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# setup -------------------------------------------------------------------
# library(data.table)
library(tidyverse)
library(readxl)
# library(lubridate)
library(labelled)
# data loading ------------------------------------------------------------
set.seed(42)
data.raw <- tibble(id=gl(2, 10), group = gl(2, 10), outcome = rnorm(20))
# data.raw <- read_excel("dataset/file.xlsx") %>%
# janitor::clean_names()
# data cleaning -----------------------------------------------------------
# data.raw <- data.raw %>%
# filter() %>%
# select()
# data wrangling ----------------------------------------------------------
# data.raw <- data.raw %>%
# mutate(
#
# )
# labels ------------------------------------------------------------------
data.raw <- data.raw %>%
set_variable_labels(
group = "Study group",
outcome = "Study outcome",
)
# analytical dataset ------------------------------------------------------
analytical <- data.raw %>%
# select analytic variables
select(
id,
group,
outcome,
)
# mockup of analytical dataset for SAP and public SAR
analytical_mockup <- tibble( id = c( "1", "2", "3", "...", as.character(nrow(analytical)) ) ) %>%
left_join(analytical %>% head(0), by = "id") %>%
mutate_all(as.character) %>%
replace(is.na(.), "")
|
/scripts/input.R
|
no_license
|
philsf-biostat/SAR-2021-013-VB
|
R
| false | false | 1,301 |
r
|
# setup -------------------------------------------------------------------
# library(data.table)
library(tidyverse)
library(readxl)
# library(lubridate)
library(labelled)
# data loading ------------------------------------------------------------
set.seed(42)
data.raw <- tibble(id=gl(2, 10), group = gl(2, 10), outcome = rnorm(20))
# data.raw <- read_excel("dataset/file.xlsx") %>%
# janitor::clean_names()
# data cleaning -----------------------------------------------------------
# data.raw <- data.raw %>%
# filter() %>%
# select()
# data wrangling ----------------------------------------------------------
# data.raw <- data.raw %>%
# mutate(
#
# )
# labels ------------------------------------------------------------------
data.raw <- data.raw %>%
set_variable_labels(
group = "Study group",
outcome = "Study outcome",
)
# analytical dataset ------------------------------------------------------
analytical <- data.raw %>%
# select analytic variables
select(
id,
group,
outcome,
)
# mockup of analytical dataset for SAP and public SAR
analytical_mockup <- tibble( id = c( "1", "2", "3", "...", as.character(nrow(analytical)) ) ) %>%
left_join(analytical %>% head(0), by = "id") %>%
mutate_all(as.character) %>%
replace(is.na(.), "")
|
library(RPostgreSQL)
library(dplyr)
library(dbplyr)
#Uvoz:
source("auth.R", encoding="UTF-8")
source("uvoz in urejanje podatkov/tabela.R", encoding="UTF-8")
# Povezemo se z gonilnikom za PostgreSQL
drv <- dbDriver("PostgreSQL")
# Funkcija za brisanje tabel
delete_table <- function(){
# Uporabimo funkcijo tryCatch,
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo z bazo
conn <- dbConnect(drv, dbname = db, host = host, user = user, password = password)
# Če tabela obstaja, jo zbrišemo, ter najprej zbrišemo tiste,
# ki se navezujejo na druge
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS driver CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS team CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS results"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS grand_prix CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS has"))
}, finally = {
dbDisconnect(conn)
})
}
pravice <- function(){
# Uporabimo tryCatch,(da se povežemo in bazo in odvežemo)
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo
conn <- dbConnect(drv, dbname = db, host = host,#drv=s čim se povezujemo
user = user, password = password)
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO urosk"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO domenh"))
dbSendQuery(conn, build_sql("GRANT ALL ON SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO javnost"))
}, finally = {
# Na koncu nujno prekinemo povezavo z bazo,
# saj preveč odprtih povezav ne smemo imeti
dbDisconnect(conn) #PREKINEMO POVEZAVO
# Koda v finally bloku se izvede, preden program konča z napako
})
}
#Funkcija, ki ustvari tabele
create_table <- function(){
# Uporabimo tryCatch,(da se povežemo in bazo in odvežemo)
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo
conn <- dbConnect(drv, dbname = db, host = host,#drv=s čim se povezujemo
user = user, password = password)
#Glavne tabele
team <- dbSendQuery(conn,build_sql("CREATE TABLE team (
id INTEGER PRIMARY KEY,
team_name TEXT NOT NULL UNIQUE,
country TEXT NOT NULL,
constructor TEXT NOT NULL,
chassis VARCHAR(13) NOT NULL UNIQUE,
power_unit VARCHAR(22) NOT NULL)"))
driver <- dbSendQuery(conn,build_sql("CREATE TABLE driver (
name TEXT NOT NULL,
surname TEXT NOT NULL,
car_number INTEGER PRIMARY KEY,
age INTEGER NOT NULL,
height INTEGER NOT NULL,
weight INTEGER NOT NULL,
country TEXT NOT NULL
)"))
grand_prix <- dbSendQuery(conn,build_sql("CREATE TABLE grand_prix (
round INTEGER PRIMARY KEY,
official_name TEXT NOT NULL UNIQUE,
name TEXT NOT NULL,
circuit_name TEXT NOT NULL,
date DATE NOT NULL,
circuit_length DECIMAL NOT NULL,
laps INTEGER NOT NULL)"))
has <- dbSendQuery(conn,build_sql("CREATE TABLE has (
team INTEGER NOT NULL REFERENCES team(id),
driver INTEGER NOT NULL REFERENCES driver(car_number),
first INTEGER NOT NULL REFERENCES grand_prix(round),
last INTEGER NOT NULL REFERENCES grand_prix(round),
PRIMARY KEY (team,driver))"))
results <- dbSendQuery(conn,build_sql("CREATE TABLE results (
position INTEGER,
car_number INTEGER REFERENCES driver(car_number),
laps INTEGER,
time INTERVAL,
points INTEGER,
grand_prix INTEGER REFERENCES grand_prix(round),
start_position INTEGER NOT NULL)"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO jurez WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO jurez WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO javnost"))
}, finally = {
# Na koncu nujno prekinemo povezavo z bazo,
# saj preveč odprtih povezav ne smemo imeti
dbDisconnect(conn) #PREKINEMO POVEZAVO
# Koda v finally bloku se izvede, preden program konča z napako
})
}
#Funcija, ki vstavi podatke
insert_data <- function(){
tryCatch({
conn <- dbConnect(drv, dbname = db, host = host,
user = user, password = password)
dbWriteTable(conn, name="driver", tabeladirkacev, append=T, row.names=FALSE)
dbWriteTable(conn, name="team", tabelaekip, append=T, row.names=FALSE)
dbWriteTable(conn, name="grand_prix", tabelaGrandPrix16, append=T, row.names=FALSE)
dbWriteTable(conn, name="results", ultimatetabela, append=T, row.names=FALSE)
dbWriteTable(conn, name="has", data.has, append=T, row.names=FALSE)
}, finally = {
dbDisconnect(conn)
})
}
delete_table()
pravice()
create_table()
insert_data()
con <- src_postgres(dbname = db, host = host, user = user, password = password)
|
/baza/baza.r
|
permissive
|
UrosKrampelj/Formula-1
|
R
| false | false | 7,037 |
r
|
library(RPostgreSQL)
library(dplyr)
library(dbplyr)
#Uvoz:
source("auth.R", encoding="UTF-8")
source("uvoz in urejanje podatkov/tabela.R", encoding="UTF-8")
# Povezemo se z gonilnikom za PostgreSQL
drv <- dbDriver("PostgreSQL")
# Funkcija za brisanje tabel
delete_table <- function(){
# Uporabimo funkcijo tryCatch,
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo z bazo
conn <- dbConnect(drv, dbname = db, host = host, user = user, password = password)
# Če tabela obstaja, jo zbrišemo, ter najprej zbrišemo tiste,
# ki se navezujejo na druge
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS driver CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS team CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS results"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS grand_prix CASCADE"))
dbSendQuery(conn,build_sql("DROP TABLE IF EXISTS has"))
}, finally = {
dbDisconnect(conn)
})
}
pravice <- function(){
# Uporabimo tryCatch,(da se povežemo in bazo in odvežemo)
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo
conn <- dbConnect(drv, dbname = db, host = host,#drv=s čim se povezujemo
user = user, password = password)
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO urosk"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO domenh"))
dbSendQuery(conn, build_sql("GRANT ALL ON SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT CONNECT ON DATABASE sem2017_jurez TO javnost"))
}, finally = {
# Na koncu nujno prekinemo povezavo z bazo,
# saj preveč odprtih povezav ne smemo imeti
dbDisconnect(conn) #PREKINEMO POVEZAVO
# Koda v finally bloku se izvede, preden program konča z napako
})
}
#Funkcija, ki ustvari tabele
create_table <- function(){
# Uporabimo tryCatch,(da se povežemo in bazo in odvežemo)
# da prisilimo prekinitev povezave v primeru napake
tryCatch({
# Vzpostavimo povezavo
conn <- dbConnect(drv, dbname = db, host = host,#drv=s čim se povezujemo
user = user, password = password)
#Glavne tabele
team <- dbSendQuery(conn,build_sql("CREATE TABLE team (
id INTEGER PRIMARY KEY,
team_name TEXT NOT NULL UNIQUE,
country TEXT NOT NULL,
constructor TEXT NOT NULL,
chassis VARCHAR(13) NOT NULL UNIQUE,
power_unit VARCHAR(22) NOT NULL)"))
driver <- dbSendQuery(conn,build_sql("CREATE TABLE driver (
name TEXT NOT NULL,
surname TEXT NOT NULL,
car_number INTEGER PRIMARY KEY,
age INTEGER NOT NULL,
height INTEGER NOT NULL,
weight INTEGER NOT NULL,
country TEXT NOT NULL
)"))
grand_prix <- dbSendQuery(conn,build_sql("CREATE TABLE grand_prix (
round INTEGER PRIMARY KEY,
official_name TEXT NOT NULL UNIQUE,
name TEXT NOT NULL,
circuit_name TEXT NOT NULL,
date DATE NOT NULL,
circuit_length DECIMAL NOT NULL,
laps INTEGER NOT NULL)"))
has <- dbSendQuery(conn,build_sql("CREATE TABLE has (
team INTEGER NOT NULL REFERENCES team(id),
driver INTEGER NOT NULL REFERENCES driver(car_number),
first INTEGER NOT NULL REFERENCES grand_prix(round),
last INTEGER NOT NULL REFERENCES grand_prix(round),
PRIMARY KEY (team,driver))"))
results <- dbSendQuery(conn,build_sql("CREATE TABLE results (
position INTEGER,
car_number INTEGER REFERENCES driver(car_number),
laps INTEGER,
time INTERVAL,
points INTEGER,
grand_prix INTEGER REFERENCES grand_prix(round),
start_position INTEGER NOT NULL)"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO jurez WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL TABLES IN SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO jurez WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO urosk WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO domenh WITH GRANT OPTION"))
dbSendQuery(conn, build_sql("GRANT SELECT ON ALL TABLES IN SCHEMA public TO javnost"))
}, finally = {
# Na koncu nujno prekinemo povezavo z bazo,
# saj preveč odprtih povezav ne smemo imeti
dbDisconnect(conn) #PREKINEMO POVEZAVO
# Koda v finally bloku se izvede, preden program konča z napako
})
}
#Funcija, ki vstavi podatke
insert_data <- function(){
tryCatch({
conn <- dbConnect(drv, dbname = db, host = host,
user = user, password = password)
dbWriteTable(conn, name="driver", tabeladirkacev, append=T, row.names=FALSE)
dbWriteTable(conn, name="team", tabelaekip, append=T, row.names=FALSE)
dbWriteTable(conn, name="grand_prix", tabelaGrandPrix16, append=T, row.names=FALSE)
dbWriteTable(conn, name="results", ultimatetabela, append=T, row.names=FALSE)
dbWriteTable(conn, name="has", data.has, append=T, row.names=FALSE)
}, finally = {
dbDisconnect(conn)
})
}
delete_table()
pravice()
create_table()
insert_data()
con <- src_postgres(dbname = db, host = host, user = user, password = password)
|
/plot1.R
|
no_license
|
molinerojo/ExData_Plotting1
|
R
| false | false | 4,659 |
r
| ||
print.summary.rfit <- function (x, digits = max(5, .Options$digits - 2), ...) {
cat("Call:\n")
print(x$call)
cat("\nCoefficients:\n")
est <- x$coef
printCoefmat(x$coefficients, P.values = TRUE, has.Pvalue = TRUE)
cat("\nMultiple R-squared (Robust):", x$R2, "\n")
cat("Reduction in Dispersion Test:", round(x$dropstat, digits = digits),
"p-value:", round(x$droppval, digits = digits), "\n")
cat("\n")
}
|
/Rfit/R/print.summary.rfit.R
|
no_license
|
ingted/R-Examples
|
R
| false | false | 443 |
r
|
print.summary.rfit <- function (x, digits = max(5, .Options$digits - 2), ...) {
cat("Call:\n")
print(x$call)
cat("\nCoefficients:\n")
est <- x$coef
printCoefmat(x$coefficients, P.values = TRUE, has.Pvalue = TRUE)
cat("\nMultiple R-squared (Robust):", x$R2, "\n")
cat("Reduction in Dispersion Test:", round(x$dropstat, digits = digits),
"p-value:", round(x$droppval, digits = digits), "\n")
cat("\n")
}
|
options(repos = c(CRAN = "http://cran.rstudio.com"))
if (!require(devtools)){
install.packages("devtools", dependencies = TRUE)
}
if (!require(dplyr)){
install.packages("dplyr", dependencies = TRUE)
}
if (!require(ggplot2)){
install.packages("ggplot2", dependencies = TRUE)
}
if (!require(readxl)){
install.packages("readxl", dependencies = TRUE)
}
if (!require(pwr)){
install.packages("pwr", dependencies = TRUE)
}
if (!require(effsize)){
install.packages("effsize", dependencies = TRUE)
}
if (!require(gmodels)){
install.packages("gmodels", dependencies = TRUE)
}
if (!require(coin)){
install.packages("coin", dependencies = TRUE)
}
if (!require(gdata)){
install.packages("gdata", dependencies = TRUE)
}
if (!require(Hmisc)){
install.packages("Hmisc", dependencies = TRUE)
require(Hmisc)
}
|
/install.r
|
no_license
|
mochodek/qmese
|
R
| false | false | 837 |
r
|
options(repos = c(CRAN = "http://cran.rstudio.com"))
if (!require(devtools)){
install.packages("devtools", dependencies = TRUE)
}
if (!require(dplyr)){
install.packages("dplyr", dependencies = TRUE)
}
if (!require(ggplot2)){
install.packages("ggplot2", dependencies = TRUE)
}
if (!require(readxl)){
install.packages("readxl", dependencies = TRUE)
}
if (!require(pwr)){
install.packages("pwr", dependencies = TRUE)
}
if (!require(effsize)){
install.packages("effsize", dependencies = TRUE)
}
if (!require(gmodels)){
install.packages("gmodels", dependencies = TRUE)
}
if (!require(coin)){
install.packages("coin", dependencies = TRUE)
}
if (!require(gdata)){
install.packages("gdata", dependencies = TRUE)
}
if (!require(Hmisc)){
install.packages("Hmisc", dependencies = TRUE)
require(Hmisc)
}
|
library(ape)
testtree <- read.tree("2957_28.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2957_28_unrooted.txt")
|
/codeml_files/newick_trees_processed/2957_28/rinput.R
|
no_license
|
DaniBoo/cyanobacteria_project
|
R
| false | false | 137 |
r
|
library(ape)
testtree <- read.tree("2957_28.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2957_28_unrooted.txt")
|
data("Train", package="mlogit")
Train$ID <- Train$id
Train$CHOICE <- as.numeric(Train$choice)
generate_default_availabilities(Train, 5)
|
/R/examples/generate_default_availabilities.R
|
no_license
|
joemolloy/fast-mixed-mnl
|
R
| false | false | 135 |
r
|
data("Train", package="mlogit")
Train$ID <- Train$id
Train$CHOICE <- as.numeric(Train$choice)
generate_default_availabilities(Train, 5)
|
packages.used=c("shiny","ggmap","leaflet","dplyr","shinyBS","plotly","extrafont","grDevices","shinyjs")
# check packages that need to be installed.
packages.needed=setdiff(packages.used,intersect(installed.packages()[,1],packages.used))
# install additional packages
if(length(packages.needed)>0){
install.packages(packages.needed, dependencies = TRUE)
}
library(shiny)
library(ggmap)
library(leaflet)
library(dplyr)
library(shinyBS)
library(plotly)
library(extrafont)
library(grDevices)
library(shinyjs)
#Read in files
college.filtered = readRDS("../data/school.select.rds")
college = readRDS("../data/College2014_15_new.rds")
#Support data frames
major = c("Agriculture, Agriculture Operations, And Related Sciences","Natural Resources And Conservation", "Architecture And Related Services","Area, Ethnic, Cultural, Gender, And Group Studies"," Communication, Journalism, And Related Programs","Communications Technologies/Technicians And Support Services","Computer And Information Sciences And Support Services","Personal And Culinary Services"," Education","Engineering","Engineering Technologies And Engineering-Related Fields","Foreign Languages, Literatures, And Linguistics"," Family And Consumer Sciences/Human Sciences","Legal Professions And Studies","English Language And Literature/Letters","Liberal Arts And Sciences, General Studies And Humanities","Library Science"," Biological And Biomedical Sciences","Mathematics And Statistics","Military Technologies And Applied Sciences","Multi/Interdisciplinary Studies","Parks, Recreation, Leisure, And Fitness Studies","Philosophy And Religious Studies","Theology And Religious Vocations"," Physical Sciences"," Science Technologies/Technicians"," Psychology"," Homeland Security, Law Enforcement, Firefighting And Related Protective Services","Public Administration And Social Service Professions","Social Sciences","Construction Trades","Mechanic And Repair Technologies/Technicians","Precision Production","Transportation And Materials Moving","Visual And Performing Arts","Health Professions And Related Programs","Business, Management, Marketing, And Related Support Services","History")
major.index =c("PCIP01","PCIP03","PCIP04","PCIP05","PCIP09","PCIP10","PCIP11","PCIP12","PCIP13","PCIP14","PCIP15","PCIP16","PCIP19","PCIP22","PCIP23","PCIP24","PCIP25","PCIP26","PCIP27","PCIP29","PCIP30","PCIP31","PCIP38","PCIP39","PCIP40","PCIP41","PCIP42","PCIP43","PCIP44","PCIP45","PCIP46","PCIP47","PCIP48","PCIP49","PCIP50","PCIP51","PCIP52","PCIP54")
major.frame = data.frame(major = major, index = major.index)
shinyUI(fluidPage(
div(id="canvas",
navbarPage(strong("NY School Hunter
",style="color: white;"), theme="style.css",
tabPanel(strong(tags$i("Map")),
div(class="outer",
# lealfet map
uiOutput("map"),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 100, left = 5, bottom = "auto",
width = "auto", height = "auto", cursor = "move",
wellPanel(style = "overflow-y:scroll; max-height: 600px",
bsCollapse(id="collapse.filter",open="Filter",
bsCollapsePanel(tags$strong("Filter"),style="primary",
fluidRow(column(12,checkboxGroupInput("filter","Filtered By...",choices=list("Scores","Major","Tuition","None"),selected="None",inline = TRUE))),
fluidRow(column(10,uiOutput("ui.filter")))),
bsCollapsePanel(tags$strong("Filter Options"),style = "primary",
bsCollapsePanel(tags$strong("Major"),style="info",
fluidRow(column(10,selectInput("major",tags$strong("Your Major"),choices = c(major),selected = ""))
)
),
bsCollapsePanel(tags$strong("SAT"),style="info",
fluidRow(column(4,numericInput("sat.reading",tags$strong("Read"),value=800,min=0,max=800,step=10)),
column(4,numericInput("sat.math",tags$strong("Math"),value=800,min=0,max=800,step=10),offset = 0),
column(4,numericInput("sat.writing",tags$strong("Write"),value=800,min=20,max=800,step=10),offset = 0)
)
),
bsCollapsePanel(tags$strong("ACT"),style="info",
fluidRow(column(10,numericInput("score.act",tags$strong("Cumulative Scores"),value=36,min=0,max=36,step=1))
)
),
bsCollapsePanel(tags$strong("Tuition"),style="info",
fluidRow(
column(10,numericInput("max",tags$strong("Max Tution"),min = 0, max = 90000, value = 10000))),
fluidRow(column(10, offset = 1,radioButtons("location",tags$strong("Tuition Options"),choices = list("State Resident", "Non-State Resident"),selected = "State Resident", inline = FALSE))
)
)
)
),
bsCollapsePanel(tags$strong("Map Options"),style="primary",
fluidRow(column(10,selectInput("Focus",tags$strong("Area of Focus"),choices = c("New York State","New York City","Western New York","Finger Lakes","Southern Tier","Central New York","North Country","Mohawk Valley","Capital District","Hudson Valley","Long Island"), selected = "New York Sate"))
),
fluidRow(column(12,radioButtons("output.cluster",tags$strong("Classification Options"),choices=list("Degree","Length","Transfer Rate","Type"),selected = "Degree",inline=TRUE)))
),
actionButton("search", tags$strong("Searching!"))
)#WellPanel ends here
),
absolutePanel(class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 50, right = 0, bottom = "auto",
width = 450, height = 30, cursor = "move",
bsCollapsePanel(tags$strong("Classification Map"),style = "primary",
leafletOutput('myMap_1', width = "102%", height = 450)
)#Collapse panel ends here
)
)
,div(class="footer", "Applied Data Science Group 4")
),
#Comparison
tabPanel(strong(tags$i("Comparision!")),
###########################################TEAM 2 IMPLEMENTATION STARTS##########################################################
wellPanel(id = "tPanel",style = "overflow-y:scroll; max-height: 600px",
tags$hr(style="border-color: #6088B1;"),
h1("Side-by-Side Two School Comparison",align= "center",style = "color: #333333; font-family: Times;
font-size:50pt"),
tags$hr(style="border-color: #6088B1;"),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
uiOutput("ui.1"),
uiOutput("ui.2")
)
),
br(),br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
imageOutput("logo1",height = "400", width = "400"),imageOutput("logo2",height = "400", width = "400")
)),
br(),
# ==== Title in Orange
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("Basic Information" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
# === Some text to explain the Figure:
br(),
# === display instnm
fluidRow(align = "center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"Institution Name: ")),textOutput("instnm1")),
fluidRow(strong(column(width=2,offset=1,"Institution Name: ")),textOutput("instnm2")))
),br(),
# === display city
fluidRow(align = "justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"City: ")),textOutput("city1")),
fluidRow(strong(column(width=2,offset=1,"City: ")),textOutput("city2")))
),br(),
# === display clevel
fluidRow(align = "justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Level of Institution: ")),textOutput("iclevel1")),
fluidRow(strong(column(width=4,offset=1,"Level of Institution: ")),textOutput("iclevel2")))
),br(),
# === display control
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Control of Institution: ")),textOutput("control1")),
fluidRow(strong(column(width=4,offset=1,"Control of Institution: ")),textOutput("control2")))
),br(),
# === display highest degree
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=3,offset=1,"Highest Degree: ")),textOutput("highdeg1")),
fluidRow(strong(column(width=3,offset=1,"Highest Degree: ")),textOutput("highdeg2")))
),br(),
# === display locale
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"Locale: ")),textOutput("locale1")),
fluidRow(strong(column(width=2,offset=1,"Locale: ")),textOutput("locale2")))
),br(),
# === display admission rate
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Admission Rate: ")),textOutput("adm_rate1")),
fluidRow(strong(column(width=4,offset=1,"Admission Rate: ")),textOutput("adm_rate2")))
),br(),
# === display in-state tuition
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"In-State Tuition: ")),textOutput("tuitionfee_in1")),
fluidRow(strong(column(width=4,offset=1,"In-State Tuition: ")),textOutput("tuitionfee_in2")))
),br(),
# === display out-of-state tuition
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Out-of-State Tuition: ")),textOutput("tuitionfee_out1")),
fluidRow(strong(column(width=4,offset=1,"Out-of-State Tuition: ")),textOutput("tuitionfee_out2")))
),br(),
# === display percentage of federal loans
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=7,offset=1,"Percentage of Students Receiving Federal Loans: ")),
textOutput("pctfloan1")),
fluidRow(strong(column(width=7,offset=1,"Percentage of Students Receiving Federal Loans: ")),
textOutput("pctfloan2")))
),br(),
# === display total Undergraduates Seeking Degrees
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=5,offset=1,"Total Undergraduates Seeking Degrees: ")),
textOutput("ugds1")),
fluidRow(strong(column(width=5,offset=1,"Total Undergraduates Seeking Degrees: ")),
textOutput("ugds2")))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("Calculate Median Debt by Family Income" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
br(),
# === display sliderInput for family income
fluidRow(align="center",sliderInput("fincome","Family Income: ",
min=0,max=200000,value=0,width = 600)
),br(),
# === display median debt based on family income input
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(column(width=7,offset=1,textOutput("school1")),
strong(column(width=7,offset = 1,"Median Debt based on Family Income : ")),br(),
textOutput("debt1")),
fluidRow(column(width=7,offset=1,textOutput("school2")),
strong(column(width=7,offset=1,"Median Debt based on Family Income: ")),br(),
textOutput("debt2")))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("SAT & ACT Scores" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
helpText( strong("25th-75th Percentile" , style="color:#6088B1 ; font-family: 'times'; font-size:20pt ; font-type:bold" )),
hr(style="color:#808080")
)),
br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
textOutput("school1.2"),
textOutput("school2.2"))
),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
tableOutput("sat1"),
tableOutput("sat2"))
),br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
tableOutput("act1"),
tableOutput("act2"))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),
hr(style="color:#808080"),
helpText( strong("Demographics of Students" , style="color: #6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
br(),
# === Bar with corresponding widget
fluidRow(align="center",column(4,h2("Major Diversity",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("my_barplot1" , height = "500px"),
plotlyOutput("my_barplot2" , height = "500px")
)
),br(),br(),
# === pie chart of ethnicity
fluidRow(align="center",column(8,h2("Degree-Seeking Undergraduates by Ethnicity",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("demographics1",height="550"),
plotlyOutput("demographics2",height="550"))
),br(),
fluidRow(align="center",column(8,h2("Degree-Seeking Undergraduates by Gender",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("female1",height="450"),
plotlyOutput("female2",height="450")
))
############################################TEAM 2 IMPLEMENTATION ENDS############################################################
)
),#Comparison ends here
#Data presentation
#Introduction
tabPanel(strong(tags$i("About us")),
mainPanel(width=12,
h1("Project: NY School Hunter - A Shiny App Development"),
h3("Background"),
p("Our project takes all available data on colleges and universities in New York State and creates a useful shiny app that allows users to explore and compare schools based on user-specific filtering criteria. The purpose of our design is to provide users with a bird's eye view of New York colleges and universities; allow them to filter, search, and group schools by their preferred criteria; and further compare two schools on a more micro level."),#Our motivation
h3("Project summary"),
p("A distinguishing feature of our app is the map search function - users can see all the specified schools on the map (normal map view or satellite map view), focus in on a specific area of New York State.
The side-by-side school comparison feature allows users to see a detailed breakdown of meaningful data and statistics from our available data on each school."),#What did we do
h3("Team Members"),
p(" - ",strong("I. Ka Heng (Helen) Lo - Presenter")),
p(" - ",strong("II. Boxuan Zhao")),
p(" - ",strong("III. Zijun Nie")),
p(" - ",strong("IV. Senyao Han")),
p(" - ",strong("V. Song Wang")),
p(""),
p(""),
br(),
p(em("Release 02/22/2017.","VERSION 1.0.0")),
p(em(a("Github link",href="https://github.com/TZstatsADS/Spr2017-proj2-grp4.git"))))
,div(class="footer", "Applied Data Science Group 4")
)#Introduciton ends here
)#navarbarPage ends here
)
))
|
/app/ui.r
|
no_license
|
bz2290/Spr2017-proj2-grp4
|
R
| false | false | 26,020 |
r
|
packages.used=c("shiny","ggmap","leaflet","dplyr","shinyBS","plotly","extrafont","grDevices","shinyjs")
# check packages that need to be installed.
packages.needed=setdiff(packages.used,intersect(installed.packages()[,1],packages.used))
# install additional packages
if(length(packages.needed)>0){
install.packages(packages.needed, dependencies = TRUE)
}
library(shiny)
library(ggmap)
library(leaflet)
library(dplyr)
library(shinyBS)
library(plotly)
library(extrafont)
library(grDevices)
library(shinyjs)
#Read in files
college.filtered = readRDS("../data/school.select.rds")
college = readRDS("../data/College2014_15_new.rds")
#Support data frames
major = c("Agriculture, Agriculture Operations, And Related Sciences","Natural Resources And Conservation", "Architecture And Related Services","Area, Ethnic, Cultural, Gender, And Group Studies"," Communication, Journalism, And Related Programs","Communications Technologies/Technicians And Support Services","Computer And Information Sciences And Support Services","Personal And Culinary Services"," Education","Engineering","Engineering Technologies And Engineering-Related Fields","Foreign Languages, Literatures, And Linguistics"," Family And Consumer Sciences/Human Sciences","Legal Professions And Studies","English Language And Literature/Letters","Liberal Arts And Sciences, General Studies And Humanities","Library Science"," Biological And Biomedical Sciences","Mathematics And Statistics","Military Technologies And Applied Sciences","Multi/Interdisciplinary Studies","Parks, Recreation, Leisure, And Fitness Studies","Philosophy And Religious Studies","Theology And Religious Vocations"," Physical Sciences"," Science Technologies/Technicians"," Psychology"," Homeland Security, Law Enforcement, Firefighting And Related Protective Services","Public Administration And Social Service Professions","Social Sciences","Construction Trades","Mechanic And Repair Technologies/Technicians","Precision Production","Transportation And Materials Moving","Visual And Performing Arts","Health Professions And Related Programs","Business, Management, Marketing, And Related Support Services","History")
major.index =c("PCIP01","PCIP03","PCIP04","PCIP05","PCIP09","PCIP10","PCIP11","PCIP12","PCIP13","PCIP14","PCIP15","PCIP16","PCIP19","PCIP22","PCIP23","PCIP24","PCIP25","PCIP26","PCIP27","PCIP29","PCIP30","PCIP31","PCIP38","PCIP39","PCIP40","PCIP41","PCIP42","PCIP43","PCIP44","PCIP45","PCIP46","PCIP47","PCIP48","PCIP49","PCIP50","PCIP51","PCIP52","PCIP54")
major.frame = data.frame(major = major, index = major.index)
shinyUI(fluidPage(
div(id="canvas",
navbarPage(strong("NY School Hunter
",style="color: white;"), theme="style.css",
tabPanel(strong(tags$i("Map")),
div(class="outer",
# lealfet map
uiOutput("map"),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 100, left = 5, bottom = "auto",
width = "auto", height = "auto", cursor = "move",
wellPanel(style = "overflow-y:scroll; max-height: 600px",
bsCollapse(id="collapse.filter",open="Filter",
bsCollapsePanel(tags$strong("Filter"),style="primary",
fluidRow(column(12,checkboxGroupInput("filter","Filtered By...",choices=list("Scores","Major","Tuition","None"),selected="None",inline = TRUE))),
fluidRow(column(10,uiOutput("ui.filter")))),
bsCollapsePanel(tags$strong("Filter Options"),style = "primary",
bsCollapsePanel(tags$strong("Major"),style="info",
fluidRow(column(10,selectInput("major",tags$strong("Your Major"),choices = c(major),selected = ""))
)
),
bsCollapsePanel(tags$strong("SAT"),style="info",
fluidRow(column(4,numericInput("sat.reading",tags$strong("Read"),value=800,min=0,max=800,step=10)),
column(4,numericInput("sat.math",tags$strong("Math"),value=800,min=0,max=800,step=10),offset = 0),
column(4,numericInput("sat.writing",tags$strong("Write"),value=800,min=20,max=800,step=10),offset = 0)
)
),
bsCollapsePanel(tags$strong("ACT"),style="info",
fluidRow(column(10,numericInput("score.act",tags$strong("Cumulative Scores"),value=36,min=0,max=36,step=1))
)
),
bsCollapsePanel(tags$strong("Tuition"),style="info",
fluidRow(
column(10,numericInput("max",tags$strong("Max Tution"),min = 0, max = 90000, value = 10000))),
fluidRow(column(10, offset = 1,radioButtons("location",tags$strong("Tuition Options"),choices = list("State Resident", "Non-State Resident"),selected = "State Resident", inline = FALSE))
)
)
)
),
bsCollapsePanel(tags$strong("Map Options"),style="primary",
fluidRow(column(10,selectInput("Focus",tags$strong("Area of Focus"),choices = c("New York State","New York City","Western New York","Finger Lakes","Southern Tier","Central New York","North Country","Mohawk Valley","Capital District","Hudson Valley","Long Island"), selected = "New York Sate"))
),
fluidRow(column(12,radioButtons("output.cluster",tags$strong("Classification Options"),choices=list("Degree","Length","Transfer Rate","Type"),selected = "Degree",inline=TRUE)))
),
actionButton("search", tags$strong("Searching!"))
)#WellPanel ends here
),
absolutePanel(class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 50, right = 0, bottom = "auto",
width = 450, height = 30, cursor = "move",
bsCollapsePanel(tags$strong("Classification Map"),style = "primary",
leafletOutput('myMap_1', width = "102%", height = 450)
)#Collapse panel ends here
)
)
,div(class="footer", "Applied Data Science Group 4")
),
#Comparison
tabPanel(strong(tags$i("Comparision!")),
###########################################TEAM 2 IMPLEMENTATION STARTS##########################################################
wellPanel(id = "tPanel",style = "overflow-y:scroll; max-height: 600px",
tags$hr(style="border-color: #6088B1;"),
h1("Side-by-Side Two School Comparison",align= "center",style = "color: #333333; font-family: Times;
font-size:50pt"),
tags$hr(style="border-color: #6088B1;"),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
uiOutput("ui.1"),
uiOutput("ui.2")
)
),
br(),br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
imageOutput("logo1",height = "400", width = "400"),imageOutput("logo2",height = "400", width = "400")
)),
br(),
# ==== Title in Orange
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("Basic Information" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
# === Some text to explain the Figure:
br(),
# === display instnm
fluidRow(align = "center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"Institution Name: ")),textOutput("instnm1")),
fluidRow(strong(column(width=2,offset=1,"Institution Name: ")),textOutput("instnm2")))
),br(),
# === display city
fluidRow(align = "justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"City: ")),textOutput("city1")),
fluidRow(strong(column(width=2,offset=1,"City: ")),textOutput("city2")))
),br(),
# === display clevel
fluidRow(align = "justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Level of Institution: ")),textOutput("iclevel1")),
fluidRow(strong(column(width=4,offset=1,"Level of Institution: ")),textOutput("iclevel2")))
),br(),
# === display control
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Control of Institution: ")),textOutput("control1")),
fluidRow(strong(column(width=4,offset=1,"Control of Institution: ")),textOutput("control2")))
),br(),
# === display highest degree
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=3,offset=1,"Highest Degree: ")),textOutput("highdeg1")),
fluidRow(strong(column(width=3,offset=1,"Highest Degree: ")),textOutput("highdeg2")))
),br(),
# === display locale
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=2,offset=1,"Locale: ")),textOutput("locale1")),
fluidRow(strong(column(width=2,offset=1,"Locale: ")),textOutput("locale2")))
),br(),
# === display admission rate
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Admission Rate: ")),textOutput("adm_rate1")),
fluidRow(strong(column(width=4,offset=1,"Admission Rate: ")),textOutput("adm_rate2")))
),br(),
# === display in-state tuition
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"In-State Tuition: ")),textOutput("tuitionfee_in1")),
fluidRow(strong(column(width=4,offset=1,"In-State Tuition: ")),textOutput("tuitionfee_in2")))
),br(),
# === display out-of-state tuition
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=4,offset=1,"Out-of-State Tuition: ")),textOutput("tuitionfee_out1")),
fluidRow(strong(column(width=4,offset=1,"Out-of-State Tuition: ")),textOutput("tuitionfee_out2")))
),br(),
# === display percentage of federal loans
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=7,offset=1,"Percentage of Students Receiving Federal Loans: ")),
textOutput("pctfloan1")),
fluidRow(strong(column(width=7,offset=1,"Percentage of Students Receiving Federal Loans: ")),
textOutput("pctfloan2")))
),br(),
# === display total Undergraduates Seeking Degrees
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
fluidRow(strong(column(width=5,offset=1,"Total Undergraduates Seeking Degrees: ")),
textOutput("ugds1")),
fluidRow(strong(column(width=5,offset=1,"Total Undergraduates Seeking Degrees: ")),
textOutput("ugds2")))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("Calculate Median Debt by Family Income" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
br(),
# === display sliderInput for family income
fluidRow(align="center",sliderInput("fincome","Family Income: ",
min=0,max=200000,value=0,width = 600)
),br(),
# === display median debt based on family income input
fluidRow(align="justify",splitLayout(cellWidths = c("50%","50%"),
fluidRow(column(width=7,offset=1,textOutput("school1")),
strong(column(width=7,offset = 1,"Median Debt based on Family Income : ")),br(),
textOutput("debt1")),
fluidRow(column(width=7,offset=1,textOutput("school2")),
strong(column(width=7,offset=1,"Median Debt based on Family Income: ")),br(),
textOutput("debt2")))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),hr(style="color:#808080"),
helpText( strong("SAT & ACT Scores" , style="color:#6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
helpText( strong("25th-75th Percentile" , style="color:#6088B1 ; font-family: 'times'; font-size:20pt ; font-type:bold" )),
hr(style="color:#808080")
)),
br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
textOutput("school1.2"),
textOutput("school2.2"))
),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
tableOutput("sat1"),
tableOutput("sat2"))
),br(),
fluidRow(align="center",splitLayout(cellWidths = c("50%","50%"),
tableOutput("act1"),
tableOutput("act2"))
),
br(),
br(),
fluidRow(align="center",
style="opacity:0.9; background-color: white ;margin-top: 0px; width: 100%;",
column(6,offset=3,
br(),
hr(style="color:#808080"),
helpText( strong("Demographics of Students" , style="color: #6088B1 ; font-family: 'times'; font-size:30pt ; font-type:bold" )) ,
hr(style="color:#808080")
)),
br(),
# === Bar with corresponding widget
fluidRow(align="center",column(4,h2("Major Diversity",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("my_barplot1" , height = "500px"),
plotlyOutput("my_barplot2" , height = "500px")
)
),br(),br(),
# === pie chart of ethnicity
fluidRow(align="center",column(8,h2("Degree-Seeking Undergraduates by Ethnicity",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("demographics1",height="550"),
plotlyOutput("demographics2",height="550"))
),br(),
fluidRow(align="center",column(8,h2("Degree-Seeking Undergraduates by Gender",
style="color:#4C4C4C ; font-family: Times"),
tags$hr(style="border-color: #6088B1;")),br()),
fluidRow(align="center",
splitLayout(cellWidths = c("50%","50%"),
plotlyOutput("female1",height="450"),
plotlyOutput("female2",height="450")
))
############################################TEAM 2 IMPLEMENTATION ENDS############################################################
)
),#Comparison ends here
#Data presentation
#Introduction
tabPanel(strong(tags$i("About us")),
mainPanel(width=12,
h1("Project: NY School Hunter - A Shiny App Development"),
h3("Background"),
p("Our project takes all available data on colleges and universities in New York State and creates a useful shiny app that allows users to explore and compare schools based on user-specific filtering criteria. The purpose of our design is to provide users with a bird's eye view of New York colleges and universities; allow them to filter, search, and group schools by their preferred criteria; and further compare two schools on a more micro level."),#Our motivation
h3("Project summary"),
p("A distinguishing feature of our app is the map search function - users can see all the specified schools on the map (normal map view or satellite map view), focus in on a specific area of New York State.
The side-by-side school comparison feature allows users to see a detailed breakdown of meaningful data and statistics from our available data on each school."),#What did we do
h3("Team Members"),
p(" - ",strong("I. Ka Heng (Helen) Lo - Presenter")),
p(" - ",strong("II. Boxuan Zhao")),
p(" - ",strong("III. Zijun Nie")),
p(" - ",strong("IV. Senyao Han")),
p(" - ",strong("V. Song Wang")),
p(""),
p(""),
br(),
p(em("Release 02/22/2017.","VERSION 1.0.0")),
p(em(a("Github link",href="https://github.com/TZstatsADS/Spr2017-proj2-grp4.git"))))
,div(class="footer", "Applied Data Science Group 4")
)#Introduciton ends here
)#navarbarPage ends here
)
))
|
# Part 1
#Load CSV
precorpus <- read.csv("~/Desktop/Third Semester/BA/Trump and Forbe 500/Part1.csv",header=TRUE, stringsAsFactors = FALSE)
dim(precorpus) # dim of file
names(precorpus)
str(precorpus)
#### Corpus mission and Core Value
# create a corpus of mission statement
require(quanteda)
newscorpus1<- corpus(precorpus$Mission,
docnames=precorpus$Document_ID,
docvar=data.frame(name=precorpus$Name,Subject= precorpus$Description))
#explore the corpus of mission
names(newscorpus1) #to explore
summary(newscorpus1) #summary of corpus
head(newscorpus1)
# create a corpus of Core value
newscorpus2<- corpus(precorpus$Description,
docnames=precorpus$Document_ID,
docvar=data.frame(name=precorpus$Name,Subject= precorpus$Mission))
#explore the corpus of Core Value
names(newscorpus2) #to explore
summary(newscorpus2) #summary of corpus
head(newscorpus2)
#### Clean corpus
#mission
newscorpus1<- toLower(newscorpus1, keepAcronyms = FALSE)
cleancorpus1 <- tokenize(newscorpus1,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
# core value
newscorpus2<- toLower(newscorpus2, keepAcronyms = FALSE)
cleancorpus2 <- tokenize(newscorpus2,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
### create DFM
dfm.mission<- dfm(cleancorpus1,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
dfm.core<- dfm(cleancorpus2,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
#### To display most frequent terms in dfm
topfeatures1<-topfeatures(dfm.mission, n=50)
topfeatures1 # words:can,s every,way
topfeatures2<-topfeatures(dfm.core, n=50)
topfeatures2 # words:s,make use
###to create a custom dictionary list of stop words
swlist1 = c("can", "s", "every","way") # remove those words
dfm_mission.stem<- dfm(cleancorpus1, toLower = TRUE,
ignoredFeatures = c(swlist1, stopwords("english")), #put stopwords
verbose=TRUE, # show process
stem=TRUE) # roots of word
topfeatures1.stem<-topfeatures(dfm_mission.stem, n=50) # top 50 words
topfeatures1.stem
quartz()
plot(dfm_mission.stem)
swlist2 =c("make", "s", "use") # remove those words
dfm_core.stem<- dfm(cleancorpus2, toLower = TRUE,
ignoredFeatures = c(swlist2, stopwords("english")), #put stopwords
verbose=TRUE, # show process
stem=TRUE) #roots of word
topfeatures2.stem<-topfeatures(dfm_core.stem, n=50) # top 50 words
topfeatures2.stem
quartz()
plot(dfm_core.stem)
#### Dfm with bigrams
#mission bigrams
cleancorpus1 <- tokenize(newscorpus1,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
ngrams=2, verbose=TRUE) # ngrams=2 ,combinged 2 words
dfm_mission.bigram<- dfm(cleancorpus1, toLower = TRUE,
ignoredFeatures = c(swlist1, stopwords("english")),
verbose=TRUE,
stem=FALSE)
topfeatures1.bigram<-topfeatures(dfm_mission.bigram, n=50)
topfeatures1.bigram
# Core bigrams
cleancorpus2 <- tokenize(newscorpus2,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
ngrams=2, verbose=TRUE) # ngrams=2 ,combinged 2 words
dfm_core.bigram<- dfm(cleancorpus2, toLower = TRUE,
ignoredFeatures = c(swlist2, stopwords("english")),
verbose=TRUE,
stem=FALSE)
topfeatures1.bigram<-topfeatures(dfm_core.bigram, n=50)
topfeatures1.bigram
#specifying a correlation limit of 0.5
library(tm)
#mission
dfm_mission.tm<-convert(dfm_mission.stem, to="tm")
findAssocs(dfm_mission.tm,
c("data", "analyt", "busi"), # correlation
corlimit=0.6)
# core
dfm_core.tm<-convert(dfm_core.stem, to="tm")
findAssocs(dfm_core.tm,
c("data","analytics", "world" ), # correlation
corlimit=0.7)
##########################
### Topic Modeling
##########################
library(stm)
###### Mission #######
#Process the data for analysis.
help("textProcessor")
temp<-textProcessor(documents=precorpus$Mission, metadata = precorpus)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
#running stm for top 20 topics
prevfit <-stm(docs , vocab ,
K=20,
verbose=TRUE,
data=meta,
max.em.its=25)
topics1 <-labelTopics(prevfit , topics=c(1:20))
topics1 #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
help("findThoughts")
findThoughts(prevfit, texts = precorpus$Mission, topics = 10, n = 2)
help("plot.STM")
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(3,12,13))
plot.STM(prevfit, type="perspectives", topics = c(3,12))
# to aid on assigment of labels & intepretation of topics
help(topicCorr)
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
######## Core ############
#Process the data for analysis.
help("textProcessor")
temp<-textProcessor(documents=precorpus$core, metadata = precorpus)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
#running stm for top 20 topics
prevfit <-stm(docs , vocab ,
K=20,
verbose=TRUE,
data=meta,
max.em.its=25)
topics2 <-labelTopics(prevfit , topics=c(1:20))
topics2 #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
help("findThoughts")
findThoughts(prevfit, texts = precorpus$core, topics = 10, n = 2)
help("plot.STM")
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(3,12,13))
plot.STM(prevfit, type="perspectives", topics = c(3,12))
# to aid on assigment of labels & intepretation of topics
help(topicCorr)
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
# Part 2
#Load CSV
Part2 <- read.csv("~/Desktop/Third Semester/BA/Trump and Forbe 500/Part2.csv", header=TRUE, stringsAsFactors = FALSE)
#### Corpus mission and Core Value
require(quanteda)
newscorpus3<- corpus(Part2$Speech,
docnames=Part2$Document_ID,
docvar=data.frame(name=Part2$Name))
names(newscorpus3) #to explore the output of the corpus function: "documents" "metadata" "settings" "tokens"
summary(newscorpus3) #summary of corpus
#clean corpus: removes punctuation, digits, converts to lower case
newscorpus3<- toLower(newscorpus3, keepAcronyms = FALSE)
cleancorpus3 <- tokenize(newscorpus3,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
# DFM
dfm.speech<- dfm(cleancorpus3,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
### Frequency analysis of word usage
topfeatures3<-topfeatures(dfm.speech, n=50)
topfeatures3
quartz()
plot(dfm.speech)
#to create a custom dictionary list of stop words
swlist3 = c("will", "s", "t","come","day","put","just") # remove these words
dfm_speech.stem<- dfm(cleancorpus3, toLower = TRUE,
ignoredFeatures = c(swlist3, stopwords("english")),
verbose=TRUE,
stem=TRUE)
topfeatures3.stem<-topfeatures(dfm_speech.stem, n=50) # top 50 words
topfeatures3.stem
#Sentiment Analysis
help(dfm)
mydict <- dictionary(list(negative = c("detriment*", "bad*", "awful*", "terrib*", "horribl*"),
postive = c("good", "great", "super*", "excellent", "yay")))
dfm_speech.sentiment <- dfm(cleancorpus3, dictionary = mydict)
topfeatures(dfm_speech.sentiment)
View(dfm_speech.sentiment)
##########################
### Topic Modeling
##########################
library(stm)
temp<-textProcessor(documents=Part2$Speech, metadata = Part2)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
temp
#running stm for top 20 topics
help("stm")
prevfit <-stm(docs , vocab ,
K=3,
verbose=TRUE,
data=meta,
max.em.its=20)
topics <-labelTopics(prevfit, topics=c(1:3))
topics #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
findThoughts(prevfit, texts = Part2$Speech, topics = 3, n = 2)
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(1,2,3))
plot.STM(prevfit, type="perspectives", topics = c(1,2))
plot.STM(prevfit, type="perspectives", topics = c(2,3))
# to aid on assigment of labels & intepretation of topics
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
|
/Homework.R
|
no_license
|
wjcdenis/Dennis10
|
R
| false | false | 10,197 |
r
|
# Part 1
#Load CSV
precorpus <- read.csv("~/Desktop/Third Semester/BA/Trump and Forbe 500/Part1.csv",header=TRUE, stringsAsFactors = FALSE)
dim(precorpus) # dim of file
names(precorpus)
str(precorpus)
#### Corpus mission and Core Value
# create a corpus of mission statement
require(quanteda)
newscorpus1<- corpus(precorpus$Mission,
docnames=precorpus$Document_ID,
docvar=data.frame(name=precorpus$Name,Subject= precorpus$Description))
#explore the corpus of mission
names(newscorpus1) #to explore
summary(newscorpus1) #summary of corpus
head(newscorpus1)
# create a corpus of Core value
newscorpus2<- corpus(precorpus$Description,
docnames=precorpus$Document_ID,
docvar=data.frame(name=precorpus$Name,Subject= precorpus$Mission))
#explore the corpus of Core Value
names(newscorpus2) #to explore
summary(newscorpus2) #summary of corpus
head(newscorpus2)
#### Clean corpus
#mission
newscorpus1<- toLower(newscorpus1, keepAcronyms = FALSE)
cleancorpus1 <- tokenize(newscorpus1,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
# core value
newscorpus2<- toLower(newscorpus2, keepAcronyms = FALSE)
cleancorpus2 <- tokenize(newscorpus2,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
### create DFM
dfm.mission<- dfm(cleancorpus1,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
dfm.core<- dfm(cleancorpus2,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
#### To display most frequent terms in dfm
topfeatures1<-topfeatures(dfm.mission, n=50)
topfeatures1 # words:can,s every,way
topfeatures2<-topfeatures(dfm.core, n=50)
topfeatures2 # words:s,make use
###to create a custom dictionary list of stop words
swlist1 = c("can", "s", "every","way") # remove those words
dfm_mission.stem<- dfm(cleancorpus1, toLower = TRUE,
ignoredFeatures = c(swlist1, stopwords("english")), #put stopwords
verbose=TRUE, # show process
stem=TRUE) # roots of word
topfeatures1.stem<-topfeatures(dfm_mission.stem, n=50) # top 50 words
topfeatures1.stem
quartz()
plot(dfm_mission.stem)
swlist2 =c("make", "s", "use") # remove those words
dfm_core.stem<- dfm(cleancorpus2, toLower = TRUE,
ignoredFeatures = c(swlist2, stopwords("english")), #put stopwords
verbose=TRUE, # show process
stem=TRUE) #roots of word
topfeatures2.stem<-topfeatures(dfm_core.stem, n=50) # top 50 words
topfeatures2.stem
quartz()
plot(dfm_core.stem)
#### Dfm with bigrams
#mission bigrams
cleancorpus1 <- tokenize(newscorpus1,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
ngrams=2, verbose=TRUE) # ngrams=2 ,combinged 2 words
dfm_mission.bigram<- dfm(cleancorpus1, toLower = TRUE,
ignoredFeatures = c(swlist1, stopwords("english")),
verbose=TRUE,
stem=FALSE)
topfeatures1.bigram<-topfeatures(dfm_mission.bigram, n=50)
topfeatures1.bigram
# Core bigrams
cleancorpus2 <- tokenize(newscorpus2,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
ngrams=2, verbose=TRUE) # ngrams=2 ,combinged 2 words
dfm_core.bigram<- dfm(cleancorpus2, toLower = TRUE,
ignoredFeatures = c(swlist2, stopwords("english")),
verbose=TRUE,
stem=FALSE)
topfeatures1.bigram<-topfeatures(dfm_core.bigram, n=50)
topfeatures1.bigram
#specifying a correlation limit of 0.5
library(tm)
#mission
dfm_mission.tm<-convert(dfm_mission.stem, to="tm")
findAssocs(dfm_mission.tm,
c("data", "analyt", "busi"), # correlation
corlimit=0.6)
# core
dfm_core.tm<-convert(dfm_core.stem, to="tm")
findAssocs(dfm_core.tm,
c("data","analytics", "world" ), # correlation
corlimit=0.7)
##########################
### Topic Modeling
##########################
library(stm)
###### Mission #######
#Process the data for analysis.
help("textProcessor")
temp<-textProcessor(documents=precorpus$Mission, metadata = precorpus)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
#running stm for top 20 topics
prevfit <-stm(docs , vocab ,
K=20,
verbose=TRUE,
data=meta,
max.em.its=25)
topics1 <-labelTopics(prevfit , topics=c(1:20))
topics1 #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
help("findThoughts")
findThoughts(prevfit, texts = precorpus$Mission, topics = 10, n = 2)
help("plot.STM")
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(3,12,13))
plot.STM(prevfit, type="perspectives", topics = c(3,12))
# to aid on assigment of labels & intepretation of topics
help(topicCorr)
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
######## Core ############
#Process the data for analysis.
help("textProcessor")
temp<-textProcessor(documents=precorpus$core, metadata = precorpus)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
#running stm for top 20 topics
prevfit <-stm(docs , vocab ,
K=20,
verbose=TRUE,
data=meta,
max.em.its=25)
topics2 <-labelTopics(prevfit , topics=c(1:20))
topics2 #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
help("findThoughts")
findThoughts(prevfit, texts = precorpus$core, topics = 10, n = 2)
help("plot.STM")
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(3,12,13))
plot.STM(prevfit, type="perspectives", topics = c(3,12))
# to aid on assigment of labels & intepretation of topics
help(topicCorr)
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
# Part 2
#Load CSV
Part2 <- read.csv("~/Desktop/Third Semester/BA/Trump and Forbe 500/Part2.csv", header=TRUE, stringsAsFactors = FALSE)
#### Corpus mission and Core Value
require(quanteda)
newscorpus3<- corpus(Part2$Speech,
docnames=Part2$Document_ID,
docvar=data.frame(name=Part2$Name))
names(newscorpus3) #to explore the output of the corpus function: "documents" "metadata" "settings" "tokens"
summary(newscorpus3) #summary of corpus
#clean corpus: removes punctuation, digits, converts to lower case
newscorpus3<- toLower(newscorpus3, keepAcronyms = FALSE)
cleancorpus3 <- tokenize(newscorpus3,
removeNumbers=TRUE,
removePunct = TRUE,
removeSeparators=TRUE,
removeTwitter=FALSE,
verbose=TRUE)
# DFM
dfm.speech<- dfm(cleancorpus3,
toLower = TRUE,
ignoredFeatures =stopwords("english"), # stopword
verbose=TRUE,
stem=FALSE)
### Frequency analysis of word usage
topfeatures3<-topfeatures(dfm.speech, n=50)
topfeatures3
quartz()
plot(dfm.speech)
#to create a custom dictionary list of stop words
swlist3 = c("will", "s", "t","come","day","put","just") # remove these words
dfm_speech.stem<- dfm(cleancorpus3, toLower = TRUE,
ignoredFeatures = c(swlist3, stopwords("english")),
verbose=TRUE,
stem=TRUE)
topfeatures3.stem<-topfeatures(dfm_speech.stem, n=50) # top 50 words
topfeatures3.stem
#Sentiment Analysis
help(dfm)
mydict <- dictionary(list(negative = c("detriment*", "bad*", "awful*", "terrib*", "horribl*"),
postive = c("good", "great", "super*", "excellent", "yay")))
dfm_speech.sentiment <- dfm(cleancorpus3, dictionary = mydict)
topfeatures(dfm_speech.sentiment)
View(dfm_speech.sentiment)
##########################
### Topic Modeling
##########################
library(stm)
temp<-textProcessor(documents=Part2$Speech, metadata = Part2)
names(temp) # produces: "documents", "vocab", "meta", "docs.removed"
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
temp
#running stm for top 20 topics
help("stm")
prevfit <-stm(docs , vocab ,
K=3,
verbose=TRUE,
data=meta,
max.em.its=20)
topics <-labelTopics(prevfit, topics=c(1:3))
topics #shows topics with highest probability words
#explore the topics in context. Provides an example of the text
findThoughts(prevfit, texts = Part2$Speech, topics = 3, n = 2)
plot.STM(prevfit, type="summary")
plot.STM(prevfit, type="labels", topics=c(1,2,3))
plot.STM(prevfit, type="perspectives", topics = c(1,2))
plot.STM(prevfit, type="perspectives", topics = c(2,3))
# to aid on assigment of labels & intepretation of topics
mod.out.corr <- topicCorr(prevfit) #Estimates a graph of topic correlations topic
plot.topicCorr(mod.out.corr)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BPmix.R
\name{BPmix}
\alias{BPmix}
\title{Binomial mixtures with Poisson Trials via Kiefer Wolfowitz NPMLE}
\usage{
BPmix(x, m, v = 50, weights = NULL, ...)
}
\arguments{
\item{x}{Count of "successes" for binomial observations}
\item{m}{Number of trials for binomial observations}
\item{v}{Grid Values for the mixing distribution defaults to equal
spacing of length v on [eps, 1- eps], if v is scalar.}
\item{weights}{replicate weights for x obervations, should sum to 1}
\item{...}{Other arguments to be passed to KWDual to control optimization}
}
\value{
An object of class density with components:
\item{v}{grid points of evaluation of the success probabilities}
\item{u}{grid points of evaluation of the Poisson rate for number of trials}
\item{y}{function values of the mixing density at (v,u)}
\item{g}{estimates of the mixture density at the distinct data values}
\item{logLik}{Log Likelihood value at the estimate}
\item{status}{exit code from the optimizer}
}
\description{
Interior point solution of Kiefer-Wolfowitz NPMLE for mixture of Poisson Binomials
}
\details{
The joint distribution of the probabilities of success and the number of trials
is estimated. The grid specification for success probabilities is as for \code{Bmix}
whereas the grid for the Poisson rate parameters is currently the support of the
observed trials. There is no predict method as yet. See \code{demo(BPmix1)}.
}
\references{
Kiefer, J. and J. Wolfowitz Consistency of the Maximum
Likelihood Estimator in the Presence of Infinitely Many Incidental
Parameters \emph{Ann. Math. Statist}. 27, (1956), 887-906.
}
\author{
R. Koenker
}
\keyword{nonparametric}
|
/man/BPmix.Rd
|
no_license
|
cran/REBayes
|
R
| false | true | 1,742 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BPmix.R
\name{BPmix}
\alias{BPmix}
\title{Binomial mixtures with Poisson Trials via Kiefer Wolfowitz NPMLE}
\usage{
BPmix(x, m, v = 50, weights = NULL, ...)
}
\arguments{
\item{x}{Count of "successes" for binomial observations}
\item{m}{Number of trials for binomial observations}
\item{v}{Grid Values for the mixing distribution defaults to equal
spacing of length v on [eps, 1- eps], if v is scalar.}
\item{weights}{replicate weights for x obervations, should sum to 1}
\item{...}{Other arguments to be passed to KWDual to control optimization}
}
\value{
An object of class density with components:
\item{v}{grid points of evaluation of the success probabilities}
\item{u}{grid points of evaluation of the Poisson rate for number of trials}
\item{y}{function values of the mixing density at (v,u)}
\item{g}{estimates of the mixture density at the distinct data values}
\item{logLik}{Log Likelihood value at the estimate}
\item{status}{exit code from the optimizer}
}
\description{
Interior point solution of Kiefer-Wolfowitz NPMLE for mixture of Poisson Binomials
}
\details{
The joint distribution of the probabilities of success and the number of trials
is estimated. The grid specification for success probabilities is as for \code{Bmix}
whereas the grid for the Poisson rate parameters is currently the support of the
observed trials. There is no predict method as yet. See \code{demo(BPmix1)}.
}
\references{
Kiefer, J. and J. Wolfowitz Consistency of the Maximum
Likelihood Estimator in the Presence of Infinitely Many Incidental
Parameters \emph{Ann. Math. Statist}. 27, (1956), 887-906.
}
\author{
R. Koenker
}
\keyword{nonparametric}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RmRMR-auxiliary.R
\name{balancedOrderingDependency}
\alias{balancedOrderingDependency}
\title{Balanced ordering-based dependency measure}
\usage{
balancedOrderingDependency(attrVec, target, numericTarget = NULL,
nSamples = 100, sampleSize = 10, ...)
}
\arguments{
\item{attrVec, target}{two numeric attributes or an attribute and a decision vector
(not necessarily ordered). At least the first argument has to be numeric.}
\item{numericTarget}{logical indicating whether the \code{target} is ordered and numeric. The
default is \code{NULL} in which case the function makes a guess using a simple heuristic.}
\item{...}{optional arguments (currently omitted).}
}
\value{
a numeric value expressing ordering dependency between \code{attrVec} and \code{target}
}
\description{
A balanced version of the function for measuring ordering dependency between two vectors
(the ordering measure).
}
\examples{
#############################################
data(methaneSampleData)
## an experiment on a sample from the data used in a data mining competition -
## IJCRS'15 Data Challenge: Mining Data from Coal Mines
## (https://knowledgepit.fedcsis.org/contest/view.php?id=109).
## The whole data set can be downloaded from the competition web page.
mrmrAttrs = mRMRfs(dataT = methaneData$methaneTraining,
target = methaneData$methaneTrainingLabels[, V2],
dependencyF = balancedOrderingDependency, sampleSize = 10)
mrmrAttrs
}
\author{
Andrzej Janusz
}
\references{
Andrzej Janusz and Marek Grzegorowski. Efficient Attribute Quality Assessment Using
a Decision Ordering Measure. 2017.
}
|
/man/balancedOrderingDependency.Rd
|
no_license
|
janusza/RmRMR
|
R
| false | true | 1,706 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RmRMR-auxiliary.R
\name{balancedOrderingDependency}
\alias{balancedOrderingDependency}
\title{Balanced ordering-based dependency measure}
\usage{
balancedOrderingDependency(attrVec, target, numericTarget = NULL,
nSamples = 100, sampleSize = 10, ...)
}
\arguments{
\item{attrVec, target}{two numeric attributes or an attribute and a decision vector
(not necessarily ordered). At least the first argument has to be numeric.}
\item{numericTarget}{logical indicating whether the \code{target} is ordered and numeric. The
default is \code{NULL} in which case the function makes a guess using a simple heuristic.}
\item{...}{optional arguments (currently omitted).}
}
\value{
a numeric value expressing ordering dependency between \code{attrVec} and \code{target}
}
\description{
A balanced version of the function for measuring ordering dependency between two vectors
(the ordering measure).
}
\examples{
#############################################
data(methaneSampleData)
## an experiment on a sample from the data used in a data mining competition -
## IJCRS'15 Data Challenge: Mining Data from Coal Mines
## (https://knowledgepit.fedcsis.org/contest/view.php?id=109).
## The whole data set can be downloaded from the competition web page.
mrmrAttrs = mRMRfs(dataT = methaneData$methaneTraining,
target = methaneData$methaneTrainingLabels[, V2],
dependencyF = balancedOrderingDependency, sampleSize = 10)
mrmrAttrs
}
\author{
Andrzej Janusz
}
\references{
Andrzej Janusz and Marek Grzegorowski. Efficient Attribute Quality Assessment Using
a Decision Ordering Measure. 2017.
}
|
mat <- compute_correspondence_tables(data, "LandUse", "HgBand")
display_table(mat$P_r, "Conditional Hg given LandUse", "cond. likelihood")
# Burt matrix analogue of variance-covariance matrix for discrete data.
display_table(mat$var_covar_mat, "Burt Matrix", "var. covar")
require(corrplot)
corrplot(mat$var_covar_mat)
# row marginal probabilities
barplot(mat$P_row_margins)
# column marginal probabilities
barplot(mat$P_col_margins)
## TODO: Example of plotting profile coordinates.
## Row profiles G_P and H_S
## Column profiles G_s and H_p
## Both profiles G_p and H_p
draw_profiles(mat, "LandUse vs HgBand")
v <- mat$R_inertia$R_eigen$vectors
plot(v[1,], v[2,])
|
/test2.R
|
no_license
|
cxd/notes_correspondence
|
R
| false | false | 678 |
r
|
mat <- compute_correspondence_tables(data, "LandUse", "HgBand")
display_table(mat$P_r, "Conditional Hg given LandUse", "cond. likelihood")
# Burt matrix analogue of variance-covariance matrix for discrete data.
display_table(mat$var_covar_mat, "Burt Matrix", "var. covar")
require(corrplot)
corrplot(mat$var_covar_mat)
# row marginal probabilities
barplot(mat$P_row_margins)
# column marginal probabilities
barplot(mat$P_col_margins)
## TODO: Example of plotting profile coordinates.
## Row profiles G_P and H_S
## Column profiles G_s and H_p
## Both profiles G_p and H_p
draw_profiles(mat, "LandUse vs HgBand")
v <- mat$R_inertia$R_eigen$vectors
plot(v[1,], v[2,])
|
## Set working directory.
setwd("C://Tejas//Learnings//Coursera//Exploratory_Data_Analysis//Project1")
getwd()
## Load required libraries
##install.packages('data.table')
library(data.table)
## Check if file exists else download the file and extract in the folder
fileurl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
if (!file.exists('./household_power_consumption.zip')){
download.file(fileurl,'./household_power_consumption.zip', mode = 'wb')
unzip("household_power_consumption.zip", exdir = '.')
}
## Read Household Power Consumption data into dataset using fread for dates 2007-02-01 and 2007-02-02.
## Skip all rows upto 2007-02-01. read next 2 days of data with 1 min interval. 24(hr)*60(min)*2(days)
hpc_data <- fread('./household_power_consumption.txt',sep = ';',header = FALSE, na.strings = "?",skip = 66637,nrows = 2879,check.names = FALSE)
var_names <- names(hpc_data)
## Get column headers
hpc_cols <- fread('./household_power_consumption.txt',sep = ';',header = TRUE, na.strings = "?",nrows = 1)
hpc_cols <- names(hpc_cols)
## Apply back column headers
setnames(hpc_data,var_names,hpc_cols)
## Convert dates
col_datetime <- paste(as.Date(hpc_data$Date, format="%d/%m/%Y"), hpc_data$Time)
hpc_data$Datetime <- as.POSIXct(col_datetime)
## Generate Plot-3 (Multiple Lines)
with (hpc_data,
{plot(Sub_metering_1 ~ Datetime, type="l", ylab="Energy sub metering",xlab="")
lines(Sub_metering_2 ~ Datetime,col='Red')
lines(Sub_metering_3 ~ Datetime,col='Blue')
}
)
## Add Legend
legend("topright", col=c("black", "red", "blue"), lty=1,
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),cex = 0.75)
## Save plot to .PNG file
dev.copy(png, file="plot3.png", height=480, width=480)
dev.off()
|
/Plot3.R
|
no_license
|
tejasatalati/ExData_Plotting1
|
R
| false | false | 1,811 |
r
|
## Set working directory.
setwd("C://Tejas//Learnings//Coursera//Exploratory_Data_Analysis//Project1")
getwd()
## Load required libraries
##install.packages('data.table')
library(data.table)
## Check if file exists else download the file and extract in the folder
fileurl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
if (!file.exists('./household_power_consumption.zip')){
download.file(fileurl,'./household_power_consumption.zip', mode = 'wb')
unzip("household_power_consumption.zip", exdir = '.')
}
## Read Household Power Consumption data into dataset using fread for dates 2007-02-01 and 2007-02-02.
## Skip all rows upto 2007-02-01. read next 2 days of data with 1 min interval. 24(hr)*60(min)*2(days)
hpc_data <- fread('./household_power_consumption.txt',sep = ';',header = FALSE, na.strings = "?",skip = 66637,nrows = 2879,check.names = FALSE)
var_names <- names(hpc_data)
## Get column headers
hpc_cols <- fread('./household_power_consumption.txt',sep = ';',header = TRUE, na.strings = "?",nrows = 1)
hpc_cols <- names(hpc_cols)
## Apply back column headers
setnames(hpc_data,var_names,hpc_cols)
## Convert dates
col_datetime <- paste(as.Date(hpc_data$Date, format="%d/%m/%Y"), hpc_data$Time)
hpc_data$Datetime <- as.POSIXct(col_datetime)
## Generate Plot-3 (Multiple Lines)
with (hpc_data,
{plot(Sub_metering_1 ~ Datetime, type="l", ylab="Energy sub metering",xlab="")
lines(Sub_metering_2 ~ Datetime,col='Red')
lines(Sub_metering_3 ~ Datetime,col='Blue')
}
)
## Add Legend
legend("topright", col=c("black", "red", "blue"), lty=1,
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),cex = 0.75)
## Save plot to .PNG file
dev.copy(png, file="plot3.png", height=480, width=480)
dev.off()
|
##
## Metric to calculate the correlation between two streams of seismic data
##
## Copyright (C) 2012 Mazama Science, Inc.
## by Jonathan Callahan, jonathan@mazamascience.com
##
## 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 2 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, write to the Free Software
## Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
################################################################################
#
correlationMetric <- function(st1, st2) {
# Get the first traces
tr1 <- st1@traces[[1]]
tr2 <- st2@traces[[1]]
starttime <- st1@requestedStarttime
endtime <- st1@requestedEndtime
# Sanity check stream lengths
if (st2@requestedStarttime != starttime || st2@requestedEndtime != endtime) {
stop(paste("correlationMetric: Incompatible starttimes or endtimes"))
}
# Sanity check sampling rates
if (tr1@stats@sampling_rate != tr2@stats@sampling_rate) {
stop(paste("correlationMetric: Incompatible sampling rates"))
}
# NOTE: Correlation demands vectors of the same length (even though we will ignore NA).
# NOTE: We will truncate by up to one second to ensure this.
# Sanity check lengths
l1 <- length(tr1)
l2 <- length(tr2)
# Only complain if if sample lengths differ AND the difference is greater than the inverse sampling rate
if ( (abs(l1 - l2) > 1) && (abs(l1 - l2) > 1/tr1@stats@sampling_rate) ) {
stop(paste("correlationMetric: Incompatible lengths tr1 =",l1,", tr2 =",l2))
} else {
min_length <- min(l1,l2)
}
# Sanity check network and station
if (tr1@stats@network != tr2@stats@network || tr1@stats@station != tr2@stats@station) {
stop(paste("correlationMetric: Incompatible trace ids '", tr1@id, "', '", tr2@id, "'", sep=""))
}
# Create two-channel ids if needed
locations <- tr1@stats@location
if (tr2@stats@location != tr1@stats@location) {
locations <- paste(locations, ":", tr2@stats@location, sep="")
}
channels <- tr1@stats@channel
if (tr2@stats@channel != tr1@stats@channel) {
channels <- paste(channels, ":", tr2@stats@channel, sep="")
}
snclq <- paste(tr1@stats@network, tr1@stats@station, locations, channels, tr1@stats@quality, sep=".")
# Calculate the correlation metric
cor <- cor(tr1@data[1:min_length], tr2@data[1:min_length], use="na.or.complete")
# Create and return a list of Metric objects
m1 <- new("SingleValueMetric", snclq=snclq, starttime=starttime, endtime=endtime, metricName="cross_talk", value=cor)
return(c(m1))
}
|
/IRISMustangMetrics/R/correlationMetric.R
|
no_license
|
ingted/R-Examples
|
R
| false | false | 3,102 |
r
|
##
## Metric to calculate the correlation between two streams of seismic data
##
## Copyright (C) 2012 Mazama Science, Inc.
## by Jonathan Callahan, jonathan@mazamascience.com
##
## 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 2 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, write to the Free Software
## Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
################################################################################
#
correlationMetric <- function(st1, st2) {
# Get the first traces
tr1 <- st1@traces[[1]]
tr2 <- st2@traces[[1]]
starttime <- st1@requestedStarttime
endtime <- st1@requestedEndtime
# Sanity check stream lengths
if (st2@requestedStarttime != starttime || st2@requestedEndtime != endtime) {
stop(paste("correlationMetric: Incompatible starttimes or endtimes"))
}
# Sanity check sampling rates
if (tr1@stats@sampling_rate != tr2@stats@sampling_rate) {
stop(paste("correlationMetric: Incompatible sampling rates"))
}
# NOTE: Correlation demands vectors of the same length (even though we will ignore NA).
# NOTE: We will truncate by up to one second to ensure this.
# Sanity check lengths
l1 <- length(tr1)
l2 <- length(tr2)
# Only complain if if sample lengths differ AND the difference is greater than the inverse sampling rate
if ( (abs(l1 - l2) > 1) && (abs(l1 - l2) > 1/tr1@stats@sampling_rate) ) {
stop(paste("correlationMetric: Incompatible lengths tr1 =",l1,", tr2 =",l2))
} else {
min_length <- min(l1,l2)
}
# Sanity check network and station
if (tr1@stats@network != tr2@stats@network || tr1@stats@station != tr2@stats@station) {
stop(paste("correlationMetric: Incompatible trace ids '", tr1@id, "', '", tr2@id, "'", sep=""))
}
# Create two-channel ids if needed
locations <- tr1@stats@location
if (tr2@stats@location != tr1@stats@location) {
locations <- paste(locations, ":", tr2@stats@location, sep="")
}
channels <- tr1@stats@channel
if (tr2@stats@channel != tr1@stats@channel) {
channels <- paste(channels, ":", tr2@stats@channel, sep="")
}
snclq <- paste(tr1@stats@network, tr1@stats@station, locations, channels, tr1@stats@quality, sep=".")
# Calculate the correlation metric
cor <- cor(tr1@data[1:min_length], tr2@data[1:min_length], use="na.or.complete")
# Create and return a list of Metric objects
m1 <- new("SingleValueMetric", snclq=snclq, starttime=starttime, endtime=endtime, metricName="cross_talk", value=cor)
return(c(m1))
}
|
#' Load a user-provided token
#'
#' @description
#' This function does very little when called directly with a token:
#' * If input has class `request`, i.e. it is a token that has been prepared
#' with [httr::config()], the `auth_token` component is extracted. For
#' example, such input could be produced by `googledrive::drive_token()`
#' or `bigrquery::bq_token()`.
#' * Checks that the input appears to be a Google OAuth token, based on
#' the embedded `oauth_endpoint`.
#' * Refreshes the token, if it's refreshable.
#' * Returns its input.
#'
#' There is no point providing `scopes`. They are ignored because the `scopes`
#' associated with the token have already been baked in to the token itself and
#' gargle does not support incremental authorization. The main point of
#' `credentials_byo_oauth2()` is to allow `token_fetch()` (and packages that
#' wrap it) to accommodate a "bring your own token" workflow.
#'
#' This also makes it possible to obtain a token with one package and then
#' register it for use with another package. For example, the default scope
#' requested by googledrive is also sufficient for operations available in
#' googlesheets4. You could use a shared token like so:
#' ```
#' library(googledrive)
#' library(googlesheets4)
#' drive_auth(email = "jane_doe@example.com")
#' sheets_auth(token = drive_token())
#' # work with both packages freely now
#' ```
#'
#' @inheritParams token_fetch
#' @inheritParams token-info
#'
#' @return An [Token2.0][httr::Token-class].
#' @family credential functions
#' @export
#' @examples
#' \dontrun{
#' # assume `my_token` is a Token2.0 object returned by a function such as
#' # httr::oauth2.0_token() or gargle::gargle2.0_token()
#' credentials_byo_oauth2(token = my_token)
#' }
credentials_byo_oauth2 <- function(scopes = NULL, token, ...) {
gargle_debug("trying {.fun credentials_byo_oauth}")
if (inherits(token, "request")) {
token <- token$auth_token
}
stopifnot(inherits(token, "Token2.0"))
if (!is.null(scopes)) {
gargle_debug(c(
"{.arg scopes} cannot be specified when user brings their own OAuth token",
"{.arg scopes} are already implicit in the token"
))
}
check_endpoint(token$endpoint)
if (token$can_refresh()) {
token$refresh()
}
token
}
check_endpoint <- function(endpoint) {
stopifnot(inherits(endpoint, "oauth_endpoint"))
urls <- endpoint[c("authorize", "access", "validate", "revoke")]
urls_ok <- all(grepl("google", urls))
if (!urls_ok) {
gargle_abort("Token doesn't use Google's OAuth endpoint")
}
endpoint
}
|
/R/credentials_byo_oauth2.R
|
permissive
|
jimsforks/gargle
|
R
| false | false | 2,593 |
r
|
#' Load a user-provided token
#'
#' @description
#' This function does very little when called directly with a token:
#' * If input has class `request`, i.e. it is a token that has been prepared
#' with [httr::config()], the `auth_token` component is extracted. For
#' example, such input could be produced by `googledrive::drive_token()`
#' or `bigrquery::bq_token()`.
#' * Checks that the input appears to be a Google OAuth token, based on
#' the embedded `oauth_endpoint`.
#' * Refreshes the token, if it's refreshable.
#' * Returns its input.
#'
#' There is no point providing `scopes`. They are ignored because the `scopes`
#' associated with the token have already been baked in to the token itself and
#' gargle does not support incremental authorization. The main point of
#' `credentials_byo_oauth2()` is to allow `token_fetch()` (and packages that
#' wrap it) to accommodate a "bring your own token" workflow.
#'
#' This also makes it possible to obtain a token with one package and then
#' register it for use with another package. For example, the default scope
#' requested by googledrive is also sufficient for operations available in
#' googlesheets4. You could use a shared token like so:
#' ```
#' library(googledrive)
#' library(googlesheets4)
#' drive_auth(email = "jane_doe@example.com")
#' sheets_auth(token = drive_token())
#' # work with both packages freely now
#' ```
#'
#' @inheritParams token_fetch
#' @inheritParams token-info
#'
#' @return An [Token2.0][httr::Token-class].
#' @family credential functions
#' @export
#' @examples
#' \dontrun{
#' # assume `my_token` is a Token2.0 object returned by a function such as
#' # httr::oauth2.0_token() or gargle::gargle2.0_token()
#' credentials_byo_oauth2(token = my_token)
#' }
credentials_byo_oauth2 <- function(scopes = NULL, token, ...) {
gargle_debug("trying {.fun credentials_byo_oauth}")
if (inherits(token, "request")) {
token <- token$auth_token
}
stopifnot(inherits(token, "Token2.0"))
if (!is.null(scopes)) {
gargle_debug(c(
"{.arg scopes} cannot be specified when user brings their own OAuth token",
"{.arg scopes} are already implicit in the token"
))
}
check_endpoint(token$endpoint)
if (token$can_refresh()) {
token$refresh()
}
token
}
check_endpoint <- function(endpoint) {
stopifnot(inherits(endpoint, "oauth_endpoint"))
urls <- endpoint[c("authorize", "access", "validate", "revoke")]
urls_ok <- all(grepl("google", urls))
if (!urls_ok) {
gargle_abort("Token doesn't use Google's OAuth endpoint")
}
endpoint
}
|
#' deFinetti
#'
#' A package for plotting a de Finetti diagram and distributions of F-statistics of genotypes.
#' @name deFinetti
#' @rdname deFinetti-package
#' @docType package
#' @author Masahiro Kanai
#' @author Kenji Yamane
NULL
#' Genotype Frequency
#'
#' An example input.
#' @docType data
#' @keywords datasets
#' @name GenotypeFreq
#' @usage data(GenotypeFreq)
#' @format A data frame with 3 rows and 3 variables
NULL
|
/R/deFinetti-package.R
|
no_license
|
mkanai/deFinetti
|
R
| false | false | 429 |
r
|
#' deFinetti
#'
#' A package for plotting a de Finetti diagram and distributions of F-statistics of genotypes.
#' @name deFinetti
#' @rdname deFinetti-package
#' @docType package
#' @author Masahiro Kanai
#' @author Kenji Yamane
NULL
#' Genotype Frequency
#'
#' An example input.
#' @docType data
#' @keywords datasets
#' @name GenotypeFreq
#' @usage data(GenotypeFreq)
#' @format A data frame with 3 rows and 3 variables
NULL
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{dodgr_cache_off}
\alias{dodgr_cache_off}
\title{dodgr_cache_off}
\usage{
dodgr_cache_off()
}
\value{
Nothing; the function invisibly returns \code{TRUE} if successful.
}
\description{
Turn off all dodgr caching in current session. This is useful is speed is
paramount, and if graph contraction is not needed. Caching can be switched
back on with \link{dodgr_cache_on}.
}
|
/fuzzedpackages/dodgr/man/dodgr_cache_off.Rd
|
no_license
|
akhikolla/testpackages
|
R
| false | true | 463 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{dodgr_cache_off}
\alias{dodgr_cache_off}
\title{dodgr_cache_off}
\usage{
dodgr_cache_off()
}
\value{
Nothing; the function invisibly returns \code{TRUE} if successful.
}
\description{
Turn off all dodgr caching in current session. This is useful is speed is
paramount, and if graph contraction is not needed. Caching can be switched
back on with \link{dodgr_cache_on}.
}
|
library(VaRES)
### Name: burr7
### Title: Burr XII distribution
### Aliases: dburr7 pburr7 varburr7 esburr7
### Keywords: Value at risk, expected shortfall
### ** Examples
x=runif(10,min=0,max=1)
dburr7(x)
pburr7(x)
varburr7(x)
esburr7(x)
|
/data/genthat_extracted_code/VaRES/examples/burr7.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 244 |
r
|
library(VaRES)
### Name: burr7
### Title: Burr XII distribution
### Aliases: dburr7 pburr7 varburr7 esburr7
### Keywords: Value at risk, expected shortfall
### ** Examples
x=runif(10,min=0,max=1)
dburr7(x)
pburr7(x)
varburr7(x)
esburr7(x)
|
library(phylosim)
### Name: getInsertHook.GeneralInsertor
### Title: Get the insert hook function
### Aliases: getInsertHook.GeneralInsertor GeneralInsertor.getInsertHook
### getInsertHook,GeneralInsertor-method
### ** Examples
# create a GeneralInsertor object
i<-GeneralInsertor(
rate=0.5,
propose.by=function(process){sample(c(5:10),1)}, # inserts between 5 and 10
template.seq=NucleotideSequence(length=5,processes=list(list(JC69())))
)
# set a dummy insert hook
setInsertHook(i,function(seq){return(seq)})
# set a new insert hook via virtual field
i$insertHook<-function(seq){
seq$processes<-list(list(GTR())) # replace the subsitution process
return(seq)
}
# get the insert hook via virtual field
i$insertHook
# get the insert hook
getInsertHook(i)
|
/data/genthat_extracted_code/phylosim/examples/getInsertHook.GeneralInsertor.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 787 |
r
|
library(phylosim)
### Name: getInsertHook.GeneralInsertor
### Title: Get the insert hook function
### Aliases: getInsertHook.GeneralInsertor GeneralInsertor.getInsertHook
### getInsertHook,GeneralInsertor-method
### ** Examples
# create a GeneralInsertor object
i<-GeneralInsertor(
rate=0.5,
propose.by=function(process){sample(c(5:10),1)}, # inserts between 5 and 10
template.seq=NucleotideSequence(length=5,processes=list(list(JC69())))
)
# set a dummy insert hook
setInsertHook(i,function(seq){return(seq)})
# set a new insert hook via virtual field
i$insertHook<-function(seq){
seq$processes<-list(list(GTR())) # replace the subsitution process
return(seq)
}
# get the insert hook via virtual field
i$insertHook
# get the insert hook
getInsertHook(i)
|
#for using ddply
library(plyr)
#reading data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#split data by year, sum up all the emissions, recombine
plot2data <- ddply(NEI[NEI$fips== "24510",], .(year),summarize,totalEM = sum(Emissions))
png("plot2.png")
plot(plot2data$year,plot2data$totalEM,type="b",pch=2,bg = "transparent",xlab="Year",ylab="Total PM25 Emission in Baltimore")
dev.off()
|
/plot2.R
|
no_license
|
Hdooster/Exploratory-CP2
|
R
| false | false | 433 |
r
|
#for using ddply
library(plyr)
#reading data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#split data by year, sum up all the emissions, recombine
plot2data <- ddply(NEI[NEI$fips== "24510",], .(year),summarize,totalEM = sum(Emissions))
png("plot2.png")
plot(plot2data$year,plot2data$totalEM,type="b",pch=2,bg = "transparent",xlab="Year",ylab="Total PM25 Emission in Baltimore")
dev.off()
|
#read in a vector of variable names, and keep only those with 'mean' or 'std'
# (i.e. standard deviation) in the name
features <- read.table("~/Desktop/UCI HAR Dataset/features.txt")
features <- features[grep("mean\\(|std",features[[2]]),2]
#create vector of activity names in the right order
act <- c("walking", "walking up", "walking down", "sitting", "standing", "laying")
# read in training data, select measurements on mean and standard deviation,
# replace activity numbers with activity names, label the activity and subject
# variables, put the columns together, convert to a table
X_train <- read.table("~/Desktop/UCI HAR Dataset/train/X_train.txt") #7352 x 561
y_train <- read.table("~/Desktop/UCI HAR Dataset/train/y_train.txt") #7352 x 1
subject_train <- read.table("~/Desktop/UCI HAR Dataset/train/subject_train.txt") #7352 x 1
X_train <- select(X_train, features)
y_train <- mutate(y_train, activity = act[V1], V1 = NULL)
subject_train <- rename(subject_train, subject = V1)
train_data <- cbind(y_train, subject_train, X_train)
train_data <- tbl_df(train_data)
# do the same thing, but now with the test data
X_test <- read.table("~/Desktop/UCI HAR Dataset/test/X_test.txt") #2947 x 561
y_test <- read.table("~/Desktop/UCI HAR Dataset/test/y_test.txt") #2947 x 1
subject_test <- read.table("~/Desktop/UCI HAR Dataset/test/subject_test.txt") #2947 x 1
X_test <- select(X_test,features)
y_test <- mutate(y_test, activity = act[V1], V1 = NULL)
subject_test <- rename(subject_test, subject = V1)
test_data <- cbind(y_test, subject_test, X_test)
test_data <- tbl_df(test_data)
# merge the training and test data sets vertically, give variables descriptive names
data <- rbind(train_data, test_data)
names(data)[3:68] <- as.character(features)
# group dataset by activity and subject, then produce a second dataset with the
# averages of each variable for each activity and subject
data <- group_by(data, activity, subject)
averaged_data <- summarise_all(data, mean)
write.table(averaged_data, "~/Desktop/averaged_data.txt")
# here is the code for reading in and viewing "averaged_data.txt"
#data <- read.table("~/Desktop/averaged_data.txt", header = TRUE)
#View(data)
|
/run_analysis.R
|
no_license
|
MJSpong/getting_data
|
R
| false | false | 2,198 |
r
|
#read in a vector of variable names, and keep only those with 'mean' or 'std'
# (i.e. standard deviation) in the name
features <- read.table("~/Desktop/UCI HAR Dataset/features.txt")
features <- features[grep("mean\\(|std",features[[2]]),2]
#create vector of activity names in the right order
act <- c("walking", "walking up", "walking down", "sitting", "standing", "laying")
# read in training data, select measurements on mean and standard deviation,
# replace activity numbers with activity names, label the activity and subject
# variables, put the columns together, convert to a table
X_train <- read.table("~/Desktop/UCI HAR Dataset/train/X_train.txt") #7352 x 561
y_train <- read.table("~/Desktop/UCI HAR Dataset/train/y_train.txt") #7352 x 1
subject_train <- read.table("~/Desktop/UCI HAR Dataset/train/subject_train.txt") #7352 x 1
X_train <- select(X_train, features)
y_train <- mutate(y_train, activity = act[V1], V1 = NULL)
subject_train <- rename(subject_train, subject = V1)
train_data <- cbind(y_train, subject_train, X_train)
train_data <- tbl_df(train_data)
# do the same thing, but now with the test data
X_test <- read.table("~/Desktop/UCI HAR Dataset/test/X_test.txt") #2947 x 561
y_test <- read.table("~/Desktop/UCI HAR Dataset/test/y_test.txt") #2947 x 1
subject_test <- read.table("~/Desktop/UCI HAR Dataset/test/subject_test.txt") #2947 x 1
X_test <- select(X_test,features)
y_test <- mutate(y_test, activity = act[V1], V1 = NULL)
subject_test <- rename(subject_test, subject = V1)
test_data <- cbind(y_test, subject_test, X_test)
test_data <- tbl_df(test_data)
# merge the training and test data sets vertically, give variables descriptive names
data <- rbind(train_data, test_data)
names(data)[3:68] <- as.character(features)
# group dataset by activity and subject, then produce a second dataset with the
# averages of each variable for each activity and subject
data <- group_by(data, activity, subject)
averaged_data <- summarise_all(data, mean)
write.table(averaged_data, "~/Desktop/averaged_data.txt")
# here is the code for reading in and viewing "averaged_data.txt"
#data <- read.table("~/Desktop/averaged_data.txt", header = TRUE)
#View(data)
|
library(rPref)
### Name: plot_front
### Title: Pareto Front Plot
### Aliases: plot_front
### ** Examples
# plots Pareto fronts for the hp/mpg values of mtcars
show_front <- function(pref) {
plot(mtcars$hp, mtcars$mpg)
sky <- psel(mtcars, pref)
plot_front(mtcars, pref, col = rgb(0, 0, 1))
points(sky$hp, sky$mpg, lwd = 3)
}
# do this for all four combinations of Pareto compositions
show_front(low(hp) * low(mpg))
show_front(low(hp) * high(mpg))
show_front(high(hp) * low(mpg))
show_front(high(hp) * high(mpg))
# compare this to the front of a intersection preference
show_front(high(hp) | high(mpg))
|
/data/genthat_extracted_code/rPref/examples/plot_front.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 623 |
r
|
library(rPref)
### Name: plot_front
### Title: Pareto Front Plot
### Aliases: plot_front
### ** Examples
# plots Pareto fronts for the hp/mpg values of mtcars
show_front <- function(pref) {
plot(mtcars$hp, mtcars$mpg)
sky <- psel(mtcars, pref)
plot_front(mtcars, pref, col = rgb(0, 0, 1))
points(sky$hp, sky$mpg, lwd = 3)
}
# do this for all four combinations of Pareto compositions
show_front(low(hp) * low(mpg))
show_front(low(hp) * high(mpg))
show_front(high(hp) * low(mpg))
show_front(high(hp) * high(mpg))
# compare this to the front of a intersection preference
show_front(high(hp) | high(mpg))
|
# This R file accomanies the .Rmd blog post
# _source/2016-02-28-third-post.Rmd
|
/_source/third-post.R
|
permissive
|
SCgeeker/knitr-jekyll
|
R
| false | false | 81 |
r
|
# This R file accomanies the .Rmd blog post
# _source/2016-02-28-third-post.Rmd
|
# Checklist builder
# Keaton Wilson
# keatonwilson@me.com
# 2020-05-29
# packages
library(tidyverse)
library(raster)
library(rgeos)
#test raster
test_rast = raster("./data/thresh_maps_rasters/checklist_rasters/aglais_milberti_all_thresh.tif")
# NFS polygons
nfs = readRDS("./data/nfs_small.rds")
#same proj
crs(test_rast) = crs(nfs)
# intersection
angeles = subset(nfs, nfs$FORESTNAME == "Angeles National Forest")
intersect = raster::intersect(test_rast, angeles)
# building a checklist for one species for all forests
forest_list = split(nfs, nfs$FORESTNAME, drop = TRUE)
# getting all species threshold file names
files = list.files("./data/thresh_maps_rasters/checklist_rasters/",
full.names = TRUE)
to_run = files[str_detect(files, "all")]
df_out = data.frame(forest_name = c(),
species = c(),
present = c())
for(j in 1:length(to_run)){
spec_rast = raster(to_run[j])
crs(spec_rast) = crs(nfs)
species_name = str_remove(to_run[j],
"./data/thresh_maps_rasters/checklist_rasters//") %>%
str_remove("_all_thresh.tif")
for(i in 1:length(forest_list)){
check_intersect = try(raster::intersect(spec_rast, forest_list[[i]]))
df = data.frame(forest_name = names(forest_list[i]),
species = species_name,
present = ifelse(class(check_intersect) == "try-error",
FALSE,
ifelse(sum(check_intersect@data@values) > 0,
TRUE,
FALSE)))
df_out = rbind(df_out, df)
print(paste(species_name, names(forest_list[i])))
}
}
write_csv(df_out, "checklist.csv")
|
/scripts/checklists.R
|
no_license
|
keatonwilson/butterfly_mapper
|
R
| false | false | 1,766 |
r
|
# Checklist builder
# Keaton Wilson
# keatonwilson@me.com
# 2020-05-29
# packages
library(tidyverse)
library(raster)
library(rgeos)
#test raster
test_rast = raster("./data/thresh_maps_rasters/checklist_rasters/aglais_milberti_all_thresh.tif")
# NFS polygons
nfs = readRDS("./data/nfs_small.rds")
#same proj
crs(test_rast) = crs(nfs)
# intersection
angeles = subset(nfs, nfs$FORESTNAME == "Angeles National Forest")
intersect = raster::intersect(test_rast, angeles)
# building a checklist for one species for all forests
forest_list = split(nfs, nfs$FORESTNAME, drop = TRUE)
# getting all species threshold file names
files = list.files("./data/thresh_maps_rasters/checklist_rasters/",
full.names = TRUE)
to_run = files[str_detect(files, "all")]
df_out = data.frame(forest_name = c(),
species = c(),
present = c())
for(j in 1:length(to_run)){
spec_rast = raster(to_run[j])
crs(spec_rast) = crs(nfs)
species_name = str_remove(to_run[j],
"./data/thresh_maps_rasters/checklist_rasters//") %>%
str_remove("_all_thresh.tif")
for(i in 1:length(forest_list)){
check_intersect = try(raster::intersect(spec_rast, forest_list[[i]]))
df = data.frame(forest_name = names(forest_list[i]),
species = species_name,
present = ifelse(class(check_intersect) == "try-error",
FALSE,
ifelse(sum(check_intersect@data@values) > 0,
TRUE,
FALSE)))
df_out = rbind(df_out, df)
print(paste(species_name, names(forest_list[i])))
}
}
write_csv(df_out, "checklist.csv")
|
"%&%" = function(a,b) paste(a,b,sep="")
pheno_name <- c("CHOL_rank", "HDL_rank", "LDL_rank", "TRIG_rank")
database_tissues <- read.table('/home/angela/px_yri_chol/PrediXcan/database_tissues2.txt')
database_tissues <- database_tissues$V1
for (i in pheno_name){
for (j in database_tissues){
MetaXcan <- 'python /home/angela/MetaXcan-master/software/MetaXcan.py --model_db_path /home/wheelerlab1/Data/PrediXcan_db/GTEx-V6p-HapMap-2016-09-08/' %&% j %&% '_0.5.db --gwas_folder /home/angela/px_cebu_chol/GEMMA/MichGEMMA/output/' %&% i %&% '/ --snp_column rs --effect_allele_column allele1 --non_effect_allele_column allele0 --chromosome_column chr --position_column ps --freq_column af --beta_column beta --se_column se --pvalue_column p_wald --covariance /home/wheelerlab1/Data/PrediXcan_db/GTEx-V6p-HapMap-2016-09-08/' %&% j %&% '.txt.gz --output_file /home/angela/MetaXcan-master/GEMMA_Cebu/Mich/' %&% j %&%'_0.5.db/' %&% i %&% '.csv'
system(MetaXcan)
}
}
|
/06_runMetaXcan.r
|
no_license
|
quanrd/px_chol
|
R
| false | false | 978 |
r
|
"%&%" = function(a,b) paste(a,b,sep="")
pheno_name <- c("CHOL_rank", "HDL_rank", "LDL_rank", "TRIG_rank")
database_tissues <- read.table('/home/angela/px_yri_chol/PrediXcan/database_tissues2.txt')
database_tissues <- database_tissues$V1
for (i in pheno_name){
for (j in database_tissues){
MetaXcan <- 'python /home/angela/MetaXcan-master/software/MetaXcan.py --model_db_path /home/wheelerlab1/Data/PrediXcan_db/GTEx-V6p-HapMap-2016-09-08/' %&% j %&% '_0.5.db --gwas_folder /home/angela/px_cebu_chol/GEMMA/MichGEMMA/output/' %&% i %&% '/ --snp_column rs --effect_allele_column allele1 --non_effect_allele_column allele0 --chromosome_column chr --position_column ps --freq_column af --beta_column beta --se_column se --pvalue_column p_wald --covariance /home/wheelerlab1/Data/PrediXcan_db/GTEx-V6p-HapMap-2016-09-08/' %&% j %&% '.txt.gz --output_file /home/angela/MetaXcan-master/GEMMA_Cebu/Mich/' %&% j %&%'_0.5.db/' %&% i %&% '.csv'
system(MetaXcan)
}
}
|
num <- scan("stdin", what = integer(), quiet = TRUE)
cas <- num[1]
ans <- integer(cas)
for (i in 1:cas) {
if (num[i + 1] == 1) {
ans[i] = 1
} else {
ans[i] = num[i + 1] %/% 2
}
}
write(as.integer(ans), file = "", sep = "\n")
|
/Round1/A/A.r
|
no_license
|
Rachelwwwwb/TPC
|
R
| false | false | 260 |
r
|
num <- scan("stdin", what = integer(), quiet = TRUE)
cas <- num[1]
ans <- integer(cas)
for (i in 1:cas) {
if (num[i + 1] == 1) {
ans[i] = 1
} else {
ans[i] = num[i + 1] %/% 2
}
}
write(as.integer(ans), file = "", sep = "\n")
|
library("dplyr")
library("gdxtools")
# BASE FUNCTIONS --------------------------------------
#' Extract simple variable
#' @noRd
getGDX_Variable<- function(gdx_filename,
variable_name,
unit = NULL){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)]
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
#' Extract nty variable
#' @noRd
getGDX_Variable_nty <- function( variable_name,
gdx_filename,
year_start = 0,
year_limit = 2300,
unit = NULL
){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)] %>%
mutate(year = t_to_y(as.integer(t))) %>%
mutate(year =as.integer(year)) %>%
mutate(t = as.integer(t)) %>%
dplyr::filter(year >= year_start) %>%
dplyr::filter(year <= year_limit)
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate(unit = unit))}
}
#' Extract ty variable
#' @noRd
getGDX_Variable_ty <- function( variable_name,
gdx_filename,
year_start = 0,
year_limit = 2300,
unit = NULL
){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)] %>%
mutate(year = t_to_y(as.integer(t)) ) %>%
mutate(year =as.integer(year)) %>%
mutate(t = as.integer(t)) %>%
dplyr::filter(year >= year_start) %>%
dplyr::filter(year <= year_limit)
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
# AGGREGATING FUNCTIONS -----------------------
#' Extract cumulated (5-years-period) n variable
#' @noRd
getGDX_Variable_CUML5y_n <- function(variable_name,
gdx_filename,
unit = NULL,
year_start = 0,
year_limit=2300){
myvar =getGDX_Variable_nty( variable_name,
gdx_filename,
year_start,
year_limit ) %>%
group_by(n) %>%
summarise(value = sum(value*5)) %>% # multiplied because is the
dplyr::select(n, value) %>%
as.data.frame()
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
#' Extract world variable summing across countries
#' @noRd
getGDX_Variable_WORLDagg_ntySUMty <- function( variable_name,
gdx_filename,
unit = NULL,
year_start = 0,
year_limit = 2300 ) {
VAR_nty = getGDX_Variable_nty(variable_name, gdx_filename, year_start,year_limit)
VAR_ty = WORLDaggr_ntySUMty(VAR_nty)
if(is.null(unit)){return(VAR_ty)}
else{return(VAR_ty %>% mutate("unit" = unit))}
}
## -------------: DISAGGREGATING FUNCTIONS :-----------------------
#
#
# getGDX_Variable_dsagg_ntyTOiso3ty <- function( variable_name,
# gdx_file,
# unit = NULL,
# year_start = 0,
# year_limit = 2300 ){
#
# d_n = getGDX_Variable_nty(variable_name, gdx_file, year_start,year_limit)
# map_n_iso3 = getGDX_Parameter(gdx_file,"map_n_iso3")
# iso3_variable = merge(map_n_iso3,d_n,by=c("n")) %>% sanitizeISO3()
#
# if(is.null(unit)){return(iso3_variable)}
# else{return(iso3_variable %>% mutate("unit" = unit))}
#
# }
#
#
#
#
# getGDX_Parameter_dsagg_ntyTOiso3ty <- function( parameter_name,
# gdx_file,
# unit = NULL,
# year_start = 0,
# year_limit = 2300 ){
#
# d_n = getGDX_Parameter_nty(parameter_name, gdx_file, year_start,year_limit)
# map_n_iso3 = getGDX_Parameter(gdx_file,"map_n_iso3")
# iso3_parameter = merge(map_n_iso3,d_n,by=c("n")) %>% sanitizeISO3()
#
# if(is.null(unit)){return(iso3_parameter)}
# else{return(iso3_parameter %>% mutate("unit" = unit))}
# }
#
#
|
/R/02_gdx_extract_utils.R
|
permissive
|
gappix/rice50xplots
|
R
| false | false | 4,704 |
r
|
library("dplyr")
library("gdxtools")
# BASE FUNCTIONS --------------------------------------
#' Extract simple variable
#' @noRd
getGDX_Variable<- function(gdx_filename,
variable_name,
unit = NULL){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)]
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
#' Extract nty variable
#' @noRd
getGDX_Variable_nty <- function( variable_name,
gdx_filename,
year_start = 0,
year_limit = 2300,
unit = NULL
){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)] %>%
mutate(year = t_to_y(as.integer(t))) %>%
mutate(year =as.integer(year)) %>%
mutate(t = as.integer(t)) %>%
dplyr::filter(year >= year_start) %>%
dplyr::filter(year <= year_limit)
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate(unit = unit))}
}
#' Extract ty variable
#' @noRd
getGDX_Variable_ty <- function( variable_name,
gdx_filename,
year_start = 0,
year_limit = 2300,
unit = NULL
){
gdx_file = gdx(gdx_filename)
myvar = gdx_file[as.character(variable_name)] %>%
mutate(year = t_to_y(as.integer(t)) ) %>%
mutate(year =as.integer(year)) %>%
mutate(t = as.integer(t)) %>%
dplyr::filter(year >= year_start) %>%
dplyr::filter(year <= year_limit)
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
# AGGREGATING FUNCTIONS -----------------------
#' Extract cumulated (5-years-period) n variable
#' @noRd
getGDX_Variable_CUML5y_n <- function(variable_name,
gdx_filename,
unit = NULL,
year_start = 0,
year_limit=2300){
myvar =getGDX_Variable_nty( variable_name,
gdx_filename,
year_start,
year_limit ) %>%
group_by(n) %>%
summarise(value = sum(value*5)) %>% # multiplied because is the
dplyr::select(n, value) %>%
as.data.frame()
if(is.null(unit)){return(myvar)}
else{return(myvar %>% mutate("unit" = unit))}
}
#' Extract world variable summing across countries
#' @noRd
getGDX_Variable_WORLDagg_ntySUMty <- function( variable_name,
gdx_filename,
unit = NULL,
year_start = 0,
year_limit = 2300 ) {
VAR_nty = getGDX_Variable_nty(variable_name, gdx_filename, year_start,year_limit)
VAR_ty = WORLDaggr_ntySUMty(VAR_nty)
if(is.null(unit)){return(VAR_ty)}
else{return(VAR_ty %>% mutate("unit" = unit))}
}
## -------------: DISAGGREGATING FUNCTIONS :-----------------------
#
#
# getGDX_Variable_dsagg_ntyTOiso3ty <- function( variable_name,
# gdx_file,
# unit = NULL,
# year_start = 0,
# year_limit = 2300 ){
#
# d_n = getGDX_Variable_nty(variable_name, gdx_file, year_start,year_limit)
# map_n_iso3 = getGDX_Parameter(gdx_file,"map_n_iso3")
# iso3_variable = merge(map_n_iso3,d_n,by=c("n")) %>% sanitizeISO3()
#
# if(is.null(unit)){return(iso3_variable)}
# else{return(iso3_variable %>% mutate("unit" = unit))}
#
# }
#
#
#
#
# getGDX_Parameter_dsagg_ntyTOiso3ty <- function( parameter_name,
# gdx_file,
# unit = NULL,
# year_start = 0,
# year_limit = 2300 ){
#
# d_n = getGDX_Parameter_nty(parameter_name, gdx_file, year_start,year_limit)
# map_n_iso3 = getGDX_Parameter(gdx_file,"map_n_iso3")
# iso3_parameter = merge(map_n_iso3,d_n,by=c("n")) %>% sanitizeISO3()
#
# if(is.null(unit)){return(iso3_parameter)}
# else{return(iso3_parameter %>% mutate("unit" = unit))}
# }
#
#
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/epanetmsx.rpt-s3.r
\name{summary.epanetmsx.rpt}
\alias{summary.epanetmsx.rpt}
\title{Summary of Epanet-msx Simulation Results}
\usage{
\method{summary}{epanetmsx.rpt}(object, ...)
}
\arguments{
\item{object}{of epanetmsx.rpt class}
\item{...}{further arguments passed to summary()}
}
\description{
Provides a basic summary of simulation results
}
|
/man/summary.epanetmsx.rpt.Rd
|
permissive
|
gintaem/epanetReader
|
R
| false | true | 427 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/epanetmsx.rpt-s3.r
\name{summary.epanetmsx.rpt}
\alias{summary.epanetmsx.rpt}
\title{Summary of Epanet-msx Simulation Results}
\usage{
\method{summary}{epanetmsx.rpt}(object, ...)
}
\arguments{
\item{object}{of epanetmsx.rpt class}
\item{...}{further arguments passed to summary()}
}
\description{
Provides a basic summary of simulation results
}
|
library(readxl)
data=read_xlsx("E:/Data_Nilai_Pegawai.xlsx")
View(data)
library(ggplot2)
library(RColorBrewer)
library(doBy)
summary(data)
#variabel Tingkat Pendidikan Akhir
counts=table(data$`Tingkat Pendidikan Akhir`)
barplot(counts,main="Tingkat Pendidikan Akhir Pegawai",
xlab="pendidikan akhir", ylab="jumlah",col=brewer.pal(8,"Set3"))
mytable=table(data$`Tingkat Pendidikan Akhir`)
pct=((mytable/sum(mytable))*100)
pct=round(pct,digit=2)
lbls=paste(names(mytable),pct)
lbls=paste(lbls,"%",sep="")
pie(mytable,labels = lbls,
main="Pie Chart dari Tingkat Pendidikan Akhir Pegawai")
boxplot(data$Nilai~data$`Tingkat Pendidikan Akhir`,
data=data,main="Perbedaan Boxplot untuk Masing-Masing Tingkat Pendidikan Akhir",
xlab="Tingkat Pendidikan Akhir",
ylab="Nilai", col="orange",border="brown")
#variabel Satuan Kerja
counts=table(data$`Satuan Kerja`)
barplot(counts,main="Satuan Kerja",
ylab="jumlah",col=brewer.pal(6,"Set2"))
mytable=table(data$`Satuan Kerja`)
pct=((mytable/sum(mytable))*100)
pct=round(pct,digit=2)
lbls=paste(names(mytable),pct)
lbls=paste(lbls,"%",sep="")
pie(mytable,labels = lbls,
col=rainbow(length(lbls)),
main="Pie Chart dari Satuan Kerja Pegawai")
boxplot(data$Nilai~data$`Satuan Kerja`,
data=data,main="Perbedaan Boxplot untuk Masing-Masing Satuan Kerja",
xlab="Satuan Kerja",
ylab="Nilai", col="orange",border="brown")
#variabel Usia
plot(data$Usia,data$Nilai,main="Persebaran Nilai Berdasarkan Usia",ylab="Nilai",xlab="Usia",col=brewer.pal(3,"Set2"))
#ANOVA AND TUKEY TEST
Anova_result=aov(data$Nilai~data$`Tingkat Pendidikan Akhir`,data=data)
summary(Anova_result)
tukey=TukeyHSD(Anova_result)
tukey
plot(tukey)
Anova_result=aov(data$Nilai~data$`Satuan Kerja`,data=data)
summary(Anova_result)
tukey=TukeyHSD(Anova_result)
tukey
plot(tukey)
|
/JDS_.R
|
no_license
|
Novitadwiutami/analisis-evaluasi-kinerja-asn-pemprov-jabar
|
R
| false | false | 1,923 |
r
|
library(readxl)
data=read_xlsx("E:/Data_Nilai_Pegawai.xlsx")
View(data)
library(ggplot2)
library(RColorBrewer)
library(doBy)
summary(data)
#variabel Tingkat Pendidikan Akhir
counts=table(data$`Tingkat Pendidikan Akhir`)
barplot(counts,main="Tingkat Pendidikan Akhir Pegawai",
xlab="pendidikan akhir", ylab="jumlah",col=brewer.pal(8,"Set3"))
mytable=table(data$`Tingkat Pendidikan Akhir`)
pct=((mytable/sum(mytable))*100)
pct=round(pct,digit=2)
lbls=paste(names(mytable),pct)
lbls=paste(lbls,"%",sep="")
pie(mytable,labels = lbls,
main="Pie Chart dari Tingkat Pendidikan Akhir Pegawai")
boxplot(data$Nilai~data$`Tingkat Pendidikan Akhir`,
data=data,main="Perbedaan Boxplot untuk Masing-Masing Tingkat Pendidikan Akhir",
xlab="Tingkat Pendidikan Akhir",
ylab="Nilai", col="orange",border="brown")
#variabel Satuan Kerja
counts=table(data$`Satuan Kerja`)
barplot(counts,main="Satuan Kerja",
ylab="jumlah",col=brewer.pal(6,"Set2"))
mytable=table(data$`Satuan Kerja`)
pct=((mytable/sum(mytable))*100)
pct=round(pct,digit=2)
lbls=paste(names(mytable),pct)
lbls=paste(lbls,"%",sep="")
pie(mytable,labels = lbls,
col=rainbow(length(lbls)),
main="Pie Chart dari Satuan Kerja Pegawai")
boxplot(data$Nilai~data$`Satuan Kerja`,
data=data,main="Perbedaan Boxplot untuk Masing-Masing Satuan Kerja",
xlab="Satuan Kerja",
ylab="Nilai", col="orange",border="brown")
#variabel Usia
plot(data$Usia,data$Nilai,main="Persebaran Nilai Berdasarkan Usia",ylab="Nilai",xlab="Usia",col=brewer.pal(3,"Set2"))
#ANOVA AND TUKEY TEST
Anova_result=aov(data$Nilai~data$`Tingkat Pendidikan Akhir`,data=data)
summary(Anova_result)
tukey=TukeyHSD(Anova_result)
tukey
plot(tukey)
Anova_result=aov(data$Nilai~data$`Satuan Kerja`,data=data)
summary(Anova_result)
tukey=TukeyHSD(Anova_result)
tukey
plot(tukey)
|
\name{lagmess}
\alias{lagmess}
\alias{print.lagmess}
\alias{print.summary.lagmess}
\alias{summary.lagmess}
\alias{residuals.lagmess}
\alias{deviance.lagmess}
\alias{coef.lagmess}
\alias{fitted.lagmess}
\alias{logLik.lagmess}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Matrix exponential spatial lag model}
\description{The function fits a matrix exponential spatial lag model, using \code{optim} to find the value of \code{alpha}, the spatial coefficient.}
\usage{
lagmess(formula, data = list(), listw, zero.policy = NULL, na.action = na.fail,
q = 10, start = -2.5, control=list(), method="BFGS", verbose=NULL,
use_expm=FALSE)
\method{summary}{lagmess}(object, ...)
\method{print}{lagmess}(x, ...)
\method{print}{summary.lagmess}(x, digits = max(5, .Options$digits - 3),
signif.stars = FALSE, ...)
\method{residuals}{lagmess}(object, ...)
\method{deviance}{lagmess}(object, ...)
\method{coef}{lagmess}(object, ...)
\method{fitted}{lagmess}(object, ...)
\method{logLik}{lagmess}(object, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{formula}{a symbolic description of the model to be fit. The details
of model specification are given for \code{lm()}}
\item{data}{an optional data frame containing the variables in the model.
By default the variables are taken from the environment which the function
is called.}
\item{listw}{a \code{listw} object created for example by \code{nb2listw}}
\item{zero.policy}{default NULL, use global option value; if TRUE assign zero to the lagged value of zones without
neighbours, if FALSE assign NA - causing \code{lagmess()} to terminate with an error}
\item{na.action}{a function (default \code{options("na.action")}), can also be \code{na.omit} or \code{na.exclude} with consequences for residuals and fitted values - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to \code{nb2listw} may be subsetted.}
\item{q}{default 10; number of powers of the spatial weights to use}
\item{start}{starting value for numerical optimization, should be a small negative number}
\item{control}{control parameters passed to \code{optim}}
\item{method}{default \code{BFGS}, method passed to \code{optim}}
\item{verbose}{default NULL, use global option value; if TRUE report function values during optimization}
\item{use_expm}{default FALSE; if TRUE use \code{expm::expAtv} instead of a truncated power series of W}
\item{x,object}{Objects of classes \code{lagmess} or \code{summary.lagmess} to be passed to methods}
\item{digits}{the number of significant digits to use when printing}
\item{signif.stars}{logical. If TRUE, "significance stars" are printed
for each coefficient.}
\item{\dots}{further arguments passed to or from other methods}
}
\details{The underlying spatial lag model:
\deqn{y = \rho W y + X \beta + \varepsilon}{y = rho W y + X beta + e}
where \eqn{\rho}{rho} is the spatial parameter may be fitted by maximum likelihood. In that case, the log likelihood function includes the logartithm of cumbersome Jacobian term \eqn{|I - \rho W|}{|I - rho W|}. If we rewrite the model as:
\deqn{S y = X \beta + \varepsilon}{S y = X beta + e}
we see that in the ML case \eqn{S y = (I - \rho W) y}{S y = (I - rho W) y}. If W is row-stochastic, S may be expressed as a linear combination of row-stochastic matrices. By pre-computing the matrix \eqn{[y Wy, W^2y, ..., W^{q-1}y]}{[y Wy, W^2y, ..., W^{q-1}y]}, the term \eqn{S y (\alpha)}{S y (alpha)} can readily be found by numerical optimization using the matrix exponential approach. \eqn{\alpha}{alpha} and \eqn{\rho}{rho} are related as \eqn{\rho = 1 - \exp{\alpha}}{rho = 1 - exp(alpha)}, conditional on the number of matrix power terms taken \code{q}.}
\value{
The function returns an object of class \code{lagmess} with components:
\item{lmobj}{the \code{lm} object returned after fitting \code{alpha}}
\item{alpha}{the spatial coefficient}
\item{alphase}{the standard error of the spatial coefficient using the numerical Hessian}
\item{rho}{the value of \code{rho} implied by \code{alpha}}
\item{bestmess}{the object returned by \code{optim}}
\item{q}{the number of powers of the spatial weights used}
\item{start}{the starting value for numerical optimization used}
\item{na.action}{(possibly) named vector of excluded or omitted observations if non-default na.action argument used}
\item{nullLL}{the log likelihood of the aspatial model for the same data}
}
\references{J. P. LeSage and R. K. Pace (2007) A matrix exponential specification. Journal of Econometrics, 140, 190-214; J. P. LeSage and R. K. Pace (2009) Introduction to Spatial Econometrics. CRC Press, Chapter 9.}
\author{Roger Bivand \email{Roger.Bivand@nhh.no} and Eric Blankmeyer}
\seealso{\code{\link{lagsarlm}}, \code{\link[stats]{optim}}}
\examples{
data(baltimore)
baltimore$AGE <- ifelse(baltimore$AGE < 1, 1, baltimore$AGE)
lw <- nb2listw(knn2nb(knearneigh(cbind(baltimore$X, baltimore$Y), k=7)))
obj1 <- lm(log(PRICE) ~ PATIO + log(AGE) + log(SQFT),
data=baltimore)
lm.morantest(obj1, lw)
lm.LMtests(obj1, lw, test="all")
system.time(obj2 <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw))
summary(obj2)
system.time(obj2a <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw,
use_expm=TRUE))
summary(obj2a)
obj3 <- lagsarlm(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw)
summary(obj3)
data(boston)
lw <- nb2listw(boston.soi)
gp2 <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
+ AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
data=boston.c, lw, method="Matrix")
summary(gp2)
gp2a <- lagmess(CMEDV ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
+ AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
data=boston.c, lw)
summary(gp2a)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{spatial}
|
/man/lagmess.Rd
|
no_license
|
jsjae2000/spdep-1
|
R
| false | false | 6,182 |
rd
|
\name{lagmess}
\alias{lagmess}
\alias{print.lagmess}
\alias{print.summary.lagmess}
\alias{summary.lagmess}
\alias{residuals.lagmess}
\alias{deviance.lagmess}
\alias{coef.lagmess}
\alias{fitted.lagmess}
\alias{logLik.lagmess}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Matrix exponential spatial lag model}
\description{The function fits a matrix exponential spatial lag model, using \code{optim} to find the value of \code{alpha}, the spatial coefficient.}
\usage{
lagmess(formula, data = list(), listw, zero.policy = NULL, na.action = na.fail,
q = 10, start = -2.5, control=list(), method="BFGS", verbose=NULL,
use_expm=FALSE)
\method{summary}{lagmess}(object, ...)
\method{print}{lagmess}(x, ...)
\method{print}{summary.lagmess}(x, digits = max(5, .Options$digits - 3),
signif.stars = FALSE, ...)
\method{residuals}{lagmess}(object, ...)
\method{deviance}{lagmess}(object, ...)
\method{coef}{lagmess}(object, ...)
\method{fitted}{lagmess}(object, ...)
\method{logLik}{lagmess}(object, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{formula}{a symbolic description of the model to be fit. The details
of model specification are given for \code{lm()}}
\item{data}{an optional data frame containing the variables in the model.
By default the variables are taken from the environment which the function
is called.}
\item{listw}{a \code{listw} object created for example by \code{nb2listw}}
\item{zero.policy}{default NULL, use global option value; if TRUE assign zero to the lagged value of zones without
neighbours, if FALSE assign NA - causing \code{lagmess()} to terminate with an error}
\item{na.action}{a function (default \code{options("na.action")}), can also be \code{na.omit} or \code{na.exclude} with consequences for residuals and fitted values - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to \code{nb2listw} may be subsetted.}
\item{q}{default 10; number of powers of the spatial weights to use}
\item{start}{starting value for numerical optimization, should be a small negative number}
\item{control}{control parameters passed to \code{optim}}
\item{method}{default \code{BFGS}, method passed to \code{optim}}
\item{verbose}{default NULL, use global option value; if TRUE report function values during optimization}
\item{use_expm}{default FALSE; if TRUE use \code{expm::expAtv} instead of a truncated power series of W}
\item{x,object}{Objects of classes \code{lagmess} or \code{summary.lagmess} to be passed to methods}
\item{digits}{the number of significant digits to use when printing}
\item{signif.stars}{logical. If TRUE, "significance stars" are printed
for each coefficient.}
\item{\dots}{further arguments passed to or from other methods}
}
\details{The underlying spatial lag model:
\deqn{y = \rho W y + X \beta + \varepsilon}{y = rho W y + X beta + e}
where \eqn{\rho}{rho} is the spatial parameter may be fitted by maximum likelihood. In that case, the log likelihood function includes the logartithm of cumbersome Jacobian term \eqn{|I - \rho W|}{|I - rho W|}. If we rewrite the model as:
\deqn{S y = X \beta + \varepsilon}{S y = X beta + e}
we see that in the ML case \eqn{S y = (I - \rho W) y}{S y = (I - rho W) y}. If W is row-stochastic, S may be expressed as a linear combination of row-stochastic matrices. By pre-computing the matrix \eqn{[y Wy, W^2y, ..., W^{q-1}y]}{[y Wy, W^2y, ..., W^{q-1}y]}, the term \eqn{S y (\alpha)}{S y (alpha)} can readily be found by numerical optimization using the matrix exponential approach. \eqn{\alpha}{alpha} and \eqn{\rho}{rho} are related as \eqn{\rho = 1 - \exp{\alpha}}{rho = 1 - exp(alpha)}, conditional on the number of matrix power terms taken \code{q}.}
\value{
The function returns an object of class \code{lagmess} with components:
\item{lmobj}{the \code{lm} object returned after fitting \code{alpha}}
\item{alpha}{the spatial coefficient}
\item{alphase}{the standard error of the spatial coefficient using the numerical Hessian}
\item{rho}{the value of \code{rho} implied by \code{alpha}}
\item{bestmess}{the object returned by \code{optim}}
\item{q}{the number of powers of the spatial weights used}
\item{start}{the starting value for numerical optimization used}
\item{na.action}{(possibly) named vector of excluded or omitted observations if non-default na.action argument used}
\item{nullLL}{the log likelihood of the aspatial model for the same data}
}
\references{J. P. LeSage and R. K. Pace (2007) A matrix exponential specification. Journal of Econometrics, 140, 190-214; J. P. LeSage and R. K. Pace (2009) Introduction to Spatial Econometrics. CRC Press, Chapter 9.}
\author{Roger Bivand \email{Roger.Bivand@nhh.no} and Eric Blankmeyer}
\seealso{\code{\link{lagsarlm}}, \code{\link[stats]{optim}}}
\examples{
data(baltimore)
baltimore$AGE <- ifelse(baltimore$AGE < 1, 1, baltimore$AGE)
lw <- nb2listw(knn2nb(knearneigh(cbind(baltimore$X, baltimore$Y), k=7)))
obj1 <- lm(log(PRICE) ~ PATIO + log(AGE) + log(SQFT),
data=baltimore)
lm.morantest(obj1, lw)
lm.LMtests(obj1, lw, test="all")
system.time(obj2 <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw))
summary(obj2)
system.time(obj2a <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw,
use_expm=TRUE))
summary(obj2a)
obj3 <- lagsarlm(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw)
summary(obj3)
data(boston)
lw <- nb2listw(boston.soi)
gp2 <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
+ AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
data=boston.c, lw, method="Matrix")
summary(gp2)
gp2a <- lagmess(CMEDV ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2)
+ AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
data=boston.c, lw)
summary(gp2a)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{spatial}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/terrain_analysis.R
\name{wbt_plan_curvature}
\alias{wbt_plan_curvature}
\title{Plan curvature}
\usage{
wbt_plan_curvature(dem, output, zfactor = 1, wd = NULL, verbose_mode = FALSE)
}
\arguments{
\item{dem}{Input raster DEM file.}
\item{output}{Output raster file.}
\item{zfactor}{Optional multiplier for when the vertical and horizontal units are not the same.}
\item{wd}{Changes the working directory.}
\item{verbose_mode}{Sets verbose mode. If verbose mode is False, tools will not print output messages.}
}
\value{
Returns the tool text outputs.
}
\description{
Calculates a plan (contour) curvature raster from an input DEM.
}
|
/man/wbt_plan_curvature.Rd
|
permissive
|
gitWayneZhang/whiteboxR
|
R
| false | true | 713 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/terrain_analysis.R
\name{wbt_plan_curvature}
\alias{wbt_plan_curvature}
\title{Plan curvature}
\usage{
wbt_plan_curvature(dem, output, zfactor = 1, wd = NULL, verbose_mode = FALSE)
}
\arguments{
\item{dem}{Input raster DEM file.}
\item{output}{Output raster file.}
\item{zfactor}{Optional multiplier for when the vertical and horizontal units are not the same.}
\item{wd}{Changes the working directory.}
\item{verbose_mode}{Sets verbose mode. If verbose mode is False, tools will not print output messages.}
}
\value{
Returns the tool text outputs.
}
\description{
Calculates a plan (contour) curvature raster from an input DEM.
}
|
#' @title Function to compute the confidence interval for the Spearman
#' correelation coefficient
#'
#' @description
#' This function enables to compute the confidence interval for the Spearman
#' correelation coefficient using the Fischer Z transformation.
#'
#' @usage
#' spearmanCI(x, n, alpha = 0.05)
#'
#' @param x Spearman correlation coefficient rho.
#' @param n the sample size used to compute the Spearman rho.
#' @param alpha alpha level for confidence interval.
#'
#' @return
#' A vector containing the lower, upper values for the confidence interval
#' and p-value for Spearman rho
#'
#' @examples
#' spearmanCI(x=0.2, n=100, alpha=0.05)
#'
#' @md
#' @export
spearmanCI <-
function (x, n, alpha=0.05) {
zz <- sqrt((n-3)/1.06) * survcomp::fisherz(x)
zz.se <- 1/sqrt(n - 3)
ll <- zz - qnorm(p=alpha / 2, lower.tail=FALSE) * zz.se
ll <- survcomp::fisherz(ll / sqrt((n-3)/1.06), inv=TRUE)
uu <- zz + qnorm(p=alpha / 2, lower.tail=FALSE) * zz.se
uu <- survcomp::fisherz(uu / sqrt((n-3)/1.06), inv=TRUE)
pp <- pnorm(q=zz, lower.tail=x < 0)
res <- c("lower"=ll, "upper"=uu, "p.value"=pp)
return(res)
}
|
/R/SpearmanCI.R
|
no_license
|
bhklab/genefu
|
R
| false | false | 1,153 |
r
|
#' @title Function to compute the confidence interval for the Spearman
#' correelation coefficient
#'
#' @description
#' This function enables to compute the confidence interval for the Spearman
#' correelation coefficient using the Fischer Z transformation.
#'
#' @usage
#' spearmanCI(x, n, alpha = 0.05)
#'
#' @param x Spearman correlation coefficient rho.
#' @param n the sample size used to compute the Spearman rho.
#' @param alpha alpha level for confidence interval.
#'
#' @return
#' A vector containing the lower, upper values for the confidence interval
#' and p-value for Spearman rho
#'
#' @examples
#' spearmanCI(x=0.2, n=100, alpha=0.05)
#'
#' @md
#' @export
spearmanCI <-
function (x, n, alpha=0.05) {
zz <- sqrt((n-3)/1.06) * survcomp::fisherz(x)
zz.se <- 1/sqrt(n - 3)
ll <- zz - qnorm(p=alpha / 2, lower.tail=FALSE) * zz.se
ll <- survcomp::fisherz(ll / sqrt((n-3)/1.06), inv=TRUE)
uu <- zz + qnorm(p=alpha / 2, lower.tail=FALSE) * zz.se
uu <- survcomp::fisherz(uu / sqrt((n-3)/1.06), inv=TRUE)
pp <- pnorm(q=zz, lower.tail=x < 0)
res <- c("lower"=ll, "upper"=uu, "p.value"=pp)
return(res)
}
|
## Put comments here that give an overall description of what your
## functions do.
## Write a short comment describing this function
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
invs <- NULL
sets <- function(y) {
x <<- y
invs <<- NULL
}
gets <- function() x
set_Inverse <- function(inverse) invs <<- inverse
get_Inverse <- function() invs
list(sets = sets,
gets = gets,
set_Inverse = set_Inverse,
get_Inverse = get_Inverse)
}
## This function computes the inverse of the special "matrix" created by
## makeCacheMatrix above. If the inverse has already been calculated (and the
## matrix has not changed), then it should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
invs <- x$get_Inverse()
if (!is.null(invs)) {
message("getting cached data")
return(invs)
}
mat <- x$gets()
invs <- solve(mat, ...)
x$set_Inverse(invs)
invs
}
|
/cachematrix.R
|
no_license
|
PrajwalP54/ProgrammingAssignment2
|
R
| false | false | 1,222 |
r
|
## Put comments here that give an overall description of what your
## functions do.
## Write a short comment describing this function
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
invs <- NULL
sets <- function(y) {
x <<- y
invs <<- NULL
}
gets <- function() x
set_Inverse <- function(inverse) invs <<- inverse
get_Inverse <- function() invs
list(sets = sets,
gets = gets,
set_Inverse = set_Inverse,
get_Inverse = get_Inverse)
}
## This function computes the inverse of the special "matrix" created by
## makeCacheMatrix above. If the inverse has already been calculated (and the
## matrix has not changed), then it should retrieve the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
invs <- x$get_Inverse()
if (!is.null(invs)) {
message("getting cached data")
return(invs)
}
mat <- x$gets()
invs <- solve(mat, ...)
x$set_Inverse(invs)
invs
}
|
gelev <-
c(-1.02375613068687, -0.821718941602767, -0.93505687694263, -1.58551807106706,
-0.163866142999653, -0.3043066280947, -0.686206192826845, -0.806935732645394,
-0.964623294857376, -0.922737536144818, -1.07549736203768, -0.95723169037869,
-1.24057652872835, -1.04593094412293, -0.88085177743226, -1.5436323123545,
-0.962159426697814, -0.656639774912098, -0.306770496254262, 0.146581245105187,
-0.200824165393087, -0.585187598284794, -0.666495247550347, -0.774905446571085,
-0.797080260007145, -0.765049973932836, -0.632001093316476, -0.59257920276348,
-0.641856565954725, -0.686206192826845, -0.654175906752536, -0.73548355601809,
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gchap <-
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|
/Supplementary_Material_A/grid_covariates.R
|
no_license
|
dill/varprop-suppmaterials
|
R
| false | false | 165,935 |
r
|
gelev <-
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gfor <-
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0.896554064656628, 0.126762298262344, 0.212294716750598, 0.383359553727105,
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1.3242161570979, 1.28144994785377, 0.212294716750598, -0.0870687479582905,
-0.728561886620194, -0.00153632947003662, 0.982086483144882,
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0.426125762971232, 0.939320273900755, 1.4952809940744, 1.58081341256266,
1.88017687727155, 1.02485269238901, -0.0870687479582905, -0.300899794178925,
0.383359553727105, 1.88017687727155, 1.36698236634202, 0.126762298262344,
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0.682723018435993, -0.00153632947003662, -0.386432212667179,
-0.471964631155432, -0.215367375690671, -0.00153632947003662,
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1.4952809940744, 1.62357962180679, 0.59719059994774, -0.471964631155432,
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-1.02792535132908, -0.899626723596701, -0.985159142084955, -0.600263258887813,
-0.942392932840828, -0.985159142084955, -0.0870687479582905,
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-1.15622397906146, -0.728561886620194, -0.77132809586432)
|
library(evmix)
### Name: fhpdcon
### Title: MLE Fitting of Hybrid Pareto Extreme Value Mixture Model with
### Single Continuity Constraint
### Aliases: fhpdcon lhpdcon nlhpdcon profluhpdcon nluhpdcon lhpdcon
### fhpdcon nlhpdcon profluhpdcon nluhpdcon nlhpdcon fhpdcon lhpdcon
### profluhpdcon nluhpdcon profluhpdcon fhpdcon lhpdcon nlhpdcon
### nluhpdcon nluhpdcon fhpdcon lhpdcon nlhpdcon profluhpdcon
### ** Examples
## Not run:
##D set.seed(1)
##D par(mfrow = c(2, 1))
##D
##D x = rnorm(1000)
##D xx = seq(-4, 4, 0.01)
##D y = dnorm(xx)
##D
##D # Hybrid Pareto provides reasonable fit for some asymmetric heavy upper tailed distributions
##D # but not for cases such as the normal distribution
##D
##D # Continuity constraint
##D fit = fhpdcon(x)
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D
##D # No continuity constraint
##D fit2 = fhpd(x)
##D with(fit2, lines(xx, dhpd(xx, nmean, nsd, xi), col="blue"))
##D abline(v = fit2$u, col = "blue")
##D legend("topleft", c("True Density","No continuity constraint","With continuty constraint"),
##D col=c("black", "blue", "red"), lty = 1)
##D
##D # Profile likelihood for initial value of threshold and fixed threshold approach
##D fitu = fhpdcon(x, useq = seq(-2, 2, length = 20))
##D fitfix = fhpdcon(x, useq = seq(-2, 2, length = 20), fixedu = TRUE)
##D
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D with(fitu, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="purple"))
##D abline(v = fitu$u, col = "purple")
##D with(fitfix, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="darkgreen"))
##D abline(v = fitfix$u, col = "darkgreen")
##D legend("topleft", c("True Density","Default initial value (90% quantile)",
##D "Prof. lik. for initial value", "Prof. lik. for fixed threshold"),
##D col=c("black", "red", "purple", "darkgreen"), lty = 1)
##D
##D # Notice that if tail fraction is included a better fit is obtained
##D fittailfrac = fnormgpdcon(x)
##D
##D par(mfrow = c(1, 1))
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D with(fittailfrac, lines(xx, dnormgpdcon(xx, nmean, nsd, u, xi), col="blue"))
##D abline(v = fittailfrac$u)
##D legend("topright", c("Standard Normal", "Hybrid Pareto Continuous", "Normal+GPD Continuous"),
##D col=c("black", "red", "blue"), lty = 1)
## End(Not run)
|
/data/genthat_extracted_code/evmix/examples/fhpdcon.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 2,680 |
r
|
library(evmix)
### Name: fhpdcon
### Title: MLE Fitting of Hybrid Pareto Extreme Value Mixture Model with
### Single Continuity Constraint
### Aliases: fhpdcon lhpdcon nlhpdcon profluhpdcon nluhpdcon lhpdcon
### fhpdcon nlhpdcon profluhpdcon nluhpdcon nlhpdcon fhpdcon lhpdcon
### profluhpdcon nluhpdcon profluhpdcon fhpdcon lhpdcon nlhpdcon
### nluhpdcon nluhpdcon fhpdcon lhpdcon nlhpdcon profluhpdcon
### ** Examples
## Not run:
##D set.seed(1)
##D par(mfrow = c(2, 1))
##D
##D x = rnorm(1000)
##D xx = seq(-4, 4, 0.01)
##D y = dnorm(xx)
##D
##D # Hybrid Pareto provides reasonable fit for some asymmetric heavy upper tailed distributions
##D # but not for cases such as the normal distribution
##D
##D # Continuity constraint
##D fit = fhpdcon(x)
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D
##D # No continuity constraint
##D fit2 = fhpd(x)
##D with(fit2, lines(xx, dhpd(xx, nmean, nsd, xi), col="blue"))
##D abline(v = fit2$u, col = "blue")
##D legend("topleft", c("True Density","No continuity constraint","With continuty constraint"),
##D col=c("black", "blue", "red"), lty = 1)
##D
##D # Profile likelihood for initial value of threshold and fixed threshold approach
##D fitu = fhpdcon(x, useq = seq(-2, 2, length = 20))
##D fitfix = fhpdcon(x, useq = seq(-2, 2, length = 20), fixedu = TRUE)
##D
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D with(fitu, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="purple"))
##D abline(v = fitu$u, col = "purple")
##D with(fitfix, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="darkgreen"))
##D abline(v = fitfix$u, col = "darkgreen")
##D legend("topleft", c("True Density","Default initial value (90% quantile)",
##D "Prof. lik. for initial value", "Prof. lik. for fixed threshold"),
##D col=c("black", "red", "purple", "darkgreen"), lty = 1)
##D
##D # Notice that if tail fraction is included a better fit is obtained
##D fittailfrac = fnormgpdcon(x)
##D
##D par(mfrow = c(1, 1))
##D hist(x, breaks = 100, freq = FALSE, xlim = c(-4, 4))
##D lines(xx, y)
##D with(fit, lines(xx, dhpdcon(xx, nmean, nsd, u, xi), col="red"))
##D abline(v = fit$u, col = "red")
##D with(fittailfrac, lines(xx, dnormgpdcon(xx, nmean, nsd, u, xi), col="blue"))
##D abline(v = fittailfrac$u)
##D legend("topright", c("Standard Normal", "Hybrid Pareto Continuous", "Normal+GPD Continuous"),
##D col=c("black", "red", "blue"), lty = 1)
## End(Not run)
|
library(shiny)
library(ggplot2)
library(caret)
data("iris")
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
set.seed(1233214)
intrain<-createDataPartition(y=iris$Species,p=0.7,list=FALSE)
training<-iris[intrain,]
testing<-iris[-intrain,]
fit_lda<-train(Species~.,data = training,method="lda",
trainControl=trainControl(method="cv",number = 10))
fit_rf<-train(Species~.,data = training,method="rf",
trainControl=trainControl(method="cv",number = 10))
fit_svm<-train(Species~.,data = training,method="svmRadial",
trainControl=trainControl(method="cv",number = 10))
output$predicted_specie<-renderText({
if(input$models=="LDA"){
model_pred_lda<-reactive({
predict(fit_lda, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_lda()[1]
}
else if(input$models=="RF"){
model_pred_rf<-reactive({
predict(fit_rf, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_rf()[1]
}
else{
model_pred_svm<-reactive({
predict(fit_svm, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_svm()[1]
}
})
output$stats1<-renderPrint({
if(input$models=="LDA"){
(confusionMatrix(predict(fit_lda,newdata=testing[-5]),testing$Species))$overall
}else if (input$models=="RF"){
confusionMatrix(predict(fit_rf,newdata=testing[-5]),testing$Species)$overall
}else if (input$models=="SVM") { confusionMatrix(predict(fit_svm,newdata=testing[-5]),testing$Species)$overall}
})
})
|
/predict_species/server.R
|
no_license
|
AhmedKharbech/iris_shinyapp
|
R
| false | false | 2,998 |
r
|
library(shiny)
library(ggplot2)
library(caret)
data("iris")
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
set.seed(1233214)
intrain<-createDataPartition(y=iris$Species,p=0.7,list=FALSE)
training<-iris[intrain,]
testing<-iris[-intrain,]
fit_lda<-train(Species~.,data = training,method="lda",
trainControl=trainControl(method="cv",number = 10))
fit_rf<-train(Species~.,data = training,method="rf",
trainControl=trainControl(method="cv",number = 10))
fit_svm<-train(Species~.,data = training,method="svmRadial",
trainControl=trainControl(method="cv",number = 10))
output$predicted_specie<-renderText({
if(input$models=="LDA"){
model_pred_lda<-reactive({
predict(fit_lda, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_lda()[1]
}
else if(input$models=="RF"){
model_pred_rf<-reactive({
predict(fit_rf, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_rf()[1]
}
else{
model_pred_svm<-reactive({
predict(fit_svm, newdata=data.frame("Sepal.Length"=input$sepal_length,
"Sepal.Width"=input$sepal_width,
"Petal.Length"=input$petal_length,
"Petal.Width" =input$petal_width))
})
model_pred_svm()[1]
}
})
output$stats1<-renderPrint({
if(input$models=="LDA"){
(confusionMatrix(predict(fit_lda,newdata=testing[-5]),testing$Species))$overall
}else if (input$models=="RF"){
confusionMatrix(predict(fit_rf,newdata=testing[-5]),testing$Species)$overall
}else if (input$models=="SVM") { confusionMatrix(predict(fit_svm,newdata=testing[-5]),testing$Species)$overall}
})
})
|
# Scatterplot matrix of DMC,DC,wind,rain,temp
data_1 = read.csv("C:/Users/ENVY X360/Desktop/R/forestfires.csv")
pairs(~DMC+DC+wind+rain+temp,data=data_1,
main="Scatterplot Matrix")
attach(data_1)
dev.copy(pdf,"scatterplot.pdf")
dev.off()
dev.copy(png,"scatterplot.png")
dev.off()
# 3D Scatterplot of wind,rain,area
library(scatterplot3d)
scatterplot3d(wind,rain,area,main="3D Scatterplot")
dev.copy(pdf,"3D_scatterplot.pdf")
dev.off()
# Interactive 3D Scatterplot of wind,rain,area
library(rgl)
plot3d(wind,rain,area, col="red", size=3)
dev.copy(pdf,"3D_interactiveplot.pdf")
dev.off()
# Boxplot of X and Y
boxplot(X~Y,data=data_1, main="Boxplot",
xlab="X", ylab="Y")
dev.copy(pdf,"Boxplot.pdf")
dev.off()
# Simple bar plot of temp, wind, rain [horizontal and vertical]
#Horizantal
counts =table(data_1$temp)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
counts =table(data_1$wind)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
counts =table(data_1$rain)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
#Vertical
counts =table(data_1$temp)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
counts =table(data_1$wind)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
counts =table(data_1$rain)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
# Grouped bar plot of X and Y
counts <- table(data_1$X, data_1$Y)
barplot(counts, main="Distribution by X and Y",
xlab="X", col=c("darkblue","red"),
legend = rownames(counts), beside=TRUE)
dev.copy(pdf,"groundbar_plot.pdf")
dev.off()
dev.copy(png,"groundbar_plot.png")
dev.off()
# Histogram of probability distribution of X, Y, wind, temp, area along with line density
hist(data_1$X,
main="Histogram for X",
xlab="X",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$X))
hist(data_1$Y,
main="Histogram for Y",
xlab="Y",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$Y))
hist(data_1$wind,
main="Histogram for wind",
xlab="wind",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$wind))
hist(data_1$temp,
main="Histogram for temp",
xlab="temp",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$temp))
hist(data_1$area,
main="Histogram for area",
xlab="area",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$area))
# Histogram of frequency distribution of X, Y, wind, temp, area
hist(data_1$X,main="Histogram for X",xlab="X",col = "Blue")
hist(data_1$Y,main="Histogram for Y",xlab="Y",col = "Blue")
hist(data_1$wind,main="Histogram for wind",xlab="wind",col = "Blue")
hist(data_1$temp,main="Histogram for temp",xlab="temp",col = "Blue")
# Pie Chart of area, wind, rain, temp by month
data_1$month
data_1_pivot <- summarise(group_by(data_1,month),area=sum(area))
slices <- data_1_pivot[["area"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of area")
data_1_pivot <- summarise(group_by(data_1,month),wind=sum(wind))
slices <- data_1_pivot[["wind"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of wind")
data_1_pivot <- summarise(group_by(data_1,month),rain=sum(rain))
slices <- data_1_pivot[["rain"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of rain")
data_1_pivot <- summarise(group_by(data_1,month),temp=sum(temp))
slices <- data_1_pivot[["temp"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of temp")
# Pie Chart of area, wind, rain, temp by day
library(dplyr)
data_1_pivot <- summarize(group_by(data_1,day),area=sum(area))
slices <- data_1_pivot[["area"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of area")
data_1_pivot <- summarize(group_by(data_1,day),wind=sum(wind))
slices <- data_1_pivot[["wind"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of wind")
data_1_pivot <- summarize(group_by(data_1,day),rain=sum(rain))
slices <- data_1_pivot[["rain"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of rain")
data_1_pivot <- summarize(group_by(data_1,day),temp=sum(temp))
slices <- data_1_pivot[["temp"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of temp")
# Map Plot of sourceAirportID
airports <- read.csv("C:/Users/ENVY X360/Desktop/R/airports.dat")
head(airports)
colnames(airports) <- c("ID", "name", "city", "country", "IATA_FAA", "ICAO", "lat", "lon", "altitude", "timezone", "DST")
head(airports)
routes <- read.csv("C:/Users/ENVY X360/Desktop/R/routes.dat")
colnames(routes) <- c("airline", "airlineID", "sourceAirport", "sourceAirportID", "destinationAirport", "destinationAirportID", "codeshare", "stops", "equipment")
head(routes)
library(plyr)
departures <- ddply(routes, .(sourceAirportID), "nrow")
names(departures)[2] <- "flights"
arrivals <- ddply(routes, .(destinationAirportID), "nrow")
names(arrivals)[2] <- "flights"
airportA <- merge(airports, departures, by.x = "ID", by.y = "sourceAirportID")
# install.packages("ggmap")
library(ggmap)
map <- get_map(location = 'World', zoom = 4)
mapPoints <- ggmap(map) +
geom_point(aes(x = lon, y = lat, size = sqrt(flights)), data = airportA, alpha = .5)
# Map Plot of destinationAirportID
airportB <- merge(airports, arrivals, by.x = "ID", by.y = "destinationAirportID")
library(ggmap)
map <- get_map(location = 'World', zoom = 4)
mapPoints <- ggmap(map) +
geom_point(aes(x = lon, y = lat, size = sqrt(flights)), data = airportB, alpha = .5)
mapPoints
dev.copy(pdf,"map.pdf")
dev.off()
|
/Assignment_plot 3 solution.R
|
no_license
|
Hegdesachin87/RLanguage
|
R
| false | false | 5,935 |
r
|
# Scatterplot matrix of DMC,DC,wind,rain,temp
data_1 = read.csv("C:/Users/ENVY X360/Desktop/R/forestfires.csv")
pairs(~DMC+DC+wind+rain+temp,data=data_1,
main="Scatterplot Matrix")
attach(data_1)
dev.copy(pdf,"scatterplot.pdf")
dev.off()
dev.copy(png,"scatterplot.png")
dev.off()
# 3D Scatterplot of wind,rain,area
library(scatterplot3d)
scatterplot3d(wind,rain,area,main="3D Scatterplot")
dev.copy(pdf,"3D_scatterplot.pdf")
dev.off()
# Interactive 3D Scatterplot of wind,rain,area
library(rgl)
plot3d(wind,rain,area, col="red", size=3)
dev.copy(pdf,"3D_interactiveplot.pdf")
dev.off()
# Boxplot of X and Y
boxplot(X~Y,data=data_1, main="Boxplot",
xlab="X", ylab="Y")
dev.copy(pdf,"Boxplot.pdf")
dev.off()
# Simple bar plot of temp, wind, rain [horizontal and vertical]
#Horizantal
counts =table(data_1$temp)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
counts =table(data_1$wind)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
counts =table(data_1$rain)
barplot(counts, main="Temperature Distribution", horiz=TRUE)
#Vertical
counts =table(data_1$temp)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
counts =table(data_1$wind)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
counts =table(data_1$rain)
barplot(counts, main="Temperature Distribution",
xlab="Temp")
# Grouped bar plot of X and Y
counts <- table(data_1$X, data_1$Y)
barplot(counts, main="Distribution by X and Y",
xlab="X", col=c("darkblue","red"),
legend = rownames(counts), beside=TRUE)
dev.copy(pdf,"groundbar_plot.pdf")
dev.off()
dev.copy(png,"groundbar_plot.png")
dev.off()
# Histogram of probability distribution of X, Y, wind, temp, area along with line density
hist(data_1$X,
main="Histogram for X",
xlab="X",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$X))
hist(data_1$Y,
main="Histogram for Y",
xlab="Y",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$Y))
hist(data_1$wind,
main="Histogram for wind",
xlab="wind",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$wind))
hist(data_1$temp,
main="Histogram for temp",
xlab="temp",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$temp))
hist(data_1$area,
main="Histogram for area",
xlab="area",
border="blue",
col="green",
las=1,
breaks=5,
prob = TRUE)
lines(density(data_1$area))
# Histogram of frequency distribution of X, Y, wind, temp, area
hist(data_1$X,main="Histogram for X",xlab="X",col = "Blue")
hist(data_1$Y,main="Histogram for Y",xlab="Y",col = "Blue")
hist(data_1$wind,main="Histogram for wind",xlab="wind",col = "Blue")
hist(data_1$temp,main="Histogram for temp",xlab="temp",col = "Blue")
# Pie Chart of area, wind, rain, temp by month
data_1$month
data_1_pivot <- summarise(group_by(data_1,month),area=sum(area))
slices <- data_1_pivot[["area"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of area")
data_1_pivot <- summarise(group_by(data_1,month),wind=sum(wind))
slices <- data_1_pivot[["wind"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of wind")
data_1_pivot <- summarise(group_by(data_1,month),rain=sum(rain))
slices <- data_1_pivot[["rain"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of rain")
data_1_pivot <- summarise(group_by(data_1,month),temp=sum(temp))
slices <- data_1_pivot[["temp"]]
pie(slices, labels=data_1[["month"]], main="Pie Chart of temp")
# Pie Chart of area, wind, rain, temp by day
library(dplyr)
data_1_pivot <- summarize(group_by(data_1,day),area=sum(area))
slices <- data_1_pivot[["area"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of area")
data_1_pivot <- summarize(group_by(data_1,day),wind=sum(wind))
slices <- data_1_pivot[["wind"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of wind")
data_1_pivot <- summarize(group_by(data_1,day),rain=sum(rain))
slices <- data_1_pivot[["rain"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of rain")
data_1_pivot <- summarize(group_by(data_1,day),temp=sum(temp))
slices <- data_1_pivot[["temp"]]
pie(slices, labels=data_1[["day"]], main="Pie Chart of temp")
# Map Plot of sourceAirportID
airports <- read.csv("C:/Users/ENVY X360/Desktop/R/airports.dat")
head(airports)
colnames(airports) <- c("ID", "name", "city", "country", "IATA_FAA", "ICAO", "lat", "lon", "altitude", "timezone", "DST")
head(airports)
routes <- read.csv("C:/Users/ENVY X360/Desktop/R/routes.dat")
colnames(routes) <- c("airline", "airlineID", "sourceAirport", "sourceAirportID", "destinationAirport", "destinationAirportID", "codeshare", "stops", "equipment")
head(routes)
library(plyr)
departures <- ddply(routes, .(sourceAirportID), "nrow")
names(departures)[2] <- "flights"
arrivals <- ddply(routes, .(destinationAirportID), "nrow")
names(arrivals)[2] <- "flights"
airportA <- merge(airports, departures, by.x = "ID", by.y = "sourceAirportID")
# install.packages("ggmap")
library(ggmap)
map <- get_map(location = 'World', zoom = 4)
mapPoints <- ggmap(map) +
geom_point(aes(x = lon, y = lat, size = sqrt(flights)), data = airportA, alpha = .5)
# Map Plot of destinationAirportID
airportB <- merge(airports, arrivals, by.x = "ID", by.y = "destinationAirportID")
library(ggmap)
map <- get_map(location = 'World', zoom = 4)
mapPoints <- ggmap(map) +
geom_point(aes(x = lon, y = lat, size = sqrt(flights)), data = airportB, alpha = .5)
mapPoints
dev.copy(pdf,"map.pdf")
dev.off()
|
## ----global_options, include = FALSE----------------------------------------------------------
try(source("../../../.Rprofile"))
## import platform as platform
## print(platform.release())
## # This assums using an EC2 instance where amzn is in platform name
## if 'amzn' in platform.release():
## s3_status = True
## else:
## s3_status = False
## print(s3_status)
## import boto3
## s3 = boto3.client('s3')
## spn_local_path_file_name = "C:/Users/fan/pyfan/vig/aws/setup/_data/iris_s3.dta"
## str_bucket_name = "fans3testbucket"
## spn_remote_path_file_name = "_data/iris_s3.dta"
## s3.upload_file(spn_local_path_file_name, str_bucket_name, spn_remote_path_file_name)
|
/vig/aws/s3/htmlpdfr/fs_aws_s3.R
|
permissive
|
fagan2888/pyfan
|
R
| false | false | 696 |
r
|
## ----global_options, include = FALSE----------------------------------------------------------
try(source("../../../.Rprofile"))
## import platform as platform
## print(platform.release())
## # This assums using an EC2 instance where amzn is in platform name
## if 'amzn' in platform.release():
## s3_status = True
## else:
## s3_status = False
## print(s3_status)
## import boto3
## s3 = boto3.client('s3')
## spn_local_path_file_name = "C:/Users/fan/pyfan/vig/aws/setup/_data/iris_s3.dta"
## str_bucket_name = "fans3testbucket"
## spn_remote_path_file_name = "_data/iris_s3.dta"
## s3.upload_file(spn_local_path_file_name, str_bucket_name, spn_remote_path_file_name)
|
context("poll multiple processes")
test_that("single process", {
cmd <- switch(
os_type(),
"unix" = "sleep 1; ls",
paste0(sleep(1), " && dir /b")
)
p <- process$new(commandline = cmd, stdout = "|")
## Timeout
expect_equal(
poll(list(p), 0),
list(c(output = "timeout", error = "nopipe"))
)
p$wait()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
p$read_output_lines()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
p$kill()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
close(p$get_output_connection())
expect_equal(
poll(list(p), -1),
list(c(output = "closed", error = "nopipe"))
)
})
test_that("multiple processes", {
cmd1 <- switch(
os_type(),
"unix" = "sleep 1; ls",
paste0(sleep(1), " && dir /b")
)
cmd2 <- switch(
os_type(),
"unix" = "sleep 2; ls 1>&2",
paste0(sleep(1), " && dir /b 1>&2")
)
p1 <- process$new(commandline = cmd1, stdout = "|")
p2 <- process$new(commandline = cmd2, stderr = "|")
## Timeout
res <- poll(list(p1 = p1, p2 = p2), 0)
expect_equal(
res,
list(
p1 = c(output = "timeout", error = "nopipe"),
p2 = c(output = "nopipe", error = "timeout")
)
)
p1$wait()
res <- poll(list(p1 = p1, p2 = p2), -1)
expect_equal(res$p1, c(output = "ready", error = "nopipe"))
expect_equal(res$p2[["output"]], "nopipe")
expect_true(res$p2[["error"]] %in% c("silent", "ready"))
close(p1$get_output_connection())
p2$wait()
res <- poll(list(p1 = p1, p2 = p2), -1)
expect_equal(
res,
list(
p1 = c(output = "closed", error = "nopipe"),
p2 = c(output = "nopipe", error = "ready")
)
)
close(p2$get_error_connection())
res <- poll(list(p1 = p1, p2 = p2), 0)
expect_equal(
res,
list(
p1 = c(output = "closed", error = "nopipe"),
p2 = c(output = "nopipe", error = "closed")
)
)
})
|
/tests/testthat/test-poll2.R
|
permissive
|
wch/processx
|
R
| false | false | 2,018 |
r
|
context("poll multiple processes")
test_that("single process", {
cmd <- switch(
os_type(),
"unix" = "sleep 1; ls",
paste0(sleep(1), " && dir /b")
)
p <- process$new(commandline = cmd, stdout = "|")
## Timeout
expect_equal(
poll(list(p), 0),
list(c(output = "timeout", error = "nopipe"))
)
p$wait()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
p$read_output_lines()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
p$kill()
expect_equal(
poll(list(p), -1),
list(c(output = "ready", error = "nopipe"))
)
close(p$get_output_connection())
expect_equal(
poll(list(p), -1),
list(c(output = "closed", error = "nopipe"))
)
})
test_that("multiple processes", {
cmd1 <- switch(
os_type(),
"unix" = "sleep 1; ls",
paste0(sleep(1), " && dir /b")
)
cmd2 <- switch(
os_type(),
"unix" = "sleep 2; ls 1>&2",
paste0(sleep(1), " && dir /b 1>&2")
)
p1 <- process$new(commandline = cmd1, stdout = "|")
p2 <- process$new(commandline = cmd2, stderr = "|")
## Timeout
res <- poll(list(p1 = p1, p2 = p2), 0)
expect_equal(
res,
list(
p1 = c(output = "timeout", error = "nopipe"),
p2 = c(output = "nopipe", error = "timeout")
)
)
p1$wait()
res <- poll(list(p1 = p1, p2 = p2), -1)
expect_equal(res$p1, c(output = "ready", error = "nopipe"))
expect_equal(res$p2[["output"]], "nopipe")
expect_true(res$p2[["error"]] %in% c("silent", "ready"))
close(p1$get_output_connection())
p2$wait()
res <- poll(list(p1 = p1, p2 = p2), -1)
expect_equal(
res,
list(
p1 = c(output = "closed", error = "nopipe"),
p2 = c(output = "nopipe", error = "ready")
)
)
close(p2$get_error_connection())
res <- poll(list(p1 = p1, p2 = p2), 0)
expect_equal(
res,
list(
p1 = c(output = "closed", error = "nopipe"),
p2 = c(output = "nopipe", error = "closed")
)
)
})
|
#Calcualtes the AIC-Criteria for the estimated density.
my.AIC <- function(penden.env,lambda0,opt.Likelihood=NULL) {
if(!is.null(opt.Likelihood)) {val1 <- -opt.Likelihood}
if(is.null(opt.Likelihood)) {val1 <- -pen.log.like(penden.env,lambda0=0)}
df <- my.positive.definite.solve(get("Derv2.pen",penden.env))%*%get("Derv2.cal",penden.env)
mytrace <- sum(diag(df))
return(list(myAIC=(val1+mytrace),mytrace=mytrace))
}
|
/R/my.AIC.R
|
no_license
|
cran/pendensity
|
R
| false | false | 426 |
r
|
#Calcualtes the AIC-Criteria for the estimated density.
my.AIC <- function(penden.env,lambda0,opt.Likelihood=NULL) {
if(!is.null(opt.Likelihood)) {val1 <- -opt.Likelihood}
if(is.null(opt.Likelihood)) {val1 <- -pen.log.like(penden.env,lambda0=0)}
df <- my.positive.definite.solve(get("Derv2.pen",penden.env))%*%get("Derv2.cal",penden.env)
mytrace <- sum(diag(df))
return(list(myAIC=(val1+mytrace),mytrace=mytrace))
}
|
library(shiny)
data(iris)
shinyServer(
function(input, output) {
colm <- reactive({
as.numeric(input$var)
})
output$text1 <- renderText({
paste("Dataset Variable/Coloumn name is", names(iris[colm()]))
})
output$text2 <- renderText({
paste("Warna Histogram is", input$color)
})
output$text3 <- renderText({
paste("Nomor dari Histogram Bins is", input$bins)
})
output$sum <- renderPrint({
summary(iris)
})
output$str <- renderPrint({
str(iris)
})
output$data <- renderTable({
colm <- as.numeric(input$var)
iris[colm]
#head(iris)
})
output$myhist <- renderPlot(
{
#colm <- as.numeric(input$var)
hist(iris[,colm()], breaks = seq(0, max(iris[,colm()]), l = input$bins+1), col=input$color, xlim = c(0,max(iris[,colm()])), main = "Histogram dari Iris Dataset", xlab = names(iris[colm()]))
}
)
}
)
|
/server.R
|
no_license
|
diditwbw/shyni-with-R
|
R
| false | false | 980 |
r
|
library(shiny)
data(iris)
shinyServer(
function(input, output) {
colm <- reactive({
as.numeric(input$var)
})
output$text1 <- renderText({
paste("Dataset Variable/Coloumn name is", names(iris[colm()]))
})
output$text2 <- renderText({
paste("Warna Histogram is", input$color)
})
output$text3 <- renderText({
paste("Nomor dari Histogram Bins is", input$bins)
})
output$sum <- renderPrint({
summary(iris)
})
output$str <- renderPrint({
str(iris)
})
output$data <- renderTable({
colm <- as.numeric(input$var)
iris[colm]
#head(iris)
})
output$myhist <- renderPlot(
{
#colm <- as.numeric(input$var)
hist(iris[,colm()], breaks = seq(0, max(iris[,colm()]), l = input$bins+1), col=input$color, xlim = c(0,max(iris[,colm()])), main = "Histogram dari Iris Dataset", xlab = names(iris[colm()]))
}
)
}
)
|
# Exploratory Data Analysis - Assignment 2
# Question 4
# Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?
# Yang Fong September 20, 2017
# Source code to plot4.png
ibrary("data.table")
library("ggplot2")
setwd("~/Documents/courseradatascience/Exploratory_Data_Analysis/project2")
path <- getwd()
download.file(url = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
, destfile = paste(path, "dataFiles.zip", sep = "/"))
unzip(zipfile = "dataFiles.zip")
# Load the NEI & SCC data frames.
NEI <- data.table::as.data.table(x = readRDS("summarySCC_PM25.rds"))
SCC <- data.table::as.data.table(x = readRDS("Source_Classification_Code.rds"))
# Subset coal combustion related NEI data
combustionRelated <- grepl("comb", SCC[, SCC.Level.One], ignore.case=TRUE)
coalRelated <- grepl("coal", SCC[, SCC.Level.Four], ignore.case=TRUE)
combustionSCC <- SCC[combustionRelated & coalRelated, SCC]
combustionNEI <- NEI[NEI[,SCC] %in% combustionSCC]
png("plot4.png")
ggplot(combustionNEI,aes(x = factor(year),y = Emissions/10^5)) +
geom_bar(stat="identity", fill ="#FF9999", width=0.75) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008"))
dev.off()
|
/plot4.R
|
no_license
|
foamy1881/ExData_Plotting2
|
R
| false | false | 1,357 |
r
|
# Exploratory Data Analysis - Assignment 2
# Question 4
# Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?
# Yang Fong September 20, 2017
# Source code to plot4.png
ibrary("data.table")
library("ggplot2")
setwd("~/Documents/courseradatascience/Exploratory_Data_Analysis/project2")
path <- getwd()
download.file(url = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
, destfile = paste(path, "dataFiles.zip", sep = "/"))
unzip(zipfile = "dataFiles.zip")
# Load the NEI & SCC data frames.
NEI <- data.table::as.data.table(x = readRDS("summarySCC_PM25.rds"))
SCC <- data.table::as.data.table(x = readRDS("Source_Classification_Code.rds"))
# Subset coal combustion related NEI data
combustionRelated <- grepl("comb", SCC[, SCC.Level.One], ignore.case=TRUE)
coalRelated <- grepl("coal", SCC[, SCC.Level.Four], ignore.case=TRUE)
combustionSCC <- SCC[combustionRelated & coalRelated, SCC]
combustionNEI <- NEI[NEI[,SCC] %in% combustionSCC]
png("plot4.png")
ggplot(combustionNEI,aes(x = factor(year),y = Emissions/10^5)) +
geom_bar(stat="identity", fill ="#FF9999", width=0.75) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008"))
dev.off()
|
\name{jmap}
\alias{jmap}
\title{Retrieve jetset mapped probe sets}
\description{This function retrieves probe sets corresponding to the queried genes}
\usage{
jmap(chip, eg, symbol, alias, ensembl)
}
\arguments{
\item{chip}{ Chip name }
\item{eg}{ A vector of Entrez GeneIDs (optional) }
\item{symbol}{ A vector of gene symbols (optional) }
\item{alias}{ A vector of gene aliases (optional) }
\item{ensembl}{ A vector of Ensembl IDs (optional) }
}
\details{
Currently, \code{chip} can be \code{"hgu95av2"}, \code{"hgu133a"}, \code{"hgu133plus2"}, or \code{"u133x3p"}.
Queried genes must be specified by either \code{eg}, \code{symbol}, \code{alias}, or \code{ensembl}.
If the query is not recognized, or is ambiguous, or corresponds to a gene
that is not detected by the array, \code{NA} will be returned.
Details about the jetset algorithm are available in the vignette.
}
\value{A character vector of probe set IDs}
\references{
Qiyuan Li, Nicolai J. Birkbak, Balazs Gyorffy, Zoltan Szallasi and Aron C. Eklund. (2011) Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics. 12:474.
}
\seealso{The underlying Entrez ID to probeset data is available in (e.g.) \code{\link{scores.hgu95av2}}.
Symbol, alias, and ensembl lookups are generated from e.g.
\code{\link[org.Hs.eg.db]{org.Hs.egSYMBOL2EG}}.
}
\examples{
genes <- c('MKI67', 'CHD5', 'ESR1', 'FGF19', 'ERBB2', 'NoSuchGene')
# This generates several informative warnings
jmap('hgu133a', symbol = genes)
}
\keyword{ misc }
|
/man/jmap.Rd
|
no_license
|
aroneklund/jetset
|
R
| false | false | 1,536 |
rd
|
\name{jmap}
\alias{jmap}
\title{Retrieve jetset mapped probe sets}
\description{This function retrieves probe sets corresponding to the queried genes}
\usage{
jmap(chip, eg, symbol, alias, ensembl)
}
\arguments{
\item{chip}{ Chip name }
\item{eg}{ A vector of Entrez GeneIDs (optional) }
\item{symbol}{ A vector of gene symbols (optional) }
\item{alias}{ A vector of gene aliases (optional) }
\item{ensembl}{ A vector of Ensembl IDs (optional) }
}
\details{
Currently, \code{chip} can be \code{"hgu95av2"}, \code{"hgu133a"}, \code{"hgu133plus2"}, or \code{"u133x3p"}.
Queried genes must be specified by either \code{eg}, \code{symbol}, \code{alias}, or \code{ensembl}.
If the query is not recognized, or is ambiguous, or corresponds to a gene
that is not detected by the array, \code{NA} will be returned.
Details about the jetset algorithm are available in the vignette.
}
\value{A character vector of probe set IDs}
\references{
Qiyuan Li, Nicolai J. Birkbak, Balazs Gyorffy, Zoltan Szallasi and Aron C. Eklund. (2011) Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics. 12:474.
}
\seealso{The underlying Entrez ID to probeset data is available in (e.g.) \code{\link{scores.hgu95av2}}.
Symbol, alias, and ensembl lookups are generated from e.g.
\code{\link[org.Hs.eg.db]{org.Hs.egSYMBOL2EG}}.
}
\examples{
genes <- c('MKI67', 'CHD5', 'ESR1', 'FGF19', 'ERBB2', 'NoSuchGene')
# This generates several informative warnings
jmap('hgu133a', symbol = genes)
}
\keyword{ misc }
|
#' To Look for Area from Codes
#'
#' @param codes id codes
#' @param data data after lookfor_area() function
#'
#' @return dataframe
#' @export
#'
#' @examples
#' \donttest{
#' df=get_data()
#' codes=c(32999999,320324,320323,320381)
#' lookfor_area(codes,df)
#' }
lookfor_area <-function(codes,data){
for (i in 1:length(codes)) {
if (i==1) df=NULL
code.i=codes[i]
dd1=data[data[,4]==do::left(code.i,2),]
if (nrow(dd1)==0){
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230'))
codes[i]=NA
next(i)
}
dd2=dd1[dd1[,5]==do::mid(code.i,3,2),]
if (nrow(dd2)==0){
df=plyr::rbind.fill(df,unique(dd1[,c(1,4)]))
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230 \u5E02'))
next(i)
}
df.i=dd2[dd2[,6]==do::mid(code.i,5,2),]
if (nrow(df.i)==0){
df=plyr::rbind.fill(df,unique(dd2[,-c(3,6)]))
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230 \u53BF'))
next(i)
}else{
df=plyr::rbind.fill(df,df.i)
}
}
rownames(df)=NULL
df=cbind(df[,!grepl(tmcn::toUTF8('\u7F16\u7801'),colnames(df))],
df[,grepl(tmcn::toUTF8('\u7F16\u7801'),colnames(df))])
codes=codes[!is.na(codes)]
df=cbind(code=codes,df)
colnames(df)[1]=tmcn::toUTF8('\u6240\u67E5\u7F16\u7801')
return(df)
}
|
/R/lookfor_area.R
|
no_license
|
yikeshu0611/admin.number
|
R
| false | false | 1,464 |
r
|
#' To Look for Area from Codes
#'
#' @param codes id codes
#' @param data data after lookfor_area() function
#'
#' @return dataframe
#' @export
#'
#' @examples
#' \donttest{
#' df=get_data()
#' codes=c(32999999,320324,320323,320381)
#' lookfor_area(codes,df)
#' }
lookfor_area <-function(codes,data){
for (i in 1:length(codes)) {
if (i==1) df=NULL
code.i=codes[i]
dd1=data[data[,4]==do::left(code.i,2),]
if (nrow(dd1)==0){
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230'))
codes[i]=NA
next(i)
}
dd2=dd1[dd1[,5]==do::mid(code.i,3,2),]
if (nrow(dd2)==0){
df=plyr::rbind.fill(df,unique(dd1[,c(1,4)]))
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230 \u5E02'))
next(i)
}
df.i=dd2[dd2[,6]==do::mid(code.i,5,2),]
if (nrow(df.i)==0){
df=plyr::rbind.fill(df,unique(dd2[,-c(3,6)]))
message(code.i,tmcn::toUTF8(' \u6CA1\u6709\u67E5\u5230 \u53BF'))
next(i)
}else{
df=plyr::rbind.fill(df,df.i)
}
}
rownames(df)=NULL
df=cbind(df[,!grepl(tmcn::toUTF8('\u7F16\u7801'),colnames(df))],
df[,grepl(tmcn::toUTF8('\u7F16\u7801'),colnames(df))])
codes=codes[!is.na(codes)]
df=cbind(code=codes,df)
colnames(df)[1]=tmcn::toUTF8('\u6240\u67E5\u7F16\u7801')
return(df)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Npat.getNpat.R
\name{Npat.getNpat}
\alias{Npat.getNpat}
\title{Get a candidate's most recently filled out NPAT/PCT (Political Courage Test)}
\usage{
Npat.getNpat(candidateId)
}
\arguments{
\item{candidateId}{a character string or list of character strings with the candidate ID(s) (see references for details)}
}
\value{
A data frame with a row for each candidate and columns with the following variables describing the candidate:\cr bio.candidate.crpId (OpenSecrets ID),\cr bio.candidate.firstName,\cr bio.candidate.nickName,\cr bio.candidate.middleName,\cr bio.candidate.lastName,\cr bio.candidate.suffix,\cr bio.candidate.birthDate,\cr bio.candidate.birthPlace,\cr bio.candidate.pronunciation,\cr bio.candidate.gender,\cr bio.candidate.family,\cr bio.candidate.photo,\cr bio.candidate.homeCity,\cr bio.candidate.homeState,\cr bio.candidate.education,\cr bio.candidate.profession,\cr bio.candidate.political,\cr bio.candidate.religion,\cr bio.candidate.congMembership,\cr bio.candidate.orgMembership,\cr bio.candidate.specialMsg,\cr bio.office.parties,\cr bio.office.title,\cr bio.office.shortTitle,\cr bio.office.name,\cr bio.office.type,\cr bio.office.status,\cr bio.office.firstElect,\cr bio.office.lastElect,\cr bio.office.nextElect,\cr bio.office.termStart,\cr bio.office.termEnd,\cr bio.office.district,\cr bio.office.districtId,\cr bio.office.stateId,\cr bio.office.committee*.committeeId,\cr bio.office.committee*.committeeName,\cr bio.election*.office,\cr bio.election*.officeId,\cr bio.election*.officeType,\cr bio.election*.parties,\cr bio.election*.district,\cr bio.election*.districtId,\cr bio.election*.status,\cr bio.election*.ballotName.
}
\description{
This function is a wrapper for the Npat.getNpat() method of the PVS API Npat class which returns the candidate's most recently filled out NPAT/PCT. The function sends a request with this method to the PVS API for all candidate IDs given as a function input, extracts the XML values from the returned XML file(s) and returns them arranged in one data frame.
}
\examples{
# First, make sure your personal PVS API key is saved as an option
# (options("pvs.key" = "yourkey")) or in the pvs.key variable:
\dontrun{pvs.key <- "yourkey"}
# get political courage tests of Barack Obama and John Sidney McCain III
\dontrun{pcts <- Npat.getNpat(list(9490,53270))}
\dontrun{head(pcts$survey)}
\dontrun{head(pcts$candidate)}
}
\references{
http://api.votesmart.org/docs/CandidateBio.html\cr
Use Candidates.getByOfficeState(), Candidates.getByOfficeTypeState(), Candidates.getByLastname(), Candidates.getByLevenshtein(), Candidates.getByElection(), Candidates.getByDistrict() or Candidates.getByZip() to get a list of candidate IDs.\cr
See also: Matter U, Stutzer A (2015) pvsR: An Open Source Interface to Big Data on the American Political Sphere. PLoS ONE 10(7): e0130501. doi: 10.1371/journal.pone.0130501
}
\author{
Ulrich Matter <ulrich.matter-at-unibas.ch>
}
|
/man/Npat.getNpat.Rd
|
no_license
|
umatter/pvsR
|
R
| false | true | 3,003 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Npat.getNpat.R
\name{Npat.getNpat}
\alias{Npat.getNpat}
\title{Get a candidate's most recently filled out NPAT/PCT (Political Courage Test)}
\usage{
Npat.getNpat(candidateId)
}
\arguments{
\item{candidateId}{a character string or list of character strings with the candidate ID(s) (see references for details)}
}
\value{
A data frame with a row for each candidate and columns with the following variables describing the candidate:\cr bio.candidate.crpId (OpenSecrets ID),\cr bio.candidate.firstName,\cr bio.candidate.nickName,\cr bio.candidate.middleName,\cr bio.candidate.lastName,\cr bio.candidate.suffix,\cr bio.candidate.birthDate,\cr bio.candidate.birthPlace,\cr bio.candidate.pronunciation,\cr bio.candidate.gender,\cr bio.candidate.family,\cr bio.candidate.photo,\cr bio.candidate.homeCity,\cr bio.candidate.homeState,\cr bio.candidate.education,\cr bio.candidate.profession,\cr bio.candidate.political,\cr bio.candidate.religion,\cr bio.candidate.congMembership,\cr bio.candidate.orgMembership,\cr bio.candidate.specialMsg,\cr bio.office.parties,\cr bio.office.title,\cr bio.office.shortTitle,\cr bio.office.name,\cr bio.office.type,\cr bio.office.status,\cr bio.office.firstElect,\cr bio.office.lastElect,\cr bio.office.nextElect,\cr bio.office.termStart,\cr bio.office.termEnd,\cr bio.office.district,\cr bio.office.districtId,\cr bio.office.stateId,\cr bio.office.committee*.committeeId,\cr bio.office.committee*.committeeName,\cr bio.election*.office,\cr bio.election*.officeId,\cr bio.election*.officeType,\cr bio.election*.parties,\cr bio.election*.district,\cr bio.election*.districtId,\cr bio.election*.status,\cr bio.election*.ballotName.
}
\description{
This function is a wrapper for the Npat.getNpat() method of the PVS API Npat class which returns the candidate's most recently filled out NPAT/PCT. The function sends a request with this method to the PVS API for all candidate IDs given as a function input, extracts the XML values from the returned XML file(s) and returns them arranged in one data frame.
}
\examples{
# First, make sure your personal PVS API key is saved as an option
# (options("pvs.key" = "yourkey")) or in the pvs.key variable:
\dontrun{pvs.key <- "yourkey"}
# get political courage tests of Barack Obama and John Sidney McCain III
\dontrun{pcts <- Npat.getNpat(list(9490,53270))}
\dontrun{head(pcts$survey)}
\dontrun{head(pcts$candidate)}
}
\references{
http://api.votesmart.org/docs/CandidateBio.html\cr
Use Candidates.getByOfficeState(), Candidates.getByOfficeTypeState(), Candidates.getByLastname(), Candidates.getByLevenshtein(), Candidates.getByElection(), Candidates.getByDistrict() or Candidates.getByZip() to get a list of candidate IDs.\cr
See also: Matter U, Stutzer A (2015) pvsR: An Open Source Interface to Big Data on the American Political Sphere. PLoS ONE 10(7): e0130501. doi: 10.1371/journal.pone.0130501
}
\author{
Ulrich Matter <ulrich.matter-at-unibas.ch>
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smoothobs_mul.R
\name{smoothobs_mul}
\alias{smoothobs_mul}
\title{Multiplicative De-biasing With Smoothed Observation Climatology}
\usage{
smoothobs_mul(fcst, obs, fcst.out = fcst, span = min(1, 31/nrow(fcst)), ...)
}
\arguments{
\item{fcst}{n x m x k array of n lead times, m forecasts, of k ensemble
members}
\item{obs}{n x m matrix of veryfing observations}
\item{fcst.out}{array of forecast values to which bias correction should be
applied (defaults to \code{fcst})}
\item{span}{the parameter which controls the degree of smoothing (see
\code{\link{loess}})}
\item{...}{additional arguments for compatibility with other bias correction
methods}
}
\description{
Computes multiplicative de-biasing with loess smoothing (obs. only)
}
\details{
The bias corrected forecast is scaled by the lead-time dependent
ratio of observed to forecast climatology, where the observed climatology
is smoothed using a loess smoothing.
}
\examples{
## initialise forcast observation pairs
signal <- outer(1.5 + sin(seq(0,4,length=215)), rnorm(30)**2, '*')
fcst <- array(rnorm(length(signal)*15)**2, c(dim(signal), 15)) * c(signal)
obs <- rnorm(length(signal), mean=1.4)**2 * signal
fcst.debias <- biascorrection:::smoothobs_mul(fcst[,1:20,], obs[,1:20], fcst.out=fcst, span=0.5)
}
\seealso{
smooth_mul smoothobs
}
\keyword{util}
|
/man/smoothobs_mul.Rd
|
no_license
|
arulalant/biascorrection
|
R
| false | true | 1,408 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smoothobs_mul.R
\name{smoothobs_mul}
\alias{smoothobs_mul}
\title{Multiplicative De-biasing With Smoothed Observation Climatology}
\usage{
smoothobs_mul(fcst, obs, fcst.out = fcst, span = min(1, 31/nrow(fcst)), ...)
}
\arguments{
\item{fcst}{n x m x k array of n lead times, m forecasts, of k ensemble
members}
\item{obs}{n x m matrix of veryfing observations}
\item{fcst.out}{array of forecast values to which bias correction should be
applied (defaults to \code{fcst})}
\item{span}{the parameter which controls the degree of smoothing (see
\code{\link{loess}})}
\item{...}{additional arguments for compatibility with other bias correction
methods}
}
\description{
Computes multiplicative de-biasing with loess smoothing (obs. only)
}
\details{
The bias corrected forecast is scaled by the lead-time dependent
ratio of observed to forecast climatology, where the observed climatology
is smoothed using a loess smoothing.
}
\examples{
## initialise forcast observation pairs
signal <- outer(1.5 + sin(seq(0,4,length=215)), rnorm(30)**2, '*')
fcst <- array(rnorm(length(signal)*15)**2, c(dim(signal), 15)) * c(signal)
obs <- rnorm(length(signal), mean=1.4)**2 * signal
fcst.debias <- biascorrection:::smoothobs_mul(fcst[,1:20,], obs[,1:20], fcst.out=fcst, span=0.5)
}
\seealso{
smooth_mul smoothobs
}
\keyword{util}
|
# The main demo file for quanteda
str(uk2010immig)
mycorpus <- corpus(uk2010immig,
docvars=list(party=names(uk2010immig)),
notes="Immigration-related sections from UK 2010 party manifestos.")
encoding(mycorpus) <- "UTF-8"
language(mycorpus) <- "english"
summary(mycorpus, showmeta=TRUE)
kwic(mycorpus, "deport", 3)
mydfm <- dfm(mycorpus, stem=TRUE, stopwords=TRUE)
docnames(mydfm)
features(mydfm)
# open a nicer quartz device on a mac
if (Sys.info()[1]=="Darwin") quartz("BNP 2010 word cloud", 7,7)
# this is necessary currently because no subset is yet implemented for dfm objects
plot(dfm(subset(mycorpus, party=="BNP"), stem=TRUE, stopwords=TRUE))
# some examples of tokenization and string cleaning
library(quantedaData)
data(exampleString)
tokenize(exampleString)
clean(exampleString)
wordstem(exampleString)
# topic models
library(topicmodels)
prescorpus <- subset(inaugCorpus, Year>1900)
presdfm <- dfm(prescorpus, stopwords=TRUE, stem=TRUE)
presdfm <- trimdfm(presdfm, minCount=10, minDoc=5)
presTriplet <- dfm2tmformat(presdfm)
presLDA <- LDA(presTriplet, method="VEM", k=20)
# which terms contribute most to each topic
get_terms(presLDA, k=15)
# which is the dominant topic for each document
get_topics(presLDA)
# the topic contribution of each topic to each document
postTopics <- data.frame(posterior(presLDA)$topics)
# dictionaries
data(iebudgets)
mydict <- list(christmas=c("Christmas", "Santa", "holiday"),
opposition=c("Opposition", "reject", "notincorpus"),
taxing="taxing",
taxation="taxation",
taxregex="tax*")
dictDfm <- dfm(mycorpus, dictionary=mydict)
dictDfm
# simple lexical diversity measures
data(iebudgets)
finMins <- subset(iebudgets, no=="01")
finDfm <- dfm(finMins)
types <- rowSums(finDfm > 0)
tokens <- rowSums(finDfm)
ttrs <- types/tokens
plot(2008:2012, ttrs,
ylim=c(.18,.25), # set the y axis range
type="b", # points connected by lines
xlab="Budget Year",
ylab="Type/Token Ratios")
|
/demo/quanteda.R
|
no_license
|
behrica/quanteda
|
R
| false | false | 2,058 |
r
|
# The main demo file for quanteda
str(uk2010immig)
mycorpus <- corpus(uk2010immig,
docvars=list(party=names(uk2010immig)),
notes="Immigration-related sections from UK 2010 party manifestos.")
encoding(mycorpus) <- "UTF-8"
language(mycorpus) <- "english"
summary(mycorpus, showmeta=TRUE)
kwic(mycorpus, "deport", 3)
mydfm <- dfm(mycorpus, stem=TRUE, stopwords=TRUE)
docnames(mydfm)
features(mydfm)
# open a nicer quartz device on a mac
if (Sys.info()[1]=="Darwin") quartz("BNP 2010 word cloud", 7,7)
# this is necessary currently because no subset is yet implemented for dfm objects
plot(dfm(subset(mycorpus, party=="BNP"), stem=TRUE, stopwords=TRUE))
# some examples of tokenization and string cleaning
library(quantedaData)
data(exampleString)
tokenize(exampleString)
clean(exampleString)
wordstem(exampleString)
# topic models
library(topicmodels)
prescorpus <- subset(inaugCorpus, Year>1900)
presdfm <- dfm(prescorpus, stopwords=TRUE, stem=TRUE)
presdfm <- trimdfm(presdfm, minCount=10, minDoc=5)
presTriplet <- dfm2tmformat(presdfm)
presLDA <- LDA(presTriplet, method="VEM", k=20)
# which terms contribute most to each topic
get_terms(presLDA, k=15)
# which is the dominant topic for each document
get_topics(presLDA)
# the topic contribution of each topic to each document
postTopics <- data.frame(posterior(presLDA)$topics)
# dictionaries
data(iebudgets)
mydict <- list(christmas=c("Christmas", "Santa", "holiday"),
opposition=c("Opposition", "reject", "notincorpus"),
taxing="taxing",
taxation="taxation",
taxregex="tax*")
dictDfm <- dfm(mycorpus, dictionary=mydict)
dictDfm
# simple lexical diversity measures
data(iebudgets)
finMins <- subset(iebudgets, no=="01")
finDfm <- dfm(finMins)
types <- rowSums(finDfm > 0)
tokens <- rowSums(finDfm)
ttrs <- types/tokens
plot(2008:2012, ttrs,
ylim=c(.18,.25), # set the y axis range
type="b", # points connected by lines
xlab="Budget Year",
ylab="Type/Token Ratios")
|
testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120192875e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 7.15714506860012e-155, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L)
result <- do.call(myTAI:::cpp_bootMatrix,testlist)
str(result)
|
/myTAI/inst/testfiles/cpp_bootMatrix/AFL_cpp_bootMatrix/cpp_bootMatrix_valgrind_files/1615767014-test.R
|
no_license
|
akhikolla/updatedatatype-list3
|
R
| false | false | 1,803 |
r
|
testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120192875e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 7.15714506860012e-155, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L)
result <- do.call(myTAI:::cpp_bootMatrix,testlist)
str(result)
|
library(readr)
library(reshape2)
library(ggplot2)
library(data.table)
lib <- fread("~/Desktop/ROP299/phase2/phase2.2_data_analysis/library_sort.csv")
lib[,c(3,4)] <- NULL
lib <- na.omit(lib)
colnames(lib) <-c('cis', 'barcode')
lib <- lib[, .N, by = .(cis, barcode)]
colnames(lib) <-c('cis', 'barcode', 'count')
separable <- function(bar_seq, lib) {
df <- lib[barcode == bar_seq][1:2,]
return ((df$count[1] >= 10 * df$count[2]) && df[2,]$count < 10 )
}
lib_50 <- lib[count > 30]
lib_separable <- lib_50[sapply(lib_50$barcode, separable, lib),]
write.csv()
|
/Script/Analyser/lib_filter.R
|
no_license
|
floraliu1011/ROP299
|
R
| false | false | 563 |
r
|
library(readr)
library(reshape2)
library(ggplot2)
library(data.table)
lib <- fread("~/Desktop/ROP299/phase2/phase2.2_data_analysis/library_sort.csv")
lib[,c(3,4)] <- NULL
lib <- na.omit(lib)
colnames(lib) <-c('cis', 'barcode')
lib <- lib[, .N, by = .(cis, barcode)]
colnames(lib) <-c('cis', 'barcode', 'count')
separable <- function(bar_seq, lib) {
df <- lib[barcode == bar_seq][1:2,]
return ((df$count[1] >= 10 * df$count[2]) && df[2,]$count < 10 )
}
lib_50 <- lib[count > 30]
lib_separable <- lib_50[sapply(lib_50$barcode, separable, lib),]
write.csv()
|
## this R file contains 2 functions written to compute, store, and return the inverse of any square matrix (assuming it is invertible)
## you can test the functions by creating a random matrix, such as by
## x<-matrix(rexp(n^2, rate=.1), ncol=n)
## when n is any integer
## cacheSolve(makeCacheMatrix(x)) %*% x should return an identity matrix
## alternatively store the makeCacheMatrix(x) in y first, then try cacheSolve(y) %*% x
## makeCacheMatrix defines a few functions, converting the input matrix into a list which can store its inverse
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinv <- function(inv) m <<- inv
getinv <- function() m
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## cacheSolve searches the transformed matrix for its inverse, if the inverse already exists, it is retrieved, otherwise it is computed.
cacheSolve <- function(x, ...) {
m <- x$getinv()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinv(m)
m
}
|
/cachematrix.R
|
no_license
|
paulxiep/ProgrammingAssignment2
|
R
| false | false | 1,141 |
r
|
## this R file contains 2 functions written to compute, store, and return the inverse of any square matrix (assuming it is invertible)
## you can test the functions by creating a random matrix, such as by
## x<-matrix(rexp(n^2, rate=.1), ncol=n)
## when n is any integer
## cacheSolve(makeCacheMatrix(x)) %*% x should return an identity matrix
## alternatively store the makeCacheMatrix(x) in y first, then try cacheSolve(y) %*% x
## makeCacheMatrix defines a few functions, converting the input matrix into a list which can store its inverse
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinv <- function(inv) m <<- inv
getinv <- function() m
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## cacheSolve searches the transformed matrix for its inverse, if the inverse already exists, it is retrieved, otherwise it is computed.
cacheSolve <- function(x, ...) {
m <- x$getinv()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinv(m)
m
}
|
context('toolExtractSortScaleQuitte()')
test_that(
'Test toolExtractSortScaleQuitte() results',
{
expect_equal(
object = toolExtractSortScaleQuitte(
x = quitte_example_data,
scen = 'r7552c_1p5C_Def-rem-5',
vars = c('Consumption', 'PE'),
regi = c('EUR', 'LAM'),
prd = c(2005, 2030, 2050)),
expected = quitte_example_data %>%
filter('r7552c_1p5C_Def-rem-5' == scenario,
variable %in% c('Consumption', 'PE'),
region %in% c('EUR', 'LAM'),
period %in% c(2005, 2030, 2050)) %>%
droplevels() %>%
mutate(variable = factor(variable, levels = c('Consumption', 'PE'),
ordered = TRUE))
)
})
|
/tests/testthat/test-toolExtractSortScaleQuitte.R
|
no_license
|
pik-piam/quitte
|
R
| false | false | 748 |
r
|
context('toolExtractSortScaleQuitte()')
test_that(
'Test toolExtractSortScaleQuitte() results',
{
expect_equal(
object = toolExtractSortScaleQuitte(
x = quitte_example_data,
scen = 'r7552c_1p5C_Def-rem-5',
vars = c('Consumption', 'PE'),
regi = c('EUR', 'LAM'),
prd = c(2005, 2030, 2050)),
expected = quitte_example_data %>%
filter('r7552c_1p5C_Def-rem-5' == scenario,
variable %in% c('Consumption', 'PE'),
region %in% c('EUR', 'LAM'),
period %in% c(2005, 2030, 2050)) %>%
droplevels() %>%
mutate(variable = factor(variable, levels = c('Consumption', 'PE'),
ordered = TRUE))
)
})
|
suppressPackageStartupMessages(library("optparse"))
# Make option list
option_list <- list(
make_option(c("-i", "--subdist"), type="character", default=NULL, help="Input subject distances descriptor (*.desc) file (required)", metavar="file"),
make_option(c("-m", "--mask"), type="character", default=NULL, help="Brain mask file (required)", metavar="file"),
make_option(c("-l", "--labels"), type="character", default=NULL, help="File with labels/responses where # of rows correspond to # of subjects in the subject distances matrices (required)", metavar="file"),
make_option(c("-c", "--forks"), type="integer", default=1, help="Number of computer processors to use in parallel by forking the complete processing stream [default: %default]", metavar="number"),
make_option(c("-t", "--threads"), type="integer", default=1, help="Number of computer processors to use in parallel by multi-threading matrix algebra operations [default: %default]", metavar="number"),
make_option("--cross", type="integer", default=10, help="Number of folds for cross-validation (default: %default)", metavar="option"),
make_option("--type", type="character", default=NULL, help="Type of classification: 'C-classification', 'nu-classification', 'one-classification', 'eps-regression', 'nu-regression' (default is to auto-select between C-classification or eps-regression)", metavar="option"),
make_option("--kernel", type="character", default="linear", help="Kernel used to create the support vectors: 'linear', 'polynomial', 'radial', 'sigmoid' (default: %default)", metavar="option"),
make_option("--memlimit", type="double", default=1, dest="memlimit", help="Maximum amount of RAM to use. It is preferable to keep this number as small as possible for speed reasons (this rule of thumb just applies to this script and connectir_kmeans_cross). [default: %default]", metavar="RAM"),
make_option("--overwrite", action="store_true", default=FALSE, help="Overwrite output if it already exists (default is not to overwrite already existing output)"),
make_option(c("-v", "--verbose"), action="store_true", default=TRUE, help="Print extra output [default]"),
make_option(c("-q", "--quiet"), action="store_false", dest="verbose", help="Print little output")
)
# Make class/usage
parser <- OptionParser(usage = "%prog [options] outfile",
option_list=option_list, add_help_option=TRUE)
# Parse
parser_out <- parse_args(parser, positional_arguments = TRUE)
args <- parser_out$args
opts <- parser_out$options
# Check options/arguments
if (length(args) != 1) {
print_help(parser)
quit(save="no", status=1)
}
saved_opts <- list(args=args, opts=opts)
tryCatch({
# load connectir
suppressWarnings(suppressPackageStartupMessages(library("connectir")))
# parallel processing setup
set_parallel_procs(opts$forks, opts$threads, opts$verbose)
# use foreach parallelization and shared memory?
parallel_forks <- ifelse(opts$forks == 1, FALSE, TRUE)
###
# Check Inputs
###
vcat(opts$verbose, "Checking options")
if (is.null(opts$subdist))
stop("Must specify -i/--subdist")
if (is.null(opts$mask))
stop("Must specify -m/--mask")
if (is.null(opts$labels))
stop("Must specify -l/--labels")
if (getext(opts$subdist) != "desc")
stop("Subject distances file (-i/--subdist) must have a '.desc' extension")
opts$output <- args[1]
###
# Compute Baby Compute
###
start.time <- Sys.time()
wrap_svm_subdist_cross(opts$subdist, opts$mask, opts$labels,
out_file=opts$output, overwrite=opts$overwrite,
kernel=opts$kernel, type=opts$type, cross=opts$cross,
memlimit=opts$memlimit, parallel=parallel_forks,
verbose=opts$verbose)
end.time <- Sys.time()
vcat(opts$verbose, "SVM is done! It took: %.2f minutes\n",
as.numeric(end.time-start.time, units="mins"))
}, warning = function(ex) {
cat("\nA warning was detected: \n")
cat(ex$message, "\n\n")
cat("Called by: \n")
print(ex$call)
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
}, error = function(ex) {
cat("\nAn error was detected: \n")
cat(ex$message, "\n\n")
cat("Called by: \n")
print(ex$call)
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
}, interrupt = function(ex) {
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
cat("\nKill signal sent. Trying to clean up...\n")
rm(list=ls())
gc(FALSE)
cat("...success\n")
}, finally = {
cat("\nRemoving everything from memory\n")
rm(list=ls())
gc(FALSE)
cat("...sucesss\n")
})
|
/inst/scripts/connectir_svm_cross_worker.R
|
no_license
|
onebacha/connectir
|
R
| false | false | 4,746 |
r
|
suppressPackageStartupMessages(library("optparse"))
# Make option list
option_list <- list(
make_option(c("-i", "--subdist"), type="character", default=NULL, help="Input subject distances descriptor (*.desc) file (required)", metavar="file"),
make_option(c("-m", "--mask"), type="character", default=NULL, help="Brain mask file (required)", metavar="file"),
make_option(c("-l", "--labels"), type="character", default=NULL, help="File with labels/responses where # of rows correspond to # of subjects in the subject distances matrices (required)", metavar="file"),
make_option(c("-c", "--forks"), type="integer", default=1, help="Number of computer processors to use in parallel by forking the complete processing stream [default: %default]", metavar="number"),
make_option(c("-t", "--threads"), type="integer", default=1, help="Number of computer processors to use in parallel by multi-threading matrix algebra operations [default: %default]", metavar="number"),
make_option("--cross", type="integer", default=10, help="Number of folds for cross-validation (default: %default)", metavar="option"),
make_option("--type", type="character", default=NULL, help="Type of classification: 'C-classification', 'nu-classification', 'one-classification', 'eps-regression', 'nu-regression' (default is to auto-select between C-classification or eps-regression)", metavar="option"),
make_option("--kernel", type="character", default="linear", help="Kernel used to create the support vectors: 'linear', 'polynomial', 'radial', 'sigmoid' (default: %default)", metavar="option"),
make_option("--memlimit", type="double", default=1, dest="memlimit", help="Maximum amount of RAM to use. It is preferable to keep this number as small as possible for speed reasons (this rule of thumb just applies to this script and connectir_kmeans_cross). [default: %default]", metavar="RAM"),
make_option("--overwrite", action="store_true", default=FALSE, help="Overwrite output if it already exists (default is not to overwrite already existing output)"),
make_option(c("-v", "--verbose"), action="store_true", default=TRUE, help="Print extra output [default]"),
make_option(c("-q", "--quiet"), action="store_false", dest="verbose", help="Print little output")
)
# Make class/usage
parser <- OptionParser(usage = "%prog [options] outfile",
option_list=option_list, add_help_option=TRUE)
# Parse
parser_out <- parse_args(parser, positional_arguments = TRUE)
args <- parser_out$args
opts <- parser_out$options
# Check options/arguments
if (length(args) != 1) {
print_help(parser)
quit(save="no", status=1)
}
saved_opts <- list(args=args, opts=opts)
tryCatch({
# load connectir
suppressWarnings(suppressPackageStartupMessages(library("connectir")))
# parallel processing setup
set_parallel_procs(opts$forks, opts$threads, opts$verbose)
# use foreach parallelization and shared memory?
parallel_forks <- ifelse(opts$forks == 1, FALSE, TRUE)
###
# Check Inputs
###
vcat(opts$verbose, "Checking options")
if (is.null(opts$subdist))
stop("Must specify -i/--subdist")
if (is.null(opts$mask))
stop("Must specify -m/--mask")
if (is.null(opts$labels))
stop("Must specify -l/--labels")
if (getext(opts$subdist) != "desc")
stop("Subject distances file (-i/--subdist) must have a '.desc' extension")
opts$output <- args[1]
###
# Compute Baby Compute
###
start.time <- Sys.time()
wrap_svm_subdist_cross(opts$subdist, opts$mask, opts$labels,
out_file=opts$output, overwrite=opts$overwrite,
kernel=opts$kernel, type=opts$type, cross=opts$cross,
memlimit=opts$memlimit, parallel=parallel_forks,
verbose=opts$verbose)
end.time <- Sys.time()
vcat(opts$verbose, "SVM is done! It took: %.2f minutes\n",
as.numeric(end.time-start.time, units="mins"))
}, warning = function(ex) {
cat("\nA warning was detected: \n")
cat(ex$message, "\n\n")
cat("Called by: \n")
print(ex$call)
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
}, error = function(ex) {
cat("\nAn error was detected: \n")
cat(ex$message, "\n\n")
cat("Called by: \n")
print(ex$call)
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
}, interrupt = function(ex) {
cat("\nSaving options...\n")
save(saved_opts, file="called_options.rda")
cat("\nKill signal sent. Trying to clean up...\n")
rm(list=ls())
gc(FALSE)
cat("...success\n")
}, finally = {
cat("\nRemoving everything from memory\n")
rm(list=ls())
gc(FALSE)
cat("...sucesss\n")
})
|
gini_impurity_red_Sim <-
function(Daten, index_set, vari_index, Varis, cutoff){
# Betrachtete Variable
variable<- Varis[which(Varis[,1]==vari_index), 2]
# Linke und Rechte Teilmenge der Beobachtungen berechnen
left_index_set <- intersect(index_set, which(Daten[, variable]<=cutoff))
right_index_set<- intersect(index_set, which(Daten[, variable]>cutoff))
# Tochterknoten
class <- Daten[index_set,1]
class_left <- Daten[left_index_set,1]
class_right<- Daten[right_index_set,1]
# impurity reduction
N_class <- length(class)
n_classL <- length(class_left)/N_class
n_classR <- length(class_right)/N_class
imp_reduction<- gini_index(class) - n_classL*gini_index(class_left) - n_classR*gini_index(class_right)
# Ausgabe:
return(imp_reduction)
}
|
/NHEMOtree/R/gini_impurity_red_Sim.R
|
no_license
|
ingted/R-Examples
|
R
| false | false | 850 |
r
|
gini_impurity_red_Sim <-
function(Daten, index_set, vari_index, Varis, cutoff){
# Betrachtete Variable
variable<- Varis[which(Varis[,1]==vari_index), 2]
# Linke und Rechte Teilmenge der Beobachtungen berechnen
left_index_set <- intersect(index_set, which(Daten[, variable]<=cutoff))
right_index_set<- intersect(index_set, which(Daten[, variable]>cutoff))
# Tochterknoten
class <- Daten[index_set,1]
class_left <- Daten[left_index_set,1]
class_right<- Daten[right_index_set,1]
# impurity reduction
N_class <- length(class)
n_classL <- length(class_left)/N_class
n_classR <- length(class_right)/N_class
imp_reduction<- gini_index(class) - n_classL*gini_index(class_left) - n_classR*gini_index(class_right)
# Ausgabe:
return(imp_reduction)
}
|
# Comparison of logicals
TRUE == FALSE
# Comparison of numerics
-6*14 != 17 - 101
# Comparison of character strings
"useR" == "user"
# Compare a logical with a numeric
TRUE == 1
|
/Tutorials/R Intermediate/Chapter 1/Equality.R
|
no_license
|
Lingesh2311/R
|
R
| false | false | 190 |
r
|
# Comparison of logicals
TRUE == FALSE
# Comparison of numerics
-6*14 != 17 - 101
# Comparison of character strings
"useR" == "user"
# Compare a logical with a numeric
TRUE == 1
|
#' 01305 Detergent Suds (Severity)
#'
#' A table containing the USGS Detergent Suds (Severity) parameter codes.
#'
#' @format A data frame with 5 rows and 3 variables:
#' \describe{
#' \item{Parameter Code}{USGS Parameter Code}
#' \item{Fixed Value}{Fixed Value}
#' \item{Fixed Text}{Fixed Text}
#' }
#'
#'
#' @references
#' This data is from Table 26. Parameter codes with fixed values (USGS Water Quality Samples for USA: Sample Data). See \url{https://help.waterdata.usgs.gov/codes-and-parameters/}.
#'
#'
#'
#'
"pmcode_01305"
#> [1] "pmcode_01305"
|
/R/pmcode_01305.R
|
permissive
|
cran/ie2miscdata
|
R
| false | false | 552 |
r
|
#' 01305 Detergent Suds (Severity)
#'
#' A table containing the USGS Detergent Suds (Severity) parameter codes.
#'
#' @format A data frame with 5 rows and 3 variables:
#' \describe{
#' \item{Parameter Code}{USGS Parameter Code}
#' \item{Fixed Value}{Fixed Value}
#' \item{Fixed Text}{Fixed Text}
#' }
#'
#'
#' @references
#' This data is from Table 26. Parameter codes with fixed values (USGS Water Quality Samples for USA: Sample Data). See \url{https://help.waterdata.usgs.gov/codes-and-parameters/}.
#'
#'
#'
#'
"pmcode_01305"
#> [1] "pmcode_01305"
|
#' ---
#' title: "Bayesian data analysis demo 10.1"
#' author: "Aki Vehtari, Markus Paasiniemi"
#' date: "`r format(Sys.Date())`"
#' output:
#' html_document:
#' theme: readable
#' code_download: true
#' ---
#' ## Rejection sampling example
#'
#' ggplot2 is used for plotting, tidyr for manipulating data frames
#+ setup, message=FALSE, error=FALSE, warning=FALSE
library(ggplot2)
theme_set(theme_minimal())
library(tidyr)
#' Fake interesting distribution
x <- seq(-4, 4, length.out = 200)
r <- c(1.1, 1.3, -0.1, -0.7, 0.2, -0.4, 0.06, -1.7,
1.7, 0.3, 0.7, 1.6, -2.06, -0.74, 0.2, 0.5)
#' Compute unnormalized target density (named q, to emphesize that it
#' does not need to be normalized).
q <- density(r, bw = 0.5, n = 200, from = -4, to = 4)$y
#' Gaussian proposal distribution
g_mean <- 0
g_sd <- 1.1
g <- dnorm(x, g_mean, g_sd)
#' M is computed by discrete approximation
M <- max(q/g)
g <- g*M
#' One draw at -0.8
r1 = -0.8
zi = which.min(abs(x - r1)) # find the closest grid point
r21 = 0.3 * g[zi]
r22 = 0.8 * g[zi]
#' Visualize one accepted and one rejected draw:
df1 <- data.frame(x, q, g) %>%
pivot_longer(cols = !x, names_to = "grp", values_to = "p")
# subset with only target distribution
dfq <- subset(df1 , grp == "q")
# labels
labs1 <- c('Mg(theta)','q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(aes(x[zi], r21), col = 'forestgreen', size = 2) +
geom_point(aes(x[zi], r22), col = 'red', size = 2) +
geom_segment(aes(x = x[zi], xend = x[zi], y = 0, yend = q[zi])) +
geom_segment(aes(x = x[zi], xend = x[zi], y = q[zi], yend = g[zi]),
linetype = 'dashed') +
scale_y_continuous(breaks = NULL) +
labs(y = '') +
theme(legend.position = 'bottom', legend.title = element_blank()) +
scale_linetype_manual(values = c('dashed', 'solid'), labels = labs1) +
scale_color_discrete(labels = labs1) +
annotate('text', x = x[zi] + 0.1, y = c(r21, r22),
label = c('accepted', 'rejected'), hjust = 0)
#' 200 draws from the proposal distribution
nsamp <- 200
r1s <- rnorm(nsamp, g_mean, g_sd)
zis <- sapply(r1s, function(r) which.min(abs(x - r)))
r2s <- runif(nsamp) * g[zis]
acc <- ifelse(r2s < q[zis], 'a', 'r')
#' Visualize 200 draws, only some of which are accepted
df2 <- data.frame(r1s, r2s, acc)
# labels
labs2 <- c('Accepted', 'Rejected', 'Mg(theta)', 'q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(data = df2, aes(r1s, r2s, color = acc), size = 2) +
geom_rug(data = subset(df2, acc== 'a'), aes(x = r1s, r2s),
col = 'forestgreen', sides = 'b') +
labs(y = '') +
scale_y_continuous(breaks = NULL) +
scale_linetype_manual(values = c(2, 1, 0, 0), labels = labs2) +
scale_color_manual(values=c('forestgreen','red','#00BFC4','red'), labels = labs2) +
guides(color=guide_legend(override.aes=list(
shape = c(16, 16, NA, NA), linetype = c(0, 0, 2, 1),
color=c('forestgreen', 'red', 'red', '#00BFC4'), labels = labs2)),
linetype="none") +
theme(legend.position = 'bottom', legend.title = element_blank())
#' Rejection sampling for truncated distribution
q <- g
q[x < -1.5] <- 0
df1 <- data.frame(x, q, g) %>%
pivot_longer(cols = !x, names_to = "grp", values_to = "p")
acc <- ifelse(r1s > -1.5, 'a', 'r')
df2 <- data.frame(r1s, r2s, acc)
dfq <- subset(df1 , grp == "q")
# labels
labs2 <- c('Accepted', 'Rejected', 'Mg(theta)', 'q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(data = df2, aes(r1s, r2s, color = acc), size = 2) +
geom_rug(data = subset(df2, acc== 'a'), aes(x = r1s, r2s),
col = 'forestgreen', sides = 'b') +
labs(y = '') +
scale_y_continuous(breaks = NULL) +
scale_linetype_manual(values = c(2, 1, 0, 0), labels = labs2) +
scale_color_manual(values=c('forestgreen','red','#00BFC4','red'), labels = labs2) +
guides(color=guide_legend(override.aes=list(
shape = c(16, 16, NA, NA), linetype = c(0, 0, 2, 1),
color=c('forestgreen', 'red', 'red', '#00BFC4'), labels = labs2)),
linetype="none") +
theme(legend.position = 'bottom', legend.title = element_blank())
|
/demos_ch10/demo10_1.R
|
permissive
|
avehtari/BDA_R_demos
|
R
| false | false | 4,401 |
r
|
#' ---
#' title: "Bayesian data analysis demo 10.1"
#' author: "Aki Vehtari, Markus Paasiniemi"
#' date: "`r format(Sys.Date())`"
#' output:
#' html_document:
#' theme: readable
#' code_download: true
#' ---
#' ## Rejection sampling example
#'
#' ggplot2 is used for plotting, tidyr for manipulating data frames
#+ setup, message=FALSE, error=FALSE, warning=FALSE
library(ggplot2)
theme_set(theme_minimal())
library(tidyr)
#' Fake interesting distribution
x <- seq(-4, 4, length.out = 200)
r <- c(1.1, 1.3, -0.1, -0.7, 0.2, -0.4, 0.06, -1.7,
1.7, 0.3, 0.7, 1.6, -2.06, -0.74, 0.2, 0.5)
#' Compute unnormalized target density (named q, to emphesize that it
#' does not need to be normalized).
q <- density(r, bw = 0.5, n = 200, from = -4, to = 4)$y
#' Gaussian proposal distribution
g_mean <- 0
g_sd <- 1.1
g <- dnorm(x, g_mean, g_sd)
#' M is computed by discrete approximation
M <- max(q/g)
g <- g*M
#' One draw at -0.8
r1 = -0.8
zi = which.min(abs(x - r1)) # find the closest grid point
r21 = 0.3 * g[zi]
r22 = 0.8 * g[zi]
#' Visualize one accepted and one rejected draw:
df1 <- data.frame(x, q, g) %>%
pivot_longer(cols = !x, names_to = "grp", values_to = "p")
# subset with only target distribution
dfq <- subset(df1 , grp == "q")
# labels
labs1 <- c('Mg(theta)','q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(aes(x[zi], r21), col = 'forestgreen', size = 2) +
geom_point(aes(x[zi], r22), col = 'red', size = 2) +
geom_segment(aes(x = x[zi], xend = x[zi], y = 0, yend = q[zi])) +
geom_segment(aes(x = x[zi], xend = x[zi], y = q[zi], yend = g[zi]),
linetype = 'dashed') +
scale_y_continuous(breaks = NULL) +
labs(y = '') +
theme(legend.position = 'bottom', legend.title = element_blank()) +
scale_linetype_manual(values = c('dashed', 'solid'), labels = labs1) +
scale_color_discrete(labels = labs1) +
annotate('text', x = x[zi] + 0.1, y = c(r21, r22),
label = c('accepted', 'rejected'), hjust = 0)
#' 200 draws from the proposal distribution
nsamp <- 200
r1s <- rnorm(nsamp, g_mean, g_sd)
zis <- sapply(r1s, function(r) which.min(abs(x - r)))
r2s <- runif(nsamp) * g[zis]
acc <- ifelse(r2s < q[zis], 'a', 'r')
#' Visualize 200 draws, only some of which are accepted
df2 <- data.frame(r1s, r2s, acc)
# labels
labs2 <- c('Accepted', 'Rejected', 'Mg(theta)', 'q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(data = df2, aes(r1s, r2s, color = acc), size = 2) +
geom_rug(data = subset(df2, acc== 'a'), aes(x = r1s, r2s),
col = 'forestgreen', sides = 'b') +
labs(y = '') +
scale_y_continuous(breaks = NULL) +
scale_linetype_manual(values = c(2, 1, 0, 0), labels = labs2) +
scale_color_manual(values=c('forestgreen','red','#00BFC4','red'), labels = labs2) +
guides(color=guide_legend(override.aes=list(
shape = c(16, 16, NA, NA), linetype = c(0, 0, 2, 1),
color=c('forestgreen', 'red', 'red', '#00BFC4'), labels = labs2)),
linetype="none") +
theme(legend.position = 'bottom', legend.title = element_blank())
#' Rejection sampling for truncated distribution
q <- g
q[x < -1.5] <- 0
df1 <- data.frame(x, q, g) %>%
pivot_longer(cols = !x, names_to = "grp", values_to = "p")
acc <- ifelse(r1s > -1.5, 'a', 'r')
df2 <- data.frame(r1s, r2s, acc)
dfq <- subset(df1 , grp == "q")
# labels
labs2 <- c('Accepted', 'Rejected', 'Mg(theta)', 'q(theta|y)')
ggplot() +
geom_line(data = df1, aes(x, p, color = grp, linetype = grp)) +
geom_area(data = dfq, aes(x, p), fill = 'lightblue', alpha = 0.3) +
geom_point(data = df2, aes(r1s, r2s, color = acc), size = 2) +
geom_rug(data = subset(df2, acc== 'a'), aes(x = r1s, r2s),
col = 'forestgreen', sides = 'b') +
labs(y = '') +
scale_y_continuous(breaks = NULL) +
scale_linetype_manual(values = c(2, 1, 0, 0), labels = labs2) +
scale_color_manual(values=c('forestgreen','red','#00BFC4','red'), labels = labs2) +
guides(color=guide_legend(override.aes=list(
shape = c(16, 16, NA, NA), linetype = c(0, 0, 2, 1),
color=c('forestgreen', 'red', 'red', '#00BFC4'), labels = labs2)),
linetype="none") +
theme(legend.position = 'bottom', legend.title = element_blank())
|
dotR <- file.path(Sys.getenv("HOME"), ".R")
if (!file.exists(dotR)) dir.create(dotR)
M <- file.path(dotR, ifelse(.Platform$OS.type == "windows", "Makevars.win", "Makevars"))
if (!file.exists(M)) file.create(M)
cat("\nCXX14FLAGS=-O3 -march=native -mtune=native",
if( grepl("^darwin", R.version$os)) "CXX14FLAGS += -arch x86_64 -ftemplate-depth-256" else
if (.Platform$OS.type == "windows") "CXX11FLAGS=-O3 -march=native -mtune=native" else
"CXX14FLAGS += -fPIC",
file = M, sep = "\n", append = TRUE)
require(lobstr)
require(ggridges)
require(loo)
require(rethinking)
require(tidyr)
require(tidyverse)
require(dplyr)
require(ggplot2)
require(tidyr)
require(tidybayes)
require(bayesplot)
require(brms)
require(broom)
library(lubridate)
require(imputeTS)
require(smooth)
require(Mcomp)
require(hash)
require(rlist)
require(feather)
require(modelr)
require(gridExtra)
require(scales)
require(ggrepel)
require(posterior)
require(cmdstanr)
options(warnPartialMatchDollar = TRUE)
Sys.setenv("_R_CHECK_LENGTH_1_CONDITION_" = "true")
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
options(stringsAsFactors = FALSE)
|
/init_settings.R
|
no_license
|
svats2k/BayesianAnalysis
|
R
| false | false | 1,161 |
r
|
dotR <- file.path(Sys.getenv("HOME"), ".R")
if (!file.exists(dotR)) dir.create(dotR)
M <- file.path(dotR, ifelse(.Platform$OS.type == "windows", "Makevars.win", "Makevars"))
if (!file.exists(M)) file.create(M)
cat("\nCXX14FLAGS=-O3 -march=native -mtune=native",
if( grepl("^darwin", R.version$os)) "CXX14FLAGS += -arch x86_64 -ftemplate-depth-256" else
if (.Platform$OS.type == "windows") "CXX11FLAGS=-O3 -march=native -mtune=native" else
"CXX14FLAGS += -fPIC",
file = M, sep = "\n", append = TRUE)
require(lobstr)
require(ggridges)
require(loo)
require(rethinking)
require(tidyr)
require(tidyverse)
require(dplyr)
require(ggplot2)
require(tidyr)
require(tidybayes)
require(bayesplot)
require(brms)
require(broom)
library(lubridate)
require(imputeTS)
require(smooth)
require(Mcomp)
require(hash)
require(rlist)
require(feather)
require(modelr)
require(gridExtra)
require(scales)
require(ggrepel)
require(posterior)
require(cmdstanr)
options(warnPartialMatchDollar = TRUE)
Sys.setenv("_R_CHECK_LENGTH_1_CONDITION_" = "true")
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
options(stringsAsFactors = FALSE)
|
install.packages("tidyverse")
library(tidyverse)
murders <- read.csv("data/murders.csv")
murders <- murders %>% mutate(region = factor(region), rate = total / population * 10^5)
save(murders, file = "rda/murders.rda")
|
/wrangle-data.R
|
no_license
|
ajherrick19/murders
|
R
| false | false | 217 |
r
|
install.packages("tidyverse")
library(tidyverse)
murders <- read.csv("data/murders.csv")
murders <- murders %>% mutate(region = factor(region), rate = total / population * 10^5)
save(murders, file = "rda/murders.rda")
|
#' Perform a microsimulation of tax changes using the 2016-17 two per cent
#' sample of Australian taxpayers.
#'
#' @description
#'
#' The analysis year is 2019-20. The assumptions for employment growth and wages
#' are from the 2018-19 Budget and are as follows.
#'
#' Wages 2.25%, 2.75%, 3.25%, 3.5%
#' Employment 2.75%, 1.5%, 1.5%, 1.25%
#'
#' @param keep_df Whether to keep the amended tax file, mainly useful for
#' debugging.
#' @param ... Parameters for the `calculate_tax` function
#' @param employment_growth A vector containing forecasts for employment growth
#' @param wages_growth A vector containing forecasts for wages growth
#' @param parallel Should this compute using the parallel package
#'
#' @return A microsim object
#' @export
#'
#' @examples
#' microsim(base_tax_brackets = c(18200, 37000, 80000, 1.8e5, Inf),
#' change_tax_brackets = c(18200, 37000, 87000, 1.8e5, Inf))
#'
#' @import dplyr
#' @import ozTaxData
#' @import parallel
microsim <- function(keep_df = FALSE,
employment_growth = c(.0275, .015, .015, .0125),
wages_growth = c(.0225, .0275, .0325, .035),
parallel = FALSE,
...) {
tax_file <- uprate_data(ozTaxData::sample_16_17, wages_growth)
if (parallel == TRUE) {
n_cores <- detectCores() - 1
cl <- makeCluster(n_cores)
tax_file$difference <- parSapply(cl, tax_file$Taxable_Income,
function(x) calculate_tax(x, ...)$difference)
} else {
tax_file$difference <- sapply(tax_file$Taxable_Income,
function(x) calculate_tax(x, ...)$difference)
}
tax_file <- tax_file %>% mutate(decile = ntile(Taxable_Income, 10)) %>%
select(Gender, decile, Partner_status, Tot_inc_amt, Taxable_Income,
difference)
distribution <- tax_file %>% group_by(decile) %>% summarise(
revenue = sum(variable_compound(difference, employment_growth)) * 50 / 1e6,
income_from = round(min(Taxable_Income), -2),
income_to = round(max(Taxable_Income), -2),
avg_change = mean(difference),
avg_change_share = mean(difference) / mean(Taxable_Income)
)
gender <- tax_file %>% group_by(Gender) %>% summarise(
revenue = sum(variable_compound(difference, employment_growth)) * 50 / 1e6,
avg_change = mean(difference),
avg_change_share = mean(difference) / mean(Taxable_Income)
)
revenue <- sum(variable_compound(tax_file$difference, employment_growth)) * 50 / 1e6
n_tax_cut <- variable_compound(sum(tax_file$difference < 0), employment_growth) * 50
n_tax_increase <- variable_compound(sum(tax_file$difference > 0), employment_growth) * 50
n_no_difference <- variable_compound(sum(tax_file$difference == 0), employment_growth) * 50
summary_tbl <- average_tax_table(...)
input_params <- as.list(match.call()[-1])
if (keep_df == FALSE) {
tax_file <- NULL
}
output <- list(tax_file = tax_file,
revenue = revenue,
n_affected = list(n_tax_cut = n_tax_cut,
n_tax_increase = n_tax_increase,
n_no_difference = n_no_difference),
distribution = distribution,
gender = gender,
input_params = input_params,
summary_tbl = summary_tbl)
class(output) <- "microsim"
return(output)
}
|
/R/microsim.R
|
no_license
|
thmcmahon/microsim
|
R
| false | false | 3,403 |
r
|
#' Perform a microsimulation of tax changes using the 2016-17 two per cent
#' sample of Australian taxpayers.
#'
#' @description
#'
#' The analysis year is 2019-20. The assumptions for employment growth and wages
#' are from the 2018-19 Budget and are as follows.
#'
#' Wages 2.25%, 2.75%, 3.25%, 3.5%
#' Employment 2.75%, 1.5%, 1.5%, 1.25%
#'
#' @param keep_df Whether to keep the amended tax file, mainly useful for
#' debugging.
#' @param ... Parameters for the `calculate_tax` function
#' @param employment_growth A vector containing forecasts for employment growth
#' @param wages_growth A vector containing forecasts for wages growth
#' @param parallel Should this compute using the parallel package
#'
#' @return A microsim object
#' @export
#'
#' @examples
#' microsim(base_tax_brackets = c(18200, 37000, 80000, 1.8e5, Inf),
#' change_tax_brackets = c(18200, 37000, 87000, 1.8e5, Inf))
#'
#' @import dplyr
#' @import ozTaxData
#' @import parallel
microsim <- function(keep_df = FALSE,
employment_growth = c(.0275, .015, .015, .0125),
wages_growth = c(.0225, .0275, .0325, .035),
parallel = FALSE,
...) {
tax_file <- uprate_data(ozTaxData::sample_16_17, wages_growth)
if (parallel == TRUE) {
n_cores <- detectCores() - 1
cl <- makeCluster(n_cores)
tax_file$difference <- parSapply(cl, tax_file$Taxable_Income,
function(x) calculate_tax(x, ...)$difference)
} else {
tax_file$difference <- sapply(tax_file$Taxable_Income,
function(x) calculate_tax(x, ...)$difference)
}
tax_file <- tax_file %>% mutate(decile = ntile(Taxable_Income, 10)) %>%
select(Gender, decile, Partner_status, Tot_inc_amt, Taxable_Income,
difference)
distribution <- tax_file %>% group_by(decile) %>% summarise(
revenue = sum(variable_compound(difference, employment_growth)) * 50 / 1e6,
income_from = round(min(Taxable_Income), -2),
income_to = round(max(Taxable_Income), -2),
avg_change = mean(difference),
avg_change_share = mean(difference) / mean(Taxable_Income)
)
gender <- tax_file %>% group_by(Gender) %>% summarise(
revenue = sum(variable_compound(difference, employment_growth)) * 50 / 1e6,
avg_change = mean(difference),
avg_change_share = mean(difference) / mean(Taxable_Income)
)
revenue <- sum(variable_compound(tax_file$difference, employment_growth)) * 50 / 1e6
n_tax_cut <- variable_compound(sum(tax_file$difference < 0), employment_growth) * 50
n_tax_increase <- variable_compound(sum(tax_file$difference > 0), employment_growth) * 50
n_no_difference <- variable_compound(sum(tax_file$difference == 0), employment_growth) * 50
summary_tbl <- average_tax_table(...)
input_params <- as.list(match.call()[-1])
if (keep_df == FALSE) {
tax_file <- NULL
}
output <- list(tax_file = tax_file,
revenue = revenue,
n_affected = list(n_tax_cut = n_tax_cut,
n_tax_increase = n_tax_increase,
n_no_difference = n_no_difference),
distribution = distribution,
gender = gender,
input_params = input_params,
summary_tbl = summary_tbl)
class(output) <- "microsim"
return(output)
}
|
source("prep.R")
doPlot4 <- function() {
tbl <- prepareData()
png(filename = "plot4.png", width = 480, height = 480, units = "px")
par (mfrow = c(2,2), mar = c(4,4,2,1), oma = c(0,0,2,0))
with(tbl, {
plot(DateTime, Global_active_power, xlab="", ylab="Global Active Power", type="l")
plot(DateTime, Voltage, xlab="datetime", ylab="Voltage", type="l")
cols = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
plot(DateTime, Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
lines(DateTime, Sub_metering_2, type="l", col="red")
lines(DateTime, Sub_metering_3, type="l", col="blue")
legend("topright", lty=1, lwd=1, col=c("black","blue","red"), legend=cols, bty="n")
plot(DateTime, Global_reactive_power, xlab="datetime", ylab="Global_reactive_power", type="l")
})
dev.off()
}
doPlot4()
|
/plot4.R
|
no_license
|
pRcoding/ExData_Plotting1
|
R
| false | false | 872 |
r
|
source("prep.R")
doPlot4 <- function() {
tbl <- prepareData()
png(filename = "plot4.png", width = 480, height = 480, units = "px")
par (mfrow = c(2,2), mar = c(4,4,2,1), oma = c(0,0,2,0))
with(tbl, {
plot(DateTime, Global_active_power, xlab="", ylab="Global Active Power", type="l")
plot(DateTime, Voltage, xlab="datetime", ylab="Voltage", type="l")
cols = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
plot(DateTime, Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
lines(DateTime, Sub_metering_2, type="l", col="red")
lines(DateTime, Sub_metering_3, type="l", col="blue")
legend("topright", lty=1, lwd=1, col=c("black","blue","red"), legend=cols, bty="n")
plot(DateTime, Global_reactive_power, xlab="datetime", ylab="Global_reactive_power", type="l")
})
dev.off()
}
doPlot4()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FTG-random.R
\name{rFTG}
\alias{rFTG}
\title{FTG Random Sample Generation}
\usage{
rFTG(n, threshold, scale, shape)
}
\arguments{
\item{n}{Sample size.}
\item{threshold}{Minimum value of the tail.}
\item{scale}{Scale parameter.}
\item{shape}{Shape parameter.}
}
\value{
Gives random deviates of the FTG. The length of the result is determined by n.
}
\description{
This function computes n random variates from full-tail gamma with a rejection method.
}
\examples{
x <- rFTG(100, 1, 1, 1)
hist(x, breaks = "FD")
}
\references{
del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012). The full-tails gamma distribution applied to model extreme values. ASTIN Bulletin. <doi:10.1017/asb.2017.9>.
}
\keyword{FTG}
|
/man/rFTG.Rd
|
no_license
|
SergiVilardell/distTails
|
R
| false | true | 794 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FTG-random.R
\name{rFTG}
\alias{rFTG}
\title{FTG Random Sample Generation}
\usage{
rFTG(n, threshold, scale, shape)
}
\arguments{
\item{n}{Sample size.}
\item{threshold}{Minimum value of the tail.}
\item{scale}{Scale parameter.}
\item{shape}{Shape parameter.}
}
\value{
Gives random deviates of the FTG. The length of the result is determined by n.
}
\description{
This function computes n random variates from full-tail gamma with a rejection method.
}
\examples{
x <- rFTG(100, 1, 1, 1)
hist(x, breaks = "FD")
}
\references{
del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012). The full-tails gamma distribution applied to model extreme values. ASTIN Bulletin. <doi:10.1017/asb.2017.9>.
}
\keyword{FTG}
|
# Create a shortened version of `mtcars`
mtcars_short <- mtcars[1:5, ]
test_that("the `cols_move()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `drat`
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c(mpg, cyl, disp), after = drat)
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & mpg & cyl & disp & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `drat` using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c("mpg", "cyl", "disp"), after = c("drat"))
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & mpg & cyl & disp & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `carb` (the end of the series)
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c(mpg, cyl, disp), after = carb)
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & wt & qsec & vs & am & gear & carb & mpg & cyl & disp.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
test_that("the `cols_move_to_start()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the start
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_start(columns = c(gear, carb))
# Expect a characteristic pattern
grepl(
paste0(
".*gear & carb & mpg & cyl & disp & hp & drat & wt & qsec & vs & am.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the start using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_start(columns = c("gear", "carb"))
# Expect a characteristic pattern
grepl(
paste0(
".*gear & carb & mpg & cyl & disp & hp & drat & wt & qsec & vs & am.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
test_that("the `cols_move_to_end()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the end
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_end(columns = c(gear, carb))
# Expect a characteristic pattern
grepl(
paste0(
".*mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the end using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_end(columns = c("gear", "carb"))
# Expect a characteristic pattern
grepl(
paste0(
".*mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
|
/tests/testthat/test-l_cols_move.R
|
permissive
|
steveputman/gt
|
R
| false | false | 3,216 |
r
|
# Create a shortened version of `mtcars`
mtcars_short <- mtcars[1:5, ]
test_that("the `cols_move()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `drat`
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c(mpg, cyl, disp), after = drat)
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & mpg & cyl & disp & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `drat` using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c("mpg", "cyl", "disp"), after = c("drat"))
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & mpg & cyl & disp & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `mpg`,
# `cyl`, and `drat` columns placed after `carb` (the end of the series)
tbl_latex <-
gt(mtcars_short) %>%
cols_move(columns = c(mpg, cyl, disp), after = carb)
# Expect a characteristic pattern
grepl(
paste0(
".*hp & drat & wt & qsec & vs & am & gear & carb & mpg & cyl & disp.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
test_that("the `cols_move_to_start()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the start
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_start(columns = c(gear, carb))
# Expect a characteristic pattern
grepl(
paste0(
".*gear & carb & mpg & cyl & disp & hp & drat & wt & qsec & vs & am.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the start using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_start(columns = c("gear", "carb"))
# Expect a characteristic pattern
grepl(
paste0(
".*gear & carb & mpg & cyl & disp & hp & drat & wt & qsec & vs & am.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
test_that("the `cols_move_to_end()` function works correctly", {
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the end
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_end(columns = c(gear, carb))
# Expect a characteristic pattern
grepl(
paste0(
".*mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
# Create a `tbl_latex` object with `gt()`; the `gear`,
# and `carb` columns placed at the end using vectors
tbl_latex <-
gt(mtcars_short) %>%
cols_move_to_end(columns = c("gear", "carb"))
# Expect a characteristic pattern
grepl(
paste0(
".*mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb.*"),
tbl_latex %>%
as_latex() %>% as.character()) %>%
expect_true()
})
|
\name{mpr_templates-package}
\alias{mpr_templates-package}
\alias{mpr_templates}
\docType{package}
\title{
\packageTitle{mpr_templates}
}
\description{
\packageDescription{mpr_templates}
}
\details{
The DESCRIPTION file:
\packageDESCRIPTION{mpr_templates}
\packageIndices{mpr_templates}
~~ An overview of how to use the package, including the most ~~
~~ important functions ~~
}
\author{
\packageAuthor{mpr_templates}
Maintainer: \packageMaintainer{mpr_templates}
}
\references{
~~ Literature or other references for background information ~~
}
~~ Optionally other standard keywords, one per line, from file ~~
~~ KEYWORDS in the R documentation directory ~~
\keyword{ package }
\seealso{
~~ Optional links to other man pages, e.g. ~~
~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
}
\examples{
~~ simple examples of the most important functions ~~
}
|
/man/mpr_templates-package.Rd
|
no_license
|
Huihua2015/mpr_templates
|
R
| false | false | 853 |
rd
|
\name{mpr_templates-package}
\alias{mpr_templates-package}
\alias{mpr_templates}
\docType{package}
\title{
\packageTitle{mpr_templates}
}
\description{
\packageDescription{mpr_templates}
}
\details{
The DESCRIPTION file:
\packageDESCRIPTION{mpr_templates}
\packageIndices{mpr_templates}
~~ An overview of how to use the package, including the most ~~
~~ important functions ~~
}
\author{
\packageAuthor{mpr_templates}
Maintainer: \packageMaintainer{mpr_templates}
}
\references{
~~ Literature or other references for background information ~~
}
~~ Optionally other standard keywords, one per line, from file ~~
~~ KEYWORDS in the R documentation directory ~~
\keyword{ package }
\seealso{
~~ Optional links to other man pages, e.g. ~~
~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
}
\examples{
~~ simple examples of the most important functions ~~
}
|
#rm(list = ls())
setwd(dirname(parent.frame(2)$ofile))
library(ggplot2)
library(xtable)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(kableExtra)
source('./_function_task_expand_name.r')
eps = 0.2
median_range = 100
xtabs.data.first = function (data, formular, ...) {
return(xtabs(formular, data, ...))
}
dat = expand.name(read_csv('../results/function_task_static.csv'))
dat.ggplot = subset(dat, dat$model %in% c('NMU', 'NALU', '$\\mathrm{NAC}_{\\bullet}$') & dat$operation == '${a \\cdot b}$' & dat$seed == 1) %>%
mutate(
model=droplevels(model),
iteration=step,
sparse.error = sparse.error.max,
interpolation.error = interpolation,
extrapolation.error = extrapolation
) %>%
select(model, operation, iteration, interpolation.error, extrapolation.error, sparse.error) %>%
gather('measurement', 'error', interpolation.error, extrapolation.error, sparse.error)
dat.eps = data.frame(
measurement=c('interpolation.error', 'extrapolation.error', 'sparse.error'),
epsilon=c(NA, 0.2, NA)
)
p = ggplot(dat.ggplot, aes(x=iteration, y=error, colour=model)) +
geom_line(alpha=0.7) +
geom_hline(aes(yintercept = epsilon), dat.eps, colour='black') +
scale_y_continuous(trans="log10", name = element_blank()) +
scale_color_discrete(labels = model.to.exp(levels(dat.ggplot$model))) +
xlab('Iteration') +
facet_wrap(~ measurement, scale='free_y', labeller = labeller(
measurement = c(
extrapolation.error = "Extrapolation error [MSE]",
interpolation.error = "Interpolation error [MSE]",
sparse.error = "Sparsity error"
)
)) +
theme(legend.position="bottom") +
theme(plot.margin=unit(c(5.5, 10.5, 5.5, 5.5), "points"))
print(p)
ggsave('../paper/results/function-task-static-example.pdf', p, device="pdf", width = 13.968, height = 5, scale=1.4, units = "cm")
|
/export/function_task_static_plot_example.r
|
permissive
|
AndreasMadsen/stable-nalu
|
R
| false | false | 1,847 |
r
|
#rm(list = ls())
setwd(dirname(parent.frame(2)$ofile))
library(ggplot2)
library(xtable)
library(plyr)
library(dplyr)
library(tidyr)
library(readr)
library(kableExtra)
source('./_function_task_expand_name.r')
eps = 0.2
median_range = 100
xtabs.data.first = function (data, formular, ...) {
return(xtabs(formular, data, ...))
}
dat = expand.name(read_csv('../results/function_task_static.csv'))
dat.ggplot = subset(dat, dat$model %in% c('NMU', 'NALU', '$\\mathrm{NAC}_{\\bullet}$') & dat$operation == '${a \\cdot b}$' & dat$seed == 1) %>%
mutate(
model=droplevels(model),
iteration=step,
sparse.error = sparse.error.max,
interpolation.error = interpolation,
extrapolation.error = extrapolation
) %>%
select(model, operation, iteration, interpolation.error, extrapolation.error, sparse.error) %>%
gather('measurement', 'error', interpolation.error, extrapolation.error, sparse.error)
dat.eps = data.frame(
measurement=c('interpolation.error', 'extrapolation.error', 'sparse.error'),
epsilon=c(NA, 0.2, NA)
)
p = ggplot(dat.ggplot, aes(x=iteration, y=error, colour=model)) +
geom_line(alpha=0.7) +
geom_hline(aes(yintercept = epsilon), dat.eps, colour='black') +
scale_y_continuous(trans="log10", name = element_blank()) +
scale_color_discrete(labels = model.to.exp(levels(dat.ggplot$model))) +
xlab('Iteration') +
facet_wrap(~ measurement, scale='free_y', labeller = labeller(
measurement = c(
extrapolation.error = "Extrapolation error [MSE]",
interpolation.error = "Interpolation error [MSE]",
sparse.error = "Sparsity error"
)
)) +
theme(legend.position="bottom") +
theme(plot.margin=unit(c(5.5, 10.5, 5.5, 5.5), "points"))
print(p)
ggsave('../paper/results/function-task-static-example.pdf', p, device="pdf", width = 13.968, height = 5, scale=1.4, units = "cm")
|
###############
#### model ###
###############
##### some definitions
MAE=c()
bias=c()
SSresults=c()
difference.best.worst=c()
difference.best.reference=c()
sd.best.worst=c()
sd.best.ref=c()
coverage=c()
jags.m2=list()
#####################
model2=function() {
for(i in 1:NS){
dm[i]<-d[t2[i]]-d[t1[i]]
prec[i]<-1/(SE[i]*SE[i])
y[i]~dnorm(dm[i],prec[i])}
d[1]<-0
for(i in 2:NT){
d[i]~dnorm(md,sd)}
md~dnorm(0,0.1)
sd<-1/(td*td)
td~dunif(0,2)
for (i in 1:NT){
for (j in i:NT){
D[j,i]<-d[j]-d[i]}}
#TreatmeNT hierarchy
order[1:NT]<- NT+1- rank(d[1:NT])
for(k in 1:NT) {
# this is when the outcome is positive - omit 'NT+1-' when the outcome is negative
most.effective[k]<-equals(order[k],1)
for(j in 1:NT) {
effectiveness[k,j]<- equals(order[k],j)}}
for(k in 1:NT) {
for(j in 1:NT) {
cumeffectiveness[k,j]<- sum(effectiveness[k,1:j])}}
#SUCRAS#
for(k in 1:NT) {
SUCRA[k]<- sum(cumeffectiveness[k,1:(NT-1)]) /(NT-1)}}
##################
params=c()
for (i in 1:(N.treat-1)){
for (j in (i+1):N.treat){
params=c(params, paste("D[",j,",",i,"]",sep=""))
}}
for (i in 2:(N.treat)){
params=c(params, paste("d[",i,"]",sep=""))
}
for (i in 1:(N.treat)){
params=c(params, paste("SUCRA[",i,"]",sep=""))
}
#number of D parameters
no.D=N.treat*(N.treat-1)/2
for (i in 1:N.sim){
initialval = NULL
data2 <- list(y = data1[[i]]$TE,SE=data1[[i]]$seTE, NS=length(data1[[i]]$studlab), t1=data1[[i]]$t1,t2=data1[[i]]$t2, NT=N.treat)
jags.m2[[i]] <- jags.parallel(data=data2,initialval,parameters.to.save = params, n.chains = 2, n.iter = 15000, n.thin=1, n.burnin = 5000, DIC=F, model.file = model2)
print(i)
coverage[i]=(mean(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),3]<0&jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),7]>0))
bias[i]=(mean(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),1]))
MAE[i]=mean(abs(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),1]))
SSresults[i]=sum(jags.m2[[i]]$BUGSoutput$summary[1:no.D,3]>0|jags.m2[[i]]$BUGSoutput$summary[1:no.D,7]<0) ### 95% CrI does not include 0
difference.best.worst[i]=abs(jags.m2[[i]]$BUGSoutput$summary[1,1])
sd.best.worst[i]=abs(jags.m2[[i]]$BUGSoutput$summary[1,2])
jags.m2[[i]]=NULL
}
|
/simulation study 1/Model II - Fixed Effect (Scenario C).R
|
no_license
|
esm-ispm-unibe-ch-REPRODUCIBLE/the_dark_side_of_the_force
|
R
| false | false | 2,279 |
r
|
###############
#### model ###
###############
##### some definitions
MAE=c()
bias=c()
SSresults=c()
difference.best.worst=c()
difference.best.reference=c()
sd.best.worst=c()
sd.best.ref=c()
coverage=c()
jags.m2=list()
#####################
model2=function() {
for(i in 1:NS){
dm[i]<-d[t2[i]]-d[t1[i]]
prec[i]<-1/(SE[i]*SE[i])
y[i]~dnorm(dm[i],prec[i])}
d[1]<-0
for(i in 2:NT){
d[i]~dnorm(md,sd)}
md~dnorm(0,0.1)
sd<-1/(td*td)
td~dunif(0,2)
for (i in 1:NT){
for (j in i:NT){
D[j,i]<-d[j]-d[i]}}
#TreatmeNT hierarchy
order[1:NT]<- NT+1- rank(d[1:NT])
for(k in 1:NT) {
# this is when the outcome is positive - omit 'NT+1-' when the outcome is negative
most.effective[k]<-equals(order[k],1)
for(j in 1:NT) {
effectiveness[k,j]<- equals(order[k],j)}}
for(k in 1:NT) {
for(j in 1:NT) {
cumeffectiveness[k,j]<- sum(effectiveness[k,1:j])}}
#SUCRAS#
for(k in 1:NT) {
SUCRA[k]<- sum(cumeffectiveness[k,1:(NT-1)]) /(NT-1)}}
##################
params=c()
for (i in 1:(N.treat-1)){
for (j in (i+1):N.treat){
params=c(params, paste("D[",j,",",i,"]",sep=""))
}}
for (i in 2:(N.treat)){
params=c(params, paste("d[",i,"]",sep=""))
}
for (i in 1:(N.treat)){
params=c(params, paste("SUCRA[",i,"]",sep=""))
}
#number of D parameters
no.D=N.treat*(N.treat-1)/2
for (i in 1:N.sim){
initialval = NULL
data2 <- list(y = data1[[i]]$TE,SE=data1[[i]]$seTE, NS=length(data1[[i]]$studlab), t1=data1[[i]]$t1,t2=data1[[i]]$t2, NT=N.treat)
jags.m2[[i]] <- jags.parallel(data=data2,initialval,parameters.to.save = params, n.chains = 2, n.iter = 15000, n.thin=1, n.burnin = 5000, DIC=F, model.file = model2)
print(i)
coverage[i]=(mean(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),3]<0&jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),7]>0))
bias[i]=(mean(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),1]))
MAE[i]=mean(abs(jags.m2[[i]]$BUGSoutput$summary[(no.D+N.treat+1):(no.D+2*N.treat-1),1]))
SSresults[i]=sum(jags.m2[[i]]$BUGSoutput$summary[1:no.D,3]>0|jags.m2[[i]]$BUGSoutput$summary[1:no.D,7]<0) ### 95% CrI does not include 0
difference.best.worst[i]=abs(jags.m2[[i]]$BUGSoutput$summary[1,1])
sd.best.worst[i]=abs(jags.m2[[i]]$BUGSoutput$summary[1,2])
jags.m2[[i]]=NULL
}
|
setwd("~/summer-tranning-project/r code")
mat <- read.csv("/home/sudip/summer-tranning-project/r code/LL-ALL-selected-transpose.csv")
factor(mat[,101])
#[1] LL LL LL LL LL LL LL LL LL ALL ALL ALL ALL ALL ALL ALL ALL ALL[19] ALL
#Levels: ALL LL
library("e1071")
#Breaking the data into train and test data
trainData = sample(1:19,15)
testData = setdiff(1:19,trainData)
trainData <- mat[trainData,]
testData <- mat[testData,]
#mat <- as.matrix(mat)
x <- subset(mat, select=-class)
y <- subset(mat, select=class)
svm_model <- svm(class ~ ., data=mat)
summary(svm_model)
svm_model1 <- svm(as.matrix(x),ifelse(mat[,101] == "ALL",1,0))
summary(svm_model1)
|
/Svm.R
|
no_license
|
Sudipsamanta1/summer-traning
|
R
| false | false | 664 |
r
|
setwd("~/summer-tranning-project/r code")
mat <- read.csv("/home/sudip/summer-tranning-project/r code/LL-ALL-selected-transpose.csv")
factor(mat[,101])
#[1] LL LL LL LL LL LL LL LL LL ALL ALL ALL ALL ALL ALL ALL ALL ALL[19] ALL
#Levels: ALL LL
library("e1071")
#Breaking the data into train and test data
trainData = sample(1:19,15)
testData = setdiff(1:19,trainData)
trainData <- mat[trainData,]
testData <- mat[testData,]
#mat <- as.matrix(mat)
x <- subset(mat, select=-class)
y <- subset(mat, select=class)
svm_model <- svm(class ~ ., data=mat)
summary(svm_model)
svm_model1 <- svm(as.matrix(x),ifelse(mat[,101] == "ALL",1,0))
summary(svm_model1)
|
install.packages("leaflet")
library(leaflet)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=-49.255954, lat=-25.450428, popup="Compwire")
m
|
/lab02/packages/R/package.R
|
no_license
|
lopesdiego12/CDSW-Trainning
|
R
| false | false | 185 |
r
|
install.packages("leaflet")
library(leaflet)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=-49.255954, lat=-25.450428, popup="Compwire")
m
|
library(data.table)
library(dplyr)
# load additional code to perform variable names clean up
source('clean_variable_names.R')
# will check to ensure required directory with raw data exists in working directory
if( !file.exists('UCI HAR Dataset')){
stop("Invalid script working environment. This script requires 'UCI HAR Dataset' directory in the working directory.")
}
# change work directory to directory containing raw data
setwd('UCI HAR Dataset')
# load raw data into memory
train_set <- cbind(read.table('./train/X_train.txt'),
read.table('./train/y_train.txt'),
read.table('./train/subject_train.txt'))
test_set <- cbind(read.table('./test/X_test.txt'),
read.table("./test/y_test.txt"),
read.table('./test/subject_test.txt'))
activity_labels <- read.table('./activity_labels.txt', col.names = c('levels', 'labels'))
# make combined set of train and test data
combined_set <- rbind(train_set, test_set)
features <- read.table("features.txt")
# build subset of variables we only will use to perform futture analysis
stdVariables <- features[grep("-std\\(\\)", features$V2),]$V1
meanVariables <- features[grep("-mean\\(\\)", features$V2),]$V1
variables <- c(stdVariables, meanVariables)
# will get variable names from features vector
variableNames <- as.character(features[variables, ]$V2)
# perform some clean ups on variable names(remove dashes, parenthesis etc)
variableNames <- cleanVariableNames(variableNames)
# add Subject and Activity variables to final datatset
variables <- c(563, 562, variables)
variableNames <- c("Subject", "Activity", variableNames)
# subset all selected variables to new dataset and assign varibles names
tidyResult <- data.table(combined_set[, variables])
setnames(tidyResult, old = colnames(tidyResult), new = variableNames)
tidyResult$Activity <- factor(tidyResult$Activity, activity_labels$levels, activity_labels$labels)
# calculate averaged values for all selected variables grouped by Subject and Activity
averagedTideyResult <- tidyResult[, lapply(.SD, mean), by=list(Subject, Activity)][order(Subject,Activity)]
# save tidy results to file
write.table(averagedTideyResult, "../tidy_dataset.txt", row.name=FALSE)
|
/run_analysis.R
|
no_license
|
ok-datascience/CleaningData
|
R
| false | false | 2,249 |
r
|
library(data.table)
library(dplyr)
# load additional code to perform variable names clean up
source('clean_variable_names.R')
# will check to ensure required directory with raw data exists in working directory
if( !file.exists('UCI HAR Dataset')){
stop("Invalid script working environment. This script requires 'UCI HAR Dataset' directory in the working directory.")
}
# change work directory to directory containing raw data
setwd('UCI HAR Dataset')
# load raw data into memory
train_set <- cbind(read.table('./train/X_train.txt'),
read.table('./train/y_train.txt'),
read.table('./train/subject_train.txt'))
test_set <- cbind(read.table('./test/X_test.txt'),
read.table("./test/y_test.txt"),
read.table('./test/subject_test.txt'))
activity_labels <- read.table('./activity_labels.txt', col.names = c('levels', 'labels'))
# make combined set of train and test data
combined_set <- rbind(train_set, test_set)
features <- read.table("features.txt")
# build subset of variables we only will use to perform futture analysis
stdVariables <- features[grep("-std\\(\\)", features$V2),]$V1
meanVariables <- features[grep("-mean\\(\\)", features$V2),]$V1
variables <- c(stdVariables, meanVariables)
# will get variable names from features vector
variableNames <- as.character(features[variables, ]$V2)
# perform some clean ups on variable names(remove dashes, parenthesis etc)
variableNames <- cleanVariableNames(variableNames)
# add Subject and Activity variables to final datatset
variables <- c(563, 562, variables)
variableNames <- c("Subject", "Activity", variableNames)
# subset all selected variables to new dataset and assign varibles names
tidyResult <- data.table(combined_set[, variables])
setnames(tidyResult, old = colnames(tidyResult), new = variableNames)
tidyResult$Activity <- factor(tidyResult$Activity, activity_labels$levels, activity_labels$labels)
# calculate averaged values for all selected variables grouped by Subject and Activity
averagedTideyResult <- tidyResult[, lapply(.SD, mean), by=list(Subject, Activity)][order(Subject,Activity)]
# save tidy results to file
write.table(averagedTideyResult, "../tidy_dataset.txt", row.name=FALSE)
|
###################################################
########## Population Module ########
###################################################
population_path <- "Population/"
# Data preprocessing
source(str_c(population_path, "data-preprocessing.R"), local = TRUE)
# UI
population_ui <- function(id) {
ns <- NS(id)
tabPanel(
title = "Population",
column(width = 2, class = "sidebar", box(width = 12, h4(class = "accent-color", "Options"),
h5("General"),
HTML("<label>Select country: </label>"),
selectizeInput(ns('country'), NULL, choices = countries, selected = NULL, multiple = FALSE, options = list(placeholder = 'Type a country name, e.g. Spain')),
HTML("<label>Years: </label>"),
actionButton(ns("years_reset"), class = "button-option btn btn-link", "Reset"),
actionButton(ns("years_select_all"), class = "button-option btn btn-link", "Select all"),
selectizeInput(ns('years'), NULL, choices = years, selected = NULL, multiple = TRUE,
options = list(placeholder = 'Type a year, e.g. 2001', maxItems = 15)))),
column(width = 10, class = "content", box(width = 12,
h4("Population per country and year"),
plotOutput(ns("plot.histogram.population")))))
}
# Server
population_server <- function(input, output, session) {
# years
observe({
req(input$years_reset)
values$years_selected <<- isolate(values$years[1])
})
observe({
req(input$years_select_all)
values$years_selected <<- isolate(values$years)
})
observe({
req(values$years_selected)
if (length(isolate(input$years)) != length(values$years_selected)) {
updateSelectizeInput(session, 'years', choices = isolate(values$years), selected = values$years_selected, server = TRUE)
}
})
observe({
req(input$years)
if (length(input$years) != length(isolate(values$years_selected))) {
values$years_selected <<- input$years
}
})
# plots
# histogram
output$plot.histogram.population <- renderPlot({
req(values$europe_stats)
req(values$years_selected)
req(input$country)
data <- values$europe_stats()[values$europe_stats()$year %in% values$years_selected,]
data <- data[data$country.name == input$country,]
data$year <- as.factor(data$year)
ggplot(data = data, aes(x = year, y = population)) +
geom_histogram(stat = "identity", fill = "blue", alpha = .8)
})
}
|
/exercise2/Population/module.R
|
no_license
|
javierdlrm/Data-Visualization-and-Exploration-Tool
|
R
| false | false | 2,624 |
r
|
###################################################
########## Population Module ########
###################################################
population_path <- "Population/"
# Data preprocessing
source(str_c(population_path, "data-preprocessing.R"), local = TRUE)
# UI
population_ui <- function(id) {
ns <- NS(id)
tabPanel(
title = "Population",
column(width = 2, class = "sidebar", box(width = 12, h4(class = "accent-color", "Options"),
h5("General"),
HTML("<label>Select country: </label>"),
selectizeInput(ns('country'), NULL, choices = countries, selected = NULL, multiple = FALSE, options = list(placeholder = 'Type a country name, e.g. Spain')),
HTML("<label>Years: </label>"),
actionButton(ns("years_reset"), class = "button-option btn btn-link", "Reset"),
actionButton(ns("years_select_all"), class = "button-option btn btn-link", "Select all"),
selectizeInput(ns('years'), NULL, choices = years, selected = NULL, multiple = TRUE,
options = list(placeholder = 'Type a year, e.g. 2001', maxItems = 15)))),
column(width = 10, class = "content", box(width = 12,
h4("Population per country and year"),
plotOutput(ns("plot.histogram.population")))))
}
# Server
population_server <- function(input, output, session) {
# years
observe({
req(input$years_reset)
values$years_selected <<- isolate(values$years[1])
})
observe({
req(input$years_select_all)
values$years_selected <<- isolate(values$years)
})
observe({
req(values$years_selected)
if (length(isolate(input$years)) != length(values$years_selected)) {
updateSelectizeInput(session, 'years', choices = isolate(values$years), selected = values$years_selected, server = TRUE)
}
})
observe({
req(input$years)
if (length(input$years) != length(isolate(values$years_selected))) {
values$years_selected <<- input$years
}
})
# plots
# histogram
output$plot.histogram.population <- renderPlot({
req(values$europe_stats)
req(values$years_selected)
req(input$country)
data <- values$europe_stats()[values$europe_stats()$year %in% values$years_selected,]
data <- data[data$country.name == input$country,]
data$year <- as.factor(data$year)
ggplot(data = data, aes(x = year, y = population)) +
geom_histogram(stat = "identity", fill = "blue", alpha = .8)
})
}
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/functions.R
\name{makeGRMmatrix}
\alias{makeGRMmatrix}
\title{Convert long GRM format to matrix format}
\usage{
makeGRMmatrix(grm)
}
\arguments{
\item{grm}{Result from readGRM}
}
\value{
Matrix of n x n
}
\description{
Convert long GRM format to matrix format
}
|
/man/makeGRMmatrix.Rd
|
no_license
|
explodecomputer/explodecomputr
|
R
| false | false | 349 |
rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/functions.R
\name{makeGRMmatrix}
\alias{makeGRMmatrix}
\title{Convert long GRM format to matrix format}
\usage{
makeGRMmatrix(grm)
}
\arguments{
\item{grm}{Result from readGRM}
}
\value{
Matrix of n x n
}
\description{
Convert long GRM format to matrix format
}
|
#-------------------------------------------------------------------------------
# Copyright (c) 2012 University of Illinois, NCSA.
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the
# University of Illinois/NCSA Open Source License
# which accompanies this distribution, and is available at
# http://opensource.ncsa.illinois.edu/license.html
#-------------------------------------------------------------------------------
##' Individual Meta-analysis
##'
##' Individual meta-analysis for a specific trait and PFT is run by the function
##' single.MA. This will allow power analysis to run repeated MA outside of the
##' full loop over traits and PFTs.
##' @title Single MA
##' @param data data frame generated by jagify function
##' with indexed values for greenhouse, treatment, and site (ghs, trt, site)
##' as well as Y, SE, and n for each observation or summary statistic.
##' @param j.chains number of chains in meta-analysis
##' @param j.iter number of mcmc samples
##' @param tauA prior on variance parameters
##' @param tauB prior on variance parameters
##' @param prior data.frame with columns named 'distn', 'parama', 'paramb'
##' e.g. \code{prior <- data.frame(distn = 'weibull', parama = 0.5, paramb = 10, n = 1)}
##' @param jag.model.file file to which model will be written
##' @param overdispersed if TRUE (default), chains start at overdispersed locations in parameter space (recommended)
##' @export
##' @return jags.out, an mcmc.object with results of meta-analysis
##' @author David LeBauer, Michael C. Dietze
single.MA <- function(data, j.chains, j.iter, tauA, tauB, prior, jag.model.file,
overdispersed = TRUE) {
## Convert R distributions to JAGS distributions
jagsprior <- PEcAn.utils::r2bugs.distributions(prior)
jagsprior <- jagsprior[, c("distn", "parama", "paramb", "n")]
colnames(jagsprior) <- c("distn", "a", "b", "n")
colnames(prior) <- c("distn", "a", "b", "n")
# determine what factors to include in meta-analysis
model.parms <- list(ghs = length(unique(data$ghs)),
site = length(unique(data$site)),
trt = length(unique(data$trt)))
# define regression model
reg.parms <- list(ghs = "beta.ghs[ghs[k]]", # beta.o will be included by default
site = "beta.site[site[k]]",
trt = "beta.trt[trt[k]]")
# making sure ghs and trt are factor
data$ghs <- as.factor(data$ghs)
data$trt <- as.factor(data$trt)
if (sum(model.parms > 1) == 0) {
reg.model <- ""
} else {
reg.model <- paste("+", reg.parms[model.parms > 1], collapse = " ")
}
## generate list of parameters for jags to follow and produce mcmc output for
vars <- c("beta.o", "sd.y")
for (x in c("ghs", "site", "trt")) {
if (model.parms[[x]] == 1) {
data <- data[, which(names(data) != x)]
} else {
data <- data
if (x != "ghs") {
vars <- c(vars, paste0("sd.", x))
}
# m <- min(model.parms[[x]], 5)
m <- model.parms[[x]]
for (i in seq_len(m)) {
if (i == 1 && x == "site") {
vars <- c(vars, "beta.site[1]")
}
if (i > 1) {
vars <- c(vars, paste0("beta.", x, "[", i, "]"))
}
}
}
}
### Import defaul JAGS model file
modelfile <- system.file("ma.model.template.bug", package = "PEcAn.MA")
### Write JAGS bug file based on user settings and default bug file write.ma.model (modelfile =
### paste(settings$pecanDir,'rscripts/ma.model.template.bug',sep=''),
write.ma.model(modelfile = modelfile,
outfile = jag.model.file,
reg.model = reg.model,
jagsprior$distn, jagsprior$a, jagsprior$b,
n = length(data$Y),
trt.n = model.parms[["trt"]],
site.n = model.parms[["site"]],
ghs.n = model.parms[["ghs"]],
tauA = tauA, tauB = tauB)
if (overdispersed == TRUE) {
## overdispersed chains
j.inits <- function(chain) {
list(beta.o = do.call(paste("q", prior$dist, sep = ""),
list(chain * 1 / (j.chains + 1), prior$a, prior$b)),
.RNG.seed = chain,
.RNG.name = "base::Mersenne-Twister")
}
} else if (overdispersed == FALSE) {
## chains fixed at data mean - used if above code does not converge
## invalidates assumptions about convergence, e.g. Gelman-Rubin diagnostic
j.inits <- function(chain) { list(beta.o = mean(data$Y)) }
}
j.model <- rjags::jags.model(
file = jag.model.file,
data = data,
inits = j.inits,
n.chains = j.chains
)
jags.out <- rjags::coda.samples(
model = j.model,
variable.names = vars,
n.iter = j.iter,
thin = max(c(2, j.iter / (5000 * 2)))
)
## I would have done a while loop, but it could take forever
## So just give one chance to try again
if (coda::gelman.diag(jags.out)$mpsrf > 1.2) {
PEcAn.logger::logger.warn("model did not converge; re-running with j.iter * 10")
jags.out <- rjags::coda.samples(
model = j.model,
variable.names = vars,
n.iter = j.iter * 10,
thin = max(c(2, j.iter / (500 * 2)))
)
}
return(jags.out)
} # single.MA
|
/modules/meta.analysis/R/single.MA.R
|
permissive
|
PecanProject/pecan
|
R
| false | false | 5,312 |
r
|
#-------------------------------------------------------------------------------
# Copyright (c) 2012 University of Illinois, NCSA.
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the
# University of Illinois/NCSA Open Source License
# which accompanies this distribution, and is available at
# http://opensource.ncsa.illinois.edu/license.html
#-------------------------------------------------------------------------------
##' Individual Meta-analysis
##'
##' Individual meta-analysis for a specific trait and PFT is run by the function
##' single.MA. This will allow power analysis to run repeated MA outside of the
##' full loop over traits and PFTs.
##' @title Single MA
##' @param data data frame generated by jagify function
##' with indexed values for greenhouse, treatment, and site (ghs, trt, site)
##' as well as Y, SE, and n for each observation or summary statistic.
##' @param j.chains number of chains in meta-analysis
##' @param j.iter number of mcmc samples
##' @param tauA prior on variance parameters
##' @param tauB prior on variance parameters
##' @param prior data.frame with columns named 'distn', 'parama', 'paramb'
##' e.g. \code{prior <- data.frame(distn = 'weibull', parama = 0.5, paramb = 10, n = 1)}
##' @param jag.model.file file to which model will be written
##' @param overdispersed if TRUE (default), chains start at overdispersed locations in parameter space (recommended)
##' @export
##' @return jags.out, an mcmc.object with results of meta-analysis
##' @author David LeBauer, Michael C. Dietze
single.MA <- function(data, j.chains, j.iter, tauA, tauB, prior, jag.model.file,
overdispersed = TRUE) {
## Convert R distributions to JAGS distributions
jagsprior <- PEcAn.utils::r2bugs.distributions(prior)
jagsprior <- jagsprior[, c("distn", "parama", "paramb", "n")]
colnames(jagsprior) <- c("distn", "a", "b", "n")
colnames(prior) <- c("distn", "a", "b", "n")
# determine what factors to include in meta-analysis
model.parms <- list(ghs = length(unique(data$ghs)),
site = length(unique(data$site)),
trt = length(unique(data$trt)))
# define regression model
reg.parms <- list(ghs = "beta.ghs[ghs[k]]", # beta.o will be included by default
site = "beta.site[site[k]]",
trt = "beta.trt[trt[k]]")
# making sure ghs and trt are factor
data$ghs <- as.factor(data$ghs)
data$trt <- as.factor(data$trt)
if (sum(model.parms > 1) == 0) {
reg.model <- ""
} else {
reg.model <- paste("+", reg.parms[model.parms > 1], collapse = " ")
}
## generate list of parameters for jags to follow and produce mcmc output for
vars <- c("beta.o", "sd.y")
for (x in c("ghs", "site", "trt")) {
if (model.parms[[x]] == 1) {
data <- data[, which(names(data) != x)]
} else {
data <- data
if (x != "ghs") {
vars <- c(vars, paste0("sd.", x))
}
# m <- min(model.parms[[x]], 5)
m <- model.parms[[x]]
for (i in seq_len(m)) {
if (i == 1 && x == "site") {
vars <- c(vars, "beta.site[1]")
}
if (i > 1) {
vars <- c(vars, paste0("beta.", x, "[", i, "]"))
}
}
}
}
### Import defaul JAGS model file
modelfile <- system.file("ma.model.template.bug", package = "PEcAn.MA")
### Write JAGS bug file based on user settings and default bug file write.ma.model (modelfile =
### paste(settings$pecanDir,'rscripts/ma.model.template.bug',sep=''),
write.ma.model(modelfile = modelfile,
outfile = jag.model.file,
reg.model = reg.model,
jagsprior$distn, jagsprior$a, jagsprior$b,
n = length(data$Y),
trt.n = model.parms[["trt"]],
site.n = model.parms[["site"]],
ghs.n = model.parms[["ghs"]],
tauA = tauA, tauB = tauB)
if (overdispersed == TRUE) {
## overdispersed chains
j.inits <- function(chain) {
list(beta.o = do.call(paste("q", prior$dist, sep = ""),
list(chain * 1 / (j.chains + 1), prior$a, prior$b)),
.RNG.seed = chain,
.RNG.name = "base::Mersenne-Twister")
}
} else if (overdispersed == FALSE) {
## chains fixed at data mean - used if above code does not converge
## invalidates assumptions about convergence, e.g. Gelman-Rubin diagnostic
j.inits <- function(chain) { list(beta.o = mean(data$Y)) }
}
j.model <- rjags::jags.model(
file = jag.model.file,
data = data,
inits = j.inits,
n.chains = j.chains
)
jags.out <- rjags::coda.samples(
model = j.model,
variable.names = vars,
n.iter = j.iter,
thin = max(c(2, j.iter / (5000 * 2)))
)
## I would have done a while loop, but it could take forever
## So just give one chance to try again
if (coda::gelman.diag(jags.out)$mpsrf > 1.2) {
PEcAn.logger::logger.warn("model did not converge; re-running with j.iter * 10")
jags.out <- rjags::coda.samples(
model = j.model,
variable.names = vars,
n.iter = j.iter * 10,
thin = max(c(2, j.iter / (500 * 2)))
)
}
return(jags.out)
} # single.MA
|
# Make figure 4
# RIL effect on all polymorphs
fig4 <- caco3_comp %>%
modelr::data_grid(log_weight = mean(log_weight),
log_ril = modelr::seq_range(log_ril, n = 101),
mean_T = mean(mean_T),
sqrt_asp_ratio = mean(sqrt_asp_ratio),
method = "double") %>%
tidybayes::add_epred_draws(m3_caco3_comp, resp = resp, re_formula = NA) %>%
mutate(mineral = factor(.category,
levels = paste0(c("L", "AR", "H", "M", "AC"), "umolh"),
labels = c("LMC", "Aragonite", "HMC", "MHC", "ACMC")
)
) %>%
ggplot(aes(x = exp(log_ril))) +
tidybayes::stat_lineribbon(aes(y = .epred, color = mineral), size = 0.5, .width = c(0.5, 0.8, 0.95)) +
scale_color_viridis_d(option = "C", end = 0.95, guide = "none") +
scale_fill_brewer("CI", palette = "Greys") +
facet_grid(rows = "mineral", scales = "free_y") +
ylab("Excretion rate (μmol h<sup>-1</sup>)") +
xlab("Relative intestinal length") +
theme(axis.title.y = ggtext::element_markdown(),
legend.position = "bottom",
legend.title = element_text(size = 11),
legend.text = element_text(size = 10),
legend.key.size = unit(4, "mm"),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 12))
ggsave(here::here("outputs", "figures", "fig4.png"), fig4,
width = 10, height = 24, units = "cm", dpi = 600, type = "cairo")
ggsave(here::here("outputs", "figures", "fig4.pdf"), fig4,
width = 10, height = 24, units = "cm", device = cairo_pdf)
|
/analysis/figure4.R
|
permissive
|
mattiaghilardi/FishCaCO3Model
|
R
| false | false | 1,639 |
r
|
# Make figure 4
# RIL effect on all polymorphs
fig4 <- caco3_comp %>%
modelr::data_grid(log_weight = mean(log_weight),
log_ril = modelr::seq_range(log_ril, n = 101),
mean_T = mean(mean_T),
sqrt_asp_ratio = mean(sqrt_asp_ratio),
method = "double") %>%
tidybayes::add_epred_draws(m3_caco3_comp, resp = resp, re_formula = NA) %>%
mutate(mineral = factor(.category,
levels = paste0(c("L", "AR", "H", "M", "AC"), "umolh"),
labels = c("LMC", "Aragonite", "HMC", "MHC", "ACMC")
)
) %>%
ggplot(aes(x = exp(log_ril))) +
tidybayes::stat_lineribbon(aes(y = .epred, color = mineral), size = 0.5, .width = c(0.5, 0.8, 0.95)) +
scale_color_viridis_d(option = "C", end = 0.95, guide = "none") +
scale_fill_brewer("CI", palette = "Greys") +
facet_grid(rows = "mineral", scales = "free_y") +
ylab("Excretion rate (μmol h<sup>-1</sup>)") +
xlab("Relative intestinal length") +
theme(axis.title.y = ggtext::element_markdown(),
legend.position = "bottom",
legend.title = element_text(size = 11),
legend.text = element_text(size = 10),
legend.key.size = unit(4, "mm"),
strip.background = element_blank(),
strip.text = element_text(face = "bold", size = 12))
ggsave(here::here("outputs", "figures", "fig4.png"), fig4,
width = 10, height = 24, units = "cm", dpi = 600, type = "cairo")
ggsave(here::here("outputs", "figures", "fig4.pdf"), fig4,
width = 10, height = 24, units = "cm", device = cairo_pdf)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ConstraintTaxo2newick.R
\name{ConstraintTaxo2newick}
\alias{ConstraintTaxo2newick}
\title{Build a multifurcating topological constraint tree for RAxML}
\usage{
ConstraintTaxo2newick(inputTaxo = NULL, Taxo.Hier = "Phylum2Genus",
inputConst = NULL, outputNewick = NULL)
}
\arguments{
\item{inputTaxo}{a classification table, the first column is the species name (or
the name used as tip.label by the phylogenetic tree), followed by
the different hierarchical levels of the Linnaean classification
in the subsequent columns.}
\item{Taxo.Hier}{order of entry for the different hierarchical levels of the
Linnaean classification. "Phylum2Genus" is used by default, where the second column is the highest
level (e.g. Phylum) and the last column is the lowest classification level (e.g. Genus).
The reverse can also be used, using "Genus2Phylum" (i.e. the second column contains the lowest
level and the last column contains the highest).}
\item{inputConst}{a two column table: the first column refers to the hierarchical
level of the topological constraints (e.g. 'Family', or 'Order', or
'Subdivision'; note that the names of the hierarchical levels must be the same as
the headers of the classification table); the second column contains the name
of the taxa to be constrained as monophyletic (e.g. 'Aplodactylidae',
Aulopiformes', 'Percomorphaceae').}
\item{outputNewick}{name of the output multifurcating newick tree that will
be exported in a .txt file (can also include the path to the folder).}
}
\value{
This function exports into the R environment a list of two objects. The first
object is the taxonomic table modified to include the constraints, and the
second object is the multifurcating tree converted into a 'phylo' object.
The function also exports a newick tree as a txt document that can be used to constrain
the topology in RAxML.
}
\description{
This function builds a multifurcating phylogenetic tree
from a classification table and a table of phylogenetic constraints ready to be
used by RAxML as a constraint tree (option -g in RAxML) to guide the
reconstruction of the molecular phylogenetic tree.
}
\details{
Warning: branch lengths of the multifurcating tree are misleading, only the
topology matters.
}
\examples{
# Load the table listing the topological constraints (first object of the list)
# and the classification table (second object of the list).
\dontrun{
data(TopoConstraints)
# The table details 22 topological constraints overall,
# including constraints at the Family, Order, Series, Subdivision, Division,
# Subsection, Subcohort, Cohort, Supercohort, Infraclass, and Subclass.
#
# The classification table includes 16 species from the New Zealand marine
# ray-finned fish species list.
# Create a temporary folder to store the outputs of the function.
dir.create("TempDir.TopoConstraints")
# Run the function considering all the constraints.
BackBoneTreeAll = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = TopoConstraints[[1]], outputNewick = "TempDir.TopoConstraints/BackboneTreeAll")
# Plot the constraining tree (the branch length do not matter, only the topology matters).
plot(BackBoneTreeAll[[2]], cex=0.8)
# Use only the constraints at the Family level.
FamilyConst=TopoConstraints[[1]][TopoConstraints[[1]][,1]=="Family",]
# Run the function considering only the constraints at the family level.
BackBoneTreeFamily = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = FamilyConst, outputNewick = "TempDir.TopoConstraints/BackboneTreeFamily")
# Plot the constraining tree (the branch length does not matter,
# only the topology matters), notice that only constrained taxa
# are present on the guiding tree, the other (unconstrained) taxa will
# be positioned on the tree based on their molecular affinities.
plot(BackBoneTreeFamily[[2]], cex=0.8)
# Use only the constraints at the Family and Series levels.
FamilySeriesConst=TopoConstraints[[1]][c(which(TopoConstraints[[1]][,1] == "Family"),
which(TopoConstraints[[1]][,1] == "Series")),]
# Run the function considering only the constraints at the Family and Order levels.
BackBoneTreeFamilySeries = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = FamilySeriesConst, outputNewick = "TempDir.TopoConstraints/BackboneTreeFamilySeries")
# Plot the constraining tree (the branch length does not matter,
# only the topology matters). Notice that only constrained taxa
# are present on the guiding tree, the other (unconstrained) taxa will
# be positioned on the tree based on their molecular affinities.
plot(BackBoneTreeFamilySeries[[2]], cex=0.8)
# To remove the files created while running the example do the following:
unlink("TempDir.TopoConstraints", recursive = TRUE)
}
}
|
/man/ConstraintTaxo2newick.Rd
|
no_license
|
ignacio3437/regPhylo
|
R
| false | true | 4,821 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ConstraintTaxo2newick.R
\name{ConstraintTaxo2newick}
\alias{ConstraintTaxo2newick}
\title{Build a multifurcating topological constraint tree for RAxML}
\usage{
ConstraintTaxo2newick(inputTaxo = NULL, Taxo.Hier = "Phylum2Genus",
inputConst = NULL, outputNewick = NULL)
}
\arguments{
\item{inputTaxo}{a classification table, the first column is the species name (or
the name used as tip.label by the phylogenetic tree), followed by
the different hierarchical levels of the Linnaean classification
in the subsequent columns.}
\item{Taxo.Hier}{order of entry for the different hierarchical levels of the
Linnaean classification. "Phylum2Genus" is used by default, where the second column is the highest
level (e.g. Phylum) and the last column is the lowest classification level (e.g. Genus).
The reverse can also be used, using "Genus2Phylum" (i.e. the second column contains the lowest
level and the last column contains the highest).}
\item{inputConst}{a two column table: the first column refers to the hierarchical
level of the topological constraints (e.g. 'Family', or 'Order', or
'Subdivision'; note that the names of the hierarchical levels must be the same as
the headers of the classification table); the second column contains the name
of the taxa to be constrained as monophyletic (e.g. 'Aplodactylidae',
Aulopiformes', 'Percomorphaceae').}
\item{outputNewick}{name of the output multifurcating newick tree that will
be exported in a .txt file (can also include the path to the folder).}
}
\value{
This function exports into the R environment a list of two objects. The first
object is the taxonomic table modified to include the constraints, and the
second object is the multifurcating tree converted into a 'phylo' object.
The function also exports a newick tree as a txt document that can be used to constrain
the topology in RAxML.
}
\description{
This function builds a multifurcating phylogenetic tree
from a classification table and a table of phylogenetic constraints ready to be
used by RAxML as a constraint tree (option -g in RAxML) to guide the
reconstruction of the molecular phylogenetic tree.
}
\details{
Warning: branch lengths of the multifurcating tree are misleading, only the
topology matters.
}
\examples{
# Load the table listing the topological constraints (first object of the list)
# and the classification table (second object of the list).
\dontrun{
data(TopoConstraints)
# The table details 22 topological constraints overall,
# including constraints at the Family, Order, Series, Subdivision, Division,
# Subsection, Subcohort, Cohort, Supercohort, Infraclass, and Subclass.
#
# The classification table includes 16 species from the New Zealand marine
# ray-finned fish species list.
# Create a temporary folder to store the outputs of the function.
dir.create("TempDir.TopoConstraints")
# Run the function considering all the constraints.
BackBoneTreeAll = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = TopoConstraints[[1]], outputNewick = "TempDir.TopoConstraints/BackboneTreeAll")
# Plot the constraining tree (the branch length do not matter, only the topology matters).
plot(BackBoneTreeAll[[2]], cex=0.8)
# Use only the constraints at the Family level.
FamilyConst=TopoConstraints[[1]][TopoConstraints[[1]][,1]=="Family",]
# Run the function considering only the constraints at the family level.
BackBoneTreeFamily = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = FamilyConst, outputNewick = "TempDir.TopoConstraints/BackboneTreeFamily")
# Plot the constraining tree (the branch length does not matter,
# only the topology matters), notice that only constrained taxa
# are present on the guiding tree, the other (unconstrained) taxa will
# be positioned on the tree based on their molecular affinities.
plot(BackBoneTreeFamily[[2]], cex=0.8)
# Use only the constraints at the Family and Series levels.
FamilySeriesConst=TopoConstraints[[1]][c(which(TopoConstraints[[1]][,1] == "Family"),
which(TopoConstraints[[1]][,1] == "Series")),]
# Run the function considering only the constraints at the Family and Order levels.
BackBoneTreeFamilySeries = ConstraintTaxo2newick(inputTaxo = TopoConstraints[[2]],
inputConst = FamilySeriesConst, outputNewick = "TempDir.TopoConstraints/BackboneTreeFamilySeries")
# Plot the constraining tree (the branch length does not matter,
# only the topology matters). Notice that only constrained taxa
# are present on the guiding tree, the other (unconstrained) taxa will
# be positioned on the tree based on their molecular affinities.
plot(BackBoneTreeFamilySeries[[2]], cex=0.8)
# To remove the files created while running the example do the following:
unlink("TempDir.TopoConstraints", recursive = TRUE)
}
}
|
# Reading JSON
## Reading JSON files
library(jsonlite)
jsonData <- fromJSON("https://api.github.com/users/jtleek/repos")
names(jsonData)
names(jsonData$owner)
jsonData$owner$login
## Writing data frames to JSON
myjson <- toJSON(iris, pretty = TRUE)
cat(myjson)
## Convert back to JSON
iris2 <- fromJSON(myjson)
head(iris2)
|
/3_GettingAndCleaningData/week1/Reading JSON.R
|
no_license
|
artickavenger/datasciencecoursera
|
R
| false | false | 328 |
r
|
# Reading JSON
## Reading JSON files
library(jsonlite)
jsonData <- fromJSON("https://api.github.com/users/jtleek/repos")
names(jsonData)
names(jsonData$owner)
jsonData$owner$login
## Writing data frames to JSON
myjson <- toJSON(iris, pretty = TRUE)
cat(myjson)
## Convert back to JSON
iris2 <- fromJSON(myjson)
head(iris2)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/driver_redis_api.R
\name{storr_redis_api}
\alias{driver_redis_api}
\alias{storr_redis_api}
\title{Redis object cache driver}
\usage{
storr_redis_api(prefix, con, default_namespace = "objects")
driver_redis_api(prefix, con)
}
\arguments{
\item{prefix}{Prefix for keys. We'll generate a number of keys
that start with this string. Probably terminating the string
with a punctuation character (e.g., ":") will make created
strings nicer to deal with.}
\item{con}{A \code{redis_api} connection object, as created by the
RedisAPI package. This package does not actually provide the
tools to create a connection; you need to provide one that is
good to go.}
\item{default_namespace}{Default namespace (see \code{\link{storr}}).}
}
\description{
Redis object cache driver
}
\author{
Rich FitzJohn
}
|
/man/storr_redis_api.Rd
|
no_license
|
mcdelaney/storr
|
R
| false | true | 877 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/driver_redis_api.R
\name{storr_redis_api}
\alias{driver_redis_api}
\alias{storr_redis_api}
\title{Redis object cache driver}
\usage{
storr_redis_api(prefix, con, default_namespace = "objects")
driver_redis_api(prefix, con)
}
\arguments{
\item{prefix}{Prefix for keys. We'll generate a number of keys
that start with this string. Probably terminating the string
with a punctuation character (e.g., ":") will make created
strings nicer to deal with.}
\item{con}{A \code{redis_api} connection object, as created by the
RedisAPI package. This package does not actually provide the
tools to create a connection; you need to provide one that is
good to go.}
\item{default_namespace}{Default namespace (see \code{\link{storr}}).}
}
\description{
Redis object cache driver
}
\author{
Rich FitzJohn
}
|
library(iosmooth)
### Name: bwplot.ts
### Title: Bandwidth plot for spectral density estimation
### Aliases: bwplot.ts
### Keywords: Spectral Density Flat-top kernel Smoothing parameter
### ** Examples
set.seed(123)
x <- arima.sim(list(ar=.7, ma=-.3), 100)
bwplot(x)
bwadap(x)
# Choose a smoothing parameter of 3
plot(iospecden(x, l=3), type="l")
|
/data/genthat_extracted_code/iosmooth/examples/bwplot.ts.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 354 |
r
|
library(iosmooth)
### Name: bwplot.ts
### Title: Bandwidth plot for spectral density estimation
### Aliases: bwplot.ts
### Keywords: Spectral Density Flat-top kernel Smoothing parameter
### ** Examples
set.seed(123)
x <- arima.sim(list(ar=.7, ma=-.3), 100)
bwplot(x)
bwadap(x)
# Choose a smoothing parameter of 3
plot(iospecden(x, l=3), type="l")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expressionAnalysis.R
\name{plotExpression}
\alias{plotExpression}
\title{Overlap with expression data}
\usage{
plotExpression(hmm, expression, combinations = NULL, return.marks = FALSE)
}
\arguments{
\item{hmm}{A \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.}
\item{expression}{A \code{\link{GRanges-class}} object with metadata column 'expression', containing the expression value for each range.}
\item{combinations}{A vector with combinations for which the expression overlap will be calculated. If \code{NULL} all combinations will be considered.}
\item{return.marks}{Set to \code{TRUE} if expression values for marks instead of combinations should be returned.}
}
\value{
A \code{\link{ggplot2}} object if a \code{\link{multiHMM}} was given or a named list with \code{\link{ggplot2}} objects if a \code{\link{combinedMultiHMM}} was given.
}
\description{
Get the expression values that overlap with each combinatorial state.
}
\examples{
## Load an example multiHMM
file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
package="chromstaR")
model <- get(load(file))
## Obtain expression data
data(expression_lv)
head(expression_lv)
## We need to get coordinates for each of the genes
library(biomaRt)
ensembl <- useMart('ENSEMBL_MART_ENSEMBL', host='may2012.archive.ensembl.org',
dataset='rnorvegicus_gene_ensembl')
genes <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name', 'start_position',
'end_position', 'strand', 'external_gene_id',
'gene_biotype'),
mart=ensembl)
expr <- merge(genes, expression_lv, by='ensembl_gene_id')
# Transform to GRanges
expression.SHR <- GRanges(seqnames=paste0('chr',expr$chromosome_name),
ranges=IRanges(start=expr$start, end=expr$end),
strand=expr$strand, name=expr$external_gene_id,
biotype=expr$gene_biotype,
expression=expr$expression_SHR)
# We apply an asinh transformation to reduce the effect of outliers
expression.SHR$expression <- asinh(expression.SHR$expression)
## Plot
plotExpression(model, expression.SHR) +
theme(axis.text.x=element_text(angle=0, hjust=0.5)) +
ggtitle('Expression of genes overlapping combinatorial states')
plotExpression(model, expression.SHR, return.marks=TRUE) +
ggtitle('Expression of marks overlapping combinatorial states')
}
\seealso{
\code{\link{plotting}}
}
\author{
Aaron Taudt
}
|
/man/plotExpression.Rd
|
no_license
|
zorrodong/chromstaR
|
R
| false | true | 2,646 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expressionAnalysis.R
\name{plotExpression}
\alias{plotExpression}
\title{Overlap with expression data}
\usage{
plotExpression(hmm, expression, combinations = NULL, return.marks = FALSE)
}
\arguments{
\item{hmm}{A \code{\link{multiHMM}} or \code{\link{combinedMultiHMM}} object or file that contains such an object.}
\item{expression}{A \code{\link{GRanges-class}} object with metadata column 'expression', containing the expression value for each range.}
\item{combinations}{A vector with combinations for which the expression overlap will be calculated. If \code{NULL} all combinations will be considered.}
\item{return.marks}{Set to \code{TRUE} if expression values for marks instead of combinations should be returned.}
}
\value{
A \code{\link{ggplot2}} object if a \code{\link{multiHMM}} was given or a named list with \code{\link{ggplot2}} objects if a \code{\link{combinedMultiHMM}} was given.
}
\description{
Get the expression values that overlap with each combinatorial state.
}
\examples{
## Load an example multiHMM
file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
package="chromstaR")
model <- get(load(file))
## Obtain expression data
data(expression_lv)
head(expression_lv)
## We need to get coordinates for each of the genes
library(biomaRt)
ensembl <- useMart('ENSEMBL_MART_ENSEMBL', host='may2012.archive.ensembl.org',
dataset='rnorvegicus_gene_ensembl')
genes <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name', 'start_position',
'end_position', 'strand', 'external_gene_id',
'gene_biotype'),
mart=ensembl)
expr <- merge(genes, expression_lv, by='ensembl_gene_id')
# Transform to GRanges
expression.SHR <- GRanges(seqnames=paste0('chr',expr$chromosome_name),
ranges=IRanges(start=expr$start, end=expr$end),
strand=expr$strand, name=expr$external_gene_id,
biotype=expr$gene_biotype,
expression=expr$expression_SHR)
# We apply an asinh transformation to reduce the effect of outliers
expression.SHR$expression <- asinh(expression.SHR$expression)
## Plot
plotExpression(model, expression.SHR) +
theme(axis.text.x=element_text(angle=0, hjust=0.5)) +
ggtitle('Expression of genes overlapping combinatorial states')
plotExpression(model, expression.SHR, return.marks=TRUE) +
ggtitle('Expression of marks overlapping combinatorial states')
}
\seealso{
\code{\link{plotting}}
}
\author{
Aaron Taudt
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/capitais.R
\name{capitais}
\alias{capitais}
\title{Current weather conditions of capitals}
\usage{
capitais()
}
\value{
The function returns a data frame
}
\description{
This function allows the user to retrieve data from
CPTEC/INPE current weather conditions of capitals.
The data frame returned contain actual temperature,
atmospheric pressure, humidity, direction and wind
intensity
}
\examples{
capitais()
}
\seealso{
\code{\link{prev4dias}}
}
\author{
Renato Prado Siqueira \email{rpradosiqueira@gmail.com}
}
\keyword{brazil}
\keyword{forecast}
\keyword{weather}
|
/man/capitais.Rd
|
no_license
|
rpradosiqueira/cptec
|
R
| false | true | 647 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/capitais.R
\name{capitais}
\alias{capitais}
\title{Current weather conditions of capitals}
\usage{
capitais()
}
\value{
The function returns a data frame
}
\description{
This function allows the user to retrieve data from
CPTEC/INPE current weather conditions of capitals.
The data frame returned contain actual temperature,
atmospheric pressure, humidity, direction and wind
intensity
}
\examples{
capitais()
}
\seealso{
\code{\link{prev4dias}}
}
\author{
Renato Prado Siqueira \email{rpradosiqueira@gmail.com}
}
\keyword{brazil}
\keyword{forecast}
\keyword{weather}
|
## Script to unlist big stan fit objects and save as ggs files
library(rstan)
library(ggmcmc)
bigstan <- readRDS("recruitment_stanfits.RDS")
spp_list <- c("BOGR", "HECO", "PASM", "POSE")
for(spp in spp_list){
tmp <- bigstan[[spp]]
long <- ggs(tmp)
file <- paste("recruitment_stanmcmc_", spp, ".RDS", sep="")
saveRDS(long, file)
}
ggs_caterpillar(long, family="b2")
|
/analysis/ipm/vitalRateRegs/cache/recruitment_cache/stan2ggs.R
|
no_license
|
atredennick/MicroMesoForecast
|
R
| false | false | 378 |
r
|
## Script to unlist big stan fit objects and save as ggs files
library(rstan)
library(ggmcmc)
bigstan <- readRDS("recruitment_stanfits.RDS")
spp_list <- c("BOGR", "HECO", "PASM", "POSE")
for(spp in spp_list){
tmp <- bigstan[[spp]]
long <- ggs(tmp)
file <- paste("recruitment_stanmcmc_", spp, ".RDS", sep="")
saveRDS(long, file)
}
ggs_caterpillar(long, family="b2")
|
testlist <- list(ends = c(-1125300777L, 765849512L, -1760774663L, 791623263L, 1358782356L, -128659642L, -14914341L, 1092032927L, 1837701012L, 1632068659L), pts = c(1758370433L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), starts = c(16777216L, 0L, 738263040L, 682962941L, 1612840977L, 150997320L, 747898999L, -1195392662L, 2024571419L, 808515032L, 1373469055L, -282236997L, -207881465L, -237801926L, -168118689L, -1090227888L, 235129118L, 949454105L, 1651285440L, -1119277666L, 1892621188L), members = NULL, total_members = 0L)
result <- do.call(IntervalSurgeon:::rcpp_pile,testlist)
str(result)
|
/IntervalSurgeon/inst/testfiles/rcpp_pile/AFL_rcpp_pile/rcpp_pile_valgrind_files/1609874958-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false | false | 728 |
r
|
testlist <- list(ends = c(-1125300777L, 765849512L, -1760774663L, 791623263L, 1358782356L, -128659642L, -14914341L, 1092032927L, 1837701012L, 1632068659L), pts = c(1758370433L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), starts = c(16777216L, 0L, 738263040L, 682962941L, 1612840977L, 150997320L, 747898999L, -1195392662L, 2024571419L, 808515032L, 1373469055L, -282236997L, -207881465L, -237801926L, -168118689L, -1090227888L, 235129118L, 949454105L, 1651285440L, -1119277666L, 1892621188L), members = NULL, total_members = 0L)
result <- do.call(IntervalSurgeon:::rcpp_pile,testlist)
str(result)
|
RawAccel <- read.csv('OnlyCalibratedAccelFlowData.csv')
# Calculate Gproj_XY, the magnitude of the projection of the direction vector,
# H, in the XY plane.
RawAccel$Gproj_XY <- sqrt(RawAccel$Xcal^2 + RawAccel$Ycal^2)
# Calculate tilt angle
RawAccel$tiltAngle <- atan2(RawAccel$Gproj_XY, RawAccel$Zcal) * 180 / pi
# Examining where the accel data has non-negative signs
# > sum(RawAccel$Xcal > 0 | RawAccel$Ycal > 0)
# [1] 95 (out of 25,442 data points)
|
/Scripts/ExaminingAccel.R
|
no_license
|
abby-lammers/LSM303-Data-Processing
|
R
| false | false | 460 |
r
|
RawAccel <- read.csv('OnlyCalibratedAccelFlowData.csv')
# Calculate Gproj_XY, the magnitude of the projection of the direction vector,
# H, in the XY plane.
RawAccel$Gproj_XY <- sqrt(RawAccel$Xcal^2 + RawAccel$Ycal^2)
# Calculate tilt angle
RawAccel$tiltAngle <- atan2(RawAccel$Gproj_XY, RawAccel$Zcal) * 180 / pi
# Examining where the accel data has non-negative signs
# > sum(RawAccel$Xcal > 0 | RawAccel$Ycal > 0)
# [1] 95 (out of 25,442 data points)
|
# Get and Set date and time components
# affiliate: https://linkedin-learning.pxf.io/rdatesGetSetLubridate
library(lubridate)
# see how easy!
PabloPicassoBday <- mdy_hm("October 25, 1881, 11:15 PM")
# add
year(PabloPicassoBday) + 3
# adding three years with Base R
PPB_lt <- as.POSIXlt(PabloPicassoBday)
PPB_lt$year # year is offset from 1900
PPB_lt$year <- PPB_lt$year + 3
PPB_lt$year + 3 # PPB_lt$year is -19 +3 = -16
PPB_lt
# retrieving parts of a date
date(PabloPicassoBday)
year(PabloPicassoBday)
month(PabloPicassoBday)
# sometimes lubridate is more configurable
weekdays(PabloPicassoBday) # base R
wday(PabloPicassoBday, label = TRUE, abbr = FALSE) # lubridate is a bit more complex
# Change Pablo's birthday to February
PPB_feb <- as.POSIXlt(PabloPicassoBday) # make a copy
PPB_feb$mon <- 1 # month: Jan = 0
PPB_feb
# with lubridate
PPB_feb <- PabloPicassoBday #make a copy
month(PPB_feb) <- 2
PPB_feb
# Lubridate provides semester, am/pm, dst, leap_year
semester(PabloPicassoBday)
am(PabloPicassoBday)
dst(PabloPicassoBday)
leap_year(PabloPicassoBday)
|
/chapter 03/03_03 get and set.R
|
no_license
|
mnr/R-for-Data-Science-dates-and-times
|
R
| false | false | 1,074 |
r
|
# Get and Set date and time components
# affiliate: https://linkedin-learning.pxf.io/rdatesGetSetLubridate
library(lubridate)
# see how easy!
PabloPicassoBday <- mdy_hm("October 25, 1881, 11:15 PM")
# add
year(PabloPicassoBday) + 3
# adding three years with Base R
PPB_lt <- as.POSIXlt(PabloPicassoBday)
PPB_lt$year # year is offset from 1900
PPB_lt$year <- PPB_lt$year + 3
PPB_lt$year + 3 # PPB_lt$year is -19 +3 = -16
PPB_lt
# retrieving parts of a date
date(PabloPicassoBday)
year(PabloPicassoBday)
month(PabloPicassoBday)
# sometimes lubridate is more configurable
weekdays(PabloPicassoBday) # base R
wday(PabloPicassoBday, label = TRUE, abbr = FALSE) # lubridate is a bit more complex
# Change Pablo's birthday to February
PPB_feb <- as.POSIXlt(PabloPicassoBday) # make a copy
PPB_feb$mon <- 1 # month: Jan = 0
PPB_feb
# with lubridate
PPB_feb <- PabloPicassoBday #make a copy
month(PPB_feb) <- 2
PPB_feb
# Lubridate provides semester, am/pm, dst, leap_year
semester(PabloPicassoBday)
am(PabloPicassoBday)
dst(PabloPicassoBday)
leap_year(PabloPicassoBday)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/confint-methods.R
\name{confint.fderiv}
\alias{confint.fderiv}
\title{Point-wise and simultaneous confidence intervals for derivatives of smooths}
\usage{
\method{confint}{fderiv}(object, parm, level = 0.95,
type = c("confidence", "simultaneous"), nsim = 10000, ncores = 1,
...)
}
\arguments{
\item{object}{an object of class \code{"fderiv"} containing the estimated
derivatives.}
\item{parm}{which parameters (smooth terms) are to be given intervals as a
vector of terms. If missing, all parameters are considered.}
\item{level}{numeric, \code{0 < level < 1}; the confidence level of the
point-wise or simultaneous interval. The default is \code{0.95} for a 95%
interval.}
\item{type}{character; the type of interval to compute. One of \code{"confidence"}
for point-wise intervals, or \code{"simultaneous"} for simultaneous intervals.}
\item{nsim}{integer; the number of simulations used in computing the
simultaneous intervals.}
\item{ncores}{number of cores for generating random variables from a
multivariate normal distribution. Passed to \code{mvnfast::rmvn}.
Parallelization will take place only if OpenMP is supported (but appears
to work on Windows with current \code{R}).}
\item{...}{additional arguments for methods}
}
\value{
a data frame with components:
\enumerate{
\item \code{term}; factor indicating to which term each row relates,
\item \code{lower}; lower limit of the confidence or simultaneous interval,
\item \code{est}; estimated derivative
\item \code{upper}; upper limit of the confidence or simultaneous interval.
}
}
\description{
Calculates point-wise confidence or simultaneous intervals for the first
derivatives of smooth terms in a fitted GAM.
}
\examples{
suppressPackageStartupMessages(library("mgcv"))
\dontshow{
set.seed(2)
op <- options(digits = 5)
}
dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")
## first derivatives of all smooths...
fd <- fderiv(mod)
## point-wise interval
ci <- confint(fd, type = "confidence")
head(ci)
## simultaneous interval for smooth term of x1
set.seed(42)
x1.sint <- confint(fd, parm = "x1", type = "simultaneous", nsim = 1000)
head(x1.sint)
\dontshow{options(op)}
}
\author{
Gavin L. Simpson
}
|
/man/confint.fderiv.Rd
|
permissive
|
singmann/gratia
|
R
| false | true | 2,335 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/confint-methods.R
\name{confint.fderiv}
\alias{confint.fderiv}
\title{Point-wise and simultaneous confidence intervals for derivatives of smooths}
\usage{
\method{confint}{fderiv}(object, parm, level = 0.95,
type = c("confidence", "simultaneous"), nsim = 10000, ncores = 1,
...)
}
\arguments{
\item{object}{an object of class \code{"fderiv"} containing the estimated
derivatives.}
\item{parm}{which parameters (smooth terms) are to be given intervals as a
vector of terms. If missing, all parameters are considered.}
\item{level}{numeric, \code{0 < level < 1}; the confidence level of the
point-wise or simultaneous interval. The default is \code{0.95} for a 95%
interval.}
\item{type}{character; the type of interval to compute. One of \code{"confidence"}
for point-wise intervals, or \code{"simultaneous"} for simultaneous intervals.}
\item{nsim}{integer; the number of simulations used in computing the
simultaneous intervals.}
\item{ncores}{number of cores for generating random variables from a
multivariate normal distribution. Passed to \code{mvnfast::rmvn}.
Parallelization will take place only if OpenMP is supported (but appears
to work on Windows with current \code{R}).}
\item{...}{additional arguments for methods}
}
\value{
a data frame with components:
\enumerate{
\item \code{term}; factor indicating to which term each row relates,
\item \code{lower}; lower limit of the confidence or simultaneous interval,
\item \code{est}; estimated derivative
\item \code{upper}; upper limit of the confidence or simultaneous interval.
}
}
\description{
Calculates point-wise confidence or simultaneous intervals for the first
derivatives of smooth terms in a fitted GAM.
}
\examples{
suppressPackageStartupMessages(library("mgcv"))
\dontshow{
set.seed(2)
op <- options(digits = 5)
}
dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
mod <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")
## first derivatives of all smooths...
fd <- fderiv(mod)
## point-wise interval
ci <- confint(fd, type = "confidence")
head(ci)
## simultaneous interval for smooth term of x1
set.seed(42)
x1.sint <- confint(fd, parm = "x1", type = "simultaneous", nsim = 1000)
head(x1.sint)
\dontshow{options(op)}
}
\author{
Gavin L. Simpson
}
|
# --------------------------------------
# purpose: create ensemble members of downscaling noise
# Creator: Laura Puckett, December 21 2018
# contact: plaura1@vt.edu
# --------------------------------------
# summary: creates ensemble members with noise addition (random sample from normal distribution with standard deviation equal to saved standard deviation of residuals from downscaling process.) For all variables except Shortwave, this is the standard deviaiton of the residuals after downscaling to the hourly resolution. For Shortwave, is is the value after downscaling to the daily resolution to artificually high values that result from noise in observational data from hour to hour.
# --------------------------------------
add_noise <- function(debiased, coeff.df, nmembers)
debiased.with.noise <- debiased %>%
group_by(timestamp, NOAA.member, AirTemp, RelHum, WindSpeed, ShortWave, LongWave) %>%
expand(dscale.member = 1:nmembers) %>%
ungroup() %>%
group_by(dscale.member, NOAA.member) %>%
dplyr::mutate(AirTemp = AirTemp + rnorm(mean = 0, sd = coeff.df$AirTemp[5], n = 1),
RelHum = RelHum + rnorm(mean = 0, sd = coeff.df$RelHum[5], n = 1),
WindSpeed = WindSpeed + rnorm(mean = 0, sd = coeff.df$WindSpeed[5], n = 1),
ShortWave = ifelse(ShortWave == 0, # not adding noise to night-time values
0,
ShortWave + rnorm(mean = 0, sd = coeff.df$ShortWave[3], n = 1)),
LongWave = LongWave + rnorm(mean = 0, sd = coeff.df$LongWave[5], n = 1)) %>%
ungroup() %>%
select(timestamp, dscale.member, NOAA.member, AirTemp, RelHum, WindSpeed, ShortWave, LongWave)
|
/add_noise.R
|
no_license
|
EcoDynForecast/NOAA_download_downscale
|
R
| false | false | 1,709 |
r
|
# --------------------------------------
# purpose: create ensemble members of downscaling noise
# Creator: Laura Puckett, December 21 2018
# contact: plaura1@vt.edu
# --------------------------------------
# summary: creates ensemble members with noise addition (random sample from normal distribution with standard deviation equal to saved standard deviation of residuals from downscaling process.) For all variables except Shortwave, this is the standard deviaiton of the residuals after downscaling to the hourly resolution. For Shortwave, is is the value after downscaling to the daily resolution to artificually high values that result from noise in observational data from hour to hour.
# --------------------------------------
add_noise <- function(debiased, coeff.df, nmembers)
debiased.with.noise <- debiased %>%
group_by(timestamp, NOAA.member, AirTemp, RelHum, WindSpeed, ShortWave, LongWave) %>%
expand(dscale.member = 1:nmembers) %>%
ungroup() %>%
group_by(dscale.member, NOAA.member) %>%
dplyr::mutate(AirTemp = AirTemp + rnorm(mean = 0, sd = coeff.df$AirTemp[5], n = 1),
RelHum = RelHum + rnorm(mean = 0, sd = coeff.df$RelHum[5], n = 1),
WindSpeed = WindSpeed + rnorm(mean = 0, sd = coeff.df$WindSpeed[5], n = 1),
ShortWave = ifelse(ShortWave == 0, # not adding noise to night-time values
0,
ShortWave + rnorm(mean = 0, sd = coeff.df$ShortWave[3], n = 1)),
LongWave = LongWave + rnorm(mean = 0, sd = coeff.df$LongWave[5], n = 1)) %>%
ungroup() %>%
select(timestamp, dscale.member, NOAA.member, AirTemp, RelHum, WindSpeed, ShortWave, LongWave)
|
# yamadaが遊び用に使うファイル
|
/personal/hanako_yamada/calc_sold_count.R
|
no_license
|
takahiro-umeda/file_share_practice
|
R
| false | false | 42 |
r
|
# yamadaが遊び用に使うファイル
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nf_to_annual.R
\name{nf_to_annual}
\alias{nf_to_annual}
\alias{nf_to_annual.xts}
\alias{nf_to_annual.nfd}
\alias{nf_to_annual.crss_nf}
\alias{nf_to_annual.crssi}
\title{Sum monthly natural flow data to annual data}
\usage{
nf_to_annual(x, ...)
\method{nf_to_annual}{xts}(x, ..., year = "cy", full_year = TRUE)
\method{nf_to_annual}{nfd}(x, ..., full_year = TRUE, recompute = FALSE, keep_monthly = TRUE)
\method{nf_to_annual}{crss_nf}(x, ..., full_year = TRUE, recompute = FALSE, keep_monthly = TRUE)
\method{nf_to_annual}{crssi}(x, ..., recompute = FALSE)
}
\arguments{
\item{x}{An object inheriting from \code{xts}.}
\item{...}{Other parameters passed to methods.}
\item{year}{"cy" or "wy" to sum over the calendar or water year,
respectively. For \code{nfd} like objects, this must either match the year
attribute of \code{x} or \code{keep_monthly} must be \code{FALSE}.}
\item{full_year}{Only return sums for full years when \code{TRUE}. Otherwise, will
sum all months in a year, even if that's a partial year.}
\item{recompute}{If \code{nfd} object already has annual data, should the annual
data be recomputed. An error will post if it has annual data and
\code{recompute} is \code{FALSE}.}
\item{keep_monthly}{If \code{TRUE} the monthly data are kept in the returned
object, otherwise they are dropped.}
}
\description{
\code{nf_to_annual()} sums \code{nfd}, and \code{xts} data from monthly data to annual data.
}
\examples{
# can sum monthly data to annual and get the existing stored annual data
library(CoRiverNF)
ann <- nf_to_annual(monthlyTot)
all.equal(ann, cyAnnTot, check.attributes = FALSE)
# for nfd objects, annual data will be added to object and the monthly
# data are kept by default
nf <- nfd(
monthlyTot["2000/2002"],
flow_space = "total",
time_step = "monthly"
)
nf2 <- nf_to_annual(nf)
# nf2 now has annual data, and monthly data
nf2 <- nf_to_annual(nf, keep_monthly = FALSE)
# nf2 no longer has monthly data
}
\seealso{
\code{\link[=nf_to_intervening]{nf_to_intervening()}}, \code{\link[=nf_to_total]{nf_to_total()}}
}
|
/man/nf_to_annual.Rd
|
no_license
|
BoulderCodeHub/CRSSIO
|
R
| false | true | 2,146 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nf_to_annual.R
\name{nf_to_annual}
\alias{nf_to_annual}
\alias{nf_to_annual.xts}
\alias{nf_to_annual.nfd}
\alias{nf_to_annual.crss_nf}
\alias{nf_to_annual.crssi}
\title{Sum monthly natural flow data to annual data}
\usage{
nf_to_annual(x, ...)
\method{nf_to_annual}{xts}(x, ..., year = "cy", full_year = TRUE)
\method{nf_to_annual}{nfd}(x, ..., full_year = TRUE, recompute = FALSE, keep_monthly = TRUE)
\method{nf_to_annual}{crss_nf}(x, ..., full_year = TRUE, recompute = FALSE, keep_monthly = TRUE)
\method{nf_to_annual}{crssi}(x, ..., recompute = FALSE)
}
\arguments{
\item{x}{An object inheriting from \code{xts}.}
\item{...}{Other parameters passed to methods.}
\item{year}{"cy" or "wy" to sum over the calendar or water year,
respectively. For \code{nfd} like objects, this must either match the year
attribute of \code{x} or \code{keep_monthly} must be \code{FALSE}.}
\item{full_year}{Only return sums for full years when \code{TRUE}. Otherwise, will
sum all months in a year, even if that's a partial year.}
\item{recompute}{If \code{nfd} object already has annual data, should the annual
data be recomputed. An error will post if it has annual data and
\code{recompute} is \code{FALSE}.}
\item{keep_monthly}{If \code{TRUE} the monthly data are kept in the returned
object, otherwise they are dropped.}
}
\description{
\code{nf_to_annual()} sums \code{nfd}, and \code{xts} data from monthly data to annual data.
}
\examples{
# can sum monthly data to annual and get the existing stored annual data
library(CoRiverNF)
ann <- nf_to_annual(monthlyTot)
all.equal(ann, cyAnnTot, check.attributes = FALSE)
# for nfd objects, annual data will be added to object and the monthly
# data are kept by default
nf <- nfd(
monthlyTot["2000/2002"],
flow_space = "total",
time_step = "monthly"
)
nf2 <- nf_to_annual(nf)
# nf2 now has annual data, and monthly data
nf2 <- nf_to_annual(nf, keep_monthly = FALSE)
# nf2 no longer has monthly data
}
\seealso{
\code{\link[=nf_to_intervening]{nf_to_intervening()}}, \code{\link[=nf_to_total]{nf_to_total()}}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R
\name{fitted.bife}
\alias{fitted.bife}
\title{Extract \code{bife} fitted values}
\usage{
\method{fitted}{bife}(object, ...)
}
\arguments{
\item{object}{an object of class \code{"bife"}.}
\item{...}{other arguments.}
}
\value{
The function \code{\link{fitted.bife}} returns a vector of fitted values.
}
\description{
\code{\link{fitted.bife}} is a generic function which extracts fitted values from an object
returned by \code{\link{bife}}.
}
\seealso{
\code{\link{bife}}
}
|
/man/fitted.bife.Rd
|
no_license
|
amrei-stammann/bife
|
R
| false | true | 564 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R
\name{fitted.bife}
\alias{fitted.bife}
\title{Extract \code{bife} fitted values}
\usage{
\method{fitted}{bife}(object, ...)
}
\arguments{
\item{object}{an object of class \code{"bife"}.}
\item{...}{other arguments.}
}
\value{
The function \code{\link{fitted.bife}} returns a vector of fitted values.
}
\description{
\code{\link{fitted.bife}} is a generic function which extracts fitted values from an object
returned by \code{\link{bife}}.
}
\seealso{
\code{\link{bife}}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compute_objects.R
\name{CustomerEncryptionKeyProtectedDisk}
\alias{CustomerEncryptionKeyProtectedDisk}
\title{CustomerEncryptionKeyProtectedDisk Object}
\usage{
CustomerEncryptionKeyProtectedDisk(diskEncryptionKey = NULL, source = NULL)
}
\arguments{
\item{diskEncryptionKey}{Decrypts data associated with the disk with a customer-supplied encryption key}
\item{source}{Specifies a valid partial or full URL to an existing Persistent Disk resource}
}
\value{
CustomerEncryptionKeyProtectedDisk object
}
\description{
CustomerEncryptionKeyProtectedDisk Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
No description
}
|
/googlecomputev1.auto/man/CustomerEncryptionKeyProtectedDisk.Rd
|
permissive
|
GVersteeg/autoGoogleAPI
|
R
| false | true | 735 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compute_objects.R
\name{CustomerEncryptionKeyProtectedDisk}
\alias{CustomerEncryptionKeyProtectedDisk}
\title{CustomerEncryptionKeyProtectedDisk Object}
\usage{
CustomerEncryptionKeyProtectedDisk(diskEncryptionKey = NULL, source = NULL)
}
\arguments{
\item{diskEncryptionKey}{Decrypts data associated with the disk with a customer-supplied encryption key}
\item{source}{Specifies a valid partial or full URL to an existing Persistent Disk resource}
}
\value{
CustomerEncryptionKeyProtectedDisk object
}
\description{
CustomerEncryptionKeyProtectedDisk Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
No description
}
|
## 1. read the household_power_consumption.txt file
## household_power_consumption.txt file located in the main working directory
data <- read.table("./household_power_consumption.txt", stringsAsFactors = FALSE, header = TRUE, sep =";" )
## Correct the data class
data$Date <- as.Date(data$Date, format="%d/%m/%Y")
data$Time <- format(data$Time, format="%H:%M:%S")
data$Global_active_power <- as.numeric(data$Global_active_power)
data$Global_reactive_power <- as.numeric(data$Global_reactive_power)
data$Voltage <- as.numeric(data$Voltage)
data$Global_intensity <- as.numeric(data$Global_intensity)
data$Sub_metering_1 <- as.numeric(data$Sub_metering_1)
data$Sub_metering_2 <- as.numeric(data$Sub_metering_2)
data$Sub_metering_3 <- as.numeric(data$Sub_metering_3)
## subset data date from 2007-02-01 to 2007-02-02
subsetdata <- subset(data, Date == "2007-02-01" | Date =="2007-02-02")
## plot histogram of global active power for 2 days
##dev.set
dev.set(which=2)
png("plot1.png", width=480, height=480)
hist(subsetdata$Global_active_power, col="red", border="black", main ="Global Active Power", xlab="Global Active Power (kilowatts)", ylab="Frequency")
dev.off()
|
/Plot 1.R
|
no_license
|
Maguerra94/ExData_Plotting1
|
R
| false | false | 1,178 |
r
|
## 1. read the household_power_consumption.txt file
## household_power_consumption.txt file located in the main working directory
data <- read.table("./household_power_consumption.txt", stringsAsFactors = FALSE, header = TRUE, sep =";" )
## Correct the data class
data$Date <- as.Date(data$Date, format="%d/%m/%Y")
data$Time <- format(data$Time, format="%H:%M:%S")
data$Global_active_power <- as.numeric(data$Global_active_power)
data$Global_reactive_power <- as.numeric(data$Global_reactive_power)
data$Voltage <- as.numeric(data$Voltage)
data$Global_intensity <- as.numeric(data$Global_intensity)
data$Sub_metering_1 <- as.numeric(data$Sub_metering_1)
data$Sub_metering_2 <- as.numeric(data$Sub_metering_2)
data$Sub_metering_3 <- as.numeric(data$Sub_metering_3)
## subset data date from 2007-02-01 to 2007-02-02
subsetdata <- subset(data, Date == "2007-02-01" | Date =="2007-02-02")
## plot histogram of global active power for 2 days
##dev.set
dev.set(which=2)
png("plot1.png", width=480, height=480)
hist(subsetdata$Global_active_power, col="red", border="black", main ="Global Active Power", xlab="Global Active Power (kilowatts)", ylab="Frequency")
dev.off()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{plotFragLen}
\alias{plotFragLen}
\title{Plot fragment length distribution over samples}
\usage{
plotFragLen(fitpar, col, lty)
}
\arguments{
\item{fitpar}{a list of the output of \link{fitBiasModels} over samples}
\item{col}{a vector of colors}
\item{lty}{a vector of line types}
}
\description{
Plots the fragment length distribution.
}
|
/man/plotFragLen.Rd
|
no_license
|
thomasgilgenast/alpine
|
R
| false | true | 432 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{plotFragLen}
\alias{plotFragLen}
\title{Plot fragment length distribution over samples}
\usage{
plotFragLen(fitpar, col, lty)
}
\arguments{
\item{fitpar}{a list of the output of \link{fitBiasModels} over samples}
\item{col}{a vector of colors}
\item{lty}{a vector of line types}
}
\description{
Plots the fragment length distribution.
}
|
####
# Compare the efficiency of different weights
#### 0 Simulation set up####
library(parallel)
library(scales)
library(xtable)
library(tidyr)
library(ggplot2)
## 0.1 Generate the seed for the simulation ####
set.seed(2018)
seed_i <- sample(1000000,1000)
## 0.2 Global Parameters ####
ncores <- 30
ProjectName <- "Simulation_INS9"
nsample <- 1000
## 0.3 Functions ####
## 0.3.1 Original Functions ####
GEE_UI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
# cat(theta, " \n")
return(GEE_UfuncIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
theta[1:3], theta[4:6], sigma = theta[7], xi = theta[8],
gamma1, gamma, alpha1=alpha1, alpha0=alpha0,
sigma_e))
}
GEE_SIGMA <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
return(GEE_SIGMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
theta[1:3], theta[4:6], sigma = theta[7], xi = theta[8],
gamma1, gamma, alpha1, alpha0, sigma_e))
}
GEE_GAMMA <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2){
GAMMA <- GEE_GAMMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, beta1=theta[1:3], beta2=theta[4:6],
xi=theta[8], sigma = theta[7])
return(GAMMA)
}
GEE_GAMMA.inv <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2){
GAMMA <- GEE_GAMMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, beta1=theta[1:3], beta2=theta[4:6],
xi=theta[8], sigma = theta[7])
GAMMA.inv <- solve(GAMMA,tol=1e-200)
return(GAMMA.inv)
}
GEE_cov <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
GAMMA <- GEE_GAMMA(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2)
GAMMA.inv <- GEE_GAMMA.inv(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2)
SIGMA <- GEE_SIGMA(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e)
covmatrix <- GAMMA.inv %*% SIGMA %*% t(as.matrix(GAMMA.inv))
return(covmatrix)
# return(list(GAMMA=GAMMA,SIGMA=SIGMA,covmatrix))
}
## 0.3.1 Functions with Internal Validation ####
GEE_UI_IV <- function(theta, data.validation, data.mismeasure,
gamma1, gamma, alpha1, alpha0, sigma_e, Weight){
# cat(theta, " \n")
return(GEE_UfuncInsIVWeight(Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
Weight = Weight,
beta1=theta[1:3], beta2=theta[4:6], sigma = theta[7], xi = theta[8],
gamma1=gamma1, gamma=gamma, alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e
))
}
GEE_GAMMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIV0(Y1star, Y2star, Y1, Y2,
CovMis1, CovMis2, ValidationMatrix1, ValidationMatrix2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_GAMMA_IVI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIVI(Y1star, Y2star, DesignMatrix1, DesignMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIV0(Y1star, Y2star, Y1, Y2,
CovMis1, CovMis2, ValidationMatrix1, ValidationMatrix2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_SIGMAInsIV0(Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IVI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_SIGMAInsIVI(Y1star, Y2star, DesignMatrix1, DesignMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_covIV <- function(theta, data.validation, data.mismeasure, Weight,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
nvalidation <- dim(data.validation)[1]
nsample <- dim(data.mismeasure)[1] + nvalidation
Omega <- diag(Weight)
I_Omega <- diag(1-Weight)
M0 <- GEE_GAMMA_IV0(theta,
Y1star=data.validation$Y1star,
Y2star=data.validation$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.validation[,8:9]),
CovMis2 = as.matrix(data.validation[,10]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
M1 <- GEE_GAMMA_IVI(theta,
Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
GAMMA_IV <- Omega %*% M1 + I_Omega %*% M0
B0 <- GEE_SIGMA_IV0(theta,
Y1star=data.validation$Y1star,
Y2star=data.validation$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.validation[,8:9]),
CovMis2 = as.matrix(data.validation[,10]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
B1 <- GEE_SIGMA_IVI(theta,
Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
SIGMA_IV <- Omega %*% B1 %*% Omega + I_Omega %*% B0 %*% I_Omega
GAMMA.inv <- solve(GAMMA_IV,tol=1e-200)
covmatrix <- GAMMA.inv %*% SIGMA_IV %*% t(as.matrix(GAMMA.inv))
# return(covmatrix)
return(list(M1=M1,M0=M0,B1=B1,B0=B0,GAMMA_IV=GAMMA.inv,SIGMA_IV=SIGMA_IV,covmatrix=covmatrix))
# return(list(M1=M1,M0=M0,B1=B1,B0=B0,covmatrix=covmatrix))
}
Cov01matrix <- function(cov1,cov0,nomegas,nsco){
Gammaomega1 <- cov0$M0
Gammaomega1 <- cbind(Gammaomega1,matrix(rep(0,nomegas*(nomegas+nsco)),ncol=nomegas))
Gammaomega2 <- matrix(rep(0,nomegas*nomegas),ncol=nomegas)
Gammaomega2 <- cbind(Gammaomega2,cov1$M1[1:nomegas,(nomegas+1):(nomegas+nsco)])
Gammaomega2 <- cbind(Gammaomega2,cov1$M1[1:nomegas,1:nomegas])
Gammaomega <- rbind(Gammaomega1,Gammaomega2)
Sigmaomega1 <- cov0$B0
Sigmaomega1 <- cbind(Sigmaomega1,matrix(rep(0,nomegas*(nomegas+nsco)),ncol=nomegas))
Sigmaomega2 <- matrix(rep(0,nomegas*(nomegas+nsco)),nrow=nomegas)
Sigmaomega2 <- cbind(Sigmaomega2,cov1$B1[1:nomegas,1:nomegas])
Sigmaomega <- rbind(Sigmaomega1,Sigmaomega2)
GAMMAOMG.inv <- solve(Gammaomega,tol=1e-200)
covmatrix <- GAMMAOMG.inv %*% Sigmaomega %*% t(as.matrix(GAMMAOMG.inv))
return(list(Var0 = covmatrix[1:(nomegas+nsco),1:(nomegas+nsco)],
Var1 = covmatrix[c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco)),
c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco))],
Cov01 = covmatrix[1:(nomegas+nsco),c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco))]))
}
Weightestimate<- function(intial4, omegas, data.validation, data.mismeasure,
gamma1, gamma, alpha1, alpha0,sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0,
optimal= FALSE){
nomegas <- length(omegas)
nsco <- length(c(fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0))-sum(c(fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0))
NR1 <- nleqslv(intial4, GEE_UI_IV, jacobian=T, control=list(maxit=2000),
data.validation = data.validation, data.mismeasure = data.mismeasure,
Weight = c(rep(1,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma = gamma, alpha1= alpha1, alpha0= alpha0, sigma_e = sigma_e)
betahat1 <- ifelse(abs(NR1$x)<10,NR1$x,NA)
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov1 <- GEE_covIV (betahat1, data.validation, data.mismeasure, Weight = c(rep(1,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma = gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)}
NR0 <- nleqslv(intial4, GEE_UI_IV, jacobian=T, control=list(maxit=2000),
data.validation = data.validation, data.mismeasure = data.mismeasure,
Weight = c(rep(0,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma=gamma, alpha1= alpha1, alpha0= alpha0, sigma_e = sigma_e)
betahat0 <- ifelse(abs(NR0$x)<10,NR0$x,NA)
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov0 <- GEE_covIV (betahat0, data.validation, data.mismeasure, Weight = c(rep(0,nomegas),rep(0,nsco)),
gamma1=gamma1, gamma = gamma, alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)}
Cov01matrices <- Cov01matrix(cov1,cov0,nomegas,nsco)
if (optimal) {
weiopttop <- diag(Cov01matrices$Var0) - diag(Cov01matrices$Cov01)
weioptbot <- diag(Cov01matrices$Var1) + diag(Cov01matrices$Var0) - 2* diag(Cov01matrices$Cov01)
weiopt <- weiopttop/weioptbot
weiopt <- ifelse(weiopt<0, 0, weiopt)
weiopt <- ifelse(weiopt>1, 1, weiopt)
omegas <- weiopt[1:nomegas]
}
betahat <- omegas * betahat1 + (1-omegas) * betahat0
Omega <- diag(c(omegas,rep(0,nsco)))
I_Omega <- diag(1-c(omegas,rep(0,nsco)))
Covomega <- Omega %*% Cov01matrices$Var1 %*% Omega +
I_Omega %*% Cov01matrices$Var0 %*% I_Omega +
Omega %*% Cov01matrices$Cov01 %*% I_Omega
return(list(betahat=betahat, Covomega = Covomega))
}
##### 1 Implementation Function ####
### A example in deguging
i <- 1
nsample <- 1500
nvalidation <- 500
omega_j <- -1
INS_int <- function(i, nsample, nvalidation, omega_j){
## 1.1 Set up ####
set.seed(2019)
seed_i <- sample(1000000,1000)
set.seed(seed_i[i])
library(GeneErrorMis)
library(nleqslv)
## 1.2 Data Generation ####
## true parameters
beta1 <- c(0.7,1.5,-1)
beta2 <- c(0.7,-1.5,1)
omegas <- rep(omega_j,6+2)
optimal <- ifelse (omega_j == -1,T,F)
# theta <- c(beta1, beta2, sigma, xi)
sigma <- 1
rho <- 0
sigma_e <- 0.1
gamma <- 0.8
alpha <- -2.197
# gamma <- 0.8
### Generate the true data sets
# nsample <- 1000
# nvalidation <- 500
X <- runif(nsample,-3,4)
W <- rnorm(nsample,0,sd=1)
mu1 <- beta1[1] + beta1[2] * X + beta1[3] * W
mu2 <- beta2[1] + beta2[2] * X + beta2[3] * W
expit <- function(x){
value <- exp(x)/(1+exp(x))
ifelse(is.na(value),1,value)
}
## Response
epsilon <- rnorm(nsample,0,1)
U <- runif(nsample,0,1)
mu2expit <- expit(mu2)
Y1 <- mu1 + epsilon
Y2 <- ifelse(U < mu2expit,1,0)
## obtain the actuall correlation
rho <- cor(Y1-mu1,Y2-mu2expit)
## measurement error and misclassification
e <- rnorm(nsample,0,sigma_e)
U2 <- runif(nsample,0,1)
Y1star <- Y1 + gamma * Y2 + e
Y2star <- ifelse(U2 > expit(alpha),Y2,1-Y2)
## Naive model
naive.model1 <- lm(Y1star ~ X + W)
true.model1 <- lm(Y1 ~ X + W)
naive.model2 <- glm(Y2star ~ X + W, family = binomial(link = logit))
true.model2 <- glm(Y2 ~ X + W, family = binomial(link = logit))
## 1.3 Implementation Generation ###
## 1.3.1 Preperation ###
DesignMatrix1 <- DesignMatrix2 <- cbind(rep(1,length(X)),X,W)
CovMis1 <- cbind(rep(0,length(X)),rep(1,length(X)))
CovMis2 <- c(rep(1,length(X)))
DesignMatrix1 <- as.matrix(DesignMatrix1)
DesignMatrix2 <- as.matrix(DesignMatrix2)
CovMis1 <- as.matrix(CovMis1)
CovMis2 <- as.matrix (CovMis2)
## Create the mismeasured data and the validation data
data.mismeasure <- data.frame(Y1star=Y1star[1:(nsample - nvalidation)],Y2star=Y2star[1:(nsample - nvalidation)], DesignMatrix1[1:(nsample - nvalidation),],CovMis1[1:(nsample - nvalidation),],CovMis2[1:(nsample - nvalidation),])
data.validation <- data.frame(Y1=Y1[(nsample - nvalidation+1):nsample],Y2=Y2[(nsample - nvalidation+1):nsample], Y1star=Y1star[(nsample - nvalidation+1):nsample],Y2star=Y2star[(nsample - nvalidation+1):nsample],
DesignMatrix1[(nsample - nvalidation+1):nsample,],CovMis1[(nsample - nvalidation+1):nsample,],CovMis2[(nsample - nvalidation+1):nsample,])
## 1.3.2 Prepare different choices of initial variables ###
beta_Y1_0 <- mean(Y1star)
beta_Y2_0 <- log(mean(Y2star)/(1-mean(Y2star)))
intial3 <- c(beta_Y1_0,0,0,beta_Y2_0,0,0,1,0.001)
intial4 <- c(naive.model1$coefficients,naive.model2$coefficients,1,0)
## 1.4 Estimating Procedure
## 1.4.1 Measurement Error and Misclassification Parameters
model.measure <- lm(Y1star ~ -1 + offset(Y1) + Y2,data = data.validation)
model.class1 <- glm((1-Y2star) ~ 1, data = data.validation[data.validation$Y2==1,],family = binomial(link="logit"))
model.class0 <- glm(Y2star ~ 1, data = data.validation[data.validation$Y2==0,],family = binomial(link="logit"))
gamma2 <- model.measure$coefficients
sigma_e <- sigma(model.measure)
alpha1 <- model.class1$coefficients
alpha0 <- model.class0$coefficients
tryCatch({
# 1.4.2 The proposed method ####
NR <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,gamma2), alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e,
fixgamma1=1,fixgamma=c(1,0),fixsigma_e=0,fixalpha1=0,fixalpha0=0,
optimal = optimal)
betahat <- NR$betahat
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov <- NR$Covomega
sd <- sqrt(diag(cov))} else {
sd <- rep(NA,length(betahat))
}
# 2.3.3 Naive Model of only consider the measurement error ###
measonly <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,gamma2), alpha1= -Inf, alpha0= -Inf, sigma_e=sigma_e,
fixgamma1=1,fixgamma=c(1,0),fixsigma_e=0,fixalpha1=1,fixalpha0=1,
optimal = optimal)
betahat_measonly <- measonly$betahat
if (!any(is.na(betahat_measonly))) {
cov <- measonly$Covomega
sd_measonly <- sqrt(diag(cov))} else {
sd_measonly <- rep(NA,length(betahat_measonly))
}
# 2.3.4 Naive Model of only consider the misclassification error ###
misconly <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,0), alpha1= alpha1, alpha0= alpha0, sigma_e=0,
fixgamma1=1,fixgamma=c(1,1),fixsigma_e=1,fixalpha1=0,fixalpha0=0,
optimal = optimal)
betahat_misconly <- misconly$betahat
if (!any(is.na(betahat_misconly))) {
cov <- misconly$Covomega
sd_misconly <- sqrt(diag(cov))} else {
sd_misconly <- rep(NA,length(betahat_misconly))
}
return(list(seed = seed_i[i],
naive1coef = naive.model1$coefficients,
naive1vcov = vcov(naive.model1),
naive2coef = naive.model2$coefficients,
naive2vcov = vcov(naive.model2),
betameasonly = c(betahat_measonly,gamma2,sigma_e,0,0),
sdmeasonly = c(sd_measonly,0,0),
betamisconly = c(betahat_misconly,0,0,alpha1,alpha0),
sdmisconly = c(sd_misconly[1:8],0,0,sd_misconly[9:10]),
betahat = c(betahat,gamma2,sigma_e,alpha1,alpha0),
sd = sd))
}, error = function(e) return(NULL))
}
### 4.3 Simulation 1: under different level of interation - Small Sample Size ####
results_3 <- lapply(c(0,1,0.5,-1), FUN= function(x){
results_x <- lapply(1:1000, FUN = INS_int,
nsample = 1500, nvalidation = 500, omega_j = x)
return(results_x)
})
# re1 <- INS_int(1,nsample=nsample, nvalidation=nvalidation, omega_j= omega_j)
omega <- round(c(0,1,0.5,-1),3)
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0)
save(results_3,file="WINV_R3.RData")
Results <- NULL
for (k in 1:4) {
results <- results_3[[k]]
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0,0.8,0.1,-2.197,-2.197)
naive1coef <- NULL
naive1sd <- NULL
naive2coef <- NULL
naive2sd <- NULL
CI1naive <- NULL
CI2naive <- NULL
measonlycoef <- NULL
measonlysd <- NULL
CImeasonly <- NULL
misconlycoef <- NULL
misconlysd <- NULL
CImisconly <- NULL
betas <- NULL
sds <- NULL
CIs <- NULL
for (i in 1:1000){
if (is.null(results[[i]])) {
next}
naive1coef <- rbind(naive1coef, results[[i]]$naive1coef)
naive1sd <- rbind(naive1sd,sqrt(diag( results[[i]]$naive1vcov)))
naive2coef <- rbind(naive2coef, results[[i]]$naive2coef)
naive2sd <- rbind(naive2sd,sqrt(diag( results[[i]]$naive2vcov)))
if ((!is.null(results[[i]]$betameasonly)) & (!is.null(results[[i]]$sdmeasonly))) {
measonlycoef <- rbind(measonlycoef,as.vector(results[[i]]$betameasonly))
measonlysd_i <- results[[i]]$sdmeasonly
measonlysd_i <- ifelse(abs(measonlysd_i)<10,measonlysd_i,NA)
measonlysd <- rbind(measonlysd,measonlysd_i)
CILBmeasonly <- results[[i]]$betameasonly - 1.96 *(measonlysd_i)
CIUBmeasonly <- results[[i]]$betameasonly + 1.96 *(measonlysd_i)
CImeasonly <- rbind(CImeasonly,ifelse((truebeta<as.vector(CIUBmeasonly)) & (truebeta>as.vector(CILBmeasonly)),1,0))
}
if ((!is.null(results[[i]]$betamisconly)) & (!is.null(results[[i]]$sdmisconly))) {
misconlycoef <- rbind(misconlycoef,as.vector(results[[i]]$betamisconly))
misconlysd_i <- (results[[i]]$sdmisconly)
misconlysd_i <- ifelse(abs(misconlysd_i)<10,misconlysd_i,NA)
misconlysd <- rbind(misconlysd, misconlysd_i)
CILBmisconly <- results[[i]]$betamisconly - 1.96 *(misconlysd_i)
CIUBmisconly <- results[[i]]$betamisconly + 1.96 *(misconlysd_i)
CImisconly <- rbind(CImisconly,ifelse((truebeta<as.vector(CIUBmisconly)) & (truebeta>as.vector(CILBmisconly)),1,0))
}
betahat0 <- results[[i]]$betahat
sd0 <- results[[i]]$sd
sd0 <- ifelse(abs(sd0)<5,sd0,NA)
betas <- rbind(betas, betahat0)
sds <- rbind(sds, sd0)
CILBnaive1 <- results[[i]]$naive1coef - 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CIUBnaive1 <- results[[i]]$naive1coef + 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CI1naive <- rbind(CI1naive,ifelse((truebeta[1:3]<CIUBnaive1) & (truebeta[1:3]>CILBnaive1),1,0))
CILBnaive2 <- results[[i]]$naive2coef - 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CIUBnaive2 <- results[[i]]$naive2coef + 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CI2naive <- rbind(CI2naive,ifelse((truebeta[4:6]<CIUBnaive2) & (truebeta[4:6]>CILBnaive2),1,0))
CILB <- betahat0 - 1.96 *(sd0)
CIUB <- betahat0 + 1.96 *(sd0)
CIs <- rbind(CIs,ifelse((truebeta<as.vector(CIUB)) & (truebeta>as.vector(CILB)),1,0))
}
biasnaive1 <- colMeans(naive1coef,na.rm=T)-truebeta[1:3]
biasnaive2 <- colMeans(naive2coef,na.rm=T)-truebeta[4:6]
naive_esd <- apply(cbind(naive1coef,naive2coef), MARGIN = 2 , FUN=sd, na.rm=T)
sdnaive1 <- colMeans(naive1sd,na.rm=T)
sdnaive2 <- colMeans(naive2sd,na.rm=T)
CInaive1 <- colMeans(CI1naive,na.rm=T)
CInaive2 <- colMeans(CI2naive,na.rm=T)
naivebias <- c(biasnaive1,biasnaive2,rep(0,6))
naive_esd <- c(naive_esd,rep(0,6))
naivesd <- c(sdnaive1,sdnaive2,rep(0,6))
naiveCI <- c(CInaive1,CInaive2,rep(0,6))
bias_measonly <- colMeans(na.omit(measonlycoef),na.rm = T) - truebeta
sd_emp_measonly <- apply(na.omit(measonlycoef),MARGIN = 2, FUN = sd)
sd_mod_measonly <- colMeans(na.omit(measonlysd),na.rm = T)
CI_measonly <- colMeans(na.omit(CImeasonly),na.rm = T)
bias_misconly <- colMeans(na.omit(misconlycoef),na.rm = T) - truebeta
sd_emp_misconly <- apply(na.omit(misconlycoef),MARGIN = 2, FUN = sd)
sd_mod_misconly <- colMeans(na.omit(misconlysd),na.rm = T)
CI_misconly <- colMeans(na.omit(CImisconly),na.rm = T)
bias1 <- colMeans(na.omit(betas),na.rm = T) - truebeta
sd_emp <- apply(na.omit(betas),MARGIN = 2, FUN = sd)
sd_mod <- colMeans(na.omit(sds),na.rm = T)
CIrate <- colMeans(na.omit(CIs),na.rm = T)
Results0 <- data.frame(omega = omega[k],
naivebias=round(naivebias,3),naive_esd=round(naive_esd,3),naivesd=round(naivesd,3),naiveCI=percent(round(naiveCI,3)),naiveARE=percent(round(naive_esd/naive_esd,3)),
biasprop=round(bias1,3),propose_esd=round(sd_emp,3),sdpropose=round(sd_mod,3),CI_propose=percent(round(CIrate,3)),ARE_propose=percent(round((naive_esd/sd_mod)^2,3)))
Results <- rbind(Results,Results0)
}
parnames <- c("beta10","beta11","beta12","beta20","beta21","beta22",
"sigma","xi", "gamma_2","sigma_e","alpha_1","alpha_0")
betanames <- c("beta10","beta11","beta12","beta20","beta21","beta22")
Results <- data.frame(Parameters = parnames,Results)
Results <- Results[Results$Parameters %in% betanames,]
Results <- Results[order(Results$Parameters),]
Results1 <- Results[1:6*4-3,c(1:7)]
Results1$omega <- -99
Results2 <- Results[,c(1:2,8:12)]
colnames(Results1) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
colnames(Results2) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
Results <- rbind(Results1,Results2)
Results <- Results[order(Results$Parameters),]
save(Results,file="WINV_RTable31.RData")
library(xtable)
xtable(Results,digits = 3)
### 4.3 Simulation 1: under different level of interation - Small Sample Size ####
results_3 <- lapply(c(0,1,0.5,-1), FUN= function(x){
results_x <- lapply(1:1000, FUN = INS_int,
nsample = 3000, nvalidation = 1500, omega_j = x)
return(results_x)
})
# re1 <- INS_int(1,nsample=nsample, nvalidation=nvalidation, omega_j= omega_j)
omega <- round(c(0,1,0.5,-1),3)
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0)
save(results_3,file="WINV_R3.RData")
Results <- NULL
for (k in 1:4) {
results <- results_3[[k]]
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0,0.8,0.1,-2.197,-2.197)
naive1coef <- NULL
naive1sd <- NULL
naive2coef <- NULL
naive2sd <- NULL
CI1naive <- NULL
CI2naive <- NULL
measonlycoef <- NULL
measonlysd <- NULL
CImeasonly <- NULL
misconlycoef <- NULL
misconlysd <- NULL
CImisconly <- NULL
betas <- NULL
sds <- NULL
CIs <- NULL
for (i in 1:1000){
if (is.null(results[[i]])) {
next}
naive1coef <- rbind(naive1coef, results[[i]]$naive1coef)
naive1sd <- rbind(naive1sd,sqrt(diag( results[[i]]$naive1vcov)))
naive2coef <- rbind(naive2coef, results[[i]]$naive2coef)
naive2sd <- rbind(naive2sd,sqrt(diag( results[[i]]$naive2vcov)))
if ((!is.null(results[[i]]$betameasonly)) & (!is.null(results[[i]]$sdmeasonly))) {
measonlycoef <- rbind(measonlycoef,as.vector(results[[i]]$betameasonly))
measonlysd_i <- results[[i]]$sdmeasonly
measonlysd_i <- ifelse(abs(measonlysd_i)<10,measonlysd_i,NA)
measonlysd <- rbind(measonlysd,measonlysd_i)
CILBmeasonly <- results[[i]]$betameasonly - 1.96 *(measonlysd_i)
CIUBmeasonly <- results[[i]]$betameasonly + 1.96 *(measonlysd_i)
CImeasonly <- rbind(CImeasonly,ifelse((truebeta<as.vector(CIUBmeasonly)) & (truebeta>as.vector(CILBmeasonly)),1,0))
}
if ((!is.null(results[[i]]$betamisconly)) & (!is.null(results[[i]]$sdmisconly))) {
misconlycoef <- rbind(misconlycoef,as.vector(results[[i]]$betamisconly))
misconlysd_i <- (results[[i]]$sdmisconly)
misconlysd_i <- ifelse(abs(misconlysd_i)<10,misconlysd_i,NA)
misconlysd <- rbind(misconlysd, misconlysd_i)
CILBmisconly <- results[[i]]$betamisconly - 1.96 *(misconlysd_i)
CIUBmisconly <- results[[i]]$betamisconly + 1.96 *(misconlysd_i)
CImisconly <- rbind(CImisconly,ifelse((truebeta<as.vector(CIUBmisconly)) & (truebeta>as.vector(CILBmisconly)),1,0))
}
betahat0 <- results[[i]]$betahat
sd0 <- results[[i]]$sd
sd0 <- ifelse(abs(sd0)<5,sd0,NA)
betas <- rbind(betas, betahat0)
sds <- rbind(sds, sd0)
CILBnaive1 <- results[[i]]$naive1coef - 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CIUBnaive1 <- results[[i]]$naive1coef + 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CI1naive <- rbind(CI1naive,ifelse((truebeta[1:3]<CIUBnaive1) & (truebeta[1:3]>CILBnaive1),1,0))
CILBnaive2 <- results[[i]]$naive2coef - 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CIUBnaive2 <- results[[i]]$naive2coef + 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CI2naive <- rbind(CI2naive,ifelse((truebeta[4:6]<CIUBnaive2) & (truebeta[4:6]>CILBnaive2),1,0))
CILB <- betahat0 - 1.96 *(sd0)
CIUB <- betahat0 + 1.96 *(sd0)
CIs <- rbind(CIs,ifelse((truebeta<as.vector(CIUB)) & (truebeta>as.vector(CILB)),1,0))
}
biasnaive1 <- colMeans(naive1coef,na.rm=T)-truebeta[1:3]
biasnaive2 <- colMeans(naive2coef,na.rm=T)-truebeta[4:6]
naive_esd <- apply(cbind(naive1coef,naive2coef), MARGIN = 2 , FUN=sd, na.rm=T)
sdnaive1 <- colMeans(naive1sd,na.rm=T)
sdnaive2 <- colMeans(naive2sd,na.rm=T)
CInaive1 <- colMeans(CI1naive,na.rm=T)
CInaive2 <- colMeans(CI2naive,na.rm=T)
naivebias <- c(biasnaive1,biasnaive2,rep(0,6))
naive_esd <- c(naive_esd,rep(0,6))
naivesd <- c(sdnaive1,sdnaive2,rep(0,6))
naiveCI <- c(CInaive1,CInaive2,rep(0,6))
bias_measonly <- colMeans(na.omit(measonlycoef),na.rm = T) - truebeta
sd_emp_measonly <- apply(na.omit(measonlycoef),MARGIN = 2, FUN = sd)
sd_mod_measonly <- colMeans(na.omit(measonlysd),na.rm = T)
CI_measonly <- colMeans(na.omit(CImeasonly),na.rm = T)
bias_misconly <- colMeans(na.omit(misconlycoef),na.rm = T) - truebeta
sd_emp_misconly <- apply(na.omit(misconlycoef),MARGIN = 2, FUN = sd)
sd_mod_misconly <- colMeans(na.omit(misconlysd),na.rm = T)
CI_misconly <- colMeans(na.omit(CImisconly),na.rm = T)
bias1 <- colMeans(na.omit(betas),na.rm = T) - truebeta
sd_emp <- apply(na.omit(betas),MARGIN = 2, FUN = sd)
sd_mod <- colMeans(na.omit(sds),na.rm = T)
CIrate <- colMeans(na.omit(CIs),na.rm = T)
Results0 <- data.frame(omega = omega[k],
naivebias=round(naivebias,3),naive_esd=round(naive_esd,3),naivesd=round(naivesd,3),naiveCI=percent(round(naiveCI,3)),naiveARE=percent(round(naive_esd/naive_esd,3)),
biasprop=round(bias1,3),propose_esd=round(sd_emp,3),sdpropose=round(sd_mod,3),CI_propose=percent(round(CIrate,3)),ARE_propose=percent(round((naive_esd/sd_mod)^2,3)))
Results <- rbind(Results,Results0)
}
parnames <- c("beta10","beta11","beta12","beta20","beta21","beta22",
"sigma","xi", "gamma_2","sigma_e","alpha_1","alpha_0")
betanames <- c("beta10","beta11","beta12","beta20","beta21","beta22")
Results <- data.frame(Parameters = parnames,Results)
Results <- Results[Results$Parameters %in% betanames,]
Results <- Results[order(Results$Parameters),]
Results1 <- Results[1:6*4-3,c(1:7)]
Results1$omega <- -99
Results2 <- Results[,c(1:2,8:12)]
colnames(Results1) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
colnames(Results2) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
Results <- rbind(Results1,Results2)
Results <- Results[order(Results$Parameters),]
save(Results,file="WINV_RTable32.RData")
library(xtable)
xtable(Results,digits = 3)
xtable(Results,digits = 4)
|
/code/Simulation/Simulation_INS9.R
|
no_license
|
QihuangZhang/GEEmix
|
R
| false | false | 31,958 |
r
|
####
# Compare the efficiency of different weights
#### 0 Simulation set up####
library(parallel)
library(scales)
library(xtable)
library(tidyr)
library(ggplot2)
## 0.1 Generate the seed for the simulation ####
set.seed(2018)
seed_i <- sample(1000000,1000)
## 0.2 Global Parameters ####
ncores <- 30
ProjectName <- "Simulation_INS9"
nsample <- 1000
## 0.3 Functions ####
## 0.3.1 Original Functions ####
GEE_UI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
# cat(theta, " \n")
return(GEE_UfuncIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
theta[1:3], theta[4:6], sigma = theta[7], xi = theta[8],
gamma1, gamma, alpha1=alpha1, alpha0=alpha0,
sigma_e))
}
GEE_SIGMA <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
return(GEE_SIGMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
theta[1:3], theta[4:6], sigma = theta[7], xi = theta[8],
gamma1, gamma, alpha1, alpha0, sigma_e))
}
GEE_GAMMA <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2){
GAMMA <- GEE_GAMMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, beta1=theta[1:3], beta2=theta[4:6],
xi=theta[8], sigma = theta[7])
return(GAMMA)
}
GEE_GAMMA.inv <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2){
GAMMA <- GEE_GAMMAIns(Y1star, Y2star, DesignMatrix1, DesignMatrix2, beta1=theta[1:3], beta2=theta[4:6],
xi=theta[8], sigma = theta[7])
GAMMA.inv <- solve(GAMMA,tol=1e-200)
return(GAMMA.inv)
}
GEE_cov <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e){
GAMMA <- GEE_GAMMA(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2)
GAMMA.inv <- GEE_GAMMA.inv(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2)
SIGMA <- GEE_SIGMA(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e)
covmatrix <- GAMMA.inv %*% SIGMA %*% t(as.matrix(GAMMA.inv))
return(covmatrix)
# return(list(GAMMA=GAMMA,SIGMA=SIGMA,covmatrix))
}
## 0.3.1 Functions with Internal Validation ####
GEE_UI_IV <- function(theta, data.validation, data.mismeasure,
gamma1, gamma, alpha1, alpha0, sigma_e, Weight){
# cat(theta, " \n")
return(GEE_UfuncInsIVWeight(Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
Weight = Weight,
beta1=theta[1:3], beta2=theta[4:6], sigma = theta[7], xi = theta[8],
gamma1=gamma1, gamma=gamma, alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e
))
}
GEE_GAMMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIV0(Y1star, Y2star, Y1, Y2,
CovMis1, CovMis2, ValidationMatrix1, ValidationMatrix2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_GAMMA_IVI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIVI(Y1star, Y2star, DesignMatrix1, DesignMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_GAMMAInsIV0(Y1star, Y2star, Y1, Y2,
CovMis1, CovMis2, ValidationMatrix1, ValidationMatrix2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IV0 <- function(theta, Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_SIGMAInsIV0(Y1star, Y2star, Y1, Y2, ValidationMatrix1, ValidationMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_SIGMA_IVI <- function(theta, Y1star, Y2star, DesignMatrix1, DesignMatrix2, CovMis1, CovMis2,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
return(GEE_SIGMAInsIVI(Y1star, Y2star, DesignMatrix1, DesignMatrix2,
CovMis1, CovMis2,
beta1=theta[1:3], beta2=theta[4:6], xi=theta[8], sigma=theta[7],
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)
)
}
GEE_covIV <- function(theta, data.validation, data.mismeasure, Weight,
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0){
nvalidation <- dim(data.validation)[1]
nsample <- dim(data.mismeasure)[1] + nvalidation
Omega <- diag(Weight)
I_Omega <- diag(1-Weight)
M0 <- GEE_GAMMA_IV0(theta,
Y1star=data.validation$Y1star,
Y2star=data.validation$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.validation[,8:9]),
CovMis2 = as.matrix(data.validation[,10]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
M1 <- GEE_GAMMA_IVI(theta,
Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
GAMMA_IV <- Omega %*% M1 + I_Omega %*% M0
B0 <- GEE_SIGMA_IV0(theta,
Y1star=data.validation$Y1star,
Y2star=data.validation$Y2star,
Y1 = data.validation$Y1,
Y2 = data.validation$Y2,
ValidationMatrix1 = as.matrix(data.validation[,5:7]),
ValidationMatrix2 = as.matrix(data.validation[,5:7]),
CovMis1 = as.matrix(data.validation[,8:9]),
CovMis2 = as.matrix(data.validation[,10]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
B1 <- GEE_SIGMA_IVI(theta,
Y1star=data.mismeasure$Y1star,
Y2star=data.mismeasure$Y2star,
DesignMatrix1 = as.matrix(data.mismeasure[,3:5]),
DesignMatrix2 = as.matrix(data.mismeasure[,3:5]),
CovMis1 = as.matrix(data.mismeasure[,6:7]),
CovMis2 = as.matrix(data.mismeasure[,8]),
gamma1, gamma, alpha1, alpha0, sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0)
SIGMA_IV <- Omega %*% B1 %*% Omega + I_Omega %*% B0 %*% I_Omega
GAMMA.inv <- solve(GAMMA_IV,tol=1e-200)
covmatrix <- GAMMA.inv %*% SIGMA_IV %*% t(as.matrix(GAMMA.inv))
# return(covmatrix)
return(list(M1=M1,M0=M0,B1=B1,B0=B0,GAMMA_IV=GAMMA.inv,SIGMA_IV=SIGMA_IV,covmatrix=covmatrix))
# return(list(M1=M1,M0=M0,B1=B1,B0=B0,covmatrix=covmatrix))
}
Cov01matrix <- function(cov1,cov0,nomegas,nsco){
Gammaomega1 <- cov0$M0
Gammaomega1 <- cbind(Gammaomega1,matrix(rep(0,nomegas*(nomegas+nsco)),ncol=nomegas))
Gammaomega2 <- matrix(rep(0,nomegas*nomegas),ncol=nomegas)
Gammaomega2 <- cbind(Gammaomega2,cov1$M1[1:nomegas,(nomegas+1):(nomegas+nsco)])
Gammaomega2 <- cbind(Gammaomega2,cov1$M1[1:nomegas,1:nomegas])
Gammaomega <- rbind(Gammaomega1,Gammaomega2)
Sigmaomega1 <- cov0$B0
Sigmaomega1 <- cbind(Sigmaomega1,matrix(rep(0,nomegas*(nomegas+nsco)),ncol=nomegas))
Sigmaomega2 <- matrix(rep(0,nomegas*(nomegas+nsco)),nrow=nomegas)
Sigmaomega2 <- cbind(Sigmaomega2,cov1$B1[1:nomegas,1:nomegas])
Sigmaomega <- rbind(Sigmaomega1,Sigmaomega2)
GAMMAOMG.inv <- solve(Gammaomega,tol=1e-200)
covmatrix <- GAMMAOMG.inv %*% Sigmaomega %*% t(as.matrix(GAMMAOMG.inv))
return(list(Var0 = covmatrix[1:(nomegas+nsco),1:(nomegas+nsco)],
Var1 = covmatrix[c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco)),
c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco))],
Cov01 = covmatrix[1:(nomegas+nsco),c((nomegas+nsco+1):(2*nomegas+nsco),(nomegas+1):(nomegas+nsco))]))
}
Weightestimate<- function(intial4, omegas, data.validation, data.mismeasure,
gamma1, gamma, alpha1, alpha0,sigma_e,
fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0,
optimal= FALSE){
nomegas <- length(omegas)
nsco <- length(c(fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0))-sum(c(fixgamma1,fixgamma,fixsigma_e,fixalpha1,fixalpha0))
NR1 <- nleqslv(intial4, GEE_UI_IV, jacobian=T, control=list(maxit=2000),
data.validation = data.validation, data.mismeasure = data.mismeasure,
Weight = c(rep(1,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma = gamma, alpha1= alpha1, alpha0= alpha0, sigma_e = sigma_e)
betahat1 <- ifelse(abs(NR1$x)<10,NR1$x,NA)
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov1 <- GEE_covIV (betahat1, data.validation, data.mismeasure, Weight = c(rep(1,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma = gamma, alpha1, alpha0, sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)}
NR0 <- nleqslv(intial4, GEE_UI_IV, jacobian=T, control=list(maxit=2000),
data.validation = data.validation, data.mismeasure = data.mismeasure,
Weight = c(rep(0,nomegas),rep(0,nsco)),
gamma1 = gamma1, gamma=gamma, alpha1= alpha1, alpha0= alpha0, sigma_e = sigma_e)
betahat0 <- ifelse(abs(NR0$x)<10,NR0$x,NA)
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov0 <- GEE_covIV (betahat0, data.validation, data.mismeasure, Weight = c(rep(0,nomegas),rep(0,nsco)),
gamma1=gamma1, gamma = gamma, alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e,
fixgamma1=fixgamma1, fixgamma=fixgamma, fixsigma_e=fixsigma_e, fixalpha1=fixalpha1, fixalpha0=fixalpha0)}
Cov01matrices <- Cov01matrix(cov1,cov0,nomegas,nsco)
if (optimal) {
weiopttop <- diag(Cov01matrices$Var0) - diag(Cov01matrices$Cov01)
weioptbot <- diag(Cov01matrices$Var1) + diag(Cov01matrices$Var0) - 2* diag(Cov01matrices$Cov01)
weiopt <- weiopttop/weioptbot
weiopt <- ifelse(weiopt<0, 0, weiopt)
weiopt <- ifelse(weiopt>1, 1, weiopt)
omegas <- weiopt[1:nomegas]
}
betahat <- omegas * betahat1 + (1-omegas) * betahat0
Omega <- diag(c(omegas,rep(0,nsco)))
I_Omega <- diag(1-c(omegas,rep(0,nsco)))
Covomega <- Omega %*% Cov01matrices$Var1 %*% Omega +
I_Omega %*% Cov01matrices$Var0 %*% I_Omega +
Omega %*% Cov01matrices$Cov01 %*% I_Omega
return(list(betahat=betahat, Covomega = Covomega))
}
##### 1 Implementation Function ####
### A example in deguging
i <- 1
nsample <- 1500
nvalidation <- 500
omega_j <- -1
INS_int <- function(i, nsample, nvalidation, omega_j){
## 1.1 Set up ####
set.seed(2019)
seed_i <- sample(1000000,1000)
set.seed(seed_i[i])
library(GeneErrorMis)
library(nleqslv)
## 1.2 Data Generation ####
## true parameters
beta1 <- c(0.7,1.5,-1)
beta2 <- c(0.7,-1.5,1)
omegas <- rep(omega_j,6+2)
optimal <- ifelse (omega_j == -1,T,F)
# theta <- c(beta1, beta2, sigma, xi)
sigma <- 1
rho <- 0
sigma_e <- 0.1
gamma <- 0.8
alpha <- -2.197
# gamma <- 0.8
### Generate the true data sets
# nsample <- 1000
# nvalidation <- 500
X <- runif(nsample,-3,4)
W <- rnorm(nsample,0,sd=1)
mu1 <- beta1[1] + beta1[2] * X + beta1[3] * W
mu2 <- beta2[1] + beta2[2] * X + beta2[3] * W
expit <- function(x){
value <- exp(x)/(1+exp(x))
ifelse(is.na(value),1,value)
}
## Response
epsilon <- rnorm(nsample,0,1)
U <- runif(nsample,0,1)
mu2expit <- expit(mu2)
Y1 <- mu1 + epsilon
Y2 <- ifelse(U < mu2expit,1,0)
## obtain the actuall correlation
rho <- cor(Y1-mu1,Y2-mu2expit)
## measurement error and misclassification
e <- rnorm(nsample,0,sigma_e)
U2 <- runif(nsample,0,1)
Y1star <- Y1 + gamma * Y2 + e
Y2star <- ifelse(U2 > expit(alpha),Y2,1-Y2)
## Naive model
naive.model1 <- lm(Y1star ~ X + W)
true.model1 <- lm(Y1 ~ X + W)
naive.model2 <- glm(Y2star ~ X + W, family = binomial(link = logit))
true.model2 <- glm(Y2 ~ X + W, family = binomial(link = logit))
## 1.3 Implementation Generation ###
## 1.3.1 Preperation ###
DesignMatrix1 <- DesignMatrix2 <- cbind(rep(1,length(X)),X,W)
CovMis1 <- cbind(rep(0,length(X)),rep(1,length(X)))
CovMis2 <- c(rep(1,length(X)))
DesignMatrix1 <- as.matrix(DesignMatrix1)
DesignMatrix2 <- as.matrix(DesignMatrix2)
CovMis1 <- as.matrix(CovMis1)
CovMis2 <- as.matrix (CovMis2)
## Create the mismeasured data and the validation data
data.mismeasure <- data.frame(Y1star=Y1star[1:(nsample - nvalidation)],Y2star=Y2star[1:(nsample - nvalidation)], DesignMatrix1[1:(nsample - nvalidation),],CovMis1[1:(nsample - nvalidation),],CovMis2[1:(nsample - nvalidation),])
data.validation <- data.frame(Y1=Y1[(nsample - nvalidation+1):nsample],Y2=Y2[(nsample - nvalidation+1):nsample], Y1star=Y1star[(nsample - nvalidation+1):nsample],Y2star=Y2star[(nsample - nvalidation+1):nsample],
DesignMatrix1[(nsample - nvalidation+1):nsample,],CovMis1[(nsample - nvalidation+1):nsample,],CovMis2[(nsample - nvalidation+1):nsample,])
## 1.3.2 Prepare different choices of initial variables ###
beta_Y1_0 <- mean(Y1star)
beta_Y2_0 <- log(mean(Y2star)/(1-mean(Y2star)))
intial3 <- c(beta_Y1_0,0,0,beta_Y2_0,0,0,1,0.001)
intial4 <- c(naive.model1$coefficients,naive.model2$coefficients,1,0)
## 1.4 Estimating Procedure
## 1.4.1 Measurement Error and Misclassification Parameters
model.measure <- lm(Y1star ~ -1 + offset(Y1) + Y2,data = data.validation)
model.class1 <- glm((1-Y2star) ~ 1, data = data.validation[data.validation$Y2==1,],family = binomial(link="logit"))
model.class0 <- glm(Y2star ~ 1, data = data.validation[data.validation$Y2==0,],family = binomial(link="logit"))
gamma2 <- model.measure$coefficients
sigma_e <- sigma(model.measure)
alpha1 <- model.class1$coefficients
alpha0 <- model.class0$coefficients
tryCatch({
# 1.4.2 The proposed method ####
NR <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,gamma2), alpha1=alpha1, alpha0=alpha0, sigma_e=sigma_e,
fixgamma1=1,fixgamma=c(1,0),fixsigma_e=0,fixalpha1=0,fixalpha0=0,
optimal = optimal)
betahat <- NR$betahat
### variance estimation with validation data
if (!any(is.na(betahat))) {
cov <- NR$Covomega
sd <- sqrt(diag(cov))} else {
sd <- rep(NA,length(betahat))
}
# 2.3.3 Naive Model of only consider the measurement error ###
measonly <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,gamma2), alpha1= -Inf, alpha0= -Inf, sigma_e=sigma_e,
fixgamma1=1,fixgamma=c(1,0),fixsigma_e=0,fixalpha1=1,fixalpha0=1,
optimal = optimal)
betahat_measonly <- measonly$betahat
if (!any(is.na(betahat_measonly))) {
cov <- measonly$Covomega
sd_measonly <- sqrt(diag(cov))} else {
sd_measonly <- rep(NA,length(betahat_measonly))
}
# 2.3.4 Naive Model of only consider the misclassification error ###
misconly <- Weightestimate(intial4, omegas = omegas,
data.validation = data.validation, data.mismeasure = data.mismeasure,
gamma1=1, gamma=c(0,0), alpha1= alpha1, alpha0= alpha0, sigma_e=0,
fixgamma1=1,fixgamma=c(1,1),fixsigma_e=1,fixalpha1=0,fixalpha0=0,
optimal = optimal)
betahat_misconly <- misconly$betahat
if (!any(is.na(betahat_misconly))) {
cov <- misconly$Covomega
sd_misconly <- sqrt(diag(cov))} else {
sd_misconly <- rep(NA,length(betahat_misconly))
}
return(list(seed = seed_i[i],
naive1coef = naive.model1$coefficients,
naive1vcov = vcov(naive.model1),
naive2coef = naive.model2$coefficients,
naive2vcov = vcov(naive.model2),
betameasonly = c(betahat_measonly,gamma2,sigma_e,0,0),
sdmeasonly = c(sd_measonly,0,0),
betamisconly = c(betahat_misconly,0,0,alpha1,alpha0),
sdmisconly = c(sd_misconly[1:8],0,0,sd_misconly[9:10]),
betahat = c(betahat,gamma2,sigma_e,alpha1,alpha0),
sd = sd))
}, error = function(e) return(NULL))
}
### 4.3 Simulation 1: under different level of interation - Small Sample Size ####
results_3 <- lapply(c(0,1,0.5,-1), FUN= function(x){
results_x <- lapply(1:1000, FUN = INS_int,
nsample = 1500, nvalidation = 500, omega_j = x)
return(results_x)
})
# re1 <- INS_int(1,nsample=nsample, nvalidation=nvalidation, omega_j= omega_j)
omega <- round(c(0,1,0.5,-1),3)
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0)
save(results_3,file="WINV_R3.RData")
Results <- NULL
for (k in 1:4) {
results <- results_3[[k]]
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0,0.8,0.1,-2.197,-2.197)
naive1coef <- NULL
naive1sd <- NULL
naive2coef <- NULL
naive2sd <- NULL
CI1naive <- NULL
CI2naive <- NULL
measonlycoef <- NULL
measonlysd <- NULL
CImeasonly <- NULL
misconlycoef <- NULL
misconlysd <- NULL
CImisconly <- NULL
betas <- NULL
sds <- NULL
CIs <- NULL
for (i in 1:1000){
if (is.null(results[[i]])) {
next}
naive1coef <- rbind(naive1coef, results[[i]]$naive1coef)
naive1sd <- rbind(naive1sd,sqrt(diag( results[[i]]$naive1vcov)))
naive2coef <- rbind(naive2coef, results[[i]]$naive2coef)
naive2sd <- rbind(naive2sd,sqrt(diag( results[[i]]$naive2vcov)))
if ((!is.null(results[[i]]$betameasonly)) & (!is.null(results[[i]]$sdmeasonly))) {
measonlycoef <- rbind(measonlycoef,as.vector(results[[i]]$betameasonly))
measonlysd_i <- results[[i]]$sdmeasonly
measonlysd_i <- ifelse(abs(measonlysd_i)<10,measonlysd_i,NA)
measonlysd <- rbind(measonlysd,measonlysd_i)
CILBmeasonly <- results[[i]]$betameasonly - 1.96 *(measonlysd_i)
CIUBmeasonly <- results[[i]]$betameasonly + 1.96 *(measonlysd_i)
CImeasonly <- rbind(CImeasonly,ifelse((truebeta<as.vector(CIUBmeasonly)) & (truebeta>as.vector(CILBmeasonly)),1,0))
}
if ((!is.null(results[[i]]$betamisconly)) & (!is.null(results[[i]]$sdmisconly))) {
misconlycoef <- rbind(misconlycoef,as.vector(results[[i]]$betamisconly))
misconlysd_i <- (results[[i]]$sdmisconly)
misconlysd_i <- ifelse(abs(misconlysd_i)<10,misconlysd_i,NA)
misconlysd <- rbind(misconlysd, misconlysd_i)
CILBmisconly <- results[[i]]$betamisconly - 1.96 *(misconlysd_i)
CIUBmisconly <- results[[i]]$betamisconly + 1.96 *(misconlysd_i)
CImisconly <- rbind(CImisconly,ifelse((truebeta<as.vector(CIUBmisconly)) & (truebeta>as.vector(CILBmisconly)),1,0))
}
betahat0 <- results[[i]]$betahat
sd0 <- results[[i]]$sd
sd0 <- ifelse(abs(sd0)<5,sd0,NA)
betas <- rbind(betas, betahat0)
sds <- rbind(sds, sd0)
CILBnaive1 <- results[[i]]$naive1coef - 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CIUBnaive1 <- results[[i]]$naive1coef + 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CI1naive <- rbind(CI1naive,ifelse((truebeta[1:3]<CIUBnaive1) & (truebeta[1:3]>CILBnaive1),1,0))
CILBnaive2 <- results[[i]]$naive2coef - 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CIUBnaive2 <- results[[i]]$naive2coef + 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CI2naive <- rbind(CI2naive,ifelse((truebeta[4:6]<CIUBnaive2) & (truebeta[4:6]>CILBnaive2),1,0))
CILB <- betahat0 - 1.96 *(sd0)
CIUB <- betahat0 + 1.96 *(sd0)
CIs <- rbind(CIs,ifelse((truebeta<as.vector(CIUB)) & (truebeta>as.vector(CILB)),1,0))
}
biasnaive1 <- colMeans(naive1coef,na.rm=T)-truebeta[1:3]
biasnaive2 <- colMeans(naive2coef,na.rm=T)-truebeta[4:6]
naive_esd <- apply(cbind(naive1coef,naive2coef), MARGIN = 2 , FUN=sd, na.rm=T)
sdnaive1 <- colMeans(naive1sd,na.rm=T)
sdnaive2 <- colMeans(naive2sd,na.rm=T)
CInaive1 <- colMeans(CI1naive,na.rm=T)
CInaive2 <- colMeans(CI2naive,na.rm=T)
naivebias <- c(biasnaive1,biasnaive2,rep(0,6))
naive_esd <- c(naive_esd,rep(0,6))
naivesd <- c(sdnaive1,sdnaive2,rep(0,6))
naiveCI <- c(CInaive1,CInaive2,rep(0,6))
bias_measonly <- colMeans(na.omit(measonlycoef),na.rm = T) - truebeta
sd_emp_measonly <- apply(na.omit(measonlycoef),MARGIN = 2, FUN = sd)
sd_mod_measonly <- colMeans(na.omit(measonlysd),na.rm = T)
CI_measonly <- colMeans(na.omit(CImeasonly),na.rm = T)
bias_misconly <- colMeans(na.omit(misconlycoef),na.rm = T) - truebeta
sd_emp_misconly <- apply(na.omit(misconlycoef),MARGIN = 2, FUN = sd)
sd_mod_misconly <- colMeans(na.omit(misconlysd),na.rm = T)
CI_misconly <- colMeans(na.omit(CImisconly),na.rm = T)
bias1 <- colMeans(na.omit(betas),na.rm = T) - truebeta
sd_emp <- apply(na.omit(betas),MARGIN = 2, FUN = sd)
sd_mod <- colMeans(na.omit(sds),na.rm = T)
CIrate <- colMeans(na.omit(CIs),na.rm = T)
Results0 <- data.frame(omega = omega[k],
naivebias=round(naivebias,3),naive_esd=round(naive_esd,3),naivesd=round(naivesd,3),naiveCI=percent(round(naiveCI,3)),naiveARE=percent(round(naive_esd/naive_esd,3)),
biasprop=round(bias1,3),propose_esd=round(sd_emp,3),sdpropose=round(sd_mod,3),CI_propose=percent(round(CIrate,3)),ARE_propose=percent(round((naive_esd/sd_mod)^2,3)))
Results <- rbind(Results,Results0)
}
parnames <- c("beta10","beta11","beta12","beta20","beta21","beta22",
"sigma","xi", "gamma_2","sigma_e","alpha_1","alpha_0")
betanames <- c("beta10","beta11","beta12","beta20","beta21","beta22")
Results <- data.frame(Parameters = parnames,Results)
Results <- Results[Results$Parameters %in% betanames,]
Results <- Results[order(Results$Parameters),]
Results1 <- Results[1:6*4-3,c(1:7)]
Results1$omega <- -99
Results2 <- Results[,c(1:2,8:12)]
colnames(Results1) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
colnames(Results2) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
Results <- rbind(Results1,Results2)
Results <- Results[order(Results$Parameters),]
save(Results,file="WINV_RTable31.RData")
library(xtable)
xtable(Results,digits = 3)
### 4.3 Simulation 1: under different level of interation - Small Sample Size ####
results_3 <- lapply(c(0,1,0.5,-1), FUN= function(x){
results_x <- lapply(1:1000, FUN = INS_int,
nsample = 3000, nvalidation = 1500, omega_j = x)
return(results_x)
})
# re1 <- INS_int(1,nsample=nsample, nvalidation=nvalidation, omega_j= omega_j)
omega <- round(c(0,1,0.5,-1),3)
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0)
save(results_3,file="WINV_R3.RData")
Results <- NULL
for (k in 1:4) {
results <- results_3[[k]]
truebeta <- c(0.7,1.5,-1,0.7,-1.5,1,1,0,0.8,0.1,-2.197,-2.197)
naive1coef <- NULL
naive1sd <- NULL
naive2coef <- NULL
naive2sd <- NULL
CI1naive <- NULL
CI2naive <- NULL
measonlycoef <- NULL
measonlysd <- NULL
CImeasonly <- NULL
misconlycoef <- NULL
misconlysd <- NULL
CImisconly <- NULL
betas <- NULL
sds <- NULL
CIs <- NULL
for (i in 1:1000){
if (is.null(results[[i]])) {
next}
naive1coef <- rbind(naive1coef, results[[i]]$naive1coef)
naive1sd <- rbind(naive1sd,sqrt(diag( results[[i]]$naive1vcov)))
naive2coef <- rbind(naive2coef, results[[i]]$naive2coef)
naive2sd <- rbind(naive2sd,sqrt(diag( results[[i]]$naive2vcov)))
if ((!is.null(results[[i]]$betameasonly)) & (!is.null(results[[i]]$sdmeasonly))) {
measonlycoef <- rbind(measonlycoef,as.vector(results[[i]]$betameasonly))
measonlysd_i <- results[[i]]$sdmeasonly
measonlysd_i <- ifelse(abs(measonlysd_i)<10,measonlysd_i,NA)
measonlysd <- rbind(measonlysd,measonlysd_i)
CILBmeasonly <- results[[i]]$betameasonly - 1.96 *(measonlysd_i)
CIUBmeasonly <- results[[i]]$betameasonly + 1.96 *(measonlysd_i)
CImeasonly <- rbind(CImeasonly,ifelse((truebeta<as.vector(CIUBmeasonly)) & (truebeta>as.vector(CILBmeasonly)),1,0))
}
if ((!is.null(results[[i]]$betamisconly)) & (!is.null(results[[i]]$sdmisconly))) {
misconlycoef <- rbind(misconlycoef,as.vector(results[[i]]$betamisconly))
misconlysd_i <- (results[[i]]$sdmisconly)
misconlysd_i <- ifelse(abs(misconlysd_i)<10,misconlysd_i,NA)
misconlysd <- rbind(misconlysd, misconlysd_i)
CILBmisconly <- results[[i]]$betamisconly - 1.96 *(misconlysd_i)
CIUBmisconly <- results[[i]]$betamisconly + 1.96 *(misconlysd_i)
CImisconly <- rbind(CImisconly,ifelse((truebeta<as.vector(CIUBmisconly)) & (truebeta>as.vector(CILBmisconly)),1,0))
}
betahat0 <- results[[i]]$betahat
sd0 <- results[[i]]$sd
sd0 <- ifelse(abs(sd0)<5,sd0,NA)
betas <- rbind(betas, betahat0)
sds <- rbind(sds, sd0)
CILBnaive1 <- results[[i]]$naive1coef - 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CIUBnaive1 <- results[[i]]$naive1coef + 1.96 *(sqrt(diag(results[[i]]$naive1vcov)))
CI1naive <- rbind(CI1naive,ifelse((truebeta[1:3]<CIUBnaive1) & (truebeta[1:3]>CILBnaive1),1,0))
CILBnaive2 <- results[[i]]$naive2coef - 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CIUBnaive2 <- results[[i]]$naive2coef + 1.96 *(sqrt(diag(results[[i]]$naive2vcov)))
CI2naive <- rbind(CI2naive,ifelse((truebeta[4:6]<CIUBnaive2) & (truebeta[4:6]>CILBnaive2),1,0))
CILB <- betahat0 - 1.96 *(sd0)
CIUB <- betahat0 + 1.96 *(sd0)
CIs <- rbind(CIs,ifelse((truebeta<as.vector(CIUB)) & (truebeta>as.vector(CILB)),1,0))
}
biasnaive1 <- colMeans(naive1coef,na.rm=T)-truebeta[1:3]
biasnaive2 <- colMeans(naive2coef,na.rm=T)-truebeta[4:6]
naive_esd <- apply(cbind(naive1coef,naive2coef), MARGIN = 2 , FUN=sd, na.rm=T)
sdnaive1 <- colMeans(naive1sd,na.rm=T)
sdnaive2 <- colMeans(naive2sd,na.rm=T)
CInaive1 <- colMeans(CI1naive,na.rm=T)
CInaive2 <- colMeans(CI2naive,na.rm=T)
naivebias <- c(biasnaive1,biasnaive2,rep(0,6))
naive_esd <- c(naive_esd,rep(0,6))
naivesd <- c(sdnaive1,sdnaive2,rep(0,6))
naiveCI <- c(CInaive1,CInaive2,rep(0,6))
bias_measonly <- colMeans(na.omit(measonlycoef),na.rm = T) - truebeta
sd_emp_measonly <- apply(na.omit(measonlycoef),MARGIN = 2, FUN = sd)
sd_mod_measonly <- colMeans(na.omit(measonlysd),na.rm = T)
CI_measonly <- colMeans(na.omit(CImeasonly),na.rm = T)
bias_misconly <- colMeans(na.omit(misconlycoef),na.rm = T) - truebeta
sd_emp_misconly <- apply(na.omit(misconlycoef),MARGIN = 2, FUN = sd)
sd_mod_misconly <- colMeans(na.omit(misconlysd),na.rm = T)
CI_misconly <- colMeans(na.omit(CImisconly),na.rm = T)
bias1 <- colMeans(na.omit(betas),na.rm = T) - truebeta
sd_emp <- apply(na.omit(betas),MARGIN = 2, FUN = sd)
sd_mod <- colMeans(na.omit(sds),na.rm = T)
CIrate <- colMeans(na.omit(CIs),na.rm = T)
Results0 <- data.frame(omega = omega[k],
naivebias=round(naivebias,3),naive_esd=round(naive_esd,3),naivesd=round(naivesd,3),naiveCI=percent(round(naiveCI,3)),naiveARE=percent(round(naive_esd/naive_esd,3)),
biasprop=round(bias1,3),propose_esd=round(sd_emp,3),sdpropose=round(sd_mod,3),CI_propose=percent(round(CIrate,3)),ARE_propose=percent(round((naive_esd/sd_mod)^2,3)))
Results <- rbind(Results,Results0)
}
parnames <- c("beta10","beta11","beta12","beta20","beta21","beta22",
"sigma","xi", "gamma_2","sigma_e","alpha_1","alpha_0")
betanames <- c("beta10","beta11","beta12","beta20","beta21","beta22")
Results <- data.frame(Parameters = parnames,Results)
Results <- Results[Results$Parameters %in% betanames,]
Results <- Results[order(Results$Parameters),]
Results1 <- Results[1:6*4-3,c(1:7)]
Results1$omega <- -99
Results2 <- Results[,c(1:2,8:12)]
colnames(Results1) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
colnames(Results2) <- c("Parameters","omega","bias","esd","MSD","CR","ARE")
Results <- rbind(Results1,Results2)
Results <- Results[order(Results$Parameters),]
save(Results,file="WINV_RTable32.RData")
library(xtable)
xtable(Results,digits = 3)
xtable(Results,digits = 4)
|
# http://www-bcf.usc.edu/~gareth/ISL/code.html
#------ basic commands
x = 3
y = 5
x * y
x <- c(1, 2, 4, 6, 9)
x
x = c(1, 2, 4, 6, 9)
x
y <- c(3, 5, 2, 1)
y
length(x)
length(y)
x + y
ls()
rm(x,y)
ls()
x = seq(1,10)
x
x=1:10
x
?matrix
x = matrix(data=c(1,2,3,4), nrow=2, ncol=2)
x
y = matrix(data=c(1,2,3,4), byrow=TRUE, nrow=2, ncol=2)
y
sqrt(x)
y^2
SIZE = 30
set.seed(1303)
rnorm(SIZE)
runif(SIZE ,-2, 2)
#------ basic stats -----
x=rnorm(SIZE)
y=x+rnorm(SIZE ,mean=20,sd=.1)+runif(SIZE ,-1,1)
x
y
cor(x,y)
set.seed(3)
z=rnorm(100)
mean(z)
var(z)
sqrt(var(z))
sd(z)
#------ plot -----
plot(x,y)
plot(x, y, type='b', col='green',
main='plot y against x', xlab='x name', ylab='y name')
hist(z, breaks=30)
x=seq(-pi, pi, length=50)
y=sin(x)
plot(x,y,col='red', pch=4)
#------ indexing data -----
A=matrix(1:16,4,4)
A
dim(A)
A[2,3]
A[c(1,3),c(2,4)]
A[1:3,2:4]
A[1:2,]
A[,1:2]
A[1,]
A[-c(1,3),]
A[-c(1,3),-c(1,3,4)]
#------- loading data --------
Auto = read.table('Auto.data', header=TRUE)
head(Auto)
dim(Auto)
names(Auto)
plot(Auto$weight, Auto$mpg, col='red')
hist(Auto$mpg, breaks=20, col=2)
pairs(~ mpg + horsepower + weight + acceleration, Auto)
summary(Auto)
summary(Auto$mpg)
|
/r1.R
|
no_license
|
cocobaco/r_basics1
|
R
| false | false | 1,185 |
r
|
# http://www-bcf.usc.edu/~gareth/ISL/code.html
#------ basic commands
x = 3
y = 5
x * y
x <- c(1, 2, 4, 6, 9)
x
x = c(1, 2, 4, 6, 9)
x
y <- c(3, 5, 2, 1)
y
length(x)
length(y)
x + y
ls()
rm(x,y)
ls()
x = seq(1,10)
x
x=1:10
x
?matrix
x = matrix(data=c(1,2,3,4), nrow=2, ncol=2)
x
y = matrix(data=c(1,2,3,4), byrow=TRUE, nrow=2, ncol=2)
y
sqrt(x)
y^2
SIZE = 30
set.seed(1303)
rnorm(SIZE)
runif(SIZE ,-2, 2)
#------ basic stats -----
x=rnorm(SIZE)
y=x+rnorm(SIZE ,mean=20,sd=.1)+runif(SIZE ,-1,1)
x
y
cor(x,y)
set.seed(3)
z=rnorm(100)
mean(z)
var(z)
sqrt(var(z))
sd(z)
#------ plot -----
plot(x,y)
plot(x, y, type='b', col='green',
main='plot y against x', xlab='x name', ylab='y name')
hist(z, breaks=30)
x=seq(-pi, pi, length=50)
y=sin(x)
plot(x,y,col='red', pch=4)
#------ indexing data -----
A=matrix(1:16,4,4)
A
dim(A)
A[2,3]
A[c(1,3),c(2,4)]
A[1:3,2:4]
A[1:2,]
A[,1:2]
A[1,]
A[-c(1,3),]
A[-c(1,3),-c(1,3,4)]
#------- loading data --------
Auto = read.table('Auto.data', header=TRUE)
head(Auto)
dim(Auto)
names(Auto)
plot(Auto$weight, Auto$mpg, col='red')
hist(Auto$mpg, breaks=20, col=2)
pairs(~ mpg + horsepower + weight + acceleration, Auto)
summary(Auto)
summary(Auto$mpg)
|
# Coursera Exploratory Data Analysis - Course Project 1 #
## Plot 2 ##
# I downloaded the data (in my working directory) using:
# $ wget https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# $ mv exdata%2Fdata%2Fhousehold_power_consumption.zip exdata_data_household_power_consumption.zip
# $ unzip exdata_data_household_power_consumption.zip
# I created a subset of the data using:
# $ awk '/^[1|2]\/2\/2007;/' household_power_consumption.txt > Feb2007subset.txt
# This subset contains 2880 rows and no header
# for plot background transparency: see comments in the other plots
# This script uses 2 libraries:
library(dplyr) # for subsetting data
library(lubridate) # for coercing date and time values
# Read in the data in R
feb07 <- tbl_df(read.table("Feb2007subset.txt",
sep = ";",
col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage",
"Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
colClasses = c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")))
# Subset the data using dplyr methods
plot_data <- feb07 %>%
mutate(datetime = dmy_hms(paste(Date, Time))) %>%
select(-Global_intensity)
# open png device
png(filename = "plot4.png")
# adjust mfrow parameter to create 2 rows, 2 columns
par(mfrow = c(2, 2))
# create plots and annotations
# top left
plot(plot_data$datetime, plot_data$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
# top right
plot(plot_data$datetime, plot_data$Voltage, type = "l", xlab = "datetime", ylab = "Voltage")
# bottom left
plot( plot_data$datetime, plot_data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering")
lines(plot_data$datetime, plot_data$Sub_metering_2, type = "l", col = "red")
lines(plot_data$datetime, plot_data$Sub_metering_3, type = "l", col = "blue")
legend("topright", legend = colnames(plot_data)[6:8], col = c("black", "red", "blue"), lty = 1, bty = "n")
# bottom right
plot(plot_data$datetime, plot_data$Global_reactive_power, type = "l", xlab = "datetime", ylab = "Global_reactive_power")
# always device off
dev.off()
|
/ExData-plotting1/plot4.R
|
no_license
|
wbkoetsier/Coursera
|
R
| false | false | 2,326 |
r
|
# Coursera Exploratory Data Analysis - Course Project 1 #
## Plot 2 ##
# I downloaded the data (in my working directory) using:
# $ wget https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# $ mv exdata%2Fdata%2Fhousehold_power_consumption.zip exdata_data_household_power_consumption.zip
# $ unzip exdata_data_household_power_consumption.zip
# I created a subset of the data using:
# $ awk '/^[1|2]\/2\/2007;/' household_power_consumption.txt > Feb2007subset.txt
# This subset contains 2880 rows and no header
# for plot background transparency: see comments in the other plots
# This script uses 2 libraries:
library(dplyr) # for subsetting data
library(lubridate) # for coercing date and time values
# Read in the data in R
feb07 <- tbl_df(read.table("Feb2007subset.txt",
sep = ";",
col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage",
"Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
colClasses = c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")))
# Subset the data using dplyr methods
plot_data <- feb07 %>%
mutate(datetime = dmy_hms(paste(Date, Time))) %>%
select(-Global_intensity)
# open png device
png(filename = "plot4.png")
# adjust mfrow parameter to create 2 rows, 2 columns
par(mfrow = c(2, 2))
# create plots and annotations
# top left
plot(plot_data$datetime, plot_data$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
# top right
plot(plot_data$datetime, plot_data$Voltage, type = "l", xlab = "datetime", ylab = "Voltage")
# bottom left
plot( plot_data$datetime, plot_data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering")
lines(plot_data$datetime, plot_data$Sub_metering_2, type = "l", col = "red")
lines(plot_data$datetime, plot_data$Sub_metering_3, type = "l", col = "blue")
legend("topright", legend = colnames(plot_data)[6:8], col = c("black", "red", "blue"), lty = 1, bty = "n")
# bottom right
plot(plot_data$datetime, plot_data$Global_reactive_power, type = "l", xlab = "datetime", ylab = "Global_reactive_power")
# always device off
dev.off()
|
gmse_paras$land_type = "equal"
gmse_paras$yield_value = 0.8
gmse_paras$ytb_type = "beta1"
years = gmse_paras$years
sims = gmse_paras$sims
yield_value = gmse_paras$yield_value
ytb_type = gmse_paras$ytb_type
|
/sims/nullModel-YTB2/paras_nullModel-YTB2.R
|
no_license
|
jejoenje/gmse_vary
|
R
| false | false | 208 |
r
|
gmse_paras$land_type = "equal"
gmse_paras$yield_value = 0.8
gmse_paras$ytb_type = "beta1"
years = gmse_paras$years
sims = gmse_paras$sims
yield_value = gmse_paras$yield_value
ytb_type = gmse_paras$ytb_type
|
library(tm)
library(NLP)
library(e1071)
library(Rstem)
library(ggplot2)
library(RCurl)
library(ggmap)
library(xml2)
library(sentiment)
library(RTextTools)
library(RColorBrewer)
library(class)
library(gmodels)
library(MASS)
data<-read.csv("test.csv")
matrix= create_matrix(data$review, language="english",
removeStopwords=TRUE, removeNumbers=TRUE,
stemWords=TRUE)
container = create_container(matrix, as.numeric(as.factor(data[,1])),
trainSize=1:1300, testSize=1301:1513,virgin=FALSE)
models = train_models(container, algorithms="SVM")
results = classify_models(container, models)
results
table(as.numeric(as.factor(data[1301:1513, 2])), results[,"SVM_LABEL"])
plot(results)
recall_accuracy(as.numeric(as.factor(data[1301:1513, 2])), results[,"SVM_LABEL"])
analytics = create_analytics(container, results)
summary(analytics)
head(analytics@document_summary)
N=4
set.seed(2014)
cross_validate(container,N,"SVM")
|
/projeTest/svm2.R
|
no_license
|
praneethsaiii/Opinion-mining-for-Mcdonalds-services-using-machine-learning
|
R
| false | false | 991 |
r
|
library(tm)
library(NLP)
library(e1071)
library(Rstem)
library(ggplot2)
library(RCurl)
library(ggmap)
library(xml2)
library(sentiment)
library(RTextTools)
library(RColorBrewer)
library(class)
library(gmodels)
library(MASS)
data<-read.csv("test.csv")
matrix= create_matrix(data$review, language="english",
removeStopwords=TRUE, removeNumbers=TRUE,
stemWords=TRUE)
container = create_container(matrix, as.numeric(as.factor(data[,1])),
trainSize=1:1300, testSize=1301:1513,virgin=FALSE)
models = train_models(container, algorithms="SVM")
results = classify_models(container, models)
results
table(as.numeric(as.factor(data[1301:1513, 2])), results[,"SVM_LABEL"])
plot(results)
recall_accuracy(as.numeric(as.factor(data[1301:1513, 2])), results[,"SVM_LABEL"])
analytics = create_analytics(container, results)
summary(analytics)
head(analytics@document_summary)
N=4
set.seed(2014)
cross_validate(container,N,"SVM")
|
library(RNAseqNet)
### Name: GLMnetwork
### Title: Infer a network from RNA-seq expression.
### Aliases: GLMnetwork
### ** Examples
data(lung)
lambdas <- 4 * 10^(seq(0, -2, length = 10))
ref_lung <- GLMnetwork(lung, lambdas = lambdas)
|
/data/genthat_extracted_code/RNAseqNet/examples/GLMnetwork.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false | false | 243 |
r
|
library(RNAseqNet)
### Name: GLMnetwork
### Title: Infer a network from RNA-seq expression.
### Aliases: GLMnetwork
### ** Examples
data(lung)
lambdas <- 4 * 10^(seq(0, -2, length = 10))
ref_lung <- GLMnetwork(lung, lambdas = lambdas)
|
#' @title Destroy all or part of the data store.
#' @export
#' @family clean
#' @description Destroy all or part of the data store written
#' by [tar_make()] and similar functions.
#' @return Nothing.
#' @inheritParams tar_validate
#' @param destroy Character of length 1, what to destroy. Choices:
#' * `"all"`: destroy the entire data store (default: `_targets/`)
#' * `"meta"`: just delete the metadata file at `meta/meta` in the
#' data store, which invalidates all the targets but keeps the data.
#' * `"process"`: just delete the progress data file at
#' `meta/process` in the data store, which resets the metadata
#' of the main process.
#' * `"progress"`: just delete the progress data file at
#' `meta/progress` in the data store,
#' which resets the progress tracking info.
#' * `"objects"`: delete all the target
#' return values in `objects/` in the data
#' store but keep progress and metadata.
#' Dynamic files are not deleted this way.
#' * `"scratch"`: temporary files saved during [tar_make()] that should
#' automatically get deleted except if R crashed.
#' * `"workspaces"`: compressed files in `workspaces/` in the data store with
#' the saved workspaces of targets. See [tar_workspace()] for details.
#' @param ask Logical of length 1, whether to pause with a menu prompt
#' before deleting files. To disable this menu, set the `TAR_ASK`
#' environment variable to `"false"`. `usethis::edit_r_environ()`
#' can help set environment variables.
#' @examples
#' if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
#' tar_dir({ # tar_dir() runs code from a temporary directory.
#' tar_script(list(tar_target(x, 1 + 1)), ask = FALSE)
#' tar_make() # Creates the _targets/ data store.
#' tar_destroy()
#' print(file.exists("_targets")) # Should be FALSE.
#' })
#' }
tar_destroy <- function(
destroy = c(
"all",
"meta",
"process",
"progress",
"objects",
"scratch",
"workspaces"
),
ask = NULL,
store = targets::tar_config_get("store")
) {
path <- switch(
match.arg(destroy),
all = store,
meta = path_meta(store),
process = path_process(store),
progress = path_progress(store),
objects = path_objects_dir(store),
scratch = path_scratch_dir(store),
workspaces = path_workspaces_dir(store)
)
if (tar_should_delete(path = path, ask = ask)) {
unlink(path, recursive = TRUE)
}
invisible()
}
|
/R/tar_destroy.R
|
permissive
|
billdenney/targets
|
R
| false | false | 2,443 |
r
|
#' @title Destroy all or part of the data store.
#' @export
#' @family clean
#' @description Destroy all or part of the data store written
#' by [tar_make()] and similar functions.
#' @return Nothing.
#' @inheritParams tar_validate
#' @param destroy Character of length 1, what to destroy. Choices:
#' * `"all"`: destroy the entire data store (default: `_targets/`)
#' * `"meta"`: just delete the metadata file at `meta/meta` in the
#' data store, which invalidates all the targets but keeps the data.
#' * `"process"`: just delete the progress data file at
#' `meta/process` in the data store, which resets the metadata
#' of the main process.
#' * `"progress"`: just delete the progress data file at
#' `meta/progress` in the data store,
#' which resets the progress tracking info.
#' * `"objects"`: delete all the target
#' return values in `objects/` in the data
#' store but keep progress and metadata.
#' Dynamic files are not deleted this way.
#' * `"scratch"`: temporary files saved during [tar_make()] that should
#' automatically get deleted except if R crashed.
#' * `"workspaces"`: compressed files in `workspaces/` in the data store with
#' the saved workspaces of targets. See [tar_workspace()] for details.
#' @param ask Logical of length 1, whether to pause with a menu prompt
#' before deleting files. To disable this menu, set the `TAR_ASK`
#' environment variable to `"false"`. `usethis::edit_r_environ()`
#' can help set environment variables.
#' @examples
#' if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
#' tar_dir({ # tar_dir() runs code from a temporary directory.
#' tar_script(list(tar_target(x, 1 + 1)), ask = FALSE)
#' tar_make() # Creates the _targets/ data store.
#' tar_destroy()
#' print(file.exists("_targets")) # Should be FALSE.
#' })
#' }
tar_destroy <- function(
destroy = c(
"all",
"meta",
"process",
"progress",
"objects",
"scratch",
"workspaces"
),
ask = NULL,
store = targets::tar_config_get("store")
) {
path <- switch(
match.arg(destroy),
all = store,
meta = path_meta(store),
process = path_process(store),
progress = path_progress(store),
objects = path_objects_dir(store),
scratch = path_scratch_dir(store),
workspaces = path_workspaces_dir(store)
)
if (tar_should_delete(path = path, ask = ask)) {
unlink(path, recursive = TRUE)
}
invisible()
}
|
x1<-readline(prompt = "Enter your x1 point value : ");
x1<-as.double(x1)
y1<-readline(prompt = "Enter your y1 point value : ");
y1<-as.double(y1)
x2<-readline(prompt = "Enter your x2 point value : ");
x2<-as.double(x2)
y2<-readline(prompt = "Enter your y2 point value : ");
y2<-as.double(y2)
X<-(x2-x1)**2
Y<-(y2-y1)**2
z<-X+Y
M<-sqrt(z)
print(paste(M))
|
/distance between two points.R
|
no_license
|
lakhyaraj/DA_LAB_ASSIGNMENT
|
R
| false | false | 367 |
r
|
x1<-readline(prompt = "Enter your x1 point value : ");
x1<-as.double(x1)
y1<-readline(prompt = "Enter your y1 point value : ");
y1<-as.double(y1)
x2<-readline(prompt = "Enter your x2 point value : ");
x2<-as.double(x2)
y2<-readline(prompt = "Enter your y2 point value : ");
y2<-as.double(y2)
X<-(x2-x1)**2
Y<-(y2-y1)**2
z<-X+Y
M<-sqrt(z)
print(paste(M))
|
library(dplyr)
## Log scales =====================
test_that("Log scales", {
# Log scales (#161)
p <- data.frame(x = c(1.5, 2, 3, 4, 4.5, 5, 6, 7, 8, 9),
y = c(11, 20, 40, 90, 11, 14, 90, 15, 15, 16)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "xy") + agg_line() +
agg_ylim(10, 90, 5) + agg_xlim(1, 10)
expect_true(check_graph(p, "misc-log-scale-both"))
p <- data.frame(x = 1:10,
y = c(11, 20, 40, 90, 11, 14, 90, 15, 15, 16)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "y") +
agg_line() + agg_ylim(10, 90, 5)
expect_true(check_graph(p, "misc-log-scale-y"))
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "xy") + agg_line() +
agg_ylim(10, 90, 5) + agg_xlim(10, 100)
expect_true(check_graph(p, "misc-log-scale-both-larger-scale"))
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "x") + agg_line() +
agg_xlim(10, 100)
expect_true(check_graph(p, "misc-log-scale-x"))
p <- data.frame(x = c(1, 2, 3), y = c(-1, 2, 3)) %>%
arphitgg(agg_aes(x, y), log_scale = "y") + agg_col() + agg_ylim(1, 2, 3)
expect_error(print(p), "y log scale plots cannot have negative data")
})
test_that("Log scale limit requirement", {
expect_error({
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "x") + agg_line()
print(p)
},
"You must manually set x axis limits for log scale plots.")
expect_error({
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "y") + agg_line()
print(p)
},
"You must manually set y axis limits for log scale plots.")
})
## joined (#101) ==================
test_that("Joined", {
foo <- data.frame(x = 1:10, y = 1:10)
foo$y[4] <- NA
p <- arphitgg(foo, agg_aes(x = x, y = y), joined = FALSE) + agg_line()
expect_true(check_graph(p, "misc-joined"))
})
## SRT ================
test_that("srt", {
foo <- data.frame(x = c("a very long label", "another very long label"),
y = c(1, 2))
p <- arphitgg(foo, agg_aes(x = x, y = y), srt = 90) + agg_col()
expect_true(check_graph(p, "misc-long-rotated-x-labels-90"))
p <- arphitgg(foo, agg_aes(x = x, y = y), srt = 45) + agg_col()
expect_true(check_graph(p, "misc-long-rotated-x-labels-45"))
})
|
/tests/testthat/test-misc.R
|
permissive
|
angusmoore/arphit
|
R
| false | false | 2,440 |
r
|
library(dplyr)
## Log scales =====================
test_that("Log scales", {
# Log scales (#161)
p <- data.frame(x = c(1.5, 2, 3, 4, 4.5, 5, 6, 7, 8, 9),
y = c(11, 20, 40, 90, 11, 14, 90, 15, 15, 16)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "xy") + agg_line() +
agg_ylim(10, 90, 5) + agg_xlim(1, 10)
expect_true(check_graph(p, "misc-log-scale-both"))
p <- data.frame(x = 1:10,
y = c(11, 20, 40, 90, 11, 14, 90, 15, 15, 16)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "y") +
agg_line() + agg_ylim(10, 90, 5)
expect_true(check_graph(p, "misc-log-scale-y"))
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "xy") + agg_line() +
agg_ylim(10, 90, 5) + agg_xlim(10, 100)
expect_true(check_graph(p, "misc-log-scale-both-larger-scale"))
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "x") + agg_line() +
agg_xlim(10, 100)
expect_true(check_graph(p, "misc-log-scale-x"))
p <- data.frame(x = c(1, 2, 3), y = c(-1, 2, 3)) %>%
arphitgg(agg_aes(x, y), log_scale = "y") + agg_col() + agg_ylim(1, 2, 3)
expect_error(print(p), "y log scale plots cannot have negative data")
})
test_that("Log scale limit requirement", {
expect_error({
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "x") + agg_line()
print(p)
},
"You must manually set x axis limits for log scale plots.")
expect_error({
p <- data.frame(x = c(10, 100, 60), y = c(11, 20, 40)) %>%
arphitgg(agg_aes(x = x, y = y), log_scale = "y") + agg_line()
print(p)
},
"You must manually set y axis limits for log scale plots.")
})
## joined (#101) ==================
test_that("Joined", {
foo <- data.frame(x = 1:10, y = 1:10)
foo$y[4] <- NA
p <- arphitgg(foo, agg_aes(x = x, y = y), joined = FALSE) + agg_line()
expect_true(check_graph(p, "misc-joined"))
})
## SRT ================
test_that("srt", {
foo <- data.frame(x = c("a very long label", "another very long label"),
y = c(1, 2))
p <- arphitgg(foo, agg_aes(x = x, y = y), srt = 90) + agg_col()
expect_true(check_graph(p, "misc-long-rotated-x-labels-90"))
p <- arphitgg(foo, agg_aes(x = x, y = y), srt = 45) + agg_col()
expect_true(check_graph(p, "misc-long-rotated-x-labels-45"))
})
|
/examples/pruebas_simples/basico_multivariantes.R
|
no_license
|
Bastianpizarro1991/SeriesTemporalesEnCastellano
|
R
| false | false | 2,686 |
r
|
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