col = 1:(ng +
1), cex = 0.8, bg = "white")
Xc = res.dmfa$Xc
for (j in 1:ng) {
auxil = res.dmfa$ind$coord[res.dmfa$call$X[, 1] ==
lev[j], axes]
prefpls(cbind(auxil, Xc[[j]][rownames(auxil), ]),
title = paste("Biplot between axes ", axes[1],
" and ", axes[2], " for group ", lev[j], sep = ""))
}
}
if (choix == "group") {
if ((new.plot)&!nzchar(Sys.getenv("RSTUDIO_USER_IDENTITY"))) dev.new()
if (is.null(palette)) palette(c("black", "red", "green3", "blue", "cyan", "magenta","darkgray", "darkgoldenrod", "darkgreen", "violet","turquoise", "orange", "lightpink", "lavender", "yellow","lightgreen", "lightgrey", "lightblue", "darkkhaki","darkmagenta", "darkolivegreen", "lightcyan", "darkorange","darkorchid", "darkred", "darksalmon", "darkseagreen","darkslateblue", "darkslategray", "darkslategrey","darkturquoise", "darkviolet", "lightgray", "lightsalmon","lightyellow", "maroon"))
coord.gr = res.dmfa$group$coord.n
lev = levels(res.dmfa$call$X[, 1])
ng = length(lev)
xlim = 1.1 * c(0, max(1, max(coord.gr[, axes[1]])))
ylim = 1.1 * c(0, max(1, max(coord.gr[, axes[2]])))
if(is.null(title)) titre = "Projection of the groups"
else titre = title
plot(0, 0, xlab = paste("Dim", axes[1]), ylab = paste("Dim",
axes[2]), xlim = xlim, ylim = ylim, col = "white",
asp = 1, main = titre,...)
for (j in 1:ng) {
points(coord.gr[j, axes[1]], coord.gr[j, axes[2]],
col = j, pch = 15,...)
if (label == "all")
text(coord.gr[j, axes[1]], coord.gr[j, axes[2]],
labels = lev[j], pos = 3,...)
}
abline(v = 0, lty = 2,...)
abline(h = 0, lty = 2,...)
}
}
|
library(ggplot2)
install.packages('XLConnect', dependencies = T)
library(XLConnect)
install.packages('tidyr', dependencies = T)
library(tidyr)
jobRaw <- readWorksheet(loadWorkbook("indicator_t above 15 employ.xlsx"),sheet=1)
colnames(jobRaw)[1] <- "country"
#Treating
job = gather(jobRaw, "year", "percentage", 2:18)
str(substr(job$year,2,5))
job$year <- as.numeric((substr(job$year,2,5)))
str(job$year)
str(job$percentage)
qplot(x=year, y=percentage, data=job, color=country, geom="line")
#That's a complete mess!
#Let's analyze this.
by(job$percent, job$country, summary)
#Country with highest job percentage.
countryHigh = job[which.max(job$percentage),1]
#Country with lowest job percentage.
countryLow = job[which.min(job$percentage),1]
#Reducing countries to a manageble number.
jobCountries = subset(job, country %in% c("Brazil","Argentina","Germany", countryHigh, countryLow))
#Line
qplot(x=year, y=percentage, data=jobCountries, color=country, geom="line")
ggsave('jobContriesLinePercentageYear.png')
#Frequency
qplot(x = percentage,
y = ..count..,
data = jobCountries,
binwidth = 11,
geom="freqpoly",
color=country
)
ggsave('jobContriesFreqPercentageContry.png')
|
/lesson3/dataFromGapminder.R
|
no_license
|
pcontop/EDA-Udacity
|
R
| false | false | 1,222 |
r
|
library(ggplot2)
install.packages('XLConnect', dependencies = T)
library(XLConnect)
install.packages('tidyr', dependencies = T)
library(tidyr)
jobRaw <- readWorksheet(loadWorkbook("indicator_t above 15 employ.xlsx"),sheet=1)
colnames(jobRaw)[1] <- "country"
#Treating
job = gather(jobRaw, "year", "percentage", 2:18)
str(substr(job$year,2,5))
job$year <- as.numeric((substr(job$year,2,5)))
str(job$year)
str(job$percentage)
qplot(x=year, y=percentage, data=job, color=country, geom="line")
#That's a complete mess!
#Let's analyze this.
by(job$percent, job$country, summary)
#Country with highest job percentage.
countryHigh = job[which.max(job$percentage),1]
#Country with lowest job percentage.
countryLow = job[which.min(job$percentage),1]
#Reducing countries to a manageble number.
jobCountries = subset(job, country %in% c("Brazil","Argentina","Germany", countryHigh, countryLow))
#Line
qplot(x=year, y=percentage, data=jobCountries, color=country, geom="line")
ggsave('jobContriesLinePercentageYear.png')
#Frequency
qplot(x = percentage,
y = ..count..,
data = jobCountries,
binwidth = 11,
geom="freqpoly",
color=country
)
ggsave('jobContriesFreqPercentageContry.png')
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Classifier/bone.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.03,family="gaussian",standardize=FALSE)
sink('./bone_016.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/Classifier/bone/bone_016.R
|
no_license
|
esbgkannan/QSMART
|
R
| false | false | 346 |
r
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Classifier/bone.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.03,family="gaussian",standardize=FALSE)
sink('./bone_016.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
# Yige Wu @WashU Apr 2020
## make barcode to cell type mapping table for the integrated dataset
## 2020-10-27 added Alla's latest immune cell types
# set up libraries and output directory -----------------------------------
## set working directory
dir_base = "~/Box/Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/"
setwd(dir_base)
source("./ccRCC_snRNA_analysis/load_pkgs.R")
source("./ccRCC_snRNA_analysis/functions.R")
source("./ccRCC_snRNA_analysis/variables.R")
## set run id
version_tmp <- 1
run_id <- paste0(format(Sys.Date(), "%Y%m%d") , ".v", version_tmp)
## set output directory
dir_out <- paste0(makeOutDir(), run_id, "/")
dir.create(dir_out)
# input dependencies ------------------------------------------------------
## input barcode to cluster mapping table from
all_integrated_barcode2cluster_df <- fread(input = "./Resources/Analysis_Results/integration/30_aliquot_integration/fetch_data/20200212.v3/30_aliquot_integration.20200212.v3.umap_data.tsv", data.table = F)
## input cluster to cell type mapping table for all clusters in the integrated dataset
all_integrated_cluster2celltype_df <- fread(input = "./Resources/snRNA_Processed_Data/Cell_Type_Assignment/Integration_AllClusters/integration.allcluster2celltype.20200213.v3.tsv")
## input Alla's immune cell type assignment
### should be 33004
# immune_barcode2celltype_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_immune_cells/20200626.v1/Barcode2ImmuneCellType.20200626.v1.tsv")
immune_barcode2celltype_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_immune_cells_with_patch/20201027.v1/Barcode2ImmuneCellType.20201027.v1.tsv")
## input tumor subclustering cell type assignment: 93277 cells
tumor_barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_barcode_with_manual_tumorsubcluster_id/20200616.v1/Barcode2TumorSubclusterId.20200616.v1.tsv", data.table = F)
## input barcode-to-cell-type table
normal_epithelial_barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_normal_epithelial_cells/20200720.v1/normal_epithelial_reclustered.barcode2celltype.20200720.v1.tsv", data.table = F)
## input patch barcode2cell type table
bc2celltype_patch_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_for_C3L-00088-N/20200904.v1/Barcode2CellType.C3L-00088-N.20200904.v1.tsv")
# get barcode2celltype from integrated data -------------------------------
nrow(all_integrated_barcode2cluster_df)
## merge
all_integrated_barcode2celltype_df <- merge(x = all_integrated_barcode2cluster_df, y = all_integrated_cluster2celltype_df, by.x = c("ident"), by.y = c("Cluster"), all.x = T)
nrow(all_integrated_barcode2celltype_df)
## add individual barcode
all_integrated_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
rename(integrated_barcode = barcode) %>%
mutate(individual_barcode = str_split_fixed(string = integrated_barcode, pattern = "_", n = 2)[,1])
# format normal epithelial cells ------------------------------------------
normal_epithelial_barcode2celltype_df <- normal_epithelial_barcode2celltype_df %>%
mutate(Id_TumorManualCluster = NA) %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# map stromal cells -------------------------------------------------------
stroma_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
filter(Most_Enriched_Cell_Group == "Stroma") %>%
mutate(Cell_type.shorter = Most_Enriched_Cell_Type1) %>%
mutate(Cell_type.detailed = Most_Enriched_Cell_Type1)
## see what needs to be corrected
table(stroma_barcode2celltype_df$Most_Enriched_Cell_Type1)
table(stroma_barcode2celltype_df$Most_Enriched_Cell_Type2)
## make short cell type names for myofibroblasts
stroma_barcode2celltype_df$Cell_type.shorter[stroma_barcode2celltype_df$Most_Enriched_Cell_Type2 == "Myofibroblasts"] <- "Myofibroblasts"
stroma_barcode2celltype_df$Cell_type.detailed[stroma_barcode2celltype_df$Most_Enriched_Cell_Type2 == "Myofibroblasts"] <- "Myofibroblasts"
## add columns
stroma_barcode2celltype_df <- stroma_barcode2celltype_df %>%
mutate(Id_TumorManualCluster = NA) %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# map unknown cells -------------------------------------------------------
assigned_integrated_barcodes <- c(immune_barcode2celltype_df$integrated_barcode,
tumor_barcode2celltype_df$integrated_barcode,
stroma_barcode2celltype_df$integrated_barcode,
normal_epithelial_barcode2celltype_df$integrated_barcode)
length(assigned_integrated_barcodes)
nrow(all_integrated_barcode2celltype_df)
unknown_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
filter(!(integrated_barcode %in% assigned_integrated_barcodes)) %>%
mutate(Id_TumorManualCluster = NA) %>%
mutate(Cell_type.shorter = "Unknown") %>%
mutate(Cell_type.detailed = "Unknown")
unknown_barcode2celltype_df$Most_Enriched_Cell_Group <- "Unknown"
unknown_barcode2celltype_df$Most_Enriched_Cell_Type1 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type2 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type3 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type4 <- ""
unknown_barcode2celltype_df <- unknown_barcode2celltype_df %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# merge the immune and tumor cells and other info ------------------------------------
barcode2celltype_df <- rbind(immune_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Most_Enriched_Cell_Group == "Immune", "Immune", "Unknown")),
tumor_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Cell_type.shorter == "Tumor cells", "Tumor cells", "Unknown")),
stroma_barcode2celltype_df %>%
mutate(Cell_group = "Stroma"),
normal_epithelial_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Most_Enriched_Cell_Group == "Nephron_Epithelium", "Normal epithelial cells", "Unknown")),
unknown_barcode2celltype_df %>%
mutate(Cell_group = "Unknown"))
nrow(barcode2celltype_df)
table(barcode2celltype_df$Cell_type.shorter)
# add patch ---------------------------------------------------------------
barcode2celltype_df <- rbind(barcode2celltype_df, bc2celltype_patch_df)
nrow(barcode2celltype_df) ## 138547
table(barcode2celltype_df$Cell_type.shorter)
table(barcode2celltype_df$Cell_type.detailed)
# write output ------------------------------------------------------------
write.table(x = barcode2celltype_df, file = paste0(dir_out, "31AliquotIntegration.Barcode2CellType.TumorManualCluster.", run_id, ".tsv"), quote = F, sep = "\t", row.names = F)
|
/annotate_barcode/map_celltype_to_all_cells_with_patch.R
|
no_license
|
ding-lab/ccRCC_snRNA_analysis
|
R
| false | false | 7,649 |
r
|
# Yige Wu @WashU Apr 2020
## make barcode to cell type mapping table for the integrated dataset
## 2020-10-27 added Alla's latest immune cell types
# set up libraries and output directory -----------------------------------
## set working directory
dir_base = "~/Box/Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/"
setwd(dir_base)
source("./ccRCC_snRNA_analysis/load_pkgs.R")
source("./ccRCC_snRNA_analysis/functions.R")
source("./ccRCC_snRNA_analysis/variables.R")
## set run id
version_tmp <- 1
run_id <- paste0(format(Sys.Date(), "%Y%m%d") , ".v", version_tmp)
## set output directory
dir_out <- paste0(makeOutDir(), run_id, "/")
dir.create(dir_out)
# input dependencies ------------------------------------------------------
## input barcode to cluster mapping table from
all_integrated_barcode2cluster_df <- fread(input = "./Resources/Analysis_Results/integration/30_aliquot_integration/fetch_data/20200212.v3/30_aliquot_integration.20200212.v3.umap_data.tsv", data.table = F)
## input cluster to cell type mapping table for all clusters in the integrated dataset
all_integrated_cluster2celltype_df <- fread(input = "./Resources/snRNA_Processed_Data/Cell_Type_Assignment/Integration_AllClusters/integration.allcluster2celltype.20200213.v3.tsv")
## input Alla's immune cell type assignment
### should be 33004
# immune_barcode2celltype_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_immune_cells/20200626.v1/Barcode2ImmuneCellType.20200626.v1.tsv")
immune_barcode2celltype_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_immune_cells_with_patch/20201027.v1/Barcode2ImmuneCellType.20201027.v1.tsv")
## input tumor subclustering cell type assignment: 93277 cells
tumor_barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_barcode_with_manual_tumorsubcluster_id/20200616.v1/Barcode2TumorSubclusterId.20200616.v1.tsv", data.table = F)
## input barcode-to-cell-type table
normal_epithelial_barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_to_normal_epithelial_cells/20200720.v1/normal_epithelial_reclustered.barcode2celltype.20200720.v1.tsv", data.table = F)
## input patch barcode2cell type table
bc2celltype_patch_df <- fread(data.table = F, input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_for_C3L-00088-N/20200904.v1/Barcode2CellType.C3L-00088-N.20200904.v1.tsv")
# get barcode2celltype from integrated data -------------------------------
nrow(all_integrated_barcode2cluster_df)
## merge
all_integrated_barcode2celltype_df <- merge(x = all_integrated_barcode2cluster_df, y = all_integrated_cluster2celltype_df, by.x = c("ident"), by.y = c("Cluster"), all.x = T)
nrow(all_integrated_barcode2celltype_df)
## add individual barcode
all_integrated_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
rename(integrated_barcode = barcode) %>%
mutate(individual_barcode = str_split_fixed(string = integrated_barcode, pattern = "_", n = 2)[,1])
# format normal epithelial cells ------------------------------------------
normal_epithelial_barcode2celltype_df <- normal_epithelial_barcode2celltype_df %>%
mutate(Id_TumorManualCluster = NA) %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# map stromal cells -------------------------------------------------------
stroma_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
filter(Most_Enriched_Cell_Group == "Stroma") %>%
mutate(Cell_type.shorter = Most_Enriched_Cell_Type1) %>%
mutate(Cell_type.detailed = Most_Enriched_Cell_Type1)
## see what needs to be corrected
table(stroma_barcode2celltype_df$Most_Enriched_Cell_Type1)
table(stroma_barcode2celltype_df$Most_Enriched_Cell_Type2)
## make short cell type names for myofibroblasts
stroma_barcode2celltype_df$Cell_type.shorter[stroma_barcode2celltype_df$Most_Enriched_Cell_Type2 == "Myofibroblasts"] <- "Myofibroblasts"
stroma_barcode2celltype_df$Cell_type.detailed[stroma_barcode2celltype_df$Most_Enriched_Cell_Type2 == "Myofibroblasts"] <- "Myofibroblasts"
## add columns
stroma_barcode2celltype_df <- stroma_barcode2celltype_df %>%
mutate(Id_TumorManualCluster = NA) %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# map unknown cells -------------------------------------------------------
assigned_integrated_barcodes <- c(immune_barcode2celltype_df$integrated_barcode,
tumor_barcode2celltype_df$integrated_barcode,
stroma_barcode2celltype_df$integrated_barcode,
normal_epithelial_barcode2celltype_df$integrated_barcode)
length(assigned_integrated_barcodes)
nrow(all_integrated_barcode2celltype_df)
unknown_barcode2celltype_df <- all_integrated_barcode2celltype_df %>%
filter(!(integrated_barcode %in% assigned_integrated_barcodes)) %>%
mutate(Id_TumorManualCluster = NA) %>%
mutate(Cell_type.shorter = "Unknown") %>%
mutate(Cell_type.detailed = "Unknown")
unknown_barcode2celltype_df$Most_Enriched_Cell_Group <- "Unknown"
unknown_barcode2celltype_df$Most_Enriched_Cell_Type1 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type2 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type3 <- ""
unknown_barcode2celltype_df$Most_Enriched_Cell_Type4 <- ""
unknown_barcode2celltype_df <- unknown_barcode2celltype_df %>%
select(orig.ident, individual_barcode, integrated_barcode,
Most_Enriched_Cell_Group,
Cell_type.shorter, Cell_type.detailed,
Most_Enriched_Cell_Type1, Most_Enriched_Cell_Type2, Most_Enriched_Cell_Type3, Most_Enriched_Cell_Type4, Id_TumorManualCluster)
# merge the immune and tumor cells and other info ------------------------------------
barcode2celltype_df <- rbind(immune_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Most_Enriched_Cell_Group == "Immune", "Immune", "Unknown")),
tumor_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Cell_type.shorter == "Tumor cells", "Tumor cells", "Unknown")),
stroma_barcode2celltype_df %>%
mutate(Cell_group = "Stroma"),
normal_epithelial_barcode2celltype_df %>%
mutate(Cell_group = ifelse(Most_Enriched_Cell_Group == "Nephron_Epithelium", "Normal epithelial cells", "Unknown")),
unknown_barcode2celltype_df %>%
mutate(Cell_group = "Unknown"))
nrow(barcode2celltype_df)
table(barcode2celltype_df$Cell_type.shorter)
# add patch ---------------------------------------------------------------
barcode2celltype_df <- rbind(barcode2celltype_df, bc2celltype_patch_df)
nrow(barcode2celltype_df) ## 138547
table(barcode2celltype_df$Cell_type.shorter)
table(barcode2celltype_df$Cell_type.detailed)
# write output ------------------------------------------------------------
write.table(x = barcode2celltype_df, file = paste0(dir_out, "31AliquotIntegration.Barcode2CellType.TumorManualCluster.", run_id, ".tsv"), quote = F, sep = "\t", row.names = F)
|
get_seasons <- function(df) {
paste0('seas_', unique(df$seas))
}
ps_filter_df <- function(df, .pt_sprd) {
dplyr::filter(df, ps == .pt_sprd)
}
get_n_percentile <- function(df, .ps_col, .n=.98){
df %>%
dplyr::pull({{.ps_col}}) %>%
quantile(.,.n) %>%
floor()
}
|
/R/utils_helpers.R
|
permissive
|
cswaters/bayesPushchartNFL
|
R
| false | false | 285 |
r
|
get_seasons <- function(df) {
paste0('seas_', unique(df$seas))
}
ps_filter_df <- function(df, .pt_sprd) {
dplyr::filter(df, ps == .pt_sprd)
}
get_n_percentile <- function(df, .ps_col, .n=.98){
df %>%
dplyr::pull({{.ps_col}}) %>%
quantile(.,.n) %>%
floor()
}
|
#' @export
doPadRows <- function(dataToBePadded, dataThatSuppliesRowCount) {
#####################
# This function adds NA rows to the bottom of data.frame dataToBePadded
# to ensure that it has the the number of observations (rows) as
# dataThatSuppliesRowCount. The number of columns comes from dataToBePadded
# Execution halts if nrow(dataThatSuppliesRowCount) < nrow(dataToBePadded)
# Returns dataToBePadded with NA rows at the bottom.
##
nRowsToAdd <- nrow(dataThatSuppliesRowCount) - nrow(dataToBePadded)
if (nRowsToAdd < 0){
stop(paste("Model data frame has", abs(nRowsToAdd), "fewer rows than target."))
}
dfToAppend <- as.data.frame(matrix(NA, ncol=ncol(dataToBePadded), nrow=nRowsToAdd))
colnames(dfToAppend) <- colnames(dataToBePadded)
return(rbind(dataToBePadded, dfToAppend))
}
#' @export
padRows <- function(countryAbbrev, df, baseHistorical){
#####################
# This function adds NA rows to the bottom of data.frame df to ensure that it has the
# the number of observations (rows) as the country data set for countryAbbrev
# This function is a convenience wrapper function that simply loads
# the data.frame for countryAbbrev and then calls doPadRows
# returns a modified version of df that includes the padded rows filled with "NA".
##
countryData <- loadData(countryAbbrev=countryAbbrev, baseHistorical=baseHistorical)
return(doPadRows(dataToBePadded=df, dataThatSuppliesRowCount=countryData))
}
#' @export
columnIndex <- function(data, factor){
##############################
# Returns an integer representing the column index for some data
# data the data.frame in which you want to change column names
# factor should be a string and one of Year, Y, K, L, Q, X, or U
##
if (factor == "Year"){
colName <- "iYear"
} else if (factor == "Y"){
colName <- "iGDP"
} else if (factor == "K"){
colName <- "iK"
} else if (factor == "L"){
colName <- "iL"
} else if (factor == "Q"){
colName <- "iQ"
} else if (factor == "X"){
colName <- "iX"
} else if (factor == "U"){
colName <- "iU"
} else {
print(paste("Unknown factor:", factor, "in colIndex. Terminating execution."))
quit()
}
# Get the desired column index.
colIndex <- which(names(data) %in% colName) #Find index of desired column
return(colIndex)
}
#' @export
replaceColName <- function(data, factor, newName){
##############################
# Replaces a column name with the given string
# data the data.frame that you're working with
# factor should be a string and one of Year, Y, K, L, Q, X, or U
# newName should be a string and the desired new name of the column
# returns data.frame with a new name for one of its factor column.
##
colIndex <- columnIndex(data=data, factor=factor)
# colnames(data)[colIndex] <- newName #Change desired column name to newName
data[,newName] <- data[,colIndex]
return(data)
}
|
/Packages/EconModels0/R/huh.R
|
no_license
|
anhnguyendepocen/MacroGrowth
|
R
| false | false | 2,934 |
r
|
#' @export
doPadRows <- function(dataToBePadded, dataThatSuppliesRowCount) {
#####################
# This function adds NA rows to the bottom of data.frame dataToBePadded
# to ensure that it has the the number of observations (rows) as
# dataThatSuppliesRowCount. The number of columns comes from dataToBePadded
# Execution halts if nrow(dataThatSuppliesRowCount) < nrow(dataToBePadded)
# Returns dataToBePadded with NA rows at the bottom.
##
nRowsToAdd <- nrow(dataThatSuppliesRowCount) - nrow(dataToBePadded)
if (nRowsToAdd < 0){
stop(paste("Model data frame has", abs(nRowsToAdd), "fewer rows than target."))
}
dfToAppend <- as.data.frame(matrix(NA, ncol=ncol(dataToBePadded), nrow=nRowsToAdd))
colnames(dfToAppend) <- colnames(dataToBePadded)
return(rbind(dataToBePadded, dfToAppend))
}
#' @export
padRows <- function(countryAbbrev, df, baseHistorical){
#####################
# This function adds NA rows to the bottom of data.frame df to ensure that it has the
# the number of observations (rows) as the country data set for countryAbbrev
# This function is a convenience wrapper function that simply loads
# the data.frame for countryAbbrev and then calls doPadRows
# returns a modified version of df that includes the padded rows filled with "NA".
##
countryData <- loadData(countryAbbrev=countryAbbrev, baseHistorical=baseHistorical)
return(doPadRows(dataToBePadded=df, dataThatSuppliesRowCount=countryData))
}
#' @export
columnIndex <- function(data, factor){
##############################
# Returns an integer representing the column index for some data
# data the data.frame in which you want to change column names
# factor should be a string and one of Year, Y, K, L, Q, X, or U
##
if (factor == "Year"){
colName <- "iYear"
} else if (factor == "Y"){
colName <- "iGDP"
} else if (factor == "K"){
colName <- "iK"
} else if (factor == "L"){
colName <- "iL"
} else if (factor == "Q"){
colName <- "iQ"
} else if (factor == "X"){
colName <- "iX"
} else if (factor == "U"){
colName <- "iU"
} else {
print(paste("Unknown factor:", factor, "in colIndex. Terminating execution."))
quit()
}
# Get the desired column index.
colIndex <- which(names(data) %in% colName) #Find index of desired column
return(colIndex)
}
#' @export
replaceColName <- function(data, factor, newName){
##############################
# Replaces a column name with the given string
# data the data.frame that you're working with
# factor should be a string and one of Year, Y, K, L, Q, X, or U
# newName should be a string and the desired new name of the column
# returns data.frame with a new name for one of its factor column.
##
colIndex <- columnIndex(data=data, factor=factor)
# colnames(data)[colIndex] <- newName #Change desired column name to newName
data[,newName] <- data[,colIndex]
return(data)
}
|
# Yige Wu @WashU Aug 2020
# set up libraries and output directory -----------------------------------
## getting the path to the current script
thisFile <- function() {
cmdArgs <- commandArgs(trailingOnly = FALSE)
needle <- "--file="
match <- grep(needle, cmdArgs)
if (length(match) > 0) {
# Rscript
return(normalizePath(sub(needle, "", cmdArgs[match])))
} else {
# 'source'd via R console
return(normalizePath(sys.frames()[[1]]$ofile))
}
}
path_this_script <- thisFile()
## set working directory
dir_base = "/diskmnt/Projects/ccRCC_scratch/ccRCC_snRNA/"
# dir_base = "~/Box/Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/"
setwd(dir_base)
source("./ccRCC_snRNA_analysis/load_pkgs.R")
source("./ccRCC_snRNA_analysis/functions.R")
source("./ccRCC_snRNA_analysis/variables.R")
library(tibble)
## set run id
version_tmp <- 1
run_id <- paste0(format(Sys.Date(), "%Y%m%d") , ".v", version_tmp)
## set output directory
dir_out <- paste0(makeOutDir_katmai(path_this_script), run_id, "/")
dir.create(dir_out)
# input dependencies ------------------------------------------------------
## input the integrated data
path_rds <- "./Resources/Analysis_Results/integration/30_aliquot_integration/docker_run_integration/20200212.v3/30_aliquot_integration.20200212.v3.RDS"
srat <- readRDS(file = path_rds)
print("Finish reading RDS file")
## input the barcode-cell-type table
barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_corrected_by_individual_sample_inspection/20200811.v1/31Aliquot.Barcode2CellType.20200811.v1.tsv", data.table = F)
barcode2celltype_df <- as.data.frame(barcode2celltype_df)
cat("finish reading the barcode-to-cell type table!\n")
## specify the cell group
cellgroup2process <- "Stroma"
# subset seurat object ----------------------------------------------------
BC <- srat@meta.data %>% rownames
#### get original barcode
srat@meta.data$original_barcode <- BC %>% strsplit("_") %>% lapply("[[",1) %>% unlist
srat@meta.data <- srat@meta.data %>% rownames_to_column
srat@meta.data <- merge(srat@meta.data,barcode2celltype_df,
by.x=c("orig.ident","original_barcode"), by.y=c("orig.ident","individual_barcode"))
srat@meta.data <- column_to_rownames(srat@meta.data,"rowname")[BC,]
Idents(srat) <- "Cell_group.shorter"
## subset data
cat("Start subsetting\n")
srat.new <- subset(srat, idents = "Stroma")
cat(dim(srat.new))
rm(srat)
## set default assay
DefaultAssay(object = srat.new) <- "RNA"
cat("###########################################\n")
# find variable genes -----------------------------------------------------
cat("Start running FindVariableFeatures\n")
srat.new <- FindVariableFeatures(srat.new, selection.method = "vst", nfeatures = 3000, verbose = T)
cat("###########################################\n")
# write output ------------------------------------------------------------
file2write <- paste0(dir_out, "findvariablefeatures.", "cellgroup.", cellgroup2process, ".", run_id, ".tsv")
var_features_df <- data.frame(gene = srat.new@assays$RNA@var.features)
write.table(var_features_df, file = file2write, quote = F, sep = "\t", row.names = F)
cat("Finished saving the output\n")
cat("###########################################\n")
|
/findvariablefeatures/findvariablefeatures_cellgroup_stroma.R
|
no_license
|
ding-lab/ccRCC_snRNA_analysis
|
R
| false | false | 3,260 |
r
|
# Yige Wu @WashU Aug 2020
# set up libraries and output directory -----------------------------------
## getting the path to the current script
thisFile <- function() {
cmdArgs <- commandArgs(trailingOnly = FALSE)
needle <- "--file="
match <- grep(needle, cmdArgs)
if (length(match) > 0) {
# Rscript
return(normalizePath(sub(needle, "", cmdArgs[match])))
} else {
# 'source'd via R console
return(normalizePath(sys.frames()[[1]]$ofile))
}
}
path_this_script <- thisFile()
## set working directory
dir_base = "/diskmnt/Projects/ccRCC_scratch/ccRCC_snRNA/"
# dir_base = "~/Box/Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/"
setwd(dir_base)
source("./ccRCC_snRNA_analysis/load_pkgs.R")
source("./ccRCC_snRNA_analysis/functions.R")
source("./ccRCC_snRNA_analysis/variables.R")
library(tibble)
## set run id
version_tmp <- 1
run_id <- paste0(format(Sys.Date(), "%Y%m%d") , ".v", version_tmp)
## set output directory
dir_out <- paste0(makeOutDir_katmai(path_this_script), run_id, "/")
dir.create(dir_out)
# input dependencies ------------------------------------------------------
## input the integrated data
path_rds <- "./Resources/Analysis_Results/integration/30_aliquot_integration/docker_run_integration/20200212.v3/30_aliquot_integration.20200212.v3.RDS"
srat <- readRDS(file = path_rds)
print("Finish reading RDS file")
## input the barcode-cell-type table
barcode2celltype_df <- fread(input = "./Resources/Analysis_Results/annotate_barcode/map_celltype_corrected_by_individual_sample_inspection/20200811.v1/31Aliquot.Barcode2CellType.20200811.v1.tsv", data.table = F)
barcode2celltype_df <- as.data.frame(barcode2celltype_df)
cat("finish reading the barcode-to-cell type table!\n")
## specify the cell group
cellgroup2process <- "Stroma"
# subset seurat object ----------------------------------------------------
BC <- srat@meta.data %>% rownames
#### get original barcode
srat@meta.data$original_barcode <- BC %>% strsplit("_") %>% lapply("[[",1) %>% unlist
srat@meta.data <- srat@meta.data %>% rownames_to_column
srat@meta.data <- merge(srat@meta.data,barcode2celltype_df,
by.x=c("orig.ident","original_barcode"), by.y=c("orig.ident","individual_barcode"))
srat@meta.data <- column_to_rownames(srat@meta.data,"rowname")[BC,]
Idents(srat) <- "Cell_group.shorter"
## subset data
cat("Start subsetting\n")
srat.new <- subset(srat, idents = "Stroma")
cat(dim(srat.new))
rm(srat)
## set default assay
DefaultAssay(object = srat.new) <- "RNA"
cat("###########################################\n")
# find variable genes -----------------------------------------------------
cat("Start running FindVariableFeatures\n")
srat.new <- FindVariableFeatures(srat.new, selection.method = "vst", nfeatures = 3000, verbose = T)
cat("###########################################\n")
# write output ------------------------------------------------------------
file2write <- paste0(dir_out, "findvariablefeatures.", "cellgroup.", cellgroup2process, ".", run_id, ".tsv")
var_features_df <- data.frame(gene = srat.new@assays$RNA@var.features)
write.table(var_features_df, file = file2write, quote = F, sep = "\t", row.names = F)
cat("Finished saving the output\n")
cat("###########################################\n")
|
library(tswge)
# Randomly generating signal plus noise
gen.sigplusnoise.wge(100, b0 = 2, b1 = 4, vara = 100)
gen.sigplusnoise.wge(100, b0 = 0, b1 = 0, vara = 10)
gen.sigplusnoise.wge(100, b0 = 0, b1 = 0, phi = 0.975, vara = 10)
# periodic Signal
gen.sigplusnoise.wge(100, coef = c(5,0), freq = c(.1,0), psi = c(0,0.25),phi = 0.975, vara = 10)
#AR(4) from the slides
parms = mult.wge(c(.975), c(.2, -.45),c(-.53))
parms$model.coef
gen.arma.wge(160, phi = parms$model.coef, vara = 1)
# (1-B)X_t = a_t
x = gen.arima.wge(200, phi = 0, var = 1, d = 1, sn =31)
acf(x)
# The following command differences the data in x
y = artrans.wge(x, phi.tr = 1)
#This simply means that y(i) = x(i) -x(i-1)
# y has n-1 becuse x(1) has no x(0) before it.
# example
x = c(1,3,6,10,25)
y = artrans.wge(x, phi.tr = 1)
y # shows the 4 differences
# Arima with 2
data = gen.arima.wge(200, phi = c(1.5,-.8), d = 2, theta = c(-.8), sn = 31)
FirstDif = artrans.wge(data,phi.tr = 1)
SecondDif = artrans.wge(FirstDif, phi.tr = 1)
aic5.wge(SecondDif)
### ARUMA Models (seasonal)
# (1-B^4) = a_t
x1 = gen.aruma.wge(n=80, s = 4, sn = 6)
plotts.sample.wge(x1)
#(1-B^4) with ARMA(2,1)
x2 = gen.aruma.wge(n = 80, phi =c(1,-.6), s = 4, theta = -.5, sn = 6, d = 1)
plotts.sample.wge(x2)
factor.wge(phi = c(1,-.6))
factor.wge(phi = -.5)
#(1-B^12) with ARMA(2,1)
x3 = gen.aruma.wge(n = 180, phi =c(1,-.6), s = 12, theta = -.5, sn = 6)
plotts.sample.wge(x3, lag.max = 48)
x2 = gen.aruma.wge(n = 180, phi =c(.6,-.94), s = 7, theta = -.3, sn = 19)
plotts.sample.wge(x2)
factor.wge(phi = c(.6,-.94))
factor.wge(phi = -.3)
### Stationarize the quaterly data (1-b^4)X_t = a_t
x = gen.aruma.wge(n=80, s=4, sn = 81)
dif = artrans.wge(x,c(0,0,0,1)) # Take out the (1-B^4)
aic5.wge(dif)
## (1-.4B - .6B^2 + .74B^3)(1-B^12)X_t = (1 + .7B)a_t
x = gen.aruma.wge(n = 80, phi = c(.4,.6,-.74), theta = -.7, s =12, sn = 31)
dif = artrans.wge(x,c(rep(0,11),1)) # take out the (1-B^12)
aic5.wge(dif)
x = gen.aruma.wge(n = 500, phi = c(.6,-.8), theta = c(.3,-.7), s =12, sn = 37)
dif = artrans.wge(x,c(rep(0,11),1)) # take out the (1-B^12)
dif = artrans.wge(dif,c(rep(0,11),1)) # take out the (1-B^12)
aic5.wge(dif)
#factor ARUMA models
#factor.wge(phi = c(0,0,0,1)) or factor.wge(phi = c(rep(0,11),1)) for (1-B^12)
# (.2B - .4B^2 -.49B^3 -B^12 -.2B^13 + .4B^14 + .49B^15)
factor.wge(c(-.2, .4, .49, rep(0,5), 1, .2, -.4, -.49))
factor.wge(c(-.5, .2, 0, -1, .5, -.2))
factor.wge(c(-.3, 1.2, .4,0,.5, rep(0,6), 1, .3, -1.2, -.4))
factor.wge(c(-.3, 1.2, .4,0,.5, rep(0,6), 1, .3, -1.2, -.4))
#Pre-live
energy = read.csv("~/Documents/datascience/DS6373/Dataset/Appliance_Energy/energydata_complete.csv", header = TRUE)
plotts.wge(energy$Appliances)
aic5.wge(energy$Appliances)
factor.wge(c(rep(0,11),1))
factor.wge(c(0.6996, 0.2599, 0.0079, -0.0646, 0.1381, -0.0953, 0.0235, -0.0969, 0.1770, -0.1191, 0.1030, 0.7754, -0.4590, -0.4099, 0.0501))
|
/Unit 6/unit6.r
|
no_license
|
nwaoguIkenna/DS6373_Time_Series
|
R
| false | false | 2,929 |
r
|
library(tswge)
# Randomly generating signal plus noise
gen.sigplusnoise.wge(100, b0 = 2, b1 = 4, vara = 100)
gen.sigplusnoise.wge(100, b0 = 0, b1 = 0, vara = 10)
gen.sigplusnoise.wge(100, b0 = 0, b1 = 0, phi = 0.975, vara = 10)
# periodic Signal
gen.sigplusnoise.wge(100, coef = c(5,0), freq = c(.1,0), psi = c(0,0.25),phi = 0.975, vara = 10)
#AR(4) from the slides
parms = mult.wge(c(.975), c(.2, -.45),c(-.53))
parms$model.coef
gen.arma.wge(160, phi = parms$model.coef, vara = 1)
# (1-B)X_t = a_t
x = gen.arima.wge(200, phi = 0, var = 1, d = 1, sn =31)
acf(x)
# The following command differences the data in x
y = artrans.wge(x, phi.tr = 1)
#This simply means that y(i) = x(i) -x(i-1)
# y has n-1 becuse x(1) has no x(0) before it.
# example
x = c(1,3,6,10,25)
y = artrans.wge(x, phi.tr = 1)
y # shows the 4 differences
# Arima with 2
data = gen.arima.wge(200, phi = c(1.5,-.8), d = 2, theta = c(-.8), sn = 31)
FirstDif = artrans.wge(data,phi.tr = 1)
SecondDif = artrans.wge(FirstDif, phi.tr = 1)
aic5.wge(SecondDif)
### ARUMA Models (seasonal)
# (1-B^4) = a_t
x1 = gen.aruma.wge(n=80, s = 4, sn = 6)
plotts.sample.wge(x1)
#(1-B^4) with ARMA(2,1)
x2 = gen.aruma.wge(n = 80, phi =c(1,-.6), s = 4, theta = -.5, sn = 6, d = 1)
plotts.sample.wge(x2)
factor.wge(phi = c(1,-.6))
factor.wge(phi = -.5)
#(1-B^12) with ARMA(2,1)
x3 = gen.aruma.wge(n = 180, phi =c(1,-.6), s = 12, theta = -.5, sn = 6)
plotts.sample.wge(x3, lag.max = 48)
x2 = gen.aruma.wge(n = 180, phi =c(.6,-.94), s = 7, theta = -.3, sn = 19)
plotts.sample.wge(x2)
factor.wge(phi = c(.6,-.94))
factor.wge(phi = -.3)
### Stationarize the quaterly data (1-b^4)X_t = a_t
x = gen.aruma.wge(n=80, s=4, sn = 81)
dif = artrans.wge(x,c(0,0,0,1)) # Take out the (1-B^4)
aic5.wge(dif)
## (1-.4B - .6B^2 + .74B^3)(1-B^12)X_t = (1 + .7B)a_t
x = gen.aruma.wge(n = 80, phi = c(.4,.6,-.74), theta = -.7, s =12, sn = 31)
dif = artrans.wge(x,c(rep(0,11),1)) # take out the (1-B^12)
aic5.wge(dif)
x = gen.aruma.wge(n = 500, phi = c(.6,-.8), theta = c(.3,-.7), s =12, sn = 37)
dif = artrans.wge(x,c(rep(0,11),1)) # take out the (1-B^12)
dif = artrans.wge(dif,c(rep(0,11),1)) # take out the (1-B^12)
aic5.wge(dif)
#factor ARUMA models
#factor.wge(phi = c(0,0,0,1)) or factor.wge(phi = c(rep(0,11),1)) for (1-B^12)
# (.2B - .4B^2 -.49B^3 -B^12 -.2B^13 + .4B^14 + .49B^15)
factor.wge(c(-.2, .4, .49, rep(0,5), 1, .2, -.4, -.49))
factor.wge(c(-.5, .2, 0, -1, .5, -.2))
factor.wge(c(-.3, 1.2, .4,0,.5, rep(0,6), 1, .3, -1.2, -.4))
factor.wge(c(-.3, 1.2, .4,0,.5, rep(0,6), 1, .3, -1.2, -.4))
#Pre-live
energy = read.csv("~/Documents/datascience/DS6373/Dataset/Appliance_Energy/energydata_complete.csv", header = TRUE)
plotts.wge(energy$Appliances)
aic5.wge(energy$Appliances)
factor.wge(c(rep(0,11),1))
factor.wge(c(0.6996, 0.2599, 0.0079, -0.0646, 0.1381, -0.0953, 0.0235, -0.0969, 0.1770, -0.1191, 0.1030, 0.7754, -0.4590, -0.4099, 0.0501))
|
# This script returns the plots shown in Figure 1.
#
# Author: KLEY
###############################################################################
source("../../R/indecomposablePartitions.R")
source("../../R/comp_bound.R")
## Case 1: eps_t ~ N(0,1)
kappa_s_1 <- c(0, 1, 0, 0, 0, 0, 0, 0)
## Case 2: eps_t ~ nu^{-1/2} (nu - 2)^{1/2} t_nu
nu <- 14
kappa_s_2 <- c(0, 1, 0, 6 / (nu - 4), 0, 240 / ((nu - 4) * (nu - 6)), 0,
5040 * (5*nu - 22) / ((nu - 4)^2 * (nu - 6) * (nu - 8)))
nu <- 9
kappa_s_3 <- c(0, 1, 0, 6 / (nu - 4), 0, 240 / ((nu - 4) * (nu - 6)), 0,
5040 * (5*nu - 22) / ((nu - 4)^2 * (nu - 6) * (nu - 8)))
# Fig 1
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_1, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_N_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4.8)
par(mar = c(4, 5, 6, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = paste("n =", n_s[i]))
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
# Fig 2
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_3, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_t9_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4)
# plot(b$m_s, b$results[[i]]$bound_s, type = "l",
# ylim = c(0, max(b$results[[i]]$bound_s)),
# xlab = expression(m), ylab = "value of the bound",
# main = paste("n =", n_s[i], "B_n(m*) =", round(b$results[[i]]$bound_star, 3)))
# abline(v = b$results[[i]]$m_star, lty = 2)
# abline(h = b$results[[i]]$bound_star, col = "gray")
# points(b$m_s, b$results[[i]]$bound_s)
par(mar = c(4, 5, 2, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = "")
if (i == 2) {
mtext(expression(m), side = 1, line = 3, cex = 2)
}
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
# Fig 3
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_2, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_t14_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4)
# plot(b$m_s, b$results[[i]]$bound_s, type = "l",
# ylim = c(0, max(b$results[[i]]$bound_s)),
# xlab = expression(m), ylab = "value of the bound",
# main = paste("n =", n_s[i], "B_n(m*) =", round(b$results[[i]]$bound_star, 3)))
# abline(v = b$results[[i]]$m_star, lty = 2)
# abline(h = b$results[[i]]$bound_star, col = "gray")
# points(b$m_s, b$results[[i]]$bound_s)
par(mar = c(4, 5, 2, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = "")
if (i == 1) {
mtext(expression(B[n](m)), side = 2, line = 3, cex = 2)
}
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
|
/sim/3_tables_figures/figure_1.R
|
no_license
|
tobiaskley/ccf_bounds_replication_package
|
R
| false | false | 4,106 |
r
|
# This script returns the plots shown in Figure 1.
#
# Author: KLEY
###############################################################################
source("../../R/indecomposablePartitions.R")
source("../../R/comp_bound.R")
## Case 1: eps_t ~ N(0,1)
kappa_s_1 <- c(0, 1, 0, 0, 0, 0, 0, 0)
## Case 2: eps_t ~ nu^{-1/2} (nu - 2)^{1/2} t_nu
nu <- 14
kappa_s_2 <- c(0, 1, 0, 6 / (nu - 4), 0, 240 / ((nu - 4) * (nu - 6)), 0,
5040 * (5*nu - 22) / ((nu - 4)^2 * (nu - 6) * (nu - 8)))
nu <- 9
kappa_s_3 <- c(0, 1, 0, 6 / (nu - 4), 0, 240 / ((nu - 4) * (nu - 6)), 0,
5040 * (5*nu - 22) / ((nu - 4)^2 * (nu - 6) * (nu - 8)))
# Fig 1
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_1, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_N_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4.8)
par(mar = c(4, 5, 6, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = paste("n =", n_s[i]))
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
# Fig 2
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_3, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_t9_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4)
# plot(b$m_s, b$results[[i]]$bound_s, type = "l",
# ylim = c(0, max(b$results[[i]]$bound_s)),
# xlab = expression(m), ylab = "value of the bound",
# main = paste("n =", n_s[i], "B_n(m*) =", round(b$results[[i]]$bound_star, 3)))
# abline(v = b$results[[i]]$m_star, lty = 2)
# abline(h = b$results[[i]]$bound_star, col = "gray")
# points(b$m_s, b$results[[i]]$bound_s)
par(mar = c(4, 5, 2, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = "")
if (i == 2) {
mtext(expression(m), side = 1, line = 3, cex = 2)
}
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
# Fig 3
k <- 0
a <- 0.7
n_s <- c(25, 500, 2000)
b <- comp_bound_AR1(n_s, a, k, kappa_s_2, m_incr = 30, m_max = 30)
for (i in 1:3) {
pdf(paste("bound_fct_t14_a07_n", n_s[i], ".pdf", sep = ""), width = 6, height = 4)
# plot(b$m_s, b$results[[i]]$bound_s, type = "l",
# ylim = c(0, max(b$results[[i]]$bound_s)),
# xlab = expression(m), ylab = "value of the bound",
# main = paste("n =", n_s[i], "B_n(m*) =", round(b$results[[i]]$bound_star, 3)))
# abline(v = b$results[[i]]$m_star, lty = 2)
# abline(h = b$results[[i]]$bound_star, col = "gray")
# points(b$m_s, b$results[[i]]$bound_s)
par(mar = c(4, 5, 2, 1) + 0.1)
plot(b$m_s, b$results[[i]]$bound_s, type = "l",
cex.main = 2, cex.axis = 1.5,
ylim = c(0, max(b$results[[i]]$bound_s)),
xlab = "", ylab = "",
main = "")
if (i == 1) {
mtext(expression(B[n](m)), side = 2, line = 3, cex = 2)
}
mtext(substitute(paste(B[n](m^symbol("\052"))," = ",b),
list(b = round(b$results[[i]]$bound_star, 3))),
side = 3, line = 0, outer = FALSE, cex = 1.5)
abline(v = b$results[[i]]$m_star, lty = 2)
abline(h = b$results[[i]]$bound_star, col = "gray")
points(b$m_s, b$results[[i]]$bound_s)
dev.off()
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/score_forecasts.R
\name{score_forecasts}
\alias{score_forecasts}
\title{Score forecasts}
\usage{
score_forecasts(
forecasts,
truth,
return_format = "wide",
metrics = c("abs_error", "wis", "wis_components", "interval_coverage",
"quantile_coverage"),
use_median_as_point = FALSE
)
}
\arguments{
\item{forecasts}{required data.frame with forecasts in the format returned
by \code{\link[=load_forecasts]{load_forecasts()}}}
\item{truth}{required data.frame with forecasts in the format returned
by \code{\link[=load_truth]{load_truth()}}}
\item{return_format}{string: \code{"long"} returns long format with a column for
\code{"score_name"} and a column for \code{"score_value"}; \code{"wide"} returns wide format with
a separate column for each score. Defaults to \code{"wide"}.}
\item{metrics}{character vector of the metrics to be returned with options
\code{"abs_error"}, \code{"wis"}, \code{"wis_components"},\code{"interval_coverage"}, and \code{"quantile_coverage"}}
\item{use_median_as_point}{logical: \code{TRUE} uses the median as the point
forecast when scoring; \code{FALSE} uses the point forecasts from the data when
scoring. Defaults to \code{FALSE}}
}
\value{
data.frame with scores. The result will have some columns that
define the observation, namely, \code{model}, \code{forecast_date}, \code{location},
\code{horizon}, \code{temporal_resolution}, \code{target_variable}, \code{horizon}, and
\code{target_end_date}.
Other columns will contain scores dependent on metrics parameter:
\itemize{
\item \code{true_value} is the observed truth at that \code{location} and \code{target_end_date} (always returned)
\item \code{abs_error} is the absolute error based on median estimate if
use_median_as_point is TRUE, and absolute error based on point forecast
if use_median_as_point is FALSE
\item \code{wis} is the weighted interval score
\item \code{sharpness} the component of WIS made up of interval widths
\item \code{overprediction} the component of WIS made up of overprediction of intervals
\item \code{underprediction} the component of WIS made up of underprediction of intervals
\item \code{coverage_X} are prediction interval coverage at alpha level X
\item \code{quantile_coverage_0.X} are one-sided quantile coverage at quantile X
}
If return_format is \code{"long"}, also contains columns score_name and score_value
where \code{score_name} is the type of score calculated and \code{score_value} has the numeric
value of the score.
If return_format is \code{"wide"}, each calculated score is in its own column.
}
\description{
Score forecasts
}
\examples{
forecasts <- load_latest_forecasts(
models = c("COVIDhub-ensemble", "UMass-MechBayes"),
last_forecast_date = "2020-12-14",
forecast_date_window_size = 7,
locations = c("US"),
targets = paste(1:4, "wk ahead inc death"),
source = "zoltar"
)
truth <- load_truth("JHU", target_variable = "inc death", locations = "US")
score_forecasts(forecasts, truth)
forecasts <- load_latest_forecasts(
models = c("ILM-EKF"),
hub = c("ECDC", "US"), last_forecast_date = "2021-03-08",
forecast_date_window_size = 0,
locations = c("GB"),
targets = paste(1:4, "wk ahead inc death"),
source = "zoltar"
)
truth <- load_truth("JHU",
hub = c("ECDC", "US"),
target_variable = "inc death", locations = "GB"
)
score_forecasts(forecasts, truth)
}
\references{
Bracher J, Ray EL, Gneiting T, Reich NG. (2020) Evaluating epidemic forecasts
in an interval format. arXiv:2005.12881.
\url{https://arxiv.org/abs/2005.12881}.
}
|
/man/score_forecasts.Rd
|
no_license
|
Serena-Wang/covidHubUtils
|
R
| false | true | 3,595 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/score_forecasts.R
\name{score_forecasts}
\alias{score_forecasts}
\title{Score forecasts}
\usage{
score_forecasts(
forecasts,
truth,
return_format = "wide",
metrics = c("abs_error", "wis", "wis_components", "interval_coverage",
"quantile_coverage"),
use_median_as_point = FALSE
)
}
\arguments{
\item{forecasts}{required data.frame with forecasts in the format returned
by \code{\link[=load_forecasts]{load_forecasts()}}}
\item{truth}{required data.frame with forecasts in the format returned
by \code{\link[=load_truth]{load_truth()}}}
\item{return_format}{string: \code{"long"} returns long format with a column for
\code{"score_name"} and a column for \code{"score_value"}; \code{"wide"} returns wide format with
a separate column for each score. Defaults to \code{"wide"}.}
\item{metrics}{character vector of the metrics to be returned with options
\code{"abs_error"}, \code{"wis"}, \code{"wis_components"},\code{"interval_coverage"}, and \code{"quantile_coverage"}}
\item{use_median_as_point}{logical: \code{TRUE} uses the median as the point
forecast when scoring; \code{FALSE} uses the point forecasts from the data when
scoring. Defaults to \code{FALSE}}
}
\value{
data.frame with scores. The result will have some columns that
define the observation, namely, \code{model}, \code{forecast_date}, \code{location},
\code{horizon}, \code{temporal_resolution}, \code{target_variable}, \code{horizon}, and
\code{target_end_date}.
Other columns will contain scores dependent on metrics parameter:
\itemize{
\item \code{true_value} is the observed truth at that \code{location} and \code{target_end_date} (always returned)
\item \code{abs_error} is the absolute error based on median estimate if
use_median_as_point is TRUE, and absolute error based on point forecast
if use_median_as_point is FALSE
\item \code{wis} is the weighted interval score
\item \code{sharpness} the component of WIS made up of interval widths
\item \code{overprediction} the component of WIS made up of overprediction of intervals
\item \code{underprediction} the component of WIS made up of underprediction of intervals
\item \code{coverage_X} are prediction interval coverage at alpha level X
\item \code{quantile_coverage_0.X} are one-sided quantile coverage at quantile X
}
If return_format is \code{"long"}, also contains columns score_name and score_value
where \code{score_name} is the type of score calculated and \code{score_value} has the numeric
value of the score.
If return_format is \code{"wide"}, each calculated score is in its own column.
}
\description{
Score forecasts
}
\examples{
forecasts <- load_latest_forecasts(
models = c("COVIDhub-ensemble", "UMass-MechBayes"),
last_forecast_date = "2020-12-14",
forecast_date_window_size = 7,
locations = c("US"),
targets = paste(1:4, "wk ahead inc death"),
source = "zoltar"
)
truth <- load_truth("JHU", target_variable = "inc death", locations = "US")
score_forecasts(forecasts, truth)
forecasts <- load_latest_forecasts(
models = c("ILM-EKF"),
hub = c("ECDC", "US"), last_forecast_date = "2021-03-08",
forecast_date_window_size = 0,
locations = c("GB"),
targets = paste(1:4, "wk ahead inc death"),
source = "zoltar"
)
truth <- load_truth("JHU",
hub = c("ECDC", "US"),
target_variable = "inc death", locations = "GB"
)
score_forecasts(forecasts, truth)
}
\references{
Bracher J, Ray EL, Gneiting T, Reich NG. (2020) Evaluating epidemic forecasts
in an interval format. arXiv:2005.12881.
\url{https://arxiv.org/abs/2005.12881}.
}
|
########
## MLE #
########
## Jiaming Mao (jiaming.mao@gmail.com)
## https://jiamingmao.github.io/data-analysis/
library(bbmle)
rm(list=ls())
set.seed(5)
## generate data
n <- 500
e <- rnorm(n)
x1 <- runif(n)
x2 <- 0.5*x1 + 0.5*runif(n)
y <- 1 - 2.5*x1 + 5*x2 + e
# define the negative log likelihood function
nll <-function(theta){
beta0 <- theta[1]
beta1 <- theta[2]
beta2 <- theta[3]
sigma <- theta[4]
N <- length(y)
z <- (y - beta0 - beta1*x1 - beta2*x2)/sigma
logL <- -1*N*log(sigma) - 0.5*sum(z^2)
return(-logL)}
# perform optimization
mlefit <- optim(c(0,0,0,1),nll)
mlefit$par
# alternatively, use the mle2 function from the bbmle package
parnames(nll) <- c("beta0","beta1","beta2","sigma")
result <- mle2(nll,start=c(beta0=0,beta1=0,beta2=0,sigma=1))
summary(result)
|
/Materials/Estimation Methods/codes/R/mle.R
|
permissive
|
snowdj/data-analysis-2
|
R
| false | false | 797 |
r
|
########
## MLE #
########
## Jiaming Mao (jiaming.mao@gmail.com)
## https://jiamingmao.github.io/data-analysis/
library(bbmle)
rm(list=ls())
set.seed(5)
## generate data
n <- 500
e <- rnorm(n)
x1 <- runif(n)
x2 <- 0.5*x1 + 0.5*runif(n)
y <- 1 - 2.5*x1 + 5*x2 + e
# define the negative log likelihood function
nll <-function(theta){
beta0 <- theta[1]
beta1 <- theta[2]
beta2 <- theta[3]
sigma <- theta[4]
N <- length(y)
z <- (y - beta0 - beta1*x1 - beta2*x2)/sigma
logL <- -1*N*log(sigma) - 0.5*sum(z^2)
return(-logL)}
# perform optimization
mlefit <- optim(c(0,0,0,1),nll)
mlefit$par
# alternatively, use the mle2 function from the bbmle package
parnames(nll) <- c("beta0","beta1","beta2","sigma")
result <- mle2(nll,start=c(beta0=0,beta1=0,beta2=0,sigma=1))
summary(result)
|
#Formatting pitching database
#Install/ load in packages
library(baseballr)
library(tidyverse)
library(rvest)
library(stringr)
library(Hmisc)
#load in pitching data from "Making stat database.R"
#Set to your own directory
#D3Pitch <- D3Pitch1920
#Add column for WHIP
D3Pitch1 <- mutate(D3Pitch, WHIP = (BB+H)/(IP))
#Win Loss column
D3Pitch1$WL = paste(D3Pitch1$W, D3Pitch1$L, sep="-")
#Opponents batting average
D3Pitch1 <- mutate(D3Pitch1, POAB = BF-(BB+HB+SHA+SFA+IBB))
D3Pitch1 <- mutate(D3Pitch1, BAA = H/POAB)
D3Pitch1$P.OAB <- NULL
#Now to calculate FIP
#Since FIP depends on run scoring environment in ech year, we have to calculate them separately
#Break databases into three for FIP calculation
D3Pitch19 <- D3Pitch1 %>%
filter(year == "2019")
D3Pitch20 <- D3Pitch1 %>%
filter(year == "2020")
#Run each database through fip calculations and then rbind them
#FIP for 2019
D3Pitch19 <- D3Pitch19 %>% mutate(addl_outs = ((IP %% 1)*10),
ip_outs = ((floor(IP)*3 + addl_outs)),
Ip1 = ip_outs / 3)
#Get subset for BF > 25
BF2519 <- D3Pitch19 %>% filter(BF >= 25)
#League ERA
lgER = sum(BF2519$ER)
lgIp1 = sum(BF2519$Ip1)
lgERA = (lgER/lgIp1)*9
#League HR
lgHR = sum(BF2519$HR)
lgBB = sum(BF2519$BB)
lgHBP = sum(BF2519$HB)
lgK = sum(BF2519$SO)
#Actual Constant
FIPC19 = lgERA-(((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIp1)
#Calculate FIP per player
D3Pitch19 <- D3Pitch19 %>% mutate(FIP = ifelse(BF >= 25, (((13*HR.A)+(3*(IBB+HB))-(2*SO))/Ip1) + FIPC19, NA))
#FIP for 2020
D3Pitch20 <- D3Pitch20 %>% mutate(addl_outs = ((IP %% 1)*10),
ip_outs = ((floor(IP)*3 + addl_outs)),
Ip1 = ip_outs / 3)
#Get subset for BF > 25
BF2520 <- D3Pitch20 %>% filter(BF >= 25)
#League ERA
lgER = sum(BF2520$ER)
lgIp1 = sum(BF2520$Ip1)
lgERA = (lgER/lgIp1)*9
#League HR
lgHR = sum(BF2520$HR)
lgBB = sum(BF2520$BB)
lgHBP = sum(BF2520$HB)
lgK = sum(BF2520$SO)
#Actual Constant
FIPC20 = lgERA-(((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIp1)
#Calculate FIP per player
D3Pitch20 <- D3Pitch20 %>% mutate(FIP1 = ifelse(BF >= 25, (((13*HR.A)+(3*(IBB+HB))-(2*SO))/Ip1) + FIPC20, NA)) %>%
mutate(FIP = ifelse(FIP1 <= 0, 0, FIP1))
D3Pitch20$FIP1 <- NULL
#combine both databases with their respective FIP calculations
FipPitch20 <- rbind(D3Pitch19, D3Pitch20)
#Delete the 4 unnamed players, they seem to be copies of other, already accounted for players
FipPitch20 <- FipPitch20 %>% arrange(Player)
FipPitch20 <- FipPitch20[-c(1:4),]
#Fix CSE column
FipPitch20$school = str_replace(FipPitch20$school, "0", "St. Elizabeth")
FipPitch20$conference = str_replace(FipPitch20$conference, "0", "CSAC")
#Replace N/A school years with " "
FipPitch20$Yr <- str_replace(FipPitch20$Yr, "N/A", "")
#Clean up unused columns
FipPitch21 <- FipPitch20 %>% select(Player, school, Yr, ERA, WHIP, WL, SV, IP, H, R, ER, BB, SO, X2B.A, X3B.A, HR.A, BF, BAA, WP, HB, GO, FO, IBB, FIP, conference, year)
#Round columns correctly
FipPitch21$WHIP <- round(FipPitch21$WHIP, digits = 2)
FipPitch21$BAA <- round(FipPitch21$BAA, digits = 3)
FipPitch21$FIP <- round(FipPitch21$FIP, digits = 2)
#Name columns correctly
colnames(FipPitch21)[colnames(FipPitch21) == "school"] <- "School"
colnames(FipPitch21)[colnames(FipPitch21) == "X2B.A"] <- "x2B"
colnames(FipPitch21)[colnames(FipPitch21) == "X3B.A"] <- "x3B"
colnames(FipPitch21)[colnames(FipPitch21) == "HR.A"] <- "HR"
colnames(FipPitch21)[colnames(FipPitch21) == "HB"] <- "HBP"
colnames(FipPitch21)[colnames(FipPitch21) == "conference"] <- "Conference"
colnames(FipPitch21)[colnames(FipPitch21) == "year"] <- "Year"
colnames(FipPitch21)[colnames(FipPitch21) == "Yr"] <- "Class"
FinalPitchAll1 <- FipPitch21 %>% arrange(desc(IP))
#This is the pitching stats table as seen on the applicationo
|
/Player Stats/Cleaning Pitching Database.R
|
no_license
|
dantebozzuti/D3-Baseball-Graphics
|
R
| false | false | 4,308 |
r
|
#Formatting pitching database
#Install/ load in packages
library(baseballr)
library(tidyverse)
library(rvest)
library(stringr)
library(Hmisc)
#load in pitching data from "Making stat database.R"
#Set to your own directory
#D3Pitch <- D3Pitch1920
#Add column for WHIP
D3Pitch1 <- mutate(D3Pitch, WHIP = (BB+H)/(IP))
#Win Loss column
D3Pitch1$WL = paste(D3Pitch1$W, D3Pitch1$L, sep="-")
#Opponents batting average
D3Pitch1 <- mutate(D3Pitch1, POAB = BF-(BB+HB+SHA+SFA+IBB))
D3Pitch1 <- mutate(D3Pitch1, BAA = H/POAB)
D3Pitch1$P.OAB <- NULL
#Now to calculate FIP
#Since FIP depends on run scoring environment in ech year, we have to calculate them separately
#Break databases into three for FIP calculation
D3Pitch19 <- D3Pitch1 %>%
filter(year == "2019")
D3Pitch20 <- D3Pitch1 %>%
filter(year == "2020")
#Run each database through fip calculations and then rbind them
#FIP for 2019
D3Pitch19 <- D3Pitch19 %>% mutate(addl_outs = ((IP %% 1)*10),
ip_outs = ((floor(IP)*3 + addl_outs)),
Ip1 = ip_outs / 3)
#Get subset for BF > 25
BF2519 <- D3Pitch19 %>% filter(BF >= 25)
#League ERA
lgER = sum(BF2519$ER)
lgIp1 = sum(BF2519$Ip1)
lgERA = (lgER/lgIp1)*9
#League HR
lgHR = sum(BF2519$HR)
lgBB = sum(BF2519$BB)
lgHBP = sum(BF2519$HB)
lgK = sum(BF2519$SO)
#Actual Constant
FIPC19 = lgERA-(((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIp1)
#Calculate FIP per player
D3Pitch19 <- D3Pitch19 %>% mutate(FIP = ifelse(BF >= 25, (((13*HR.A)+(3*(IBB+HB))-(2*SO))/Ip1) + FIPC19, NA))
#FIP for 2020
D3Pitch20 <- D3Pitch20 %>% mutate(addl_outs = ((IP %% 1)*10),
ip_outs = ((floor(IP)*3 + addl_outs)),
Ip1 = ip_outs / 3)
#Get subset for BF > 25
BF2520 <- D3Pitch20 %>% filter(BF >= 25)
#League ERA
lgER = sum(BF2520$ER)
lgIp1 = sum(BF2520$Ip1)
lgERA = (lgER/lgIp1)*9
#League HR
lgHR = sum(BF2520$HR)
lgBB = sum(BF2520$BB)
lgHBP = sum(BF2520$HB)
lgK = sum(BF2520$SO)
#Actual Constant
FIPC20 = lgERA-(((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIp1)
#Calculate FIP per player
D3Pitch20 <- D3Pitch20 %>% mutate(FIP1 = ifelse(BF >= 25, (((13*HR.A)+(3*(IBB+HB))-(2*SO))/Ip1) + FIPC20, NA)) %>%
mutate(FIP = ifelse(FIP1 <= 0, 0, FIP1))
D3Pitch20$FIP1 <- NULL
#combine both databases with their respective FIP calculations
FipPitch20 <- rbind(D3Pitch19, D3Pitch20)
#Delete the 4 unnamed players, they seem to be copies of other, already accounted for players
FipPitch20 <- FipPitch20 %>% arrange(Player)
FipPitch20 <- FipPitch20[-c(1:4),]
#Fix CSE column
FipPitch20$school = str_replace(FipPitch20$school, "0", "St. Elizabeth")
FipPitch20$conference = str_replace(FipPitch20$conference, "0", "CSAC")
#Replace N/A school years with " "
FipPitch20$Yr <- str_replace(FipPitch20$Yr, "N/A", "")
#Clean up unused columns
FipPitch21 <- FipPitch20 %>% select(Player, school, Yr, ERA, WHIP, WL, SV, IP, H, R, ER, BB, SO, X2B.A, X3B.A, HR.A, BF, BAA, WP, HB, GO, FO, IBB, FIP, conference, year)
#Round columns correctly
FipPitch21$WHIP <- round(FipPitch21$WHIP, digits = 2)
FipPitch21$BAA <- round(FipPitch21$BAA, digits = 3)
FipPitch21$FIP <- round(FipPitch21$FIP, digits = 2)
#Name columns correctly
colnames(FipPitch21)[colnames(FipPitch21) == "school"] <- "School"
colnames(FipPitch21)[colnames(FipPitch21) == "X2B.A"] <- "x2B"
colnames(FipPitch21)[colnames(FipPitch21) == "X3B.A"] <- "x3B"
colnames(FipPitch21)[colnames(FipPitch21) == "HR.A"] <- "HR"
colnames(FipPitch21)[colnames(FipPitch21) == "HB"] <- "HBP"
colnames(FipPitch21)[colnames(FipPitch21) == "conference"] <- "Conference"
colnames(FipPitch21)[colnames(FipPitch21) == "year"] <- "Year"
colnames(FipPitch21)[colnames(FipPitch21) == "Yr"] <- "Class"
FinalPitchAll1 <- FipPitch21 %>% arrange(desc(IP))
#This is the pitching stats table as seen on the applicationo
|
\name{world.stretch.time}
\alias{world.stretch.time}
\docType{package}
\title{
Change the tempo of the speech signal
}
\description{
\code{world.stretch.time} changes the tempo of the speeh without changing the F0.
}
\usage{
world.stretch.time(worldobj,rate)
}
\arguments{
\item{worldobj}{A list calculated by \code{world.analysis()}.}
\item{rate}{A float value for changing the tempo.}
}
\details{
\code{world.stretch.time()} stretches/compresses the speech signal. The argument \code{rate} is the rate. If \code{rate=2.0}, the speech signal becomes two times as quick as the original signal.
The return value is a list of parameters analyzed by World.
}
\seealso{
\code{\link{world.analysis}}, \code{\link{world.synthesis}}, \code{\link{world.stretch.time}}, \code{\link{world.stretch.spectrum}}
}
\examples{
\dontrun{
}
}
|
/man/world.stretch.time.Rd
|
no_license
|
akinori-ito/WorldR
|
R
| false | false | 828 |
rd
|
\name{world.stretch.time}
\alias{world.stretch.time}
\docType{package}
\title{
Change the tempo of the speech signal
}
\description{
\code{world.stretch.time} changes the tempo of the speeh without changing the F0.
}
\usage{
world.stretch.time(worldobj,rate)
}
\arguments{
\item{worldobj}{A list calculated by \code{world.analysis()}.}
\item{rate}{A float value for changing the tempo.}
}
\details{
\code{world.stretch.time()} stretches/compresses the speech signal. The argument \code{rate} is the rate. If \code{rate=2.0}, the speech signal becomes two times as quick as the original signal.
The return value is a list of parameters analyzed by World.
}
\seealso{
\code{\link{world.analysis}}, \code{\link{world.synthesis}}, \code{\link{world.stretch.time}}, \code{\link{world.stretch.spectrum}}
}
\examples{
\dontrun{
}
}
|
df <- read.csv('datalog_thursdays.csv')
#Objectives
#Filter Inverter,
#string combiner box,
#data,
#time,
#series of dates and times
my.date <- as.Date(df$DATE ,format = "%d-%b-%y")
sorted <- order(my.date, df$HOUR, df$INVERTER)
df.date <- df$DATE
df.date
df2 <- df[sorted, ]
df1 <- df[sorted, ]
|
/Oscar/python/datalog_thursdays.R
|
no_license
|
docligot/web-bootcamp-1
|
R
| false | false | 307 |
r
|
df <- read.csv('datalog_thursdays.csv')
#Objectives
#Filter Inverter,
#string combiner box,
#data,
#time,
#series of dates and times
my.date <- as.Date(df$DATE ,format = "%d-%b-%y")
sorted <- order(my.date, df$HOUR, df$INVERTER)
df.date <- df$DATE
df.date
df2 <- df[sorted, ]
df1 <- df[sorted, ]
|
##monty hall problem
nrun <- 10000 #how many times to run sim
n_door_start = 3 #how many doors do we have to begin with
montyhallfx <- function(n_door_start = 3){#how many doors to start w/ - default 3
door_vector <- vector(length=n_door_start)
door_vector[1] <- TRUE #create a 'winning' door, place it in slot 1 for convenience
door_vector <- sample(door_vector) #shuffle doors around randomly
door_pick <- sample(length(door_vector),1) #pick a position for your door choice
false_doors <- which(!door_vector)#identify the false doors' locations
true_door <- which(door_vector)
is_original_pick_correct <- door_pick==true_door
is_switch_pick_correct <- door_pick!=true_door
#return door pick answer (T or F) and switch pick answer (T or F) as named DT row
return(cbind(is_original_pick_correct,is_switch_pick_correct))
}
#create answer DT for n runs
dt <- data.frame("original_pick_correct"=vector(length=nrun),
"switching_is_correct"=vector(length=nrun))
#run n timess
for(i in 1:nrun){
dt[i,] <- rbind(montyhallfx(n_door_start))
}
#stats on dtable
#first: proof it works -- there is a correct answer in every row
which(dt$original_pick_correct) == which(!dt$switching_is_correct)
#will resolve to true
#translation: if the original door is correct, then the switch option will be incorrect
#and implicitly, vice versa
hist(as.numeric(dt[,1]),main=paste0("Was the original door the winner? \n In an empirical simulation of ",n_door_start," initial doors"),
xlab='No<-..................................................................->Yes',
ylab=paste0('Frequency (x/',nrun,')'))
#check against hypothesis!
expected_odds_original <- 1/n_door_start #the expected odds of the original door being right
outcome_original <- length(which(dt$original_pick_correct))/nrun
|
/montyhall.R
|
no_license
|
kaabre/montyhall
|
R
| false | false | 1,909 |
r
|
##monty hall problem
nrun <- 10000 #how many times to run sim
n_door_start = 3 #how many doors do we have to begin with
montyhallfx <- function(n_door_start = 3){#how many doors to start w/ - default 3
door_vector <- vector(length=n_door_start)
door_vector[1] <- TRUE #create a 'winning' door, place it in slot 1 for convenience
door_vector <- sample(door_vector) #shuffle doors around randomly
door_pick <- sample(length(door_vector),1) #pick a position for your door choice
false_doors <- which(!door_vector)#identify the false doors' locations
true_door <- which(door_vector)
is_original_pick_correct <- door_pick==true_door
is_switch_pick_correct <- door_pick!=true_door
#return door pick answer (T or F) and switch pick answer (T or F) as named DT row
return(cbind(is_original_pick_correct,is_switch_pick_correct))
}
#create answer DT for n runs
dt <- data.frame("original_pick_correct"=vector(length=nrun),
"switching_is_correct"=vector(length=nrun))
#run n timess
for(i in 1:nrun){
dt[i,] <- rbind(montyhallfx(n_door_start))
}
#stats on dtable
#first: proof it works -- there is a correct answer in every row
which(dt$original_pick_correct) == which(!dt$switching_is_correct)
#will resolve to true
#translation: if the original door is correct, then the switch option will be incorrect
#and implicitly, vice versa
hist(as.numeric(dt[,1]),main=paste0("Was the original door the winner? \n In an empirical simulation of ",n_door_start," initial doors"),
xlab='No<-..................................................................->Yes',
ylab=paste0('Frequency (x/',nrun,')'))
#check against hypothesis!
expected_odds_original <- 1/n_door_start #the expected odds of the original door being right
outcome_original <- length(which(dt$original_pick_correct))/nrun
|
\name{l0approximatorR-package}
\alias{l0approximatorR-package}
\alias{l0approximatorR}
\docType{package}
\title{
A short title line describing what the package does
}
\description{
A more detailed description of what the package does. A length
of about one to five lines is recommended.
}
\details{
This section should provide a more detailed overview of how to use the
package, including the most important functions.
}
\author{
Your Name, email optional.
Maintainer: Your Name <your@email.com>
}
\references{
This optional section can contain literature or other references for
background information.
}
\keyword{ package }
\seealso{
Optional links to other man pages
}
\examples{
\dontrun{
## Optional simple examples of the most important functions
## These can be in \dontrun{} and \donttest{} blocks.
}
}
|
/man/l0approximatorR-package.Rd
|
no_license
|
boooooogey/l0approximatorR
|
R
| false | false | 845 |
rd
|
\name{l0approximatorR-package}
\alias{l0approximatorR-package}
\alias{l0approximatorR}
\docType{package}
\title{
A short title line describing what the package does
}
\description{
A more detailed description of what the package does. A length
of about one to five lines is recommended.
}
\details{
This section should provide a more detailed overview of how to use the
package, including the most important functions.
}
\author{
Your Name, email optional.
Maintainer: Your Name <your@email.com>
}
\references{
This optional section can contain literature or other references for
background information.
}
\keyword{ package }
\seealso{
Optional links to other man pages
}
\examples{
\dontrun{
## Optional simple examples of the most important functions
## These can be in \dontrun{} and \donttest{} blocks.
}
}
|
library(tidyverse)
##many bits referenced from gmoney's moth code. ty, gmoney.
datadir='/uru/Data/Nanopore/projects/nivar/paperfigs/annotation'
gff=tibble(
liftcer=file.path(datadir, 'liftoff', 'nivar_cer_lifted.gff'),
##liftalb=file.path(datadir, 'liftoff', 'nivar_alb_lifted.gff'),
liftgla=file.path(datadir, 'liftoff', 'nivar_gla_lifted.gff'),
braker=file.path(datadir, 'braker', 'braker.gff3'),
drna=file.path(datadir, 'stringtie', 'denovo_drna.gff'),
rnaseq=file.path(datadir, 'stringtie', 'denovo_rnaseq.gff'))
cols=c('seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'attribute')
liftcergff=read_tsv(gff$liftcer, col_names=cols)
##liftalbgff=read_tsv(gff$liftalb, col_names=cols)
liftglagff=read_tsv(gff$liftgla, col_names=cols)
brakergff=read_tsv(gff$braker, col_names=cols)
drnagff=read_tsv(gff$drna, col_names=cols, skip=2)
##find annots included in liftoff albicans that weren't found in liftoff cerevisiae
'''
liftalbfile=file.path(datadir, 'liftoff', 'liftcer_liftalb.nivar_alb_lifted.gff.tmap')
liftalb=read_tsv(liftalbfile) %>%
filter(class_code=='u')
fromalb=liftalbgff %>%
rowwise() %>%
mutate(geneid=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
filter(geneid %in% liftalb$qry_gene_id | geneid %in% liftalb$qry_id) %>%
select(-geneid)
lifted_annots=rbind(liftcergff, fromalb)
lifted_tsv=file.path(datadir, 'combined', 'lifted_all.gff')
write_tsv(lifted_annots, lifted_tsv, col_names=FALSE)
'''
liftcerfile=file.path(datadir, 'liftoff', 'liftgla_liftcer.nivar_cer_lifted.gff.tmap')
liftcer=read_tsv(liftcerfile) %>%
filter(class_code=='u')
fromcer=liftcergff %>%
rowwise() %>%
mutate(geneid=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
filter(geneid %in% liftcer$qry_gene_id | geneid %in% liftgla$qry_id) %>%
select(-geneid)
lifted_annots=rbind(liftcergff, fromgla)
##lifted_tsv=file.path(datadir, 'combined', 'lifted_all.gff')
lifted_tsv=file.path(datadir, 'combined', 'lifted_all_gla.gff')
write_tsv(lifted_annots, lifted_tsv, col_names=FALSE)
##find common ones between stringtie and braker
drnabrakerfile=file.path(datadir, 'braker', 'drna_braker.braker.gff3.tmap')
drnabraker=read_tsv(drnabrakerfile) %>% #keep these from drna annot
filter(class_code=='=' | class_code=='c')
drnabraker_rev=read_tsv(drnabrakerfile) %>% #keep these from braker annot
filter(class_code=='k')
fromdrna=drnagff %>%
rowwise() %>%
mutate(geneid=strsplit(attribute, '"', fixed=TRUE)[[1]][2]) %>%
filter(geneid %in% drnabraker$ref_gene_id | geneid %in% drnabraker$ref_id) %>%
select(-geneid)
frombraker=brakergff %>%
rowwise() %>%
mutate(geneidraw=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
mutate(geneid=paste0(strsplit(geneidraw, '.', fixed=TRUE)[[1]][1],'.', strsplit(geneidraw, '.', fixed=TRUE)[[1]][2])) %>%
filter(geneid %in% drnabraker_rev$qry_gene_id | geneid %in% drnabraker_rev$qry_id) %>%
#mutate(attribute=paste0('"', attribute, '"')) %>%
select(-geneid, -geneidraw)
data_annots=rbind(frombraker, fromdrna)
data_tsv=file.path(datadir, 'combined', 'data_all.gff')
write.table(data_annots, data_tsv, quote=FALSE, sep='\t', col.names=FALSE, row.names=FALSE)
|
/paperfigs/annot_compare.R
|
no_license
|
timplab/nivar
|
R
| false | false | 3,274 |
r
|
library(tidyverse)
##many bits referenced from gmoney's moth code. ty, gmoney.
datadir='/uru/Data/Nanopore/projects/nivar/paperfigs/annotation'
gff=tibble(
liftcer=file.path(datadir, 'liftoff', 'nivar_cer_lifted.gff'),
##liftalb=file.path(datadir, 'liftoff', 'nivar_alb_lifted.gff'),
liftgla=file.path(datadir, 'liftoff', 'nivar_gla_lifted.gff'),
braker=file.path(datadir, 'braker', 'braker.gff3'),
drna=file.path(datadir, 'stringtie', 'denovo_drna.gff'),
rnaseq=file.path(datadir, 'stringtie', 'denovo_rnaseq.gff'))
cols=c('seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'attribute')
liftcergff=read_tsv(gff$liftcer, col_names=cols)
##liftalbgff=read_tsv(gff$liftalb, col_names=cols)
liftglagff=read_tsv(gff$liftgla, col_names=cols)
brakergff=read_tsv(gff$braker, col_names=cols)
drnagff=read_tsv(gff$drna, col_names=cols, skip=2)
##find annots included in liftoff albicans that weren't found in liftoff cerevisiae
'''
liftalbfile=file.path(datadir, 'liftoff', 'liftcer_liftalb.nivar_alb_lifted.gff.tmap')
liftalb=read_tsv(liftalbfile) %>%
filter(class_code=='u')
fromalb=liftalbgff %>%
rowwise() %>%
mutate(geneid=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
filter(geneid %in% liftalb$qry_gene_id | geneid %in% liftalb$qry_id) %>%
select(-geneid)
lifted_annots=rbind(liftcergff, fromalb)
lifted_tsv=file.path(datadir, 'combined', 'lifted_all.gff')
write_tsv(lifted_annots, lifted_tsv, col_names=FALSE)
'''
liftcerfile=file.path(datadir, 'liftoff', 'liftgla_liftcer.nivar_cer_lifted.gff.tmap')
liftcer=read_tsv(liftcerfile) %>%
filter(class_code=='u')
fromcer=liftcergff %>%
rowwise() %>%
mutate(geneid=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
filter(geneid %in% liftcer$qry_gene_id | geneid %in% liftgla$qry_id) %>%
select(-geneid)
lifted_annots=rbind(liftcergff, fromgla)
##lifted_tsv=file.path(datadir, 'combined', 'lifted_all.gff')
lifted_tsv=file.path(datadir, 'combined', 'lifted_all_gla.gff')
write_tsv(lifted_annots, lifted_tsv, col_names=FALSE)
##find common ones between stringtie and braker
drnabrakerfile=file.path(datadir, 'braker', 'drna_braker.braker.gff3.tmap')
drnabraker=read_tsv(drnabrakerfile) %>% #keep these from drna annot
filter(class_code=='=' | class_code=='c')
drnabraker_rev=read_tsv(drnabrakerfile) %>% #keep these from braker annot
filter(class_code=='k')
fromdrna=drnagff %>%
rowwise() %>%
mutate(geneid=strsplit(attribute, '"', fixed=TRUE)[[1]][2]) %>%
filter(geneid %in% drnabraker$ref_gene_id | geneid %in% drnabraker$ref_id) %>%
select(-geneid)
frombraker=brakergff %>%
rowwise() %>%
mutate(geneidraw=substring(strsplit(attribute, ';', fixed=TRUE)[[1]][1], 4)) %>%
mutate(geneid=paste0(strsplit(geneidraw, '.', fixed=TRUE)[[1]][1],'.', strsplit(geneidraw, '.', fixed=TRUE)[[1]][2])) %>%
filter(geneid %in% drnabraker_rev$qry_gene_id | geneid %in% drnabraker_rev$qry_id) %>%
#mutate(attribute=paste0('"', attribute, '"')) %>%
select(-geneid, -geneidraw)
data_annots=rbind(frombraker, fromdrna)
data_tsv=file.path(datadir, 'combined', 'data_all.gff')
write.table(data_annots, data_tsv, quote=FALSE, sep='\t', col.names=FALSE, row.names=FALSE)
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/wrappers.R
\name{getStyles}
\alias{getStyles}
\title{Returns a list of all styles in the workbook}
\usage{
getStyles(wb)
}
\arguments{
\item{wb}{A workbook object}
}
\description{
Returns a list of all styles in the workbook
}
\examples{
## load a workbook
wb <- loadWorkbook(file = file.path(path.package("openxlsx"), "loadExample.xlsx"))
getStyles(wb)
}
\author{
Alexander Walker
}
\seealso{
\code{\link{replaceStyle}}
}
|
/man/getStyles.Rd
|
no_license
|
petersteiner/openxlsx
|
R
| false | false | 510 |
rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/wrappers.R
\name{getStyles}
\alias{getStyles}
\title{Returns a list of all styles in the workbook}
\usage{
getStyles(wb)
}
\arguments{
\item{wb}{A workbook object}
}
\description{
Returns a list of all styles in the workbook
}
\examples{
## load a workbook
wb <- loadWorkbook(file = file.path(path.package("openxlsx"), "loadExample.xlsx"))
getStyles(wb)
}
\author{
Alexander Walker
}
\seealso{
\code{\link{replaceStyle}}
}
|
\name{genhistogram}
\alias{genhistogram}
\title{ genhistogram }
\description{
Function \code{genhistogram} creates a histogram of (usually) concentrations with equidistant breaks placed in "good-looking" numbers. If plot=TRUE, the resulting histogram is plotted with several possibilities of graphical options.
}
\usage{
genhistogram(x, breaks=7, input="openair", pollutant=NA,
delta=0, plot=TRUE, distr="norm", gap=0.05,
columns=2, col="#A52375", emboss=0, xlab="Concentration",
ylab="Number of samples", main=NA)
}
\arguments{
\item{x}{ vector of concentration values or data frame of genasis/openair type. See 'Details' for more detailed description of both data types.}
\item{breaks}{ a number of breaks between individualhistogram cells.}
\item{input}{ type of data.frame in the case of data.frame input. The allowed values are "openair" (default) and "genasis". In case of vector input, this argument is meaningless.}
\item{pollutant}{ name of the pollutant, for which the plot is plotted. Not necessary if only data for one pollutant is available in x. If not specified, plots for all pollutants are drawn in a multi-plot arrangement.}
\item{delta}{ a delta value before the lowest and after the highest value of x. Positive number or 0.}
\item{plot}{ logical. Should the result be plotted?}
\item{distr}{ a name of distribution to interlace. Values "norm" and "lnorm" are possible.}
\item{gap}{ the size of a gap between columns in a relative width of the column (number between 0 and 1).}
\item{columns}{ number of columns in the multi-plot arrangement.}
\item{col}{ a specification for the default plotting color. See section 'Color Specification' in \link{par}.}
\item{emboss}{ 0,1,2 or 3 for differnet emboss efect of genhistogram columns.}
\item{xlab}{ a label for the x axis, defaults to \code{"Concentration"}.}
\item{ylab}{ a label for the x axis, defaults to \code{"Number of samples"}.}
\item{main}{ overall title for the histogram.}
}
\details{
Function \code{genhistogram} creates a histogram, e.g. distribution of given data values into several cells with equidistant breaks placed in "good-looking" numbers. The function considers both the magnitude of the data values and their precision (number of decimals) and choose the breaks to be interpretable well (not to have a lot of decimals).Some empty range is add before the first and behind the last data value in order to smooth the breaks numbers. The minimal size of this range could be determined by the delta parameter.
The function recognises three different input formats: Option \code{input="openair"} uses openair format of data frame with first column of name 'date' and type Date, optional columns of names \code{"date_end"}, \code{"temp"}, \code{"wind"} and \code{"note"} and other columns of class \code{"numeric"}" containing concentration values and named by names of the compounds. \code{input="genasis"} is used for the data frame with six columns \code{"valu"}, \code{"comp"}, \code{"date_start"}, \code{"date_end"}, \code{"temp"} and \code{"wind"} where the first, fifth and sixth are of class \code{"numeric"}", second of type character and third and fourth columns could be both \code{"character"} or \code{"Date"} class. The names of columns in "genasis" type data frame are not rigid, only their order is assumed. There is also a possibility to specify \code{x} as a numeric vector.
If \code{plot=TRUE}, the resulting histogram is plotted with several graphical possibilities. There are two options for plotting a curve of idealised distribution of concentration values. \code{distr="lnorm"} (default) draws curve of lognormal distribution, while \code{distr="norm"} draws curve of normal distribution defined by central tendency and variance of concentration data.
The argument \code{emboss} allows to induce an impression of plasticity of the histogram colums by two different graphical effects or their combination. \code{emboss=0} implies flat columns without any effect, \code{emboss=1} makes an impression of shading, \code{emboss=2} bevels edges of the columns and \code{emboss=3} combines last two effect into one.
}
\value{
a list containing:
\item{borders }{numerical vector of column cell borders }
\item{distr }{numerical vector of counts of values in the individual column cells }
}
\author{ Jiri Kalina \cr
\email{kalina@mail.muni.cz}}
\seealso{
\code{\link{genloq}, \link{genoutlier}, \link{genpastoact}, \link{genanaggr}, \link{genplot},
\link{genstatistic}, \link{gentransform}, \link{genwhisker}}
}
\examples{
## Vector input.
genhistogram(rnorm(60))
## Use of example data from the package:
data(kosetice.pas.openair)
genhistogram(kosetice.pas.openair[,1:8],col="orange",emboss=3)
data(kosetice.pas.genasis)
genhistogram(kosetice.pas.genasis[1:208,],input="genasis",
distr="lnorm",col="orange",emboss=2)
}
\keyword{ genhistogram }
|
/man/genhistogram.Rd
|
no_license
|
cran/genasis
|
R
| false | false | 5,056 |
rd
|
\name{genhistogram}
\alias{genhistogram}
\title{ genhistogram }
\description{
Function \code{genhistogram} creates a histogram of (usually) concentrations with equidistant breaks placed in "good-looking" numbers. If plot=TRUE, the resulting histogram is plotted with several possibilities of graphical options.
}
\usage{
genhistogram(x, breaks=7, input="openair", pollutant=NA,
delta=0, plot=TRUE, distr="norm", gap=0.05,
columns=2, col="#A52375", emboss=0, xlab="Concentration",
ylab="Number of samples", main=NA)
}
\arguments{
\item{x}{ vector of concentration values or data frame of genasis/openair type. See 'Details' for more detailed description of both data types.}
\item{breaks}{ a number of breaks between individualhistogram cells.}
\item{input}{ type of data.frame in the case of data.frame input. The allowed values are "openair" (default) and "genasis". In case of vector input, this argument is meaningless.}
\item{pollutant}{ name of the pollutant, for which the plot is plotted. Not necessary if only data for one pollutant is available in x. If not specified, plots for all pollutants are drawn in a multi-plot arrangement.}
\item{delta}{ a delta value before the lowest and after the highest value of x. Positive number or 0.}
\item{plot}{ logical. Should the result be plotted?}
\item{distr}{ a name of distribution to interlace. Values "norm" and "lnorm" are possible.}
\item{gap}{ the size of a gap between columns in a relative width of the column (number between 0 and 1).}
\item{columns}{ number of columns in the multi-plot arrangement.}
\item{col}{ a specification for the default plotting color. See section 'Color Specification' in \link{par}.}
\item{emboss}{ 0,1,2 or 3 for differnet emboss efect of genhistogram columns.}
\item{xlab}{ a label for the x axis, defaults to \code{"Concentration"}.}
\item{ylab}{ a label for the x axis, defaults to \code{"Number of samples"}.}
\item{main}{ overall title for the histogram.}
}
\details{
Function \code{genhistogram} creates a histogram, e.g. distribution of given data values into several cells with equidistant breaks placed in "good-looking" numbers. The function considers both the magnitude of the data values and their precision (number of decimals) and choose the breaks to be interpretable well (not to have a lot of decimals).Some empty range is add before the first and behind the last data value in order to smooth the breaks numbers. The minimal size of this range could be determined by the delta parameter.
The function recognises three different input formats: Option \code{input="openair"} uses openair format of data frame with first column of name 'date' and type Date, optional columns of names \code{"date_end"}, \code{"temp"}, \code{"wind"} and \code{"note"} and other columns of class \code{"numeric"}" containing concentration values and named by names of the compounds. \code{input="genasis"} is used for the data frame with six columns \code{"valu"}, \code{"comp"}, \code{"date_start"}, \code{"date_end"}, \code{"temp"} and \code{"wind"} where the first, fifth and sixth are of class \code{"numeric"}", second of type character and third and fourth columns could be both \code{"character"} or \code{"Date"} class. The names of columns in "genasis" type data frame are not rigid, only their order is assumed. There is also a possibility to specify \code{x} as a numeric vector.
If \code{plot=TRUE}, the resulting histogram is plotted with several graphical possibilities. There are two options for plotting a curve of idealised distribution of concentration values. \code{distr="lnorm"} (default) draws curve of lognormal distribution, while \code{distr="norm"} draws curve of normal distribution defined by central tendency and variance of concentration data.
The argument \code{emboss} allows to induce an impression of plasticity of the histogram colums by two different graphical effects or their combination. \code{emboss=0} implies flat columns without any effect, \code{emboss=1} makes an impression of shading, \code{emboss=2} bevels edges of the columns and \code{emboss=3} combines last two effect into one.
}
\value{
a list containing:
\item{borders }{numerical vector of column cell borders }
\item{distr }{numerical vector of counts of values in the individual column cells }
}
\author{ Jiri Kalina \cr
\email{kalina@mail.muni.cz}}
\seealso{
\code{\link{genloq}, \link{genoutlier}, \link{genpastoact}, \link{genanaggr}, \link{genplot},
\link{genstatistic}, \link{gentransform}, \link{genwhisker}}
}
\examples{
## Vector input.
genhistogram(rnorm(60))
## Use of example data from the package:
data(kosetice.pas.openair)
genhistogram(kosetice.pas.openair[,1:8],col="orange",emboss=3)
data(kosetice.pas.genasis)
genhistogram(kosetice.pas.genasis[1:208,],input="genasis",
distr="lnorm",col="orange",emboss=2)
}
\keyword{ genhistogram }
|
#* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
#
# Error Scaling with Mousetracking - Estimation Code
#
#* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
library(gtools)
library(bayesm)
#Set working drive
setwd("/Users/rogerbailey/Desktop/Projects/ESM/Code")
set.seed(77)
#Load data
load("Simdata_BasicErrScaling.RData")
#Set MCMC variables
R=10000 #number of iterations
keep=5#thinning
accup=100#interval size for updating RW step size
space=10
#number of betas
nbeta=natt*nlvl+1
#Set priors
nubeta=nbeta+3
Vb=nubeta*diag(nbeta)
Abeta=matrix(.01)
betadoublebar=matrix(double(nbeta),nc=1)
Omegabar=double(nM)
AOmega=1
#Set initial values
oldbetabar=matrix(double(nbeta),nc=1)#initial betabar
oldbetamat=matrix(double(nbeta*nresp),nr=nresp)#initial betas
stepsizeb=.2 #beta stepsize
stepsizeO=.025 #Omega stepsize
oldVbeta=.5*diag(nbeta)#initial Vbeta
oldVbetai=backsolve(chol(oldVbeta),diag(nbeta))#initial inv chol Vbeta
oldOmega=tOmega#matrix(double(nM),nr=1)#Initial omega vector
oldVOmega=.01*diag(nM)
oldVOmegai=backsolve(chol(oldVOmega),diag(nM))
acceptpropb=double(accup)+.23 #proportion of beta draws accepted
acceptpropO=double(accup)+.23 #proportion of Omega draws accepted
llike=matrix(double(nresp),nc=1) #log likelihood
#Setup functions
#function that returns the value of the prior for betas
logprior=function(beta,betabar,Vbi){
return((beta-betabar)%*%Vbi%*%t(beta-betabar)*(-.5))
}
#loglike function
loglike=function(xb,y){
probs=log(exp(xb))-log(matrix(rep(colSums(exp(xb)),length(
xb[,1])),byrow=T,nc=length(xb[1,])))
loc=cbind(y,c(1:(ncol(xb))))
return(sum(probs[loc]))
}
#function for determining the change in the step size
stepupdate=function(accprop){
step=1
if(is.na(accprop)){return(step)}else{
if(accprop<.21) {step=.99}
if(accprop<.19) {step=.95}
if(accprop<.15) {step=.85}
if(accprop<.10) {step=.7}
if(accprop>.25) {step=1.01}
if(accprop>.27) {step=1.05}
if(accprop>.3) {step=1.15}
if(accprop>.4) {step=1.35}
return(step)}
}
#Setup storage
betadraws=array(double(nbeta*nresp*R/keep),dim=c(R/keep,nresp,nbeta))
betabardraws=matrix(double(nbeta*R/keep),nr=R/keep)
Omegadraws=matrix(double(nM*R/keep),nr=R/keep)
llikes=matrix(double(R/keep),nr=R/keep)
acceptpropbs=matrix(double(R/keep),nr=R/keep)
acceptpropOs=matrix(double(R/keep),nr=R/keep)
stepsizebdraws=matrix(double(R/keep),nr=R/keep)
stepsizeOdraws=matrix(double(R/keep),nr=R/keep)
#timer
itime = proc.time()[3]
for(r in 1:R){
accept=matrix(double(nresp),nc=nresp)#iter accept storage
oldgammas=oldOmega%*%M
olduts=NULL
#Draw new betas and screen cutoffs for each respondent
for(i in 1:nresp){
#get new and old paramter vectors for RW
oldbeta=matrix(oldbetamat[i,],nc=1)
newbeta=oldbeta+t(chol(oldVbeta))%*%rnorm(nbeta)*stepsizeb
scaler=matrix((1+oldgammas[i]*(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
oldutmat=matrix(data[[i]]$X%*%oldbeta,nr=nalt)
newutmat=matrix(data[[i]]$X%*%newbeta,nr=nalt)
#get likelihood and prior values
oldllikeb=loglike(oldutmat*scaler,as.matrix(data[[i]]$y))
newllikeb=loglike(newutmat*scaler,as.matrix(data[[i]]$y))
oldlpriorb=logprior(t(oldbeta),t(oldbetabar),oldVbetai)
newlpriorb=logprior(t(newbeta),t(oldbetabar),oldVbetai)
diffvecb=newllikeb+newlpriorb-(oldllikeb+oldlpriorb)
alphab=min(exp(diffvecb), 1)
#accept or reject
draw=runif(1)
acceptb=0
if(alphab>draw){acceptb=1}
accept[i]=acceptb
if(acceptb==1){
oldbetamat[i,]=newbeta
oldutmat=newutmat
}
olduts=cbind(olduts,oldutmat)#save unscaled utilities for later use
}
#Draw new Omega
#create new omega matrix
newOmega=oldOmega#+rnorm(nM)*stepsizeO
newgammas=newOmega%*%M
while((min(newgammas))*ntask<(-1)){
newOmega=oldOmega+rnorm(nM)*stepsizeO
newgammas=newOmega%*%M
}
scalerold=matrix((1+oldgammas%x%(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
scalernew=matrix((1+newgammas%x%(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
#get likelihood and prior values
oldllikeO=loglike(olduts*scalerold,ycomb)
newllikeO=loglike(olduts*scalernew,ycomb)
oldlpriorO=logprior(oldOmega,Omegabar,oldVOmegai)
newlpriorO=logprior(newOmega,Omegabar,oldVOmegai)
diffvecO=newllikeO+newlpriorO-(oldllikeO+oldlpriorO)
alphaO=min(exp(diffvecO), 1)
#accept or reject
draw=runif(1)
acceptO=0
if(alphaO>draw){acceptO=1}
llike=oldllikeO
if(acceptb==1){
oldOmega=newOmega
llike=newllikeO
}
#Store acceptance proportions
acceptpropb=c(acceptpropb[2:accup],mean(accept))
acceptpropO=c(acceptpropb[2:accup],acceptO)
#Draw new values for betabar
outbetaup=rmultireg(oldbetamat,matrix(1,nr=nresp,nc=1),matrix(betadoublebar,nr=1),Abeta,nubeta,Vb)
oldbetabar=matrix(outbetaup$B)
oldVbeta=outbetaup$Sigma
oldVbetai=chol2inv(chol(oldVbeta))
#Store values
if(r%%keep==0){
betadraws[r/keep,,]=oldbetamat
betabardraws[r/keep,]=oldbetabar
Omegadraws[r/keep,]=oldOmega
acceptpropbs[r/keep]=mean(acceptpropb)
acceptpropOs[r/keep]=mean(acceptpropO)
llikes[r/keep]=llike
stepsizebdraws[r/keep]=stepsizeb
stepsizeOdraws[r/keep]=stepsizeO
}
#print progress
#print tte and chart current draw progress
if(r%%(keep*space)==0){
par(mfrow=c(3,1))
ctime = proc.time()[3]
tuntilend = ((ctime - itime)/r) * (R + 1 - r)
cat(" ", r, " (", round(tuntilend/60, 1), ")",
fill = TRUE)
plot(llikes,type="l",ylab="Log Likelihood")
matplot(betabardraws,type="l",ylab="Betabar Draws")
matplot(Omegadraws,type="l",ylab="Omega Draws")
fsh()
}
#update stepsizes
if(r%%accup==0&&r<(.3*R)){
stepsizeb=stepsizeb*stepupdate(mean(acceptpropb))
stepsizeO=stepsizeO*stepupdate(mean(acceptpropO))
}
}
|
/Code/Proxy Model/Code/EstCode_ErrorScalingwithTracking.R
|
permissive
|
jeff-dotson/mouse-tracking
|
R
| false | false | 5,829 |
r
|
#* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
#
# Error Scaling with Mousetracking - Estimation Code
#
#* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
library(gtools)
library(bayesm)
#Set working drive
setwd("/Users/rogerbailey/Desktop/Projects/ESM/Code")
set.seed(77)
#Load data
load("Simdata_BasicErrScaling.RData")
#Set MCMC variables
R=10000 #number of iterations
keep=5#thinning
accup=100#interval size for updating RW step size
space=10
#number of betas
nbeta=natt*nlvl+1
#Set priors
nubeta=nbeta+3
Vb=nubeta*diag(nbeta)
Abeta=matrix(.01)
betadoublebar=matrix(double(nbeta),nc=1)
Omegabar=double(nM)
AOmega=1
#Set initial values
oldbetabar=matrix(double(nbeta),nc=1)#initial betabar
oldbetamat=matrix(double(nbeta*nresp),nr=nresp)#initial betas
stepsizeb=.2 #beta stepsize
stepsizeO=.025 #Omega stepsize
oldVbeta=.5*diag(nbeta)#initial Vbeta
oldVbetai=backsolve(chol(oldVbeta),diag(nbeta))#initial inv chol Vbeta
oldOmega=tOmega#matrix(double(nM),nr=1)#Initial omega vector
oldVOmega=.01*diag(nM)
oldVOmegai=backsolve(chol(oldVOmega),diag(nM))
acceptpropb=double(accup)+.23 #proportion of beta draws accepted
acceptpropO=double(accup)+.23 #proportion of Omega draws accepted
llike=matrix(double(nresp),nc=1) #log likelihood
#Setup functions
#function that returns the value of the prior for betas
logprior=function(beta,betabar,Vbi){
return((beta-betabar)%*%Vbi%*%t(beta-betabar)*(-.5))
}
#loglike function
loglike=function(xb,y){
probs=log(exp(xb))-log(matrix(rep(colSums(exp(xb)),length(
xb[,1])),byrow=T,nc=length(xb[1,])))
loc=cbind(y,c(1:(ncol(xb))))
return(sum(probs[loc]))
}
#function for determining the change in the step size
stepupdate=function(accprop){
step=1
if(is.na(accprop)){return(step)}else{
if(accprop<.21) {step=.99}
if(accprop<.19) {step=.95}
if(accprop<.15) {step=.85}
if(accprop<.10) {step=.7}
if(accprop>.25) {step=1.01}
if(accprop>.27) {step=1.05}
if(accprop>.3) {step=1.15}
if(accprop>.4) {step=1.35}
return(step)}
}
#Setup storage
betadraws=array(double(nbeta*nresp*R/keep),dim=c(R/keep,nresp,nbeta))
betabardraws=matrix(double(nbeta*R/keep),nr=R/keep)
Omegadraws=matrix(double(nM*R/keep),nr=R/keep)
llikes=matrix(double(R/keep),nr=R/keep)
acceptpropbs=matrix(double(R/keep),nr=R/keep)
acceptpropOs=matrix(double(R/keep),nr=R/keep)
stepsizebdraws=matrix(double(R/keep),nr=R/keep)
stepsizeOdraws=matrix(double(R/keep),nr=R/keep)
#timer
itime = proc.time()[3]
for(r in 1:R){
accept=matrix(double(nresp),nc=nresp)#iter accept storage
oldgammas=oldOmega%*%M
olduts=NULL
#Draw new betas and screen cutoffs for each respondent
for(i in 1:nresp){
#get new and old paramter vectors for RW
oldbeta=matrix(oldbetamat[i,],nc=1)
newbeta=oldbeta+t(chol(oldVbeta))%*%rnorm(nbeta)*stepsizeb
scaler=matrix((1+oldgammas[i]*(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
oldutmat=matrix(data[[i]]$X%*%oldbeta,nr=nalt)
newutmat=matrix(data[[i]]$X%*%newbeta,nr=nalt)
#get likelihood and prior values
oldllikeb=loglike(oldutmat*scaler,as.matrix(data[[i]]$y))
newllikeb=loglike(newutmat*scaler,as.matrix(data[[i]]$y))
oldlpriorb=logprior(t(oldbeta),t(oldbetabar),oldVbetai)
newlpriorb=logprior(t(newbeta),t(oldbetabar),oldVbetai)
diffvecb=newllikeb+newlpriorb-(oldllikeb+oldlpriorb)
alphab=min(exp(diffvecb), 1)
#accept or reject
draw=runif(1)
acceptb=0
if(alphab>draw){acceptb=1}
accept[i]=acceptb
if(acceptb==1){
oldbetamat[i,]=newbeta
oldutmat=newutmat
}
olduts=cbind(olduts,oldutmat)#save unscaled utilities for later use
}
#Draw new Omega
#create new omega matrix
newOmega=oldOmega#+rnorm(nM)*stepsizeO
newgammas=newOmega%*%M
while((min(newgammas))*ntask<(-1)){
newOmega=oldOmega+rnorm(nM)*stepsizeO
newgammas=newOmega%*%M
}
scalerold=matrix((1+oldgammas%x%(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
scalernew=matrix((1+newgammas%x%(c(0:(ntask-1))%x%rep(1,nalt)))^(-1),nr=nalt)
#get likelihood and prior values
oldllikeO=loglike(olduts*scalerold,ycomb)
newllikeO=loglike(olduts*scalernew,ycomb)
oldlpriorO=logprior(oldOmega,Omegabar,oldVOmegai)
newlpriorO=logprior(newOmega,Omegabar,oldVOmegai)
diffvecO=newllikeO+newlpriorO-(oldllikeO+oldlpriorO)
alphaO=min(exp(diffvecO), 1)
#accept or reject
draw=runif(1)
acceptO=0
if(alphaO>draw){acceptO=1}
llike=oldllikeO
if(acceptb==1){
oldOmega=newOmega
llike=newllikeO
}
#Store acceptance proportions
acceptpropb=c(acceptpropb[2:accup],mean(accept))
acceptpropO=c(acceptpropb[2:accup],acceptO)
#Draw new values for betabar
outbetaup=rmultireg(oldbetamat,matrix(1,nr=nresp,nc=1),matrix(betadoublebar,nr=1),Abeta,nubeta,Vb)
oldbetabar=matrix(outbetaup$B)
oldVbeta=outbetaup$Sigma
oldVbetai=chol2inv(chol(oldVbeta))
#Store values
if(r%%keep==0){
betadraws[r/keep,,]=oldbetamat
betabardraws[r/keep,]=oldbetabar
Omegadraws[r/keep,]=oldOmega
acceptpropbs[r/keep]=mean(acceptpropb)
acceptpropOs[r/keep]=mean(acceptpropO)
llikes[r/keep]=llike
stepsizebdraws[r/keep]=stepsizeb
stepsizeOdraws[r/keep]=stepsizeO
}
#print progress
#print tte and chart current draw progress
if(r%%(keep*space)==0){
par(mfrow=c(3,1))
ctime = proc.time()[3]
tuntilend = ((ctime - itime)/r) * (R + 1 - r)
cat(" ", r, " (", round(tuntilend/60, 1), ")",
fill = TRUE)
plot(llikes,type="l",ylab="Log Likelihood")
matplot(betabardraws,type="l",ylab="Betabar Draws")
matplot(Omegadraws,type="l",ylab="Omega Draws")
fsh()
}
#update stepsizes
if(r%%accup==0&&r<(.3*R)){
stepsizeb=stepsizeb*stepupdate(mean(acceptpropb))
stepsizeO=stepsizeO*stepupdate(mean(acceptpropO))
}
}
|
# Dustin Roten 11/05/2017
# This script is responsible for metrics for the shifted dispersion scenario ONLY.
SetAlpha <- 0.3
# Load required libraries
library(ggplot2)
library(reshape)
library(ggmap)
source("TEST-DEMOFunctions.R")
# The required information for the functions used includes the (Lat, Lon) of the plant
# as well as the desired gridding resolution.
PlantLAT <- 39.28682
PlantLON <- -96.1172
Resolution <- 0.1
Dispersion <- read.delim("JEC-10000m2.txt", header = TRUE, sep = "")
Origin_Dispersion <- ShiftToOrigin("S", Dispersion, PlantLAT, PlantLON)
Shifted_Dispersion <- ShiftToOrigin("U", ShiftDispersion(Origin_Dispersion, 10), PlantLAT, PlantLON)
Rotated_Dispersion <- ShiftToOrigin("U", RotateDispersion(Origin_Dispersion, 15), PlantLAT, PlantLON)
Radial_Dispersion <- ShiftToOrigin("U", RadialDilation(Origin_Dispersion, 24), PlantLAT, PlantLON)
Angular_Dispersion <- ShiftToOrigin("U", AngularStretch(Origin_Dispersion, 55), PlantLAT, PlantLON)
map <- get_map(location = c(PlantLON + 2, PlantLAT + 4), zoom =6, maptype = "terrain", color = "bw")
Ex1 <- ggmap(map) +
geom_point(data = Shifted_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("ShiftExample.jpg", Ex1, device = "jpg", width = 10, height = 10, units = "in")
Ex2 <- ggmap(map) +
geom_point(data = Angular_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("AngularExample.jpg", Ex2, device = "jpg", width = 10, height = 10, units = "in")
Ex3 <- ggmap(map) +
geom_point(data = Radial_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("RadialExample.jpg", Ex3, device = "jpg", width = 10, height = 10, units = "in")
Ex4 <- ggmap(map) +
geom_point(data = Rotated_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("RotatedExample.jpg", Ex4, device = "jpg", width = 10, height = 10, units = "in")
##### Moving files #####
NAME <- c("ShiftExample.jpg", "AngularExample.jpg", "RadialExample.jpg", "RotatedExample.jpg")
TEST = NULL
for(i in 1:length(NAME)) {
TEST[i] <- file.exists(paste(NAME[i]))
file.copy(from = paste(NAME[i]), to = paste("C:/Users/dusti/Google Drive/NASA/HysplitPaper1/images/", NAME[i], sep = ""), overwrite = TRUE)
file.remove(paste(NAME[i]))
TEST
}
|
/Figures/POC_Images.R
|
no_license
|
DustinRoten/STACK-project
|
R
| false | false | 4,629 |
r
|
# Dustin Roten 11/05/2017
# This script is responsible for metrics for the shifted dispersion scenario ONLY.
SetAlpha <- 0.3
# Load required libraries
library(ggplot2)
library(reshape)
library(ggmap)
source("TEST-DEMOFunctions.R")
# The required information for the functions used includes the (Lat, Lon) of the plant
# as well as the desired gridding resolution.
PlantLAT <- 39.28682
PlantLON <- -96.1172
Resolution <- 0.1
Dispersion <- read.delim("JEC-10000m2.txt", header = TRUE, sep = "")
Origin_Dispersion <- ShiftToOrigin("S", Dispersion, PlantLAT, PlantLON)
Shifted_Dispersion <- ShiftToOrigin("U", ShiftDispersion(Origin_Dispersion, 10), PlantLAT, PlantLON)
Rotated_Dispersion <- ShiftToOrigin("U", RotateDispersion(Origin_Dispersion, 15), PlantLAT, PlantLON)
Radial_Dispersion <- ShiftToOrigin("U", RadialDilation(Origin_Dispersion, 24), PlantLAT, PlantLON)
Angular_Dispersion <- ShiftToOrigin("U", AngularStretch(Origin_Dispersion, 55), PlantLAT, PlantLON)
map <- get_map(location = c(PlantLON + 2, PlantLAT + 4), zoom =6, maptype = "terrain", color = "bw")
Ex1 <- ggmap(map) +
geom_point(data = Shifted_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("ShiftExample.jpg", Ex1, device = "jpg", width = 10, height = 10, units = "in")
Ex2 <- ggmap(map) +
geom_point(data = Angular_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("AngularExample.jpg", Ex2, device = "jpg", width = 10, height = 10, units = "in")
Ex3 <- ggmap(map) +
geom_point(data = Radial_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("RadialExample.jpg", Ex3, device = "jpg", width = 10, height = 10, units = "in")
Ex4 <- ggmap(map) +
geom_point(data = Rotated_Dispersion, aes(x = LON, y = LAT, color = CO2), alpha = SetAlpha) +
geom_point(data = Dispersion, aes(x = LON, y = LAT, color = CO2)) +
coord_cartesian() +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
scale_colour_gradientn(colours = c("green", "darkgreen", "yellow", "darkorange", "orange", "red"),
values = c(0, 0.0012, 0.002, 0.004, 0.009, 1))
ggsave("RotatedExample.jpg", Ex4, device = "jpg", width = 10, height = 10, units = "in")
##### Moving files #####
NAME <- c("ShiftExample.jpg", "AngularExample.jpg", "RadialExample.jpg", "RotatedExample.jpg")
TEST = NULL
for(i in 1:length(NAME)) {
TEST[i] <- file.exists(paste(NAME[i]))
file.copy(from = paste(NAME[i]), to = paste("C:/Users/dusti/Google Drive/NASA/HysplitPaper1/images/", NAME[i], sep = ""), overwrite = TRUE)
file.remove(paste(NAME[i]))
TEST
}
|
library(tree)
dataset_name <- "iris"
k_parameter <- 10
values <- c("one",2,"three",4,5)
values[2]
values[-1]
values[c(2,3)]
# minus means exclude
values[-c(2,3)]
values
# [1] means identifying which entry you are looking at
rep(values,10)
which(values == "three")
which(values == "2")
which(values == 2)
a <- 5 + 4
b <- 5 +
4
c <- 5
+ 4
sum_two_numbers <- function(a = 4,b){
return (a + b)
}
sum_two_numbers(4,5)
sum_two_numbers(,3)
A <- matrix(1:12, nrow = 3, ncol = 4, byrow = TRUE)
matrix(1:12, nrow = 3, ncol = 2, byrow = TRUE)
# it repeats the set, but n
matrix(1:12, nrow = 3, ncol = 8, byrow = TRUE)
A[1,]
A[,1]
A[,c(1,2)]
A[c(2,3),c(1,2)]
colnames(A) <- c("C1","C2","C3","C4")
rownames(A) <- c("R1","R2","R3")
write.csv(A,"data.csv")
# read.csv returns a dataframe by default
B <- read.csv(file = "data.csv")
B <- read.csv(file = "data.csv", header = TRUE,row.names = 1)
B <- matrix(1:20, nrow = 4, ncol = 5, byrow = TRUE)
A %*% B
# transpose of a matrix
t(A)
# if we want the result as a matrix use drop = FALSE
A[,2,drop = FALSE]
|
/lab-demo.R
|
no_license
|
limoneren/R-Tutorial
|
R
| false | false | 1,079 |
r
|
library(tree)
dataset_name <- "iris"
k_parameter <- 10
values <- c("one",2,"three",4,5)
values[2]
values[-1]
values[c(2,3)]
# minus means exclude
values[-c(2,3)]
values
# [1] means identifying which entry you are looking at
rep(values,10)
which(values == "three")
which(values == "2")
which(values == 2)
a <- 5 + 4
b <- 5 +
4
c <- 5
+ 4
sum_two_numbers <- function(a = 4,b){
return (a + b)
}
sum_two_numbers(4,5)
sum_two_numbers(,3)
A <- matrix(1:12, nrow = 3, ncol = 4, byrow = TRUE)
matrix(1:12, nrow = 3, ncol = 2, byrow = TRUE)
# it repeats the set, but n
matrix(1:12, nrow = 3, ncol = 8, byrow = TRUE)
A[1,]
A[,1]
A[,c(1,2)]
A[c(2,3),c(1,2)]
colnames(A) <- c("C1","C2","C3","C4")
rownames(A) <- c("R1","R2","R3")
write.csv(A,"data.csv")
# read.csv returns a dataframe by default
B <- read.csv(file = "data.csv")
B <- read.csv(file = "data.csv", header = TRUE,row.names = 1)
B <- matrix(1:20, nrow = 4, ncol = 5, byrow = TRUE)
A %*% B
# transpose of a matrix
t(A)
# if we want the result as a matrix use drop = FALSE
A[,2,drop = FALSE]
|
library(IC2)
library("rineq")
library(convey)
pof_design_moradores_mg <- readRDS("./outputs/moradores_impostos_mg_design.rds")
pof_design_moradores_prep <- convey_prep(pof_design_moradores_mg)
gini_renda_total <- coef( svygini( ~ RENDA_ANUAL_PC, pof_design_moradores_prep ) ) * 100
gini_pos_ipva <- coef( svygini( ~ renda_pos_ipva_pc, pof_design_moradores_prep )) * 100
gini_pos_icms <- coef( svygini( ~ renda_pos_icms_pc, pof_design_moradores_prep )) *100
gasto_icms_pc <- pof_design_moradores_mg$variables$ICMS_ANUAL_PC
renda_pos_icms_pc <- pof_design_moradores_mg$variables$renda_pos_icms_pc
gasto_ipva_pc <- pof_design_moradores_mg$variables$IPVA_ANUAL_pc
renda_pos_ipva_pc <- pof_design_moradores_mg$variables$renda_pos_ipva_pc
renda_pc <- pof_design_moradores_mg$variables$RENDA_ANUAL_PC
pesos <- pof_design_moradores_mg$variables$peso
renda_pos_icms_pc <- ifelse(renda_pos_icms_pc < 0, 0,renda_pos_icms_pc)
curveConcent(y = renda_pos_icms_pc, x = gasto_icms_pc, w = pesos, col = "red",
xlab = "Ranking pela renda pós ICMS")
curveConcent(y = renda_pos_ipva_pc, x = renda_pos_ipva_pc, w = pesos, col = "black",add = TRUE)
curveConcent(y = renda_pos_icms_pc, x = renda_pos_icms_pc, w = pesos, col = "grey",add = TRUE)
curveConcent(y = renda_pos_ipva_pc, x = gasto_ipva_pc, w = pesos, col = "green",add = TRUE)
legend("topleft", c("Renda pós ICMS","Renda pós IPVA","ICMS","IPVA"), pch=c(16), col=c("grey","black","red","green"),
bty = "n")
con_index_icms <- ci(gasto_icms_pc, renda_pos_icms_pc, wt=pesos, type = c("CI"))
con_index_ipva <- ci(gasto_ipva_pc, renda_pos_ipva_pc, wt=pesos, type = c("CI"))
|
/scripts/5.1 Índice de Lerman Yitzhaki.R
|
permissive
|
LucasLBrandao/Efeitos-redistributivos-dos-impostos-estaduais-MG
|
R
| false | false | 1,638 |
r
|
library(IC2)
library("rineq")
library(convey)
pof_design_moradores_mg <- readRDS("./outputs/moradores_impostos_mg_design.rds")
pof_design_moradores_prep <- convey_prep(pof_design_moradores_mg)
gini_renda_total <- coef( svygini( ~ RENDA_ANUAL_PC, pof_design_moradores_prep ) ) * 100
gini_pos_ipva <- coef( svygini( ~ renda_pos_ipva_pc, pof_design_moradores_prep )) * 100
gini_pos_icms <- coef( svygini( ~ renda_pos_icms_pc, pof_design_moradores_prep )) *100
gasto_icms_pc <- pof_design_moradores_mg$variables$ICMS_ANUAL_PC
renda_pos_icms_pc <- pof_design_moradores_mg$variables$renda_pos_icms_pc
gasto_ipva_pc <- pof_design_moradores_mg$variables$IPVA_ANUAL_pc
renda_pos_ipva_pc <- pof_design_moradores_mg$variables$renda_pos_ipva_pc
renda_pc <- pof_design_moradores_mg$variables$RENDA_ANUAL_PC
pesos <- pof_design_moradores_mg$variables$peso
renda_pos_icms_pc <- ifelse(renda_pos_icms_pc < 0, 0,renda_pos_icms_pc)
curveConcent(y = renda_pos_icms_pc, x = gasto_icms_pc, w = pesos, col = "red",
xlab = "Ranking pela renda pós ICMS")
curveConcent(y = renda_pos_ipva_pc, x = renda_pos_ipva_pc, w = pesos, col = "black",add = TRUE)
curveConcent(y = renda_pos_icms_pc, x = renda_pos_icms_pc, w = pesos, col = "grey",add = TRUE)
curveConcent(y = renda_pos_ipva_pc, x = gasto_ipva_pc, w = pesos, col = "green",add = TRUE)
legend("topleft", c("Renda pós ICMS","Renda pós IPVA","ICMS","IPVA"), pch=c(16), col=c("grey","black","red","green"),
bty = "n")
con_index_icms <- ci(gasto_icms_pc, renda_pos_icms_pc, wt=pesos, type = c("CI"))
con_index_ipva <- ci(gasto_ipva_pc, renda_pos_ipva_pc, wt=pesos, type = c("CI"))
|
preparePathways <-
function(db=c("kegg", "MSigDB"),
type=c("H","C1","C2","C3","C4","C5","C6","C7"),
genename= c("EntrezID", "symbol")) {
this.call <- match.call()
db <- match.arg(db)
m.type <- match.arg(type)
g.id <- match.arg(genename)
if (db=="kegg" && is.null(g.id)){g.id="EntrezID"}
if (db=="MSigDB" && is.null(m.type)){m.type="C2";g.id=="symbol"}
if (db=="kegg"){
paths <- graphite::pathways('hsapiens','kegg')
if (g.id=="symbol"){
paths<- lapply(paths, function(p) convertIdentifiers(p,g.id))
}
genesets <- lapply(paths, nodes)
genesets <- lapply(genesets, function(a) gsub("ENTREZID:","",a))
} else {
m_df = msigdbr::msigdbr(species = "Homo sapiens", category = m.type)
m_t2g = m_df %>% dplyr::select(.data$gs_name, .data$entrez_gene) %>% as.data.frame()
if (g.id=="symbol"){
m_t2g = m_df %>% dplyr::select(.data$gs_name, .data$gene_symbol) %>% as.data.frame()
}
gs_df <- m_t2g %>% dplyr::group_by(.data$gs_name) %>%
dplyr::summarise(entrez_gene = paste0(.data$entrez_gene, collapse=","))
genesets <- lapply(gs_df$entrez_gene, function(s) unlist(strsplit(s,",")))
names(genesets) <- gs_df$gs_name
}
return(genesets)
}
|
/R/preparePathways.R
|
no_license
|
mikehellstern/netgsa
|
R
| false | false | 1,306 |
r
|
preparePathways <-
function(db=c("kegg", "MSigDB"),
type=c("H","C1","C2","C3","C4","C5","C6","C7"),
genename= c("EntrezID", "symbol")) {
this.call <- match.call()
db <- match.arg(db)
m.type <- match.arg(type)
g.id <- match.arg(genename)
if (db=="kegg" && is.null(g.id)){g.id="EntrezID"}
if (db=="MSigDB" && is.null(m.type)){m.type="C2";g.id=="symbol"}
if (db=="kegg"){
paths <- graphite::pathways('hsapiens','kegg')
if (g.id=="symbol"){
paths<- lapply(paths, function(p) convertIdentifiers(p,g.id))
}
genesets <- lapply(paths, nodes)
genesets <- lapply(genesets, function(a) gsub("ENTREZID:","",a))
} else {
m_df = msigdbr::msigdbr(species = "Homo sapiens", category = m.type)
m_t2g = m_df %>% dplyr::select(.data$gs_name, .data$entrez_gene) %>% as.data.frame()
if (g.id=="symbol"){
m_t2g = m_df %>% dplyr::select(.data$gs_name, .data$gene_symbol) %>% as.data.frame()
}
gs_df <- m_t2g %>% dplyr::group_by(.data$gs_name) %>%
dplyr::summarise(entrez_gene = paste0(.data$entrez_gene, collapse=","))
genesets <- lapply(gs_df$entrez_gene, function(s) unlist(strsplit(s,",")))
names(genesets) <- gs_df$gs_name
}
return(genesets)
}
|
library(scales)
for (p in packages) {
dynamic_require(p)
}
#path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/USA_ONLY/"
#path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/WITH_MEXICO/"
path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/WORLDCLIM/"
setwd(path)
#files = list.files(pattern="AddedPredThin.+USA.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+zoomedin.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+addedLAyers.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+LGM.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+MID.asc$",recursive = T)
files = list.files(pattern="AddedPredThin.+worldclim.asc$",recursive = T)
doThresh=T
for (asc in files) {
print(asc)
print("--reading")
ras = raster(asc)
print("--calculating")
maximum = max(values(ras),na.rm=T)
minimum = min(values(ras),na.rm=T)
print("--scaling raster")
values(ras) = scales::rescale(values(ras),to=c(0,1))
newasc = paste(asc,"_",maximum,"_",minimum,".rescaled",sep="")
print("--writing raster")
writeRaster(ras,newasc,format="ascii",overwrite=T)
if(doThresh==T) {
print("--reading thresh")
tab = sub(pattern="BestModel",replacement="ThreshTable",x=asc)
tab = sub(pattern="asc",replacement="csv",x=tab)
newtab = paste(tab,".rescaled.csv",sep="")
read = read.csv(tab)
print("--scaling thresh")
newread = c("threshold",as.vector(sapply(read[,2:7],FUN=function(x){(x-minimum)/(maximum-minimum)})))
names(newread) = names(read)
names(newread)[1] = ""
newread = rbind(newread)
print("--writing thresh")
write.csv(newread,newtab,row.names=F)
}
}
|
/scripts/Niche_Models/convert_raster_to_range01.R
|
no_license
|
kaiyaprovost/GDM_pipeline
|
R
| false | false | 1,765 |
r
|
library(scales)
for (p in packages) {
dynamic_require(p)
}
#path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/USA_ONLY/"
#path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/WITH_MEXICO/"
path = "/Users/kprovost/Dropbox (AMNH)/Dissertation/CHAPTER3_TRAITS/ECOLOGY/OUTPUTS/3.enms/WORLDCLIM/"
setwd(path)
#files = list.files(pattern="AddedPredThin.+USA.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+zoomedin.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+addedLAyers.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+LGM.asc$",recursive = T)
#files = list.files(pattern="AddedPredThin.+MID.asc$",recursive = T)
files = list.files(pattern="AddedPredThin.+worldclim.asc$",recursive = T)
doThresh=T
for (asc in files) {
print(asc)
print("--reading")
ras = raster(asc)
print("--calculating")
maximum = max(values(ras),na.rm=T)
minimum = min(values(ras),na.rm=T)
print("--scaling raster")
values(ras) = scales::rescale(values(ras),to=c(0,1))
newasc = paste(asc,"_",maximum,"_",minimum,".rescaled",sep="")
print("--writing raster")
writeRaster(ras,newasc,format="ascii",overwrite=T)
if(doThresh==T) {
print("--reading thresh")
tab = sub(pattern="BestModel",replacement="ThreshTable",x=asc)
tab = sub(pattern="asc",replacement="csv",x=tab)
newtab = paste(tab,".rescaled.csv",sep="")
read = read.csv(tab)
print("--scaling thresh")
newread = c("threshold",as.vector(sapply(read[,2:7],FUN=function(x){(x-minimum)/(maximum-minimum)})))
names(newread) = names(read)
names(newread)[1] = ""
newread = rbind(newread)
print("--writing thresh")
write.csv(newread,newtab,row.names=F)
}
}
|
### Functions to facilitate fitting the CCl4 inhalation model
initparms <- function(...) {
arglist <- list(...)
Pm <- numeric(36)
## The Changeable parameters are ones that can be modified on input
Changeable <- c("BW", "QP", "QC", "VFC", "VLC", "VMC", "QFC", "QLC", "QMC",
"PLA", "PFA", "PMA", "PTA", "PB", "MW", "VMAX", "KM",
"CONC", "KL", "RATS", "VCHC")
## Computed parameters are strictly functions of the Changeable ones.
Computed <- c("VCH", "AI0", "PL", "PF", "PT", "PM", "VTC", "VT", "VF",
"VL", "VM", "QF", "QL", "QM", "QT")
names(Pm) <- c(Changeable,
Computed
)
### Physiological parameters
Pm["BW"] <- 0.182 # Body weight (kg)
Pm["QP"] <- 4.0 # Alveolar ventilation rate (hr^-1)
Pm["QC"] <- 4.0 # Cardiac output (hr^-1)
Pm["VFC"] <- 0.08 # Fraction fat tissue (kg/(kg/BW))
Pm["VLC"] <- 0.04 # Fraction liver tissue (kg/(kg/BW))
Pm["VMC"] <- 0.74 # Fraction of muscle tissue (kg/(kg/BW))
Pm["QFC"] <- 0.05 # Fractional blood flow to fat ((hr^-1)/QC
Pm["QLC"] <- 0.15 # Fractional blood flow to liver ((hr^-1)/QC)
Pm["QMC"] <- 0.32 # Fractional blood flow to muscle ((hr^-1)/QC)
## Chemical specific parameters for chemical
Pm["PLA"] <- 16.17 # Liver/air partition coefficient
Pm["PFA"] <- 281.48 # Fat/air partition coefficient
Pm["PMA"] <- 13.3 # Muscle/air partition coefficient
Pm["PTA"] <- 16.17 # Viscera/air partition coefficient
Pm["PB"] <- 5.487 # Blood/air partition coefficient
Pm["MW"] <- 153.8 # Molecular weight (g/mol)
Pm["VMAX"] <- 0.11 # Maximum velocity of metabolism (mg/hr)
Pm["KM"] <- 1.3 # Michaelis-Menten constant (mg/l)
## Parameters for simulated experiment
Pm["CONC"] <- 1000 # Inhaled concentration
Pm["KL"] <- 0.02 # Loss rate from empty chamber /hr
Pm["RATS"] <- 1.0 # Number of rats enclosed in chamber
Pm["VCHC"] <- 3.8 # Volume of closed chamber (l)
## Now, change anything from the argument list
## First, delete anything in arglist that is not in Changeable
whichdel <- which(! names(arglist) %in% Changeable)
if (length(whichdel)) {
warning(paste("Parameters", paste(names(arglist)[whichdel], collapse=", "),
"are not in this model\n"))
}
arglist[whichdel] <- NULL
## Is there anything else
if (length(arglist)) {
Pm[names(arglist)] <- as.vector(unlist(arglist))
}
## Computed parameter values
Pm["VCH"] <- Pm["VCHC"] - Pm["RATS"]*Pm["BW"] # Net chamber volume
Pm["AI0"] <- Pm["CONC"]*Pm["VCH"]*Pm["MW"]/24450 # Initial amt. in chamber (mg)
Pm[c("PL", "PF", "PT", "PM")] <- Pm[c("PLA", "PFA", "PTA", "PMA")]/Pm["PB"]
## Fraction viscera (kg/(kg BW))
Pm["VTC"] <- 0.91 - sum(Pm[c("VLC", "VFC", "VMC")])
Pm[c("VT", "VF", "VL", "VM")] <- Pm[c("VTC", "VFC", "VLC", "VMC")]*Pm["BW"]
Pm[c("QF", "QL", "QM")] <- Pm[c("QFC", "QLC", "QMC")]*Pm["QC"]
Pm["QT"] <- Pm["QC"] - sum(Pm[c("QF", "QL", "QM")])
Pm
}
### We don't actually use these functions (though they work)
### They exist because cclmodel.orig is easier to read than ccl4modelG
### The model function also computes some values that are of interest in
### checking the model and for calculating a dose metric:
### the amount metabolized (AM)
### the area under the concentration-time curve in the liver (CLT)
### and the mass balance (MASS), which should be constant if everything
### worked right.
## State variable, y, assignments.
## CI CM CT CF CL
## AI AAM AT AF AL CLT AM
## 1 2 3 4 5 6 7
initstate.orig <- function(Pm) {
y <- rep(0, 7)
names(y) <- c("AI", "AAM", "AT", "AF", "AL", "CLT", "AM")
y["AI"] <- Pm["AI0"]
y
}
parms <- initparms()
ccl4model.orig <- with(as.list(parms), function(t, y, parms) {
conc <- y[c("AI", "AAM", "AT", "AF", "AL")]/c(VCH, VM, VT, VF, VL)
## Vconc[1] is conc in mixed venous blood
Vconc <- c(0, conc[2:5]/parms[c("PM", "PT", "PF", "PL")]) # '0' is a placeholder
Vconc[1] <- sum(Vconc[2:5]*c(QM, QT, QF, QL))/QC
## CA is conc in arterial blood
CA <- (QC * Vconc[1] + QP * conc[1])/
(QC + QP/PB)
## Exhaled chemical
CX <- CA/PB
## return the derivatives and other computed items
list(c(RATS*QP*(CX - conc[1]) - KL*y["AI"],
QM*(CA - Vconc[2]),
QT*(CA - Vconc[3]),
QF*(CA - Vconc[4]),
QL*(CA - Vconc[5]) -
(RAM <- VMAX*Vconc[5]/(KM + Vconc[5])),
conc[5],
RAM),
c(DOSE = as.vector(AI0 - y["AI"]),
MASS = as.vector(sum(y[c("AAM","AT", "AF", "AL", "AM")])*RATS),
CP=as.vector(conc[1]*24450.0/MW)
))
})
### Versions that only calculate what is needed for parameter estimation
initparmmx <- function(parms) {
mx <- matrix(nrow=5, ncol=7)
mx[1, 6] <- parms["VCH"]
mx[1, 7] <- parms["MW"]
mx[4, 6] <- parms["VL"]*parms["PL"]
mx[5, 6] <- parms["VMAX"]
mx[5, 7] <- parms["KM"]
mxx <- matrix(parms[c("QP", "QM", "QT", "QF", "QL")], nrow=5, ncol=5, byrow=TRUE)
mxx <- sweep(mxx, 2, parms[c("VCH", "VM", "VT", "VF", "VL")], "/")
mxx <- sweep(mxx, 2, c(1, parms[c("PM", "PT", "PF", "PL")]), "/")
mxx <- mxx/(parms["QC"] + parms["QP"]/parms["PB"])
mxx <- sweep(mxx, 1, c(parms["RATS"]*parms["QP"]/parms["PB"],
parms[c("QM", "QT", "QF", "QL")]), "*")
dg <- diag(c(parms["RATS"]*parms["QP"]/parms["VCH"] + parms["KL"],
parms[c("QM", "QT", "QF", "QL")]/
(parms[c("PM", "PT", "PF", "PL")]*parms[c("VM", "VT", "VF", "VL")])))
mxx <- mxx - dg
mx[1:5, 1:5] <- mxx
mx
}
### Now, include the gradients wrt Vmax, Km, and initial chamber concentration
initstateG <- function(Pm) {
y <- rep(0, 20)
names(y) <- c("AI", "AAM", "AT", "AF", "AL",
"dAIdVm", "dAAMdVm", "dATdVm", "dAFdVm", "dALdVm",
"dAIdK", "dAAMdK", "dATdK", "dAFdK", "dALdK",
"dAIdy0", "dAAMdy0", "dATdy0", "dAFdy0", "dALdy0"
)
y["AI"] <- Pm["AI0"]
y["dAIdy0"] <- Pm["VCH"] * Pm["MW"]/24450.0
y
}
ccl4modelG <- function(t, y, parms) {
list(c(parms[,1:5] %*% y[1:5] - c(0, 0, 0, 0, parms[5, 6]*y[5] /
((Kms <- parms[5, 7]*parms[4, 6]) + y[5])),
parms[, 1:5] %*% y[6:10] - c(0, 0, 0, 0,
y[5]/(Kms + y[5]) +
parms[5, 6]*Kms*y[10]/
(Kms + y[5])^2),
parms[, 1:5] %*% y[11:15] - c(0, 0, 0, 0,
parms[5, 6]*(y[15]*Kms -
parms[4, 6]*y[5])/
(Kms + y[5])^2),
parms[,1:5] %*% y[16:20] - c(0, 0, 0, 0,
parms[5, 6]*Kms*y[20]/(Kms + y[5])^2)
),
c(CP = as.vector(y[1]*(zz <- 24450.0/parms[1, 6]/parms[1, 7])),
dCPdVm = as.vector(y[6]*zz),
dCPdK = as.vector(y[11]*zz),
dCPdy0 = as.vector(y[16]*zz)
)
)
}
### Function to use in gnls. This is more complicated than usual for such
### functions, because each value for each animal depends on the previous
### value for that animal. Normal vectorization doesn't work. Work with
### log(Vmax) and log(Km)
ccl4gnls <- function(time, initconc, lVmax, lKm, lconc) {
Vmax <- if(length(lVmax) == 1) rep(exp(lVmax), length(time)) else exp(lVmax)
Km <- if (length(lKm) == 1) rep(exp(lKm), length(time)) else exp(lKm)
conc <- if (length(lconc) == 1) rep(exp(lconc), length(time)) else exp(lconc)
Concs <- levels(initconc)
CP <- numeric(length(time))
.grad <- matrix(nrow=length(time), ncol=3,
dimnames=list(NULL, c("lVmax", "lKm", "lconc")))
### Run the model once for each unique initial concentration
for (Conc in Concs) {
sel <- initconc == Conc
parms <- initparms(CONC=conc[sel][1], VMAX=Vmax[sel][1], KM=Km[sel][1])
parmmx <- initparmmx(parms)
y <- initstateG(parms)
TTime <- sort(unique(time[sel]))
if (! 0 %in% TTime) TTime <- c(0, TTime)
out <- lsoda(y, TTime, ccl4modelG, parmmx, rtol=1e-12, atol=1e-12)
CP[sel] <- out[match(time[sel], out[,"time"]),"CP"]
.grad[sel, "lVmax"] <- out[match(time[sel], out[, "time"]), "dCPdVm"]
.grad[sel, "lKm"] <- out[match(time[sel], out[, "time"]), "dCPdK"]
.grad[sel, "lconc"] <- out[match(time[sel], out[, "time"]), "dCPdy0"]
}
.grad <- .grad * cbind(Vmax, Km, conc)
attr(CP, "gradient") <- .grad
CP
}
if (require(nlme, quietly=TRUE)) {
start <- log(c(lVmax = 0.11, lKm=1.3, 25, 100, 250, 1000))
### Data are from:
### Evans, et al. (1994) Applications of sensitivity analysis to a
### physiologically
### based pharmacokinetic model for carbon tetrachloride in rats.
### Toxicology and Applied Pharmacology 128: 36--44.
data(ccl4data)
ccl4data.avg<-aggregate(ccl4data$ChamberConc,
by=ccl4data[c("time", "initconc")], mean)
names(ccl4data.avg)[3]<-"ChamberConc"
### Estimate log(Vmax), log(Km), and the logs of the initial
### concentrations with gnls
cat("\nThis may take a little while ... \n")
ccl4.gnls <- gnls(ChamberConc ~ ccl4gnls(time, factor(initconc),
lVmax, lKm, lconc),
params = list(lVmax + lKm ~ 1, lconc ~ factor(initconc)-1),
data=ccl4data.avg, start=start,
weights=varPower(fixed=1),
verbose=TRUE)
start <- coef(ccl4.gnls)
ccl4.gnls2 <- gnls(ChamberConc ~ ccl4gnls(time, factor(initconc),
lVmax, lKm, lconc),
params = list(lVmax + lKm ~ 1,
lconc ~ factor(initconc)-1),
data=ccl4data, start=start,
weights=varPower(fixed=1),
verbose=TRUE)
print(summary(ccl4.gnls2))
### Now fit a separate initial concentration for each animal
start <- c(coef(ccl4.gnls))
cat("\nApprox. 95% Confidence Intervals for Metabolic Parameters:\n")
tmp <- exp(intervals(ccl4.gnls2)[[1]][1:2,])
row.names(tmp) <- c("Vmax", "Km")
print(tmp)
cat("\nOf course, the statistical model is inappropriate, since\nthe concentrations within animal are pretty highly autocorrelated:\nsee the graph.\n")
opar <- par(ask=TRUE, no.readonly=TRUE)
plot(ChamberConc ~ time, data=ccl4data, xlab="Time (hours)",
xlim=range(c(0, ccl4data$time)),
ylab="Chamber Concentration (ppm)", log="y")
out <- predict(ccl4.gnls2, newdata=ccl4data.avg)
concentrations <- sort(unique(ccl4data$initconc))
for (conc in concentrations) {
times <- ccl4data.avg$time[sel <- ccl4data.avg$initconc == conc]
CP <- out[sel]
lines(CP ~ times)
}
par(opar)
} else {
cat("This example requires the package nlme\n")
}
|
/deSolve/demo/CCL4model.R
|
permissive
|
solgenomics/R_libs
|
R
| false | false | 11,157 |
r
|
### Functions to facilitate fitting the CCl4 inhalation model
initparms <- function(...) {
arglist <- list(...)
Pm <- numeric(36)
## The Changeable parameters are ones that can be modified on input
Changeable <- c("BW", "QP", "QC", "VFC", "VLC", "VMC", "QFC", "QLC", "QMC",
"PLA", "PFA", "PMA", "PTA", "PB", "MW", "VMAX", "KM",
"CONC", "KL", "RATS", "VCHC")
## Computed parameters are strictly functions of the Changeable ones.
Computed <- c("VCH", "AI0", "PL", "PF", "PT", "PM", "VTC", "VT", "VF",
"VL", "VM", "QF", "QL", "QM", "QT")
names(Pm) <- c(Changeable,
Computed
)
### Physiological parameters
Pm["BW"] <- 0.182 # Body weight (kg)
Pm["QP"] <- 4.0 # Alveolar ventilation rate (hr^-1)
Pm["QC"] <- 4.0 # Cardiac output (hr^-1)
Pm["VFC"] <- 0.08 # Fraction fat tissue (kg/(kg/BW))
Pm["VLC"] <- 0.04 # Fraction liver tissue (kg/(kg/BW))
Pm["VMC"] <- 0.74 # Fraction of muscle tissue (kg/(kg/BW))
Pm["QFC"] <- 0.05 # Fractional blood flow to fat ((hr^-1)/QC
Pm["QLC"] <- 0.15 # Fractional blood flow to liver ((hr^-1)/QC)
Pm["QMC"] <- 0.32 # Fractional blood flow to muscle ((hr^-1)/QC)
## Chemical specific parameters for chemical
Pm["PLA"] <- 16.17 # Liver/air partition coefficient
Pm["PFA"] <- 281.48 # Fat/air partition coefficient
Pm["PMA"] <- 13.3 # Muscle/air partition coefficient
Pm["PTA"] <- 16.17 # Viscera/air partition coefficient
Pm["PB"] <- 5.487 # Blood/air partition coefficient
Pm["MW"] <- 153.8 # Molecular weight (g/mol)
Pm["VMAX"] <- 0.11 # Maximum velocity of metabolism (mg/hr)
Pm["KM"] <- 1.3 # Michaelis-Menten constant (mg/l)
## Parameters for simulated experiment
Pm["CONC"] <- 1000 # Inhaled concentration
Pm["KL"] <- 0.02 # Loss rate from empty chamber /hr
Pm["RATS"] <- 1.0 # Number of rats enclosed in chamber
Pm["VCHC"] <- 3.8 # Volume of closed chamber (l)
## Now, change anything from the argument list
## First, delete anything in arglist that is not in Changeable
whichdel <- which(! names(arglist) %in% Changeable)
if (length(whichdel)) {
warning(paste("Parameters", paste(names(arglist)[whichdel], collapse=", "),
"are not in this model\n"))
}
arglist[whichdel] <- NULL
## Is there anything else
if (length(arglist)) {
Pm[names(arglist)] <- as.vector(unlist(arglist))
}
## Computed parameter values
Pm["VCH"] <- Pm["VCHC"] - Pm["RATS"]*Pm["BW"] # Net chamber volume
Pm["AI0"] <- Pm["CONC"]*Pm["VCH"]*Pm["MW"]/24450 # Initial amt. in chamber (mg)
Pm[c("PL", "PF", "PT", "PM")] <- Pm[c("PLA", "PFA", "PTA", "PMA")]/Pm["PB"]
## Fraction viscera (kg/(kg BW))
Pm["VTC"] <- 0.91 - sum(Pm[c("VLC", "VFC", "VMC")])
Pm[c("VT", "VF", "VL", "VM")] <- Pm[c("VTC", "VFC", "VLC", "VMC")]*Pm["BW"]
Pm[c("QF", "QL", "QM")] <- Pm[c("QFC", "QLC", "QMC")]*Pm["QC"]
Pm["QT"] <- Pm["QC"] - sum(Pm[c("QF", "QL", "QM")])
Pm
}
### We don't actually use these functions (though they work)
### They exist because cclmodel.orig is easier to read than ccl4modelG
### The model function also computes some values that are of interest in
### checking the model and for calculating a dose metric:
### the amount metabolized (AM)
### the area under the concentration-time curve in the liver (CLT)
### and the mass balance (MASS), which should be constant if everything
### worked right.
## State variable, y, assignments.
## CI CM CT CF CL
## AI AAM AT AF AL CLT AM
## 1 2 3 4 5 6 7
initstate.orig <- function(Pm) {
y <- rep(0, 7)
names(y) <- c("AI", "AAM", "AT", "AF", "AL", "CLT", "AM")
y["AI"] <- Pm["AI0"]
y
}
parms <- initparms()
ccl4model.orig <- with(as.list(parms), function(t, y, parms) {
conc <- y[c("AI", "AAM", "AT", "AF", "AL")]/c(VCH, VM, VT, VF, VL)
## Vconc[1] is conc in mixed venous blood
Vconc <- c(0, conc[2:5]/parms[c("PM", "PT", "PF", "PL")]) # '0' is a placeholder
Vconc[1] <- sum(Vconc[2:5]*c(QM, QT, QF, QL))/QC
## CA is conc in arterial blood
CA <- (QC * Vconc[1] + QP * conc[1])/
(QC + QP/PB)
## Exhaled chemical
CX <- CA/PB
## return the derivatives and other computed items
list(c(RATS*QP*(CX - conc[1]) - KL*y["AI"],
QM*(CA - Vconc[2]),
QT*(CA - Vconc[3]),
QF*(CA - Vconc[4]),
QL*(CA - Vconc[5]) -
(RAM <- VMAX*Vconc[5]/(KM + Vconc[5])),
conc[5],
RAM),
c(DOSE = as.vector(AI0 - y["AI"]),
MASS = as.vector(sum(y[c("AAM","AT", "AF", "AL", "AM")])*RATS),
CP=as.vector(conc[1]*24450.0/MW)
))
})
### Versions that only calculate what is needed for parameter estimation
initparmmx <- function(parms) {
mx <- matrix(nrow=5, ncol=7)
mx[1, 6] <- parms["VCH"]
mx[1, 7] <- parms["MW"]
mx[4, 6] <- parms["VL"]*parms["PL"]
mx[5, 6] <- parms["VMAX"]
mx[5, 7] <- parms["KM"]
mxx <- matrix(parms[c("QP", "QM", "QT", "QF", "QL")], nrow=5, ncol=5, byrow=TRUE)
mxx <- sweep(mxx, 2, parms[c("VCH", "VM", "VT", "VF", "VL")], "/")
mxx <- sweep(mxx, 2, c(1, parms[c("PM", "PT", "PF", "PL")]), "/")
mxx <- mxx/(parms["QC"] + parms["QP"]/parms["PB"])
mxx <- sweep(mxx, 1, c(parms["RATS"]*parms["QP"]/parms["PB"],
parms[c("QM", "QT", "QF", "QL")]), "*")
dg <- diag(c(parms["RATS"]*parms["QP"]/parms["VCH"] + parms["KL"],
parms[c("QM", "QT", "QF", "QL")]/
(parms[c("PM", "PT", "PF", "PL")]*parms[c("VM", "VT", "VF", "VL")])))
mxx <- mxx - dg
mx[1:5, 1:5] <- mxx
mx
}
### Now, include the gradients wrt Vmax, Km, and initial chamber concentration
initstateG <- function(Pm) {
y <- rep(0, 20)
names(y) <- c("AI", "AAM", "AT", "AF", "AL",
"dAIdVm", "dAAMdVm", "dATdVm", "dAFdVm", "dALdVm",
"dAIdK", "dAAMdK", "dATdK", "dAFdK", "dALdK",
"dAIdy0", "dAAMdy0", "dATdy0", "dAFdy0", "dALdy0"
)
y["AI"] <- Pm["AI0"]
y["dAIdy0"] <- Pm["VCH"] * Pm["MW"]/24450.0
y
}
ccl4modelG <- function(t, y, parms) {
list(c(parms[,1:5] %*% y[1:5] - c(0, 0, 0, 0, parms[5, 6]*y[5] /
((Kms <- parms[5, 7]*parms[4, 6]) + y[5])),
parms[, 1:5] %*% y[6:10] - c(0, 0, 0, 0,
y[5]/(Kms + y[5]) +
parms[5, 6]*Kms*y[10]/
(Kms + y[5])^2),
parms[, 1:5] %*% y[11:15] - c(0, 0, 0, 0,
parms[5, 6]*(y[15]*Kms -
parms[4, 6]*y[5])/
(Kms + y[5])^2),
parms[,1:5] %*% y[16:20] - c(0, 0, 0, 0,
parms[5, 6]*Kms*y[20]/(Kms + y[5])^2)
),
c(CP = as.vector(y[1]*(zz <- 24450.0/parms[1, 6]/parms[1, 7])),
dCPdVm = as.vector(y[6]*zz),
dCPdK = as.vector(y[11]*zz),
dCPdy0 = as.vector(y[16]*zz)
)
)
}
### Function to use in gnls. This is more complicated than usual for such
### functions, because each value for each animal depends on the previous
### value for that animal. Normal vectorization doesn't work. Work with
### log(Vmax) and log(Km)
ccl4gnls <- function(time, initconc, lVmax, lKm, lconc) {
Vmax <- if(length(lVmax) == 1) rep(exp(lVmax), length(time)) else exp(lVmax)
Km <- if (length(lKm) == 1) rep(exp(lKm), length(time)) else exp(lKm)
conc <- if (length(lconc) == 1) rep(exp(lconc), length(time)) else exp(lconc)
Concs <- levels(initconc)
CP <- numeric(length(time))
.grad <- matrix(nrow=length(time), ncol=3,
dimnames=list(NULL, c("lVmax", "lKm", "lconc")))
### Run the model once for each unique initial concentration
for (Conc in Concs) {
sel <- initconc == Conc
parms <- initparms(CONC=conc[sel][1], VMAX=Vmax[sel][1], KM=Km[sel][1])
parmmx <- initparmmx(parms)
y <- initstateG(parms)
TTime <- sort(unique(time[sel]))
if (! 0 %in% TTime) TTime <- c(0, TTime)
out <- lsoda(y, TTime, ccl4modelG, parmmx, rtol=1e-12, atol=1e-12)
CP[sel] <- out[match(time[sel], out[,"time"]),"CP"]
.grad[sel, "lVmax"] <- out[match(time[sel], out[, "time"]), "dCPdVm"]
.grad[sel, "lKm"] <- out[match(time[sel], out[, "time"]), "dCPdK"]
.grad[sel, "lconc"] <- out[match(time[sel], out[, "time"]), "dCPdy0"]
}
.grad <- .grad * cbind(Vmax, Km, conc)
attr(CP, "gradient") <- .grad
CP
}
if (require(nlme, quietly=TRUE)) {
start <- log(c(lVmax = 0.11, lKm=1.3, 25, 100, 250, 1000))
### Data are from:
### Evans, et al. (1994) Applications of sensitivity analysis to a
### physiologically
### based pharmacokinetic model for carbon tetrachloride in rats.
### Toxicology and Applied Pharmacology 128: 36--44.
data(ccl4data)
ccl4data.avg<-aggregate(ccl4data$ChamberConc,
by=ccl4data[c("time", "initconc")], mean)
names(ccl4data.avg)[3]<-"ChamberConc"
### Estimate log(Vmax), log(Km), and the logs of the initial
### concentrations with gnls
cat("\nThis may take a little while ... \n")
ccl4.gnls <- gnls(ChamberConc ~ ccl4gnls(time, factor(initconc),
lVmax, lKm, lconc),
params = list(lVmax + lKm ~ 1, lconc ~ factor(initconc)-1),
data=ccl4data.avg, start=start,
weights=varPower(fixed=1),
verbose=TRUE)
start <- coef(ccl4.gnls)
ccl4.gnls2 <- gnls(ChamberConc ~ ccl4gnls(time, factor(initconc),
lVmax, lKm, lconc),
params = list(lVmax + lKm ~ 1,
lconc ~ factor(initconc)-1),
data=ccl4data, start=start,
weights=varPower(fixed=1),
verbose=TRUE)
print(summary(ccl4.gnls2))
### Now fit a separate initial concentration for each animal
start <- c(coef(ccl4.gnls))
cat("\nApprox. 95% Confidence Intervals for Metabolic Parameters:\n")
tmp <- exp(intervals(ccl4.gnls2)[[1]][1:2,])
row.names(tmp) <- c("Vmax", "Km")
print(tmp)
cat("\nOf course, the statistical model is inappropriate, since\nthe concentrations within animal are pretty highly autocorrelated:\nsee the graph.\n")
opar <- par(ask=TRUE, no.readonly=TRUE)
plot(ChamberConc ~ time, data=ccl4data, xlab="Time (hours)",
xlim=range(c(0, ccl4data$time)),
ylab="Chamber Concentration (ppm)", log="y")
out <- predict(ccl4.gnls2, newdata=ccl4data.avg)
concentrations <- sort(unique(ccl4data$initconc))
for (conc in concentrations) {
times <- ccl4data.avg$time[sel <- ccl4data.avg$initconc == conc]
CP <- out[sel]
lines(CP ~ times)
}
par(opar)
} else {
cat("This example requires the package nlme\n")
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AFROC.R
\name{AFROC}
\alias{AFROC}
\title{AF\strong{ROC} curve (alternative free-response \strong{ROC} curve)}
\usage{
AFROC(t, a = 0.14, b = 0.19, x.coordinate.also = FALSE)
}
\arguments{
\item{t}{A real number which moves in the domain of FROC curve}
\item{a, b}{One of the parameter of model which characterize AFROC curve}
\item{x.coordinate.also}{Logical, whether a vector of \code{1-exp(-t)}
is included in a return value.}
}
\value{
if \code{x.coordinate.also =TRUE}, then
A list, contains two vectors as x,y cooridinates of the AFROC curve
for drawing curves.
if \code{x.coordinate.also =FALSE}, then
return is a vector as y coodinates of the AFROC curve exclueded its x-coordinates.
(x coodinates is omitted.)
}
\description{
An AFROC curve is a plane curve whose area under the curve (AUC) indicates an observer performance ability.
In the following, \eqn{\Phi()} denotes the cumulative distribution function
on the standard Gaussian disribution.
The so-called \emph{AFROC} curve is defined by
\deqn{ (\xi(t),\eta(t) ) =(1-e^{-t}, \Phi( b\Phi^{-1}(\exp(-t) )- a ) )}
for all \eqn{t >0} and some fixed real numbers \eqn{a,b}.
Specifying two real numbers \eqn{a} and \eqn{b}, we can plot an AFROC curve.
The are under the AFROC curve, or breafly AUC, is calculated as follows, whic
are used to evaluate how physicians detect lesions in radiographs.
\deqn{ AUC = \int \eta(t) d\xi(t) = \frac{ a }{ \sqrt{1+ b^2} }. }
Note that the so-called FROC curve can be interpreted as the curve of expectations of data points.
On the other hand, AFROC curve cannot be interpreted as the fitted curve, but its AUC is finite.
Because AFROC can be obtained by modifying FROC curve, it reflects obeserver performance.
}
\examples{
#========================================================================================
# Plot AFROC curve
#========================================================================================
tt <- seq(0, 1, length.out = 111)
ttt <- stats::runif(1000,0.001,100)
t <- c(tt,ttt)
a <- AFROC(t,x.coordinate.also=TRUE)
plot(a$x,a$y)
# We note that the x-coordinates of AFROC curve is not t but x = 1 - exp(-t).
# To emphasize that x-coordinates is not t, we prepare the another example
#========================================================================================
# Plot AFROC curve
#========================================================================================
tt <- seq(0, 1, length.out = 1111) #plot(1:length(tt),tt)
ttt <- stats::runif(1000,0.001,100)
t <- c(tt,ttt)
t <- c(0,tt,ttt,1)
t<-sort(t, method = "shell", index.return = FALSE)
y <- AFROC(t,x.coordinate.also=FALSE)
plot(1-exp(-t),y,type="l")
Close_all_graphic_devices() # 2020 August; Revised 2022 Jan 6
}
|
/man/AFROC.Rd
|
no_license
|
cran/BayesianFROC
|
R
| false | true | 2,872 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AFROC.R
\name{AFROC}
\alias{AFROC}
\title{AF\strong{ROC} curve (alternative free-response \strong{ROC} curve)}
\usage{
AFROC(t, a = 0.14, b = 0.19, x.coordinate.also = FALSE)
}
\arguments{
\item{t}{A real number which moves in the domain of FROC curve}
\item{a, b}{One of the parameter of model which characterize AFROC curve}
\item{x.coordinate.also}{Logical, whether a vector of \code{1-exp(-t)}
is included in a return value.}
}
\value{
if \code{x.coordinate.also =TRUE}, then
A list, contains two vectors as x,y cooridinates of the AFROC curve
for drawing curves.
if \code{x.coordinate.also =FALSE}, then
return is a vector as y coodinates of the AFROC curve exclueded its x-coordinates.
(x coodinates is omitted.)
}
\description{
An AFROC curve is a plane curve whose area under the curve (AUC) indicates an observer performance ability.
In the following, \eqn{\Phi()} denotes the cumulative distribution function
on the standard Gaussian disribution.
The so-called \emph{AFROC} curve is defined by
\deqn{ (\xi(t),\eta(t) ) =(1-e^{-t}, \Phi( b\Phi^{-1}(\exp(-t) )- a ) )}
for all \eqn{t >0} and some fixed real numbers \eqn{a,b}.
Specifying two real numbers \eqn{a} and \eqn{b}, we can plot an AFROC curve.
The are under the AFROC curve, or breafly AUC, is calculated as follows, whic
are used to evaluate how physicians detect lesions in radiographs.
\deqn{ AUC = \int \eta(t) d\xi(t) = \frac{ a }{ \sqrt{1+ b^2} }. }
Note that the so-called FROC curve can be interpreted as the curve of expectations of data points.
On the other hand, AFROC curve cannot be interpreted as the fitted curve, but its AUC is finite.
Because AFROC can be obtained by modifying FROC curve, it reflects obeserver performance.
}
\examples{
#========================================================================================
# Plot AFROC curve
#========================================================================================
tt <- seq(0, 1, length.out = 111)
ttt <- stats::runif(1000,0.001,100)
t <- c(tt,ttt)
a <- AFROC(t,x.coordinate.also=TRUE)
plot(a$x,a$y)
# We note that the x-coordinates of AFROC curve is not t but x = 1 - exp(-t).
# To emphasize that x-coordinates is not t, we prepare the another example
#========================================================================================
# Plot AFROC curve
#========================================================================================
tt <- seq(0, 1, length.out = 1111) #plot(1:length(tt),tt)
ttt <- stats::runif(1000,0.001,100)
t <- c(tt,ttt)
t <- c(0,tt,ttt,1)
t<-sort(t, method = "shell", index.return = FALSE)
y <- AFROC(t,x.coordinate.also=FALSE)
plot(1-exp(-t),y,type="l")
Close_all_graphic_devices() # 2020 August; Revised 2022 Jan 6
}
|
\name{milk}
\alias{milk}
\docType{data}
\title{Monthly Milk Production}
\description{ Average monthly milk production per cow in the US,
01/1994 - 12/2005}
\usage{data(milk)}
\format{
The format is:
'ts' int [1:144, 1] 1343 1236 1401 1396 1457 1388 1389 1369 1318 1354 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr "milk"
- attr(*, "tsp")= num [1:3] 1994 2006 12
}
\examples{
data(milk)
str(milk)
plot(milk)
}
\keyword{datasets}
|
/man/milk.Rd
|
no_license
|
cran/TSA
|
R
| false | false | 475 |
rd
|
\name{milk}
\alias{milk}
\docType{data}
\title{Monthly Milk Production}
\description{ Average monthly milk production per cow in the US,
01/1994 - 12/2005}
\usage{data(milk)}
\format{
The format is:
'ts' int [1:144, 1] 1343 1236 1401 1396 1457 1388 1389 1369 1318 1354 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr "milk"
- attr(*, "tsp")= num [1:3] 1994 2006 12
}
\examples{
data(milk)
str(milk)
plot(milk)
}
\keyword{datasets}
|
#' @title plotRatioDensity Function to plot density of ratios between two
#' treatments (using groupings from an annotation column) within an se
#' object.
#' @description This function plots the expression of the supplied se
#' object, using ratios between a pair of selected treatments.
#' @usage plotRatioDensity(se, groupings= NULL, treatment1=NULL,
#' treatment2=NULL, mode_mean=TRUE, LOG2=TRUE,...)
#' @param se An se object.
#' @param groupings A grouping (annotation); groupings="annotation.ZA"
#' @param treatment1 Symbol, treatment 1.
#' @param treatment2 Symbol, treatment 2.
#' @param mode_mean Boolean, Calculate RowMeans or RowMedians.
#' @param LOG2 Boolean, Calculate LOG2.
#' @param ... Passthrough arguments to boxplot (additional arguments affecting
#' the summary produced).
#' @details This function plots expression of the supplied se object
#' using ratios of treatment1/treatment2.
#' @return Returns an invisible data frame containing the x-values and
#' corresponding density for each applicable annotation column entry.
#' @examples
#' data(hmel.se)
#' plotRatioDensity(se, groupings='annotation.ZA', treatment1 = 'Male',
#' treatment2 = 'Female',lty=1,type="l")
#' @author AJ Vaestermark, JR Walters.
#' @references The "doseR" package, 2018 (in press).
plotRatioDensity <- function (se, groupings= NULL, treatment1=NULL,
treatment2=NULL, mode_mean=TRUE, LOG2=TRUE,...) {
MyGroups<-rowData(se)[[groupings]]
if(is.null(groupings)) {
stop ('No groupings, e.g. groupings="something"...')
return (NULL)
}
if(length(assays(se)$rpkm) == 0) {
stop ('No RPKM data saved in se object... cancelling...')
return (NULL)
}
if(is.null(treatment1) | is.null(treatment2) ) {
stop ('Indicate treatments, such as treatment1="A", treatment2="B"')
return (NULL)
}
if ( is.factor(MyGroups) ) { MyGroups <- droplevels(MyGroups) }
RM <- (
if(mode_mean) rowMeans(assays(se)$rpkm[,colData(se)$Treatment==treatment1])
else matrixStats::rowMedians(
assays(se)$rpkm[,colData(se)$Treatment==treatment1])
) / (
if(mode_mean) rowMeans(assays(se)$rpkm[,colData(se)$Treatment==treatment2])
else matrixStats::rowMedians(
assays(se)$rpkm[,colData(se)$Treatment==treatment2])
)
if (LOG2) { RM <- log2(RM) }
RM[is.infinite(RM)] <- NA
val <- split(RM, MyGroups)
xrge <- range(unlist(val), na.rm = TRUE, finite=TRUE)
myX <- density(na.omit(val[[1]]), from = xrge[1], to = xrge[2])$x
myDens <- vapply(val, FUN = function(x) { density(na.omit(x),
from = xrge[1], to = xrge[2])$y }, numeric(512) )
cat("Current order of levels: ", paste(unique(MyGroups), sep="\t") )
matplot(x = myX, y = myDens, ...)
invisible(data.frame(myX,myDens))
}# plotRatioDensity
|
/R/plotRatioDensity.R
|
no_license
|
avastermark19/doseR_0.99.8
|
R
| false | false | 2,719 |
r
|
#' @title plotRatioDensity Function to plot density of ratios between two
#' treatments (using groupings from an annotation column) within an se
#' object.
#' @description This function plots the expression of the supplied se
#' object, using ratios between a pair of selected treatments.
#' @usage plotRatioDensity(se, groupings= NULL, treatment1=NULL,
#' treatment2=NULL, mode_mean=TRUE, LOG2=TRUE,...)
#' @param se An se object.
#' @param groupings A grouping (annotation); groupings="annotation.ZA"
#' @param treatment1 Symbol, treatment 1.
#' @param treatment2 Symbol, treatment 2.
#' @param mode_mean Boolean, Calculate RowMeans or RowMedians.
#' @param LOG2 Boolean, Calculate LOG2.
#' @param ... Passthrough arguments to boxplot (additional arguments affecting
#' the summary produced).
#' @details This function plots expression of the supplied se object
#' using ratios of treatment1/treatment2.
#' @return Returns an invisible data frame containing the x-values and
#' corresponding density for each applicable annotation column entry.
#' @examples
#' data(hmel.se)
#' plotRatioDensity(se, groupings='annotation.ZA', treatment1 = 'Male',
#' treatment2 = 'Female',lty=1,type="l")
#' @author AJ Vaestermark, JR Walters.
#' @references The "doseR" package, 2018 (in press).
plotRatioDensity <- function (se, groupings= NULL, treatment1=NULL,
treatment2=NULL, mode_mean=TRUE, LOG2=TRUE,...) {
MyGroups<-rowData(se)[[groupings]]
if(is.null(groupings)) {
stop ('No groupings, e.g. groupings="something"...')
return (NULL)
}
if(length(assays(se)$rpkm) == 0) {
stop ('No RPKM data saved in se object... cancelling...')
return (NULL)
}
if(is.null(treatment1) | is.null(treatment2) ) {
stop ('Indicate treatments, such as treatment1="A", treatment2="B"')
return (NULL)
}
if ( is.factor(MyGroups) ) { MyGroups <- droplevels(MyGroups) }
RM <- (
if(mode_mean) rowMeans(assays(se)$rpkm[,colData(se)$Treatment==treatment1])
else matrixStats::rowMedians(
assays(se)$rpkm[,colData(se)$Treatment==treatment1])
) / (
if(mode_mean) rowMeans(assays(se)$rpkm[,colData(se)$Treatment==treatment2])
else matrixStats::rowMedians(
assays(se)$rpkm[,colData(se)$Treatment==treatment2])
)
if (LOG2) { RM <- log2(RM) }
RM[is.infinite(RM)] <- NA
val <- split(RM, MyGroups)
xrge <- range(unlist(val), na.rm = TRUE, finite=TRUE)
myX <- density(na.omit(val[[1]]), from = xrge[1], to = xrge[2])$x
myDens <- vapply(val, FUN = function(x) { density(na.omit(x),
from = xrge[1], to = xrge[2])$y }, numeric(512) )
cat("Current order of levels: ", paste(unique(MyGroups), sep="\t") )
matplot(x = myX, y = myDens, ...)
invisible(data.frame(myX,myDens))
}# plotRatioDensity
|
# A function that takes in a dataset and returns a list of info about it:
setwd("~/project-ianlindelI")
get_summary_info <- function(dataset) {
ret <- list()
ret$length <- length(dataset)
ret$rows <- nrow(dataset)
ret$col <- ncol(dataset)
return(ret)
}
|
/project-ianlindelI/scripts/summary-info.R
|
permissive
|
getachew67/info201-5
|
R
| false | false | 277 |
r
|
# A function that takes in a dataset and returns a list of info about it:
setwd("~/project-ianlindelI")
get_summary_info <- function(dataset) {
ret <- list()
ret$length <- length(dataset)
ret$rows <- nrow(dataset)
ret$col <- ncol(dataset)
return(ret)
}
|
install.packages("readstata13")
rm(list=ls())
library(readstata13)
dat <- read.dta13("~/Downloads/rEA1.dta")
###2018 Welcoming analysis
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat <- read.dta13("~/Downloads/birth1.dta")
dat$topp[dat$topp==1] <- "Animal welfare"
dat$topp[dat$topp==2] <- "Cause prioritisation"
dat$topp[dat$topp==3] <- "Biosecurity"
dat$topp[dat$topp==4] <- "Climate change"
dat$topp[dat$topp==5] <- "Nuclear security"
dat$topp[dat$topp==6] <- "AI Risk"
dat$topp[dat$topp==7] <- "Mental health"
dat$topp[dat$topp==8] <- "Global poverty"
dat$topp[dat$topp==9] <- "Improving rationality"
dat$topp[dat$topp==10] <- "Meta charities"
dat$topp[dat$topp==11] <- "Other X-Risk"
dat$topp<-as.factor(dat$topp)
dat <- read.dta13("~/Downloads/rEA4.dta")
dat <- read.dta13("~/Downloads/rEA5.dta")
dat$agegroup1[dat$agegroup1==0] <- "18-22"
dat$agegroup1[dat$agegroup1==1] <- "23-25"
dat$agegroup1[dat$agegroup1==2] <- "26-29"
dat$agegroup1[dat$agegroup1==3] <- "30-34"
dat$agegroup1[dat$agegroup1==4] <- "35+"
dat$agegroup1<-as.factor(dat$agegroup1)
dat <- read.dta13("~/Downloads/rEA6.dta")
dat$first_heard_ea<- as.factor(dat$first_heard_ea)
dat <- read.dta13("~/Downloads/rEA7.dta")
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$first_heard_ea <-as.factor(dat$first_heard_ea)
require(likert)
attach(dat)
mylevels <- dat$wel
# Create a dummy data frame. Note that "Item 1" has only four levels
items <- data.frame(dat$wel)
str(items)
groups <- dat$first_heard_ea
sapply(items, class) #Verify that all the columns are indeed factors
sapply(items, function(x) { length(levels(x)) } ) # The number of levels in each factor
for(i in seq_along(items)) {
items[,i] <- factor(items[,i], levels=mylevels)
}
rm(list=ls())
library(readstata13)
dat <- read.dta13("~/Downloads/rEA1.dta")
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$first_heard_ea <-as.factor(dat$first_heard_ea)
require(likert)
attach(dat)
mylevels <- dat$wel
# Create a dummy data frame. Note that "Item 1" has only four levels
items <- data.frame(dat$wel)
str(items)
lgood <- likert(items)
lgood
summary(lgood)
title <- "EA Welcomeness"
plot(lgood, ylab="")+ ggtitle(title)
dat$topprioritycause<-as.factor(dat$topprioritycause)
dat$topprioritycause = factor(dat$topprioritycause,
levels = c("Other","Unemployed","Direct Charity/Non-profit Work","Self-Employed", "Retired", "Research", "Earning to Give", "Homemaker"),
ordered = TRUE)
# Plot the bar chart
title <- "How Welcoming is EA, by Top Priority Cause"
ggplot(data=dat, aes(x=topprioritycause, y=meanwelcoming, fill=topprioritycause)) +
geom_bar(colour="black", stat="identity") + xlab("Top Priority Cause") + ylab("Mean Welcomeness")+ ggtitle(title)+
guides(fill=FALSE) + theme_tufte()+ylim(3, 4.5)
###
install.packages("ggthemes") # Install
library(ggthemes) # Load
require(likert)
lgr <- likert(items)
summary(lgr)
tp=as.data.frame(lgr$results)
tp=tp[,-2]
names(tp)[1]="Item"
lgr2=(likert(summary=tp))
title <- "EA Welcomingness by Top Priority Cause"
plot(lgr2) + ggtitle(title)
##
plot(lgood) +ggtitle(title)
lgr <- likert(items, grouping=groups)
summary(lgr)
scale_height = knitr::opts_chunk$get('fig.height')*0.5
scale_width = knitr::opts_chunk$get('fig.width')*1.25
knitr::opts_chunk$set(fig.height = scale_height, fig.width = scale_width)
theme_update(legend.text = element_text(size = rel(0.7)))
title <- "EA Welcomingness by Country"
plot(lgr, ordered=TRUE) + ggtitle(title)
plot(lgr, ordered=FALSE, group.order=names(first_heard_ea))
plot(lgr, ordered = TRUE)
desired.order <- c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming")
install.packages("Epi")
require(Epi)
require(likert)
for (i in seq_along(dat)) {
dat[,i] = Relevel(dat[,i],mylevels) # desired.order must be specified beforehand
}
# Now it plots.
lgr <- likert(items, grouping=groups)
plot(lgr)
|
/likert_2018_welcomingness.R
|
no_license
|
NeiloDull/2019-EAS
|
R
| false | false | 4,438 |
r
|
install.packages("readstata13")
rm(list=ls())
library(readstata13)
dat <- read.dta13("~/Downloads/rEA1.dta")
###2018 Welcoming analysis
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat <- read.dta13("~/Downloads/birth1.dta")
dat$topp[dat$topp==1] <- "Animal welfare"
dat$topp[dat$topp==2] <- "Cause prioritisation"
dat$topp[dat$topp==3] <- "Biosecurity"
dat$topp[dat$topp==4] <- "Climate change"
dat$topp[dat$topp==5] <- "Nuclear security"
dat$topp[dat$topp==6] <- "AI Risk"
dat$topp[dat$topp==7] <- "Mental health"
dat$topp[dat$topp==8] <- "Global poverty"
dat$topp[dat$topp==9] <- "Improving rationality"
dat$topp[dat$topp==10] <- "Meta charities"
dat$topp[dat$topp==11] <- "Other X-Risk"
dat$topp<-as.factor(dat$topp)
dat <- read.dta13("~/Downloads/rEA4.dta")
dat <- read.dta13("~/Downloads/rEA5.dta")
dat$agegroup1[dat$agegroup1==0] <- "18-22"
dat$agegroup1[dat$agegroup1==1] <- "23-25"
dat$agegroup1[dat$agegroup1==2] <- "26-29"
dat$agegroup1[dat$agegroup1==3] <- "30-34"
dat$agegroup1[dat$agegroup1==4] <- "35+"
dat$agegroup1<-as.factor(dat$agegroup1)
dat <- read.dta13("~/Downloads/rEA6.dta")
dat$first_heard_ea<- as.factor(dat$first_heard_ea)
dat <- read.dta13("~/Downloads/rEA7.dta")
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$first_heard_ea <-as.factor(dat$first_heard_ea)
require(likert)
attach(dat)
mylevels <- dat$wel
# Create a dummy data frame. Note that "Item 1" has only four levels
items <- data.frame(dat$wel)
str(items)
groups <- dat$first_heard_ea
sapply(items, class) #Verify that all the columns are indeed factors
sapply(items, function(x) { length(levels(x)) } ) # The number of levels in each factor
for(i in seq_along(items)) {
items[,i] <- factor(items[,i], levels=mylevels)
}
rm(list=ls())
library(readstata13)
dat <- read.dta13("~/Downloads/rEA1.dta")
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$wel = factor(dat$welcome,
levels = c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming"),
ordered = TRUE)
dat$first_heard_ea <-as.factor(dat$first_heard_ea)
require(likert)
attach(dat)
mylevels <- dat$wel
# Create a dummy data frame. Note that "Item 1" has only four levels
items <- data.frame(dat$wel)
str(items)
lgood <- likert(items)
lgood
summary(lgood)
title <- "EA Welcomeness"
plot(lgood, ylab="")+ ggtitle(title)
dat$topprioritycause<-as.factor(dat$topprioritycause)
dat$topprioritycause = factor(dat$topprioritycause,
levels = c("Other","Unemployed","Direct Charity/Non-profit Work","Self-Employed", "Retired", "Research", "Earning to Give", "Homemaker"),
ordered = TRUE)
# Plot the bar chart
title <- "How Welcoming is EA, by Top Priority Cause"
ggplot(data=dat, aes(x=topprioritycause, y=meanwelcoming, fill=topprioritycause)) +
geom_bar(colour="black", stat="identity") + xlab("Top Priority Cause") + ylab("Mean Welcomeness")+ ggtitle(title)+
guides(fill=FALSE) + theme_tufte()+ylim(3, 4.5)
###
install.packages("ggthemes") # Install
library(ggthemes) # Load
require(likert)
lgr <- likert(items)
summary(lgr)
tp=as.data.frame(lgr$results)
tp=tp[,-2]
names(tp)[1]="Item"
lgr2=(likert(summary=tp))
title <- "EA Welcomingness by Top Priority Cause"
plot(lgr2) + ggtitle(title)
##
plot(lgood) +ggtitle(title)
lgr <- likert(items, grouping=groups)
summary(lgr)
scale_height = knitr::opts_chunk$get('fig.height')*0.5
scale_width = knitr::opts_chunk$get('fig.width')*1.25
knitr::opts_chunk$set(fig.height = scale_height, fig.width = scale_width)
theme_update(legend.text = element_text(size = rel(0.7)))
title <- "EA Welcomingness by Country"
plot(lgr, ordered=TRUE) + ggtitle(title)
plot(lgr, ordered=FALSE, group.order=names(first_heard_ea))
plot(lgr, ordered = TRUE)
desired.order <- c("Very unwelcoming", "Unwelcoming", "Neither", "Welcoming", "Very welcoming")
install.packages("Epi")
require(Epi)
require(likert)
for (i in seq_along(dat)) {
dat[,i] = Relevel(dat[,i],mylevels) # desired.order must be specified beforehand
}
# Now it plots.
lgr <- likert(items, grouping=groups)
plot(lgr)
|
testlist <- list(n = c(-555819298L, -555819298L, -555819298L))
result <- do.call(gdalcubes:::libgdalcubes_set_threads,testlist)
str(result)
|
/gdalcubes/inst/testfiles/libgdalcubes_set_threads/libFuzzer_libgdalcubes_set_threads/libgdalcubes_set_threads_valgrind_files/1609874833-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false | false | 139 |
r
|
testlist <- list(n = c(-555819298L, -555819298L, -555819298L))
result <- do.call(gdalcubes:::libgdalcubes_set_threads,testlist)
str(result)
|
# Load all required packages
source("Helper_Functions.R")
list.of.packages = c("rpart", "lubridate", "outliers", "rpart.plot", "xgboost", "caret", "caretEnsemble", "randomForest",
"e1071", "pROC", "tidyr", "klaR", "car", "devtools", "yamldebugger", "mlbench", "Hmisc", "ggvis", "relaimpo",
"formatR", "data.table", "zoo", "ggplot2", "forecast", "reshape2", "pdp")
sapply(list.of.packages, load.packages)
# Loading the full dataset
FullSet = readRDS("FullSet")
# Generating an additional date variable
FullSet$NewDate = FullSet$Date
# Adding features with mean sales
FullSet$Store = as.factor(FullSet$Store)
FullSet$Date = as.Date(paste(FullSet$Date, sep = "-"), format = "%Y-%m-%d")
FullSet = separate(FullSet, Date, into = c("Year", "Month", "Day"), sep = "-")
features_avg = setNames(aggregate(FullSet$Sales, list(FullSet$Store), mean), c("Store", "AvgSalesPerStore"))
features_avg$AvgVisitsPerStore = aggregate(FullSet$Customers, list(FullSet$Store), mean)[, 2]
features_dow = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$DayOfWeek), mean), c("Store", "DayOfWeek",
"AvgSalesPerStorePerDayOfWeek"))
features_dow$AvgVisitsPerStorePerDayOfWeek = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$DayOfWeek),
mean)[, 3]
features_year = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$Year), mean), c("Store", "Year",
"AvgSalesPerStorePerYear"))
features_year$AvgVisitsPerStorePerYear = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$Year), mean)[,
3]
features_mon = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$Year, FullSet$Month), mean), c("Store",
"Year", "Month", "AvgSalesPerStorePerMonth"))
features_mon$AvgVisitsPerStorePerMonth = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$Year, FullSet$Month),
mean)[, 4]
# Merging new features with the dataset
FullSet = merge(FullSet, features_avg, by = "Store")
FullSet = merge(FullSet, features_dow, by = c("Store", "DayOfWeek"))
FullSet = merge(FullSet, features_year, by = c("Store", "Year"))
FullSet = merge(FullSet, features_mon, by = c("Store", "Year", "Month"))
FullSet$NewDate = as.Date(paste(FullSet$NewDate, sep = "-"), format = "%Y-%m-%d")
varlist = c("Year", "Month", "Day")
FullSet[, varlist] = lapply(FullSet[, varlist], factor)
# Plotting average sales per store by promotion
ggplot(FullSet, aes(x = Store, y = AvgSalesPerStore, color = Promo, shape = Promo)) + geom_point() + scale_color_brewer(palette = "Set2") +
ggtitle("Average Sales Per Store by Promo") + labs(x = "Store", y = "Average Sales Per Store") + theme_classic()
ggsave("Average_Sales_Per_Store_by_Promo.png")
# Plotting average sales per store by competition distance
FullSet.sub = subset(FullSet, Sales != 0 & !is.na(CompetitionDistance), drop = TRUE)
SalesByDist = aggregate(FullSet.sub$AvgSalesPerStore, by = list(FullSet.sub$CompetitionDistance), mean)
colnames(SalesByDist) = c("CompetitionDistance", "AvgSalesPerStore")
ggplot(SalesByDist, aes(x = CompetitionDistance, y = AvgSalesPerStore)) + geom_point() + scale_color_brewer(palette = "Set2") +
geom_smooth() + ggtitle("Average Sales Per Store by Competition Distance") + labs(x = "Competition Distance",
y = "Average Sales Per Store") + theme_bw()
ggsave("Average_Sales_by_Competition_Distance.png")
# Plotting the log of average sales
ggplot(SalesByDist, aes(x = log(CompetitionDistance), y = log(AvgSalesPerStore))) + geom_point() + scale_color_brewer(palette = "Set2") +
geom_smooth() + ggtitle("Log of Average Sales per Store by Log of Competition Distance") + labs(x = "Log (Competition Distance)",
y = "Log (Average Sales Per Store)") + theme_bw()
ggsave("Log_of_Sales_by_Competition Distance.png")
# Plotting dynamics of sales per store per month
ggplot(FullSet, aes(x = as.Date(NewDate), y = AvgSalesPerStorePerMonth)) + geom_smooth(size = 2) + ggtitle("Average Sales Per Store Per Month over Time") +
labs(x = "Date", y = "Average Sales Per Store Per Month") + theme_bw()
ggsave("Average_Sales_per_Store_Per_Month.png")
# Plotting dynamics of customers per store per month
ggplot(FullSet, aes(x = as.Date(NewDate), y = AvgVisitsPerStorePerMonth)) + geom_smooth(size = 2) + ggtitle("Average Customers Per Store Per Month over Time") +
labs(x = "Date", y = "Average Customers Per Store Per Month") + theme_bw()
ggsave("Average_Customers_per_Store_Per_Month.png")
# Loading xgb model for further plotting
xgb = readRDS("xgb")
# Calculating partial dependence
xgb.partialPlots = list() # empty result list
imp.var.xgb = c("Open", "CompetitionDistance", "Store", "Promo", "CompetitionSinceDate", "Date", "Promo2SinceDate",
"StoreType", "Assortment", "Promo2", "DayOfWeek", "StoreAssortmentMatch")
for (var in imp.var.xgb) {
message("Now calculating for variable ", var)
xgb.partialPlots[[var]] = do.call(partial, list(xgb, pred.var = var, type = "auto", plot = FALSE))
}
# Creating the partial dependence plots and saving as pdf
par(mfrow = c(1, 2))
for (var in names(xgb.partialPlots)) {
png(paste("PDPSales", var, ".png", sep = "_"))
plot(x = xgb.partialPlots[[var]][, 1], y = xgb.partialPlots[[var]][, 2], type = "l", xlab = var, ylab = "Sales",
ylim = c(0, 8000), main = paste("Partial dependence of Sales on", var), bg = "transparent", lwd = 2)
dev.off()
}
|
/Removed/SPL_Data_Exploration.R
|
no_license
|
maggytheirish/SPL-SS17
|
R
| false | false | 5,357 |
r
|
# Load all required packages
source("Helper_Functions.R")
list.of.packages = c("rpart", "lubridate", "outliers", "rpart.plot", "xgboost", "caret", "caretEnsemble", "randomForest",
"e1071", "pROC", "tidyr", "klaR", "car", "devtools", "yamldebugger", "mlbench", "Hmisc", "ggvis", "relaimpo",
"formatR", "data.table", "zoo", "ggplot2", "forecast", "reshape2", "pdp")
sapply(list.of.packages, load.packages)
# Loading the full dataset
FullSet = readRDS("FullSet")
# Generating an additional date variable
FullSet$NewDate = FullSet$Date
# Adding features with mean sales
FullSet$Store = as.factor(FullSet$Store)
FullSet$Date = as.Date(paste(FullSet$Date, sep = "-"), format = "%Y-%m-%d")
FullSet = separate(FullSet, Date, into = c("Year", "Month", "Day"), sep = "-")
features_avg = setNames(aggregate(FullSet$Sales, list(FullSet$Store), mean), c("Store", "AvgSalesPerStore"))
features_avg$AvgVisitsPerStore = aggregate(FullSet$Customers, list(FullSet$Store), mean)[, 2]
features_dow = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$DayOfWeek), mean), c("Store", "DayOfWeek",
"AvgSalesPerStorePerDayOfWeek"))
features_dow$AvgVisitsPerStorePerDayOfWeek = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$DayOfWeek),
mean)[, 3]
features_year = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$Year), mean), c("Store", "Year",
"AvgSalesPerStorePerYear"))
features_year$AvgVisitsPerStorePerYear = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$Year), mean)[,
3]
features_mon = setNames(aggregate(FullSet$Sales, list(FullSet$Store, FullSet$Year, FullSet$Month), mean), c("Store",
"Year", "Month", "AvgSalesPerStorePerMonth"))
features_mon$AvgVisitsPerStorePerMonth = aggregate(FullSet$Customers, list(FullSet$Store, FullSet$Year, FullSet$Month),
mean)[, 4]
# Merging new features with the dataset
FullSet = merge(FullSet, features_avg, by = "Store")
FullSet = merge(FullSet, features_dow, by = c("Store", "DayOfWeek"))
FullSet = merge(FullSet, features_year, by = c("Store", "Year"))
FullSet = merge(FullSet, features_mon, by = c("Store", "Year", "Month"))
FullSet$NewDate = as.Date(paste(FullSet$NewDate, sep = "-"), format = "%Y-%m-%d")
varlist = c("Year", "Month", "Day")
FullSet[, varlist] = lapply(FullSet[, varlist], factor)
# Plotting average sales per store by promotion
ggplot(FullSet, aes(x = Store, y = AvgSalesPerStore, color = Promo, shape = Promo)) + geom_point() + scale_color_brewer(palette = "Set2") +
ggtitle("Average Sales Per Store by Promo") + labs(x = "Store", y = "Average Sales Per Store") + theme_classic()
ggsave("Average_Sales_Per_Store_by_Promo.png")
# Plotting average sales per store by competition distance
FullSet.sub = subset(FullSet, Sales != 0 & !is.na(CompetitionDistance), drop = TRUE)
SalesByDist = aggregate(FullSet.sub$AvgSalesPerStore, by = list(FullSet.sub$CompetitionDistance), mean)
colnames(SalesByDist) = c("CompetitionDistance", "AvgSalesPerStore")
ggplot(SalesByDist, aes(x = CompetitionDistance, y = AvgSalesPerStore)) + geom_point() + scale_color_brewer(palette = "Set2") +
geom_smooth() + ggtitle("Average Sales Per Store by Competition Distance") + labs(x = "Competition Distance",
y = "Average Sales Per Store") + theme_bw()
ggsave("Average_Sales_by_Competition_Distance.png")
# Plotting the log of average sales
ggplot(SalesByDist, aes(x = log(CompetitionDistance), y = log(AvgSalesPerStore))) + geom_point() + scale_color_brewer(palette = "Set2") +
geom_smooth() + ggtitle("Log of Average Sales per Store by Log of Competition Distance") + labs(x = "Log (Competition Distance)",
y = "Log (Average Sales Per Store)") + theme_bw()
ggsave("Log_of_Sales_by_Competition Distance.png")
# Plotting dynamics of sales per store per month
ggplot(FullSet, aes(x = as.Date(NewDate), y = AvgSalesPerStorePerMonth)) + geom_smooth(size = 2) + ggtitle("Average Sales Per Store Per Month over Time") +
labs(x = "Date", y = "Average Sales Per Store Per Month") + theme_bw()
ggsave("Average_Sales_per_Store_Per_Month.png")
# Plotting dynamics of customers per store per month
ggplot(FullSet, aes(x = as.Date(NewDate), y = AvgVisitsPerStorePerMonth)) + geom_smooth(size = 2) + ggtitle("Average Customers Per Store Per Month over Time") +
labs(x = "Date", y = "Average Customers Per Store Per Month") + theme_bw()
ggsave("Average_Customers_per_Store_Per_Month.png")
# Loading xgb model for further plotting
xgb = readRDS("xgb")
# Calculating partial dependence
xgb.partialPlots = list() # empty result list
imp.var.xgb = c("Open", "CompetitionDistance", "Store", "Promo", "CompetitionSinceDate", "Date", "Promo2SinceDate",
"StoreType", "Assortment", "Promo2", "DayOfWeek", "StoreAssortmentMatch")
for (var in imp.var.xgb) {
message("Now calculating for variable ", var)
xgb.partialPlots[[var]] = do.call(partial, list(xgb, pred.var = var, type = "auto", plot = FALSE))
}
# Creating the partial dependence plots and saving as pdf
par(mfrow = c(1, 2))
for (var in names(xgb.partialPlots)) {
png(paste("PDPSales", var, ".png", sep = "_"))
plot(x = xgb.partialPlots[[var]][, 1], y = xgb.partialPlots[[var]][, 2], type = "l", xlab = var, ylab = "Sales",
ylim = c(0, 8000), main = paste("Partial dependence of Sales on", var), bg = "transparent", lwd = 2)
dev.off()
}
|
# Record start time
time_start <- Sys.time()
# Remove not needed variables
rm(list=c('MC_Z','MC_h','MC_stock_log'))
# Precalculate some numbers
stock_n <- length(stockList)
stock_days <- nrow(stock_log)
pf_days <- length(pf_ret_nday)
# Get mean returns of the stocks
stock_log_mean <- apply(stock_log,2,mean)
# Deduct the mean from every stock, for standardized news process in GARCH process
stock_log_mean0 <- sweep(stock_log,2,stock_log_mean)
#=================================================================================================
## GARCH model: Fit GARCH model on all marginal returns
# Store all relevant parameters in variables. Preallocate all variables first:
# GARCH model parameters:
GARCH_mu <- vector(mode = "list", length = stock_n)
GARCH_omega <- vector(mode = "list", length = stock_n)
GARCH_alpha <- vector(mode = "list", length = stock_n)
GARCH_beta <- vector(mode = "list", length = stock_n)
GARCH_gamma <- vector(mode = "list", length = stock_n)
# GARCH residuals, condtional volatility and standardized residuals
GARCH_residuals <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_h.t <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_sigma.t <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_Z <- matrix(nrow = stock_days,ncol = stock_n)
# For GARCH model
if(GARCH_model == 'GARCH'){
for(s in 1:stock_n){
# Estimate GARCH for every individual stocks returns, adjusted for mean 0
# Use fGARCH package. Normal GARCH model is a special case of fGARCH model.
GARCH <- garchFit(formula = ~ garch(1, 1), data=stock_log_mean0[,s], cond.dist=GARCHcondDist)
# Store estiated parameters in lists
GARCH_mu[s] <- coef(GARCH)[1]
GARCH_omega[s] <- coef(GARCH)[2]
GARCH_alpha[s] <- coef(GARCH)[3]
GARCH_beta[s] <- coef(GARCH)[4]
GARCH_residuals[,s] <- GARCH@residuals
GARCH_h.t[,s] <- GARCH@h.t
GARCH_sigma.t[,s] <- GARCH@sigma.t
}
# Standardized residuals are residuals divided by conditional volatility
GARCH_Z <- GARCH_residuals/GARCH_sigma.t
}
# For TGARCH model
if(GARCH_model == 'TGARCH'){
for(s in 1:stock_n){
# Estimate TGARCH for every individual stocks returns, adjusted for mean 0
# Use fGARCH package. GJR-GARCH model is a special case of fGARCH model with delta = 2 and leverage
GARCH <- garchFit(formula = ~ garch(1, 1), delta = 2, leverage = TRUE, data=stock_log_mean0[,s], cond.dist=GARCHcondDist)
# Store estiated parameters in lists
GARCH_mu[s] <- coef(GARCH)[1]
GARCH_omega[s] <- coef(GARCH)[2]
GARCH_alpha[s] <- coef(GARCH)[3]
GARCH_gamma[s] <- coef(GARCH)[4]
GARCH_beta[s] <- coef(GARCH)[5]
GARCH_residuals[,s] <- GARCH@residuals
GARCH_h.t[,s] <- GARCH@h.t
GARCH_sigma.t[,s] <- GARCH@sigma.t
}
# Standardized residuals are residuals divided by conditional volatility
GARCH_Z <- GARCH_residuals/GARCH_sigma.t
}
# Delete not needed variables to free working memory
rm(list='GARCH')
#=================================================================================================
## Copula
# This section creates a copula model in order to get the multivariate distribution of the marginal
# returns. The copula package is used.
# Define the used copula
copula_obj <- claytonCopula(dim=length(stockList))
# Matrix of marginal returns as pseudo observations [0,1]
copula_pobs <- pobs(GARCH_Z)
# Fit the copula
copula_fit <- fitCopula(copula_obj,copula_pobs,method='ml')
# Get the fitted copula parameter
copula_theta <- coef(copula_fit)
# Fit a t distribution on standardised redisuals of marginal returns and store parameters in vectors
copula_Z_mu <- vector(mode = "numeric", length = stock_n)
copula_Z_s <- vector(mode = "numeric", length = stock_n)
copula_Z_df <- vector(mode = "numeric", length = stock_n)
for(s in 1:stock_n){
copula_Z_fit <- fitdistr(GARCH_Z[,s],"t")
copula_Z_mu[s] <- copula_Z_fit$estimate[["m"]]
copula_Z_s[s] <- copula_Z_fit$estimate[["s"]]
copula_Z_df[s] <- copula_Z_fit$estimate[["df"]]
}
# Delete not needed variables
rm(list='copula_Z_fit')
## Create lists with parameters for use of copula function
# T distribution for all standardized residuals
copula_margins_list <- matrix(data='t',nrow=1,ncol=stock_n)[1,]
# Make a list with the degrees of freedom of the standardized residuals
copula_paramMargins_list <- vector(mode="list", length = stock_n)
copula_paramMargins_list_names <- matrix(data='df',nrow=1,ncol=stock_n)[1,]
names(copula_paramMargins_list) <- copula_paramMargins_list_names
for(s in 1:stock_n){
copula_paramMargins_list[s] <- copula_Z_df[s]
}
# Create a copula object with all correct parameters, that can later be used to generate an array with returns
copula_dist <- mvdc(copula=claytonCopula(copula_theta, dim = length(stockList)), margins=copula_margins_list,
paramMargins = copula_paramMargins_list)
#=================================================================================================
## GARCH functions to simulate conditional variance h(t) as function of h(t-1) and z(t-1) and the estimated parameters
# GARCH
GARCH_ht_function <- function(omega,alpha,beta,hPrevious,zPrevious){
epsilon <- sqrt(hPrevious)*zPrevious
h <- omega + alpha*epsilon^2 + beta*hPrevious
return(h)
}
# GJR TGARCH as special version of fGARCH. Formula from introduction to the rugarch package, p.9
TGARCH_ht_function <- function(omega,alpha,beta,gamma,hPrevious,zPrevious){
h <- omega + alpha*hPrevious*(abs(zPrevious)-gamma*zPrevious)^2 + beta*hPrevious
return(h)
}
#=================================================================================================
## Monte Carlo simulation
# For high calculating performance 4 dimensional arrays are used, to reduce the number of loops
# Dimensions: 1: the different stocks, 2: the total days of the model, 3: days of VaR, 4: Monte Carlo simulations for each day
#==============================================
## Standardized residuals
# Draw all random residuals at once with correct dependence between stocks. Use the copula object generated before.
# In array the dimensions are filled consecutively with data. In the MC_Z array the first dimension are
# the different stocks, so that they can be filled with the correct dependence. The residuals in other 3 dimensions are i.i.d.
# For memory limitation reasons this part of the program is split up depending on working memory of the pc
VaR_part_function <- function(h.t){
VaR_part_trading_days <- nrow(h.t)
# Normal that this line takes a lot of time
MC_Z <- array(as.vector(t(rMvdc(copula_dist, n=MC_n*VaR_days*VaR_part_trading_days))), dim=c(stock_n,VaR_part_trading_days,VaR_days,MC_n))
# Scale back all standardized residuals
for(s in 1:stock_n){
MC_Z[s,,,] <- MC_Z[s,,,]*copula_Z_s[s]+copula_Z_mu[s]
}
#==============================================
## Conditional variance
# Create array to store h(t)
MC_h <- array(dim=c(stock_n,VaR_part_trading_days,VaR_days,MC_n))
# The variance of the first simulated day is taken from GARCH model and is the same for all simulated days
for(s in 1:stock_n){
MC_h[s,,1,] <- matrix(h.t[,s],nrow=VaR_part_trading_days,ncol=MC_n)
}
# Apply GARCH or TGARCH function. Function works over the 2 dimensions all days of the model
# and all simulated days at the same time
if(GARCH_model == 'GARCH'){
for(s in 1:stock_n){
for(i in 2:VaR_days){
MC_h[s,,i,] <- GARCH_ht_function(GARCH_omega[[s]],GARCH_alpha[[s]],GARCH_beta[[s]],MC_h[s,,i-1,],MC_Z[s,,i-1,])
}
}
}
if(GARCH_model == 'TGARCH'){
for(s in 1:stock_n){
for(i in 2:VaR_days){
MC_h[s,,i,] <- TGARCH_ht_function(GARCH_omega[[s]],GARCH_alpha[[s]],GARCH_beta[[s]],GARCH_gamma[[s]],MC_h[s,,i-1,],MC_Z[s,,i-1,])
}
}
}
#=================================================================================================
## Simulate returns and get VaR
# Multiply sampled standardized returns with conditional volatility. Works over all 4 dimensions
MC_stock_log <- MC_Z*sqrt(MC_h)
# Add back the mean
for(s in 1:stock_n){
MC_stock_log[s,,,] <- MC_stock_log[s,,,] + stock_log_mean[s]
}
# Get cumulative returns of the n days. Apply function on the other 3 dimensions
MC_log <- apply(MC_stock_log,c(1,2,4),sum)
# Change to simple returns because they aggregate over assets
MC_ret <- exp(MC_log)-1
# Get portfolio returns as mean of the returns of the different stocks
MC_ret <- apply(MC_ret,c(2,3),mean)
# Change back to log returns
MC_log <- log(MC_ret+1)
# Apply quantile function to get VaR
VaR_part <- apply(MC_log,1,quantile,VaR_alpha)
# Return several variables
ret <- list("VaR" = VaR_part,"MC_Z" = MC_Z,"MC_stock_log" = MC_stock_log,'MC_log' = MC_log)
return(ret)
# Remove to free working memory
rm(list=c('ret','MC_Z','MC_h','MC_stock_log','MC_log'))
}
## In order to use the working memory efficiently, the simulation is split up into several parts
# This tries to estimate the optimal number of parts
VaR_part_n <- round(((MC_n*VaR_days*stock_days*stock_n*4)/(memory_mb*50000)),0)
# Loop needs at least two parts to run
if(VaR_part_n < 2){
VaR_part_n <- 2
}
# Determine the start and the end day of the parts and save them in a matrix
VaR_part_start_end <- matrix(nrow = VaR_part_n,ncol = 2)
VaR_part_length <- round(nrow(GARCH_h.t)/VaR_part_n,0)
VaR_part_start_end[1,1] <- 1
for(i in 1:(VaR_part_n-1)){
VaR_part_start_end[i,2] <- VaR_part_start_end[i,1] + VaR_part_length
VaR_part_start_end[(i+1),1] <- VaR_part_start_end[i,2] + 1
}
# Variables to store the resulting VaR
VaR_part_start_end[VaR_part_n,2] <- nrow(GARCH_h.t)
VaR <- vector(mode='numeric',length = nrow(GARCH_h.t))
# Store for plots
MC_stock_log <- matrix(nrow=nrow(GARCH_h.t),ncol=stock_n)
MC_log <- matrix(nrow=nrow(GARCH_h.t),ncol=MC_n)
# For every part calculate the VaR
for(i in 1:VaR_part_n){
# To see how far the sim is
print('Model 2:')
print(i/VaR_part_n)
# Use the function to get VaR of this part
VaR_part_ret <- VaR_part_function(GARCH_h.t[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],])
# Get the data from the returned list
VaR_part <- VaR_part_ret$VaR
VaR[VaR_part_start_end[i,1]:VaR_part_start_end[i,2]] <- VaR_part
# Store for plots
MC_Z <- VaR_part_ret$MC_Z
MC_stock_log[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],] <- t(VaR_part_ret$MC_stock_log[,,1,1])
MC_log[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],] <- VaR_part_ret$MC_log
}
#=================================================================================================
## Check model
# Exceedance ratio, code as shown in class
hitSeq <- pf_ret_nday < VaR[1:length(pf_ret_nday)]
numberOfHits <- sum(hitSeq)
exRatio <- numberOfHits/length(pf_ret_nday)
## Kupiec test
# Higher precision is needed, otherwise numerator and denumerator are treated as 0
N <- mpfr(length(pf_ret_nday),precBits= 128)
exRatio <- mpfr(exRatio,precBits = 128)
numberOfHits <- mpfr(numberOfHits,precBits = 128)
VaR_alpha <- mpfr(VaR_alpha,precBits = 128)
num <- (exRatio^numberOfHits)*(1-exRatio)^(N-numberOfHits)
den <- (VaR_alpha^numberOfHits )*(1-VaR_alpha)^(N-numberOfHits)
K <- as.numeric(2*log(num/den))
VaR_alpha <- as.numeric(VaR_alpha)
exRatio <- as.numeric(exRatio)
p <- 0.99
if(K < qchisq(p,1)){
print("VaR model is accurate at 99% level")
}else{
print("VaR model is not accurate at 99% level")
}
#=================================================================================================
## Plots
## Plots related to GARCH. Made for one stock of the portfolio
plot_stock_n <- 1
# Stock returns
plot_name <- paste('plots/',stockList[plot_stock_n],'_1_day_log_returns.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- '1 day log returns'
plot_main <- paste(stockList[plot_stock_n],'returns')
pdf(plot_name)
plot(index,stock_log[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
dev.off()
# Conditional volatility
plot_name <- paste('plots/',stockList[plot_stock_n],'_volatility.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- 'Std'
plot_main <- paste(stockList[plot_stock_n],'annual stdev.')
pdf(plot_name)
plot(index,sqrt(252)*GARCH_sigma.t[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
plot_stock_uncond_vol <- vector(mode='numeric',length = length(index))
# Unconditional variance as mean of conditional variance
plot_stock_uncond_vol[] <- mean(sqrt(252)*GARCH_sigma.t[,plot_stock_n][1:length(index)])
lines(index,plot_stock_uncond_vol,col='green',lwd = 2)
legend('topright',c(expression(paste(sqrt('h'['t']),': GARCH cond. stdev.')), expression(paste(sigma,': uncond. stdev.'))),col=c('black', 'green'), lwd=2)
dev.off()
# Standardized residuals
plot_name <- paste('plots/',stockList[plot_stock_n],'_standardized_residuals.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- 'Std'
plot_main <- paste(stockList[plot_stock_n],'standardized residuals')
pdf(plot_name)
plot(index,GARCH_Z[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
dev.off()
#==============================================
## Plots related to copula
# Fitting of t dist on standardized residuals
plot_name <- paste('plots/',stockList[plot_stock_n],'_hist_standardized_residuals.pdf',sep='')
plot_xlab <- 'Std'
plot_ylab <- 'Density'
plot_main <- paste(stockList[plot_stock_n],'histogram standardized residuals')
hist(sample(MC_Z[plot_stock_n,,,],10000),breaks=80,freq=F,col='blue',xlim=c(-6,6),ylim=c(0,0.6),main=plot_main,xlab=plot_xlab,ylab=plot_ylab)
pdf(plot_name)
hist(sample(MC_Z[plot_stock_n,,,],10000),breaks=80,freq=F,col='blue',xlim=c(-6,6),ylim=c(0,0.6),main=plot_main,xlab=plot_xlab,ylab=plot_ylab)
lines(seq(-6,6,0.01),dt((seq(-6,6,0.01)-copula_Z_mu[plot_stock_n])/copula_Z_s[plot_stock_n],copula_Z_df[plot_stock_n])/copula_Z_s[plot_stock_n],col='red',lwd=2)
legend('topright',c('10000 standardized \nresiduals','Fitted t-distribution'),col=c('blue','red'), lwd=2)
dev.off()
# Plot dependence between two stocks returns, observed and simulated with copula
plot_stock_n <- 1
plot_stock_n2 <- 2
plot_name <- paste('plots/',stockList[plot_stock_n],'-',stockList[plot_stock_n2],'_observed_vs_simulated_returns.pdf',sep='')
plot_main <- 'Observed vs. simulated returns'
plot_xlab <- paste(stockList[plot_stock_n],'log returns')
plot_ylab <- paste(stockList[plot_stock_n2],'log returns')
plot_stock_dependence <- matrix(nrow = nrow(GARCH_h.t),ncol=2)
plot_stock_dependence[,plot_stock_n] <- MC_stock_log[,plot_stock_n]
plot_stock_dependence[,plot_stock_n2] <- MC_stock_log[,plot_stock_n2]
#plot_stock_dependence[,plot_stock_n] <- as.vector(MC_stock_log[plot_stock_n,1,,])
#plot_stock_dependence[,plot_stock_n2] <- as.vector(MC_stock_log[plot_stock_n2,1,,])
#plot_stock_dependence <- plot_stock_dependence[1:nrow(stock_log),]
plot_xmin <- min(min(stock_log[,plot_stock_n]),min(plot_stock_dependence[,plot_stock_n]))
plot_xmax <- max(max(stock_log[,plot_stock_n]),max(plot_stock_dependence[,plot_stock_n]))
plot_ymin <- min(min(stock_log[,plot_stock_n2]),min(plot_stock_dependence[,plot_stock_n2]))
plot_ymax <- max(max(stock_log[,plot_stock_n2]),max(plot_stock_dependence[,plot_stock_n2]))
pdf(plot_name)
plot(stock_log[,plot_stock_n],stock_log[,plot_stock_n2],xlim=c(plot_xmin,plot_xmax),ylim=c(plot_ymin,plot_ymax),xlab=plot_xlab,ylab=plot_ylab, main=plot_main,pch=16,cex=0.5)
points(plot_stock_dependence[,plot_stock_n],plot_stock_dependence[,plot_stock_n2],col='red',pch=16,cex=0.5)
legend('topleft',c('Observed','Simulated'),col=c('black','red'),pch=16,cex=1)
dev.off()
# Check correct sampling with dependence between modeled standardised residuals
plot_name <- paste('plots/pf',toString(pf_n),'_copula_simulated_returns_spearmans_rho.pdf',sep='')
plot_main <- expression(paste("Spearman's ",rho, " simulated returns"))
MC_residuals_plot <- t(MC_stock_log)
pdf(plot_name)
pairs.panels(t(MC_Z[,1,1,]),method='spearman',main=plot_main)
dev.off()
# VaR and portfolio returns
plot_name <- paste('plots/VaR_model2_pf',toString(pf_n),'_',toString(VaR_days),'day_',toString(VaR_alpha),'VaR_',MC_n,'simulations_',strftime(Sys.time(),format = "%Y-%m-%d--%H-%M-%S"),'.pdf',sep='')
plot_main <- paste('Portfolio ',toString(pf_n),' - ',toString(VaR_days),' day ','- ',toString(VaR_alpha*100),'% VaR',sep='')
plot_ylab <- paste(toString(VaR_days),'day log returns')
plot_xlab <- 'Year'
index = index[1:length(pf_log_nday)]
pdf(plot_name)
plot(index, pf_log_nday, main = plot_main,xlab = plot_xlab,ylab = plot_ylab)
lines(index, VaR[1:length(pf_log_nday)], col="red" )
points(index[hitSeq], pf_log_nday[hitSeq], pch="+", col="green")
legend('topright',c('VaR Model 2','Exceedances'),col=c('red','green'), pch=c('-','+'))
dev.off()
#=================================================================================================
## Calculate some unconditional summary statistics for simulated portfolio returns
# For simulated n day portfolio returns
MC_log_mean <- mean(MC_log)
MC_log_std <- sqrt(var(as.vector(MC_log)))
MC_log_skewness <- skewness(as.vector(MC_log))
MC_log_kurtosis <- kurtosis(as.vector(MC_log),method = 'moment')
hist(as.vector(MC_log))
#=================================================================================================
## Output file
time_end <- Sys.time()
time_model <- time_end- time_start
writeFile(2)
results <- fillData(2)
|
/model2.R
|
no_license
|
HugoCasa/daf-project
|
R
| false | false | 17,476 |
r
|
# Record start time
time_start <- Sys.time()
# Remove not needed variables
rm(list=c('MC_Z','MC_h','MC_stock_log'))
# Precalculate some numbers
stock_n <- length(stockList)
stock_days <- nrow(stock_log)
pf_days <- length(pf_ret_nday)
# Get mean returns of the stocks
stock_log_mean <- apply(stock_log,2,mean)
# Deduct the mean from every stock, for standardized news process in GARCH process
stock_log_mean0 <- sweep(stock_log,2,stock_log_mean)
#=================================================================================================
## GARCH model: Fit GARCH model on all marginal returns
# Store all relevant parameters in variables. Preallocate all variables first:
# GARCH model parameters:
GARCH_mu <- vector(mode = "list", length = stock_n)
GARCH_omega <- vector(mode = "list", length = stock_n)
GARCH_alpha <- vector(mode = "list", length = stock_n)
GARCH_beta <- vector(mode = "list", length = stock_n)
GARCH_gamma <- vector(mode = "list", length = stock_n)
# GARCH residuals, condtional volatility and standardized residuals
GARCH_residuals <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_h.t <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_sigma.t <- matrix(nrow = stock_days,ncol = stock_n)
GARCH_Z <- matrix(nrow = stock_days,ncol = stock_n)
# For GARCH model
if(GARCH_model == 'GARCH'){
for(s in 1:stock_n){
# Estimate GARCH for every individual stocks returns, adjusted for mean 0
# Use fGARCH package. Normal GARCH model is a special case of fGARCH model.
GARCH <- garchFit(formula = ~ garch(1, 1), data=stock_log_mean0[,s], cond.dist=GARCHcondDist)
# Store estiated parameters in lists
GARCH_mu[s] <- coef(GARCH)[1]
GARCH_omega[s] <- coef(GARCH)[2]
GARCH_alpha[s] <- coef(GARCH)[3]
GARCH_beta[s] <- coef(GARCH)[4]
GARCH_residuals[,s] <- GARCH@residuals
GARCH_h.t[,s] <- GARCH@h.t
GARCH_sigma.t[,s] <- GARCH@sigma.t
}
# Standardized residuals are residuals divided by conditional volatility
GARCH_Z <- GARCH_residuals/GARCH_sigma.t
}
# For TGARCH model
if(GARCH_model == 'TGARCH'){
for(s in 1:stock_n){
# Estimate TGARCH for every individual stocks returns, adjusted for mean 0
# Use fGARCH package. GJR-GARCH model is a special case of fGARCH model with delta = 2 and leverage
GARCH <- garchFit(formula = ~ garch(1, 1), delta = 2, leverage = TRUE, data=stock_log_mean0[,s], cond.dist=GARCHcondDist)
# Store estiated parameters in lists
GARCH_mu[s] <- coef(GARCH)[1]
GARCH_omega[s] <- coef(GARCH)[2]
GARCH_alpha[s] <- coef(GARCH)[3]
GARCH_gamma[s] <- coef(GARCH)[4]
GARCH_beta[s] <- coef(GARCH)[5]
GARCH_residuals[,s] <- GARCH@residuals
GARCH_h.t[,s] <- GARCH@h.t
GARCH_sigma.t[,s] <- GARCH@sigma.t
}
# Standardized residuals are residuals divided by conditional volatility
GARCH_Z <- GARCH_residuals/GARCH_sigma.t
}
# Delete not needed variables to free working memory
rm(list='GARCH')
#=================================================================================================
## Copula
# This section creates a copula model in order to get the multivariate distribution of the marginal
# returns. The copula package is used.
# Define the used copula
copula_obj <- claytonCopula(dim=length(stockList))
# Matrix of marginal returns as pseudo observations [0,1]
copula_pobs <- pobs(GARCH_Z)
# Fit the copula
copula_fit <- fitCopula(copula_obj,copula_pobs,method='ml')
# Get the fitted copula parameter
copula_theta <- coef(copula_fit)
# Fit a t distribution on standardised redisuals of marginal returns and store parameters in vectors
copula_Z_mu <- vector(mode = "numeric", length = stock_n)
copula_Z_s <- vector(mode = "numeric", length = stock_n)
copula_Z_df <- vector(mode = "numeric", length = stock_n)
for(s in 1:stock_n){
copula_Z_fit <- fitdistr(GARCH_Z[,s],"t")
copula_Z_mu[s] <- copula_Z_fit$estimate[["m"]]
copula_Z_s[s] <- copula_Z_fit$estimate[["s"]]
copula_Z_df[s] <- copula_Z_fit$estimate[["df"]]
}
# Delete not needed variables
rm(list='copula_Z_fit')
## Create lists with parameters for use of copula function
# T distribution for all standardized residuals
copula_margins_list <- matrix(data='t',nrow=1,ncol=stock_n)[1,]
# Make a list with the degrees of freedom of the standardized residuals
copula_paramMargins_list <- vector(mode="list", length = stock_n)
copula_paramMargins_list_names <- matrix(data='df',nrow=1,ncol=stock_n)[1,]
names(copula_paramMargins_list) <- copula_paramMargins_list_names
for(s in 1:stock_n){
copula_paramMargins_list[s] <- copula_Z_df[s]
}
# Create a copula object with all correct parameters, that can later be used to generate an array with returns
copula_dist <- mvdc(copula=claytonCopula(copula_theta, dim = length(stockList)), margins=copula_margins_list,
paramMargins = copula_paramMargins_list)
#=================================================================================================
## GARCH functions to simulate conditional variance h(t) as function of h(t-1) and z(t-1) and the estimated parameters
# GARCH
GARCH_ht_function <- function(omega,alpha,beta,hPrevious,zPrevious){
epsilon <- sqrt(hPrevious)*zPrevious
h <- omega + alpha*epsilon^2 + beta*hPrevious
return(h)
}
# GJR TGARCH as special version of fGARCH. Formula from introduction to the rugarch package, p.9
TGARCH_ht_function <- function(omega,alpha,beta,gamma,hPrevious,zPrevious){
h <- omega + alpha*hPrevious*(abs(zPrevious)-gamma*zPrevious)^2 + beta*hPrevious
return(h)
}
#=================================================================================================
## Monte Carlo simulation
# For high calculating performance 4 dimensional arrays are used, to reduce the number of loops
# Dimensions: 1: the different stocks, 2: the total days of the model, 3: days of VaR, 4: Monte Carlo simulations for each day
#==============================================
## Standardized residuals
# Draw all random residuals at once with correct dependence between stocks. Use the copula object generated before.
# In array the dimensions are filled consecutively with data. In the MC_Z array the first dimension are
# the different stocks, so that they can be filled with the correct dependence. The residuals in other 3 dimensions are i.i.d.
# For memory limitation reasons this part of the program is split up depending on working memory of the pc
VaR_part_function <- function(h.t){
VaR_part_trading_days <- nrow(h.t)
# Normal that this line takes a lot of time
MC_Z <- array(as.vector(t(rMvdc(copula_dist, n=MC_n*VaR_days*VaR_part_trading_days))), dim=c(stock_n,VaR_part_trading_days,VaR_days,MC_n))
# Scale back all standardized residuals
for(s in 1:stock_n){
MC_Z[s,,,] <- MC_Z[s,,,]*copula_Z_s[s]+copula_Z_mu[s]
}
#==============================================
## Conditional variance
# Create array to store h(t)
MC_h <- array(dim=c(stock_n,VaR_part_trading_days,VaR_days,MC_n))
# The variance of the first simulated day is taken from GARCH model and is the same for all simulated days
for(s in 1:stock_n){
MC_h[s,,1,] <- matrix(h.t[,s],nrow=VaR_part_trading_days,ncol=MC_n)
}
# Apply GARCH or TGARCH function. Function works over the 2 dimensions all days of the model
# and all simulated days at the same time
if(GARCH_model == 'GARCH'){
for(s in 1:stock_n){
for(i in 2:VaR_days){
MC_h[s,,i,] <- GARCH_ht_function(GARCH_omega[[s]],GARCH_alpha[[s]],GARCH_beta[[s]],MC_h[s,,i-1,],MC_Z[s,,i-1,])
}
}
}
if(GARCH_model == 'TGARCH'){
for(s in 1:stock_n){
for(i in 2:VaR_days){
MC_h[s,,i,] <- TGARCH_ht_function(GARCH_omega[[s]],GARCH_alpha[[s]],GARCH_beta[[s]],GARCH_gamma[[s]],MC_h[s,,i-1,],MC_Z[s,,i-1,])
}
}
}
#=================================================================================================
## Simulate returns and get VaR
# Multiply sampled standardized returns with conditional volatility. Works over all 4 dimensions
MC_stock_log <- MC_Z*sqrt(MC_h)
# Add back the mean
for(s in 1:stock_n){
MC_stock_log[s,,,] <- MC_stock_log[s,,,] + stock_log_mean[s]
}
# Get cumulative returns of the n days. Apply function on the other 3 dimensions
MC_log <- apply(MC_stock_log,c(1,2,4),sum)
# Change to simple returns because they aggregate over assets
MC_ret <- exp(MC_log)-1
# Get portfolio returns as mean of the returns of the different stocks
MC_ret <- apply(MC_ret,c(2,3),mean)
# Change back to log returns
MC_log <- log(MC_ret+1)
# Apply quantile function to get VaR
VaR_part <- apply(MC_log,1,quantile,VaR_alpha)
# Return several variables
ret <- list("VaR" = VaR_part,"MC_Z" = MC_Z,"MC_stock_log" = MC_stock_log,'MC_log' = MC_log)
return(ret)
# Remove to free working memory
rm(list=c('ret','MC_Z','MC_h','MC_stock_log','MC_log'))
}
## In order to use the working memory efficiently, the simulation is split up into several parts
# This tries to estimate the optimal number of parts
VaR_part_n <- round(((MC_n*VaR_days*stock_days*stock_n*4)/(memory_mb*50000)),0)
# Loop needs at least two parts to run
if(VaR_part_n < 2){
VaR_part_n <- 2
}
# Determine the start and the end day of the parts and save them in a matrix
VaR_part_start_end <- matrix(nrow = VaR_part_n,ncol = 2)
VaR_part_length <- round(nrow(GARCH_h.t)/VaR_part_n,0)
VaR_part_start_end[1,1] <- 1
for(i in 1:(VaR_part_n-1)){
VaR_part_start_end[i,2] <- VaR_part_start_end[i,1] + VaR_part_length
VaR_part_start_end[(i+1),1] <- VaR_part_start_end[i,2] + 1
}
# Variables to store the resulting VaR
VaR_part_start_end[VaR_part_n,2] <- nrow(GARCH_h.t)
VaR <- vector(mode='numeric',length = nrow(GARCH_h.t))
# Store for plots
MC_stock_log <- matrix(nrow=nrow(GARCH_h.t),ncol=stock_n)
MC_log <- matrix(nrow=nrow(GARCH_h.t),ncol=MC_n)
# For every part calculate the VaR
for(i in 1:VaR_part_n){
# To see how far the sim is
print('Model 2:')
print(i/VaR_part_n)
# Use the function to get VaR of this part
VaR_part_ret <- VaR_part_function(GARCH_h.t[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],])
# Get the data from the returned list
VaR_part <- VaR_part_ret$VaR
VaR[VaR_part_start_end[i,1]:VaR_part_start_end[i,2]] <- VaR_part
# Store for plots
MC_Z <- VaR_part_ret$MC_Z
MC_stock_log[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],] <- t(VaR_part_ret$MC_stock_log[,,1,1])
MC_log[VaR_part_start_end[i,1]:VaR_part_start_end[i,2],] <- VaR_part_ret$MC_log
}
#=================================================================================================
## Check model
# Exceedance ratio, code as shown in class
hitSeq <- pf_ret_nday < VaR[1:length(pf_ret_nday)]
numberOfHits <- sum(hitSeq)
exRatio <- numberOfHits/length(pf_ret_nday)
## Kupiec test
# Higher precision is needed, otherwise numerator and denumerator are treated as 0
N <- mpfr(length(pf_ret_nday),precBits= 128)
exRatio <- mpfr(exRatio,precBits = 128)
numberOfHits <- mpfr(numberOfHits,precBits = 128)
VaR_alpha <- mpfr(VaR_alpha,precBits = 128)
num <- (exRatio^numberOfHits)*(1-exRatio)^(N-numberOfHits)
den <- (VaR_alpha^numberOfHits )*(1-VaR_alpha)^(N-numberOfHits)
K <- as.numeric(2*log(num/den))
VaR_alpha <- as.numeric(VaR_alpha)
exRatio <- as.numeric(exRatio)
p <- 0.99
if(K < qchisq(p,1)){
print("VaR model is accurate at 99% level")
}else{
print("VaR model is not accurate at 99% level")
}
#=================================================================================================
## Plots
## Plots related to GARCH. Made for one stock of the portfolio
plot_stock_n <- 1
# Stock returns
plot_name <- paste('plots/',stockList[plot_stock_n],'_1_day_log_returns.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- '1 day log returns'
plot_main <- paste(stockList[plot_stock_n],'returns')
pdf(plot_name)
plot(index,stock_log[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
dev.off()
# Conditional volatility
plot_name <- paste('plots/',stockList[plot_stock_n],'_volatility.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- 'Std'
plot_main <- paste(stockList[plot_stock_n],'annual stdev.')
pdf(plot_name)
plot(index,sqrt(252)*GARCH_sigma.t[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
plot_stock_uncond_vol <- vector(mode='numeric',length = length(index))
# Unconditional variance as mean of conditional variance
plot_stock_uncond_vol[] <- mean(sqrt(252)*GARCH_sigma.t[,plot_stock_n][1:length(index)])
lines(index,plot_stock_uncond_vol,col='green',lwd = 2)
legend('topright',c(expression(paste(sqrt('h'['t']),': GARCH cond. stdev.')), expression(paste(sigma,': uncond. stdev.'))),col=c('black', 'green'), lwd=2)
dev.off()
# Standardized residuals
plot_name <- paste('plots/',stockList[plot_stock_n],'_standardized_residuals.pdf',sep='')
plot_xlab <- 'Year'
plot_ylab <- 'Std'
plot_main <- paste(stockList[plot_stock_n],'standardized residuals')
pdf(plot_name)
plot(index,GARCH_Z[,plot_stock_n][1:length(index)],type='l',main = plot_main,xlab = plot_xlab,ylab=plot_ylab)
dev.off()
#==============================================
## Plots related to copula
# Fitting of t dist on standardized residuals
plot_name <- paste('plots/',stockList[plot_stock_n],'_hist_standardized_residuals.pdf',sep='')
plot_xlab <- 'Std'
plot_ylab <- 'Density'
plot_main <- paste(stockList[plot_stock_n],'histogram standardized residuals')
hist(sample(MC_Z[plot_stock_n,,,],10000),breaks=80,freq=F,col='blue',xlim=c(-6,6),ylim=c(0,0.6),main=plot_main,xlab=plot_xlab,ylab=plot_ylab)
pdf(plot_name)
hist(sample(MC_Z[plot_stock_n,,,],10000),breaks=80,freq=F,col='blue',xlim=c(-6,6),ylim=c(0,0.6),main=plot_main,xlab=plot_xlab,ylab=plot_ylab)
lines(seq(-6,6,0.01),dt((seq(-6,6,0.01)-copula_Z_mu[plot_stock_n])/copula_Z_s[plot_stock_n],copula_Z_df[plot_stock_n])/copula_Z_s[plot_stock_n],col='red',lwd=2)
legend('topright',c('10000 standardized \nresiduals','Fitted t-distribution'),col=c('blue','red'), lwd=2)
dev.off()
# Plot dependence between two stocks returns, observed and simulated with copula
plot_stock_n <- 1
plot_stock_n2 <- 2
plot_name <- paste('plots/',stockList[plot_stock_n],'-',stockList[plot_stock_n2],'_observed_vs_simulated_returns.pdf',sep='')
plot_main <- 'Observed vs. simulated returns'
plot_xlab <- paste(stockList[plot_stock_n],'log returns')
plot_ylab <- paste(stockList[plot_stock_n2],'log returns')
plot_stock_dependence <- matrix(nrow = nrow(GARCH_h.t),ncol=2)
plot_stock_dependence[,plot_stock_n] <- MC_stock_log[,plot_stock_n]
plot_stock_dependence[,plot_stock_n2] <- MC_stock_log[,plot_stock_n2]
#plot_stock_dependence[,plot_stock_n] <- as.vector(MC_stock_log[plot_stock_n,1,,])
#plot_stock_dependence[,plot_stock_n2] <- as.vector(MC_stock_log[plot_stock_n2,1,,])
#plot_stock_dependence <- plot_stock_dependence[1:nrow(stock_log),]
plot_xmin <- min(min(stock_log[,plot_stock_n]),min(plot_stock_dependence[,plot_stock_n]))
plot_xmax <- max(max(stock_log[,plot_stock_n]),max(plot_stock_dependence[,plot_stock_n]))
plot_ymin <- min(min(stock_log[,plot_stock_n2]),min(plot_stock_dependence[,plot_stock_n2]))
plot_ymax <- max(max(stock_log[,plot_stock_n2]),max(plot_stock_dependence[,plot_stock_n2]))
pdf(plot_name)
plot(stock_log[,plot_stock_n],stock_log[,plot_stock_n2],xlim=c(plot_xmin,plot_xmax),ylim=c(plot_ymin,plot_ymax),xlab=plot_xlab,ylab=plot_ylab, main=plot_main,pch=16,cex=0.5)
points(plot_stock_dependence[,plot_stock_n],plot_stock_dependence[,plot_stock_n2],col='red',pch=16,cex=0.5)
legend('topleft',c('Observed','Simulated'),col=c('black','red'),pch=16,cex=1)
dev.off()
# Check correct sampling with dependence between modeled standardised residuals
plot_name <- paste('plots/pf',toString(pf_n),'_copula_simulated_returns_spearmans_rho.pdf',sep='')
plot_main <- expression(paste("Spearman's ",rho, " simulated returns"))
MC_residuals_plot <- t(MC_stock_log)
pdf(plot_name)
pairs.panels(t(MC_Z[,1,1,]),method='spearman',main=plot_main)
dev.off()
# VaR and portfolio returns
plot_name <- paste('plots/VaR_model2_pf',toString(pf_n),'_',toString(VaR_days),'day_',toString(VaR_alpha),'VaR_',MC_n,'simulations_',strftime(Sys.time(),format = "%Y-%m-%d--%H-%M-%S"),'.pdf',sep='')
plot_main <- paste('Portfolio ',toString(pf_n),' - ',toString(VaR_days),' day ','- ',toString(VaR_alpha*100),'% VaR',sep='')
plot_ylab <- paste(toString(VaR_days),'day log returns')
plot_xlab <- 'Year'
index = index[1:length(pf_log_nday)]
pdf(plot_name)
plot(index, pf_log_nday, main = plot_main,xlab = plot_xlab,ylab = plot_ylab)
lines(index, VaR[1:length(pf_log_nday)], col="red" )
points(index[hitSeq], pf_log_nday[hitSeq], pch="+", col="green")
legend('topright',c('VaR Model 2','Exceedances'),col=c('red','green'), pch=c('-','+'))
dev.off()
#=================================================================================================
## Calculate some unconditional summary statistics for simulated portfolio returns
# For simulated n day portfolio returns
MC_log_mean <- mean(MC_log)
MC_log_std <- sqrt(var(as.vector(MC_log)))
MC_log_skewness <- skewness(as.vector(MC_log))
MC_log_kurtosis <- kurtosis(as.vector(MC_log),method = 'moment')
hist(as.vector(MC_log))
#=================================================================================================
## Output file
time_end <- Sys.time()
time_model <- time_end- time_start
writeFile(2)
results <- fillData(2)
|
source("tmsRunner.R")
targetGene <- "CTBP2"
chromosome <- "chr10"
start.generous <- 124715246
end.generous <- 125430101
viz <- FALSE
if(viz){ # determine start.focused, end.focused
igv <- start.igv(targetGene, "hg38")
showGenomicRegion(igv, sprintf("%s:%d-%d", chromosome, start.generous, end.generous))
ghdb <- GeneHancerDB()
tbl.gh <- retrieveEnhancersFromDatabase(ghdb, targetGene, tissues="all") # "Common myeloid progenitor CD34+")
track <- DataFrameQuantitativeTrack("gh", tbl.gh[, c("chrom", "start", "end", "combinedscore")], autoscale=TRUE, color="brown")
displayTrack(igv, track)
tbl.atac.merged <- get(load("~/github/TrenaProjectErythropoiesis/inst/extdata/genomicRegions/tbl.atacMerged.RData"))
loc <- getGenomicRegion(igv)
tbl.atac.sub <- subset(tbl.atac.merged, chrom==chromosome & start >= loc$start & end <= loc$end)
dim(tbl.atac.sub)
track <- DataFrameAnnotationTrack("atac", tbl.atac.sub, color="red")
displayTrack(igv, track)
}
# first model, just atac in gh promoter: 12.5k
start.focused <- 125145702
end.focused <- 125163569
# second model, many extra atac regions added: 71kb
start.focused <- 124979054
end.focused <- 125211343
printf("width: %5.2f", (1 + end.focused - start.focused)/1000)
mtx.rna <- getExpressionMatrix(trenaProject, "brandLabDifferentiationTimeCourse-27171x28")
#mtx <- getExpressionMatrix(trenaProject, "brandLabDifferentiationTimeCourse-27171x28-namesCorrected")
x <- runTMS(targetGene, chromosome, start.focused, end.focused, mtx.rna)
lm.tables <- x$get.lm.tables()
lm.rsquareds <- x$get.lm.Rsquareds()
filename <- sprintf("%s-model-%s:%d-%d.RData", targetGene, chromosome,
start.focused, end.focused)
save(x, file=filename)
lm.tables <- x$get.lm.tables()
lapply(names(lm.tables), function(name) {
printf("-------- %s", name)
print(subset(lm.tables[[name]])) # , p.value < 0.25))
})
print(x$get.lm.Rsquareds())
|
/tools/runner/v1/ctbp2.R
|
permissive
|
PriceLab/TrenaMultiScore
|
R
| false | false | 1,963 |
r
|
source("tmsRunner.R")
targetGene <- "CTBP2"
chromosome <- "chr10"
start.generous <- 124715246
end.generous <- 125430101
viz <- FALSE
if(viz){ # determine start.focused, end.focused
igv <- start.igv(targetGene, "hg38")
showGenomicRegion(igv, sprintf("%s:%d-%d", chromosome, start.generous, end.generous))
ghdb <- GeneHancerDB()
tbl.gh <- retrieveEnhancersFromDatabase(ghdb, targetGene, tissues="all") # "Common myeloid progenitor CD34+")
track <- DataFrameQuantitativeTrack("gh", tbl.gh[, c("chrom", "start", "end", "combinedscore")], autoscale=TRUE, color="brown")
displayTrack(igv, track)
tbl.atac.merged <- get(load("~/github/TrenaProjectErythropoiesis/inst/extdata/genomicRegions/tbl.atacMerged.RData"))
loc <- getGenomicRegion(igv)
tbl.atac.sub <- subset(tbl.atac.merged, chrom==chromosome & start >= loc$start & end <= loc$end)
dim(tbl.atac.sub)
track <- DataFrameAnnotationTrack("atac", tbl.atac.sub, color="red")
displayTrack(igv, track)
}
# first model, just atac in gh promoter: 12.5k
start.focused <- 125145702
end.focused <- 125163569
# second model, many extra atac regions added: 71kb
start.focused <- 124979054
end.focused <- 125211343
printf("width: %5.2f", (1 + end.focused - start.focused)/1000)
mtx.rna <- getExpressionMatrix(trenaProject, "brandLabDifferentiationTimeCourse-27171x28")
#mtx <- getExpressionMatrix(trenaProject, "brandLabDifferentiationTimeCourse-27171x28-namesCorrected")
x <- runTMS(targetGene, chromosome, start.focused, end.focused, mtx.rna)
lm.tables <- x$get.lm.tables()
lm.rsquareds <- x$get.lm.Rsquareds()
filename <- sprintf("%s-model-%s:%d-%d.RData", targetGene, chromosome,
start.focused, end.focused)
save(x, file=filename)
lm.tables <- x$get.lm.tables()
lapply(names(lm.tables), function(name) {
printf("-------- %s", name)
print(subset(lm.tables[[name]])) # , p.value < 0.25))
})
print(x$get.lm.Rsquareds())
|
library(pinnacle.API)
### Name: showOddsDF
### Title: showOddsDF - Takes a GetOdds JSON response and combines with
### Fixtures and Inrunning
### Aliases: showOddsDF
### ** Examples
## No test:
SetCredentials("TESTAPI","APITEST")
AcceptTermsAndConditions(accepted=TRUE)
# Run without arguments, it will prompt you for the sport
showOddsDF()
## End(No test)
|
/data/genthat_extracted_code/pinnacle.API/examples/showOddsDF.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 367 |
r
|
library(pinnacle.API)
### Name: showOddsDF
### Title: showOddsDF - Takes a GetOdds JSON response and combines with
### Fixtures and Inrunning
### Aliases: showOddsDF
### ** Examples
## No test:
SetCredentials("TESTAPI","APITEST")
AcceptTermsAndConditions(accepted=TRUE)
# Run without arguments, it will prompt you for the sport
showOddsDF()
## End(No test)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/proceduralnames-package.R
\docType{package}
\name{proceduralnames-package}
\alias{proceduralnames}
\alias{proceduralnames-package}
\title{proceduralnames: Several Methods for Procedural Name Generation}
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
A small, dependency-free way to generate random names. Methods provided include the adjective-surname approach of Docker containers ('\url{https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go}'), and combinations of common English or Spanish words.
}
\seealso{
Useful links:
\itemize{
\item \url{https://mikemahoney218.github.io/proceduralnames/}
\item \url{https://github.com/mikemahoney218/proceduralnames}
\item Report bugs at \url{https://github.com/mikemahoney218/proceduralnames/issues}
}
}
\author{
\strong{Maintainer}: Michael Mahoney \email{mike.mahoney.218@gmail.com} (\href{https://orcid.org/0000-0003-2402-304X}{ORCID})
}
\keyword{internal}
|
/man/proceduralnames-package.Rd
|
permissive
|
mikemahoney218/proceduralnames
|
R
| false | true | 1,066 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/proceduralnames-package.R
\docType{package}
\name{proceduralnames-package}
\alias{proceduralnames}
\alias{proceduralnames-package}
\title{proceduralnames: Several Methods for Procedural Name Generation}
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
A small, dependency-free way to generate random names. Methods provided include the adjective-surname approach of Docker containers ('\url{https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go}'), and combinations of common English or Spanish words.
}
\seealso{
Useful links:
\itemize{
\item \url{https://mikemahoney218.github.io/proceduralnames/}
\item \url{https://github.com/mikemahoney218/proceduralnames}
\item Report bugs at \url{https://github.com/mikemahoney218/proceduralnames/issues}
}
}
\author{
\strong{Maintainer}: Michael Mahoney \email{mike.mahoney.218@gmail.com} (\href{https://orcid.org/0000-0003-2402-304X}{ORCID})
}
\keyword{internal}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotMA.R
\name{plotAllelicCounts}
\alias{plotAllelicCounts}
\title{plotAllelicCounts}
\usage{
plotAllelicCounts(
data,
title = NULL,
padjCutoff = 0.05,
allelicRatioCutoff = 0.8,
rare_column = NULL
)
}
\arguments{
\item{data}{A data.frame containing the counts or results table from \code{DESeq4MAE} function}
\item{title}{The plot's title}
\item{padjCutoff}{The significance level}
\item{allelicRatioCutoff}{The minimum allelic ratio ALT/(ALT+REF) to be
considered signficant}
\item{rare_column}{The name of the column that indicates if a variant is rare or not.
Default is \code{null} which means it won't be plotted.}
}
\value{
A ggplot object containing the MA plot.
}
\description{
Creates an allelic counts plot, ie, counts of the alternative
vs counts of the reference on the log10 scale. If significant and minor
allele frequency columns present, dots will be highlighted accordingly.
}
\examples{
file <- system.file("extdata", "allelic_counts_HG00187.csv",
package = "tMAE", mustWork = TRUE)
maeCounts <- fread(file)
res <- DESeq4MAE(maeCounts)
plotAllelicCounts(res)
}
\author{
Vicente Yepez
}
|
/man/plotAllelicCounts.Rd
|
permissive
|
KalinNonchev/tMAE
|
R
| false | true | 1,209 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotMA.R
\name{plotAllelicCounts}
\alias{plotAllelicCounts}
\title{plotAllelicCounts}
\usage{
plotAllelicCounts(
data,
title = NULL,
padjCutoff = 0.05,
allelicRatioCutoff = 0.8,
rare_column = NULL
)
}
\arguments{
\item{data}{A data.frame containing the counts or results table from \code{DESeq4MAE} function}
\item{title}{The plot's title}
\item{padjCutoff}{The significance level}
\item{allelicRatioCutoff}{The minimum allelic ratio ALT/(ALT+REF) to be
considered signficant}
\item{rare_column}{The name of the column that indicates if a variant is rare or not.
Default is \code{null} which means it won't be plotted.}
}
\value{
A ggplot object containing the MA plot.
}
\description{
Creates an allelic counts plot, ie, counts of the alternative
vs counts of the reference on the log10 scale. If significant and minor
allele frequency columns present, dots will be highlighted accordingly.
}
\examples{
file <- system.file("extdata", "allelic_counts_HG00187.csv",
package = "tMAE", mustWork = TRUE)
maeCounts <- fread(file)
res <- DESeq4MAE(maeCounts)
plotAllelicCounts(res)
}
\author{
Vicente Yepez
}
|
## Load required packages
library(data.table)
library(lubridate)
library(ggplot2)
## Set working Directory and load the data
setwd("D:/SVN - BIDs/branches/sandpit/flintan/Data Science Coursera/Course Project 1 - DataViz")
EPC_data <- read.table("household_power_consumption.txt",
header = TRUE,
sep = ";",
colClasses = c("character", "character", rep("numeric", 7)),
na.strings = "?")
# Subset data to use observations from 2007-02-01 and 2007-02-02
EPC_data$Date = as.Date(EPC_data$Date, format = "%d/%m/%Y")
EPC_data_subset <- EPC_data[EPC_data$Date >= "2007-02-01" & EPC_data$Date <= "2007-02-02",]
EPC_data_subset$Date.Time <- ymd_hms(paste0(EPC_data_subset$Date, " ", EPC_data_subset$Time))
EPC_data_subset$Day <- wday(EPC_data_subset$Date.Time)
EPC_data_subset$Global_active_power <- as.numeric(as.character(EPC_data_subset$Global_active_power))
rm(EPC_data)
## Plot 1
png(filename = "plot1.png", width = 480, height = 480)
hist(EPC_data_subset$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
dev.off()
|
/Plot1.R
|
no_license
|
nicoflinta/ExData_Plotting1
|
R
| false | false | 1,197 |
r
|
## Load required packages
library(data.table)
library(lubridate)
library(ggplot2)
## Set working Directory and load the data
setwd("D:/SVN - BIDs/branches/sandpit/flintan/Data Science Coursera/Course Project 1 - DataViz")
EPC_data <- read.table("household_power_consumption.txt",
header = TRUE,
sep = ";",
colClasses = c("character", "character", rep("numeric", 7)),
na.strings = "?")
# Subset data to use observations from 2007-02-01 and 2007-02-02
EPC_data$Date = as.Date(EPC_data$Date, format = "%d/%m/%Y")
EPC_data_subset <- EPC_data[EPC_data$Date >= "2007-02-01" & EPC_data$Date <= "2007-02-02",]
EPC_data_subset$Date.Time <- ymd_hms(paste0(EPC_data_subset$Date, " ", EPC_data_subset$Time))
EPC_data_subset$Day <- wday(EPC_data_subset$Date.Time)
EPC_data_subset$Global_active_power <- as.numeric(as.character(EPC_data_subset$Global_active_power))
rm(EPC_data)
## Plot 1
png(filename = "plot1.png", width = 480, height = 480)
hist(EPC_data_subset$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
dev.off()
|
library(testthat)
library(queryr)
test_check("queryr")
|
/tests/testthat.R
|
no_license
|
paulhendricks/queryr
|
R
| false | false | 56 |
r
|
library(testthat)
library(queryr)
test_check("queryr")
|
#Example data
library(sva)
i <- "/labs/mpsnyder/slancast/fiber/rnaseq/fall2019/batch_correction/rna_raw_counts.csv"
rna_df = read.csv(i, sep=",", header=TRUE, row.names = 1)
print(i)
#Full#
rna_metadata_rows = 5
rna_metadata = head(rna_df,rna_metadata_rows)
rna_df2 <- tail(rna_df, -rna_metadata_rows) #Now getting rid of the metadata
rna_df2 <- data.frame(lapply(rna_df2, as.character), stringsAsFactors=FALSE, row.names = rownames(rna_df2)) #To change to a double, this needs to go through character first
rna_df2 <- data.frame(lapply(rna_df2, as.numeric), stringsAsFactors=FALSE, row.names = rownames(rna_df2)) #then numeric
rna_metadata = data.frame(t(rna_metadata))
rna_df2 <- as.matrix(rna_df2)
rna_df2 <- rna_df2[apply(rna_df2, 1, var) != 0,]
rna_df2 <- log(rna_df2+1,2)
rna_df2 <- na.omit(rna_df2)
batch <- rna_metadata$batch
modcombat <- cbind(as.numeric(rna_metadata$condition),as.numeric(rna_metadata$week),as.numeric(rna_metadata$participant))
rownames(modcombat) <- rownames(rna_metadata)
combat_edata = ComBat(dat=rna_df2, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE)
class(combat_edata) <- "character"
rna_metadata = data.frame(t(rna_metadata))
combat_edata2 <- rbind(rna_metadata,combat_edata)
write.table(combat_edata2, file="/labs/mpsnyder/slancast/fiber/rnaseq/fall2019/batch_correction/rna-final-combat.txt", sep="\t")
|
/Batch_Effects_rna_scg.R
|
no_license
|
linlin0026/fiber_-_10.1016-j.chom.2022.03.036
|
R
| false | false | 1,364 |
r
|
#Example data
library(sva)
i <- "/labs/mpsnyder/slancast/fiber/rnaseq/fall2019/batch_correction/rna_raw_counts.csv"
rna_df = read.csv(i, sep=",", header=TRUE, row.names = 1)
print(i)
#Full#
rna_metadata_rows = 5
rna_metadata = head(rna_df,rna_metadata_rows)
rna_df2 <- tail(rna_df, -rna_metadata_rows) #Now getting rid of the metadata
rna_df2 <- data.frame(lapply(rna_df2, as.character), stringsAsFactors=FALSE, row.names = rownames(rna_df2)) #To change to a double, this needs to go through character first
rna_df2 <- data.frame(lapply(rna_df2, as.numeric), stringsAsFactors=FALSE, row.names = rownames(rna_df2)) #then numeric
rna_metadata = data.frame(t(rna_metadata))
rna_df2 <- as.matrix(rna_df2)
rna_df2 <- rna_df2[apply(rna_df2, 1, var) != 0,]
rna_df2 <- log(rna_df2+1,2)
rna_df2 <- na.omit(rna_df2)
batch <- rna_metadata$batch
modcombat <- cbind(as.numeric(rna_metadata$condition),as.numeric(rna_metadata$week),as.numeric(rna_metadata$participant))
rownames(modcombat) <- rownames(rna_metadata)
combat_edata = ComBat(dat=rna_df2, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE)
class(combat_edata) <- "character"
rna_metadata = data.frame(t(rna_metadata))
combat_edata2 <- rbind(rna_metadata,combat_edata)
write.table(combat_edata2, file="/labs/mpsnyder/slancast/fiber/rnaseq/fall2019/batch_correction/rna-final-combat.txt", sep="\t")
|
# Exercise 2: working with `dplyr`
# Note that this exercise repeats the analysis from Exercise 1, but should be
# performed using `dplyr` (do not directly access or manipulate the data frames)
# Install and load the "fueleconomy" package
#install.packages("devtools")
#devtools::install_github("hadley/fueleconomy")
library(fueleconomy)
# Install and load the "dplyr" library
install.packages("dplyr")
library("dplyr")
# Select the different manufacturers (makes) of the cars in this data set.
# Save this vector in a variable
makes <- select(vehicles, make)
# Use the `distinct()` function to determine how many different car manufacturers
# are represented by the data set
nrow(distinct(vehicles,make))
length(unique(makes$make))
# Filter the data set for vehicles manufactured in 1997
cars_1997 <-filter(vehicles, year == 1997)
# Arrange the 1997 cars by highway (`hwy`) gas milage
cars_1997 <- arrange(cars_1997, hwy)
# Mutate the 1997 cars data frame to add a column `average` that has the average
# gas milage (between city and highway mpg) for each car
cars_1997 <- mutate(cars_1997, average = (hwy + cty) / 2)
# Filter the whole vehicles data set for 2-Wheel Drive vehicles that get more
# than 20 miles/gallon in the city.
# Save this new data frame in a variable.
two_wheel_20_mpg <- filter(vehicles, drive == '2-Wheel Drive', cty > 20)
# Of the above vehicles, what is the vehicle ID of the vehicle with the worst
# hwy mpg?
# Hint: filter for the worst vehicle, then select its ID.
filtered <- filter(two_wheel_20_mpg, hwy == min(hwy))
worst_hwy <- select(filtered, id)
# Write a function that takes a `year_choice` and a `make_choice` as parameters,
# and returns the vehicle model that gets the most hwy miles/gallon of vehicles
# of that make in that year.
# You'll need to filter more (and do some selecting)!
make_year <- function(make_choice, year_choice) {
filtered <- filter(vehicles, make == make_choice, year == year_choice)
filtered <- filter(filtered, hwy == max(hwy))
selected <- select(filtered, model)
selected
}
# What was the most efficient Honda model of 1995?
make_year('Honda', 1995)
|
/exercise-2/exercise.R
|
permissive
|
tifftruong/ch10-dplyr
|
R
| false | false | 2,141 |
r
|
# Exercise 2: working with `dplyr`
# Note that this exercise repeats the analysis from Exercise 1, but should be
# performed using `dplyr` (do not directly access or manipulate the data frames)
# Install and load the "fueleconomy" package
#install.packages("devtools")
#devtools::install_github("hadley/fueleconomy")
library(fueleconomy)
# Install and load the "dplyr" library
install.packages("dplyr")
library("dplyr")
# Select the different manufacturers (makes) of the cars in this data set.
# Save this vector in a variable
makes <- select(vehicles, make)
# Use the `distinct()` function to determine how many different car manufacturers
# are represented by the data set
nrow(distinct(vehicles,make))
length(unique(makes$make))
# Filter the data set for vehicles manufactured in 1997
cars_1997 <-filter(vehicles, year == 1997)
# Arrange the 1997 cars by highway (`hwy`) gas milage
cars_1997 <- arrange(cars_1997, hwy)
# Mutate the 1997 cars data frame to add a column `average` that has the average
# gas milage (between city and highway mpg) for each car
cars_1997 <- mutate(cars_1997, average = (hwy + cty) / 2)
# Filter the whole vehicles data set for 2-Wheel Drive vehicles that get more
# than 20 miles/gallon in the city.
# Save this new data frame in a variable.
two_wheel_20_mpg <- filter(vehicles, drive == '2-Wheel Drive', cty > 20)
# Of the above vehicles, what is the vehicle ID of the vehicle with the worst
# hwy mpg?
# Hint: filter for the worst vehicle, then select its ID.
filtered <- filter(two_wheel_20_mpg, hwy == min(hwy))
worst_hwy <- select(filtered, id)
# Write a function that takes a `year_choice` and a `make_choice` as parameters,
# and returns the vehicle model that gets the most hwy miles/gallon of vehicles
# of that make in that year.
# You'll need to filter more (and do some selecting)!
make_year <- function(make_choice, year_choice) {
filtered <- filter(vehicles, make == make_choice, year == year_choice)
filtered <- filter(filtered, hwy == max(hwy))
selected <- select(filtered, model)
selected
}
# What was the most efficient Honda model of 1995?
make_year('Honda', 1995)
|
library(MASS) #box-cox
library(DAAG) #cv
library(car) #step
library(tidyverse)
library(ggplot2)
###########################################
## LINEAR MODELING: PREDICTING BIKE COUNT
###########################################
# Data Preprocessing ----------------------------------------------------
day<-read.csv("by_day.csv")
hour<-read.csv("by_hour.csv")
# Treat categorical variables as factors
day$Is.Weekend<-as.factor(day$Is.Weekend)
day$Month<-as.factor(day$Month)
day$Weekday<-as.factor(day$Weekday)
day$Rain <- as.factor(day$Rain)
day$Fog <- as.factor(day$Fog)
day$Snow <- as.factor(day$Snow)
day$Thunderstorm <- as.factor(day$Thunderstorm)
# Subset variables we're interested in
bikedata <- day[, c(2, 8:33)]
# Investigate NAs
colSums(is.na(bikedata))
# There are no NAs.
# Linear Model (full) ----------------------------------------------------
lm.full<-lm(Bike.Count~.,data=bikedata)
summary(lm.full)
# Residual standard error: 1206 on 59 degrees of freedom
# Multiple R-squared: 0.8735, Adjusted R-squared: 0.8092
# F-statistic: 13.59 on 30 and 59 DF, p-value: < 2.2e-16
# Model is significant.
# The model identified Day, Wind_Avg, Rain_Inches, Snow, Weekday(Sunday) and Is.Holiday
# as significant variables at the 5% significance level.
# Is.Weekend and Vis_High were removed from the model because of singularities.
# We do not know if the beta estimates above are being affected by multicollinearity.
# We need to investigate multicollinearity in the data and consider transforming
# and removing variables.
# Investigate Multicollinearity --------------------------------------------------------------------------
# Look at correlation matrix between numeric variables
is.num <- sapply(bikedata, is.numeric)
num.df <- bikedata[, is.num]
cor(num.df)
# There are severe near-linear dependencies (corr > 0.9) between Temp_High and Temp_Avg,
# Temp_Low and Temp_Avg, Dew_High and Dew_Avg, Dew_Low and Dew_Avg, Hum_High and Hum_Avg,
# Pres_High and Pres_Avg, and Pres_Low and Pres_Avg.
# Look at Variance Inflation Factors
lm.full<-lm(Bike.Count~.-Is.Weekend -Vis_High,data=bikedata)
vif(lm.full)
# Again, many of weather variables have VIF > 5, so again, we need to deal with them.
# Dealing With Multicollinearity --------------------------------------------------------------------------
# Collapse High/Low Variables into Range Variables.
# This way, we keep the information contained in these variables and hopefully avoid multicollinearity
# with the Avg.
bikedata$Temp_Range <- bikedata$Temp_High-bikedata$Temp_Low
bikedata$Dew_Range <- bikedata$Dew_High-bikedata$Dew_Low
bikedata$Hum_Range <- bikedata$Hum_High-bikedata$Hum_Low
bikedata$Pres_Range <- bikedata$Pres_High-bikedata$Pres_Low
bikedata$Vis_Range <- bikedata$Vis_High-bikedata$Vis_Low
# Drop and High and Low variables. We will also drop Is.Weekend because it is perfectly linearly
# dependent with Weekday==Saturday and Weekday==Sunday.
bikedata <- bikedata %>%
select(-Is.Weekend, -Temp_High, -Temp_Low, -Dew_High, -Dew_Low, -Hum_High, -Hum_Low,
-Pres_High, -Pres_Low, -Vis_High, -Vis_Low)
# Look at correlation matrix between numeric variables
is.num <- sapply(bikedata, is.numeric)
num.df <- bikedata[, is.num]
cor(num.df)
# No more correlations above > 0.9
lm.full2 <- lm(Bike.Count~. ,data=bikedata)
summary(lm.full2)
# Residual standard error: 1174 on 63 degrees of freedom
# Multiple R-squared: 0.872, Adjusted R-squared: 0.8192
# F-statistic: 16.51 on 26 and 63 DF, p-value: < 2.2e-16
# Model is significant.
# Our Adj-R^2 improved from 0.8092 to 0.8192.
# Variables identified by the model as significant at the 5% significance level:
# Month(Jan), Day, Temp_Avg, Wind_Avg, Rain_Inches, Snow, Weekday(Sunday), Is.Holiday.
vif(lm.full2)
# VIF values are much lower now. However, Temp_Avg, Dew_Avg, Hum_Avg, Vis_Avg have VIF > 10.
# Vis_Range has VIF > 5 and < 10.
# According to the correlation matrix, Hum_Avg and Temp_Avg are both correlated with Dew_Avg
# by 0.85317738 and 0.80249561 respectively.
# What happens if we drop Dew_Avg and Vis_Range?
lm.full3 <- lm(Bike.Count~. -Dew_Avg -Vis_Range,data=bikedata)
summary(lm.full3)
# Snow is no longer significant.
# Residual standard error: 1179 on 65 degrees of freedom
# Multiple R-squared: 0.8669, Adjusted R-squared: 0.8178
# F-statistic: 17.65 on 24 and 65 DF, p-value: < 2.2e-16
# Model is significant.
vif(lm.full3)
# Vis_Avg and Hum_Avg have GVIF ~ 5 or 6, but overall, looks much better.
# Drop Dew_Avg and Vis_Range from dataframe.
bikedata <- bikedata %>%
select(-Dew_Avg, -Vis_Range)
lm.full4 <- lm(Bike.Count~.,data=bikedata)
# Stepwise/Foward/Backward Selection -----------------------------------------------------------------
## Begin by defining the models with no variables (null) and all variables (full)
lm.null <- lm(Bike.Count~1,data=bikedata)
## step selection
lm.step<-step(lm.null, scope=list(lower=lm.null, upper=lm.full4), direction="both")
# Step: AIC=1281.19
# Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches + Weekday + Is.Holiday +
# Wind_Avg + Month + Day + Snow + Rain
summary(lm.step)
# Residual standard error: 1134 on 73 degrees of freedom
# Multiple R-squared: 0.8617, Adjusted R-squared: 0.8314
# F-statistic: 28.43 on 16 and 73 DF, p-value: < 2.2e-16
# Model is significant.
# Forward selection:
lm.forward <- step(lm.null, scope=list(lower=lm.null, upper=lm.full4), direction="forward")
summary(lm.forward)
# Step: AIC=1281.19
# Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches + Weekday + Is.Holiday +
# Wind_Avg + Month + Day + Snow + Rain
# Forward selection picked the exact same model.
# Backward elimination:
lm.back <- step(lm.full4, scope=list(lower=lm.null, upper=lm.full4), direction="backward")
# Step: AIC=1281.19
# Bike.Count ~ Month + Day + Temp_Avg + Hum_Avg + Wind_Avg + Rain_Inches +
# Rain + Snow + Weekday + Is.Holiday
# Backward elimination also picked the exact same model.
# Residual Analysis/Influential Points ----------------------------------------------
## R-student residuals
ti<-rstudent(lm.step)
## Normal probabilty plot
qqnorm(ti)
qqline(ti)
# Looks normal except for two points on the right tail.
## Residual vs. fitted values plot
plot(fitted(lm.step),ti)
# Seems normal with the exception of 2 points.
summary(influence.measures(lm.step))
# Pt 84 is an influential point, with a COVRATIO of 0.07, a DFFIT of 1.94 and a DFBETA of 1.03 for Temp_Avg.
# This suggests that point 84 has unusually high influence over the beta estimate of Temp_Avg,
# and that the fitted values are being affected by the presence of point 84.
# Pt 50 has a DFFIT of 1.56 and a COVRATIO of 0.1, suggesting that this point also exerts an unusual amount of
# influence on the fitted values.
bikedata[c(50, 84),]
# These points correspond to Feb 19, 2017 and Mar 25, 2017.
# Bike.Count Month Day Temp_Avg Hum_Avg Pres_Avg Vis_Avg Wind_Avg Rain_Inches Rain Fog Snow Thunderstorm Weekday Is.Holiday
# 50 12350 Feb 19 60 60 29.91 10 9 0.01 1 0 0 0 Sunday 0
# 84 16191 March 25 66 58 30.17 10 9 0.00 0 0 0 0 Saturday 0
summary(bikedata$Bike.Count)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1583 5382 7237 7183 8608 16190
# In terms of bike count, both these days are in the topmost quartile and the bike count for Pt 84 corresponds
# to the max bike count.
# Does removing these two points change the model's beta coefficients?
lm.full5 <- lm(Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches +
Weekday + Is.Holiday + Wind_Avg + Month + Day + Snow + Rain,
data = bikedata[-c(50, 84),])
summary(lm.full5)
# Residual standard error: 946.7 on 71 degrees of freedom
# Multiple R-squared: 0.8881, Adjusted R-squared: 0.8629
# F-statistic: 35.22 on 16 and 71 DF, p-value: < 2.2e-16
# An improvement in Adjusted R^2 compared to lm.step (up from 0.8314).
lm.full5$coefficients
# (Intercept) Temp_Avg Hum_Avg Rain_Inches WeekdayMonday WeekdaySaturday WeekdaySunday
# 1540.024182 174.092476 -13.834248 -4194.415747 146.885718 -801.287525 -2075.045727
# WeekdayThursday WeekdayTuesday WeekdayWednesday Is.Holiday Wind_Avg MonthJan MonthMarch
# 12.829004 -7.907761 -92.695427 -1789.780420 -92.307566 -850.845000 324.486204
# Day Snow1 Rain1
# 30.187008 491.145747 -547.949283
lm.step$coefficients
# (Intercept) Temp_Avg Hum_Avg Rain_Inches WeekdayMonday WeekdaySaturday WeekdaySunday
# 742.37329 199.83904 -20.72061 -4095.33739 193.89938 -387.05692 -1709.29852
# WeekdayThursday WeekdayTuesday WeekdayWednesday Is.Holiday Wind_Avg MonthJan MonthMarch
# -86.16931 -40.64184 -198.33614 -1733.17718 -93.83794 -813.51719 321.05561
# Day Snow1 Rain1
# 31.76259 679.38475 -570.10086
# Yes, there are large differences in these coefficient estimates.
# However, we cannot justify removing these points in our model. An internet search reveals that
# nothing special happened on Feb 19, 2017 or Mar 25, 2017 in Washington D.C. We have no evidence that
# these two observations are bad data points. It's possible that they seem influential in this model
# because we do not have enough data points (n = 90).
# Plotting residuals against explanatory variables.
plot(bikedata$Temp_Avg, ti) # Looks ok except the two points in the upper right hand corner.
plot(bikedata$Hum_Avg, ti) # Looks fine except for two points
plot(bikedata$Rain_Inches, ti) # Highly abnormal residuals - !!!!!
plot(bikedata$Wind_Avg, ti) # Some irregularities, but mostly ok
# Looking At Rain_Inches -----------------------------------------------
plot(bikedata$Rain_Inches, bikedata$Bike.Count)
# There are many days where Rain_Inches == 0, leading to the strange residual plot found above.
# There seems to be no good way to transform this variable. Furthermore, it is
# a significant variable, so we will leave it in as is.
# Cross-Validate -----------------------------------------------
x.cv <- cv.lm(data=bikedata, form.lm=lm.step, m=2, plotit=T)
# Sum of squares = 1.21e+08 Mean square = 2698332 n = 45
#
# Overall (Sum over all 45 folds)
# ms
# 2352489
# Plot shows our final model is pretty good.
# PRESS statistic
pr <- resid(lm.step)/(1-lm.influence(lm.step)$hat)
sum(pr^2)
# PRESS = 1.44e+08
#######################################################
## LINEAR MODELING: PREDICTING DAILY TOTAL DISTANCE
#######################################################
plot(day$Bike.Count, day$Total.Dist)
# We thought we would make a separating model predicting total distance travelled by all bikeshare
# users on a given day, but the plot above shows that BikeCount and Total.Dist are very highly
# correlated. A model predicting Total.Dist would use the exact same preidctors as the model above.
#######################################################
## LINEAR MODELING: PREDICTING HOURLY BIKE COUNT
#######################################################
## data preprocess
by_hour <- hour %>%
group_by(Date, Start.Hour) %>%
summarise(Bike.Count = n())
by_hour$Start.Hour <- as.factor(by_hour$Start.Hour)
predicted <- data.frame(Date = day$Date, Predicted.BC = fitted(lm.step)/24)
by_hour <- merge(x = by_hour, y = predicted, by = "Date", all.x=TRUE)
by_hour$Predicted.Residual <- by_hour$Bike.Count - by_hour$Predicted.BC
## Plot
ggplot(by_hour, aes_string(x="Start.Hour", y="Predicted.Residual")) +
geom_point(position=position_jitter(w=0.0, h=0.4)) +
theme_light(base_size=20) +
xlab("Hour of the Day") +
ylab("Predicted Residual") +
theme(plot.title=element_text(size=18))
## lm
hour.lm <- lm(Predicted.Residual ~ Start.Hour, data=by_hour)
summary(hour.lm)
# Residual standard error: 177 on 2119 degrees of freedom
# Multiple R-squared: 0.641, Adjusted R-squared: 0.637
# F-statistic: 164 on 23 and 2119 DF, p-value: <2e-16
# Model is significant.
# Residual analysis:
hour.ti<-rstudent(hour.lm)
## Normal probabilty plot
qqnorm(hour.ti)
qqline(hour.ti)
# The points in the middle are fine but the tails are extremely heavy and strange.
## Residual vs. fitted values plot
plot(fitted(hour.lm),hour.ti)
# Highly abnormal.
plot(as.numeric(by_hour$Start.Hour), by_hour$Predicted.Residual)
## polynomial
by_hour$Start.Hour<-as.numeric(by_hour$Start.Hour)
for (i in c(exp(1),1:10)){
hour.lm.n <- lm(Predicted.Residual ~ poly(Start.Hour,i), data=by_hour)
print(paste("i: ",i))
print(paste("r.squared: ",summary(hour.lm.n)$r.squared))
}
# [1] "i: 1"
# [1] "r.squared: 0.107612701237565"
# [1] "i: 2"
# [1] "r.squared: 0.358878782045627"
# [1] "i: 2.71828182845905"
# [1] "r.squared: 0.358878782045627"
# [1] "i: 3"
# [1] "r.squared: 0.427634603544068"
# [1] "i: 4"
# [1] "r.squared: 0.427796302964034"
# [1] "i: 5"
# [1] "r.squared: 0.431068009612039"
# [1] "i: 6"
# [1] "r.squared: 0.482216200704182"
# [1] "i: 7"
# [1] "r.squared: 0.517578873090201"
# [1] "i: 8"
# [1] "r.squared: 0.552582585118866"
# [1] "i: 9"
# [1] "r.squared: 0.566206272061443"
# [1] "i: 10"
# [1] "r.squared: 0.567498518111798"
|
/day_analysis.R
|
no_license
|
Jo-Pan/stat-final-bikeshare
|
R
| false | false | 13,465 |
r
|
library(MASS) #box-cox
library(DAAG) #cv
library(car) #step
library(tidyverse)
library(ggplot2)
###########################################
## LINEAR MODELING: PREDICTING BIKE COUNT
###########################################
# Data Preprocessing ----------------------------------------------------
day<-read.csv("by_day.csv")
hour<-read.csv("by_hour.csv")
# Treat categorical variables as factors
day$Is.Weekend<-as.factor(day$Is.Weekend)
day$Month<-as.factor(day$Month)
day$Weekday<-as.factor(day$Weekday)
day$Rain <- as.factor(day$Rain)
day$Fog <- as.factor(day$Fog)
day$Snow <- as.factor(day$Snow)
day$Thunderstorm <- as.factor(day$Thunderstorm)
# Subset variables we're interested in
bikedata <- day[, c(2, 8:33)]
# Investigate NAs
colSums(is.na(bikedata))
# There are no NAs.
# Linear Model (full) ----------------------------------------------------
lm.full<-lm(Bike.Count~.,data=bikedata)
summary(lm.full)
# Residual standard error: 1206 on 59 degrees of freedom
# Multiple R-squared: 0.8735, Adjusted R-squared: 0.8092
# F-statistic: 13.59 on 30 and 59 DF, p-value: < 2.2e-16
# Model is significant.
# The model identified Day, Wind_Avg, Rain_Inches, Snow, Weekday(Sunday) and Is.Holiday
# as significant variables at the 5% significance level.
# Is.Weekend and Vis_High were removed from the model because of singularities.
# We do not know if the beta estimates above are being affected by multicollinearity.
# We need to investigate multicollinearity in the data and consider transforming
# and removing variables.
# Investigate Multicollinearity --------------------------------------------------------------------------
# Look at correlation matrix between numeric variables
is.num <- sapply(bikedata, is.numeric)
num.df <- bikedata[, is.num]
cor(num.df)
# There are severe near-linear dependencies (corr > 0.9) between Temp_High and Temp_Avg,
# Temp_Low and Temp_Avg, Dew_High and Dew_Avg, Dew_Low and Dew_Avg, Hum_High and Hum_Avg,
# Pres_High and Pres_Avg, and Pres_Low and Pres_Avg.
# Look at Variance Inflation Factors
lm.full<-lm(Bike.Count~.-Is.Weekend -Vis_High,data=bikedata)
vif(lm.full)
# Again, many of weather variables have VIF > 5, so again, we need to deal with them.
# Dealing With Multicollinearity --------------------------------------------------------------------------
# Collapse High/Low Variables into Range Variables.
# This way, we keep the information contained in these variables and hopefully avoid multicollinearity
# with the Avg.
bikedata$Temp_Range <- bikedata$Temp_High-bikedata$Temp_Low
bikedata$Dew_Range <- bikedata$Dew_High-bikedata$Dew_Low
bikedata$Hum_Range <- bikedata$Hum_High-bikedata$Hum_Low
bikedata$Pres_Range <- bikedata$Pres_High-bikedata$Pres_Low
bikedata$Vis_Range <- bikedata$Vis_High-bikedata$Vis_Low
# Drop and High and Low variables. We will also drop Is.Weekend because it is perfectly linearly
# dependent with Weekday==Saturday and Weekday==Sunday.
bikedata <- bikedata %>%
select(-Is.Weekend, -Temp_High, -Temp_Low, -Dew_High, -Dew_Low, -Hum_High, -Hum_Low,
-Pres_High, -Pres_Low, -Vis_High, -Vis_Low)
# Look at correlation matrix between numeric variables
is.num <- sapply(bikedata, is.numeric)
num.df <- bikedata[, is.num]
cor(num.df)
# No more correlations above > 0.9
lm.full2 <- lm(Bike.Count~. ,data=bikedata)
summary(lm.full2)
# Residual standard error: 1174 on 63 degrees of freedom
# Multiple R-squared: 0.872, Adjusted R-squared: 0.8192
# F-statistic: 16.51 on 26 and 63 DF, p-value: < 2.2e-16
# Model is significant.
# Our Adj-R^2 improved from 0.8092 to 0.8192.
# Variables identified by the model as significant at the 5% significance level:
# Month(Jan), Day, Temp_Avg, Wind_Avg, Rain_Inches, Snow, Weekday(Sunday), Is.Holiday.
vif(lm.full2)
# VIF values are much lower now. However, Temp_Avg, Dew_Avg, Hum_Avg, Vis_Avg have VIF > 10.
# Vis_Range has VIF > 5 and < 10.
# According to the correlation matrix, Hum_Avg and Temp_Avg are both correlated with Dew_Avg
# by 0.85317738 and 0.80249561 respectively.
# What happens if we drop Dew_Avg and Vis_Range?
lm.full3 <- lm(Bike.Count~. -Dew_Avg -Vis_Range,data=bikedata)
summary(lm.full3)
# Snow is no longer significant.
# Residual standard error: 1179 on 65 degrees of freedom
# Multiple R-squared: 0.8669, Adjusted R-squared: 0.8178
# F-statistic: 17.65 on 24 and 65 DF, p-value: < 2.2e-16
# Model is significant.
vif(lm.full3)
# Vis_Avg and Hum_Avg have GVIF ~ 5 or 6, but overall, looks much better.
# Drop Dew_Avg and Vis_Range from dataframe.
bikedata <- bikedata %>%
select(-Dew_Avg, -Vis_Range)
lm.full4 <- lm(Bike.Count~.,data=bikedata)
# Stepwise/Foward/Backward Selection -----------------------------------------------------------------
## Begin by defining the models with no variables (null) and all variables (full)
lm.null <- lm(Bike.Count~1,data=bikedata)
## step selection
lm.step<-step(lm.null, scope=list(lower=lm.null, upper=lm.full4), direction="both")
# Step: AIC=1281.19
# Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches + Weekday + Is.Holiday +
# Wind_Avg + Month + Day + Snow + Rain
summary(lm.step)
# Residual standard error: 1134 on 73 degrees of freedom
# Multiple R-squared: 0.8617, Adjusted R-squared: 0.8314
# F-statistic: 28.43 on 16 and 73 DF, p-value: < 2.2e-16
# Model is significant.
# Forward selection:
lm.forward <- step(lm.null, scope=list(lower=lm.null, upper=lm.full4), direction="forward")
summary(lm.forward)
# Step: AIC=1281.19
# Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches + Weekday + Is.Holiday +
# Wind_Avg + Month + Day + Snow + Rain
# Forward selection picked the exact same model.
# Backward elimination:
lm.back <- step(lm.full4, scope=list(lower=lm.null, upper=lm.full4), direction="backward")
# Step: AIC=1281.19
# Bike.Count ~ Month + Day + Temp_Avg + Hum_Avg + Wind_Avg + Rain_Inches +
# Rain + Snow + Weekday + Is.Holiday
# Backward elimination also picked the exact same model.
# Residual Analysis/Influential Points ----------------------------------------------
## R-student residuals
ti<-rstudent(lm.step)
## Normal probabilty plot
qqnorm(ti)
qqline(ti)
# Looks normal except for two points on the right tail.
## Residual vs. fitted values plot
plot(fitted(lm.step),ti)
# Seems normal with the exception of 2 points.
summary(influence.measures(lm.step))
# Pt 84 is an influential point, with a COVRATIO of 0.07, a DFFIT of 1.94 and a DFBETA of 1.03 for Temp_Avg.
# This suggests that point 84 has unusually high influence over the beta estimate of Temp_Avg,
# and that the fitted values are being affected by the presence of point 84.
# Pt 50 has a DFFIT of 1.56 and a COVRATIO of 0.1, suggesting that this point also exerts an unusual amount of
# influence on the fitted values.
bikedata[c(50, 84),]
# These points correspond to Feb 19, 2017 and Mar 25, 2017.
# Bike.Count Month Day Temp_Avg Hum_Avg Pres_Avg Vis_Avg Wind_Avg Rain_Inches Rain Fog Snow Thunderstorm Weekday Is.Holiday
# 50 12350 Feb 19 60 60 29.91 10 9 0.01 1 0 0 0 Sunday 0
# 84 16191 March 25 66 58 30.17 10 9 0.00 0 0 0 0 Saturday 0
summary(bikedata$Bike.Count)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1583 5382 7237 7183 8608 16190
# In terms of bike count, both these days are in the topmost quartile and the bike count for Pt 84 corresponds
# to the max bike count.
# Does removing these two points change the model's beta coefficients?
lm.full5 <- lm(Bike.Count ~ Temp_Avg + Hum_Avg + Rain_Inches +
Weekday + Is.Holiday + Wind_Avg + Month + Day + Snow + Rain,
data = bikedata[-c(50, 84),])
summary(lm.full5)
# Residual standard error: 946.7 on 71 degrees of freedom
# Multiple R-squared: 0.8881, Adjusted R-squared: 0.8629
# F-statistic: 35.22 on 16 and 71 DF, p-value: < 2.2e-16
# An improvement in Adjusted R^2 compared to lm.step (up from 0.8314).
lm.full5$coefficients
# (Intercept) Temp_Avg Hum_Avg Rain_Inches WeekdayMonday WeekdaySaturday WeekdaySunday
# 1540.024182 174.092476 -13.834248 -4194.415747 146.885718 -801.287525 -2075.045727
# WeekdayThursday WeekdayTuesday WeekdayWednesday Is.Holiday Wind_Avg MonthJan MonthMarch
# 12.829004 -7.907761 -92.695427 -1789.780420 -92.307566 -850.845000 324.486204
# Day Snow1 Rain1
# 30.187008 491.145747 -547.949283
lm.step$coefficients
# (Intercept) Temp_Avg Hum_Avg Rain_Inches WeekdayMonday WeekdaySaturday WeekdaySunday
# 742.37329 199.83904 -20.72061 -4095.33739 193.89938 -387.05692 -1709.29852
# WeekdayThursday WeekdayTuesday WeekdayWednesday Is.Holiday Wind_Avg MonthJan MonthMarch
# -86.16931 -40.64184 -198.33614 -1733.17718 -93.83794 -813.51719 321.05561
# Day Snow1 Rain1
# 31.76259 679.38475 -570.10086
# Yes, there are large differences in these coefficient estimates.
# However, we cannot justify removing these points in our model. An internet search reveals that
# nothing special happened on Feb 19, 2017 or Mar 25, 2017 in Washington D.C. We have no evidence that
# these two observations are bad data points. It's possible that they seem influential in this model
# because we do not have enough data points (n = 90).
# Plotting residuals against explanatory variables.
plot(bikedata$Temp_Avg, ti) # Looks ok except the two points in the upper right hand corner.
plot(bikedata$Hum_Avg, ti) # Looks fine except for two points
plot(bikedata$Rain_Inches, ti) # Highly abnormal residuals - !!!!!
plot(bikedata$Wind_Avg, ti) # Some irregularities, but mostly ok
# Looking At Rain_Inches -----------------------------------------------
plot(bikedata$Rain_Inches, bikedata$Bike.Count)
# There are many days where Rain_Inches == 0, leading to the strange residual plot found above.
# There seems to be no good way to transform this variable. Furthermore, it is
# a significant variable, so we will leave it in as is.
# Cross-Validate -----------------------------------------------
x.cv <- cv.lm(data=bikedata, form.lm=lm.step, m=2, plotit=T)
# Sum of squares = 1.21e+08 Mean square = 2698332 n = 45
#
# Overall (Sum over all 45 folds)
# ms
# 2352489
# Plot shows our final model is pretty good.
# PRESS statistic
pr <- resid(lm.step)/(1-lm.influence(lm.step)$hat)
sum(pr^2)
# PRESS = 1.44e+08
#######################################################
## LINEAR MODELING: PREDICTING DAILY TOTAL DISTANCE
#######################################################
plot(day$Bike.Count, day$Total.Dist)
# We thought we would make a separating model predicting total distance travelled by all bikeshare
# users on a given day, but the plot above shows that BikeCount and Total.Dist are very highly
# correlated. A model predicting Total.Dist would use the exact same preidctors as the model above.
#######################################################
## LINEAR MODELING: PREDICTING HOURLY BIKE COUNT
#######################################################
## data preprocess
by_hour <- hour %>%
group_by(Date, Start.Hour) %>%
summarise(Bike.Count = n())
by_hour$Start.Hour <- as.factor(by_hour$Start.Hour)
predicted <- data.frame(Date = day$Date, Predicted.BC = fitted(lm.step)/24)
by_hour <- merge(x = by_hour, y = predicted, by = "Date", all.x=TRUE)
by_hour$Predicted.Residual <- by_hour$Bike.Count - by_hour$Predicted.BC
## Plot
ggplot(by_hour, aes_string(x="Start.Hour", y="Predicted.Residual")) +
geom_point(position=position_jitter(w=0.0, h=0.4)) +
theme_light(base_size=20) +
xlab("Hour of the Day") +
ylab("Predicted Residual") +
theme(plot.title=element_text(size=18))
## lm
hour.lm <- lm(Predicted.Residual ~ Start.Hour, data=by_hour)
summary(hour.lm)
# Residual standard error: 177 on 2119 degrees of freedom
# Multiple R-squared: 0.641, Adjusted R-squared: 0.637
# F-statistic: 164 on 23 and 2119 DF, p-value: <2e-16
# Model is significant.
# Residual analysis:
hour.ti<-rstudent(hour.lm)
## Normal probabilty plot
qqnorm(hour.ti)
qqline(hour.ti)
# The points in the middle are fine but the tails are extremely heavy and strange.
## Residual vs. fitted values plot
plot(fitted(hour.lm),hour.ti)
# Highly abnormal.
plot(as.numeric(by_hour$Start.Hour), by_hour$Predicted.Residual)
## polynomial
by_hour$Start.Hour<-as.numeric(by_hour$Start.Hour)
for (i in c(exp(1),1:10)){
hour.lm.n <- lm(Predicted.Residual ~ poly(Start.Hour,i), data=by_hour)
print(paste("i: ",i))
print(paste("r.squared: ",summary(hour.lm.n)$r.squared))
}
# [1] "i: 1"
# [1] "r.squared: 0.107612701237565"
# [1] "i: 2"
# [1] "r.squared: 0.358878782045627"
# [1] "i: 2.71828182845905"
# [1] "r.squared: 0.358878782045627"
# [1] "i: 3"
# [1] "r.squared: 0.427634603544068"
# [1] "i: 4"
# [1] "r.squared: 0.427796302964034"
# [1] "i: 5"
# [1] "r.squared: 0.431068009612039"
# [1] "i: 6"
# [1] "r.squared: 0.482216200704182"
# [1] "i: 7"
# [1] "r.squared: 0.517578873090201"
# [1] "i: 8"
# [1] "r.squared: 0.552582585118866"
# [1] "i: 9"
# [1] "r.squared: 0.566206272061443"
# [1] "i: 10"
# [1] "r.squared: 0.567498518111798"
|
#' Convert a stars or stars-proxy object into an EE Image object
#'
#' @param x stars or stars-proxy object to be converted into an ee$Image.
#' @param assetId Character. Destination asset ID for the uploaded file.
#' @param command_line_tool_path Character. Path to the Earth Engine command line
#' tool (CLT). If NULL, rgee assumes that CLT is set in the system PATH.
#' (ignore if \code{via} is not defined as "gcs_to_asset").
#' @param overwrite Logical. If TRUE, the assetId will be overwritten.
#' @param bucket Character. Name of the GCS bucket.
#' @param predefinedAcl Specify user access to object. Passed to
#' \code{googleCloudStorageR::gcs_upload}.
#' @param monitoring Logical. If TRUE the exportation task will be monitored.
#' @param quiet Logical. Suppress info message.
#' @param ... parameter(s) passed on to \code{\link{ee_utils_create_manifest_image}}
#'
#' @return An ee$Image object
#' @family image upload functions
#' @examples
#' \dontrun{
#' library(rgee)
#' library(stars)
#' ee_Initialize(gcs = TRUE)
#'
#' # Get the filename of a image
#' tif <- system.file("tif/L7_ETMs.tif", package = "stars")
#' x <- read_stars(tif)
#' assetId <- sprintf("%s/%s",ee_get_assethome(),'stars_l7')
#'
#' # # Method 1
#' # 1. Move from local to gcs
#' gs_uri <- local_to_gcs(x = tif, bucket = 'rgee_dev')
#'
#' # 2. Create a manifest
#' manifest <- ee_utils_create_manifest_image(gs_uri, assetId)
#'
#' # 3. Pass from gcs to asset
#' gcs_to_ee_image(
#' manifest = manifest,
#' overwrite = TRUE
#' )
#'
#' # OPTIONAL: Monitoring progress
#' ee_monitoring(max_attempts = Inf)
#'
#' # OPTIONAL: Display results
#' ee_stars_01 <- ee$Image(assetId)
#' Map$centerObject(ee_stars_01)
#' Map$addLayer(ee_stars_01, list(min = 0, max = 255))
#'
#' # Method 2
#' ee_stars_02 <- stars_as_ee(
#' x = x,
#' overwrite = TRUE,
#' assetId = assetId,
#' bucket = "rgee_dev"
#' )
#' Map$centerObject(ee_stars_02)
#' Map$addLayer(ee_stars_02, list(min = 0, max = 255))
#' }
#' @export
stars_as_ee <- function(x,
assetId,
bucket = NULL,
predefinedAcl = "bucketLevel",
command_line_tool_path = NULL,
overwrite = FALSE,
monitoring = TRUE,
quiet = FALSE,
...) {
# Folder to save intermediate upload files
ee_temp <- tempdir()
if (is.null(command_line_tool_path)) {
command_line_tool_path <- ""
}
if (!quiet) {
message("1. Converting stars (raster) object to GeoTIFF ... saving in /tmp")
}
stars_proxy <- ee_as_proxystars(x, temp_dir = ee_temp)
if (!quiet) {
message("2. From local to GCS")
}
gcs_filename <- local_to_gcs(
x = stars_proxy[[1]],
bucket = bucket,
predefinedAcl = predefinedAcl,
quiet = quiet
)
if (!quiet) {
message("3. Creating the manifest")
}
manifest <- ee_utils_create_manifest_image(
gs_uri = gcs_filename,
assetId = assetId,
...
)
if (!quiet) {
message("4. From GCS to Earth Engine")
}
# Verify is the EE assets path is valid
assetId <- ee_verify_filename(
path_asset = assetId,
strict = FALSE
)
gcs_to_ee_image(
manifest,
overwrite = overwrite,
command_line_tool_path = command_line_tool_path
)
if (isTRUE(monitoring)) {
ee_monitoring(max_attempts = Inf)
ee$Image(assetId)
} else {
ee$Image(assetId)
}
}
#' Convert a Raster* object into an EE Image object
#'
#' @param x RasterLayer, RasterStack or RasterBrick object to be converted into
#' an ee$Image.
#' @param assetId Character. Destination asset ID for the uploaded file.
#' @param command_line_tool_path Character. Path to the Earth Engine command line
#' tool (CLT). If NULL, rgee assumes that CLT is set in the system PATH.
#' (ignore if \code{via} is not defined as "gcs_to_asset").
#' @param overwrite Logical. If TRUE, the assetId will be overwritten.
#' @param bucket Character. Name of the GCS bucket.
#' @param predefinedAcl Specify user access to object. Passed to
#' \code{googleCloudStorageR::gcs_upload}.
#' @param monitoring Logical. If TRUE the exportation task will be monitored.
#' @param quiet Logical. Suppress info message.
#' @param ... parameter(s) passed on to \code{\link{ee_utils_create_manifest_image}}
#'
#' @return An ee$Image object
#' @family image upload functions
#'
#' @examples
#' \dontrun{
#' library(raster)
#' library(stars)
#' library(rgee)
#'
#' ee_Initialize(gcs = TRUE)
#'
#' # Get the filename of a image
#' tif <- system.file("tif/L7_ETMs.tif", package = "stars")
#' x <- stack(tif)
#' assetId <- sprintf("%s/%s",ee_get_assethome(),'raster_l7')
#'
#' # Method 1
#' # 1. Move from local to gcs
#' gs_uri <- local_to_gcs(x = tif, bucket = 'rgee_dev')
#'
#' # 2. Create a manifest
#' manifest <- ee_utils_create_manifest_image(gs_uri, assetId)
#'
#' # 3. Pass from gcs to asset
#' gcs_to_ee_image(
#' manifest = manifest,
#' overwrite = TRUE
#' )
#'
#' # OPTIONAL: Monitoring progress
#' ee_monitoring(max_attempts = Inf)
#'
#' # OPTIONAL: Display results
#' ee_stars_01 <- ee$Image(assetId)
#' Map$centerObject(ee_stars_01)
#' Map$addLayer(ee_stars_01, list(min = 0, max = 255))
#'
#' # Method 2
#' ee_stars_02 <- raster_as_ee(
#' x = x,
#' overwrite = TRUE,
#' assetId = assetId,
#' bucket = "rgee_dev"
#' )
#' Map$centerObject(ee_stars_02)
#' Map$addLayer(ee_stars_02, list(min = 0, max = 255))
#' }
#' @export
raster_as_ee <- stars_as_ee
|
/R/raster_as_ee.R
|
permissive
|
r-spatial/rgee
|
R
| false | false | 5,492 |
r
|
#' Convert a stars or stars-proxy object into an EE Image object
#'
#' @param x stars or stars-proxy object to be converted into an ee$Image.
#' @param assetId Character. Destination asset ID for the uploaded file.
#' @param command_line_tool_path Character. Path to the Earth Engine command line
#' tool (CLT). If NULL, rgee assumes that CLT is set in the system PATH.
#' (ignore if \code{via} is not defined as "gcs_to_asset").
#' @param overwrite Logical. If TRUE, the assetId will be overwritten.
#' @param bucket Character. Name of the GCS bucket.
#' @param predefinedAcl Specify user access to object. Passed to
#' \code{googleCloudStorageR::gcs_upload}.
#' @param monitoring Logical. If TRUE the exportation task will be monitored.
#' @param quiet Logical. Suppress info message.
#' @param ... parameter(s) passed on to \code{\link{ee_utils_create_manifest_image}}
#'
#' @return An ee$Image object
#' @family image upload functions
#' @examples
#' \dontrun{
#' library(rgee)
#' library(stars)
#' ee_Initialize(gcs = TRUE)
#'
#' # Get the filename of a image
#' tif <- system.file("tif/L7_ETMs.tif", package = "stars")
#' x <- read_stars(tif)
#' assetId <- sprintf("%s/%s",ee_get_assethome(),'stars_l7')
#'
#' # # Method 1
#' # 1. Move from local to gcs
#' gs_uri <- local_to_gcs(x = tif, bucket = 'rgee_dev')
#'
#' # 2. Create a manifest
#' manifest <- ee_utils_create_manifest_image(gs_uri, assetId)
#'
#' # 3. Pass from gcs to asset
#' gcs_to_ee_image(
#' manifest = manifest,
#' overwrite = TRUE
#' )
#'
#' # OPTIONAL: Monitoring progress
#' ee_monitoring(max_attempts = Inf)
#'
#' # OPTIONAL: Display results
#' ee_stars_01 <- ee$Image(assetId)
#' Map$centerObject(ee_stars_01)
#' Map$addLayer(ee_stars_01, list(min = 0, max = 255))
#'
#' # Method 2
#' ee_stars_02 <- stars_as_ee(
#' x = x,
#' overwrite = TRUE,
#' assetId = assetId,
#' bucket = "rgee_dev"
#' )
#' Map$centerObject(ee_stars_02)
#' Map$addLayer(ee_stars_02, list(min = 0, max = 255))
#' }
#' @export
stars_as_ee <- function(x,
assetId,
bucket = NULL,
predefinedAcl = "bucketLevel",
command_line_tool_path = NULL,
overwrite = FALSE,
monitoring = TRUE,
quiet = FALSE,
...) {
# Folder to save intermediate upload files
ee_temp <- tempdir()
if (is.null(command_line_tool_path)) {
command_line_tool_path <- ""
}
if (!quiet) {
message("1. Converting stars (raster) object to GeoTIFF ... saving in /tmp")
}
stars_proxy <- ee_as_proxystars(x, temp_dir = ee_temp)
if (!quiet) {
message("2. From local to GCS")
}
gcs_filename <- local_to_gcs(
x = stars_proxy[[1]],
bucket = bucket,
predefinedAcl = predefinedAcl,
quiet = quiet
)
if (!quiet) {
message("3. Creating the manifest")
}
manifest <- ee_utils_create_manifest_image(
gs_uri = gcs_filename,
assetId = assetId,
...
)
if (!quiet) {
message("4. From GCS to Earth Engine")
}
# Verify is the EE assets path is valid
assetId <- ee_verify_filename(
path_asset = assetId,
strict = FALSE
)
gcs_to_ee_image(
manifest,
overwrite = overwrite,
command_line_tool_path = command_line_tool_path
)
if (isTRUE(monitoring)) {
ee_monitoring(max_attempts = Inf)
ee$Image(assetId)
} else {
ee$Image(assetId)
}
}
#' Convert a Raster* object into an EE Image object
#'
#' @param x RasterLayer, RasterStack or RasterBrick object to be converted into
#' an ee$Image.
#' @param assetId Character. Destination asset ID for the uploaded file.
#' @param command_line_tool_path Character. Path to the Earth Engine command line
#' tool (CLT). If NULL, rgee assumes that CLT is set in the system PATH.
#' (ignore if \code{via} is not defined as "gcs_to_asset").
#' @param overwrite Logical. If TRUE, the assetId will be overwritten.
#' @param bucket Character. Name of the GCS bucket.
#' @param predefinedAcl Specify user access to object. Passed to
#' \code{googleCloudStorageR::gcs_upload}.
#' @param monitoring Logical. If TRUE the exportation task will be monitored.
#' @param quiet Logical. Suppress info message.
#' @param ... parameter(s) passed on to \code{\link{ee_utils_create_manifest_image}}
#'
#' @return An ee$Image object
#' @family image upload functions
#'
#' @examples
#' \dontrun{
#' library(raster)
#' library(stars)
#' library(rgee)
#'
#' ee_Initialize(gcs = TRUE)
#'
#' # Get the filename of a image
#' tif <- system.file("tif/L7_ETMs.tif", package = "stars")
#' x <- stack(tif)
#' assetId <- sprintf("%s/%s",ee_get_assethome(),'raster_l7')
#'
#' # Method 1
#' # 1. Move from local to gcs
#' gs_uri <- local_to_gcs(x = tif, bucket = 'rgee_dev')
#'
#' # 2. Create a manifest
#' manifest <- ee_utils_create_manifest_image(gs_uri, assetId)
#'
#' # 3. Pass from gcs to asset
#' gcs_to_ee_image(
#' manifest = manifest,
#' overwrite = TRUE
#' )
#'
#' # OPTIONAL: Monitoring progress
#' ee_monitoring(max_attempts = Inf)
#'
#' # OPTIONAL: Display results
#' ee_stars_01 <- ee$Image(assetId)
#' Map$centerObject(ee_stars_01)
#' Map$addLayer(ee_stars_01, list(min = 0, max = 255))
#'
#' # Method 2
#' ee_stars_02 <- raster_as_ee(
#' x = x,
#' overwrite = TRUE,
#' assetId = assetId,
#' bucket = "rgee_dev"
#' )
#' Map$centerObject(ee_stars_02)
#' Map$addLayer(ee_stars_02, list(min = 0, max = 255))
#' }
#' @export
raster_as_ee <- stars_as_ee
|
#'
#'@title Compare multiple size comps.
#'
#'@description Function to compare multiple size comps.
#'
#'@param n_xmsz - array (or list of arrays) dimensioned xmsz
#'@param title - title for plot
#'@param showPlot - flag to show plot immediately
#'
#'@return ggplot2 object
#'
#'@import ggplot2
#'@import reshape2
#'
#'@export
#'
compareSizeCompsGG<-function(n_xmsz=NULL,
title='',
ylab='Abundance (millions)',
showPlot=TRUE){
oneModel<-FALSE;
#size comps come in as array(s)
if (is.array(n_xmsz)){
mdfr<-melt(n_xmsz,value.name='val');
mdfr$model<-'';
oneModel<-TRUE;
} else if (is.list(n_xmsz)) {
#n_xmsz is a list of array objects with models as names
mdls<-names(n_xmsz);
mdfr<-NULL;
for (mdl in mdls){
mdfrp<-melt(n_xmsz[[mdl]],value.name='val');
mdfrp$model<-mdl;
mdfr<-rbind(mdfr,mdfrp);
}
} else {
cat('Invalid specification for n_xmsz in compareSizeCompsGG\n');
cat('n_xmsz must be either an array or a list of arrays\n');
cat("Aborting...\n")
stop();
}
if (oneModel){
#plotting one model
pl <- ggplot(aes(x=z,y=val,fill=s),data=mdfr);
pl <- pl + facet_grid(m~x);
} else {
#plotting multiple models
pl <- ggplot(aes(x=z,y=val,fill=model),data=mdfr);
pl <- pl + facet_grid(m+s~x);
}
pl <- pl + geom_bar(alpha=1,stat='identity',position='dodge');
pl <- pl + labs(x='size (mm)',y=ylab);
pl <- pl + guides(fill=guide_legend(''));
if (title!='') pl <- pl + ggtitle(title);
if (showPlot) print(pl);
return(invisible(pl))
}
|
/R/compareSizeCompsGG.R
|
permissive
|
wStockhausen/rsimTCSAM
|
R
| false | false | 1,777 |
r
|
#'
#'@title Compare multiple size comps.
#'
#'@description Function to compare multiple size comps.
#'
#'@param n_xmsz - array (or list of arrays) dimensioned xmsz
#'@param title - title for plot
#'@param showPlot - flag to show plot immediately
#'
#'@return ggplot2 object
#'
#'@import ggplot2
#'@import reshape2
#'
#'@export
#'
compareSizeCompsGG<-function(n_xmsz=NULL,
title='',
ylab='Abundance (millions)',
showPlot=TRUE){
oneModel<-FALSE;
#size comps come in as array(s)
if (is.array(n_xmsz)){
mdfr<-melt(n_xmsz,value.name='val');
mdfr$model<-'';
oneModel<-TRUE;
} else if (is.list(n_xmsz)) {
#n_xmsz is a list of array objects with models as names
mdls<-names(n_xmsz);
mdfr<-NULL;
for (mdl in mdls){
mdfrp<-melt(n_xmsz[[mdl]],value.name='val');
mdfrp$model<-mdl;
mdfr<-rbind(mdfr,mdfrp);
}
} else {
cat('Invalid specification for n_xmsz in compareSizeCompsGG\n');
cat('n_xmsz must be either an array or a list of arrays\n');
cat("Aborting...\n")
stop();
}
if (oneModel){
#plotting one model
pl <- ggplot(aes(x=z,y=val,fill=s),data=mdfr);
pl <- pl + facet_grid(m~x);
} else {
#plotting multiple models
pl <- ggplot(aes(x=z,y=val,fill=model),data=mdfr);
pl <- pl + facet_grid(m+s~x);
}
pl <- pl + geom_bar(alpha=1,stat='identity',position='dodge');
pl <- pl + labs(x='size (mm)',y=ylab);
pl <- pl + guides(fill=guide_legend(''));
if (title!='') pl <- pl + ggtitle(title);
if (showPlot) print(pl);
return(invisible(pl))
}
|
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