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9ae94990b07de39a247481095951213b41c1ccd1 | d556d8044376ffd8bf959b42f02f696d28b40b8d | /regressao_linear.R | 428bc020da0996089ac6dad5694dfaee22323f82 | [] | no_license | cassioancn/Regress-o-Linear-Simples | 8705fd8483d1d6b40b4c5824f706b3ac8b068f38 | ff55a4b099479921d82f974386024f27b16528f3 | refs/heads/main | 2023-03-12T16:49:30.919820 | 2021-03-08T04:26:19 | 2021-03-08T04:26:19 | 345,529,355 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,654 | r | regressao_linear.R | library(readxl)
dados <- read_excel("~/1UNISINOS/ALURA/RSTUDIO/Regressão Linear/regressaolinear_r-master/dados.xlsx")
View(dados)
#Plotar os gráficos de dispersão
plot(dados$area, dados$preco, main = "Diagrama de Dispersão", xlab = "Área", ylab = "Preço", pch = 19, col = "blue")
cor(dados$area, dados$preco)
cor.test(dados$area, dados$preco)
plot(dados$tempo, dados$preco, main = "Diagrama de Dispersão", xlab = "Tempo", ylab = "Preço", pch = 10, col = "red")
cor(dados$tempo, dados$preco)
cor.test(dados$tempo, dados$preco)
#Plotar o boxplot de preço
boxplot(dados$preco)
#Identificar a casa do outlier pelo pacote car
install.packages("car")
library(car)
Boxplot(dados$preco)
#Descobrir o valor do outlier pela casa dele
dados$preco[79]
#Descobrir quais são as casas do terceiro quartil (caixa de cima)
which(dados$preco > quantile(dados$preco, 0.75))
##Equação da reta
mod1 = lm(preco ~ area, data = dados)
mod1
## Fórmulada da reta para estimar o preço em razão da área. Substituir os coefs na fórmula : preco = b0 + b1*area
preco_70 = 502347 + 7851*(70)
preco_70
## Outro modo de fazer a mesma previsão
mod1$coefficients[[1]] + mod1$coefficients[[2]]*70
##Gerar o gráfico disso
plot(dados$area, dados$preco, main = "Diagrama e reta", xlab = "Área (m²)", ylab = "Preço (R$)", pch = 19, col = "blue") + abline(mod1, col = "red")
##Validar o modelo: verificar se a área influi no preço das casas
summary(mod1)
##Outro mpétodo de validar os dados
names(summary(mod1))
summary(mod1)$r.squared
##Verficar o tempo em relação ao tempo
mod2 = lm(preco ~ tempo, data = dados)
mod2
summary(mod2)
###Verificar os resíduos do modelo
plot(mod1$residuals)
identify(mod1$residuals, n = 2)
mod1$residuals[82]
###Remover uma posição desejada
dados_59 = dados[-59,]
###Remover duas casas desejadas
dados_59_82 = dados[c(-59, -82),]
###Teste de independência dos erros do modelo
install.packages("lmtest")
library(lmtest)
dwtest(mod1)
plot(mod1$fitted.values, mod1$residuals)
bptest(mod1)
plot(mod1, 2)
shapiro.test(mod1$residuals)
####Previsão
dados_novos = data.frame(area = c(60, 70))
predict(mod1, newdata = dados_novos)####Vai retornar os valores estimados para o preço em função das áreas requisitadas no vetor
####Previsão de um intervalo
predict(mod1, newdata = dados_novos, interval = "prediction") #fit=estimativa pontual; lwr=limite inferior; upr=limite superior
####Predição com intervalo de confiânça
predict(mod1, newdata = dados_novos, interval = "confidence")
|
629314643eb48983984502fa6de8d932e788d113 | b482c98ff5065b055887827fb8dc86e3055c6438 | /man/trawl_DExp.Rd | 2f712f91a91aa8b1d25e5868dd413cbda765d8c9 | [] | no_license | cran/trawl | bdb7d749804b73f5170d348e0614aff4d3d253fd | 4013b1fa141f3ae3c0c13447488e45125a675b9c | refs/heads/master | 2021-06-15T02:31:33.763015 | 2021-02-22T16:30:02 | 2021-02-22T16:30:02 | 145,911,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 883 | rd | trawl_DExp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SimulateTrawl.R
\name{trawl_DExp}
\alias{trawl_DExp}
\title{Evaluates the double exponential trawl function}
\usage{
trawl_DExp(x, w, lambda1, lambda2)
}
\arguments{
\item{x}{the argument at which the double exponential trawl function will be
evaluated}
\item{w}{parameter in the double exponential trawl}
\item{lambda1}{the parameter \eqn{\lambda_1} in the double exponential trawl}
\item{lambda2}{the parameter \eqn{\lambda_2} in the double exponential trawl}
}
\value{
The double exponential trawl function evaluated at x
}
\description{
Evaluates the double exponential trawl function
}
\details{
The trawl function is parametrised by parameters \eqn{0\leq w\leq 1}
and \eqn{\lambda_1, \lambda_2 > 0} as follows: \deqn{g(x) = w e^{\lambda_1
x}+(1-w) e^{\lambda_2 xz}, \mbox{ for } x \le 0.}
}
|
add1585f37917f937885e9d252d9ebe4c9335fb3 | 68c51b6b053872846d5e2bd273f3b172b0c1bbf7 | /Various Regression Techniques/OrdinalRegression.R | 7925c4c755cfa2f57ccfcf2990889181855c8373 | [] | no_license | akshatshah14/Machine-Learning | 62f158eafc20ce91668e225fe49d503773d9ed5c | fb4048a77f3ba15df91af5ae1d8c3d8e1bd5c0aa | refs/heads/master | 2021-07-10T16:58:55.787972 | 2017-10-09T05:54:02 | 2017-10-09T05:54:02 | 106,143,613 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,973 | r | OrdinalRegression.R | birthweight = read.csv("C:\\Users\\Akshat\\Dropbox\\Spring 2017- ML for Data Science\\Datasets\\lowbwt.csv");
colnames(birthweight)
birthweight$WT = 0
birthweight[birthweight$BWT >0 & birthweight$BWT <2500,]$WT = 4
birthweight[birthweight$BWT >=2500 & birthweight$BWT <3000,]$WT = 3
birthweight[birthweight$BWT >=3000 & birthweight$BWT <3500,]$WT = 2
birthweight[birthweight$BWT >=3500,]$WT = 1
require(foreign)
require(ggplot2)
require(MASS)
require(Hmisc)
require(reshape2)
polr.fit = polr(as.factor(WT) ~ AGE + LWT + as.factor(RACE) + as.factor(SMOKE) + as.factor(PTL) + as.factor(HT) + as.factor(UI) + as.factor(FTV), data = birthweight, Hess=TRUE)
summary(polr.fit)
(ctable <- coef(summary(polr.fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
(ctable <- cbind(ctable, "p value" = p))
ci = confint.default(polr.fit)
## odds ratios
exp(coef(polr.fit))
## OR and CI
exp(cbind(OR = coef(polr.fit), ci))
sf <- function(y) {
c('Y>=1' = qlogis(mean(y >= 1)),
'Y>=2' = qlogis(mean(y >= 2)),
'Y>=3' = qlogis(mean(y >= 3)),
'Y>=4' = qlogis(mean(y >= 4)))
}
(s <- with(birthweight, summary(as.numeric(WT) ~ AGE + LWT + as.factor(RACE) + as.factor(SMOKE) + as.factor(PTL) + as.factor(HT) + as.factor(UI) + as.factor(FTV), fun=sf)))
#evaluate the parallel slopes assumption
glm(I(as.numeric(WT) >= 2) ~ AGE + LWT + as.factor(RACE) + as.factor(SMOKE) + as.factor(PTL) + as.factor(HT) + as.factor(UI) + as.factor(FTV), family="binomial", data = birthweight)
glm(I(as.numeric(WT) >= 3) ~ AGE + LWT + as.factor(RACE) + as.factor(SMOKE) + as.factor(PTL) + as.factor(HT) + as.factor(UI) + as.factor(FTV), family="binomial", data = birthweight)
glm(I(as.numeric(WT) >= 4) ~ AGE + LWT + as.factor(RACE) + as.factor(SMOKE) + as.factor(PTL) + as.factor(HT) + as.factor(UI) + as.factor(FTV), family="binomial", data = birthweight)
s[,5] = s[,5]-s[,4]
s[,4] = s[,4]-s[,3]
s[,3] = s[,3]-s[,3]
s
|
90bca9ff7672d771a14b0b0c190c706dd28e405c | 1f6e9cc31324632eb8bfe1a1ef066369e48eb839 | /run_analysis.R | 550503ca63965affcdb1556cd823a18dd757b542 | [] | no_license | grejpfrut/Getting-and-Cleaning-Data-Course-Project | 54213f216fa0e0cbb5d2c9f86e2e49c1af377af8 | 2990ba1d5605ffded0f012ab7e55d50d146f0e4e | refs/heads/master | 2020-03-27T06:43:48.492508 | 2018-08-25T21:55:44 | 2018-08-25T21:55:44 | 146,130,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,101 | r | run_analysis.R | run_analysis.R <-
##SUBJECT
subject_test <- read.table("test/subject_test.txt", sep = ",")
subject_train <- read.table("train/subject_train.txt", sep = ",")
subject <- rbind(subject_test,subject_train)
colnames(subject) <- c("subject")
## Y-test
y_test <- read.table("test/y_test.txt", sep = ",")
y_train <- read.table("train/y_train.txt", sep = ",")
y_total <- rbind(y_test, y_train)
colnames(y_total) <- c("activities")
##X
train_Xnames <- read.table("features.txt", sep = " ")
traincharnames <- as.character(train_Xnames$V2)
library(tidyr)
test_X <- read.table("test/X_test.txt", nrows = 2947 , sep = "", na.strings=c(""," ","NA"))
train_X <- read.table("train/X_train.txt" , sep = "", na.strings=c(""," ","NA"))
X_total <- rbind(test_X, train_X)
colnames(X_total) <- traincharnames
table <- cbind(subject, y_total, X_total)
##subsetting mean and std
tableme <- grep("mean",colnames(table))
tablestd <- grep("std",colnames(table))
table <- table[ ,c(1:2,tableme,tablestd)]
##changing activity names
table$activities <- as.character(table$activities)
table$activities[table$activities == "1"] <- "WALKING"
table$activities[table$activities == "2"] <- "WALKING_UPSTAIRS"
table$activities[table$activities == "3"] <- "WALKING_DOWNSTAIRS"
table$activities[table$activities == "4"] <- "SITTING"
table$activities[table$activities == "5"] <- "STANDING"
table$activities[table$activities == "6"] <- "LAYING"
##giving descriptive variable names
names(table) <- sub("\\()","",names(table))
names(table) <- gsub("-",".",names(table))
names(table) <- sub("^t","time",names(table))
names(table) <- sub("^f","freq",names(table))
##creating new tidy data set
library(dplyr)
library(datasets)
empty_as_na <- function(x){
if("factor" %in% class(x)) x <- as.character(x)
ifelse(as.character(x)!="", x, NA)
}
table <- table %>% mutate_all(funs(empty_as_na))
table$subject <- as.factor(table$subject)
table$activities <- as.factor(table$activities)
table <- tbl_df(table)
table <- group_by(table, subject, activities)
final <- summarise_all(table, mean, na.rm = TRUE)
View(final) |
ba1091de78f8a317754085b408014b78d05f811c | 711e4cc88c3d4302a5e8cb87a74bdfea187647c1 | /R/little_helpers.R | fe85baf1d799a417e14d455a13650cc7fb59e9d9 | [] | no_license | sib-swiss/dsSwissKnife | 43dc22d6f19ea7f0162e2bdcb1ee19018e8c874d | b52dc57ccbc4ebaa58274d1736df3276dc5ca3df | refs/heads/master | 2023-06-27T10:36:55.336669 | 2023-06-14T13:24:55 | 2023-06-14T13:24:55 | 311,978,958 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,053 | r | little_helpers.R | #' @title Initialize the environment
#' @description Set a number of defaults for some of the functions.
#' These are:
#'
#' 1) hidden.fields - fields that must show no information (patient identifiers for instance)
#'
#' 2) allowed.functions - supplementary functions allowed in dssDeriveColumn (see the documentation for that function)
#'
#' 3) join.pivot.col - the default column to join (or pivot) by. In the absence of this default
#' the 'by.col' argument in dssJoin and dssPivot becomes mandatory
#'
#' 4) forbidden.options - a number of opal administrative options that are not allowed to be changed by dssSetOption
#'
#' In order to set/modify any of these options the node administrator must create an .Rprofile file
#' in the rserver conf directory (normally /var/lib/rserver/conf) and populate it with the necessary
#' R commands to set the above options (see example). Then the following line must be added to the file
#' /var/lib/rserver/conf/Rserv.conf:
#'
#' source /var/lib/rserver/conf/.Rprofile
#
#'@examples
#' # content of the /var/lib/rserver/conf/.Rprofile file that adds as.POSIXct among
#' # the allowed functions in dssDeriveColumn:
#' options(allowed.functions='as.POSIXct')
#'
#'
.init <- function(){
.mycache <<- new.env(parent=.GlobalEnv)
assign('hidden', getOption('hidden.fields'), envir = .mycache)
allowed.funcs <- c('abs', 'round', 'ceiling', 'floor', 'signif', 'length', 'paste0', 'as.Date', 'as.character',
'as.numeric','egfr', 'as.factor', 'sub', 'gsub', 'grep','grepl', 'sqrt', getOption('allowed.functions'))
#ign('allowed.funcs', allowed.funcs, envir = .mycache)
assign('allowed.funcs', allowed.funcs, envir = .mycache)
if(!is.null(getOption('join.pivot.col'))){
assign('by.col', getOption('join.pivot.col'), envir = .mycache)
}
#dissalow change of critical options:
forbidden.options <- c(grep('^default\\.|^datashield\\.',names(options()), value = TRUE), 'hidden.fields', 'allowed.functions', 'join.pivot.col','dsQueryLibrary.enforce_strict_privacy',getOption('forbidden.options'))
assign('forbidden.options', forbidden.options, envir = .mycache)
#test gitd
}
.split.numerics <- function(x, collist = NULL){
nums <- sapply(x, is.numeric)
nums <- nums[is.null(collist) | names(nums) %in% collist] # only collist if supplied
nums <- nums[nums == TRUE]
#if(length(nums) == 0){
# return(NA)
#}
#list(numerics = as.data.frame(subset(x, TRUE, nums)), others = as.data.frame(subset(x, TRUE, !nums)))
nums <- nums[.trim_hidden_fields(names(nums))] # no hidden fields
list(numerics = x[,names(nums), drop= FALSE], others = x[,!(colnames(x) %in% names(nums)), drop = FALSE])
}
#' @title Decode from base64 and deserialize from json if necessary
#' @description Work around the restrictions imposed by the Opal server on function arguments
#' The Opal server is very picky as regards function arguments. The workaround is
#' to serialize and encode them on the client and strip the right padding.
#' @details It looks for the string 'base64' in the argument to determine if it's encoded
#' @param some.thing the thing to be decoded and deserialized from json if necessary
#' @return the decoded and deserialized argument
#'
.decode.arg <- function(some.thing, simplifyMatrix = FALSE){
if(length(some.thing) == 1 && grepl('base64', some.thing, ignore.case = TRUE)){
some.thing <- gsub('base64', '', some.thing, ignore.case =TRUE)
serialized <- FALSE
if(grepl('serialized', some.thing, ignore.case = TRUE)){
serialized <- TRUE
some.thing <- gsub('serialized', '', some.thing, ignore.case =TRUE)
}
my.dictionary = c('-plus-' = '+', '-slash-' = '/', '-equals-' = '=')
sapply(names(my.dictionary), function(x){
some.thing <<- gsub(x, my.dictionary[x], some.thing)
})
#
if(serialized){
some.thing <- jsonlite::unserializeJSON(RCurl::base64Decode(some.thing))
} else {
some.thing <- jsonlite::fromJSON(RCurl::base64Decode(some.thing), simplifyMatrix = simplifyMatrix)
}
}
return(some.thing)
}
.extract <- function (input, start.env = NULL){
#modified version of dsBase:::extract
#it returns a list of (inputname = object)
#works for dataframes embedded in lists
input <- unlist(input)
output <- list()
for (i in input) {
inputterms <- unlist(strsplit(i, "\\$", perl = TRUE))
if(!is.null(start.env)){
inputterms <- inputterms[2:length(inputterms)]
inputterms <- inputterms[!is.na(inputterms)]
obj <- as.environment(start.env)
} else {
obj <- parent.frame()
}
for(objname in inputterms){
this.env <- as.environment(obj)
obj <- get(objname, envir = this.env)
}
output[[i]] <- obj
}
return(output)
}
.dsBase_extract <- function (input){
input <- unlist(input)
output1 <- c()
output2 <- c()
for (i in 1:length(input)) {
inputterms <- unlist(strsplit(input[i], "\\$", perl = TRUE))
if (length(inputterms) > 1) {
obj1 <- strsplit(input[i], "\\$", perl = TRUE)[[1]][1]
obj2 <- strsplit(input[i], "\\$", perl = TRUE)[[1]][2]
}
else {
obj1 <- NA
obj2 <- strsplit(input[i], "\\$", perl = TRUE)[[1]][1]
}
output1 <- append(output1, obj1)
output2 <- append(output2, obj2)
}
output <- list(holders = output1, elements = output2)
return(output)
}
#'
#' @title returns all objects in all environments
#' @description helper function for dssSubset and dssPivot
#' @param start a character the environment name where to start (default .GlobalEnv)
#' @return a list of environment names and the respective objects defined in each environment
#'
.ls.all <- function(start = '.GlobalEnv'){
envir <- get(start)
objs <- ls(envir, all.names = TRUE)
ret <- list()
ret[[start]] <- objs
more.envs <- names(which(sapply(objs, function(x)is.environment(get(x)))==TRUE))
c(ret,sapply(more.envs,function(x) ls(get(x), all.names = TRUE), USE.NAMES = TRUE, simplify = FALSE))
}
#'
#' @title locks or unlocks bindings in environments
#' @description helper function for dssSubset and dssPivot
#' @param what a list of environments and their respective objects - the output of ls.all above
#' @param func a function, either lockBinding or unlockBinding
#'
.lock.unlock <- function(what , func){
stopifnot(deparse(substitute(func)) %in% c('lockBinding', 'unlockBinding'))
invisible(lapply(names(what), function(x){
lapply(what[[x]], function(y){
func(y,get(x))
})
}))
}
#' @title removes objects from the current workspace
#' @description helper function for dssSubset and dssPivot
#' @param what a list of environments and their respective objects - the output of a previous call to ls.all
#' @param start a character the environment name where to start (default .GlobalEnv)
#'
.cleanup <- function(initial, start = '.GlobalEnv'){
objs <- .ls.all(start)
new.envs <- setdiff(names(objs), names(initial))
Map(function(x){
rm(get(x))
objs[x] <- NULL
}, new.envs)
invisible(Map(function(x){
new.objs <- setdiff(objs[[x]], initial[[x]])
rm(list = new.objs, pos = get(x))
}, names(objs)))
}
.dsBase_isValidDSS <- function (obj) {
nfilter <- .dsBase_setFilterDSS()
if (class(obj) == "character" | class(obj) == "integer" |
class(obj) == "logical" | class(obj) == "numeric") {
if (length(obj) > 0 & length(obj) < nfilter) {
return(FALSE)
}
else {
return(TRUE)
}
}
else {
if (class(obj) == "factor") {
tt <- tabulate(obj)
xx <- which(tt > 0 & tt < nfilter)
if (length(xx) > 0) {
return(FALSE)
}
else {
return(TRUE)
}
}
else {
if (class(obj) == "data.frame" | class(obj) == "matrix") {
if (dim(obj)[1] > 0 & dim(obj)[1] < nfilter) {
return(FALSE)
}
else {
return(TRUE)
}
}
else {
return(FALSE)
}
}
}
}
.dsBase_setFilterDSS <- function (x = getOption("datashield.privacyLevel", default = 5)) {
# from dsBase
a <- as.numeric(as.character(x))
return(a)
}
.dsBase_numNADSS <- function (xvect){
# from dsBase
out <- length(which(is.na(xvect)))
return(out)
}
.get_memory_usage <- function(envir = .GlobalEnv){
objnames <- ls(envir = envir, all.names = TRUE)
sapply(objnames, function(x){
thisobj <- get(x, envir = envir)
out <- c()
if(class(thisobj) == 'environment'){
out <- unlist(.get_memory_usage(thisobj))
} else {
out <- as.numeric(object.size(thisobj))
}
out
}, simplify = TRUE)
}
.trim_hidden_fields <- function(cols){
#first pass:
cols <- setdiff(cols, getOption('hidden.fields'))
for (r in getOption('hidden.fields.regexes')){
cols <- grep(r, cols, value = TRUE, perl = TRUE, invert = TRUE)
}
cols
}
.betterExtract <- function(what, startEnv = parent.frame()){
terms <- strsplit(what, '$' , fixed = TRUE)[[1]]
Reduce(function(x,y){
get(y, envir = as.environment(x))
}, terms, init = startEnv)
}
|
6fad7247553ce5cf64ad5e4e74fc8e84c6a77cb4 | fef507ac41bb7749840e7f5141ba87fde26d6f95 | /code/spython/tangram_libd/code/03_nn_run/09_manual_figure.R | 4a59418c545e5514b86479f306b0f8f293ebc2e9 | [] | no_license | LieberInstitute/spatialDLPFC | 84bc2f0b694473182d315b4d39ab3d18c2dd3536 | 840700ae86cdd414e024d9658b09dd11712ef470 | refs/heads/main | 2023-08-04T03:37:18.425698 | 2023-07-25T18:27:36 | 2023-07-25T18:27:36 | 314,001,778 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,304 | r | 09_manual_figure.R | # The "main tangram deconvolution figure" (Fig 4a from the paper) is hard to
# generate outside of squidpy, and requires cell centroid info, which doesn't
# seem critically important to the value of the figure. Here I explore if we
# can generate a similar figure that only requires cell counts per spot.
library("here")
library("ggplot2")
library("png")
library("spatialLIBD")
library("jaffelab")
library("rjson")
library("grid")
sample_id <- "151508"
img_path <- here(
"tangram_libd", "raw-data", "03_nn_run", "hires_histology",
paste0(sample_id, ".png")
)
clusters_path <- here(
"tangram_libd", "processed-data", "03_nn_run", "tangram_out_DLPFC",
sample_id, "clusters.csv"
)
scale_path <- file.path(
"/dcl02/lieber/ajaffe/SpatialTranscriptomics/HumanPilot/10X",
sample_id, "scalefactors_json.json"
)
plot_path <- here(
"tangram_libd", "plots", "03_nn_run", "DLPFC",
paste0("my_spot_deconvo_fig_", sample_id, ".pdf")
)
spatial_coords_names <- c("pxl_row_in_fullres", "pxl_col_in_fullres")
# Fetch and subset SPE to this sample
spe <- fetch_data(type = "spe")
spe <- spe[, spe$sample_id == sample_id]
# Read in image, cell counts, and image scale factors
img <- readPNG(img_path)
clusters <- read.csv(clusters_path)
scale_json <- fromJSON(file = scale_path)
# Add spatial coordinates to 'clusters', the data frame of cell counts per
# spot
clusters$barcode <- ss(clusters$key, "_")
stopifnot(all(clusters$barcode == rownames(spatialCoords(spe))))
clusters <- cbind(clusters, spatialCoords(spe))
# Infer the cell types used
cell_types <- colnames(clusters)[
!(colnames(clusters) %in% c(
"key", "cell_count", "barcode", spatial_coords_names
)
)
]
# Make a long data frame, where each row is a cell
df_list <- list()
i <- 1
for (barcode in rownames(clusters)) {
for (cell_type in cell_types) {
for (j in seq_len(clusters[barcode, cell_type])) {
df_list[[i]] <- c(
barcode,
as.numeric(clusters[barcode, spatial_coords_names]),
cell_type
)
i <- i + 1
}
}
}
df_long <- data.frame(do.call(rbind, df_list))
colnames(df_long) <- c("barcode", spatial_coords_names, "cell_type")
# Make sure spatialCoords are numeric, and scaled to represent
# high-resolution pixels
for (colname in spatial_coords_names) {
df_long[, colname] <- scale_json$tissue_hires_scalef *
as.numeric(df_long[, colname])
}
# Reverse y coord of spatialCoords(spe) to agree with the coordinate system
# ggplot2 is using
df_long[[spatial_coords_names[2]]] <- dim(img)[2] -
df_long[[spatial_coords_names[2]]]
# Improve plotting speed and file size by keeping a copy of just the spots.
# Get spot radius in high-res pixels
spots <- df_long[match(unique(df_long$barcode), df_long$barcode), ]
spots$spot_radius <- scale_json$spot_diameter_fullres *
scale_json$tissue_hires_scalef / 2
# Create the deconvolution figure
p <- ggplot() +
# The high-res image as the plot's background
annotation_custom(
rasterGrob(
img,
width = unit(1, "npc"), height = unit(1, "npc")
),
-Inf, Inf, -Inf, Inf
) +
# The axes should exactly span the pixel range of the image, and pixels
# should be squares
scale_x_continuous(limits = c(0, dim(img)[1]), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, dim(img)[2]), expand = c(0, 0)) +
coord_fixed() +
# Plot the individual cells, jittered within a spot
geom_jitter(
data = df_long,
aes_string(
x = spatial_coords_names[1], y = spatial_coords_names[2],
fill = "cell_type"
),
size = 0.45, width = 4, height = 4, color = "black", shape = 21,
stroke = 0.05
) +
# Plot empty circles for each spot (to show the spot grid)
geom_circle(
data = spots,
mapping = aes_string(
x0 = spatial_coords_names[1], y0 = spatial_coords_names[2],
r = "spot_radius"
),
size = 0.1
) +
# Brighten colors and improve legend
scale_fill_hue(l = 80, name = "Cell type") +
guides(fill = guide_legend(override.aes = list(size = 5)))
pdf(plot_path)
print(p)
dev.off()
|
c94465345fbe4d857ee135e4a23bf5d1e3161268 | f643705ec1baf66a44a237c1cb144ed47c498445 | /tests/testthat/test_base_convertMLBenchObjToTask.R | 3e057ae1a107005540b0b635f55ce29019cbd05e | [] | no_license | HeidiSeibold/mlr | 8046328a572604260d6b68add3e10b34ea8961f8 | 3b24e632b1cfa326147eae7abb0775e09ac26b93 | refs/heads/master | 2020-06-11T19:39:31.204273 | 2016-12-02T22:03:30 | 2016-12-02T22:03:30 | 75,626,955 | 1 | 0 | null | 2016-12-05T13:24:50 | 2016-12-05T13:24:50 | null | UTF-8 | R | false | false | 830 | r | test_base_convertMLBenchObjToTask.R | context("convertMLBenchObjToTask")
test_that("convertMLbenchObjToTask", {
requirePackages("!mlbench")
# get all mlbench.* functions, 1spiral does not work
fs = ls("package:mlbench", pattern = "mlbench")
n = 77L
for (f in setdiff(fs, c("mlbench.1spiral"))) {
task = convertMLBenchObjToTask(f, n = n)
expect_is(task, "Task")
# for some, n is not properly respected in mlbench
if (f %nin% c("mlbench.corners", "mlbench.hypercube", "mlbench.simplex"))
expect_equal(getTaskSize(task), n)
}
# get all mlbench datasets, HouseVotes84 and Ozone have NAs in target col
ds = data(package = "mlbench")
ds = ds$results[, "Item"]
for (d in setdiff(ds, c("HouseVotes84", "Ozone"))) {
task = convertMLBenchObjToTask(d, n = n)
expect_is(task, "Task")
expect_equal(getTaskId(task), d)
}
})
|
13df9c43ae6ac8ea486a760c547f29039accadb7 | b5bee2eb1150fa5e8e60fa7a259cc2e8aaefb0ef | /man/loadLibraryGloballyAs.Rd | 2d4dc621fa59c492d203a0a1a7032f5fe28c29d9 | [
"Apache-2.0"
] | permissive | SimonSchafferer/short-ncrna-annotation | f5f71e44d9f0ba34ab583c0fdae1a4b5a45e9c34 | 0c69f1b01198ba9a065c0becf5739c1281f6f584 | refs/heads/master | 2021-05-16T02:04:29.189010 | 2015-02-10T14:00:47 | 2015-02-10T14:00:47 | 20,757,069 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 507 | rd | loadLibraryGloballyAs.Rd | % Generated by roxygen2 (4.0.1.99): do not edit by hand
\docType{methods}
\name{loadLibraryGloballyAs}
\alias{loadLibraryGloballyAs}
\title{'Loads an R object by a given name into the globalEnvironment}
\usage{
loadLibraryGloballyAs(name, filename)
}
\arguments{
\item{name}{Object name one wants to define}
\item{filename}{Path to file and name}
\item{envir}{environment were the object should be loaded to}
}
\value{
TRUE
}
\description{
'Loads an R object by a given name into the globalEnvironment
}
|
7ef989db6bf225fd2937b5b9c0d893d00c2fd9bb | d410fdbad98532e294e51da43fbf798cbc04ebc5 | /NaiveBayes/NaiveBayes.R | 2bf1cb076c0940d7d587bf5e1600a58e839953e7 | [] | no_license | muneerulhudha/Machine-Learning-Assignments | b5662745055a42c0fffb9b9fea45019b983657be | 829e1cf2bf58e034f599a8a065fac95718d08859 | refs/heads/master | 2021-01-10T06:53:56.362437 | 2016-01-25T19:30:54 | 2016-01-25T19:30:54 | 50,374,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 730 | r | NaiveBayes.R | library(caret)
library(e1071)
dataset <- read.table(file = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data", header = FALSE, sep = ",")
nb <- function(){
accuracy <- 0
primaPart <- createDataPartition(dataset$V9, p = .9,list = FALSE,times = 1)
trainingData <- dataset[primaPart,]
testData <- dataset[-primaPart,]
model <- naiveBayes(as.factor(V9) ~ ., data = trainingData)
pred <- predict(model, testData)
accuracy <- sum(testData$V9 == pred)/length(pred)
return(accuracy)
}
acc <- c()
for(i in 1:10){
acc <- c(acc,nb())
}
print("Accuracy in each iteration: ")
print(acc)
overallAcc <- mean(acc)
print("Overall Accuracy: ")
print(overallAcc) |
82471ea70ba0945fb98dd6da0abb02a88906a947 | 6ec9b39141936db1b7ad61c20465e6ff5c3988bb | /Data Mining/fold Cross Validation/cmatrixInfo.r | f78290126189d8408904809ffa5d6632c0bc7589 | [] | no_license | Kalovelo/ihu-survival-lecture-notes | 4806daafc9aea246be3d760ab66bb2b0f43cf59e | 837dd9fe4395a9240e58feb16eb46aaf953f1a04 | refs/heads/master | 2021-05-22T14:44:36.418659 | 2020-09-07T12:39:11 | 2020-09-07T12:39:11 | 252,968,079 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 886 | r | cmatrixInfo.r |
#correct predictions
accuracy <- function(tp,tn,fp,fn) {
correct <- tn+tp
all<- tp+fp+tn+fn
return (correct/all)
}
#correct event predictions
precision <- function(tp,fp) {
predictions<- tp+fp
return(tp/predictions)
}
#event predictions against events
recall<- function(tp,fn) {
events<-tp+fn
return(tp/events)
}
CMatrixStats<- function(cMatrix) {
tp = cMatrix['P','P']
tn = cMatrix['N','N']
fn = cMatrix['P','N']
fp = cMatrix['N','P']
results = matrix(0,nrow=1,ncol=3)
colnames(results)<- c('Accuracy','Precision','Recall')
results[1,'Accuracy'] <- accuracy(tp,tn,fn,fp)
results[1,'Precision'] <- precision(tp,fp)
results[1,'Recall'] <- recall(tp,fn)
return(results)
}
generateCMatrix<- function() {
cMatrix = matrix(0,nrow=2,ncol=2)
colnames(cMatrix)<- c('P','N')
rownames(cMatrix)<- c('P','N')
return (cMatrix)
}
|
64650a9d5bbe783d6f0937954f15d264294161a7 | cafc5841d654b41cac6d462b95c28ab16bee8a43 | /BART_iter.R | 91f42548debc41dd9511de0623b28acf2e996257 | [
"MIT"
] | permissive | xylimeng/OIS | 4caefb253f4c84f599d878653049d39c0f7d7b86 | 29164546c1a552ddd4eba9f8fe67b40f359ae3d4 | refs/heads/main | 2023-07-16T18:41:43.962066 | 2021-09-02T15:46:40 | 2021-09-02T15:46:40 | 376,896,853 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,712 | r | BART_iter.R | BART_iter <- function(X, y, head, dimen,
X_selected = NULL,
head_selected = NULL,
dimen_selected = NULL,
num_trees = 20,
num_burn_in = 10000,
num_iterations_after_burn_in = 5000,
num_reps_for_avg = 10,
num_permute_samples = 50,
standardize = TRUE,
train_idx = NULL,
seed = NULL) {
X <- as.matrix(X)
if (!is.null(X_selected)) X_selected <- as.matrix(X_selected)
if (is.null(train_idx)) {
X_train <- X
y_train <- y
} else {
# Training data
X_train <- X[train_idx, ]
y_train <- y[train_idx]
# Testing data
X_test <- X[-train_idx, ]
y_test <- y[-train_idx]
}
# bartMachine only takes dataframe
if (standardize == TRUE){
X_train_scale <- as.data.frame(scale(X_train))
} else {
X_train_scale <- as.data.frame(X_train)
}
if(is.null(seed)){
bart_machine <- bartMachine(X_train_scale, y_train,
num_trees = num_trees,
num_burn_in = num_burn_in,
num_iterations_after_burn_in = num_iterations_after_burn_in,
run_in_sample = FALSE,
serialize = FALSE,
verbose = FALSE)
} else{
bart_machine <- bartMachine(X_train_scale, y_train,
num_trees = num_trees,
num_burn_in = num_burn_in,
num_iterations_after_burn_in = num_iterations_after_burn_in,
run_in_sample = FALSE,
serialize = FALSE,
seed = seed,
verbose = FALSE)
}
var_sel <- var_selection_by_permute(bart_machine,
num_reps_for_avg = num_reps_for_avg,
num_permute_samples = num_permute_samples,
num_trees_for_permute = 20,
plot = FALSE)
pos_idx <- sort(var_sel$important_vars_global_se_col_nums)
# Check if BART selected any variable
if (is.null(X_selected) && length(pos_idx) == 0) {
stop("BART did not select any variable, trying another seed...")
} else {
if (!is.null(X_selected)) {
X_selected <- cbind(X_selected, X[, pos_idx])
head_selected <- c(head_selected, head[pos_idx])
if (is.null(dimen)) {
dimen_selected <- NULL
} else {
dimen_selected <- c(dimen_selected, dimen[pos_idx])
}
# Remove duplicated data
temp <- round(X_selected, digits = 6)
dup_index <- duplicated(temp, MARGIN = 2)
if (any(dup_index == TRUE)) {
X_selected <- X_selected[, !dup_index]
head_selected <- head_selected[!dup_index]
if (is.null(dimen)) {
dimen_selected <- NULL
} else {
dimen_selected <- dimen_selected[!dup_index]
}
}
} else {
X_selected <- X[, pos_idx]
head_selected <- head[pos_idx]
if (is.null(dimen)) {
dimen_selected <- NULL
} else {
dimen_selected <- dimen[pos_idx]
}
}
BART_output <- list(X_selected = X_selected,
head_selected = head_selected,
dimen_selected = dimen_selected)
return(BART_output)
}
}
|
6722cae7a3104ef364dc83d677e00426ff115435 | 7f186fa9c8dd406f6d9666b9ff65319fc377412b | /man/flowdata.Rd | 1d7ba5bb15435da12a0dc1f9801a02c484122b6e | [] | no_license | garciadejalon/FlowRegEnvCost | 5419a0f0a94555a065aec2d3a3e977fbb01a70ce | 998ea71b15123c2eaa83816bb560680c82775365 | refs/heads/master | 2021-01-23T16:26:11.101896 | 2017-11-29T16:11:25 | 2017-11-29T16:11:25 | 102,742,161 | 2 | 0 | null | 2017-11-29T16:11:27 | 2017-09-07T13:45:32 | R | UTF-8 | R | false | true | 693 | rd | flowdata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{flowdata}
\alias{flowdata}
\title{River water flow data.}
\format{An example data frame with 17166 rows and 2 variables:
\describe{
\item{Date}{Date (dd/mm/yyyy)}
\item{Flow}{Water flow, in m^{3}/s}
}}
\source{
Source: \url{https://doi.org/10.1007/s11269-017-1663-0}
}
\usage{
flowdata
}
\description{
A dataset containing daily river water flow data for the Esla river at the Riaño dam (Northern Spain) from 01/10/1964 to 30/09/2011. The library adds missing days within the whole period automatically with NA flow values when you enter your own time series data.
}
\keyword{datasets}
|
3635bff44a4e3fe5c33fc498de3c1d333b62e711 | 42abe0fef0d12287d170fd2445864f9fb9aec74b | /man/neuprint_common_connectivity.Rd | fda6db905d6496f77793f06f5c2c869b372e0632 | [] | no_license | natverse/neuprintr | 45bf13ea5f351c7088ad0e9de36a737342e070f3 | 1bf392fb896710d64e1b03cdddb38ea05a54f5d1 | refs/heads/master | 2023-08-26T15:19:05.332544 | 2023-08-16T03:32:48 | 2023-08-16T03:32:48 | 176,496,271 | 3 | 2 | null | 2023-09-07T19:59:04 | 2019-03-19T11:22:52 | R | UTF-8 | R | false | true | 2,002 | rd | neuprint_common_connectivity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/connectivity.R
\name{neuprint_common_connectivity}
\alias{neuprint_common_connectivity}
\title{Get the common synaptic partners for a set of neurons}
\usage{
neuprint_common_connectivity(
bodyids,
statuses = NULL,
prepost = c("PRE", "POST"),
all_segments = FALSE,
dataset = NULL,
conn = NULL,
...
)
}
\arguments{
\item{bodyids}{the cypher by which to make your search}
\item{statuses}{if not NULL, only bodies with the given status are
considered. Statuses include: Unimportant,0.5assign,Leaves,Prelim Roughly
Traced, Anchor, Orphan.}
\item{prepost}{whether to look for partners presynaptic or postsynaptic to
the given bodyids. So when \code{prepost="PRE"} you will return inputs
(upstream partners) of your starting neurons.}
\item{all_segments}{if TRUE, all bodies are considered, if FALSE, only
'Neurons', i.e. bodies with a status roughly traced status.}
\item{dataset}{optional, a dataset you want to query. If \code{NULL}, the
default specified by your R environ file is used or, failing that the
current connection, is used. See \code{\link{neuprint_login}} for details.}
\item{conn}{optional, a neuprintr connection object, which also specifies the
neuPrint server. If NULL, the defaults set in your
\code{\link[=Startup]{.Rprofile}} or \code{\link[=Startup]{.Renviron}} are
used. See \code{\link{neuprint_login}} for details.}
\item{...}{methods passed to \code{neuprint_login}}
}
\value{
a n x m matrix where n correspond to the neurons that all connect to
m bodyids
}
\description{
Get the neurons that are shared synaptic partners for a set of
given bodyids, either upstream or downstream.
}
\examples{
\donttest{
da2s=neuprint_search('.*DA2.*')
da2conn = neuprint_common_connectivity(da2s$bodyid[1:2], prepost='PRE')
plot(t(da2conn))
head(cbind(t(da2conn), sum=colSums(da2conn)))
}
}
\seealso{
\code{\link{neuprint_simple_connectivity}},
\code{\link{neuprint_get_adjacency_matrix}}
}
|
5eaa5918324aff63c9434840a2eac652e3c09038 | 8b2f41a3710354f0778a84680eb6c72eed060312 | /External Election Data.R | 01b8bf05cbca4239ff7eb68aebd15d425316762d | [] | no_license | paviellehaines/citunited | 5714431eac4f550a75142d07397603ae2c358a55 | 6adb07abfdeacd42faf1cb5079864ad6ea0c49ee | refs/heads/master | 2021-05-08T21:14:31.350201 | 2018-01-31T04:24:22 | 2018-01-31T04:24:22 | 119,633,082 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,892 | r | External Election Data.R | # READ IN AND CLEAN DATA --------------------------------------------------
library(foreign)
library(data.table)
library(dplyr)
library(plyr)
library(tidyr)
elections1 <- read.dta("34297-0001-Data.dta")
elections2 <- read.csv("SLERs2011to2012_only_2013_05_14.csv")
col14 <- read.csv("2014GeneralPrecinctResults.csv")
# ORGANIZE AND GENERATE MISSING DATA FOR 2011-2012 ------------------------
elections2 <- subset(elections2, V17 == 1) #Remove non-final elections
elections2$V11a <- paste(elections2$V08, elections2$V09, elections2$V10, sep = ".")
elections2$election_id <- elections2 %>% group_indices(V02, V05, V11a, V16, V07)#Create unique election ID
elections2$candid <- elections2 %>% group_indices(election_id, V19) #Create unique candidate ID
totvotes <- aggregate(elections2$V23, by = list(elections2$election_id), FUN = sum) #Sum total votes per election
colnames(totvotes) <- c("election_id", "totalvote")
totvotes$election_id <- as.integer(totvotes$election_id)
elections2 <- subset(elections2, V19 != "WRITEIN") #Remove write-ins for candidate count
candvotes <- aggregate(elections2$V23, by = list(elections2$candid), FUN = sum) #Sum total votes per candidate per election
colnames(candvotes) <- c("candid", "candvotes")
candvotes$candid <- as.integer(candvotes$candid)
duplicates <- data.frame(table(elections2$candid)) #Count duplicate candidates in each election
colnames(duplicates) <- c("candid", "duplicates")
duplicates$candid <- as.integer(duplicates$candid)
duplicates$duplicates <- as.integer(duplicates$duplicates)
totvotes <- data.table(totvotes) #Prepare data for merge
candvotes <- data.table(candvotes)
duplicates <- data.table(duplicates)
elections2 <- data.table(elections2)
elections2 <- merge(elections2, totvotes, by="election_id") #Merge data
elections2 <- merge(elections2, candvotes, by="candid")
elections2 <- merge(elections2, duplicates, by="candid")
elections2$duplicates <- as.integer(elections2$duplicates)
elections2a <- subset(elections2, duplicates == 1) #Subset candidates without duplictaes
reconcile <- subset(elections2, duplicates > 1) #Remove and subset duplicated candidate ID's
reconcile1 <- arrange(reconcile, candid, desc(V23))
reconcile2 <- aggregate(reconcile1$V23, by = list(reconcile1$candid), FUN = max) #Remove duplicates
colnames(reconcile2) <- c("candid", "V23")
reconcile2$max <- 1
reconcile3 <- merge(reconcile2,reconcile, by= c("candid", "V23"), all = TRUE)
reconcile4 <- subset(reconcile3, !is.na(reconcile3$max))
reconcile5 <- subset(reconcile4, select = -c(max))
elections2 <- rbind(elections2a, reconcile5)
candidatenum <- data.frame(table(elections2$election_id)) #Count number of candidates in each election
colnames(candidatenum) <- c("election_id", "numrow")
candidatenum$election_id <- as.integer(candidatenum$election_id)
candidatenum <- data.table(candidatenum) #Prepare data for merge
elections2 <- merge(elections2, candidatenum, by = "election_id")
elections2$candperc <- (elections2$candvotes/elections2$totalvote)*100 #Get candidates percentage
missing <- subset(elections2, is.na(elections2$candperc)) #Subset candidates who are missing percentages; Notice they are all races with one candidate and no runner up
elections2b <- subset(elections2, !is.na(elections2$candperc))
missing$candperc <- 100 #Set candidates percentages to 100
missing$totalvote <- 1000
elections2 <- rbind(missing, elections2b) #Put data back together
elections2[,candrank:=rank(-candvotes,ties.method="first"),by = election_id] #Add rank by race
data.frame(elections2)
nowinner <- aggregate(elections2$V24, by = list(elections2$election_id), FUN = sum) #Identify elections with no winners and remove
nowinner <- subset(nowinner, x == 0) #Notice that all elections have winners
nrow(nowinner)
noru <- subset(elections2, numrow <= V13) #Generate margin of victory for elections with no runnerups
noru$ru <- ifelse(noru$candrank - noru$V13 == 1, 1, 0)
noru$ruvotes <- 0
noru$ruperc <- 0
noru$margin <- noru$candperc - noru$ruperc
ru1 <- subset(elections2, numrow > V13) #Generate margin of victory of elections with runnerups
ru1$ru <- ifelse(ru1$candrank - ru1$V13 == 1, 1, 0)
ruperc <- subset(ru1, ru == 1, select = c("candperc", "election_id", "candvotes"))
names(ruperc) <- c("ruperc", "election_id", "ruvotes")
ru <- merge(ru1, ruperc, by = "election_id", all = TRUE)
ru$margin <- ru$candperc - ru$ruperc
elections2 <- rbind(noru, ru) #Put the data back together
elections2win <- subset(elections2, V24 == 1) #Subset only the winners
elections2win$V12 <- ifelse(elections2win$V13 > 1, "Multimember District", "Single-Member District")
elections2win <- subset(elections2win, select = c(V02, V05, V07, V08, V09, V10, V11a, V12, V13, numrow, V14, V16, V19, V21, V22, candvotes, ruvotes, totalvote, candperc, ruperc, margin))
names(elections2win) <- c("state", "year", "seat", "distname", "distnum", "distpost", "distid", "distkind", "numwinners", "numcands", "term", "electionkind", "candname", "candparty", "incumbent", "candvotes", "ruvotes", "totalvotes", "candperc", "ruperc", "margin")
# ORGANIZE AND GENERATE MISSING DATA FOR 1967-2010 ------------------------
elections1 <- subset(elections1, V17 == 1) #Remove non-final elections
elections1 <- subset(elections1, V05 >= 1980) #Remove early elections
elections1$V10new <- elections1$V10 #Create a unique district ID
elections1$V10new[is.na(elections1$V10new)] <- "."
elections1$V11a <- paste(elections1$V08, elections1$V09, elections1$V10new, sep = ".")
elections1$election_id <- elections1 %>% group_indices(V02, V05, V11a, V16, V07)#Create unique election ID
elections1$candid <- elections1 %>% group_indices(election_id, V19) #Create unique candidate ID
totvotes <- aggregate(elections1$V23, by = list(elections1$election_id), FUN = sum) #Sum total votes per election
colnames(totvotes) <- c("election_id", "totalvote")
totvotes$election_id <- as.integer(totvotes$election_id)
candvotes <- aggregate(elections1$V23, by = list(elections1$candid), FUN = sum) #Sum total votes per candidate per election
colnames(candvotes) <- c("candid", "candvotes")
candvotes$candid <- as.integer(candvotes$candid)
duplicates <- data.frame(table(elections1$candid)) #Count duplicate candidates in each election
colnames(duplicates) <- c("candid", "duplicates")
duplicates$candid <- as.integer(duplicates$candid)
duplicates$duplicates <- as.integer(duplicates$duplicates)
totvotes <- data.table(totvotes) #Prepare data for merge
candvotes <- data.table(candvotes)
duplicates <- data.table(duplicates)
elections1 <- data.table(elections1)
elections1 <- merge(elections1, totvotes, by="election_id") #Merge data
elections1 <- merge(elections1, candvotes, by="candid")
elections1 <- merge(elections1, duplicates, by="candid")
elections1a <- subset(elections1, duplicates == 1) #Subset candidates without duplictaes
reconcile <- subset(elections1, duplicates > 1) #Remove and subset duplicated candidate ID's
reconcile1 <- arrange(reconcile, candid, desc(V23))
reconcile2 <- aggregate(reconcile1$V23, by = list(reconcile1$candid), FUN = max) #Remove duplicates
colnames(reconcile2) <- c("candid", "V23")
reconcile2$max <- 1
reconcile3 <- merge(reconcile2,reconcile, by= c("candid", "V23"), all = TRUE)
reconcile4 <- subset(reconcile3, !is.na(reconcile3$max))
reconcile5 <- subset(reconcile4, select = -c(max))
elections1 <- rbind(elections1a, reconcile5)
elections1$numrow <- elections1$V25 #Number of candidats in the election
elections1$candperc <- (elections1$candvotes/elections1$totalvote)*100 #Get candidates percentage
missing <- subset(elections1, is.na(elections1$candperc)) #Subset candidates who are missing percentages, but can be deduced
elections1b <- subset(elections1, !is.na(elections1$candperc))
missingnocomp <- subset(missing, V13 == 1 & V13 >= V25) #Identify which candidates were unchallenged and give them 100% of votes
missingnocomp$candperc <- 100
missingnocomp$candvotes <- 1000
missingcomp <- subset(missing, V13 != 1 | V13 < V25) #Subset data that is missing and cannot be deduced
missingcomp$problem <- "missing"
problemdata <- subset(elections1, election_id == 480 | election_id == 27965 | election_id == 49449 | election_id == 49651 | election_id == 50794 | election_id == 52607 | election_id == 54025 | election_id == 54973 | election_id == 56072 | election_id == 56287 | election_id == 56580 | election_id == 57569 | election_id == 72767 | election_id == 75615 | election_id == 77018 | election_id == 85763 | election_id == 90869 | election_id == 90872 | election_id == 90926 | election_id == 90985 | election_id == 91071 | election_id == 91876 | election_id == 92180)
problemdata$problem <- "wrongwinner"
problems <- rbind(missingcomp, problemdata)
write.csv(problems, "Missing Data.csv")
elections1 <- rbind(missingnocomp, elections1b) #Put data back together
elections1[,candrank:=rank(-candvotes,ties.method="first"),by= election_id] #Add rank by race
data.frame(elections1)
nowinner <- aggregate(elections1$V24, by = list(elections1$election_id), FUN = sum) #Identify elections with no winners and remove
nowinner <- subset(nowinner, x == 0) #Notice that all elections have winners
nrow(nowinner)
noru <- subset(elections1, numrow <= V13) #Generate margin of victory for elections with no runnerups
noru$ru <- ifelse(noru$candrank - noru$V13 == 1, 1, 0)
noru$ruvotes <- 0
noru$ruperc <- 0
noru$margin <- noru$candperc - noru$ruperc
ru1 <- subset(elections1, numrow > V13) #Generate margin of victory of elections with runnerups
ru1$ru <- ifelse(ru1$candrank - ru1$V13 == 1, 1, 0)
ruperc <- subset(ru1, ru == 1, select = c("candperc", "election_id", "candvotes"))
names(ruperc) <- c("ruperc", "election_id", "ruvotes")
ru <- merge(ru1, ruperc, by = "election_id", all = TRUE)
ru$margin <- ru$candperc - ru$ruperc
elections1 <- rbind(noru, ru) #Put the data back together
elections1win <- subset(elections1, V24 == 1) #Subset only the winners
problems <- subset(elections1win, margin <= 0)
elections1win <- subset(elections1win, election_id != 480 & election_id != 27965 & election_id != 49449 & election_id != 49651 & election_id != 50794 & election_id != 52607 & election_id != 54025 & election_id != 54973 & election_id != 56072 & election_id != 56287 & election_id != 56580 & election_id != 57569 & election_id != 72767 & election_id != 75615 & election_id != 77018 & election_id != 85763 & election_id != 90869 & election_id != 90872 & election_id != 90926 & election_id != 90985 & election_id != 91071 & election_id != 91876 & election_id != 92180)
elections1win <- subset(elections1win, select = c(V02, V05, V07, V08, V09, V10, V11a, V12, V13, numrow, V14, V16, V19, V21, V22, candvotes, ruvotes, totalvote, candperc, ruperc, margin))
names(elections1win) <- c("state", "year", "seat", "distname", "distnum", "distpost", "distid", "distkind", "numwinners", "numcands", "term", "electionkind", "candname", "candparty", "incumbent", "candvotes", "ruvotes", "totalvotes", "candperc", "ruperc", "margin")
# MERGE 1967-2010 AND 2011-2012 -------------------------------------------
elections <- rbind(elections1win, elections2win) #Combine ICPSR datasets
elections <- data.frame(elections)
elections$data <- "icpsr"
write.csv(elections, "Final Organized Data.csv") #Write to a CSV file and combine with corrected and collected data in Excel
# ELECTION ANALYSIS -------------------------------------------------------
elections1 <- read.csv("Final Organized Data Ver 3.csv") #Read in new CSV file
head(elections1)
elections1$electionkind1 <- ifelse(elections1$electionkind == "G" | elections1$electionkind == "GR", "G", elections1$electionkind)
elections1$election_id <- elections1 %>% group_indices(state, seat, year, electionkind1) #Create unique election ID
elections1$winner <- 1 #Create a variable showing that all candidates in data are winners to allow for counting up total candidates by election
elections1$Rseats1 <- ifelse(elections1$candparty == "R", 1, 0) #Create new variables to calculate R seat and vote share
elections1$Rvotes1 <- ifelse(elections1$candparty == "R", elections1$candvotes, 0)
elections1$Pseats1 <- ifelse(elections1$candparty == "R" | elections1$candparty == "D", 1, 0)
elections1$Pvotes1 <- ifelse(elections1$candparty == "R" | elections1$candparty == "D", elections1$totalvotes, 0)
electseats <- aggregate(elections1$winner, by = list(elections1$election_id), FUN = sum) #Aggregate new variables
electvotes <- aggregate(elections1$totalvotes, by = list(elections1$election_id), FUN = sum)
Pseats <- aggregate(elections1$Pseats1, by = list(elections1$election_id), FUN = sum)
Pvotes <- aggregate(elections1$Pvotes1, by = list(elections1$election_id), FUN = sum)
Rseats <- aggregate(elections1$Rseats1, by = list(elections1$election_id), FUN = sum)
Rvotes <- aggregate(elections1$Rvotes1, by = list(elections1$election_id), FUN = sum)
names(electseats) <- c("election_id", "electseats") #Rename aggregations
names(electvotes) <- c("election_id", "electvotes")
names(Pseats) <- c("election_id", "Pseats2")
names(Pvotes) <- c("election_id", "Pvotes2")
names(Rseats) <- c("election_id", "Rseats2")
names(Rvotes) <- c("election_id", "Rvotes2")
elections2 <- merge(elections1, electseats, by = "election_id") #Merge aggregations back with original dataset
elections2 <- merge(elections2, electvotes, by = "election_id")
elections2 <- merge(elections2, Pseats, by = "election_id")
elections2 <- merge(elections2, Pvotes, by = "election_id")
elections2 <- merge(elections2, Rseats, by = "election_id")
elections2 <- merge(elections2, Rvotes, by = "election_id")
elections2$Rseats <- elections2$Rseats2/elections2$electseats*100 #Calculate R vote and seat share
elections2$Rvotes <- elections2$Rvotes2/elections2$electvotes*100
elections2$PRseats <- elections2$Rseats2/elections2$Pseats2*100
elections2$PRvotes <- elections2$Rvotes2/elections2$Pvotes2*100
elections2$originaldat <- 1
legislate <- subset(elections2, select = -c(Rseats1, Rseats2, Rvotes1, Rvotes2, Pseats1, Pvotes1, Pseats2, Pvotes2)) #Remove uncessary variables
# ORGANIZE THE DATA FOR ANALYSIS ------------------------------------------
legislate$nextelect <- legislate$year + legislate$term #Create a variable showing when the rep will next be up for reelection
i <- 1990 #Create a loop to create a file that has all sitting reps for each legislative year
#Run the loop
while (i <= 2015){
legislate1 <- subset(legislate, year < i)
legislate2 <- subset(legislate1, nextelect >= i)
legislate2$legyear <- i
data.frame(legislate2)
filename <- as.character(paste("Legislative Data ", i, ".csv", sep = ""))
write.csv(legislate2, filename)
i <- i + 1 #Reset i for the next iteration of the loop
}
year90 <- read.csv("Legislative Data 1990.csv") #Read back in each legislative year file
year91 <- read.csv("Legislative Data 1991.csv")
year92 <- read.csv("Legislative Data 1992.csv")
year93 <- read.csv("Legislative Data 1993.csv")
year94 <- read.csv("Legislative Data 1994.csv")
year95 <- read.csv("Legislative Data 1995.csv")
year96 <- read.csv("Legislative Data 1996.csv")
year97 <- read.csv("Legislative Data 1997.csv")
year98 <- read.csv("Legislative Data 1998.csv")
year99 <- read.csv("Legislative Data 1999.csv")
year00 <- read.csv("Legislative Data 2000.csv")
year01 <- read.csv("Legislative Data 2001.csv")
year02 <- read.csv("Legislative Data 2002.csv")
year03 <- read.csv("Legislative Data 2003.csv")
year04 <- read.csv("Legislative Data 2004.csv")
year05 <- read.csv("Legislative Data 2005.csv")
year06 <- read.csv("Legislative Data 2006.csv")
year07 <- read.csv("Legislative Data 2007.csv")
year08 <- read.csv("Legislative Data 2008.csv")
year09 <- read.csv("Legislative Data 2009.csv")
year10 <- read.csv("Legislative Data 2010.csv")
year11 <- read.csv("Legislative Data 2011.csv")
year12 <- read.csv("Legislative Data 2012.csv")
year13 <- read.csv("Legislative Data 2013.csv")
year14 <- read.csv("Legislative Data 2014.csv")
year15 <- read.csv("Legislative Data 2015.csv")
leganalysis <- rbind(year90, year91, year92, year93, year94, year95, year96, year97, year98, year99, year00, year01, year02, year03, year04, year05, year06, year07, year08, year09, year10, year11, year12, year13, year14, year15) #Combine legislative years
write.csv(leganalysis, "Final Legislative Analysis Data.csv") #Do light variable editing in excel to make sure all variables are is coded consistently
# PARTISAN DOMINATION -----------------------------------------------------
legislate <- read.csv("Final Legislative Analysis Data.csv") #Read in new dataset
legislate <- droplevels(subset(legislate, state != "NE" & state != "LA")) #Remove states with non-partisan elections
legislate$legislative_id <- legislate %>% group_indices(state, seat, legyear) #Create unique legislative ID
partyseats1 <- table(legislative_id = legislate$legislative_id, party = legislate$candparty) #Tabulate the number of seats held by each party for each election ID
partyseats2 <- table(legislate$legislative_id, legislate$candparty)
partyseats1 <- as.data.frame(partyseats1)
partyseats2 <- as.data.frame.matrix(partyseats2)
partyseats <- cbind(partyseats1$legislative_id, partyseats2, row.names = NULL)
names(partyseats) <- c("legislative_id", "D", "I", "R")
legislate1 <- merge(partyseats, legislate, by = "legislative_id") #Merge counts with full dataset
legislate1 <- unique(legislate1)
legislate1$majority1 <- ifelse(legislate1$D > legislate1$R, "D", ifelse(legislate1$D == legislate1$R, "T", "R")) #Identify the majority and minority party
legislate1$minority1 <- ifelse(legislate1$majority == "T", "T", ifelse(legislate1$majority == "D", "R", "D"))
legislate1$majority <- ifelse(legislate1$majority1 == "T", "D", legislate1$majority)
legislate1$minority <- ifelse(legislate1$minority1 == "T", "R", legislate1$minority)
legislate1$majorseatdiff <- abs(legislate1$D - legislate1$R) #Get difference in seats bewteen major party and minor party
legislate1$Rseatdiff <- legislate1$R - legislate1$D #Get seat difference in seats between Republicans and Democrats
legislate1$totseats <- legislate1$D + legislate1$I + legislate1$R #Count total seats
legislate1$Dperc <- legislate1$D/legislate1$totseats*100 #Calculate percent of Democratic seats
legislate1$Iperc <- legislate1$I/legislate1$totseats*100 #Calculate percent of independent seats
legislate1$Rperc <- legislate1$R/legislate1$totseats*100 #Calcualte percent of Republican seats
legislate1$majorpercdiff <- abs(legislate1$Dperc - legislate1$Rperc) #Calculate differnece in seat percentage between major and minor parties
legislate1$Rpercdiff <- legislate1$Rperc - legislate1$Dperc #Calculate seat percentage difference between Republicans and Democrats
legislate1$majorover50 <- ifelse(legislate1$majority == "D", legislate1$Dperc - 50, legislate1$Rperc - 50) #Calculate the percentage of majority party seats over 50%
legislate1$Rover50 <- legislate1$Rperc - 50 #Calculate the percentage of Republican seats over or under 50%
legislate1$nonminor <- ifelse(legislate1$candparty != legislate1$minority, 1, 0) #Identify non-minor reps
legislate1$nonminorid <- legislate1 %>% group_indices(legislative_id, nonminor) #Create unique candidate ID
legislate1 <- data.table(legislate1) #Rank candidates within each legislative year and chamber
legislate1[,candranknonminor:=rank(margin,ties.method="first"),by = nonminorid]
legislate2 <- data.frame(legislate1)
head(legislate2)
write.csv(legislate2, "Partisan Domination 1.csv") #Check data visually in excel and organize variables
legislate3 <- read.csv("Partisan Domination 2.csv") #Read data back in
legislate3a <- subset(legislate3, nonminor == 0) #Separate minor and non-minor party reps
legislate3b <- subset(legislate3, nonminor == 1)
legislate3b <- data.table(legislate3b) #Create rank by partisanship
legislate3b[,nonminorpidrank:=rank(margin,ties.method="first"),by = c("legislative_id", "candparty")]
legislate3b$D <- as.numeric(legislate3b$D) #Format data for if-then conditions
legislate3b$I <- as.numeric(legislate3b$I)
legislate3b$R <- as.numeric(legislate3b$R)
legislate3b$newD1 <- legislate3b$D #Calculate the new number of Democratic seats if the minority party won a seat, starting with the weakest rep
legislate3b$newD2 <- ifelse(legislate3b$minority == "D", legislate3b$D + legislate3b$candranknonminor, legislate3b$newD1)
legislate3b$newD <- ifelse(legislate3b$candparty == "D", legislate3b$D - legislate3b$nonminorpidrank, legislate3b$newD2)
legislate3b$newI1 <- legislate3b$I #Calculate the new number of indpendent seats if the minority party won a seat, starting with the weakest rep
legislate3b$newI2 <- ifelse(legislate3b$minority == "I", legislate3b$I + legislate3b$candranknonminor, legislate3b$newI1)
legislate3b$newI <- ifelse(legislate3b$candparty == "I", legislate3b$I - legislate3b$nonminorpidrank, legislate3b$newI2)
legislate3b$newR1 <- legislate3b$R #Calculate the new number of Republican seats if the minority party won a seat, starting with the weakest rep
legislate3b$newR2 <- ifelse(legislate3b$minority == "R", legislate3b$R + legislate3b$candranknonminor, legislate3b$newR1)
legislate3b$newR <- ifelse(legislate3b$candparty == "R", legislate3b$R - legislate3b$nonminorpidrank, legislate3b$newR2)
legislate3b <- subset(legislate3b, select = -c(newD1, newD2, newI1, newI2, newR1, newR2)) #Remove uncessary variables
write.csv(legislate3b, "Partisan Shift.csv") #Visually check data and correct elections with independents in Excel
legislate3bb <- read.csv("Partisan Shift 1.csv") #Read csv back in
legislate3bb$newmajority <- ifelse(legislate3bb$newD > legislate3bb$newR, "D", ifelse(legislate3bb$newD == legislate3bb$newR, "T", "R")) #Identify who the new majority party would be with each newly one seat
legislate3bb$controlshift <- ifelse(legislate3bb$newmajority != "T" & legislate3bb$newmajority != legislate3bb$majority, "Yes", "No") #Identify when the minor party would become the major party
write.csv(legislate3bb, "Partisan Shift 2.csv") #Visually check data and calculate required seat and margin shift in excel
legislate3bc <- read.csv("Partisan Shift 3.csv") #Read data back in
shift <- subset(legislate3bc, select = c(legislative_id, rseatshift, rmarginshift)) #Isolate the newly calculated seat and margin shifts
shift <- na.omit(shift)
legislate3bd <- subset(legislate3bc, select = -c(nonminor, nonminorid, candranknonminor, nonminorpidrank, newD, newI, newR, newmajority, controlshift, rseatshift, rmarginshift)) #Remove uncessary variables
legislate3ab <- subset(legislate3a, select = -c(nonminor, nonminorid, candranknonminor)) #Remov uncessary variables from the original dataset
legislatefinal <- rbind(legislate3ab, legislate3bd) #Combine the original and newly calculted datasets
legislatefinal <- unique(legislatefinal) #Remove duplicates
legislatefinal <- merge(legislatefinal, shift, by = "legislative_id") #Add legislative shifts to the dataset
summary(legislatefinal$margin) #View the data
legislatefinal$percent5a <- ifelse(legislatefinal$margin <= 5, 1, 0) #Identify margins less than 5%
legislatefinal$percent10a <- ifelse(legislatefinal$margin <= 10, 1, 0) #Identify margins less than 10%
legislatefinal$percent15a <- ifelse(legislatefinal$margin <= 15, 1, 0) #Identify margins less than 15%
percent5num <- aggregate(legislatefinal$percent5a, by = list(legislatefinal$legislative_id), FUN = sum) #Add margins less than 5% by legislature
names(percent5num) <- c("legislative_id", "num5")
percent5num <- data.frame(percent5num)
percent5tot <- subset(legislatefinal, select = c(legislative_id, totseats))
percent5tot <- unique(percent5tot)
percent5 <- merge(percent5num, percent5tot, by = "legislative_id")
percent5$percent5 <- percent5$num5/percent5$totseats*100
percent5 <- subset(percent5, select = c(legislative_id, percent5))
percent10num <- aggregate(legislatefinal$percent10a, by = list(legislatefinal$legislative_id), FUN = sum) #Add margins less than 10% by legislature
names(percent10num) <- c("legislative_id", "num10")
percent10num <- data.frame(percent10num)
percent10tot <- subset(legislatefinal, select = c(legislative_id, totseats))
percent10tot <- unique(percent10tot)
percent10 <- merge(percent10num, percent10tot, by = "legislative_id")
percent10$percent10 <- percent10$num10/percent10$totseats*100
percent10 <- subset(percent10, select = c(legislative_id, percent10))
percent15num <- aggregate(legislatefinal$percent15a, by = list(legislatefinal$legislative_id), FUN = sum) #Add margins less than 15% by legislature
names(percent15num) <- c("legislative_id", "num15")
percent15num <- data.frame(percent15num)
percent15tot <- subset(legislatefinal, select = c(legislative_id, totseats))
percent15tot <- unique(percent15tot)
percent15 <- merge(percent15num, percent15tot, by = "legislative_id")
percent15$percent15 <- percent15$num15/percent15$totseats*100
percent15 <- subset(percent15, select = c(legislative_id, percent15))
legislatefinal <- merge(legislatefinal, percent5, by = "legislative_id") #Combine summed margins and original data
legislatefinal <- merge(legislatefinal, percent10, by = "legislative_id")
legislatefinal <- merge(legislatefinal, percent15, by = "legislative_id")
legislatefinal <- subset(legislatefinal, select = -c(percent5a, percent10a, percent15a)) #Remove uncessary variables
write.csv(legislatefinal, "Final Election and Partisan Domination Variables.csv") #Write new dataset
# REPLICATION OF ABDUL-RAZZAK ET AL. ELECTORAL OUTCOMES -------------------
legislate <- leganalysis
legislate$banstate <- ifelse(legislate$state == "WA" | legislate$state == "OR" |legislate$state == "ID" |legislate$state == "CA" |legislate$state == "NV" |legislate$state == "UT" |legislate$state == "NM" |legislate$state == "NE" |legislate$state == "KS" | legislate$state == "MO" |legislate$state == "AR" |legislate$state == "LA" |legislate$state == "IL" |legislate$state == "IN" | legislate$state == "MS" |legislate$state == "AL" |legislate$state == "GA" |legislate$state == "SC" |legislate$state == "FL" |legislate$state == "VA" |legislate$state == "MD" |legislate$state == "DE" |legislate$state == "NJ" |legislate$state == "NY" |legislate$state == "VT" |legislate$state == "ME"| legislate$state == "HI", 0, 1)
legislate$deepsouth <- ifelse(legislate$state == "AL" | legislate$state == "GA" | legislate$state == "MS" | legislate$state == "SC" | legislate$state == "FL", 1, 0)
legislate$postcit <- ifelse(legislate$year >= 2010, 1, 0)
legislate <- droplevels(subset(legislate, state != "NE" & state != "LA"))
legislate$newchamber <- ifelse(legislate$legyear - 1 == legislate$year, 1, 0)
repseats <- subset(legislate, electionkind == "G" | electionkind == "SSG") #Remove irrelevant election data
repseats <- subset(repseats, !duplicated(election_id))
repseats <- subset(repseats, year >= 1990)
repseats$interaction <- repseats$postcit*repseats$banstate
lower <- subset(repseats, seat == "House") #Count number of elections for Senate and House
nrow(lower)
upper <- subset(repseats, seat == "Senate")
nrow(upper)
library(lfe)
fixedlower <- felm(Rseats ~ banstate:postcit + G(state) + G(year), data = lower, cluster = lower$state) #Run replication analyses (ignore warnings)
summary(fixedlower)
fixedupper <- felm(Rseats ~ banstate:postcit + G(state) + G(year), data = upper, cluster = upper$state)
summary(fixedupper)
fixedlower <- felm(Rvotes ~ banstate:postcit + G(state) + G(year), data = lower, cluster = lower$state)
summary(fixedlower)
fixedupper <- felm(PRvotes ~ banstate:postcit + G(state) + G(year), data = upper, cluster = upper$state)
summary(fixedupper)
fixedlower <- felm(Rseats ~ banstate:postcit + G(state) + G(year) + deepsouth:year, data = lower, cluster = lower$state)
summary(fixedlower)
fixedupper <- felm(Rseats ~ banstate:postcit + G(state) + G(year) + deepsouth:year, data = upper, cluster = upper$state)
summary(fixedupper)
fixedlower <- felm(Rvotes ~ banstate:postcit + G(state) + G(year) + deepsouth:year, data = lower, cluster = lower$state)
summary(fixedlower)
fixedupper <- felm(Rvotes ~ banstate:postcit + G(state) + G(year) + deepsouth:year, data = upper, cluster = upper$state)
summary(fixedupper)
fixedlower <- felm(Rseats ~ banstate:postcit + G(state) + G(year) + state:year, data = lower, cluster = lower$state)
summary(fixedlower)
fixedupper <- felm(Rseats ~ banstate:postcit + G(state) + G(year) + state:year, data = upper, cluster = upper$state)
summary(fixedupper)
fixedlower <- felm(Rvotes ~ banstate:postcit + G(state) + G(year) + state:year, data = lower, cluster = lower$state)
summary(fixedlower)
fixedupper <- felm(Rvotes ~ banstate:postcit + G(state) + G(year) + state:year, data = upper, cluster = upper$state)
summary(fixedupper)
|
dbf5ba52ce15322fef75d1cdb5edd7433059162d | 7cde1a2f5bf3161b5aab0bae82f57c7d8bfed631 | /run_analysis.R | 2c18b4af97c899095dac04d9e072cb49d09a9378 | [] | no_license | davarix/Coursera-Getting-and-Cleaning-Data-Course-Project | 50c1262220ca863185e59bb36c06432200ef090a | f12431b08c140586255b9eb5353860d808133c4a | refs/heads/master | 2021-01-11T11:30:45.282467 | 2017-01-26T12:47:56 | 2017-01-26T12:47:56 | 80,111,535 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,750 | r | run_analysis.R | rm(list = ls())
# Load all necessary packages
library(dplyr)
# Check whether the data is present in the current work directory
data.dir.name <- "UCI HAR Dataset"
files <- list.files()
if (data.dir.name %in% files == TRUE) {
files.path <- list.files(pattern = ".txt$", recursive = TRUE)
fns <- file.path(getwd(), files.path)
list2env(setNames(lapply(fns, read.table, fill = TRUE), basename(tools::file_path_sans_ext(fns))), globalenv())
} else {
filename <- "getdata_dataset.zip"
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileURL, filename, method="curl")
unzip(filename)
files.path <- list.files(pattern = ".txt$", recursive = TRUE)
fns <- file.path(getwd(), files.path)
list2env(setNames(lapply(fns, read.table, fill = TRUE), basename(tools::file_path_sans_ext(fns))), globalenv())
}
# Check whether all required data sets were loaded into R
data.to.load <- c("activity_labels", "features", "subject_test", "subject_train", "X_test", "X_train", "y_test", "y_train")
variables <- ls()
cond <- data.to.load %in% variables
if (all(cond) == FALSE) {
stop("Error loading data into R")
}
# Merges the training and the test sets to create one data set.
## merges both data sets into one dataset
xdata <- tbl_df(rbind(X_test, X_train))
# Extracts only the measurements on the mean and standard deviation for each measurement.
## Load data on data labels in dplyr
testnames <- tbl_df(features)
## Name the resulting dataframe
colnames(testnames) <- c("n", "testname")
## Selecting labels with mean and std measurements
testnames_meanstd <- filter(testnames, grepl(".*[Mm]ean.*|.*[Ss]td.*", testname))
## Renaming the measurement labels
testnames_meanstd.names <- testnames_meanstd$testname
testnames_meanstd.names <- gsub("-mean", "Mean", testnames_meanstd.names)
testnames_meanstd.names <- gsub("-std", "Std", testnames_meanstd.names)
testnames_meanstd.names <- gsub("[()-]", "", testnames_meanstd.names)
testnames_meanstd.names <- tolower(testnames_meanstd.names)
## Creating a vector with # of columns to select in xdata
select_col <- as.numeric(testnames_meanstd$n)
## Selecting those columns from xdata
colnames(xdata) <- sprintf("x%d", 1:ncol(xdata))
xdatasel <- select(xdata, num_range("x", select_col))
## Renaming the xdatasel dataframe to original variable names
rename_col <- as.vector(testnames_meanstd.names)
colnames(xdatasel) <- rename_col
#Uses descriptive activity names to name the activities in the data set
activity <- rbind(y_test, y_train)
colnames(activity) <- c("activity_id")
activity$id<-seq.int(nrow(activity))
## Name activity_labels data frame
colnames(activity_labels) <- c("activity_id", "activity_name")
## Merge activity data and activity labels
activity2 <- merge(activity, activity_labels, by="activity_id", all = TRUE, sort = FALSE)
activity3 <- tbl_df(activity2)
activity3 <- arrange(activity2, id)
activity_label <- activity3$activity_name
# Merge subject_test and subject_train
subject <- rbind(subject_test, subject_train)
colnames(subject) <- c("subject")
# Appropriately labels the data set with descriptive variable names.
final_dataset <- cbind(subject, activity_label, xdatasel)
# From the final data set, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
by_act_sub <- group_by(final_dataset, subject, activity_label)
tidy_groupped <- summarise_each(by_act_sub, funs(mean))
# Output in the current working directory
output <- file.path(getwd(), "tidy_dataset.txt")
write.table(tidy_groupped, output, row.names = FALSE)
|
49dec84cbca1f61f0e5f03447cacaf84feec54eb | 6d821c8a242005f093001cb50ffda26d98efc392 | /R/choosethres.R | c3b99a49cd9207ce4b63332b172eee83982dee54 | [] | no_license | cran/extremeIndex | 961453a27a2cb87134123df160f33f9921bcda20 | b2034ed3a729f86ae74dabceb16e3debbcfbb9d2 | refs/heads/master | 2021-11-30T22:58:05.392388 | 2021-11-24T14:30:02 | 2021-11-24T14:30:02 | 213,374,552 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,905 | r | choosethres.R | #' Function for heuristically choosing the domain where extreme value theory can be applied
#'
#' @param data a numeric vector containing the observation used for verification
#' @param thresh vector of thresholds to try
#' @param guess starting values for GPD's sigma and xi (0<xi<1)
#' @param plots which parameter plots do you want
#' @param R number of bootstrap estimates for confidence intervals
#' @param ncpus if you want to make bootstrap on several cores
#' @return three plots summarizing the stability of the parameters to threshold. The starting threshold admits kappa=1 and its confidence interval ;
#' according Papastathopoulos & Tawn (2013)
#' @return a list with thresholds used, GP parameters and CIs, optimal threshold and xi.
#' @export
#'
#' @importFrom graphics abline arrows legend lines par plot points polygon title
choosethres=function(data,thresh,guess=c(1,0.1),plots=1:3,R=200,ncpus=1){
thresh=sort(thresh)
pe=matrix(0,ncol=3,nrow=length(thresh))
se=array(0,dim=c(length(thresh),3,2))
fi=suppressWarnings(.gp.pw.fit(data[data>thresh[1]]-thresh[1],init=c(1,guess),CI=T,R=R,ncpus=ncpus))
pe[1,]=fi$fit$PWM
se[1,,]=t(fi$CI$PWM)
for (i in 2:length(thresh)){
print(i)
fi=suppressWarnings(.gp.pw.fit(data[data>thresh[i]]-thresh[i],init=c(1,guess),CI=T,R=R,ncpus=ncpus))
pe[i,]=fi$fit$PWM
se[i,,]=t(fi$CI$PWM)
}
iopt=which((se[,1,1]<1)&(se[,1,2]>1))[1]
if (is.na(iopt)) {
warning("A pareto approximation of data is not available here")
}
if (!(length(plots) == 1 && is.na(plots))) {
plots <- sort(unique(plots))
if (!isTRUE(all(plots %in% 1:3))) {
stop("Invalid plot selection. Must be a vector of integers containing indices 1, 2 or 3.")
}
old.par <- par(no.readonly = TRUE)
par(mfrow = c(length(plots), 1), mar = c(4.5, 4.5, 3.1,
0.1))
on.exit(par(old.par))
for (i in plots) {
ylims = c(min(se[, i,],pe[,i]), max(se[, i,],pe[,i]))
plot(x = thresh, y = pe[, i], pch = 20, xlab = "Threshold",
bty = "l", ylab = switch(i, expression(kappa),
expression(sigma), expression(xi)),
ylim = ylims, type = "n")
polygon(c(thresh, rev(thresh)), c( se[, i,1], rev(se[, i,2])),
col = "gray57", border = FALSE)
abline(v=thresh[iopt],lty=2,col="seagreen")
if (i == min(plots)) {
title(paste0("Parameter stability plots for EGP"))
}
if (i == 1) {
abline(h = 1, lwd = 0.5, col = "gray20", lty = 2)
}
arrows(x0 = thresh, y0 = se[, i,1], y1 = se[, i,2],
length = 0.05, angle = 90, code = 3)
points(x = thresh, y = pe[, i], type = "b", pch = 20)
}
}
return(invisible(list(par = pe, CI = se, thresh = thresh, opt=c(thresh[iopt],pe[iopt,3]))))
}
|
a2524df1dcfdf55c9b1a7c2323b854e4118ea6c2 | b5822b9c2a756f4e540c426e7e84af35dae8caec | /rockchalk/inst/examples/centeredRegression.R | a3d92f0a19202d17fdb28e0b7b164419bb4266b6 | [] | no_license | pauljohn32/rockchalk | 0c75b7a7bc142669efcfabbc70d511f60c3f47e0 | fc2d3d04396bf89ef020e824f50db3c348e3e226 | refs/heads/master | 2022-08-20T02:49:56.898990 | 2022-07-26T01:20:12 | 2022-07-26T01:20:12 | 8,965,635 | 8 | 5 | null | 2022-07-18T00:36:58 | 2013-03-23T04:07:35 | R | UTF-8 | R | false | false | 13,182 | r | centeredRegression.R | ## Paul Johnson
## pauljohn@ku.edu 2012-03-09
##
## This is an R program that uses some functions in the newly revised
## rockchalk package to demonstrate my point that "centered variables"
## don't really make a difference in regressions with interaction.
##
## Centering does not help with multicollinearity, but I mean to say
## more than that. It does not help with regression interpretation,
## if one correctly understands what the parameter estimates and the
## predicted values mean in a regression with interaction.
## Here the idea is the following. The centered b's and se's "seem" different,
## but they are actually calculated on the EXACT SAME fitted plane. The
## difference is that the notcentered model has the y axis positioned at
## x1=0,x2=0, while in the centered model it is instead at
## x1=meanx1, x2=meanx2. The predicted values for any x1, x2 combination
## are EXACTLY the same with either model, as are the estimates of
## uncertainty (in the form of confidence intervals or standard errors).
## Thus it should be possible to take the estimates from the
## notcentered regression and calculate the slopes at x1=meanx1,
## x2=meanx2, and re-produce them. AND I CAN!! This demonstrates
## that claim at the end.
library(rockchalk)
set.seed(222233)
dat3 <- genCorrelatedData3(y ~ 0.1 + 0.2 * x1 + 0.3 * x2 - 0.2 * x1:x2, N = 100000, rho = .31,
stde = 80, means = c(x1 = 50, x2 = 50), sds = c(10, 10))
## We now explore a regression model like this:
## y = b0 + b1 * x1 + b2 *x2 + b3 *x1*x2 + e
## The mean-centered model replaces x1 and x2 by the mean-centered
## versions of those variables, (x1 - mean(x1)) and (x2 - mean(x2)).
## The usual problem that causes researchers to turn to "mean centering"
## is the following. The linear model seems "good", but the interactive
## model seems "bad" without centering. Here's an example.
## First, fit the model without the interaction term.
## y = b0 + b1 * x1 + b2 *x2 + e
m1 <- lm(y ~ x1 + x2, data = dat3)
summary(m1)
dev.new()
plotPlane(m1, plotx1="x1", plotx2="x2")
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 424.1485 45.8004 9.261 5.29e-15 ***
## x1 -8.9042 0.8499 -10.477 < 2e-16 ***
## x2 -9.2701 0.8039 -11.531 < 2e-16 ***
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Residual standard error: 77.77 on 97 degrees of freedom
## Multiple R-squared: 0.8011, Adjusted R-squared: 0.797
## F-statistic: 195.4 on 2 and 97 DF, p-value: < 2.2e-16
##
## Yeah, its a "good model". Everything is "significant".
##
##
## Now the problem.
## Add an interaction. Watch, it ruins everything!
##
m2 <- lm(y ~ x1 * x2, data = dat3)
summary(m2)
dev.new()
plotPlane(m2, plotx1 = "x1", plotx2 = "x2", theta = -10)
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -74.45719 179.94096 -0.414 0.67995
## x1 1.57257 3.75575 0.419 0.67636
## x2 0.64618 3.55471 0.182 0.85614
## x1:x2 -0.20526 0.07181 -2.859 0.00522 **
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Residual standard error: 75.05 on 96 degrees of freedom
## Multiple R-squared: 0.8167, Adjusted R-squared: 0.811
## F-statistic: 142.6 on 3 and 96 DF, p-value: < 2.2e-16
## Booh, the model's "no good". x1 and x2 "don't matter"
## any more.
## We'd better mean-center x1 and x2.
m2mc <- meanCenter(m2)
summary(m2mc)
## You can verify that manually, if you don't trust my meanCenter
## function. Results are the same, of course. First, mean center the
## variables with R's scale function:
dat3$x1c <- as.numeric(scale(dat3$x1, center = TRUE, scale = FALSE))
dat3$x2c <- as.numeric(scale(dat3$x2, center = TRUE, scale = FALSE))
## The as.numeric is required because scale returns a matrix
## with one column, not a vector. How annoying!
m2manualmc <- lm(y ~ x1c*x2c, dat3)
summary(m2manualmc)
m2mcmanualpred <- fitted(m2manualmc)
## Confirmed that matches the output from meanCenter. I suppose
## you are sorry you did not trust me now.
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -456.69849 8.01647 -56.970 < 2e-16 ***
## x1 -8.38383 0.84005 -9.980 < 2e-16 ***
## x2 -9.47969 0.77920 -12.166 < 2e-16 ***
## x1:x2 -0.20526 0.07181 -2.859 0.00522 **
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## Residual standard error: 75.05 on 96 degrees of freedom
## Multiple R-squared: 0.8167, Adjusted R-squared: 0.811
## F-statistic: 142.6 on 3 and 96 DF, p-value: < 2.2e-16
## Yeah! Mean centering saved the day! Woo Hoo! All your
## t values are big and your p values are small.
## Unfortunately, the difference in the output is just a mirage. The
## two fits are describing slope estimates at a DIFFERENT POINT on the
## same curving plane.
## To see that, note that the fitted models (centered or not centered) offer
## EXACTLY THE SAME predicted values! I don't mean "similar"
## I mean exactly the same. Look like this:
par(mfcol = c(1,2))
plotPlane(m2, "x1", "x2", plotPoints = FALSE, theta = -25,
main = "Not Mean Centered", ticktype = "detailed")
plotPlane(m2mc, "x1", "x2", plotPoints = FALSE, theta = -25,
main = "Mean Centered", ticktype = "detailed")
par(mfcol = c(1,1))
## Maybe you are not into 3d illustrations. Too bad. I can show the
## same in two dimensions. Let's create a scatterplot displaying the
## predicted values from the ordinary and the mean-centered models
plot(fitted(m2), fitted(m2mc), xlab = "predicted from uncentered x",
ylab = "predicted from centered x",
main = "(Not)Centered Predictions Identical")
##
##
## So, how can it be the two models are EXACTLY the same and yet
## the mean centered one "looks better"?
##
## Here's the simple answer. This is a nonlinear model, the slope
## changes from point to point. If we pick a point where it is
## steep and make a "snapshot" of the slope, we conclude "the
## slope is huge!" If we pick a point where the slope is small
## we conclude "there's no effect of x". Its really just that
## simple. Mean centering amounts to deciding where to measure
## the slope, and sometimes we are lucky enough to accidentally
## pick a steep spot by mean centering.
##
## Now the proponents of the mean-centering approach respond to
## me as follows. "You ignore the fact that the standard errors
## are smaller in the mean centered model. So there is a difference."
## In response, I say this. Just as the slope varies from point to
## point, the standard error also changes from point to point. If you
## choose a point in the data, say where x1 = 48 and x2 = 47.2, and then
## find the equivalent mean-centered point, which is x1c = -1.24 and
## x2c = -1.305, you will find the slopes and the standard errors are
## exactly the same. The slope at x1,x2 in the not centered model is
## exactly the same as the slope in the centered model at x1c,x2c.
## More importantly, from either model, we can calculate the same
## values of not only the slope, but also the standard error, for any
## particular point in the data.
## What is the easiest way to see that the slopes and standard errors
## are the same? It seems obvious to me that, since the predicted
## value plane is identical in the two models, then the slopes have
## to be the same as well. But perhaps that relys on intuition that
## is unique to me.
## Take the the noncentered model and calculate the slope and standard
## error at the mean of x1,x2. The slope at that point in the x1
## dimension, in terms of the noncentered fit, is
## b1 + b3*x2
## Which we estimate from the noncentered model as:
coef(m2)["x1"] + coef(m2)["x1:x2"] * mean(dat3$x2, na.rm = TRUE)
## x1
## -8.383827
##
## And the slope in the x2 dimension is
## b2 + b3*x1
coef(m2)["x2"] + coef(m2)["x1:x2"] * mean(dat3$x1, na.rm = TRUE)
## x2
## -9.479689
##
## Please note, those estimates of the slopes EXACTLY match the
## coefficient estimates reported by the centered model. That is to
## say, if you restrict your attention to a particular value of
## (x1,x2), the centered and noncentered models produce EXATCLY the same
## slope estimates.
## And the standard errors are the same as well.
## Reproduce the standard errors in centered model from noncentered model
V <- vcov(m2)
sqrt(V["x1","x1"] + mean(dat3$x2)^2 * V["x1:x2","x1:x2"] +
2 * mean(dat3$x2) * V["x1","x1:x2"])
## [1] 0.8400474
## That's the SAME number reported in the Std.Err. column for the centered
## model.
sqrt(V["x2","x2"] + mean(dat3$x1)^2 * V["x1:x2","x1:x2"] +
2 * mean(dat3$x1) * V["x2","x1:x2"])
##[1] 0.7791997
##
## Bingo, Fits exactly. The estimates of the centered model are reproduced
## exactly from the notcentered model once the correct co-ordinate
## translation is put in place. The EXACT same t ratios, etc.
## Centering has NO EFFECT whatsoever on multicollinearity. It does
## not affect predicted values, it does not affect our confidence
## in estimates of slopes or predicted values.
## In short, if you understand what a multiple regression with
## interaction "really does," it is impossible to reach any conclusion
## except the following: mean centering makes no difference at all in
## the estimation or interpretation of regression models with
## interaction effects.
## Mean centering only aids the interpretation if one is too lazy to
## understand the curvature of the estimated plane and work with the
## predicted values that are relevant to a problem. It is not more
## easier to interpret the EXACT SAME NUMBER from one model or the
## other.
m2rc <- residualCenter(m2)
summary(m2rc)
op <- par(no.readonly = TRUE)
par(mar = c(2,2,2,1), mfcol = c(2,2))
plotPlane(m1, "x1", "x2", plotPoints = TRUE, theta = -25,
main = "No Interaction", ticktype = "detailed")
plotPlane(m2, "x1", "x2", plotPoints = TRUE, theta = -25,
main = "Not Centered", ticktype = "detailed")
plotPlane(m2rc, "x1", "x2", plotPoints = TRUE, theta = -25,
main = "Residual Centered", ticktype = "detailed")
plotPlane(m2mc, "x1", "x2", plotPoints = TRUE, theta = -25,
main = "Mean Centered", ticktype = "detailed")
par(mfcol = c(1,1))
## I had trouble believing those plots. Is it possible that the
## predicted values from the residual centered regression are exactly the
## same as the predictions from the orinary non-centered m2 as well as
## the mean centered m2mc? That seemed crazy, so I decided to
## double-check by manually calculating a residual-centered regression
## and extracting predicted values.
rcreg <- lm ( I(x1*x2) ~ x1 + x2, data = dat3)
rcx1x2 <- resid(rcreg)
m2manualrc <- lm(y ~ x1 + x2 + rcx1x2, data = dat3)
predm2manualrc <- predict(m2manualrc, newdata = dat3)
m2 <- lm( y ~ x1 * x2, data = dat3)
m2mc <- meanCenter(m2)
m2rc <- residualCenter(m2)
m2pred <- predict(m2, newdata = dat3)
dat3mc <- dat3
dat3mc$x1 <- dat3mc$x1c
dat3mc$x2 <- dat3mc$x2c
m2mcpred <- predict(m2mc, newdata = dat3mc)
m2rcpred <- predict(m2rc, newdata = dat3)
dat4 <- data.frame("m2pred"= m2pred, "m2mcpred" = m2mcpred,
"m2mcmaunal"= m2mcmanualpred, "m2rcpred" = m2rcpred,
"m2rcpred2" = predm2manualrc)
head(dat4)
cor(dat4)
## Well, the predicted values are the same.
## and the coefficients on the interaction terms are all the same.
## We already saw that the ordinary interaction and the
## mean-centered regressions are identical. Is it possible
## the residual centered version is just another identical
## model viewed from yet another point in the x1,x2 plane?
## Notice:
## m2
## lm(formula = y ~ x1 * x2, data = dat3)
## Coefficients:
## (Intercept) x1 x2 x1:x2
## -74.4572 1.5726 0.6462 -0.2053
## From rockchalk:::residualCenter(m2):
## m2rc
## lm(formula = y ~ x1 + x2 + x1.X.x2, data = dat)
## Coefficients:
## (Intercept) x1 x2 x1.X.x2
## 424.1485 -8.9042 -9.2701 -0.2053
## From rockchalk:::meanCenter(m2)
## > m2mc
## The centered variables are:
## [1] "x1" "x2"
## The call that requested centering was:
## meanCenter.default(model = m2)
## Call:
## lm(formula = y ~ x1 * x2, data = stddat)
## Coefficients:
## (Intercept) x1 x2 x1:x2
## -456.6985 -8.3838 -9.4797 -0.2053
## If mean-centering is identical to residual centering,
## there must be some matrix algebra to prove it.
## b-hat = (X'X)-1 X'y
## X = |1 x1 x2 x1*x2|
## mean centered:
## X = |1 x1-m(x1) x2-m(x2) (x1-m(x1))(x2-m(x2))|
## let pred(x1*x2) be the predicted value from the regression
## (x1*x2) = b0 + b1*x1 + b2*x2
## Then the design matrix for
## residual centered:
## X = |1 x1 x2 (x1*x2)-pred(x1*x2) |
## I think if I were good at matrix algebra with partitioned
## matrices, I could demonstrate why the estimated coefficients
## are actually equivalent.
|
5a4328fc439a8cf9efa53cbd8d953b4bf0178d72 | 92e5a14420bbae0b17db12e284710b2814c6de6f | /R/20201109/data_preparation_for_analysis.R | abc3d085db0d36cff52c3d5750b564498aa1b5ab | [] | no_license | jaspershen/chuchu | 25e96e8026c0f56d7b69174d1c55f9d4c60cb8c3 | e74fdd06e561ae807e81efbb52e7afa376cbba0a | refs/heads/master | 2023-01-30T00:17:55.029860 | 2020-12-09T00:46:36 | 2020-12-09T00:46:36 | 283,093,351 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,821 | r | data_preparation_for_analysis.R | sxtTools::setwd_project()
rm(list = ls())
source("R/tools.R")
# load data
load("data/lipid20200727/lipidsearch_result/lipid_table")
is_table_pos <-
readr::read_csv("data/lipid20200727/internal_standard_table/is_table_pos.csv")
is_table_neg <-
readr::read_csv("data/lipid20200727/internal_standard_table/is_table_neg.csv")
setwd("data/lipid20200727/data_preparation_for_analysis/")
library(tidyverse)
##first, we should find the is in the peak table
data1 <- is_table_pos[,c("mz", "rt")]
data2 <- peak_table_pos[,c("mz", "rt")]
match_result_pos <-
sxtTools::sxtMTmatch(data1 = as.matrix(data1),
data2 = as.matrix(data2),
mz.tol = 25,
rt.tol = 30,
rt.error.type = "abs")
data1 <- is_table_neg[,c("mz", "rt")]
data2 <- peak_table_neg[,c("mz", "rt")]
match_result_neg <-
sxtTools::sxtMTmatch(data1 = as.matrix(data1),
data2 = as.matrix(data2),
mz.tol = 25,
rt.tol = 30,
rt.error.type = "abs")
colnames(lipid_table_pos)
colnames(lipid_table_neg)
is_tag_pos <-
is_table_pos[match_result_pos[,1],]
is_data_pos <- peak_table_pos[match_result_pos[,2],] %>%
dplyr::select(contains('X'))
is_data_pos <-
cbind(is_tag_pos, is_data_pos)
write.csv(is_data_pos, "is_data_pos.csv", row.names = FALSE)
is_tag_neg <-
is_table_neg[match_result_neg[,1],]
is_data_neg <- peak_table_neg[match_result_neg[,2],] %>%
dplyr::select(contains('X'))
is_data_neg <-
cbind(is_tag_neg, is_data_neg)
write.csv(is_data_neg, "is_data_neg.csv", row.names = FALSE)
save(is_data_pos, file = "is_data_pos")
save(is_data_neg, file = "is_data_neg")
##output relative
expression_data_relative <-
rbind(lipid_table_pos, lipid_table_neg)
variable_info_relative <-
expression_data_relative[,c(1:16)]
expression_data_relative <-
expression_data_relative[,-c(1:16)]
expression_data_relative <-
expression_data_relative %>%
dplyr::select(-contains("QC")) %>%
dplyr::select(-contains("Blk"))
variable_info_relative <-
variable_info_relative %>%
dplyr::select(peak_name, name, everything())
save(expression_data_relative, file = "expression_data_relative")
save(variable_info_relative, file = "variable_info_relative")
write.csv(expression_data_relative, "expression_data_relative.csv", row.names = FALSE)
write.csv(variable_info_relative, "variable_info_relative.csv", row.names = FALSE)
###calculate concentration for each samples and lipid
##positive
is_tag_pos <-
is_data_pos[,1:9]
is_table_pos <-
is_data_pos[,-c(1:9)] %>%
dplyr::select(contains("X"))
lipid_tag_pos <-
lipid_table_pos[,c(1:16)]
lipid_table_pos <-
lipid_table_pos[,-c(1:16)] %>%
dplyr::select(contains("X"))
colnames(lipid_table_pos) == colnames(is_table_pos)
lipid_tag_pos$Class %>% table()
is_tag_pos$name
match_item_pos <-
list(
"AEA" = NA,
"Cer" = NA,
"ChE" = c(9, 15),
"CL" = NA,
"DG" = 11,
"HexlCer" = NA,
"LPC" = 7,
"LPE" = 8,
"MG" = NA,
"PC" = 1,
"PE" = 2,
"PI" = 5,
"PS" = 3,
"SM" = 13,
"SPH" = NA,
"SPHP" = NA,
"ST" = NA,
"TG" = 12
)
lipid_tag_pos$rt <-
lipid_tag_pos$rt * 60
expresion_data_abs_pos <-
cal_abs(lipid_tag = lipid_tag_pos,
lipid_table = lipid_table_pos,
is_tag = is_tag_pos,
is_table = is_table_pos,
match_item = match_item_pos)
express_data_abs_pos1 <- expresion_data_abs_pos$express_data_abs1
express_data_abs_pos2 <- expresion_data_abs_pos$express_data_abs2
variable_info_abs_pos <- expresion_data_abs_pos$variable_info_abs
##negative
is_tag_neg <-
is_data_neg[,1:9]
is_table_neg <-
is_data_neg[,-c(1:9)] %>%
dplyr::select(contains("X"))
lipid_tag_neg <-
lipid_table_neg[,c(1:16)]
lipid_table_neg <-
lipid_table_neg[,-c(1:16)] %>%
dplyr::select(contains("X"))
colnames(lipid_table_neg) == colnames(is_table_neg)
lipid_tag_neg$Class %>% table()
is_tag_neg$name
match_item_neg <-
list(
"AEA" = NA,
"Cer" = NA,
"ChE" = NA,
"CL" = NA,
"DG" = NA,
"HexlCer" = NA,
"LPC" =NA,
"LPE" = NA,
"MG" = NA,
"PC" = NA,
"PE" = c(1,3),
"PI" = NA,
"PS" = NA,
"SM" = NA,
"SPH" = NA,
"SPHP" = NA,
"ST" = NA,
"TG" = NA
)
lipid_tag_neg$rt <-
lipid_tag_neg$rt * 60
expresion_data_abs_neg <-
cal_abs(lipid_tag = lipid_tag_neg,
lipid_table = lipid_table_neg,
is_tag = is_tag_neg,
is_table = is_table_neg,
match_item = match_item_neg)
express_data_abs_neg1 <- expresion_data_abs_neg$express_data_abs1
express_data_abs_neg2 <- expresion_data_abs_neg$express_data_abs2
variable_info_abs_neg <- expresion_data_abs_neg$variable_info_abs
###combine positive and negative
express_data_abs1 <- rbind(express_data_abs_pos1, express_data_abs_neg1)
express_data_abs2 <- rbind(express_data_abs_pos2, express_data_abs_neg2)
variable_info_abs <- rbind(variable_info_abs_pos, variable_info_abs_neg)
mass <-
stringr::str_replace_all(variable_info_abs$IonFormula, " ", "") %>%
purrr::map(.f = function(x){
x %>%
Rdisop::getMolecule() %>%
Rdisop::getMass()
})
mass <- unlist(mass)
variable_info_abs <- data.frame(variable_info_abs, mass, stringsAsFactors = FALSE)
express_data_abs1 <-
express_data_abs1 %>%
as.matrix() %>%
`/`(mass)
save(express_data_abs1, file = "express_data_abs1")
save(express_data_abs2, file = "express_data_abs2")
save(variable_info_abs, file = "variable_info_abs")
write.csv(express_data_abs1, "expression_data_abs1.csv", row.names = FALSE)
write.csv(express_data_abs2, "expression_data_abs2.csv", row.names = FALSE)
write.csv(variable_info_abs, "variable_info_abs.csv", row.names = FALSE)
|
f464e9c174a236f7724e78b093e5d629ef2f68a1 | 9ee9e9842dd26cb37d1ac23de196745dc5d42ad4 | /PracticeRHadoop.R | bbda4a25c5fd617035d7ffd3bad4580454491832 | [] | no_license | raviranjan-innoplexus/R-Hadoop-Integration | 46a1be9a23ed6fe1b94585b0935fa50ace969c20 | d8ed84570bb9b54c2e42adbed44c61473cc10a18 | refs/heads/master | 2021-01-20T20:52:11.546221 | 2016-06-23T07:05:32 | 2016-06-23T07:05:32 | 61,781,549 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,824 | r | PracticeRHadoop.R | Sys.setenv(HADOOP_CMD="/home/hadoopuser/hadoop/bin/hadoop")
Sys.setenv("HADOOP_STREAMING"="~/hadoop/share/doc/hadoop/hadoop-mapreduce/hadoop-streaming-2.6.0.jar")
library(rJava)
library(rhdfs)
hdfs.init()
gd = hdfs.file("/user/hadoopuser/Rate_PUF.csv","r")
fd = hdfs.read(gd)
chardata = rawToChar(fd)
library(rmr2)
data = read.table(textConnection(chardata),quote="",sep = ",", header=T,fill = TRUE)
dim(data) #getting far less data than there is
str(data)
summary(data)
View(data)
dat = hdfs.line.reader("/user/hadoopuser/Rate_PUF.csv")
x = dat$read()
class(x)
datarate = from.dfs("/user/hadoopuser/Rate_PUF.csv",format = make.input.format("csv",sep = ","))
library(plyr)
datafr = as.data.frame(datarate$val)
dataldply = ldply(datarate, data.frame)
#dataldply = as.data.frame(dataldply)
datacolrem = dataldply[ ,-1]
colnames(datacolrem) = as.character(unlist(datacolrem[1,]))
datacolrem = datacolrem[-1, ]
#data1 = read.hdfs("/user/hadoopuser/Rate_PUF.csv")
#data11 = as.matrix(data1$val)
#dim(data11)
#data11 = data11[-1,]
#str(data11)
#colnames(data11) = data11[1,]
#str(data11)
Rate_PUF <- read.csv("~/Rate_PUF.csv")
#########http://htmlpreview.github.io/?https://github.com/andrie/RHadoop-tutorial/blob/master/2-Taxi-analysis-with-RHadoop.html#/8##########
datarate = from.dfs("/user/hadoopuser/Rate_PUF.csv",format = make.input.format("csv",sep = "," , colClasses = "character" , stringsAsFactors = FALSE))
str(datarate)
head(
values(datarate)
)
headerInfo = read.csv("/user/hadoopuser/Rate_PUF.csv",stringsAsFactors = FALSE)
headerInfo
colClasses = as.character(as.vector(headerInfo[1, ]))
names(headerInfo)
colClasses
dataratefinal = from.dfs("/user/hadoopuser/Rate_PUF.csv",format = make.input.format("csv",sep = "," ,col.names = names(headerInfo), colClasses = colClasses , stringsAsFactors = FALSE))
|
3b3b7f09866d1f87dba83209752b8378bd909875 | 32901a09be158774089cbf8960d042e23764ad8d | /man/sgpca.Rd | 123b85d4457a0a3bcf49a7e2f50b989457ba9e80 | [] | no_license | cran/sGPCA | 470a4ef4651cde5d8a99ff01e0935468fe9e1f97 | f095235014f6f11cdac7ad31e03127df2f6a31c1 | refs/heads/master | 2021-01-19T16:56:35.076446 | 2012-07-05T00:00:00 | 2012-07-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,770 | rd | sgpca.Rd | \name{sgpca}
\alias{sgpca}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Sparse Generalized Principal Component Analysis
}
\description{
Computes the rank \code{K} sparse, sparse
non-negative, two-way sparse, and two-way sparse non-negative GPCA
solutions.
}
\usage{
sgpca(X, Q, R, K = 1, lamu = 0, lamvs = 0, posu = FALSE, posv = FALSE,
threshold = 1e-07, maxit = 1000, full.path = FALSE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{X}{The \code{n x p} data matrix. \code{X} must be of
class \code{matrix} with all numeric values.
}
\item{Q}{The row generalizing operator, an \code{n x n}
matrix. \code{Q} can be of class \code{matrix} or class
\code{dcGMatrix}, must be positive semi-definite, and have operator
norm one.
}
\item{R}{The column generalizing operator, an \code{p x p}
matrix. \code{R} can be of class \code{matrix} or class
\code{dcGMatrix}, must be positive semi-definite, and have operator
norm one.
}
\item{K}{The number of GPCA components to compute. The default value
is one.
}
\item{lamu}{The regularization parameter that determines the sparsity
level for the row factor, \code{U}. The default value is 0. If the
data is oriented with rows as samples, non-zero \code{lamu}
corresponds to two-way sparse methods.
}
\item{lamvs}{A scalar or vector of regularization parameters that
determine the sparsity level for the column factor, \code{V}. The
default is 0, with non-zero values corresponding to sparse or two-way
sparse methods. If
\code{lamvs} is a vector, then the BIC method is used to select the
optimal sparsity level. Alternatively, if \code{full.path} is
specified, then the solution at each value of \code{lamvs} is
returned.
}
\item{posu}{Flag indicating whether the row factor, \code{U} should be
constrained to be strictly positive. The default value is FALSE.
}
\item{posv}{Flag indicating whether the column factor, \code{V} should be
constrained to be strictly positive. The default value is FALSE.
}
\item{threshold}{Sets the threshold for convergence. The default value
is \code{.0001}.
}
\item{maxit}{Sets the maximum number of iterations. The default value
is \code{.0001}.
}
\item{full.path}{Flag indicating whether the entire solution path, or
the solution at each value of \code{lamvs}, should be returned. The
default value is FALSE.
}
}
\details{
The \code{sgpca} function has the flexibility to fit combinations of
sparsity and/or non-negativity for both the row and column generalized
PCs. Regularization is used to
encourage sparsity in the GPCA factors by placing an L1
penalty on the GPC loadings, \code{V}, and or the sample GPCs,
\code{U}. Non-negativity constraints on \code{V} and/or \code{U}
yield sparse non-negative and two-way non-negative GPCA.
Generalizing operators as described for \code{\link{gpca}} can be used
with this function and have the same properties.
When \code{lamvs=0}, \code{lamu=0}, \code{posu=0}, and \code{posv=0},
the GPCA solution also given by \code{\link{gpca}} is returned. The
magnitude of the regularization parameters, \code{lamvs} and
\code{lamu}, determine the level of sparsity of the factors
\code{U} and \code{V}, with higher regularization parameter values
yielding sparser factors.
If more than one regularization value \code{lamvs} is given, then
\code{sgpca} finds the optimal regularization parameter
\code{lamvs} by minimizing the BIC derived from the generalized
least squares update for each factor.
If \code{full.path = TRUE}, then the full path of solutions (\code{U},
\code{D}, and \code{V}) is returned for each value of \code{lamvs}
given. This option is best used with 50 or 100 values of
\code{lamvs} to well approximate the regularization paths.
Numerically, the path begins with the GPCA solution, \code{lamvs=0},
and uses warm starts at each step as \code{lamvs}
increases.
Proximal gradient descent is used to compute each rank-one solution.
Multiple components are calculated in a greedy manner via deflation.
Each rank-one solution is solved by iteratively fitting generalized
least squares problems with penalties or non-negativity constraints.
These regression problems are solved by the Iterative Soft-Thresholding
Algorithm (ISTA) or projected gradient descent.
}
\value{
\item{U}{The left sparse GPCA factors, an \code{n x K} matrix. If
\code{full.path} is specified with \code{r} values of \code{lamvs},
then \code{U} is a \code{n x K x r} array.}
\item{V}{The right sparse GPCA factors, a \code{p x K} matrix. If
\code{full.path} is specified with \code{r} values of \code{lamvs},
then \code{V} is a \code{p x K x r} array.}
\item{D}{A vector of the K sparse GPCA values. If
\code{full.path} is specified with \code{r} values of \code{lamvs},
then \code{D} is a \code{K x r} matrix.}
\item{cumulative.prop.var}{The cumulative proportion of variance explained
by the components}
\item{bics}{The BIC values computed for each value of \code{lamvs} and each of
the \code{K} components.}
\item{optlams}{Optimal regularization parameter as chosen by the BIC
method for each of the \code{K} components.}
}
\references{
Genevera I. Allen, Logan Grosenick, and Jonathan Taylor, "A generalized
least squares matrix decomposition", arXiv:1102.3074, 2011.
Genevera I. Allen and Mirjana Maletic-Savatic, "Sparse Non-negative
Generalized PCA
with Applications to Metabolomics", Bioinformatics, 27:21, 3029-3035,
2011.
}
\author{
Frederick Campbell
}
\examples{
data(ozone2)
ind = which(apply(is.na(ozone2$y),2,sum)==0)
X = ozone2$y[,ind]
n = nrow(X)
p = ncol(X)
#Generalizing Operators - Spatio-Temporal Smoothers
R = Exp.cov(ozone2$lon.lat[ind,],theta=5)
er = eigen(R,only.values=TRUE);
R = R/max(er$values)
Q = Exp.cov(c(1:n),c(1:n),theta=3)
eq = eigen(Q,only.values=TRUE)
Q = Q/max(eq$values)
#Sparse GPCA
fit = sgpca(X,Q,R,K=1,lamu=0,lamvs=c(.5,1))
fit$prop.var #proportion of variance explained
fit$optlams #optimal regularization param chosen by BIC
fit$bics #BIC values for each lambda
#Sparse Non-negative GPCA
fit = sgpca(X,Q,R,K=1,lamu=0,lamvs=1,posv=TRUE)
#Two-way Sparse GPCA
fit = sgpca(X,Q,R,K=1,lamu=1,lamvs=1)
#Two-way Sparse Non-negative GPCA
fit = sgpca(X,Q,R,K=1,lamu=1,lamvs=1,posu=TRUE,posv=TRUE)
#Return full regularization paths for inputted lambda values
fit = sgpca(X,Q,R,K=1,lamu=0,lamvs=c(.1,.5,1),full.path=TRUE)
}
\keyword{ PCA }
\keyword{principal components analysis}
\keyword{multivariate analysis}
\keyword{matrix factorization}
\keyword{Sparse PCA}
\keyword{Non-negative Matrix Factorization}
\keyword{Non-negative PCA}
|
dbb6930058401389fa780b187297e3b08479e5d8 | 56a3fc1ea8c5308031d6622e0969ad8fe2e85305 | /AlignementScoreComparison/AlignmentScoreComparison_1stPass/100Kreads_AScompare.R | d183572a31ada3ed649aac1453f944296de7b305 | [] | no_license | shellywanamaker/C_virginica | fd9b5d6a53e29ecc6c2a6bd0b45ed0e929d561d6 | 666f010186d73dab7412356d95d61ae19aae4b25 | refs/heads/master | 2023-01-03T22:41:05.521829 | 2019-07-23T23:09:51 | 2019-07-23T23:09:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,989 | r | 100Kreads_AScompare.R | #another place for install tips: (https://github.com/al2na/methylKit/blob/master/README.md)
#steven's solution for proper install: (https://sr320.github.io/MethylKittens/)
#from command line, change the permissions on R library folders to make them writable
#sudo chmod -R 777 /Users/Shelly/Library/R/
#sudo chmod -R 777 /Library/Frameworks/
#these are steps suggested by the Bioconductor troubleshooting guide (https://bioconductor.org/install/#install-bioconductor-packages)
remove.packages("BiocInstaller", lib=.libPaths())
source("http://bioconductor.org/biocLite.R")
biocLite("methylKit")
library(methylKit)
file.list=list("/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_1_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_2_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_3_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_4_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_5_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_6_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_7_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_8_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_9_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt2/zr2096_10_dedup.sorted.bam")
file.list2=list("/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_1_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_2_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_3_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_4_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_5_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_6_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_7_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_8_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_9_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt3/zr2096_10_dedup.sorted.bam")
file.list3=list("/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_1_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_2_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_3_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_4_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_5_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_6_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_7_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_8_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_9_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt4/zr2096_10_dedup.sorted.bam")
file.list4=list("/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_1_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_2_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_3_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_4_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_5_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_6_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_7_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_8_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_9_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt5/zr2096_10_dedup.sorted.bam")
file.list5=list("/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_1_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_2_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_3_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_4_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_5_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_6_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_7_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_8_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_9_dedup.sorted.bam",
"/Volumes/web/metacarcinus/Cvirginica/Bismark_attempt6/zr2096_10_dedup.sorted.bam")
myobj <- processBismarkAln(location = file.list, sample.id = list("1","2","3","4","5","6","7","8","9","10"),
assembly = "v3", read.context="CpG", mincov=3, treatment = c(0,0,0,0,0,1,1,1,1,1))
#For each analysis
myobj2 <- processBismarkAln(location = file.list2, sample.id = list("1","2","3","4","5","6","7","8","9","10"),
assembly = "v3", read.context="CpG", mincov=3, treatment = c(0,0,0,0,0,1,1,1,1,1))
myobj3 <- processBismarkAln(location = file.list3, sample.id = list("1","2","3","4","5","6","7","8","9","10"),
assembly = "v3", read.context="CpG", mincov=3, treatment = c(0,0,0,0,0,1,1,1,1,1))
myobj4 <- processBismarkAln(location = file.list4, sample.id = list("1","2","3","4","5","6","7","8","9","10"),
assembly = "v3", read.context="CpG", mincov=3, treatment = c(0,0,0,0,0,1,1,1,1,1))
myobj5 <- processBismarkAln(location = file.list5, sample.id = list("1","2","3","4","5","6","7","8","9","10"),
assembly = "v3", read.context="CpG", mincov=3, treatment = c(0,0,0,0,0,1,1,1,1,1))
#Find region coverage
tiles <- tileMethylCounts(myobj,win.size=1000,step.size=1000)
tiles2 <- tileMethylCounts(myobj2,win.size=1000,step.size=1000)
tiles3 <- tileMethylCounts(myobj3,win.size=1000,step.size=1000)
tiles4 <- tileMethylCounts(myobj4,win.size=1000,step.size=1000)
tiles5 <- tileMethylCounts(myobj5,win.size=1000,step.size=1000)
#find regions covered
mmeth <- unite(tiles, min.per.group = 1L)
mmeth2 <- unite(tiles2, min.per.group = 1L)
mmeth3 <- unite(tiles3, min.per.group = 1L)
mmeth4 <- unite(tiles4, min.per.group = 1L)
mmeth5 <- unite(tiles5, min.per.group = 1L)
RegionsCovered <- getData(mmeth)[,1:3]
RegionsCovered2 <- getData(mmeth2)[,1:3]
RegionsCovered3 <- getData(mmeth3)[,1:3]
RegionsCovered4 <- getData(mmeth4)[,1:3]
RegionsCovered5 <- getData(mmeth5)[,1:3]
#sum coverage across samples
RegionsCovered$cov_sum <- rowSums(getData(mmeth)[,grep("coverage", colnames(getData(mmeth)))], na.rm = TRUE)
RegionsCovered2$cov_sum <- rowSums(getData(mmeth2)[,grep("coverage", colnames(getData(mmeth2)))], na.rm = TRUE)
RegionsCovered3$cov_sum <- rowSums(getData(mmeth3)[,grep("coverage", colnames(getData(mmeth3)))], na.rm = TRUE)
RegionsCovered4$cov_sum <- rowSums(getData(mmeth4)[,grep("coverage", colnames(getData(mmeth4)))], na.rm = TRUE)
RegionsCovered5$cov_sum <- rowSums(getData(mmeth5)[,grep("coverage", colnames(getData(mmeth5)))], na.rm = TRUE)
RegionsCovered$par <- "AS1.2"
RegionsCovered2$par <- "AS0.6"
RegionsCovered3$par <- "AS2"
RegionsCovered4$par <- "AS1.2I60"
RegionsCovered5$par <- "AS1.2I150"
allRegionsCovered <- rbind(RegionsCovered,RegionsCovered2, RegionsCovered3, RegionsCovered4, RegionsCovered5)
#plot coverage
ggplot(allRegionsCovered, aes(start, cov_sum)) + geom_point(aes(colour = par, shape = par), alpha = 0.7) + scale_color_manual(values = c("orange","green","blue", "magenta","darkgray")) + xlab("position") + ylab("coverage") + facet_wrap(~chr)
filter_myobj <- filterByCoverage(myobj,lo.count=3,lo.perc=NULL,
hi.count=NULL,hi.perc=99.9)
meth_filter=unite(filter_myobj, destrand=TRUE)
meth <-unite(filter_myobj)
myDiff <- calculateDiffMeth(meth)
mmeth_diff <- calculateDiffMeth(mmeth)
getMethylationStats(tiles[[2]],plot = TRUE, both.strands = FALSE)
getCoverageStats(tiles[[2]],plot = TRUE, both.strands = FALSE)
|
2498f9aea967f08d0a5db8db309265a56f1326e7 | 7889f55c00a87518344f2e86c6f4828618a37505 | /man/examplemetaheur.Rd | 5a62ef48c2e0a7ed3f878fd537a5b9859e1563f7 | [] | no_license | mvattulainen/preprocomb | b034947015200511d96c1184443b8d1135444617 | 3898d40541632c4097028cbe7a79cf5ee3e74f91 | refs/heads/master | 2021-01-18T22:50:20.107393 | 2016-11-09T10:08:44 | 2016-11-09T10:08:44 | 42,282,758 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 366 | rd | examplemetaheur.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/00Utils.R
\docType{data}
\name{examplemetaheur}
\alias{examplemetaheur}
\title{metaheur example}
\format{A MetaheurClass object}
\usage{
examplemetaheur
}
\description{
examplemetaheur <- metaheur(examplegrid, model="svmRadial", iterations = 30, nholdout = 400) \cr
}
\keyword{internal}
|
cbbd0d95fa5a630fbf434d9186cb9da3a0ced444 | 088cfbc6e9b5bff3ab028adbf137be835d7687e5 | /Life_expectancy_chang_M.R | b1bbbcfb3b6847c0531f6aff1eed744ce0a5e3f9 | [] | no_license | facarranza/shareR | a7cf84eda859d226857fce07c7e2df442b42b368 | e641a79b939df5bc3b8e2d80cb2f7748acb595fe | refs/heads/master | 2020-10-02T03:42:05.904714 | 2020-04-17T20:22:07 | 2020-04-17T20:22:07 | 227,693,229 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,573 | r | Life_expectancy_chang_M.R | "
***************************************************************************
life_expectancy_chang_M.R
---------------------
Date : December 2019
Copyright : (C) 2019 by Felipe Carranza
Email : fcarranza@protonmail.com
***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the MIT License *
* *
* *
***************************************************************************
__author__ = 'Felipe Carranza'
__date__ = 'December 2019'
__copyright__ = '(C) 2019, Felipe Carranza'
"
"Real case applied to El Salvador microdata census 2007 for municipal research"
library(foreign)
#Read the census microdata more than 5 Millions of rows
poblation <- read.dta("BaseTotalPer.dta",convert.dates = T,convert.factors = F)
str(poblation)
#Show the variables
library(tibble)
glimpse(poblation) #alternativ to str
view(poblation)
A=data.frame(head(poblation[1:10,]))
library(tidyverse)
#select the variables that you need, in this case is munic code, Age, Sex
Studysection = poblation %>%
group_by(munic) %>%
select(munic,p02,p03a,p09)
#saving
write.csv(Studysection,"investigacion_demo2.csv")
##############################################
#FInding population by munic code, Age, Sex
Studysection <- read.csv("investigacion_demo2.csv")
Studysection = data.frame(Studysection$munic,Studysection$p02, Studysection$p03a)
vivos=NULL
vivos = Studysection %>%
group_by(Studysection$Studysection.munic,Studysection$Studysection.p02, Studysection$Studysection.p03a) %>%
count(n())
Studysection=NULL
write.csv(vivos, "vivos.csv")
#Finding deaths by munic code, Age, Sex
library(foreign)
#reading microdata census of mortality section
poblation <- read.dta("BaseTotalMorta.dta",convert.dates = T,convert.factors = F)
library(tibble)
glimpse(poblation) #alternati to str
#selecting munic, age, sex
Studysection = poblation %>%
group_by(munic) %>%
select(munic,d01,d02)
#saving
write.csv(Studysection,"investigacion_demo2_muertos.csv")
poblation=NULL
#validating NA cases
Studysection <- Studysection[!is.na(Studysection$d01),]
#counting
table(Studysection$munic,Studysection$d01, Studysection$d02)
#new dataframe
Studysection = data.frame(Studysection$munic,Studysection$d01, Studysection$d02)
muerto=NULL
#counting death number by munix, age, sex
muertos = Studysection %>%
group_by(Studysection$Studysection.munic,Studysection$Studysection.d01, Studysection$Studysection.d02) %>%
count(n())
#saving
write.csv(muertos,"muertos.csv")
#rename cols life people
colnames(vivos)[colnames(vivos) == "n()"] <- "vivos_count"
colnames(vivos)[colnames(vivos) == "n"] <- "vivos_coun_auxiliar"
colnames(vivos)[colnames(vivos) == "Studysection$Studysection.p02"] <- "sexo"
colnames(vivos)[colnames(vivos) == "Studysection$Studysection.p03a"] <- "edad"
#rename cols death people
colnames(muertos)[colnames(muertos) == "n()"] <- "muertos_count"
colnames(muertos)[colnames(muertos) == "n"] <- "muertos_coun_auxiliar"
colnames(muertos)[colnames(muertos) == "Studysection$Studysection.d02"] <- "sexo"
colnames(muertos)[colnames(muertos) == "Studysection$Studysection.d01"] <- "edad"
#joining
vivos_muertos=NULL
vivos_muertos = vivos %>%
left_join(muertos)
#saving
write.csv(vivos_muertos,"vivos_muertos.csv")
muertos=NULL
poblation=NULL
Studysection=NULL
vivos=NULL
#finding mortality life table by munic, sex
#Munic man age
vivos_muertos=data.frame(vivos_muertos)
vivos_muertos_hombres = vivos_muertos %>%
dplyr::filter(vivos_muertos$sexo == 1)
write.csv(vivos_muertos_hombres,"vivos_muertos_hombres.csv")
#Munic woman age
vivos_muertos_mujeres = vivos_muertos %>%
dplyr::filter(vivos_muertos$sexo == 2)
write.csv(vivos_muertos_mujeres,"vivos_muertos_mujeres.csv")
municipios = vivos_muertos$Studysection.Studysection.munic
municipios = data.frame(municipios)
municipios_list= municipios %>%
group_by(municipios) %>%
count()
write.csv(municipios_list,"Municipios_list.csv")
mun
#expecting life, for man, repeat the same procedure for woman
#NA issue
#TODO: Adjust for ages wihtout mortality cases
vivos_muertos_hombres = vivos_muertos_hombres %>%
mutate_all(funs(replace(., is.na(.), 0)))
#saving
write.csv(vivos_muertos_hombres,"vivos_muertos_hombres.csv")
municipios_list[1,1]
municipios_list = data.frame(municipios_list)
vivos_muertos_hombres =data.frame(vivos_muertos_hombres)
data11 = vivos_muertos_hombres %>%
filter(vivos_muertos_hombres$Studysection.Studysection.munic == municipios_list[10,1])
library(tidyverse)
data11=data.frame(data11)
dat11a = data11 %>%
mutate(mdx = data11$muertos_count/ data11$vivos_count ) %>%
mutate(qx = (2*mdx) / (2 + mdx)) %>% # death probability
mutate(px=1-qx) #survival probability
dat11a$lx=0
dat11a$lx[1]=100000 #hipotetical cohort
for(i in 2:nrow(dat11a)){
#lx
dat11a$lx[i] = dat11a$px[i-1]*dat11a$lx[i-1]
}
dat11a = dat11a %>%
mutate(Lx= (0.5)*(lx + lead(lx)) ) %>% #Age persons lifed LX
mutate(Lx= if_else(is.na(Lx),lx,Lx))%>%
mutate(Tx= rev(cumsum(rev(Lx)))) %>% # Age for to live
mutate(ex= Tx/lx)# %>% # Life expectancy
# mutate(exsum= ex + x) %>% # Life expectancy other alternative
# mutate(hx = (log(SupPcohortes) -log(lead(SupPcohortes) ))/n) #Hazard ratio if you need it
|
d22162d0699a632310cfc017bb19c9d2625d4f1c | 47d321f1c911444704812e2c7aa403553163fe2f | /scripts/genSimData.R | 2f23d9377ffedf5bfd80d163bec1598eccc71864 | [] | no_license | jaredpark/cleanNLF | e57502fe5f15d6676f64fdb7a0c50266d4351f95 | f7e129fa9f1a16215d69a99b756cf48db17333b7 | refs/heads/master | 2020-03-30T21:32:32.212472 | 2013-07-09T23:52:35 | 2013-07-09T23:52:35 | 11,297,064 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,044 | r | genSimData.R | gameInfoDate = 'sim'
mmDate = 'sim'
nTargetRows = 1600
nGamesPerWeek = 14
firstWeek = 5
lastWeek = 16
nSeas = round(nTargetRows/(nGamesPerWeek*(lastWeek - firstWeek + 1)), 0)
weeks = firstWeek:lastWeek
gameWeeks = rep(weeks, rep(nGamesPerWeek, length(weeks)))
weekColumn = rep(gameWeeks, nSeas)
seasColumn = rep((2007-nSeas+1):2007, rep(nGamesPerWeek*(lastWeek-firstWeek+1), nSeas))
nRows = length(seasColumn)
feat1 = abs(rnorm(nRows, 10, 2))
feat2 = abs(rnorm(nRows, 200, 40))
feat3 = abs(rnorm(nRows, 2, .25))
nNoiseFeat = 20
genNoise = function(n){
return(rnorm(n))
}
noiseFeat = replicate(nNoiseFeat, genNoise(nRows))
response = 2*sqrt(feat1)+5.1*feat3^2+10*log(feat2)
MM = data.frame(cbind('Season' = seasColumn, 'Week' = weekColumn, 'Home' = seq(1:nRows),
'Away' = seq(1:nRows), feat1, feat2, feat3, noiseFeat, 'Response' = response))
gameInfo = MM[,1:4]
save(gameInfo, file = paste(gameInfoDir, '/gameInfo_', gameInfoDate, '.Rout', sep = ''))
save(MM, file = paste(gameInfoDir, '/MM_', mmDate, '.Rout', sep = '')) |
0664d4dec0ef58cf8108d8f33a0621fd38cfc3e9 | f9a9f9e260498c0ff96809dee0fb9697c75b064e | /time_series_blog_1/timeseries/timeseries2.R | 0bbb4a6ade70e6e7f7a6fec9911e077162609238 | [] | no_license | kayfay/R_programming | 4b94f9af3684589c0d29ac47a7f740bb313908a8 | 3f1b3325c2f16d3f578c5f672341b216879aa263 | refs/heads/master | 2021-04-24T20:54:29.637163 | 2019-07-07T17:09:26 | 2019-07-07T17:09:26 | 117,136,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,454 | r | timeseries2.R | # Federal Reserve Economic Data - from Quandl FRED/A791RX0Q048SBEA Retailer Sales FRED/RETAILSMSA
# Data ranges from Jan 1992 to Sep 2017
# Using the Quandl package to aquire a dataset from the Federal Reserve Economic Data
library(forecast)
library(Quandl)
feddata <- Quandl("FRED/RETAILSMSA", type="ts")
print("Rplot9")
png(filename="Rplot9.png")
plot.ts(feddata, main = "Federal Reserve Economic Data: Retail Sales", ylab = "Dollars in Thousands",
xlab = "Years")
dev.off()
# autocorrelation function for differenced series
print("Rplot10")
png(filename="Rplot10%d.png")
acf(diff(feddata), xaxp = c(0, 75, 4), lag.max=75, main="")
pacf(diff(feddata), xaxp = c(0, 75, 4), lag.max=75, main="")
dev.off()
# autocorrelation function for an autoregressive integrated moving average
arima_fit <- auto.arima(feddata)
# inspecting
arima_fit
# The coefficients significantly different and are close to a
# mean of zero standard deviations of the standard error
# autocorrelation function for arima_fit looks good
print("Rplot111 Rplot 112")
png(filename="Rplot11%d.png")
acf(arima_fit$residuals, main="")
pacf(arima_fit$residuals, main="")
dev.off()
# our lag values also aren't crossing the significant zones
print("Rplot121, Rplot122, Rplot123")
png(filename="Rplot12%d.png")
# plotting
plot(arima_fit$residuals, main="", ylab = "Residuals", xlab="Time")
abline(a=0, b=0)
# histogram
hist(arima_fit$residuals, main="", ylab="Frequency", xlab="Residuals")
# qq plot
qqnorm(arima_fit$residuals, ylab="Sample Quantiles", xlab="Theoretical Quantiles", main="")
qqline(arima_fit$residuals)
dev.off()
plot(forecast(feddata, h=12),
main = "Forecast into 2018 of Retailer Sales",
xlab = "Time in Months",
ylab = "Millions of Dollars",)
# Graph
png(filename="Rplot13%d.png")
op <- par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.6, mgp = c(3.5, 1, 0), cex.lab = 1.5, font.lab = 2, cex.axis = 1.5, bty = "n", las = 1)
plot(forecast(feddata, h = 24), xlab = "", ylab = "", main = "Forecast into 2018 of Retailer Sales", type="l", axes = FALSE)
axis(1) ; axis(2)
mtext("Time in Months", side = 1, line = 2.5, cex = 1.5)
mtext("Millions of Dollars", side = 2, line = 5, cex = 1.5, las = 0)
dev.off()
# lb/ub of forecast
print("ub/lb of forecast")
f.p <- forecast(feddata, h=12)
abs(tail(feddata)[6] - tail(f.p$upper)[12]) / tail(feddata)[6] # [1] 0.1330501
abs(tail(feddata)[6] - tail(f.p$lower)[12]) / tail(feddata)[6] # [1] 0.04680049
|
bc7b52919706deb4a93a0aa4a7661937f1b82bef | ac15e81168f92c4bd7190703434d7a076fa5c6dc | /plot3.R | 9f17332173f136c747ceec3ea427389a0e6b2a16 | [] | no_license | suswaram/ExData_Plotting2 | a7caaaf19a4ea0e3fc147b762470fd30121e2fe3 | e3740609178e3517f7f7ce0a0d2b2231d12892bb | refs/heads/master | 2016-09-06T14:21:27.025211 | 2014-05-12T21:52:24 | 2014-05-12T21:52:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,081 | r | plot3.R | #install plyr package if not installed
library(plyr)
#install ggplot2 package if not already installed
#install.packages("ggplot2")
library(ggplot2)
source("loadData.R")
# Task3 Of the four types of sources indicated by the type (point,
# nonpoint, onroad, nonroad) variable, which of these four sources
# have seen decreases in emissions from 1999¨C2008 for Baltimore City?
# Which have seen increases in emissions from 1999¨C2008? Use the
# ggplot2 plotting system to make a plot for this assignment.
# create subset only contain data about baltimore
baltimore <- subset(NEI,NEI$fips == "24510")
# calculating different sources' contribution to total emission
pm25TypeBaltimore <- ddply(baltimore, .(year, type), summarise, totalEmissions = sum(Emissions))
# creating the plot
png("plot3.png", width = 800, height = 600)
g <- qplot(year, totalEmissions, data = pm25TypeBaltimore, facets = .~type)
g + geom_point(aes(color = type), size = 4) + labs(y = "Total PM2.5 Emissions", title = "Sources of PM2.5 Emissions Per Year in Baltimore")
ggsave('plot3.png')
dev.off() |
7335a55c4164bd9439615e2986155a26b0627373 | a1bb4782472b046285e29132ea8faee76cecc6f8 | /R/ch2.R | d9ffe8b005baf54cc8528f9655c82767336ec828 | [] | no_license | cran/orth | bb155ab9fc0e26274f3c5925b71e0c4090b378df | cc72f36de41107427c5445a9700608bb40cf5e6c | refs/heads/master | 2021-01-17T05:56:39.888184 | 2011-01-25T00:00:00 | 2011-01-25T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 52 | r | ch2.R | `ch2` <-
function(n)
{
return(n*(n-1)/2)
}
|
342606e277a68aa904fd54aeb71889bb32dced25 | f56d31e470b05737626c1c379b8277c66bfb078b | /R/coin_datacheck.R | 7a87e710c1aeba5bd0b83595d79bd74fe7c3525a | [
"MIT"
] | permissive | Lucas-OlivCouto/COINr | 81a242d6b0910b6d9286234b390cda4693a5263f | bda637b7fa25f1b09de730248b1eb263f0214ea0 | refs/heads/master | 2023-08-12T07:48:08.190793 | 2021-10-11T14:13:40 | 2021-10-11T14:13:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,490 | r | coin_datacheck.R | #' Detailed unit data check and screener by data availability
#'
#' Gives detailed tables of data availability, and optionally screens units based on a data
#' availability threshold and presence of zeros. Units can be optionally "forced" to be included or excluded, making
#' exceptions for the data availability threshold.
#'
#' The two main criteria of interest are `NA` values, and zeros. The summary table gives percentages of
#' `NA` values for each unit, across indicators, and percentage zero values (*as a percentage of non-`NA` values*).
#' Each unit is flagged as having low data or too many zeros based on thresholds.
#'
#' This function currently only supports COINs as inputs, not data frames.
#'
#' @param COIN The COIN object
#' @param dset The data set to be checked/screened
#' @param ind_thresh A data availability threshold used for flagging low data and screening units if `unit_screen != "none"`. Default 0.66. Specify as a fraction.
#' @param zero_thresh As ind_thresh but for non-zero values. Defaults to 0.05, i.e. it will flag any units with less than 5% non-zero values (equivalently more than 95% zero values).
#' @param unit_screen Specifies whether and how to screen units based on data availability or zero values.
#' * If set to `"none"` (default), does not screen any units.
#' * If set to `"byNA"`, screens units with data availability below `ind_thresh`
#' * If set to `"byzeros"`, screens units with non-zero values below `zero_thresh`
#' * If set to `"byNAandzeros"`, screens units based on either of the previous two criteria being true.
#' * If you simply want to force a unit or units to be excluded (without any other screening), use the `Force` argument and set `unit_screen = TRUE`.
#' `unit_screen != "none"` outputs a new data set .$Data$Screened.
#' @param Force A data frame with any additional countries to force inclusion or exclusion. First column is `"UnitCode"`. Second column `"Status"` either `"Include"` or `"Exclude"` for each country to force.
#' @param out2 Where to output the results. If `"COIN"` (default for COIN input), appends to updated COIN,
#' otherwise if `"list"` outputs to data frame.
#'
#' @importFrom dplyr select starts_with pull mutate filter
#'
#' @examples
#' # build ASEM COIN
#' ASEM <- assemble(IndData = ASEMIndData, IndMeta = ASEMIndMeta, AggMeta = ASEMAggMeta)
#' # return stats to the COIN, plus screened data set, return to list
#' ScreenedData <- checkData(ASEM, dset = "Raw", unit_screen = "byNA",
#' ind_thresh = 0.9, out2 = "list")
#' # See which units were removed
#' print(ScreenedData$RemovedUnits)
#'
#' @return An updated COIN with data frames showing missing data in `.$Analysis`, and if `unit_screen != "none"` outputs a new data set .$Data$Screened.
#' If `out2 = "list"` wraps missing data stats and screened data set into a list.
#'
#' @export
checkData <- function(COIN, dset = NULL, ind_thresh = NULL, zero_thresh = NULL,
unit_screen = "none", Force = NULL, out2 = "COIN"){
# Check input type. If not COIN, exit.
if (!is.coin(COIN)){ # COIN obj
stop("This function currently only supports COINs as inputs.")
}
# Check for dset. If not specified, exit.
if (is.null(dset)){
stop("dset is NULL. Please specify which data set to operate on.")
}
##----- SET DEFAULTS -------##
# Done here because otherwise if we use regen, this input could be input as NULL
if(is.null(ind_thresh)){
ind_thresh <- 2/3
}
if(is.null(zero_thresh)){
zero_thresh <- 0.05
}
if(is.null(unit_screen)){
unit_screen <- "none"
}
# Write function arguments to object, FTR
COIN$Method$checkData$dset <- dset
COIN$Method$checkData$ind_thresh <- ind_thresh
COIN$Method$checkData$zero_thresh <- zero_thresh
COIN$Method$checkData$unit_screen <- unit_screen
COIN$Method$checkData$Force <- Force
# Isolate indicator data
out1 <- getIn(COIN, dset = dset)
ind_data_only <- out1$ind_data_only
#--- Check overall data availability
nabyrow <- rowSums(is.na(ind_data_only)) # number of missing data by row
zerobyrow <- rowSums(ind_data_only == 0, na.rm = TRUE) # number of zeros for each row
nazerobyrow <- nabyrow + zerobyrow # number of zeros or NAs for each row
Prc_avail = 1 - nabyrow/ncol(ind_data_only) # the percentage of data available
Prc_nonzero = 1 - zerobyrow/(ncol(ind_data_only) - nabyrow) # the percentage of non zeros
#Prc_avail_and_nonzero = 1 - nazerobyrow/ncol(ind_data_only) # the percentage of non zeros AND not NA
data_avail <- data.frame(UnitCode = out1$UnitCodes,
N_missing = nabyrow,
N_zero = zerobyrow,
N_miss_or_zero = nazerobyrow,
PrcDataAll = Prc_avail*100,
PrcNonZero = Prc_nonzero*100,
#PrcDataAndNonZero = Prc_avail_and_nonzero*100,
LowDataAll = Prc_avail < ind_thresh,
ZeroFlag = Prc_nonzero < zero_thresh,
LowDatOrZeroFlag = (Prc_avail < ind_thresh) | (Prc_nonzero < zero_thresh))
#--- Check data availability by group
# the easiest way to do this is to loop over groups. Get first the index structure
# (selects indicator codes plus all aggregation level columns/codes)
agg_levels <- dplyr::select(COIN$Input$IndMeta, "IndCode" | dplyr::starts_with("Agg"))
data_avail_bygroup <- data.frame("UnitCode" = out1$UnitCodes)
for (ilev in 1:(ncol(agg_levels)-1)){ # loop over aggregation levels, except the last one
agg1 <- dplyr::pull(agg_levels,1) # names of indicators
agg2 <- dplyr::pull(agg_levels,ilev+1) # names of aggregation groups in the level above
agg2_names <- unique(agg2) # only the names of agg level above (no repetitions)
# pre-allocate a data frame for prc data availability
d_avail_lev <- as.data.frame(matrix(NA, nrow = nrow(ind_data_only), ncol = length(agg2_names)))
for (igroup in 1:length(agg2_names)){ # now looping over groups inside this level
gname <- agg2_names[igroup] # select group name
# get indicator codes belonging to group
gcodes <- agg1[agg2 == gname]
# get corresponding indicator columns
ginds <- ind_data_only[gcodes]
# now count prc data available and add to data frame
d_avail_lev[,igroup] <- 100*rowSums(!is.na(ginds))/ncol(ginds)
}
# add column names (aggregation group names) to data availability table
colnames(d_avail_lev) <- agg2_names
# add to big table
data_avail_bygroup <- cbind(data_avail_bygroup, d_avail_lev)
}
# Now add final column which says if country is included or not, if asked for
if (unit_screen == "byNA"){
data_avail <- cbind(data_avail, Included = data_avail$LowDataAll == FALSE)
} else if (unit_screen == "byzeros"){
data_avail <- cbind(data_avail, Included = data_avail$ZeroFlag == FALSE)
} else if (unit_screen == "byNAandzeros"){
data_avail <- cbind(data_avail, Included = data_avail$LowDatOrZeroFlag == FALSE)
} else {
data_avail <- cbind(data_avail, Included = TRUE)
}
if (!is.null(Force)){ # if some countries to force include/exclude
# convert to logical
Force[2] <- Force[2]=="Include"
# substitute in output table
data_avail$Included[ data_avail$UnitCode %in% Force$UnitCode[Force$Status == TRUE] ] <- TRUE
data_avail$Included[ data_avail$UnitCode %in% Force$UnitCode[Force$Status == FALSE] ] <- FALSE
}
if (unit_screen != "none"){
# create new data set which filters out the countries that didn't make the cut
ScreenedData <- dplyr::filter(out1$ind_data, data_avail$Included)
# units that are removed
ScreenedUnits <- data_avail$UnitCode[!data_avail$Included]
}
if (out2 == "list"){
# write to a list
return(list(
MissDatSummary = data_avail,
MissDatByGroup = data_avail_bygroup,
ScreenedData = ScreenedData,
RemovedUnits = ScreenedUnits
))
} else if (out2 == "COIN") {
# add summary tables to COIN
eval(parse(text=paste0("COIN$Analysis$",dset,"$MissDatSummary<- data_avail")))
eval(parse(text=paste0("COIN$Analysis$",dset,"$MissDatByGroup<- data_avail_bygroup")))
if (unit_screen != "none"){
COIN$Data$Screened <- ScreenedData
eval(parse(text=paste0("COIN$Analysis$",dset,"$RemovedUnits<- ScreenedUnits")))
}
return(COIN)
} else {
stop("out2 not recognised, should be either COIN or list")
}
}
|
bbeb4881dd67593b1e4532e3ebb8ba1be902447e | ba9dc103b4a474fe3b64b501559017f329e9653f | /Scripts/Exercises_with_solutions.R | 2afb7134d6b842b0711c91bf7eb6df990ef13c37 | [] | no_license | edinkasia/Intro_DSHSC-Tutorial_week6 | ffb534158066ded744a568ccbf13ce6dddba1838 | 8105b7e5bd9323dc83d741620ca6dde2eb84e8d4 | refs/heads/main | 2023-09-02T10:38:48.405000 | 2021-11-03T15:46:58 | 2021-11-03T15:46:58 | 424,154,352 | 0 | 0 | null | 2021-11-03T15:46:59 | 2021-11-03T09:01:43 | R | UTF-8 | R | false | false | 9,867 | r | Exercises_with_solutions.R | # You will need to load the required libraries first: tidyverse and here.
library(tidyverse)
library(here)
# We will be using traumatic brain injury data from a Tidy Tuesday challenge
# Learn more about the challenge and the data here (spend max 3 minutes looking at the linked page):
# https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-03-24/readme.md
# Let's start by loading in the data
tbi_age <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-24/tbi_age.csv')
tbi_year <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-24/tbi_year.csv')
### Task 1
# Save both datasets in the Raw_Data folder (use the 'here' library, as I showed in the demo)
# Insert your code for Task 1 below this line:
# Use a write_csv function, with the here() syntax
# Remember, the folder name goes first, followed by file name
write_csv(tbi_age, file = here("Raw_Data", "tbi_age.csv"))
write_csv(tbi_year, file = here("Raw_Data", "tbi_year.csv"))
### Task 2
# We'll look at tbi_age first. Use your favourite functions for eyeballing data and write a couple of sentences about
# this dataset (its dimensions, the variables inside, types of data, etc.)
# Insert your code below this line:
head(tbi_age)
str(tbi_age)
# Your description of the dataset
# E.g. This dataset has 231 rows and 5 columns. It includes 5 variables:
# age group, type of outcome, injury mechanism, estimated number of injuries
# in total, and estimated rate per 100000 population. Estimated number and rate
# are numeric, type of outcome and injury mechanism, are categorical nominal
# variables stored as strings, and age is a categorical ordinal variable,
# also stored as a string.
# Task 3
# Are there any variables in this dataset that should be treated as factors?
# Decide which ones, and turn them into factors.
# Once you've done that, think about the order of factor levels. Does it need to be changed? If so, change it.
# Insert your code below this line:
# Solution: type, injury_mechanism and age_group are all categorical variables,
# and so should be stored as factors. We can use the mutate() function to
# change those variables, and the factor() function to turn them into factors.
# No additional arguments are needed.
tbi_age <- tbi_age %>%
mutate(type = factor(type),
injury_mechanism = factor(injury_mechanism),
age_group = factor(age_group))
# When we used the default settings, the factor levels of age were a little
# mixed up. We're able to sort them using the levels argument.
# Notice how we've got overlapping age_group levels for children -
# this would cause trouble if we just added different age groups up.
tbi_age <- tbi_age %>%
mutate(age_group = factor(age_group, levels = c("0-17", "0-4", "5-14", "15-24",
"25-34", "35-44", "45-54", "55-64",
"65-74", "75+", "Total")))
# Task 4
# Imagine that you are preparing a brief for the local health authority, and they have asked you to
# answer the following questions. For each question, write a chunk of code that would provide the
# answer in a clean and reproducible way.
# Task 4.1
# Which injury mechanism caused the most deaths in total?
# Insert your code below this line:
# First, think about subsetting the data - which variables do you need?
# You don't want to look at any particular age group, so you can
# set age_group to be Total. You are interested in deaths,
# so you can set type to be Deaths. Use the filter function,
# with both of these criteria applied:
tbi_age %>%
filter(age_group == "Total" & type == "Deaths")
# Using the line above, you already get a pretty small tibble,
# from which you can read out the highest number.
# If you wanted to make your life even easier, you can arrange
# this tibble, with the highest number first:
tbi_age %>%
filter(age_group == "Total" & type == "Deaths") %>%
arrange(desc(number_est))
# This solution is good for eyeballing, as we can see the number of injuries
# in descending order.
# Task 4.2
# Looking just at the totals, what is the number of ED visits, hospitalisations and deaths
# associated with each injury mechanism? Present the results as a tibble. Save this tibble in your
# Tables folder.
# Insert your code below this line:
# As before, start by thinking about filtering - you want all types of outcome,
# but you're interested in the Total across age groups. So, you can set
# age_group to "Total". You will want to save the tibble, so assign it into a new object.
table_age <- tbi_age %>%
filter(age_group == "Total")
# If you wanted to, you could add the `arrange` function (like above),
# just to ease interpretation.
table_age <- tbi_age %>%
filter(age_group == "Total") %>%
arrange(desc(number_est))
# And then, for writing into a file, we use the write_csv function,
# with the here helper, to put it into the required folder.
write_csv(table_age, here("Tables", "table_age.csv"))
# Task 4.3
# Focusing on the oldest age group (75+), draw a bar chart illustrating the number of hospitalisations
# associated with each injury mechanism. Make sure that you add a title and appropriate axis labels.
# Save this chart in your Plots folder.
# Insert your code below this line:
plot1 <- tbi_age %>%
filter(age_group == "75+" & type == "Hospitalizations") %>%
# the str_wrap function from stringr adds line breaks in our injury_mechanism
# labels, so they look better in the plot
mutate(injury_mechanism = str_wrap(injury_mechanism, width = 15)) %>%
# reorder will sort the bars by height, so that the injury mechanism
# with most cases is first
ggplot(aes(x = reorder(injury_mechanism, desc(-number_est)), y = number_est)) +
geom_bar(stat = "identity") + #or use geom_col
labs(title = "Injuries, by mechanism, in the 75+ age group",
x = "Injury mechanism", y = "Number of injuries") +
coord_flip() # this swaps x and y coordinates, making the labels more visible
# Note that the labels are very long, and there are different ways to deal with that:
# https://stackoverflow.com/questions/1330989/rotating-and-spacing-axis-labels-in-ggplot2
ggsave(here("Plots", "Injuries_oldest_group.pdf"))
# ggsave saves the most recent plot by default, but you can specify the plot
# with the 'plot' argument
### Let's now look at tbi_year dataset.
### Task 5
# Use functions for eyeballing data and write a couple of sentences about
# tbi_year dataset (its dimensions, the variables inside, types of data, etc.)
# Insert your code below this line:
head(tbi_year)
str(tbi_year)
# Your description of the dataset:
# E.g. This dataset has 216 rows and 5 columns. It includes 5 variables:
# injury mechanism, type of outcome, year, estimated number of injuries
# in total, and estimated rate per 100000 population. Year, estimated number
# and rate are numeric, type of outcome and injury mechanism, are categorical
# nominal variables stored as strings.
# Task 6
# Are there any variables in this dataset that should be treated as factors?
# Decide which ones, and turn them into factors.
# Once you've done that, think about the order of factor levels. Does it need to be changed?
# Insert your code below this line:
tbi_year <- tbi_year %>%
mutate(injury_mechanism = factor(injury_mechanism),
type = factor(type))
# We don't need to change the factor levels for any variable.
# Task 7 intro
# Imagine that you are preparing a brief for the local health authority, and they have asked you to
# answer the following questions. For each question, write a chunk of code that would provide the
# answer in a clean and reproducible way.
# Task 7.1
# Plot a line chart that depicts the rate of deaths, per 100000 population,
# from traumatic brain injury in years 2006-2014.
# Save this chart in your Plots folder.
# Insert your code below this line:
plot2 <- tbi_year %>%
filter(injury_mechanism == "Total" & type == "Deaths") %>%
ggplot(aes(x = year, y = rate_est)) +
geom_line() +
geom_point() + # using two geoms makes the data points more visible
labs(title = "Rate of deaths, per 100000 population, from traumatic brain injury",
x = "Year", y = "Estimated rate")
plot2
ggsave(here("Plots", "Total_death_rates.pdf"))
# Task 7.2
# Go to this URL: https://www.cdc.gov/traumaticbraininjury/pdf/TBI-Surveillance-Report-FINAL_508.pdf
# On page 14 of the report (page 15 of the pdf document), you'll find Figure 2A,
# showing the rates, per 100000 population, of TBI-related ED visits, by year,
# and mechanism of injury, 2006-2014.
# Try and re-create this figure.
# First, focus just on depicting the data (you can use the default settings of ggplot).
# If you still have time, try reproducing this figure as closely as possible (colours, style, etc.).
# Save the final version of your figure in your Plots folder.
# Insert your code below this line:
plot3 <- tbi_year %>%
filter(type == "Emergency Department Visit" & injury_mechanism != "Total") %>%
ggplot(aes(x = year, y = rate_est, colour = injury_mechanism)) +
geom_line() +
geom_point() +
labs(title = "FIGURE 2A: ESTIMATED AGE-ADJUSTED RATES, PER 100,000 POPULATION,OF TRAUMATIC BRAIN INJURY–
RELATED EMERGENCY DEPARTMENT VISITS, BY YEAR AND MECHANISM OF INJURY, 2006-2014",
y = "Age-adjusted rates of TBI-related ED visits") +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.position = "bottom",
legend.title = element_blank())
plot3
ggsave(here("Plots", "TBI_related_ED_visits.pdf"), width=11, height=8.5)
|
30ebb438cd73a04dfc9996ae8e61eb7c56bf0a24 | a1ec9c7e27a2ddae773164976f7a31072ceaffbc | /Plot2.R | bef7ee6f6afcca90928733f6c04962260418bdaf | [] | no_license | drg0919/ExData_Plotting1 | 00a722747687e4869af13afd47178c33bfb10e7c | 41ccc5d7f46fbd19e66fa36ec6cf258d9f76c356 | refs/heads/master | 2021-01-08T18:29:28.290030 | 2020-02-21T09:57:44 | 2020-02-21T09:57:44 | 242,107,377 | 0 | 0 | null | 2020-02-21T09:55:56 | 2020-02-21T09:55:55 | null | UTF-8 | R | false | false | 659 | r | Plot2.R | my_data<-read.table("household_power_consumption.txt",sep=";",header=TRUE)
my_subset<-subset(my_data,Date=="1/2/2007"|Date=="2/2/2007")
my_subset$Date <- as.Date(my_subset$Date, format="%d/%m/%Y")
my_subset$Time <- strptime(my_subset$Time, format="%H:%M:%S")
my_subset[1:1440,"Time"] <- format(my_subset[1:1440,"Time"],"2007-02-01 %H:%M:%S")
my_subset[1441:2880,"Time"] <- format(my_subset[1441:2880,"Time"],"2007-02-02 %H:%M:%S")
png("Plot 2.png", height=480, width=480)
plot(my_subset$Time,as.numeric(as.character(my_subset$Global_active_power)),type="l",xlab="",ylab="Global Active Power (kilowatts)")
title(main="Global Active my_data Vs Time")
dev.off() |
96b078205e39a58658abd4b0e8a080406781c692 | 6c6235ddf3aee099cc429e6bf09c4f0453015993 | /Text Processing bagian 1.R | a866255cb5654c81dd3a4c245469788900e5aebc | [] | no_license | GANGGAANURAGA/Text-Mining-di-R | 6e9e55835ac165fe5d61ba15b2072ab2ab86abe5 | a30f907d3f46fb54e8cb6134b60855d62aa75ce2 | refs/heads/master | 2022-12-10T21:23:36.167245 | 2020-09-13T13:41:49 | 2020-09-13T13:41:49 | 295,157,281 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,495 | r | Text Processing bagian 1.R | # library yang digunakan
library(tm)
library(NLP)
library(wordcloud)
library(RColorBrewer)
library(tokenizers)
# data yang digunakan
datatext = read.csv("DataLatihText.csv")
dim(datatext)
# membuat korpus / kumpulan dokumen
corpusdc <- iconv(datatext$narasi, to = "UTF-8") # UTF koding karakter
corpusdc <- Corpus(VectorSource(corpusdc))
inspect(corpusdc[1:6]) # memunculkan 6 dokumen teratas
# pembersihan data text / cleaning text
# set kalimat dalam bentuk huruf kecil semua
corpusdc <- tm_map(corpusdc,tolower)
inspect(corpusdc[1:6])
# punctuation period (full stop) ( . ) comma ( , ) question mark ( ? )
# exclamation mark ( ! ) colon ( : ) semicolon ( ; )
# single quotation marks ( ' ' ) double quotation marks ( " " )
corpusdc <- tm_map(corpusdc, removePunctuation)
corpusdc <- tm_map(corpusdc, removeNumbers)
inspect(corpusdc[1:6])
# stopword
library(stopwords)
stopwordID <- read.delim("ID-Stopwords.txt")
ccStopwordID <- readline(stopwordID)
# tokenize_words(corpusdc, stopwords = ccStopwordID)
corpusdc <- tm_map(corpusdc, removeWords, ccStopwordID)
inspect(corpusdc[1:6])
corpusdc <- tm_map(corpusdc, removeWords,
c('dan','yg','yth','ini','dari','ada','tidak','itu',
'yang','untuk','dengan','dalam','dgn','bahwa',
'lagi','sebagai','jangan','bisa','satu',
'sudah','akan', 'kita', 'akan','dia','utk',
'telah','kepada','saya','kami','saat','jadi','tak','tdk','v',
'oleh','karena','mereka','pada','hanya','seperti','masih',
'anda','tersebut','adalah','the','orang','mau','atau','hari',
'semua','bukan','saja','para','tau','sdh','anak','jika',
'tahun','nya','kalau','banyak','masuk','gak','apa','belum',
'sampai','...','foto','lebih','seorang','tapi','baru',
'terjadi','dapat','info','ternyata','bagi','buat',
'juga','atas','tadi','kalo','bila','kali'))
inspect(corpusdc[1:6])
corpusdc <- tm_map(corpusdc, stripWhitespace)
inspect(corpusdc[1:6])
length(corpusdc)
# pembobotan kata tf-idf
dtmbdc = DocumentTermMatrix(corpusdc)
# write.csv((as.matrix(dtmbdc)), "tfbdc.csv")
dtmbdc.matrix = as.matrix(dtmbdc)
inspect(dtmbdc)
dtmtfidff <- weightTfIdf(dtmbdc, normalize = TRUE)
dtmtfidff.matrix = as.matrix(dtmtfidff)
inspect(dtmtfidff)
# write.csv((as.matrix(dtmtfidff)), "tfidfbdc.csv")
barplot <- rowSums(t(dtmbdc.matrix))
barplot
barplot <- subset(barplot, barplot>=10)
barplot
barplot(barplot, las=2, col=rainbow(50), cex.names = 0.8)
# wordcloud
library(wordcloud)
dtmbdcc = TermDocumentMatrix(corpusdc)
dtmbdcc = as.matrix(dtmbdcc)
bar <- sort(rowSums(dtmbdcc),decreasing = TRUE)
set.seed(222)
wordcloud(words = names(bar),freq = bar, max.words = 200,
min.freq = 5, colors = brewer.pal(8,"Dark2"),
scale = c(5,0,3), rot.per = 0.25)
library(wordcloud2)
g <- data.frame(names(bar), bar)
colnames(g) <- c('narasi','freq')
head(g)
wordcloud2(g)
wordcloud2(g,size = 0.8,shape = 'star',rotateRatio = 0.5,minSize = 1)
# Sentimen analisis
library(syuzhet)
library(lubridate)
library(ggplot2)
library(scales)
library(reshape2)
library(dplyr)
bdc <- iconv(corpusdc, to ='UTF-8')
g1 <- get_nrc_sentiment(bdc)
head(g1)
barplot(colSums(g1), las=2, col=rainbow(10),main='Sentiment Analysis Untuk Berita Hoax')
|
456098a917e76d32b34c8982a2837bb0f828a6c2 | c409ff3ea8b7c62efd962d37c83793d4fc0dc1bc | /tests/testthat.R | 95928d8662da6d94d023551289976728b9df3982 | [
"MIT"
] | permissive | markdly/conquestr | e225ecb1347957dc025c5c719d46624f56e01207 | 7994b3768e26acf1be4ac20821da66ba7f564deb | refs/heads/master | 2021-04-30T10:41:43.513747 | 2018-09-12T05:46:02 | 2018-09-12T05:46:02 | 121,339,581 | 1 | 0 | null | 2018-09-12T05:46:03 | 2018-02-13T04:43:27 | R | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(conquestr)
test_check("conquestr")
|
2150ac614d8d2cabab7061b2eca65b54b80b0f82 | bc0058818a923d1fc477fb55200365adfc1ea2e9 | /plot1.R | 9a311090bb314337a19cbe1466c740899432ebe2 | [] | no_license | makoto/ExData_Plotting1 | a149c2ee86afb448f5f6c8e7ae26a9bbfaaa961f | 96a368f6a99c39a89fb78e4e2ca6b4e9f88f4270 | refs/heads/master | 2021-01-22T17:14:00.407784 | 2015-05-10T22:51:07 | 2015-05-10T22:51:07 | 35,388,928 | 0 | 0 | null | 2015-05-10T21:35:57 | 2015-05-10T21:35:56 | null | UTF-8 | R | false | false | 367 | r | plot1.R | png("plot1.png", width=4, height=4, units="in", res=300)
power <- read.csv('./household_2002.txt', sep=";")
power$Date <- as.Date(power$Date, "%d/%m/%Y")
power$Time <- strptime(power$Time, '%H:%M:%S', tz = "UTC")
hist(power$Global_active_power,
breaks=20, col='red',
main='Global Active Power',
xlab='Global Active Power(kilowatts)')
dev.off() |
8e3a91f6470dc30eb1785f2ff57a77c11f1756d9 | 59004c819451c7f552159ec5b2ce500fa365d70d | /R/predict.QRNLMM.R | 9be0521b7769e88d2f4d261dd894f319bc8a8706 | [] | no_license | cran/qrNLMM | 168ca22144c733fa192ef9bda53e93917a46279b | ac1d52d97a4f81205151895054cd553a3c5fd608 | refs/heads/master | 2022-09-05T00:17:55.503771 | 2022-08-18T11:40:05 | 2022-08-18T11:40:05 | 30,884,909 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 12,040 | r | predict.QRNLMM.R | "predict.QRNLMM" = function(object,x = NULL,groups = NULL,covar = NULL,y = NULL,MC=1000,...){
thin = 5
if(!inherits(object,"QRNLMM"))
stop("The object must be an object of the class QRNLMM.")
if(is.null(x) & is.null(groups) & is.null(covar) & is.null(y)){
message("NOTE: No newdata provided. Returning fitted values for original dataset.")
if(is.null(object$res$beta)){
# several quantiles
nquant = length(object)
p = as.numeric(lapply(object, function (x) x$res$p))
new.values = matrix(unlist(lapply(object, function (x) x$res$fitted.values)),
ncol = nquant,
byrow = FALSE)
new.values = as.data.frame(new.values)
colnames(new.values) = p
}else{
# one quantile
nquant = 1
new.values = data.frame(new.values = object$res$fitted.values)
colnames(new.values) = object$res$p
}
return(new.values)
}
# Second case
# no 'y' provided
if(is.null(x) | is.null(groups))
stop("At least both 'x' and 'groups' must be provided.")
if(any(is.na(x)))
stop("There are some NA's values in x")
if(any(is.na(groups)==TRUE))
stop("There are some NA's values in groups")
if(nrow(as.matrix(x)) != length(groups))
stop("groups does not match with the provided data in x. (nrow(x) != length(groups))")
if(is.null(object$res$beta)){
# many quantiles
obj = object[[1]]
p = as.numeric(lapply(object, function (x) x$res$p))
}else{
#one quantile
obj = object
p = object$res$p
}
dqnc = countall2(
gsub(" ",
"",
paste0(
deparse(
obj$res$nlmodel),
collapse = ""),
fixed = TRUE)
)
d = dqnc[1]
q = dqnc[2]
nc = dqnc[3]
if(nc > 0){
# are there covariates?
if(is.null(covar)){
stop("Covariates must be provided for the fitted model.")
}
if(any(is.na(covar)))
stop("There are some NA's values in covar")
if(nrow(as.matrix(covar)) != length(groups))
stop("'groups' does not match with the provided data in 'covar'. (nrow(covar) != length(groups))")
}
if(is.null(y)){
message("NOTE: response 'y' not provided. Population curves will be computed.")
}else{
if(nrow(as.matrix(y)) != length(groups))
stop("NOTE: response y does not match with the provided data in groups. (length(y) != length(groups))")
message("NOTE: response 'y' provided. Individual curves will be computed.")
}
groups = as.numeric(groups)
#d,q,nc
n = dim(obj$res$weights)[1] #subjects
nquant = length(p)
nj = c(as.data.frame(table(groups))[,2])
rand = rep(0,q)
new.values = matrix(NA,sum(nj),nquant)
# no covariates
pb <- progress_bar$new(
format = " Predicting... [:bar] :percent eta: :eta",
total = length(nj)*nquant, clear = TRUE, width= 60, show_after = 0)
pb$tick(0)
#Sys.sleep(0.2)
if(nc == 0){
# no covariates
# one quantile
if(nquant == 1){
for (j in 1:length(nj)){
pos = (sum(nj[1:j-1])+1):(sum(nj[1:j]))
if(is.null(y)){
# sds = apply(
# X = object$res$weights,
# MARGIN = 2,
# FUN = function(x) return(
# fitdistrplus::fitdist(
# x,
# distr = "norm",
# fix.arg = list(mean = 0))$estimate
# )
# )
#
# rand = qnorm(p = p,sd = sds)
rand = apply(object$res$weights,2,quantile,probs = p)
new.values[pos,1] = object$res$nlmodel(x = x[pos],
fixed = object$res$beta,
random = rand)
pb$tick()
}else{
chutebi0 = chutebi = rep(0,q)
chutebibi0 = chutebibi = object$res$Psi # bibi^T
bmetro = matrix(MHbi3(j=j,M=MC,
y1 = y[pos],x1 = x[pos],
cov1 = covar[pos,,drop = FALSE],
bi=chutebi0,
bibi=chutebibi0,
d=d,q=q,p=p,nj=nj,
beta=object$res$beta,
sigmae=object$res$sigma,
D=object$res$Psi,
nlmodel=object$res$nlmodel),q,MC)
rand_j = apply(bmetro[,seq(1,MC,by = thin)],1,mean)
new.values[pos,1] = object$res$nlmodel(x = x[pos],
fixed = object$res$beta,
random = rand_j)
pb$tick()
}
}
#no covariates
# several quantiles
}else{
for (j in 1:length(nj)){
pos = (sum(nj[1:j-1])+1):(sum(nj[1:j]))
for(k in 1:nquant){
if(is.null(y)){
# sds = apply(
# X = object[[k]]$res$weights,
# MARGIN = 2,
# FUN = function(x) return(
# fitdistrplus::fitdist(
# x,
# distr = "norm",
# fix.arg = list(mean = 0))$estimate
# )
# )
#
# rand_k = qnorm(p = p[k],sd = sds)
rand_k = apply(object[[k]]$res$weights,2,quantile,probs = p[k])
new.values[pos,1] = obj$res$nlmodel(x = x[pos],
fixed = object[[k]]$res$beta,
random = rand_k)
pb$tick()
}else{
chutebi0 = chutebi = rep(0,q)
chutebibi0 = chutebibi = object[[k]]$res$Psi/n # bibi^T
bmetro = matrix(MHbi3(j=j,M=MC,
y1 = y[pos],x1 = x[pos],
cov1 = covar[pos,,drop = FALSE],
bi=chutebi0,
bibi=chutebibi0,
d=d,q=q,p=p[k],nj=nj,
beta=object[[k]]$res$beta,
sigmae=object[[k]]$res$sigma,
D=object[[k]]$res$Psi,
nlmodel=object[[k]]$res$nlmodel),q,MC)
rand_j = apply(bmetro[,seq(1,MC,by = thin)],1,mean)
new.values[pos,k] = obj$res$nlmodel(x = x[pos],
fixed = object[[k]]$res$beta,
random = rand_j)
pb$tick()
}
}
}
}
}else{
# with covariates
# one quantile
covar = as.matrix(covar)
# one quantile
if(nquant == 1){
for (j in 1:length(nj)){
pos = (sum(nj[1:j-1])+1):(sum(nj[1:j]))
if(is.null(y)){
# sds = apply(
# X = object$res$weights,
# MARGIN = 2,
# FUN = function(x) return(
# fitdistrplus::fitdist(
# x,
# distr = "norm",
# fix.arg = list(mean = 0))$estimate
# )
# )
#
# rand = qnorm(p = p,sd = sds)
rand = apply(object$res$weights,2,quantile,probs = p)
new.values[pos,1] = object$res$nlmodel(x = x[pos],
fixed = object$res$beta,
random = rand,
covar=covar[pos,,drop=FALSE])
pb$tick()
}else{
chutebi0 = chutebi = rep(0,q)
chutebibi0 = chutebibi = object$res$Psi # bibi^T
bmetro = matrix(MHbi3(j=j,M=MC,
y1 = y[pos],x1 = x[pos],
cov1 = covar[pos,,drop = FALSE],
bi=chutebi0,
bibi=chutebibi0,
d=d,q=q,p=p,nj=nj,
beta=object$res$beta,
sigmae=object$res$sigma,
D=object$res$Psi,
nlmodel=object$res$nlmodel),q,MC)
rand_j = apply(bmetro[,seq(1,MC,by = thin)],1,mean)
pb$tick()
new.values[pos,1] = object$res$nlmodel(x = x[pos],
fixed = object$res$beta,
random = rand_j,
covar=covar[pos,,drop=FALSE])
}
}
# several quantiles
}else{
for (j in 1:length(nj)){
pos = (sum(nj[1:j-1])+1):(sum(nj[1:j]))
for(k in 1:nquant){
if(is.null(y)){
# sds = apply(
# X = object[[k]]$res$weights,
# MARGIN = 2,
# FUN = function(x) return(
# fitdistrplus::fitdist(
# x,
# distr = "norm",
# fix.arg = list(mean = 0))$estimate
# )
# )
#
# rand_k = qnorm(p = p[k],sd = sds)
rand_k = apply(object[[k]]$res$weights,2,quantile,probs = p[k])
new.values[pos,k] = obj$res$nlmodel(x = x[pos],
fixed = object[[k]]$res$beta,
random = rand_k,
covar=covar[pos,,drop=FALSE])
pb$tick()
}else{
chutebi0 = chutebi = rep(0,q)
chutebibi0 = chutebibi = object[[k]]$res$Psi/n # bibi^T
bmetro = matrix(MHbi3(j=j,M=MC,
y1 = y[pos],x1 = x[pos],
cov1 = covar[pos,,drop = FALSE],
bi=chutebi0,
bibi=chutebibi0,
d=d,q=q,p=p[k],nj=nj,
beta=object[[k]]$res$beta,
sigmae=object[[k]]$res$sigma,
D=object[[k]]$res$Psi,
nlmodel=obj$res$nlmodel),q,MC)
rand_j = apply(bmetro[,seq(1,MC,by = thin)],1,mean)
pb$tick()
new.values[pos,k] = obj$res$nlmodel(x = x[pos],
fixed = object[[k]]$res$beta,
random = rand_j,
covar=covar[pos,,drop=FALSE])
}
}
}
}
}
new.values = as.data.frame(new.values)
colnames(new.values) = p
return(new.values)
}
|
7d318447df8c5484ba508c679661edf0adeaa68b | 29585dff702209dd446c0ab52ceea046c58e384e | /saeSim/tests/testthat/test-select_cont.R | 23b42fdd5dbfee2cc7785cf91d2a4a7ccb56aab5 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,583 | r | test-select_cont.R | context("select_cont")
test_that(desc = "select_cont", {
dat1 <- sim_base(base_id(nDomains=5, nUnits = 2)) %>% sim_gen_e() %>% as.data.frame
expect_equal(sum(select_cont(dat1, 1L, "unit", "idD", TRUE)$e == 0), 5)
expect_equal(sum(select_cont(dat1, 1L, "unit", "idD", FALSE)$e == 0), 5)
expect_equal(sum(select_cont(dat1, 1L, "area", "idD", TRUE)$e == 0), 8)
expect_equal(sum(select_cont(dat1, 1L, "area", "idD", FALSE)$e == 0), 8)
expect_equal(sum(select_cont(dat1, 1L, "unit", NULL, TRUE)$e == 0), 9)
expect_equal(sum(select_cont(dat1, 1L, "unit", NULL, FALSE)$e == 0), 9)
expect_true(sum(select_cont(dat1, 0.5, "unit", "idD", TRUE)$e == 0) > 0)
expect_true(sum(select_cont(dat1, 0.5, "area", "idD", TRUE)$e == 0) > 0)
expect_true(sum(select_cont(dat1, 0.5, "unit", NULL, TRUE)$e == 0) > 0)
dat2 <- sim_base(base_id(nDomains = 5, nUnits = 1:5)) %>% sim_gen_e() %>% as.data.frame
expect_equal(sum(select_cont(dat2, 1L, "unit", "idD", TRUE)$e == 0), 10)
expect_equal(sum(select_cont(dat2, 1L, "unit", "idD", FALSE)$e == 0), 10)
expect_equal(sum(select_cont(dat2, 1L, "area", "idD", TRUE)$e == 0), 10)
expect_equal(sum(select_cont(dat2, 1L, "unit", NULL, TRUE)$e == 0), 14)
expect_equal(sum(select_cont(dat2, 1L, "unit", NULL, FALSE)$e == 0), 14)
numberOfContObs <- (select_cont(dat2, nCont = c(0, 0, 0, 0.2, 1), "unit", "idD", TRUE)$idC %>% sum)
(numberOfContObs >= 5 & numberOfContObs <= 9) %>%
expect_true
testthat::expect_equal(sum(select_cont(dat2, c(1, 4), "area", "idD", TRUE)$e == 0), 10)
})
|
c416698e16116a7aa1c9701d86c406524a512d04 | 24a20d7690d8053c7f9e24091aca6446d758c0f1 | /bus_setup.R | 0c5fe6b7178e6f70b015fc72242a87d55a7c0d44 | [] | no_license | brendanjodowd/bus | 8d019e2aadeb22b69093456c2ff6dadc1dd89bad | e4525bada08a91f9d9b304ef5d8ed2f22fb34716 | refs/heads/master | 2020-04-22T01:50:47.162669 | 2019-02-10T21:35:49 | 2019-02-10T21:35:49 | 170,026,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 425 | r | bus_setup.R | library(rjstat)
library(rvest)
library(dplyr)
library(later)
bus_url <- "http://rtpi.ie/Text/WebDisplay.aspx?stopRef="
interval_time <- 10
# 46a oconnell street 6059
# pearse street 346
my_bus_stop <- "346"
supervalue_stop <- "4616"
call_times <- function(stop_number){
paste0(bus_url,stop_number) %>%
read_html() %>%
html_node("table") %>%
html_table() %>% data.frame()
}
times1 <- call_times(my_bus_stop) |
f4594dc9105f190c12e7bbf0e54a5eb881b1c899 | fecaed3dea0f640b38c43ea515a5fd615d127164 | /OptimalControl/hamilSymTest/stateHiddenInput.R | f82ec418420ac1fa825bcc24eeafb487b59e23a3 | [] | no_license | Dominik12345/R | 57f272b1afb554090e4311a509c964f1bcc9b828 | 4843d80e9b3c4771df5dbefe32670a44773d7084 | refs/heads/master | 2018-09-03T08:35:56.354468 | 2018-06-04T05:32:39 | 2018-06-04T05:32:39 | 109,264,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 404 | r | stateHiddenInput.R | hiddenInputState <- function(t, x, parameters, input) {
with (as.list(parameters),{
w <- sapply(input, mapply, t)
alpha1 = parameters[1]
alpha2 = parameters[2]
alpha3 = parameters[3]
beta1 = parameters[4]
beta2 = parameters[5]
dx1= -alpha1 * x[1]+ t+ w[1]
dx2= -alpha2 * x[2]+ beta1 * x[1]+ w[2]
dx3= -alpha3 * x[3]+ beta2 * x[1]+ w[3]
list(c(dx1,dx2,dx3))
})
}
|
b80dfe9ce29d79cfd7bd023f45d60681046f47ca | 0c780d32356a4b3a2fefa9392ac545bd078de909 | /R/sr-est.R | ebb361bbb1b03a0b3df5e6fc3b3ab30e34f6343e | [
"MIT"
] | permissive | heavywatal/futurervpa | 80fdeb533017eba10478d760fd9e15c1cd5b34d5 | 8fc6cfa6e831c2b6e8245b4eb83d2a769cc508e9 | refs/heads/master | 2020-04-02T03:56:01.315555 | 2018-10-21T09:20:12 | 2018-10-21T09:20:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,644 | r | sr-est.R | ############
# RVPAの結果からMSYを計算する関数
# 主に使うのはSR.est(再生産関係をフィットし、MSYを計算)とSR.plot（フィットした結果をプロット）
############
############
# 使い方
############
if(0){
# マサバ太平洋のデータを読み込み; modelAはvpaの帰り値
modelA <- readRDS("modelA_res.Rdata")
# MSY計算
res1 <- SR.est(modelA,
what.est=c(TRUE,TRUE,TRUE), # HS,BH,RIのどれをフィットするか。
bref.year=2013:2015, # 生物パラメータを用いる期間
years=c(1970:2013), # 観測されたSR関係を用いる期間
er.log=TRUE, # 誤差。TRUEで対数正規誤差
fc.year=2013:2015, # MSY計算のさいに選択率を平均する期間
seed=1 # 乱数の種。この値を変えると乱数が変わるので結果も変わる
)
res1$summary # 推定パラメータ、管理基準値の確認
# 再生産パラメータa,bはエクセルとほぼ一致するはずだが、管理基準値は確率的シミュレーションをもとに計算しているので、エクセルとは必ずしも一致しない。±５％くらいの違いはあるみたい
# 結果のプロット(HSのみ)
res1.pred <- plot.SR(res1,what.plot=c("hs"))
# 結果のプロット(HS,BH,RIを全て)
res1.pred <- plot.SR(res1,what.plot=c("hs","bh","ri"))
allplot(res1) # 要約表・グラフの出力
}
############
# fit to S-R relationship & MSE estimation -- old version?
############
SR.est <- function(vpares,SSB.dat=NULL,R.dat=NULL,gamma1=0.0001,er.log=TRUE,
years=as.numeric(colnames(vpares$naa)), # 親子関係に推定に使う年のベクトル
bref.year=2011:2013,# B0やMSYを計算するさいの生物パラメータの範囲(2011-2013に変更、2016-06-06）
fc.year=bref.year, # 将来予測をするときに仮定する選択率をとる年の範囲
seed=1,n.imputation=1,
nyear=100,
bias.correction=TRUE, # 確率的な将来予測を行うときにbias correctionをするかどうか
eyear=0, # 将来予測の最後のeyear+1年分を平衡状態とする
# FUN=median, # 漁獲量の何を最大化するか？
FUN=mean, # 漁獲量の何を最大化するか？
sigma=-1, #加入変動のCV。-1の場合にはobservedの値を使う
N=1000, # stochastic計算するときの繰り返し回数
is.small=FALSE, # 将来予測の結果を返さない。
is.boot=1000,# 正の値であれば、SRフィットのノンパラメトリックブートストラップを行う
is.Kobe=c(FALSE,FALSE,FALSE), # Kobeの計算をするかどうか。順番に、HS, BH, RIの順
is.5perlower=FALSE, # HSの折れ点を5%の確率で下回るときの親魚資源量
PGY=NULL, # PGY管理基準値を計算するかどうか。計算しない場合はNULLを、計算する場合はc(0.8,0.9,0.95)のように割合を入れる
what.est=c(TRUE,TRUE,TRUE) # MSY等を推定するか。順番に、HS, BH, RIの順
){
#####-------- 内部で使う関数の定義
HS <- function(p,R,SSB,gamma=gamma1,er.log=er.log,MLE=FALSE,a=NULL,b=NULL){
if(!is.null(a)) p[1] <- a
if(!is.null(b)) p[2] <- b
a <- exp(p[1])
b <- max(SSB)/(1+exp(-p[2]))
if(isTRUE(MLE)) sigma <- exp(p[3])
Pred <- function(SSB) a*(SSB+sqrt(b^2+gamma^2/4)-sqrt((SSB-b)^2+gamma^2/4))
if(!isTRUE(MLE)){
if(er.log==FALSE) return(sum((R-Pred(SSB))^2))
else return(sum((log(R)-log(Pred(SSB)))^2))
}
else{
if(er.log==FALSE){
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (R-Pred(SSB))^2 )
return(-obj)
}
else{
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (log(R)-log(Pred(SSB)))^2 )
return(-obj)
}
}
}
BH <- function(p,R,SSB,er.log=er.log,MLE=FALSE){
a <- exp(p[1])
b <- exp(p[2])
if(isTRUE(MLE)) sigma <- exp(p[3])
Pred <- function(SSB) a*SSB/(1+b*SSB)
if(!isTRUE(MLE)){
if(er.log==FALSE) return(sum((R-Pred(SSB))^2))
else return(sum((log(R)-log(Pred(SSB)))^2))
}
else{
if(er.log==FALSE){
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (R-Pred(SSB))^2 )
return(-obj)
}
else{
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (log(R)-log(Pred(SSB)))^2 )
return(-obj)
}
}
}
SL <- function(p,R,SSB,er.log=er.log,MLE=FALSE){
a <- exp(p[1])
# b <- exp(p[2])
if(isTRUE(MLE)) sigma <- exp(p[2])
Pred <- function(SSB) a*SSB
if(!isTRUE(MLE)){
if(er.log==FALSE) return(sum((R-Pred(SSB))^2))
else return(sum((log(R)-log(Pred(SSB)))^2))
}
else{
if(er.log==FALSE){
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (R-Pred(SSB))^2 )
return(-obj)
}
else{
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (log(R)-log(Pred(SSB)))^2 )
return(-obj)
}
}
}
RI <- function(p,R,SSB,er.log=er.log,MLE=FALSE){
a <- exp(p[1])
b <- exp(p[2])
if(isTRUE(MLE)) sigma <- exp(p[3])
Pred <- function(SSB) a*SSB*exp(-b*SSB)
if(!isTRUE(MLE)){
if(er.log==FALSE) return(sum((R-Pred(SSB))^2))
else return(sum((log(R)-log(Pred(SSB)))^2))
}
else{
if(er.log==FALSE){
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (R-Pred(SSB))^2 )
return(-obj)
}
else{
obj <- length(R)*log(1/(sqrt(2*pi)*sigma))-1/2/sigma^2*sum( (log(R)-log(Pred(SSB)))^2 )
return(-obj)
}
}
}
# HSを推定するための関数
get.HS <- function(R,SSB,er.log,gamma1,do.profile=TRUE){
reg0 <- lm(R~SSB-1)
a0 <- reg0$coef
b0 <- 0.9
# hockey-stick
res.HS <- optim(c(log(a0),logit(b0)),HS,R=R,SSB=SSB,er.log=er.log,gamma=gamma1)
s <- 1
for (j in seq(0.95,0.1,by=-0.05)){
res.HS0 <- optim(c(log(a0),logit(j)),HS,R=R,SSB=SSB,er.log=er.log,gamma=gamma1)
if (res.HS0$value < res.HS$value) res.HS <- res.HS0
}
res.HS <- optim(res.HS$par,HS,R=R,SSB=SSB,method="BFGS",er.log=er.log,gamma=gamma1)
ofv.least.square <- res.HS$value
res.HS <- optim(c(res.HS$par,log(sqrt(res.HS$value/length(R)))),HS,R=R,SSB=SSB,method="BFGS",er.log=er.log,gamma=gamma1,MLE=TRUE)
a.HS <- exp(res.HS$par[1])
names(a.HS) <- NULL
b.HS <- max(SSB)/(1+exp(-res.HS$par[2])) # 曲がるところのx軸(ssb_hs)
# r0の計算
r0.HS <- pred.HS(b.HS,a=a.HS,b=b.HS,gamma=gamma1)
# もし、b.HSが最大・最小SSBよりも大きい・小さかったら
if(b.HS>max(SSB)|b.HS<min(SSB)){
b.HS <- ifelse(b.HS>max(SSB),max(SSB),b.HS)
b.HS <- ifelse(b.HS<min(SSB),min(SSB),b.HS)
tmpfunc <- function(x,r0,...) (pred.HS(a=x,...)-r0)^2
tmp <- optimize(tmpfunc,c(0,a.HS*10),b=b.HS,gamma=gamma1,SSB=b.HS,r0=r0.HS)
a.HS <- tmp$minimum
}
# 尤度surfaceの計算
if(isTRUE(do.profile)){
a.grid <- c(seq(from=0.1,to=0.9,by=0.1),seq(from=0.91,to=1.09,by=0.02),seq(from=1.1,to=1.5,by=0.1)) * a.HS
b.grid <- c(seq(from=0.1,to=0.9,by=0.1),seq(from=0.91,to=1.09,by=0.02),seq(from=1.1,to=1.5,by=0.1)) * b.HS
b.grid <- b.grid[b.grid<max(SSB)]
obj.data <- expand.grid(a=a.grid,b=b.grid)
obj.data$obj <- NA
obj.data$log.a <- log(obj.data$a)
obj.data$conv.b <- -log(max(SSB)/obj.data$b-1)
for(i in 1:nrow(obj.data))
{
obj.data$obj[i] <- HS(c(obj.data$log.a[i],obj.data$conv.b[i]),R=R,SSB=SSB,
MLE=FALSE,er.log=er.log,gamma=gamma1)
}
}
else{
obj.data <- NA
}
return(list(a=a.HS,b=b.HS,r0=r0.HS,res=res.HS,obj.data=obj.data,ofv.least.square=ofv.least.square))
}
# Beverton-Holt
get.BH <- function(R,SSB,er.log){
reg0 <- lm(R~SSB-1)
a0 <- reg0$coef
b0 <- max(SSB)
res.BH <- optim(c(log(a0),log(1/b0)),BH,R=R,SSB=SSB,method="BFGS",er.log=er.log)
for (j in seq(0.9,0.1,by=-0.1)){
res.BH0 <- optim(c(log(a0),log(j/b0)),BH,R=R,SSB=SSB,er.log=er.log)
if (res.BH0$value < res.BH$value) res.BH <- res.BH0
}
# 最尤法で計算しなおしたもので上書き
res.BH <- optim(c(res.BH$par,log(sqrt(res.BH$value/length(R)))),BH,R=R,SSB=SSB,method="BFGS",er.log=er.log,MLE=TRUE)
a.BH <- exp(res.BH$par[1])
b.BH <- exp(res.BH$par[2])
return(list(a=a.BH,b=b.BH,res=res.BH))
}
get.RI <- function(R,SSB,er.log){
reg0 <- lm(R~SSB-1)
a0 <- reg0$coef
b0 <- max(SSB)
# Ricker
res.RI <- optim(c(log(a0),log(1/b0)),RI,R=R,SSB=SSB,method="BFGS",er.log=er.log)
for (j in seq(0.9,0.1,by=-0.1)){
res.RI0 <- optim(c(log(a0),log(j/b0)),RI,R=R,SSB=SSB,er.log=er.log)
if (res.RI0$value < res.RI$value) res.RI <- res.RI0
}
# 最尤法
res.RI <- optim(c(res.RI$par,log(sqrt(res.RI$value/length(R)))),
RI,R=R,SSB=SSB,method="BFGS",er.log=er.log,MLE=TRUE)
a.RI <- exp(res.RI$par[1])
b.RI <- exp(res.RI$par[2])
return(list(a=a.RI,b=b.RI,res=res.RI))
}
##### 関数定義終わり
# R.datとSSB.datだけが与えられた場合、それを使ってシンプルにフィットする
if(!is.null(R.dat) & !is.null(SSB.dat)){
dat <- data.frame(R=R.dat,SSB=SSB.dat,years=1:length(R.dat))
}
else{
vpares$Fc.at.age <- rowMeans(vpares$faa[as.character(fc.year)])
# データの整形
n <- ncol(vpares$naa)
L <- as.numeric(rownames(vpares$naa)[1])
dat <- list()
dat$R <- as.numeric(vpares$naa[1,])
dat$SSB <- as.numeric(colSums(vpares$ssb))
dat$year <- as.numeric(colnames(vpares$ssb))
# 加入年齢分だけずらす
dat$R <- dat$R[(L+1):n]
dat$SSB <- dat$SSB[1:(n-L)]
dat$year <- dat$year[(L+1):n]
# データの抽出
dat <- as.data.frame(dat)
dat <- dat[dat$year%in%years,]
}
R <- dat$R
SSB <- dat$SSB
# HS推定
# if(what.est[1]==TRUE){
tmp <- get.HS(R,SSB,er.log,gamma1)
a.HS <- tmp$a; b.HS <- tmp$b ; r0.HS <- tmp$r0
sd.HS <- exp(tmp$res$par[3])
surface.HS <- tmp$obj.data
ofv.least.square <- tmp$ofv.least.square
res.HS <- tmp$res
boot.HS <- matrix(NA,is.boot,3)
jack.HS <- matrix(NA,length(R),3)
colnames(boot.HS) <- colnames(jack.HS) <- c("a","b","r0")
if(what.est[1]==TRUE&&is.boot>0){ # ブートストラップ
for(i in 1:is.boot){
rand <- sample(length(R),size=length(R)*n.imputation,replace=TRUE)
tmp <- get.HS(R[rand],SSB[rand],er.log,gamma1,do.profile=FALSE)
boot.HS[i,] <- unlist(tmp[c("a","b","r0")])
}
for(i in 1:length(R)){
tmp <- get.HS(R[-i],SSB[-i],er.log,gamma1,do.profile=FALSE)
jack.HS[i,] <- unlist(tmp[c("a","b","r0")])
}
# rownames(jack.HS) <- years
}
# 予測値
dat$pred.HS <- pred.HS(dat$SSB,a=a.HS,b=b.HS,gamma=gamma1)
dat$log.resid.HS <- log(dat$R) - log(dat$pred.HS)
# }
if(0){
# 直線回帰
reg0 <- lm(R~SSB-1)
a0 <- reg0$coef
res.SL <- optimize(SL,c(0,log(a0)*10),R=R,SSB=SSB,er.log=er.log)
res.SL <- optim(c(res.SL$minimum,log(sqrt(res.SL$objective/length(R)))),
SL,R=R,SSB=SSB,er.log=er.log,MLE=TRUE)
# res.SL$value <- res.SL$objective
a.SL <- exp(res.SL$par[1])
boot.SL <- rep(NA,is.boot)
jack.SL <- rep(NA,length(R))
if(is.boot>0){
for(i in 1:is.boot){
rand <- sample(length(R),replace=TRUE)
tmp <- optimize(SL,c(0,log(a0)*10),R=R[rand],SSB=SSB[rand],er.log=er.log)
boot.SL[i] <- exp(tmp$minimum[1])
}
for(i in 1:length(R)){
tmp <- optimize(SL,c(0,log(a0)*10),R=R[-i],SSB=SSB[-i],er.log=er.log)
jack.SL[i] <- exp(tmp$minimum[1])
}
rownames(jack.SL) <- years
}
}
if(what.est[2]==TRUE){
# BH推定
tmp <- get.BH(R,SSB,er.log)
a.BH <- tmp$a; b.BH <- tmp$b; res.BH <- tmp$res
sd.BH <- exp(tmp$res$par[3])
boot.BH <- matrix(NA,is.boot,2)
jack.BH <- matrix(NA,length(R),2)
colnames(boot.BH) <- colnames(jack.BH) <- c("a","b")
if(is.boot>0){ # ブートストラップ
for(i in 1:is.boot){
rand <- sample(length(R),replace=TRUE)
tmp <- get.BH(R[rand],SSB[rand],er.log)
boot.BH[i,] <- unlist(tmp[c("a","b")])
}
# ジャックナイフも
for(i in 1:length(R)){
tmp <- get.BH(R[-i],SSB[-i],er.log)
jack.BH[i,] <- unlist(tmp[c("a","b")])
}
rownames(jack.BH) <- years
}
##
dat$pred.BH <- pred.BH(dat$SSB,a=a.BH,b=b.BH)
dat$log.resid.BH <- log(dat$R) - log(dat$pred.BH)
}
if(what.est[3]==TRUE){
# RI推定
tmp <- get.RI(R,SSB,er.log)
a.RI <- tmp$a ; b.RI <- tmp$b ; res.RI <- tmp$res
sd.RI <- exp(tmp$res$par[3])
boot.RI <- matrix(NA,is.boot,2)
jack.RI <- matrix(NA,length(R),2)
colnames(boot.RI) <- colnames(jack.RI) <- c("a","b")
if(is.boot>0){ # ブートストラップ
for(i in 1:is.boot){
rand <- sample(length(R),replace=TRUE)
tmp <- get.RI(R[rand],SSB[rand],er.log)
boot.RI[i,] <- unlist(tmp[c("a","b")])
}
# ジャックナイフも
for(i in 1:length(R)){
tmp <- get.RI(R[-i],SSB[-i],er.log)
jack.RI[i,] <- unlist(tmp[c("a","b")])
}
rownames(jack.RI) <- years
}
##
dat$pred.RI <- pred.RI(dat$SSB,a=a.RI,b=b.RI)
dat$log.resid.RI <- log(dat$R) - log(dat$pred.RI)
}
# 単に回帰だけする場合
if(!is.null(R.dat) & !is.null(SSB.dat)){
res <- list()
paste2 <- function(x,...) paste(x,...,sep="")
for(j in which(what.est)){
SR <- c("HS","BH","RI")
xx <- c(get(paste2("a.",SR[j])),
get(paste2("b.",SR[j])),
get(paste2("sd.",SR[j])),
get(paste2("res.",SR[j]))$value)
names(xx) <- c("a","b","sd","value")
res[[j]] <- list(parameter=xx,
boot=get(paste2("boot.",SR[j])),
jack=get(paste2("jack.",SR[j])))
}
names(res) <- SR[what.est]
return(res)
}
#-------------------- B0 & MSY for HS --------------------
# function to minimize
# シミュレーション回数ぶんの漁獲量のFUN（mean, geomean, median）を最大化するFを選ぶ
tmpfunc <- function(x,f.arg,FUN=FUN,eyear=eyear){
f.arg$multi <- x
fout <- do.call(future.vpa2,f.arg)
return(-FUN(fout$vwcaa[(nrow(fout$vwcaa)-eyear):nrow(fout$vwcaa),-1]))
}
tmpfunc2 <- function(x,f.arg,FUN=FUN,eyear=eyear,hsp=0){
f.arg$multi <- x
fout <- do.call(future.vpa2,f.arg)
tmp <- as.numeric(fout$vssb[(nrow(fout$vssb)-eyear):nrow(fout$vssb),-1])
lhs <- sum(tmp<hsp)/length(tmp)
return( (lhs-0.05)^2 + as.numeric(lhs==0) + as.numeric(lhs==1) )
}
get.Fhist <- function(farg,vpares,eyear,trace,hsp=0){
Fhist <- NULL
original.sel <- farg$res0$Fc.at.age # original F
for(j in 1:ncol(vpares$faa)){
farg$res0$Fc.at.age <- vpares$faa[,j] # change the selectivity
farg$multi <- 1
tmp <- do.call(future.vpa2,farg)
tmp2 <- get.stat(tmp,eyear=eyear,hsp=hsp)
# browser()
xx <- which.min(abs(trace$ssb.median-tmp2$ssb.median))+c(-1,1)
range.tmp <- trace$fmulti[xx]
if(is.na(range.tmp[2])) range.tmp[2] <- max(trace$fmulti)*2
if(xx[1]==0) range.tmp <- c(0,range.tmp[1])
tmpfunc <- function(x,farg,ssb.target,eyear){
farg$multi <- x
return((get.stat(do.call(future.vpa2,farg),eyear=eyear,hsp=hsp)$ssb.mean-ssb.target)^2)
}
farg$res0$Fc.at.age <- original.sel # current Fをもとにもどす
# originalな選択率のもとで、それを何倍にすればi年目のFで漁獲した時の親魚資源量と同じになるか
ores <- optimize(tmpfunc,range.tmp,farg=farg,ssb.target=tmp2$ssb.mean,eyear=eyear)
# farg$multi <- ores$minimum
# tmp3 <- do.call(future.vpa2,farg)
tmp2$fmulti <- ores$minimum
Fhist <- rbind(Fhist,tmp2)
}
return(as.data.frame(Fhist))
}
trace.func <- function(farg,eyear,hsp=0,
fmulti=c(seq(from=0,to=0.9,by=0.1),1,seq(from=1.1,to=2,by=0.1),3:5,7,20,100)){
trace.res <- NULL
farg$outtype <- "FULL"
for(i in 1:length(fmulti)){
farg$multi <- fmulti[i]
tmp <- do.call(future.vpa2,farg)
# trace.res <- rbind(trace.res,get.stat(tmp,eyear=eyear,hsp=hsp))
tmp2 <- cbind(get.stat(tmp,eyear=eyear,hsp=hsp),
get.stat2(tmp,eyear=eyear,hsp=hsp))
trace.res <- rbind(trace.res,tmp2)
if(tmp2$"ssb.mean"<trace.res$"ssb.mean"[1]/1000){
fmulti <- fmulti[1:i]
break()
}
}
trace.res <- as.data.frame(trace.res)
trace.res$fmulti <- fmulti
return(trace.res)
}
b0.HS <- b0.BH <- b0.RI <- numeric() # B0
fout.HS <- fout.BH <- fout.RI <- list()
fout0.HS <- fout0.BH <- fout0.RI <- list()
trace.HS <- trace.BH <- trace.RI <- list()
Fhist.HS <- Fhist.BH <- Fhist.RI <- list()
fout.HS.5per <- list()
for(kk in 1:length(sigma)){
ref.year <- as.numeric(rev(colnames(vpares$naa))[1])
if(sigma[kk]==-1){
if(isTRUE(what.est[1])){
sigma.tmp <- exp(res.HS$par[3])
}
else{
if(isTRUE(what.est[2])) sigma.tmp <- exp(res.BH$par[3])
if(isTRUE(what.est[3])) sigma.tmp <- exp(res.RI$par[3])
}
}
else{
sigma.tmp <- sigma[kk]
}
#--------- Hockey stick
fout0.HS[[kk]] <- future.vpa2(vpares,multi=0,nyear=nyear,start.year=ref.year,
N=ifelse(sigma[kk]==0,1,N),
ABC.year=ref.year+1,waa.year=bref.year,maa.year=bref.year,
M.year=bref.year,is.plot=FALSE,
recfunc=HS.rec,seed=seed,outtype="simple",
rec.arg=list(a=a.HS,b=b.HS,gamma=gamma1,sd=sigma.tmp,bias.correction=bias.correction))
# b0.HS[kk] <- fout0$vssb[nrow(fout0$vssb),1] #static b0
farg.HS <- fout0.HS[[kk]]$input
which.min2 <- function(x){
max(which(min(x)==x))
}
if(isTRUE(what.est[1])){
trace.HS[[kk]] <- trace.func(farg.HS,eyear,hsp=b.HS)
xx <- which.max(trace.HS[[kk]]$catch.median)+c(-1,1)
range.tmp <- trace.HS[[kk]]$fmulti[xx]
if(xx[1]==0) range.tmp <- c(0,range.tmp)
# if(is.na(xx[2])) range.tmp[2] <- max(trace.HS[[kk]]$fmulti)*10
if(is.na(range.tmp[2])) range.tmp[2] <- max(trace.HS[[kk]]$fmulti)*10
tmp <- optimize(tmpfunc,range.tmp,f.arg=farg.HS,eyear=eyear,FUN=FUN)
farg.HS$multi <- tmp$minimum # Fc.at.a * multiがFmsy
fout.HS[[kk]] <- do.call(future.vpa2,farg.HS)
Fmsy.HS <- tmp$minimum * farg.HS$res0$Fc.at.age
## ここでtraceを追加
trace.HS[[kk]] <- rbind(trace.HS[[kk]],trace.func(farg.HS,eyear,hsp=b.HS,
fmulti=tmp$minimum+c(-0.025,-0.05,-0.075,0,0.025,0.05,0.075)))
trace.HS[[kk]] <- trace.HS[[kk]][order(trace.HS[[kk]]$fmulti),]
###
if(is.Kobe[1]) Fhist.HS[[kk]] <- get.Fhist(farg.HS,vpares,eyear=eyear,trace=trace.HS[[kk]])
if(is.5perlower){
xx <- which.min2((trace.HS[[kk]]$lower-0.05)^2)+c(-1,1)
range.tmp <- trace.HS[[kk]]$fmulti[xx]
if(xx[1]==0) range.tmp <- c(0,range.tmp)
if(is.na(xx[2])) range.tmp[2] <- max(trace.HS[[kk]]$fmulti)*10
tmp <- optimize(tmpfunc2,range.tmp,f.arg=farg.HS,eyear=eyear,FUN=FUN,hsp=b.HS)
farg.HS$multi <- tmp$minimum
fout.HS.5per[[kk]] <- do.call(future.vpa2,farg.HS)
}
}
#---------------------- calculation of MSY for BH
if(isTRUE(what.est[2])){
if(sigma[kk]==-1){
sigma.tmp <- exp(res.BH$par[3])
}
else{
sigma.tmp <- sigma[kk]
}
farg.BH <- farg.HS
farg.BH$recfunc <- BH.rec
farg.BH$rec.arg <- list(a=a.BH,b=b.BH,sd=sigma.tmp,bias.correction=bias.correction)
farg.BH$multi <- 0
fout0.BH[[kk]] <- do.call(future.vpa2,farg.BH)
# b0.BH[kk] <- fout0.BH$vssb[nrow(fout0$vssb),1] #static b0
trace.BH[[kk]] <- trace.func(farg.BH,eyear)
# tmp <- optimize(tmpfunc,c(0,10),f.arg=farg.BH,eyear=eyear,FUN=FUN)
xx <- which.max(trace.BH[[kk]]$catch.median)+c(-1,1)
range.tmp <- trace.BH[[kk]]$fmulti[xx]
if(xx[1]==0) range.tmp <- c(0,range.tmp)
# if(is.na(xx[2])) range.tmp[2] <- max(trace.BH[[kk]]$fmulti)*10
if(is.na(range.tmp[2])) range.tmp[2] <- max(trace.BH[[kk]]$fmulti)*10
tmp <- optimize(tmpfunc,range.tmp,f.arg=farg.BH,eyear=eyear,FUN=FUN)
farg.BH$multi <- tmp$minimum
fout.BH[[kk]] <- do.call(future.vpa2,farg.BH)
Fmsy.BH <- tmp$minimum * farg.BH$res0$Fc.at.age
if(is.Kobe[2]) Fhist.BH[[kk]] <- get.Fhist(farg.BH,vpares,eyear=eyear,trace.BH[[kk]])
## ここでtraceを追加
trace.BH[[kk]] <- rbind(trace.BH[[kk]],
trace.func(farg.BH,eyear,hsp=b.BH,fmulti=tmp$minimum+c(-0.025,-0.05,-0.075,0,0.025,0.05,0.075)))
trace.BH[[kk]] <- trace.BH[[kk]][order(trace.BH[[kk]]$fmulti),]
###
}
#------------------- calculation of MSY for RI
if(isTRUE(what.est[3])){
if(sigma[kk]==-1){
sigma.tmp <- exp(res.RI$par[3])
}
else{
sigma.tmp <- sigma[kk]
}
farg.RI <- farg.HS
farg.RI$recfunc <- RI.rec
farg.RI$rec.arg <- list(a=a.RI,b=b.RI,sd=sigma.tmp,bias.correction=bias.correction)
farg.RI$multi <- 0
fout0.RI[[kk]] <- do.call(future.vpa2,farg.RI)
# b0.RI[kk] <- fout0$vssb[nrow(fout0$vssb),1] #static b0
trace.RI[[kk]] <- trace.func(farg.RI,eyear)
xx <- which.max(trace.RI[[kk]]$catch.median)+c(-1,1)
range.tmp <- trace.RI[[kk]]$fmulti[xx]
if(xx[1]==0) range.tmp <- c(0,range.tmp)
# if(is.na(xx[2])) range.tmp[2] <- max(trace.RI[[kk]]$fmulti)*10
if(is.na(range.tmp[2])) range.tmp[2] <- max(trace.RI[[kk]]$fmulti)*10
tmp <- optimize(tmpfunc,range.tmp,f.arg=farg.RI,eyear=eyear,FUN=FUN)
farg.RI$multi <- tmp$minimum
fout.RI[[kk]] <- do.call(future.vpa2,farg.RI)
Fmsy.RI <- tmp$minimum * farg.RI$res0$Fc.at.age
if(is.Kobe[3]) Fhist.RI[[kk]] <- get.Fhist(farg.RI,vpares,eyear=eyear,trace.RI[[kk]])
## ここでtraceを追加
trace.RI[[kk]] <- rbind(trace.RI[[kk]],
trace.func(farg.RI,eyear,hsp=b.RI,
fmulti=tmp$minimum+c(-0.025,-0.05,-0.075,0,0.025,0.05,0.075)))
trace.RI[[kk]] <- trace.RI[[kk]][order(trace.RI[[kk]]$fmulti),]
###
}
}
#--------------------------------------
if(isTRUE(is.5perlower)){
tmp <- as.data.frame(t(sapply(fout.HS.5per,get.stat,eyear=eyear,hsp=b.HS)))
tmp$f <- sapply(fout.HS.5per,function(x)x$multi)
}
else{
tmp <- NA
}
# 関数を返すとsaveしたときに異常にファイルサイズが大きくなる。原因は不明。
# とりあえず、関数を返すのをやめる
output <- list(dat=dat,sigma=sigma,vpares=vpares)
if(what.est[1]==TRUE)
output$hs <- list(a=a.HS,b=b.HS,sd=sd.HS,gamma=gamma1,ofv=res.HS$value,ofv.least.square=ofv.least.square,
res=res.HS,r0=r0.HS,Fhist=Fhist.HS,
trace=trace.HS,boot=as.data.frame(boot.HS),
jack=as.data.frame(jack.HS),farg=farg.HS,
f.msy=sapply(fout.HS,function(x)x$multi),
Fmsy=Fmsy.HS,surface=surface.HS,
fout=fout.HS,
# 最大化したときのFを使って将来予測したときのサマリーをMSYのreference pointとする
MSY=as.data.frame(t(sapply(fout.HS,get.stat,eyear=eyear,hsp=b.HS))),
B0=as.data.frame(t(sapply(fout0.HS,get.stat,eyear=eyear,hsp=b.HS))),
per5=tmp)
# sl=list(a=a.SL,
# res=res.SL,jack=jack.SL,boot=boot.SL),
if(what.est[2]==TRUE)
output$bh <- list(a=a.BH,b=b.BH,sd=sd.BH,
res=res.BH,r0=NA,#R0を入れないといけない
Fhist=Fhist.BH,ofv=res.BH$value,
trace=trace.BH,b0=b0.BH,boot=as.data.frame(boot.BH),jack=as.data.frame(jack.BH),
f.msy=sapply(fout.BH,function(x)x$multi),
fout=fout.BH,
Fmsy=Fmsy.BH,farg=farg.BH,
MSY=as.data.frame(t(sapply(fout.BH,get.stat,eyear=eyear))),
B0=as.data.frame(t(sapply(fout0.BH,get.stat,eyear=eyear))))
if(what.est[3]==TRUE)
output$ri <- list(a=a.RI,b=b.RI,sd=sd.RI,ofv=res.RI$value,
res=res.RI,r0=NA,#R0を入れないといけない,
Fhist=Fhist.RI,farg=farg.RI,
trace=trace.RI,b0=b0.RI,boot=as.data.frame(boot.RI),
jack=as.data.frame(jack.RI),
fout=fout.RI,
f.msy=sapply(fout.RI,function(x)x$multi),
Fmsy=Fmsy.RI,
MSY=as.data.frame(t(sapply(fout.RI,get.stat,eyear=eyear))),
B0=as.data.frame(t(sapply(fout0.RI,get.stat,eyear=eyear))))
index <- c("a","b","R0","sd","MSY","B0","f.msy","Fmsy")
tmp <- NULL
if(what.est[1]==TRUE) tmp <- rbind(tmp,unlist(output$hs[index]))
if(what.est[2]==TRUE) tmp <- rbind(tmp,unlist(output$bh[index]))
if(what.est[3]==TRUE) tmp <- rbind(tmp,unlist(output$ri[index]))
tmp <- as.data.frame(tmp)
rownames(tmp) <- c("hs","bh","ri")[what.est]
# tmp$nLL <- output$ofv
output$summary0 <- tmp
colnames(output$summary0)[1] <- "a"
output$summary <- output$summary0[c("a","b","sd","MSY.ssb.mean.ssb.mean",
"MSY.biom.mean.biom.mean",
"MSY.U.mean.U.mean",
"MSY.catch.mean.catch.mean",
"B0.ssb.mean.ssb.mean",
"B0.biom.mean.biom.mean","f.msy")]
colnames(output$summary) <- c("a","b","sd","SSB_MSY","B_MSY","U_MSY","MSY","B0(SSB)","B0(Biomass)","FMSY/Fcurrent")
output$summary <- cbind(output$summary,output$summary0[,substr(colnames(output$summary0),1,4)=="Fmsy"])
class(output) <- "SR"
##--- PGY管理基準値を計算する
if(!is.null(PGY)){
k.tmp <- which(what.est)
for(k in 1:length(k.tmp)){
fout.list2 <- list()
s <- 1
for(j in 1:length(PGY)){
outtmp <- output[[which(names(output)==c("hs","bh","ri")[k.tmp[k]])[1]]]
# outtmp$trace
# frange.list <- list(c(output[[which(names(output)==c("hs","bh","ri")[1])[1]]]$f.msy,2),
# c(0.01,output[[which(names(output)==c("hs","bh","ri")[1])[1]]]$f.msy))
ttmp <- outtmp$trace[[1]]$catch.mean-PGY[j]*output$summary$MSY[k]
ttmp <- which(diff(sign(ttmp))!=0)
frange.list <- list(outtmp$trace[[1]]$fmulti[ttmp[1]+0:1],
outtmp$trace[[1]]$fmulti[ttmp[2]+0:1])
# browser()
for(i in 1:2){
if(k.tmp[k]==1) farg.tmp <- farg.HS
if(k.tmp[k]==2) farg.tmp <- farg.BH
if(k.tmp[k]==3) farg.tmp <- farg.RI
farg.tmp$outtype <- NULL
farg.tmp$Frec <- list(stochastic=TRUE,
future.year=rev(rownames(outtmp$fout[[1]]$vssb))[1],
Blimit=PGY[j]*output$summary$MSY[k],
scenario="catch.mean",Frange=frange.list[[i]])
fout.list2[[s]] <- do.call(future.vpa,farg.tmp)
s <- s+1
}}
PGY.biom <- as.data.frame(t(sapply(fout.list2,get.stat,eyear=eyear)))
rownames(PGY.biom) <- paste("PGY",rep(PGY,each=2),rep(c("upper","lower"),length(PGY)),c("hs","bh","ri")[k.tmp[k]],sep="_")
PGY.biom$target.catch <- rep(PGY*output$summary$MSY[k],each=2)
if(k.tmp[k]==1) output$PGY.biom.hs <- PGY.biom
if(k.tmp[k]==2) output$PGY.biom.bh <- PGY.biom
if(k.tmp[k]==3) output$PGY.biom.ri <- PGY.biom
}
}
##---
if(isTRUE(is.small)){
output$hs$fout <- NULL
output$bh$fout <- NULL
output$ri$fout <- NULL
}
return(output)
}
pred.RI <- function(SSB,a,b) a*SSB*exp(-b*SSB)
pred.BH <- function(SSB,a,b) a*SSB/(1+b*SSB)
pred.HS <- function(SSB,a,b,gamma) a*(SSB+sqrt(b^2+gamma^2/4)-sqrt((SSB-b)^2+gamma^2/4))
pred.SL <- function(SSB,a) a*SSB
|
20eda438ad86c9c28581ecb71e81937c9db86292 | b186a76a1f5535019bef4d6102832445a58a408f | /R/0_data_acquisition.R | 206274cdae0ba7fee915c03a864819b50709da71 | [] | no_license | brunomc-eco/covidmunBR | 117fc9010b0eb70c9e1fccf8f3f41ce2e3dcad94 | 3a7b86204cfc82f10c57f949f80e0b1961c4ed49 | refs/heads/master | 2022-11-10T17:58:10.549667 | 2020-06-26T19:38:28 | 2020-06-26T19:38:28 | 267,056,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 827 | r | 0_data_acquisition.R | # COVID-19 Dashboard for IBGE
# Bruno Carvalho, brunomc.eco@gmail.com
# Downloading raw data from public sources
library(readr)
## ocupacao de leitos, Ministério da Saúde
## https://gestaoleitos.saude.gov.br/app/kibana#/dashboard/18685ad0-81b4-11ea-9f02-bfa3f55b2dad
# no final do dashboard, exportar arquivo "Formatted"
# salvar na pasta "./data/leitos_MS"
# renomear arquivo para "leitos_MS.csv"
## IDB Inter-American Development Bank
## Coronavirus Traffic Congestion Impact in Latin America with Waze Data
## https://www.iadb.org/en/topics-effectiveness-improving-lives/coronavirus-impact-dashboard
daily <- read_csv('http://tiny.cc/idb-traffic-daily')
metadata <- read_csv('http://tiny.cc/idb-traffic-metadata')
write.csv(daily, file = "./data/idb/daily.csv")
write.csv(metadata, file = "./data/idb/metadata.csv")
|
583f21233e35dcf906c3775a27c4d3da6e7becd9 | 7e503ed19de6efbd965a8d27a74b3b6f2728b3de | /R/simulate_baselers.R | 7cbdd391304408aaecafe03ab99e222759885e40 | [] | no_license | anhnguyendepocen/baselers | 4bf80ae72cbb763c911ce3291fb638297acaf60f | 702a2ab2cc7b91c79c269b324122ce3037b82c67 | refs/heads/master | 2020-04-14T23:59:40.080592 | 2018-06-29T10:40:37 | 2018-06-29T10:40:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,989 | r | simulate_baselers.R | #' Simulate baselers dataset
#'
#' This function can be used to simulate nsim participants. It draws samples from
#' a mulitvariate normal distribution with a prespecified correlation matrix (corMat_f
#' for women and corMat_m for men). Most (but not all) of the means (but not the
#' correlations) are based on actual data of the swiss population. Details can be
#' found in the data helpfile you can call with ?baselers if the package is loaded.
#'
#' @param nsim integer. The number of participants to simulate (same number of men
#' and women). If an uneven number is specified, the returned dataset will contain
#' one additional row.
#' @param corMat_f double matrix. A symmetric correlation matrix. If not specified
#' the default matrix is used.
#' @param corMat_m double matrix. A symmetric correlation matrix. If not specified
#' the default matrix is used.
#' @param seed integer. Is passed to set.seed, specify the same number to replicate
#' results. If not provided, results will vary randomly.
#'
#' @export
#'
#' @return A tibble with nsim rows, containing continous and categorical simulated
#' data of inhabitants of Basel.
#' @import dplyr
#' @importFrom stats runif
simulate_baselers <- function(nsim = 1000,
corMat_f = NULL,
corMat_m = NULL,
seed = NULL){
# some sources:
# age: https://www.bfs.admin.ch/bfs/de/home/statistiken/kataloge-datenbanken/tabellen.assetdetail.3202980.html
# weight, height: https://www.laenderdaten.info/durchschnittliche-koerpergroessen.php
# tattoo: https://www.migrosmagazin.ch/tattoos-ohne-grenzen
# income: https://www.srf.ch/news/schweiz/mit-7100-franken-pro-monat-ueberleben-oder-etwa-doch-weniger
# consultations: https://www.krankenkasse-vergleich.ch/news/das-schweizer-gesundheitssystem-in-der-oecd-statistik, https://www.bfs.admin.ch/bfs/de/home/statistiken/kataloge-datenbanken/tabellen.assetdetail.250255.html
if (!is.null(seed) && is.numeric(seed)){
set.seed(seed = seed)
}
# continuous variables:
# age, income (per month), weight (kg), height (cm), children,
# happiness (0 to 10), fitness (0 to 10), food, alcohol, tattoos,
# rhine (no per month), datause (no of times phone is checked per day),
# consultations, hiking
# means and sds for females and males
mu_f <- c(43.14, 7112, 64, 164, 1.54, 6, 5, 438, 25, 3, 4, 88, 4.5, 60)
stddev_f <- c(20, 1200, 8, 10, 1.25, 2, 2, 80, 10, 2.8, 2.5, 20, 2, 20)
var_f <- stddev_f ^ 2
mu_m <- c(41.01, 7112, 84.9, 178, 1.54, 6, 5, 438, 40, 3.8, 4, 88, 3, 60)
stddev_m <- c(20, 1200, 12, 11, 1.25, 2, 2, 80, 15, 2.8, 2.5, 20, 1.5, 25)
var_m <- stddev_m ^ 2
if (is.null(corMat_f)){
# correlation matrix females
corMat_f <- matrix(c( var_f[1], .3, -.1, -.15, .2, .1, -.25, -.1, 0, -.45, -.15, -.23, .5, 0,
.3, var_f[2], 0, 0, -.1, .15, 0, .5, .2, -.08, 0, 0, -.05, 0,
-.1, 0, var_f[3], .6, 0, 0, -.3, 0, .15, 0, 0, 0, .15, -.15,
-.15, 0, .6, var_f[4], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
.2, -.1, 0, 0, var_f[5], 0, 0, .4, 0, 0, -.1, 0, 0, 0,
.1, .15, 0, 0, 0, var_f[6], .15, .1, 0, 0, .2, -.3, -.15, .25,
-.25, 0, -.3, 0, 0, .15, var_f[7], 0, -.05, .15, .2, 0, -.1, .3,
-.1, .5, 0, 0, .4, .1, 0, var_f[8], 0, 0, 0, 0, 0, 0,
0, .2, .15, 0, 0, 0, -.05, 0, var_f[9], 0, 0, 0, .15, 0,
-.45, -.08, 0, 0, 0, 0, .15, 0, 0, var_f[10], 0, 0, 0, 0,
-.15, 0, 0, 0, -.1, .2, .2, 0, 0, 0, var_f[11], 0, 0, .1,
-.23, 0, 0, 0, 0, -.3, 0, 0, 0, 0, 0, var_f[12], 0, 0,
.5, -.05, .15, 0, 0, -.15, -.1, 0, .15, 0, 0, 0, var_f[13], -.15,
0, 0, -.15, 0, 0, .25, .3, 0, 0, 0, .1, 0, -.15, var_f[14]),
ncol = 14)
}
if (is.null(corMat_m)){
# correlation matrix for males
corMat_m <- matrix(c( var_m[1], .3, -.1, -.15, .2, .1, -.25, -.1, 0, -.25, -.15, -.23, .5, 0,
.3, var_m[2], 0, 0, -.1, .15, 0, .5, .2, -.08, 0, 0, -.05, 0,
-.1, 0, var_m[3], .6, 0, 0, -.3, 0, .15, 0, 0, 0, .15, -.15,
-.15, 0, .6, var_m[4], 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
.2, -.1, 0, 0, var_m[5], 0, 0, .4, 0, 0, -.1, 0, 0, 0,
.1, .15, 0, 0, 0, var_m[6], .15, .1, 0, 0, .2, -.3, -.15, .25,
-.25, 0, -.3, 0, 0, .15, var_m[7], 0, -.05, .15, .2, 0, -.1, .3,
-.1, .5, 0, 0, .4, .1, 0, var_m[8], 0, 0, 0, 0, 0, 0,
0, .2, .15, 0, 0, 0, -.05, 0, var_m[9], 0, 0, 0, .15, 0,
-.25, -.08, 0, 0, 0, 0, .15, 0, 0, var_m[10], 0, 0, 0, 0,
-.15, 0, 0, 0, -.1, .2, .2, 0, 0, 0, var_m[11], 0, 0, .1,
-.23, 0, 0, 0, 0, -.3, 0, 0, 0, 0, 0, var_m[12], 0, 0,
.5, -.05, .15, 0, 0, -.15, -.1, 0, .15, 0, 0, 0, var_m[13], -.15,
0, 0, -.15, 0, 0, .25, .3, 0, 0, 0, .1, 0, -.15, var_m[14]),
ncol = 14)
}
# if matrices are not positive definite, force them to be
if (!corpcor::is.positive.definite(corMat_f)){
corMat_f <- corpcor::make.positive.definite(corMat_f, tol=1e-3)
}
if (!corpcor::is.positive.definite(corMat_m)){
corMat_m <- corpcor::make.positive.definite(corMat_m, tol=1e-3)
}
# draw samples from multinormal distribution
mat_f <- MASS::mvrnorm(n = round(nsim / 2), mu = mu_f, Sigma = corMat_f,
empirical = TRUE)
mat_m <- MASS::mvrnorm(n = round(nsim / 2), mu = mu_m, Sigma = corMat_m,
empirical = TRUE)
tib_f <- tibble::as_tibble(mat_f)
names(tib_f) <- c("age", "income", "weight", "height", "children",
"happiness", "fitness", "food", "alcohol",
"tattoos", "rhine", "datause", "consultations",
"hiking")
tib_m <- tibble::as_tibble(mat_m)
names(tib_m) <- c("age", "income", "weight", "height", "children",
"happiness", "fitness", "food", "alcohol",
"tattoos", "rhine", "datause", "consultations",
"hiking")
tib_f$sex <- "female"
tib_m$sex <- "male"
tib_f$education <- sample(c("obligatory_school", "apprenticeship", "SEK_II",
"SEK_III"), size = nrow(tib_f), replace = TRUE,
prob = c(.146, .447, .093, .314))
tib_m$education <- sample(c("obligatory_school", "apprenticeship", "SEK_II",
"SEK_III"), size = nrow(tib_m), replace = TRUE,
prob = c(.101, .38, .05, .469))
tib_f$confession <- ifelse(tib_f$education == "SEK_III",
sample(c("confessionless", "muslim", "other", "catholic",
"evangelical-reformed"), size = nrow(tib_f),
replace = TRUE, prob = c(.353, .026, .049, .347, .225)),
sample(c("confessionless", "muslim", "other", "catholic",
"evangelical-reformed"), size = nrow(tib_f),
replace = TRUE, prob = c(.253, .051, .074, .372, .25)))
tib_m$confession <- ifelse(tib_m$education == "SEK_III",
sample(c("confessionless", "muslim", "other", "catholic",
"evangelical-reformed"), size = nrow(tib_m),
replace = TRUE, prob = c(.353, .026, .049, .347, .225)),
sample(c("confessionless", "muslim", "other", "catholic",
"evangelical-reformed"), size = nrow(tib_m),
replace = TRUE, prob = c(.253, .051, .074, .372, .25)))
tib_f$fasnacht <- sample(c("yes", "no"), size = nrow(tib_f), replace = TRUE,
prob = c(.02, .98))
tib_m$fasnacht <- sample(c("yes", "no"), size = nrow(tib_m), replace = TRUE,
prob = c(.035, .965))
tib <- rbind(tib_f, tib_m)
tib$alcohol <- tib$alcohol + ifelse(tib$fasnacht == "yes",
runif(1, 0:40), 0)
tib$eyecor <- sample(c("yes", "no"), size = nsim, replace = TRUE,
prob = c(.66, .37))
tib <- tib[sample(1:nsim),]
# id_scramble <- paste0("bas_", sapply(1:nsim, FUN = function(x) {paste(sample(LETTERS, size = 5, replace = TRUE), collapse = "")}))
id_scramble <- 1:nsim
tib <- tib %>%
mutate(id = id_scramble,
age = case_when(age < 18 | age > 105 ~ runif(1, 18, 85),
TRUE ~ age),
age = round(age, 0),
height = round(height, 1),
# weight
weight = round(weight, 1),
## make 10% of cases NA
weight = case_when(runif(nsim) < .15 ~ NA_real_,
TRUE ~ weight),
## make 15% of cases NA
income = case_when(runif(nsim) < .15 ~ NA_real_,
TRUE ~ income),
children = case_when(children < 0 ~ runif(1, 0, 3),
TRUE ~ children),
children = round(children),
happiness = case_when(happiness > 10 ~ runif(1, 6, 9),
happiness < 5 & runif(1, 0, 1) < .35 ~ runif(1, 6, 9),
TRUE ~ happiness),
happiness = case_when(happiness < 1 ~ runif(1, 1, 10),
TRUE ~ happiness),
happiness = round(happiness),
fitness = case_when(fitness < 1 | fitness > 10 ~ runif(1, 1, 10),
TRUE ~ fitness),
fitness = round(fitness),
### income
# as a function of happiness, height, fitness, tattoos age
income = income + -100 * happiness + 2 * height + 50 * fitness - 50 * tattoos + 150 * age + rnorm(nsim, mean = -150 * 40, sd = 200),
income = round(income / 100, 0) * 100,
income = case_when(income < 1000 ~ runif(1, 1000, 10000),
TRUE ~ income),
### alcohol
alcohol = case_when(alcohol < 0 ~ runif(1, 5, 50),
TRUE ~ alcohol),
alcohol = round(alcohol),
## make 15% of cases 0
alcohol = case_when(runif(nsim) < .15 ~ 0,
TRUE ~ alcohol),
## Food as a function of happiness income and alcohol
food = 50 * happiness + .1 * income + -10 * alcohol + rnorm(nsim, mean = 0, sd = 200),
food = round(food / 10) * 10,
## Tattoos
tattoos = case_when(tattoos < 0 ~ 0,
TRUE ~ tattoos),
tattoos = round(tattoos),
## make 50% of cases 0
tattoos = case_when(runif(nsim) < .5 ~ 0,
TRUE ~ tattoos),
rhine = case_when(rhine < 0 ~ 0,
TRUE ~ rhine),
rhine = round(rhine),
datause = round(datause),
consultations = case_when(consultations < 0 ~ runif(1, 0, 10),
TRUE ~ consultations),
consultations = round(consultations),
hiking = case_when(hiking < 0 ~ 0,
TRUE ~ hiking),
hiking = round(hiking),
# confession
## make 5% of cases NA
confession = case_when(runif(nsim) < .15 ~ NA_character_,
TRUE ~ confession)
)
# Change order a bit
tib <- tib %>% select(id, sex, age, height, weight, income, education, confession, everything())
tib
}
|
adf469da8e155492bf7b8e14e2f523cbb712cc48 | 9dac5dc85beb8dc395fae36ea401a53cb4d1289e | /Visualizations/covariance_matrix_approximation.R | aabd4d7de5d4134f213fbbcc41a97ee6af43d456 | [] | no_license | jamiecoops22/MISCADA_PROJECT | e60c491964b420e3901f9912bdcf946ab000b18a | a005d7930cf22bcac42c6c8773dee9e63262b254 | refs/heads/main | 2023-07-22T10:11:32.022856 | 2021-09-04T11:45:24 | 2021-09-04T11:45:24 | 403,041,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,041 | r | covariance_matrix_approximation.R | library(spBayes)
library(GpGp)
library(matlib)
# useful functions
eucldist <- function(c1, c2){
d1 <- (c1[1] - c2[1])
d2 <- (c1[2] - c2[2])
d <- sqrt(d1**2 + d2**2)
return(d)
}
vecMatch <- function(x, want) {
if (length(x) == length(want)){
if (x[1]==want[1]&x[2]==want[2]&x[3]==want[3]){
return(1)
}
else {
return(NULL)
}
}
tfv <- apply(x, 1, function(x, want) isTRUE(all.equal(x, want)), want)
which(tfv == TRUE)
}
hist_set <- function(m, ref_set, start_point){
times <- ref_set[,3]
r <- length(times)
sqrm <- sqrt(m)
coords <- ref_set[,1:2]
sind <- vecMatch(ref_set, start_point)
#print(sind)
start_index <- sind
#print(start_index)
if (length(start_index) == 0){
# start_point not in ref set
spirs <- FALSE
}
else {
spirs <- TRUE
}
#print(spirs)
start_time <- start_point[3]
history_set <- matrix(c(0,0,0),nrow=1, ncol=3)
hist_times <- which(times < start_time)
#print(hist_times)
history_set <- rbind(history_set, ref_set[hist_times,])
current_times <- which(times == start_time)
#print(current_times)
if (spirs){
is <- c()
for (i in current_times){
if (i < start_index){
is <- c(is, i)
}
}
#print(is)
if (length(is) != 0){
history_set <- rbind(history_set, ref_set[is,])
}
}
else {
history_set <- rbind(history_set, ref_set[current_times,])
}
#history_set <- rbind(history_set, ref_set[current_times,])
#print(history_set)
return((history_set <- history_set[-1,]))
}
nn_from_hist <- function(m, history_set, start_point){
### DEFINE TIME GAP
#print(history_set)
time_gap <- 1
if (length(history_set) == 3){
return(history_set)
}
h <- length(history_set[,3])
times <- history_set[,3]
coords <- history_set[,1:2]
rm <- sqrt(m)
if (h <= m){
#print('here')
return(unique.matrix(history_set))
}
start_time <- start_point[3]
nns <- matrix(c(0,0,0),nrow=1, ncol=3)
# previous times
for (i in (start_time-time_gap):(start_time-(rm+1)*time_gap)){
eds <- rep(1e6, h)
for (j in seq(1, h)){
#print('times_j')
#print(times[j])
#print('i')
#print(i)
if (format(round(times[j], 5), nsmall = 5) == format(round(i, 5), nsmall = 5)){
#print('YES')
ed <- eucldist(start_point[1:2], coords[j,])
eds[j] <- ed
}
}
cor_inds <- which(eds < 1e6)
#print(cor_inds)
if (length(cor_inds) != 0){
space_order_i <- order(eds)[1:min(length(cor_inds),rm)]
#print(space_order_i)
nearest_spatial_i <- history_set[space_order_i,]
nns <- rbind(nns, (nearest_spatial_i))
}
}
nns <- nns[-1,]
# current time
current_times <- which(times == start_time)
#print('current times')
#print(current_times)
if (length(current_times) != 0){
edst <- rep(1e6, length(current_times))
for (i in current_times){
ed <- eucldist(start_point[1:2], coords[i,])
edst[i] <- ed
}
tinds <- which(edst < 1e6)
time_order <- order(edst)[1:min(length(tinds),rm)]
nearest_times <- history_set[time_order,]
nns <- rbind(nns, nearest_times)
}
return(nns)
}
# overall nearest neighbor function
nnsfunc <- function(m, ref_set, start_point){
history_set <- hist_set(m, ref_set, start_point)
nns <- nn_from_hist(m, history_set, start_point)
#print(nns)
if (length(nns) <= 3){
return(nns)
}
else {
return(unique.matrix(nns))
}
}
# correlation function as of DNNGP article
corrf <- function(c1, c2, sigma_squ, a, c, kap){
s1 <- c1[1:2]
s2 <- c2[1:2]
t1 <- c1[3]
t2 <- c2[3]
h <- eucldist(s1, s2)
u <- abs(t1-t2)
f <- sigma_squ/((a*(u**2) + 1)**kap)
s <- exp((-c*h)/((a*(u**2)+1)**(kap/2)))
return(f*s)
}
# covariance matrix for all location possibilities
corrmat <- function(locs1, locs2, sigma_squ, a, c, kap){
locs1_test <- rbind(locs1, c(0, 0, 0))
locs2_test <- rbind(locs2, c(0, 0, 0))
if (length(locs1_test[,1]) == 2){
nrows <- 1
}
else {
nrows <- length(locs1[,1])
}
if (length(locs2_test[,1]) == 2){
ncols <- 1
}
else {
ncols <- length(locs2[,1])
}
cMat <- matrix(0, nrow = nrows, ncol = ncols)
for (i in 1:nrows){
for (j in 1:ncols){
if (nrows == 1 & ncols == 1){
cMat[i,j] <- corrf(locs1, locs2, sigma_squ, a, c, kap)
}
else if (nrows == 1 & ncols != 1){
cMat[i,j] <- corrf(locs1, locs2[j,], sigma_squ, a, c, kap)
}
else if (nrows != 1 & ncols == 1){
cMat[i,j] <- corrf(locs1[i,], locs2, sigma_squ, a, c, kap)
}
else {
cMat[i,j] <- corrf(locs1[i,], locs2[j,], sigma_squ, a, c, kap)
}
}
}
return(cMat)
}
# function to compute a_N(li)
a_vector <- function(l_i, nns, sigma_squ, a, c, kap){
C_NN <- corrmat(nns, nns, sigma_squ, a, c, kap)
C_Nl <- corrmat(nns, l_i, sigma_squ, a, c, kap)
if (length(C_NN) == 1){
iC <- 1/(C_NN)
}
else {
# print('nns')
# print(nns)
# print('C_NN')
# print(C_NN)
iC <- inv(C_NN)
}
return(iC %*% C_Nl)
}
# function to compute diagonal element of matrix F i.e. f_li
f_li <- function(l_i, nns, sigma_squ, a, c, kap){
corr <- corrf(l_i, l_i, sigma_squ, a, c, kap)
C_lN <- corrmat(l_i, nns, sigma_squ, a, c, kap)
C_NN <- corrmat(nns, nns, sigma_squ, a, c, kap)
C_Nl <- corrmat(nns, l_i, sigma_squ, a, c, kap)
#print((C_NN))
if (length(C_NN) == 1){
iC <- 1/(C_NN)
}
else {
iC <- inv(C_NN)
}
return(corr - ((C_lN %*% iC) %*% C_Nl))
}
# function to compute F matrix
F_mat <- function(ref_set, m, sigma_squ, a, c, kap){
r <- length(ref_set[,1])
Fmatrix <- matrix(0, nrow = r, ncol = r)
Fmatrix[1,1] <- corrf(ref_set[1,], ref_set[1,], sigma_squ, a, c, kap)
for (i in 2:r){
l_i <- ref_set[i,]
nns <- nnsfunc(m, ref_set, l_i)
#print(nns)
f <- f_li(l_i, nns, sigma_squ, a, c, kap)
#print(f)
Fmatrix[i,i] <- f
}
return(Fmatrix)
}
# function to compute V matrix
V_mat <- function(ref_set, m, sigma_squ, a, c, kap){
r <- length(ref_set[,1])
V <- matrix(1e6, nrow = r, ncol = r)
V[1,1] <- 1
for (i in 2:r){
V[i,1] <- 0
}
for (j in 2:r){
nns_j <- nnsfunc(m, ref_set, ref_set[j,])
inds <- c()
for (i in 1:r){
if (i == j){
V[i,j] <- 1
}
else {
ind_match <- vecMatch(nns_j, ref_set[i,])
if (length(ind_match) == 0){
# li not in N_lj
V[i,j] <- 0
}
else {
inds <- c(inds, i)
}
}
}
a_Nlj <- a_vector(ref_set[j,], nns_j, sigma_squ, a, c, kap)
for (i in 1:length(inds)){
V[inds[i],j] <- -a_Nlj[i]
}
}
return(V)
}
### K FUNCTION
K_func <- function(ref_set, m, sigma_squ, a, c, kap){
V <- V_mat(ref_set, m, sigma_squ, a, c, kap)
#view(V)
F_matrix <- F_mat(ref_set, m, sigma_squ, a, c, kap)
#view(F_matrix)
iF <- inv(F_matrix)
Vt <- t(V)
iK <- (Vt %*% (iF %*% V))
#view(iK)
return(iK)
}
K_func_test <- function(V, F_matrix){
iF <- inv(F_matrix)
Vt <- t(V)
iK <- (Vt %*% iF) %*% V
return(iK)
}
C_func_K_known <- function(l_i, l_j, ref_set, K, m, sigma_squ, a, c, kap){
p <- vecMatch(ref_set, l_i)
if (length(p) != 0){
i_in_rs <- TRUE
}
else {
i_in_rs <- FALSE
}
q <- vecMatch(ref_set, l_j)
if (length(q) != 0){
j_in_rs <- TRUE
}
else {
j_in_rs <- FALSE
}
if (i_in_rs){
if (j_in_rs){
return(K[p,q])
}
else {
nns_lj <- nnsfunc(m, ref_set, l_j)
a_lj <- a_vector(l_j, nns_lj, sigma_squ, a, c, kap)
return((t(a_lj))%*%K[,p])
}
}
else {
if (j_in_rs){
nns_li <- nnsfunc(m, ref_set, l_i)
a_li <- a_vector(l_i, nns_li, sigma_squ, a, c, kap)
return((t(a_li))%*%K[,q])
}
else {
nns_li <- nnsfunc(m, ref_set, l_i)
a_li <- a_vector(l_i, nns_li, sigma_squ, a, c, kap)
nns_lj <- nnsfunc(m, ref_set, l_j)
a_lj <- a_vector(l_j, nns_lj, sigma_squ, a, c, kap)
if (vecMatch(l_i, l_j) == 1){
extr <- f_li(l_i, nns_li, sigma_squ, a, c, kap)
}
else {
extr <- 0
}
main <- ((t(a_li))%*%K)%*%a_lj
return(main + extr)
}
}
}
C_func <- function(l_i, l_j, ref_set, m, sigma_squ, a, c, kap){
K <- K_func(ref_set, m, sigma_squ, a, c, kap)
C <- C_func_K_known(l_i, l_j, ref_set, K, m, sigma_squ, a, c, kap)
return(C)
}
C_mat <- function(ref_set, m, sigma_squ, a, c, kap){
r <- length(ref_set[,1])
K <- K_func(ref_set, m, sigma_squ, a, c, kap)
print('K found')
C <- matrix(0, nrow = r, ncol = r)
count <- 0
for (i in 1:r){
for (j in 1:r){
C[i,j] <- C_func_K_known(ref_set[i,], ref_set[j,], ref_set, K, m, sigma_squ, a, c, kap)
count <- count + 1
print(count)
}
}
return(C)
}
### visualization of matrices
sigma_squ <- 1
a <- 50
c <- 25
kap <- 0.75
tau_squ <- 0.1
# full cov mat is C
cs <- cbind(synth_full$coords.V1, synth_full$coords.V2, synth_full$time)
V_test <- V_mat(cs, 4, sigma_squ, a, c, kap)
F_test <- F_mat(cs, 4, sigma_squ, a, c, kap)
K_test <- K_func_test(V_test, F_test)
iK <- K_test
iC <- inv(C)
C_plot_x <- c()
C_plot_y <- c()
K_plot_x <- c()
K_plot_y <- c()
for (i in 1:125){
for (j in 1:125){
if (iC[i,j] != 0){
C_plot_x <- c(C_plot_x, i)
C_plot_y <- c(C_plot_y, -j)
}
if (iK[i,j] != 0){
K_plot_x <- c(K_plot_x, i)
K_plot_y <- c(K_plot_y, -j)
}
}
}
par(mfrow = c(1,2))
plot(C_plot_x, C_plot_y, xaxt='n', yaxt='n', xlab="", ylab = "", main = expression(C^{-1}))
plot(K_plot_x, K_plot_y,xaxt='n', yaxt='n', xlab="", ylab = "", main = expression(tilde(C)^{-1})) |
71e58c1c2aaf16db22f337608624a825d1bc16c0 | 7a4f14629fbaa162e4cb9ecbf50442ba3ff86c5a | /load_data.R | f29551a17230e5a80cccb806915c629a21af82e2 | [] | no_license | bryanzk/ExData_Plotting1 | 800dc2c9e693b380e0b24dbdf1d05a20b5669ab9 | 4a71ca4eb8c4315383c25343fec8b9a143735bdd | refs/heads/master | 2021-01-15T18:14:00.104240 | 2015-03-13T06:08:21 | 2015-03-13T06:08:21 | 32,133,150 | 0 | 0 | null | 2015-03-13T06:05:29 | 2015-03-13T06:05:29 | null | UTF-8 | R | false | false | 729 | r | load_data.R | require(data.table)
dt <- fread("household_power_consumption.txt",na.strings="?", stringsAsFactors = FALSE)
tar1 <- dt[dt$Date=="1/2/2007",]
tar2 <- dt[dt$Date=="2/2/2007",]
pdt = rbind(tar1, tar2)
pdt$Date <- as.Date(pdt$Date, "%d/%m/%Y")
pdt$Time <- as.character(strptime(paste(pdt$Date, pdt$Time), format="%Y-%m-%d %H:%M:%S"))
pdt$Global_active_power <- as.numeric(pdt$Global_active_power)
pdt$Global_reactive_power <- as.numeric(pdt$Global_reactive_power)
pdt$Voltage <- as.numeric(as.character(pdt$Voltage))
pdt$Global_intensity <- as.numeric(pdt$Global_intensity)
pdt$Sub_metering_1 <- as.numeric(pdt$Sub_metering_1)
pdt$Sub_metering_2 <- as.numeric(pdt$Sub_metering_2)
pdt$Sub_metering_3 <- as.double(pdt$Sub_metering_3)
|
e096cb42514da2f4053f1c269c363d9ac9cf3048 | 12098bccebee4216aa474ddad99aa8b7c721ef9a | /Scripts/Bar Graph.R | 9f769af5048d359e4b0ea4a086c1ac0db5fd86fa | [] | no_license | feltem/info-201-final-project | 57cb7439a7a6922108e31c8ef83e552feeec11d4 | c1775b97455822ae9f84acda70d78153682dcbe6 | refs/heads/master | 2020-12-30T12:24:20.306394 | 2017-06-01T02:45:31 | 2017-06-01T02:45:31 | 91,385,904 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 745 | r | Bar Graph.R | # Setup
library(dplyr)
library(plotly)
# Interactive Plotly Graph
BuildBar <- function(y.vars, error.vars) {
plot <- plot_ly(x = c("Past Month", "Past Year", "Percieved Risk Average"),
y = y.vars,
type = "bar",
error_y = ~list(value = error.vars,
color = '#000000'),
marker = list(color = c('rgb(58,95,11)', 'rgb(58,95,11)', 'rgb(169,169,169)'), width = 1.5,
line = list(color = 'rgb(169,169,169)'))) %>%
layout(title = "Marijuana Usage & Risk",
xaxis = list(title = "Marijuana Usage"),
yaxis = list(title = "Percent"))
return(plot)
}
|
ce3ac16ed2ac37c35f75c1a4f039a8e63c7768d3 | 79d5106cad12c2a6a6afb2bf4a663f5ce50557d8 | /R/convert_rfv.R | 850f11cf9ec424aaa7a2b9577223afcf4f54ae8a | [] | no_license | yuluc/parseRPDR | 6b55b2de21ddd2b5c43f8fd730a771168897bf16 | db9c656a0cd98017b69c1822d95c8c8836932153 | refs/heads/master | 2023-08-02T13:51:27.927674 | 2021-09-10T11:10:02 | 2021-09-10T11:10:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,435 | r | convert_rfv.R | #' @title Searches columns for given reason for visit defined by ERFV codes.
#' @export
#'
#' @description Analyzes reason for visit data loaded using \emph{load_rfv}.
#' If requested, the data.table is returned with new columns corresponding to boolean values, whether given group of ERFV are present in the given columns.
#' If \emph{collapse} is given, then the information is aggregated based-on the \emph{collapse} column and the earliest of latest time of the given diagnosis is provided.
#'
#' @param d data.table, database containing reason for visit information data loaded using the \emph{load_rfv} function.
#' @param code string vector, an array of column names to search.
#' @param codes_to_find list, a list of arrays corresponding to sets of ERFV codes. The function searches the columns in code and the name of each list element will be created.
#' These columns are indicators whether the given disease is present in the set of ERFV codes or not.
#' @param collapse string, a column name on which to collapse the data.table.
#' Used in case we wish to assess whether given ERFV are present within all the same instances of \emph{collapse}. See vignette for details.
#' @param code_time string, column name of the time column. Defaults to \emph{time_rfv_start}. Used in case collapse is present to provide the earliest or latest instance of diagnosing the given disease.
#' @param time_type string, if multiple diagnoses are present within the same case of \emph{collapse}, which timepoint to return. Supported are: "earliest" or "latest". Defaults to \emph{earliest}.
#' @param nThread integer, number of threads to use by \emph{dopar} for parallelization. If it is set to 1, then no parallel backends are created and the function is executed sequentially.
#' On windows machines sockets are used, while on other operating systems fork parallelization is used.
#'
#' @return data.table, with indicator columns if provided.
#' If \emph{collapse} is present, then only unique ID and the summary columns are returned.
#'
#' @encoding UTF-8
#'
#' @examples \dontrun{
#' #Parse reason for visit columns
#' #and create indicator variables for the following reasons and summarize per patient,
#' #whether there are any encounters where the given reasons were registered
#' reasons <- list(Pain = c("ERFV:160357", "ERFV:140012"), Visit = c("ERFV:501"))
#' data_rfv_disease <- convert_rfv(d = data_rfv, keep = FALSE,
#' codes_to_find = reasons, nThread = 2, collapse = "ID_MERGE")
#' }
convert_rfv <- function(d, code = "rfv_concept_id", codes_to_find = NULL,
collapse = NULL, code_time = "time_rfv_start", time_type = "earliest", nThread = 4) {
.SD=.N=.I=.GRP=.BY=.EACHI=..=..code=.SDcols=i=j=time_to_db=..which_ids_to=..which_ids_from=..collapse=. <- NULL
#Initialize multicore
if(nThread == 1 | length(codes_to_find) == 1) {
`%exec%` <- foreach::`%do%`
} else {
if(length(codes_to_find) > 0 & length(codes_to_find) < nThread) {nThread <- length(codes_to_find)}
if(.Platform$OS.type == "windows") {
cl <- parallel::makeCluster(nThread, outfile = "", type = "PSOCK", methods = FALSE, useXDR = FALSE)
} else{
cl <- parallel::makeCluster(nThread, outfile = "", type = "FORK", methods = FALSE, useXDR = FALSE)
}
doParallel::registerDoParallel(cl)
`%exec%` <- foreach::`%dopar%`
}
#Find diagnoses if requested
if(!is.null(codes_to_find)) {
message(paste0("Finding reasons within specified columns."))
#Find diagnoses per row
result <- foreach::foreach(i = 1:length(codes_to_find), .combine="cbind",
.inorder=TRUE,
.errorhandling = c("pass"), .verbose=FALSE) %exec%
{
if(is.null(collapse)) {
diag_coll <- d[, any(.SD %in% unlist(codes_to_find[i])), .SDcols = code, by=1:nrow(d)]
diag_coll$nrow <- NULL
data.table::setnames(diag_coll, "V1", names(codes_to_find[i]))
diag_coll
} else {
d[, names(codes_to_find[i]) := any(.SD %in% unlist(codes_to_find[i])), .SDcols = code, by=1:nrow(d)]
ID_dt <- unique(d[, collapse, with = FALSE]) #Get IDs
if(time_type == "earliest") { #Find time
diag_coll <- d[, .(var_time = min(get(code_time))), by=c(collapse, names(codes_to_find[i]))]
} else {
diag_coll <- d[, .(var_time = max(get(code_time))), by=c(collapse, names(codes_to_find[i]))]
}
diag_coll <- diag_coll[get(names(codes_to_find[i]))] #Remove negative cases
diag_coll <- data.table::merge.data.table(ID_dt, diag_coll, by = collapse, all.x = TRUE, all.y = FALSE) #Merge with IDs to get db
diag_coll[[names(codes_to_find[i])]][is.na(diag_coll[[names(codes_to_find[i])]])] <- FALSE
data.table::setnames(diag_coll, "var_time", paste0("time_", names(codes_to_find[i])))
diag_coll
}
}
if(exists("cl") & nThread>1) {parallel::stopCluster(cl)}
if(is.null(collapse)) { #Remove unnecessary info and combine with original data if non-collapse
result <- cbind(d, result)
}
if(!is.null(collapse) & length(codes_to_find)>1) { #Remove unnecessary ID columns if multiple codes_to_find
result[, seq(4, dim(result)[2], 3)] <- NULL
}
return(result)
} else { #If no diagnoses
if(exists("cl") & nThread>1) {parallel::stopCluster(cl)}
return(d)
}
}
|
f2de2bc0e4f646b4efd2e793b2a8170914d151de | 0b1e68824e5f5ca9e4353a35c40843cf7827d4ea | /Modified/M3_005.R | 53295cc7f3a76f953f558c72901a0a94d128c403 | [
"MIT"
] | permissive | Gpower01/WQU_Econometrics | e199d365e1966db29ba3f7957a45178fce3c8813 | f9a310c98f0665741b36d99dd3ce5aa8f85ab8a3 | refs/heads/main | 2023-04-13T12:28:08.392780 | 2021-04-26T16:27:17 | 2021-04-26T16:27:17 | 352,192,713 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,656 | r | M3_005.R | install.packages("XML")
install.packages("YieldCurve")
library(quantmod)
# 1.1 Obtain Treasury yield data
t2yr = getSymbols(Symbols = "DGS2", src = "FRED", auto.assign = FALSE)
t2yr = t2yr["2019-10/2019-11"]
t2yr
plot(x = index(t2yr),
y = t2yr$DGS2,
xlab = "Date",
ylab = "Yield (%)",
type = "l",
col = "red",
main = "10-Year US Treasury Yields")
plot(x = index(t2yr),
y = t2yr$DGS2,
xlab = "Date",
ylab = "Yield (%)",
type = "l",
col = "red",
main = "10-Year US Treasury Yields")
t3yr = getSymbols(Symbols = "DGS3", src = "FRED", auto.assign = FALSE)
t3yr = t3yr["2019-10/2019-11"]
t5yr = getSymbols(Symbols = "DGS5", src = "FRED", auto.assign = FALSE)
t5yr = t5yr["2019-10/2019-11"]
t7yr = getSymbols(Symbols = "DGS7", src = "FRED", auto.assign = FALSE)
t7yr = t7yr["2019-10/2019-11"]
t10yr = getSymbols(Symbols = "DGS10", src = "FRED", auto.assign = FALSE)
t10yr = t10yr["2019-10/2019-11"]
t30yr = getSymbols(Symbols = "DGS30", src = "FRED", auto.assign = FALSE)
t30yr = t30yr["2019-10/2019-11"]
plot( x = index( t3yr ), y = t3yr$DGS3 )
plot( x = index( t5yr ), y = t5yr$DGS5 )
plot( x = index( t7yr ), y = t7yr$DGS7 )
plot( x = index( t10yr ), y = t10yr$DGS10 )
plot( x = index( t30yr ), y = t30yr$DGS30 )
# 1.2 import GLD ETF
getSymbols(Symbols = "GLD", from = "2019-10-01", to = "2019-11-30" , src = "yahoo")
Gld_Prices <- GLD$GLD.Close
plot( x = index( GLD ), y = Gld_Prices )
# 1.3 import my ETF
# U.K Oil and Gas Investment PLC (UKOG.L)
getSymbols(Symbols = "UKOG.L", from = "2019-10-01", to = "2019-11-30" , src = "yahoo")
UKOG.L
Ukog_prices <- UKOG.L$UKOG.L.Close
plot( x = index( UKOG.L ), y = Ukog_prices )
# 2.1 calcualte the log returns
GLD$GLD.Close
gldlog = diff( log( GLD$GLD.Close ) )
gldlog
#gldlog2 = na.omit( gldlog )
gldlog2 = gldlog[-1]
gldlog2
plot(gldlog2)
UKOG.L$UKOG.L.Close
ukoglog = diff( log( UKOG.L$UKOG.L.Close ) )
ukoglog
#ukoglog2 = na.omit(ukoglog )
ukoglog2 = ukoglog[-1]
ukoglog2
plot(ukoglog2)
# 3.1 calculate benchmark security average yield for Oct and Nov
t2yr = na.omit( t2yr )
t2yravr1 = mean( t2yr$DGS2["2019-10"] )
t2yravr1
t2yravr2 = mean( t2yr$DGS2["2019-11"] )
t2yravr2
t3yr = na.omit( t3yr )
t3yravr1 = mean( t3yr$DGS3["2019-10"] )
t3yravr1
t3yravr2 = mean( t3yr$DGS3["2019-11"] )
t3yravr2
t5yr = na.omit( t5yr )
t5yravr1 = mean( t5yr$DGS5["2019-10"] )
t5yravr1
t5yravr2 = mean( t5yr$DGS5["2019-11"] )
t5yravr2
t7yr = na.omit( t7yr )
t7yravr1 = mean( t7yr$DGS7["2019-10"] )
t7yravr1
t7yravr2 = mean( t7yr$DGS7["2019-11"] )
t7yravr2
t10yr = na.omit( t10yr )
t10yravr1 = mean( t10yr$DGS10["2019-10"] )
t10yravr1
t10yravr2 = mean( t10yr$DGS10["2019-11"] )
t10yravr2
t30yr = na.omit( t30yr )
t30yravr1 = mean( t30yr$DGS30["2019-10"] )
t30yravr1
t30yravr2 = mean( t30yr$DGS30["2019-11"] )
t30yravr2
# 3.2 average price of gold ETF
gldavr1 = mean( GLD$GLD.Close["2019-10"])
gldavr1
gldavr2 = mean( GLD$GLD.Close["2019-11"])
gldavr2
# 3.3 average price of equity ETF
ukogavr1 = mean( UKOG.L$UKOG.L.Close["2019-10"])
ukogavr1
ukogavr2 = mean( UKOG.L$UKOG.L.Close["2019-11"])
ukogavr2
# 3.4 std of benchmark security std for Oct and Nov
t2yr = na.omit( t2yr )
t2yrsd1 = sd( t2yr$DGS2["2019-10"] )
t2yrsd1
t2yrsd2 = sd( t2yr$DGS2["2019-11"] )
t2yrsd2
t3yr = na.omit( t3yr )
t3yrsd1 = sd( t3yr$DGS3["2019-10"] )
t3yrsd1
t3yrsd2 = sd( t3yr$DGS3["2019-11"] )
t3yrsd2
t5yr = na.omit( t5yr )
t5yrsd1 = sd( t5yr$DGS5["2019-10"] )
t5yrsd1
t5yrsd2 = sd( t5yr$DGS5["2019-11"] )
t5yrsd2
t7yr = na.omit( t7yr )
t7yrsd1 = sd( t7yr$DGS7["2019-10"] )
t7yrsd1
t7yrsd2 = sd( t7yr$DGS7["2019-11"] )
t7yrsd2
t10yr = na.omit( t10yr )
t10yrsd1 = sd( t10yr$DGS10["2019-10"] )
t10yrsd1
t10yrsd2 = sd( t10yr$DGS10["2019-11"] )
t10yrsd2
t30yr = na.omit( t30yr )
t30yrsd1 = sd( t30yr$DGS30["2019-10"] )
t30yrsd1
t30yrsd2 = sd( t30yr$DGS30["2019-11"] )
t30yrsd2
# 3.5 std price of gold ETF
gldsd1 = sd( GLD$GLD.Close["2019-10"])
gldsd1
gldsd2 = sd( GLD$GLD.Close["2019-11"])
gldsd2
# 3.6 std price of gold ETF
ukogsd1 = sd( UKOG.L$UKOG.L.Close["2019-10"])
ukogsd1
ukogsd2 = sd( UKOG.L$UKOG.L.Close["2019-11"])
ukogsd2
# 4.1 graph 6 benchmarks
plot( x = index( t2yr ), y = t2yr$DGS2, type = "l", col = "black", xlab = "Time", ylab = "US Treasury yields", ylim = c(0.5,2.5) )
lines( x = index( t3yr ), y = t3yr$DGS3, type = "l", col = "red")
lines( x = index( t5yr ), y = t5yr$DGS5, type = "l", col = "blue")
lines( x = index( t7yr ), y = t7yr$DGS7, type = "l", col = "gray")
lines( x = index( t10yr ), y = t10yr$DGS10, type = "l", col = "purple")
lines( x = index( t30yr ), y = t30yr$DGS30, type = "l", col = "green")
legend( "bottom", legend=c("2 year", "3 year", "5 year", "7 year", "10 year", "30 year"), lty=1, col=c("black","red", "blue","gray","purple","green"), ncol = 3)
# 4.2 graph gold ETF
plot( x = index( GLD ), y = GLD$GLD.Close, type = "l", col = "black", xlab = "Time", ylab = "GLD ETF price" )
# 4.3 graph equity ETF at the same plot
plot( x = index( GLD ), y = GLD$GLD.Close, type = "l", col = "black", xlab = "Time", ylab = "GLD ETF price" )
par(mar=c(5, 4, 4, 6) + 0.1)
par(new=TRUE)
plot( x = index( UKOG.L ), y = UKOG.L$UKOG.L.Close, type = "l", col = "red", xlab = "", ylab = "", axes=FALSE)
mtext("UKOG.L ETF price",side=4,col="red",line=4)
axis(4, ylim=c(0,7000), col="red",col.axis="red",las=1)
legend("topright",legend=c("GLDF","UKOG.L ETF"),
text.col=c("black","red"),col=c("black","red"), lty=1, cex = 0.75)
# 5.1 Nelson-Siegel
# Equation : https://en.wikipedia.org/wiki/Fixed-income_attribution
# Equation : y_t(τ) = β_{0t} + β_{1t} \frac{1-\exp(-λ τ)}{λ τ} + β_{2t} ≤ft(\frac{1-\exp(-λ τ)}{λ τ} - \exp(-λ τ) \right)
library(xts)
library(zoo)
library(YieldCurve)
tyr = cbind( t2yr$DGS2, t3yr$DGS3, t5yr$DGS5, t7yr$DGS7, t10yr$DGS10, t30yr$DGS30)
tyr
maturity.tyr = c( 2, 3, 5, 7, 10, 30 )
NSParameters <- Nelson.Siegel( rate= tyr, maturity=maturity.tyr )
NSParameters
nsp_y = NSrates( NSParameters, maturity.tyr)
nsp_y
# 5.2 Fit yeild curve for Oct
tyr_avr1 = c( t2yravr1,t3yravr1,t5yravr1,t7yravr1,t10yravr1,t30yravr1 )
nsp_oct = c( mean(nsp_y$X2["2019-10"]),mean(nsp_y$X3["2019-10"]),mean(nsp_y$X5["2019-10"]),mean(nsp_y$X7["2019-10"]),mean(nsp_y$X10["2019-10"]),mean(nsp_y$X30["2019-10"]) )
plot( x = maturity.tyr, y = tyr_avr1, type = "b" , col = "black", xlab = "Time", ylab = "US Treasury yield" )
par(new=TRUE)
plot( x = maturity.tyr, y = nsp_oct, type = "l", col = "red", xlab = "Time", ylab = "US Treasury yield" )
legend("topleft",legend=c("US Treasury average Oct","Nelson-Siegel Fit"),
text.col=c("black","red"),col=c("black","red"), lty=1, cex = 0.75)
# 5.3 Fit yeild curve for Nov
tyr_avr2 = c( t2yravr2,t3yravr2,t5yravr2,t7yravr2,t10yravr2,t30yravr2 )
nsp_Nov = c( mean(nsp_y$X2["2019-11"]),mean(nsp_y$X3["2019-11"]),mean(nsp_y$X5["2019-11"]),mean(nsp_y$X7["2019-11"]),mean(nsp_y$X10["2019-11"]),mean(nsp_y$X30["2019-11"]) )
plot( x = maturity.tyr, y = tyr_avr2, type = "b" , col = "black", xlab = "Time", ylab = "US Treasury yield" )
par(new=TRUE)
plot( x = maturity.tyr, y = nsp_Nov, type = "l", col = "red", xlab = "Time", ylab = "US Treasury yield" )
legend("topleft",legend=c("US Treasury average Nov","Nelson-Siegel Fit"),
text.col=c("black","red"),col=c("black","red"), lty=1, cex = 0.75)
# 5.4 Comapre Oct and Nov
nsp_para_oct = c( mean( NSParameters$beta_0["2019-10"] ), mean(NSParameters$beta_1["2019-10"]), mean(NSParameters$beta_2["2019-10"]),mean(NSParameters$lambda["2019-10"]) )
nsp_para_nov = c( mean( NSParameters$beta_0["2019-11"] ), mean(NSParameters$beta_1["2019-11"]), mean(NSParameters$beta_2["2019-11"]),mean(NSParameters$lambda["2019-11"]) )
nsp_para_oct
nsp_para_nov
?Nelson.Siegel
# discussion
# b0 : is interpreted as the long run levels of interest rates (the loading is 1, it is a constant that does not decay).
# b1 : is the short-term component (it starts at 1, and decays monotonically and quickly to 0);
# b2 : is the medium-term component (it starts at 0, increases, then decays to zero);
# lambda : is the decay factor: large values produce slow decay and can better fit the curve at long maturities,
# while small values produce fast decay and can better fit the curve at short maturities;
# When we look at the overall yield curve, we can confirm that the yield curve is increasing S curve.
# This means that overheating of economy is expected and it will result in inflation.
# In the case of inflation, it is better to sell the US treasury.
# This is why the b1 and b2 are negative.
# The selling induce the increase of yield, this is why the yield curve is increasing.
# The fitting parameter of Nelson-Siegel shows the consistent expectations.
# b0 is much larger that b1 and b2 which means the long run interest rate is much larger than shor, mid-term.
# But lambda is small (1/5) that the fit is better for the short maturities.
# This can be explained by the data we collect. We picked many short maturities, yet the long matuiries are only 10, 30 years.
# When we compare the Oct and Nov fit result, all of Oct b0,2 < Nov b0,2 although Oct b1 > Nov b1.
# This means that for short and long term interest rate, Oct is lower than Nov.
# But for mid term interest, Oct is higher than Nov.
# 6.0 modeling prices
library(tseries)
library(ggplot2)
# For separate months of October and November
# import GLD ETF October
getSymbols(Symbols = "GLD", from = "2019-10-01", to = "2019-10-31" , src = "yahoo")
Oct_Gld_Prices <- GLD$GLD.Close
plot( x = index( GLD ), y = Oct_Gld_Prices )
# import GLD ETF November
getSymbols(Symbols = "GLD", from = "2019-11-01", to = "2019-11-30" , src = "yahoo")
Nov_Gld_Prices <- GLD$GLD.Close
plot(x = index( GLD ), y = Nov_Gld_Prices )
# import my UKOG Equity ETF October
getSymbols(Symbols = "UKOG.L", from = "2019-10-01", to = "2019-10-31" , src = "yahoo")
UKOG.L
Oct_Ukog_prices <- UKOG.L$UKOG.L.Close
plot( x = index( UKOG.L ), y = Oct_Ukog_prices )
# import my UKOG Equity ETF November
getSymbols(Symbols = "UKOG.L", from = "2019-11-01", to = "2019-11-30" , src = "yahoo")
UKOG.L
Nov_Ukog_prices <- UKOG.L$UKOG.L.Close
plot( x = index( UKOG.L ), y = Nov_Ukog_prices )
# ARM model for GLD prices October (Modeling prices)
# ACF & PACF tests for GLD October & November
acf(Oct_Gld_Prices)
pacf(Oct_Gld_Prices)
acf(Nov_Gld_Prices)
pacf(Nov_Gld_Prices)
# stationary test
adf.test(Oct_Gld_Prices)
adf.test(Nov_Gld_Prices)
# 6.1 ARMA model for GLD ETF prices for October
Gld_oct_prices <- arima(Oct_Gld_Prices, order = c(1,0,0))
summary(Gld_oct_prices)
Box.test(Gld_oct_prices$residuals, lag = 1)
# 6.2 ARMA model for GLD ETF prices for November
Gld_nov_prices <- arima(Nov_Gld_Prices, order = c(1, 0,0))
summary(Gld_nov_prices)
Box.test(Gld_nov_prices$residuals, lag = 1)
# ACF and PACF for UKOG Equity ETF October & November
acf(Oct_Ukog_prices)
pacf(Oct_Ukog_prices)
acf(Nov_Ukog_prices)
pacf(Nov_Ukog_prices)
# stationary tests
adf.test(Oct_Ukog_prices)
adf.test(Nov_Ukog_prices)
# 6.3 ARMA model for UKOG ETF Prices for October
UKog_oct_prices <- arima(Oct_Ukog_prices, order = c(1,0,0))
summary(UKog_oct_prices)
Box.test(UKog_oct_prices$residuals, lag = 1)
# 6.4 ARMA model for UKOG ETF prices for November
UKog_nov_prices <- arima(Nov_Ukog_prices, order = c(1,0,0))
summary(UKog_nov_prices)
Box.test(UKog_nov_prices$residuals, lag = 1)
# 6.5 Which model performs best
# The results indicates that none of the model shows to be statistically significant implying that
# the model is not parsimonious. However, ARMA model estimation for the month of
# October appears to perform better than the ARMA models for the month of November
# for both GLD and UKOG prices.The coefficient for the month of October appears to be generally lower
# the coefficient of November models. Also the AIC for the month of October are generally lower than
# AIC in November. So comparing both the coefficient and AIC, it suggests that UKOG ETF ARMA October model
# appears to perform better with coefficient of 0.5435 and aic = -83.41.
# 6.6 What are the significant changes? if any
# The significant changes observed in the models includes changes in the coefficient and aic. GLD October ARMA mode
# indicate a coefficient of 0.4101 and aic of 52.54, while the GLD ARMA model for November indicated
# a coeficient of 0.8936 and aic of 59.3. Similarly, the ARMA model of UKOG October prices indicates a coefficient of 0.5435
# and aic of 83.41 while the ARMA model of November UKOG prices indicated coefficient of 0.8851 and aic of 93.28.
# ACF test indicates UKOG October prices autocorrelation of about 0.5 that rapidly decayed.
# 7.0 Modelling Volatility
# 7.1 GLD ETF Daily high minus low for October
Gld_Oct_high <- max(Oct_Gld_Prices)
print(Gld_Oct_high)
Gld_Oct_low <- min(Oct_Gld_Prices)
print(Gld_Oct_low)
# October Dialy high minus low
Gld_oct_diff <- Gld_Oct_high - Gld_Oct_low
print(Gld_oct_diff)
# GLD ETF Daily high minus low for November
Gld_nov_high <- max(Nov_Gld_Prices)
print(Gld_nov_high)
Gld_nov_low <- min(Nov_Gld_Prices)
print(Gld_nov_low)
# November Daily high minus low
Gld_nov_diff <- Gld_nov_high - Gld_nov_low
print(Gld_nov_diff)
# GLD October average
Gld_Oct_avg <- mean(Oct_Gld_Prices)
print(Gld_Oct_avg)
# GLD November average
Gld_Nov_avg <- mean(Nov_Gld_Prices)
print(Gld_Nov_avg)
# 7.2 STD for GLD ETF Returns for October and November
# Log Returns for GLD October prices
gldlog_oct <- diff(log(Oct_Gld_Prices))
gldlog2_oct <- gldlog_oct[-1]
print(gldlog2_oct)
# Log Returns for GLD November prices
gldlog_nov <- diff(log(Nov_Gld_Prices))
gldlog2_nov <- gldlog_nov[-1]
print(gldlog2_nov)
# STD for GLD October and November log returns
gld_oct_sd = sd(gldlog2_oct)
print(gld_oct_sd)
gld_nov_sd = sd(gldlog2_nov)
print(gld_nov_sd)
# 7.3 Volatility GARCH (1,1) model for October GLD Return
library(rugarch)
gld_gurch <- ugarchspec(mean.model = list(armaOrder = c(0,0)),
variance.model = list(model = "sGARCH"),
distribution.model = 'norm')
gurcg_gld1 <- ugarchfit(spec =gld_gurch, data = gldlog2_oct)
print(gurcg_gld1)
# Volatility GARCH model for November GLD Return
gurcg_gld2 <- ugarchfit(spec =gld_gurch, data = gldlog2_nov)
print(gurcg_gld2)
# 7.4 What are the significant changes? if any
# GLD ETF GARCH model for October prices indicated a mean value of 0.000407
# and a p-value of 0.737075 while the GLD ETF GARCH model for November prices
# indicated a negative mean value of -0.001188 and a smaller p-value of 0.386699.
# 7.5 UKOG Daily prices high minus low for October and November, average
plot(Oct_Ukog_prices)
plot(Nov_Ukog_prices)
UKog_oct_high <- max(Oct_Ukog_prices)
print(UKog_oct_high)
Ukog_oct_low <- min(Oct_Ukog_prices)
print(Ukog_oct_low)
# October UKOG Daily High minus Low
UKog_oct_diff <- UKog_oct_high - Ukog_oct_low
print(UKog_oct_diff)
#Daily average October UKOG prices
Ukog_oct_avg <- mean(Oct_Ukog_prices)
print(Ukog_oct_avg)
# November UKOG Daily high minus low
Ukog_nov_high <- max(Nov_Ukog_prices )
print(Ukog_nov_high)
Ukog_nov_low <- min(Nov_Ukog_prices )
print(Ukog_nov_low)
# Daily high minus low UKOG Nov prices
Ukog_nov_diff <- Ukog_nov_high - Ukog_nov_low
print(Ukog_nov_diff)
# Daily average November UKOG prices
Ukog_nov_avg <- mean(Nov_Ukog_prices )
print(Ukog_nov_avg)
# 7.6 returns and STD for UKOG ETF October and November Prices
# log returns for UKOG ETF October prices
ukoglog_oct <- diff( log(Oct_Ukog_prices) )
print(ukoglog_oct)
#ukoglog2 = na.omit(ukoglog )
ukoglog2_oct = ukoglog_oct[-1]
print(ukoglog2_oct)
plot(ukoglog2_oct)
# log returns for UKOG ETF November prices
ukoglog_nov <- diff( log(Nov_Ukog_prices ) )
print(ukoglog_nov)
#ukoglog2 = na.omit(ukoglog )
ukoglog2_nov = ukoglog_nov[-1]
print(ukoglog2_nov)
plot(ukoglog2_nov)
# STD for UKOG October and November log returns
ukog_oct_sd = sd(ukoglog2_oct)
print(ukog_oct_sd)
ukog_nov_sd = sd(ukoglog2_nov)
print(ukog_nov_sd)
# 7.7 Volatility GARCH (1,1) model for October UKOG.L Return
ukog_gurch <- ugarchspec(mean.model = list(armaOrder = c(0,0)),
variance.model = list(model = "sGARCH"),
distribution.model = 'norm')
gurcg_ukog1 <- ugarchfit(spec =ukog_gurch , data = ukoglog2_oct)
print(gurcg_ukog1)
# Volatility GARCH model for November GLD Return
gurcg_ukog2 <- ugarchfit(spec =ukog_gurch, data = ukoglog2_nov)
print(gurcg_ukog2)
# 7.8 What are the significant changes? if any
# The UKOG GARCH model for October prices indicated a mean value of 0.001098 and
# p-value of 0.86797 (suggesting that is not is not statistically significant) while
# the GARCH model for November prices indicated a much better model with a mean value
# of -0.005280 and p-value of 0.25379.
# 8.0 Correlation
library(quantmod)
# 8.1 compute pearson correlation between gold and equity
getSymbols(Symbols = "GLD", from = "2019-09-30", to = "2019-11-30" , src = "yahoo")
GLD$GLD.Close
plot( x = index( GLD ), y = GLD$GLD.Close )
getSymbols(Symbols = "UKOG.L", from = "2019-09-30", to = "2019-11-30" , src = "yahoo")
UKOG.L$UKOG.L.Close
plot( x = index( UKOG.L ), y = UKOG.L$UKOG.L.Close )
gld_oct = GLD["2019-10"]
gld_oct
length( gld_oct$GLD.Close )
ukog_oct = UKOG.L["2019-10"]
length( ukog_oct$UKOG.L.Close )
cor.test(gld_oct$GLD.Close, ukog_oct$UKOG.L.Close, method=c("pearson"))
gld_nov = GLD["2019-11"]
gld_nov$GLD.Close
length( gld_nov$GLD.Close )
ukog_nov = UKOG.L["2019-11"]
ukog_nov$UKOG.L.Close
length( ukog_nov$UKOG.L.Close )
length(ukog_nov[-20,]$UKOG.L.Close)
cor.test(gld_nov$GLD.Close, ukog_nov[-20,]$UKOG.L.Close, method=c("pearson"))
|
2a53c6212ec5b3bbdd40dc4cc08f5830610d3c6f | 832669387fa12c1a02bad7d47de5d0aa57b032be | /tests/testthat/test_transform_log.R | 9b96c9943c0c643ca43f0d0a9dff92f82a01adee | [] | no_license | zoltanszebenyi/datacleaner | 5059db164c7970b9bc185ef4ebb9a37508a486ed | ed0e383809b4c1d1b182489ebcd36e0d9a87f7e1 | refs/heads/master | 2020-05-18T14:04:08.747851 | 2019-05-01T19:18:42 | 2019-05-01T19:18:42 | 184,459,696 | 0 | 0 | null | 2019-05-01T18:10:16 | 2019-05-01T18:10:16 | null | UTF-8 | R | false | false | 174 | r | test_transform_log.R | context("No negative values")
library(datacleaner)
test_that("There are no negative values in input", {
expect_error(transform_log(c(1,2,-1)), "input can't be negative")
}) |
ac80d7bd7a339f7f47618e111a646214c09a2426 | b86d6bf4662ad2bbe2083ea0915ff50f2f455e60 | /R/İstatistikVeVeriBilimi/9-)Tidyverse/7-)Summarize.R | f64e398e3461d76a8b30a589067f28a6bd18e093 | [] | no_license | DincerDogan/VeriBilimi | a8f04afcfd1a2419da0be72b6d780ffe058c42d6 | 1632b42e4d1ce744bb1382611022b79265c7896f | refs/heads/master | 2023-06-06T13:59:35.726325 | 2021-06-17T11:43:46 | 2021-06-17T11:43:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 193 | r | 7-)Summarize.R | summarise(
iris,ORTALAMA=mean(iris$Sepal.Length),
MEDYAN=median(iris$Sepal.Length),
STANDART.SAPMA=sd(iris$Sepal.Length)
)
# summarize sayesinde özet bilgilendirmelerimizi yapabiliriz |
89dc5b691460c859681a1be197a31cc163f5a0b5 | 3d7d08c45b1405297a895b74f543634eac13c2af | /base_scripts/nullnetworks.R | 749ce8d2ea738db051c62aa14c07d5f369b79f09 | [] | no_license | anshuman21111/ant-colony-networks | ed38d96cc907e81292a1701cc5d9995f8ddf14d1 | 40e1a46d66d7af78dd01590a31bef82c4393ce0a | refs/heads/master | 2022-04-30T05:54:02.002730 | 2022-04-20T15:16:05 | 2022-04-20T15:16:05 | 246,055,637 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,499 | r | nullnetworks.R | #null networks
library("VertexSort")
#strength
Antsortstrength=list()
for(i in 1:6){
set.seed(i)
X11=paste("Colony", i, sep = "")
print(X11)
Antg=dpr(Antgr[[X11]][[1]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
A=strength(Antgr1, vids = V(Antgr1), loops = TRUE)
X2=cbind(as.character(names(A)), as.numeric(A))
colnames(X2)=c("AntID", "Day1")
X2=as.data.frame(X2)
for (j in 2:41) {
Antg=dpr(Antgr[[X11]][[j]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
A=strength(Antgr1, vids = V(Antgr1), loops = TRUE)
X3=cbind(as.character(names(A)), as.numeric(A))
X3=as.data.frame(X3)
colnames(X3)=c("AntID", paste("Day", j, sep = ""))
X2=left_join(as.data.frame(X2), as.data.frame(X3), by="AntID")
print(dim(X2))
}
Antsortstrength[[X11]]=X2
X3= paste("~/Downloads/Ants/strength/sort/Colony",i,".csv",sep="")
write.csv(X2, file=X3)
}
#betweenness
Antsortbetween=list()
for(i in 1:2){
set.seed(i)
X11=paste("Colony", i, sep = "")
print(X11)
Antg=dpr(Antgr[[X11]][[1]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
A=betweenness(Antgr[[X11]][[1]], v = V(Antgr[[X11]][[1]]), directed = FALSE, nobigint = TRUE, normalized = FALSE)
X2=cbind(as.character(names(A)), as.numeric(A))
colnames(X2)=c("AntID", "Day1")
X2=as.data.frame(X2)
for (j in 2:41) {
Antg=dpr(Antgr[[X11]][[j]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
A=betweenness(Antgr[[X11]][[j]], v = V(Antgr[[X11]][[j]]), directed = FALSE, nobigint = TRUE, normalized = FALSE)
X3=cbind(as.character(names(A)), as.numeric(A))
X3=as.data.frame(X3)
colnames(X3)=c("AntID", paste("Day", j, sep = ""))
X2=left_join(as.data.frame(X2), as.data.frame(X3), by="AntID")
print(dim(X2))
}
Antsortstrength[[X11]]=X2
X3= paste("~/Downloads/Ants/betweenness/sort/Colony",i,".csv",sep="")
write.csv(X2, file=X3)
}
#bridgebetweenness
Antsortbridbetween=list()
for(i in 1:2){
set.seed(i)
X11=paste("Colony", i, sep = "")
print(X11)
Antg=dpr(Antgr[[X11]][[1]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
library(networktools)
nodea=list()
for(i in 1:6){
X11=paste("Colony", i, sep = "")
print(X11)
communities00 = Antgr[[X11]][[1]] %>% cluster_louvain(weights = NULL)
A00= bridge(Antgr[[X11]][[1]], communities00, useCommunities = "all",directed = NULL, nodes = NULL, normalize = FALSE)
for (j in 2:41) {
Antg=dpr(Antgr[[X11]][[j]], viteration_no=1000, vparallel = FALSE, vcpus = 1)
Antgr1=Antg[[1]]
for (j in 2:41) {
communities = Antgr[[X11]][[j]] %>% cluster_louvain(weights = NULL)
A= bridge(Antgr[[X11]][[j]], communities, useCommunities = "all",directed = NULL, nodes = NULL, normalize = FALSE)
for (k in 1:5) {
A00i=A00[k]
Ai=A[k]
namef=paste("A00i$`", namelist[k],"`", sep="")
X2=cbind(as.character(names(A00i$namelist[k])), as.numeric(A00i))
colnames(X2)=c("AntID", "Day1")
X2=as.data.frame(X2)
X3=cbind(as.character(names(Ai)), as.numeric(Ai))
X3=as.data.frame(X3)
colnames(X3)=c("AntID", paste("Day", j, sep = ""))
nodea[[k]]=left_join(as.data.frame(X2), as.data.frame(X3), by="AntID")
#print(dim(X2))
}
}
}
Antsortstrength[[X11]]=X2
X3= paste("~/Downloads/Ants/betweennessbridge/sort/Colony",i,".csv",sep="")
write.csv(X2, file=X3)
}
|
ff72c70ad844d1c9b3647a3d5bb9790cbfc72e65 | 6c1ef8b6a83b51a360fc281d01c47f47376a2c3b | /Ver4_Text_Messsage_mining.R | 269d000a147c243026c3a60b39b2da54613cd883 | [] | no_license | aliz20/Text-Mining | 1fbeb2be8c0924091823faffb584b554ee521cd0 | 8ca93034e3d9dd8499a41ae17bb002557bd2695d | refs/heads/master | 2021-01-17T20:16:19.538829 | 2016-07-07T06:47:47 | 2016-07-07T06:47:47 | 62,772,181 | 0 | 0 | null | 2016-07-07T06:47:47 | 2016-07-07T03:31:52 | R | UTF-8 | R | false | false | 9,839 | r | Ver4_Text_Messsage_mining.R | # Ali Zahraei, 2016
#Text message mining.
#THIS IS ANOTHER VERSION OF THIS CODE, MIGHT BE SLIGHTLY DIFFERENT THAN
# WHAT I DISCUSSED.IT DOES ANALYTICS, GRAPHICS WAS DONE BY MATLAB.
# Porbabilistic Model (Naive Bayes), and Deterministic Model (SVM- Linear & Radial Kernel] and Adaboost
# This code reads a text file containg SMS txt messages, and
# classify SMS to spam and NON-spam.
# First column, data file is the classification ("Spams and Hams").
# Originally code was written for one of my project at UC-Irvine,
# and it is majorly adapted recently for some new application.
# Code has several major sections.
#--------------------------------------------
cls <- function() cat(rep("\n",100))
cls()
# ----Section 1------------------------------
#--------------------------------------------
#----Install and call library packages
#--------------------------------------------
# Install required packages
install.packages('e1071',dependencies=TRUE)
library(e1071)
#--------------------
install.packages("ada")
library("ada")
#--------------------
install.packages("tm")
library("tm")
#---Section 2-----------------------------
#-----------------------------------------
#----Function Definition
#----------------------------------------
#--counter calculate frequency of each word within data set
#Function definition , comparing each string with repeated words
counter <- function(message,repeatedwords){
#convert to string
splittedSMS<-strsplit(toString(message), "\\s+")[[1]]
# vect is a seprate vector for each data entry, the length is equal to
# number of repeated words
vect = NULL
vect <- c(rep(0,length(repeatedwords)))
# counting frequency of the most repeated words
# in the data poll for each SMS txt message.
# Function will repeat seprately for each SMS.
for (i in 1:length(repeatedwords)){
for (j in 1:length(splittedSMS)){
if (repeatedwords[i] == splittedSMS[j]){
vect[i] <- 1 # vect[i] <- vect[i]+1
}
}
}
return(vect)
}
#--Define naive Bayes probability Function------------
prob <- function(x,stat) {
probability=NULL;
if (x==0) {
probability[1] <- stat[1,1]
probability[2] <- stat[1,2]
} else {
probability[1] <- stat[2,1]
probability[2] <- stat[2,2]}
return(probability)
}
#--Section 3----------------------------------
#---------------------------------------------
#---Loading data and few simple statistics
#---------------------------------------------
print("Loading the SMS data - Spams and Hams")
#Import the data from txt file / data downloaded from
#---Downloaded from http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
#SMSdata <-read.csv("C:/AliZ/Code/R/Sample_Project/data/SMSSpamCollection.txt", header = TRUE, sep = "\t")
SMSdata <-read.csv("SMSSpamCollection.txt", header = TRUE, sep = "\t")
colnames(SMSdata) <- c("Spam_OR_Ham","SMS_Content")
#tempdata<-SMSdata
# Replace ham and Spam with 0 and 1 (logical array)
#tempdata$Spam_OR_Ham[tempdata$Spam_OR_Ham=="spam"]<- 0
#tempdata$Spam_OR_Ham[tempdata$Spam_OR_Ham=="ham"]<- 1
#tempdata$Class=="ham"
#Simple Statistics Extracted from Original Data Set
count <- as.numeric(SMSdata$Spam_OR_Ham=="ham")
print ("The ratio of NON-SPAM SMS %"); 100*mean(count)
print ("The Varianc of data"); var(count)
# Section 4--------------------------------
#------------------------------------------
#---Sampling & Subsetting --> Training and Testing Data Set (two-fold cross validation)----
#------------------------------------------
print ("Sampling & Make two sets of data, Trainig 70%, and Testing 30%");
#
tempdata<-SMSdata
n<- nrow(tempdata)
#set.seed(100)
#
ind <- sample(1:n)
trainval <- ceiling(n* .7)
testval<- ceiling(n * .3)
#
trainData = NULL; trainData <- tempdata[ind[1:trainval],]
testData =NULL; testData <- tempdata[ind[(trainval+1):n],]
print("Training Data Set Size is"); dim(trainData);
print("Testing Data Set Size is"); dim(testData);
# Section 5---------------------------------
#-------------------------------------------
#--Feature extraction from text message (tm toolbox)----
#------------------------------------------
#(tm) Package
# Words with highest frequencies.
# We know highly used words, we use this frequency to
# train vector space model
# corpus(x, reader), VectorSource: identify the character entity of data
trsmses<-Corpus(VectorSource(trainData[,2]))
#transformation :How to modify corpus
#Extra whitespace is eliminated by
trsmses<-tm_map(trsmses, stripWhitespace)
#Conversion to lower case by
trsmses<-tm_map(trsmses, tolower)
#Removal of stopwords by
trsmses<-tm_map(trsmses, removeWords, stopwords("english"))
dtm <- DocumentTermMatrix(trsmses)
# find words with min frequency of (80)
repeatedwords<-findFreqTerms(dtm, 80)
repeatedwords <- repeatedwords[4:length(repeatedwords)]
#Section 6----------------------------------
#-------------------------------------------
#----How many of popular words are repeated whithin each text message?----
#------------------------------------------
#vectorized training data set
trdata=NULL
#vectorized test data set
tedata=NULL
#Count the number of repeated words in any data instance
#train data
for (i in 1:length(trainData[,2])){
if (trainData[i,1] == "ham") {
trdata = rbind(trdata,c(1,counter(trainData[i,2],repeatedwords)))
}
else {
trdata = rbind(trdata,c(0,counter(trainData[i,2],repeatedwords)))
}
}
#test data
for (i in 1:length(testData[,2])){
if (testData[i,1] == "ham") {
tedata = rbind(tedata,c(1,counter(testData[i,2],repeatedwords)))
}
else {
tedata = rbind(tedata,c(0,counter(testData[i,2],repeatedwords)))
}
}
#-------------------------------------------
#-------------------------------------------
#---PREDICTIVE MODELS-----------------------
#_____-----_____----------____________------
#---Section 7-------------------------------
#------------Naive Bayes--------------------
#-------Lebel Identification -------------
lable.sum <- unique(sort(trdata[,1]))
#----------Training Model---------------
stat.data.class0=NULL;stat.data.class1=NULL
# for (i in 1:length(lable.sum)){
#Binary labels 1 is first label such=successs for 1
data.class0 <- trdata[trdata[,1]==lable.sum[1],]
stat.data.class0 <- (colSums(data.class0[,2:(dim(data.class0)[2])]==lable.sum[1]))/nrow(data.class0) #nrow(trdata)
stat.data.class0 <- rbind(stat.data.class0,(colSums(data.class0[,2:(dim(data.class0)[2])]==lable.sum[2]))/nrow(data.class0))
data.class1 <- trdata[trdata[,1]==lable.sum[2],]
stat.data.class1 <- (colSums(data.class1[,2:(dim(data.class1)[2])]==lable.sum[1]))/nrow(data.class1)
stat.data.class1 <- rbind(stat.data.class1,(colSums(data.class1[,2:(dim(data.class1)[2])]==lable.sum[2]))/nrow(data.class1))
#--Testing--------------------
#--Prior Calculation----------
prior.p.set<- NULL
for (i in 1:length(lable.sum)){
prior.p.set[i] <- length(trdata[trdata[,1]==lable.sum[i],1])/dim(trdata)[1]
}
#-------Posterior Calculation-
likelihood.group0 = NULL;likelihood.group1= NULL;assigned.label=NULL;
for (i in 1:nrow(tedata)){
for (j in 2:(ncol(tedata)-1)) {
likelihood.group0 [j-1] <- prob(tedata[i,j],t(rbind(stat.data.class0[,j-1],stat.data.class1[,j-1])))[1]
likelihood.group1 [j-1] <- prob(tedata[i,j],t(rbind(stat.data.class0[,j-1],stat.data.class1[,j-1])))[2]
}
evidence <- prior.p.set[1]*prod(likelihood.group0)+ prior.p.set[2]*prod(likelihood.group1)
posterior <- NULL
posterior[1] <- (prior.p.set[1]*prod(likelihood.group0))/evidence
posterior[2] <- (prior.p.set[2]*prod(likelihood.group1))/evidence
assigned.label[i] <- which.max(posterior[])-1
}
#Contigency table
tab0 <- table(assigned.label, true=tedata[,1])
print("Naive Bayes Contigency Table")
classAgreement(tab0,match.names=FALSE)
#---Section 8-------------------------------
#----Adaboost-------------------------------
adaptiveboost<-ada(x=trdata[,-1],y=trdata[,1],test.x=tedata[,-1], test.y=tedata[,1], loss="logistic", type="gentle", iter=100)
summary(adaptiveboost)
varplot(adaptiveboost)
#-------------------------------------------
#---Section 9-------------------------------
#----SVM with Radial Kernel------------------
#Tunning SVM C & Gamma parameters-----------
#Two main parameters, 0.1 <c < 10; 10e-6 < gamma < 0.1
tuned <- tune.svm (x=trdata[,-1], y=trdata[,1],gamma=10^(-6:-1),cost=10^(-1:1))
summary(tuned)
#
model1 <- svm(x=trdata[,-1], y=trdata[,1], kernel="radial",gamma=0.01,cost=1, type= "C")
summary(model1)
prediction1 <- predict(model1,tedata[,-1])
tab1 <- table(pred1=prediction1, true=tedata[,1])
print(" Radial kernel, gamma=0.1,cost=10")
classAgreement(tab1)
#---Section 10-------------------------------
#----SVM with Linear Kernel------------------
#Tunning SVM C & Gamma parameters-----------
#-----------------------------------------------
model2 <- svm(x=trdata[,-1], y=trdata[,1], kernel="linear",gamma=0.01,cost=1, type= "C")
summary(model2)
prediction2 <- predict(model2,tedata[,-1])
tab2 <- table(pred2=prediction2, true=tedata[,1])
print(" Linear kernel, gamma=0.1,cost=10")
classAgreement(tab2,match.names=FALSE)
|
0640a2e311fb0e081f9228aff723c5810e89e7ff | 358d9bbf2cbc4ba049c293b6ccec1b7f93df9954 | /fitting/mifSearch1_TmeanB.R | c5ef211d59a71a77217ef9c0c7b35445c2d15fd3 | [] | no_license | pascualgroup/Malaria-highlands | de9c55d695c3e9bdb743a1167be83fd78eba2bd1 | c8ae3fa8c76c31530add4562dc2ff103220b9c60 | refs/heads/main | 2023-02-27T12:25:55.546119 | 2021-01-27T15:45:39 | 2021-01-27T15:45:39 | 329,729,585 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,287 | r | mifSearch1_TmeanB.R | rm(list=ls())
require(pomp)
require(plyr)
dat <- read.csv("DataCovar_1975-2000.csv",header=T) # read in data file with the malaria data and covariates
x <- as.numeric(Sys.getenv("PBS_ARRAYID"))
seqP <- seq(from = 1, to = 50000, by = 100)
now.num <- seqP[x]
y <- read.csv("parameters_TmeanB.csv")
param <- as.numeric(y[now.num,])
param.names <- colnames(y)
names(param) <- param.names
source("poObject_TmeanB.R")
for(cont in now.num:(now.num+99)){
for(i in 1:3){
seed <- ceiling(runif(1,min=1,max=2^30))
set.seed(seed)
param <- as.numeric(y[cont,])
names(param) <- param.names
cat(cont, i, "\n")
tryCatch(mif2(
po,
Nmif = 50, #change from 10 to 50
start = param,
Np = 1000, #change from 1000 to 15000
cooling.type="hyperbolic",
cooling.fraction.50 = 0.5,
rw.sd = rw.sd(muEI = 0.03, muIQ = 0.03, muIS = 0.03, muQS = 0.03, sigPRO = 0.03, sigOBS = 0.03,
tau = 0.03, rho = 0.03, betaOUT = 0.03, bT4 = 0.03, bT6 = 0.03, b1 = 0.03, b2 = 0.03,
b3 = 0.03, b4 = 0.03, b5 = 0.03, b6 = 0.03, q0 = 0.03,
S_0 = 0.03, E_0 = 0.03, I_0 = 0.03, Q_0 = 0.03 , K_0 = 0.03, F_0 = 0.03),
transform=TRUE), error = function(e) e) -> mifout
if(length(coef(mifout)) > 0){
loglik.mif <- replicate(n=5,logLik(pfilter(po,
params=coef(mifout),Np=1000,max.fail=500)))
bl <- logmeanexp(loglik.mif,se=TRUE)
loglik.mif.est <- bl[1]
loglik.mif.se <- bl[2]
cat(cont,loglik.mif.est,"\n")
### SAVE OUTPUT
if(is.finite(loglik.mif.est)){
par.out <- coef(mifout)
names(par.out) <- param.names
if(file.exists("mifOutput_TmeanB.csv")){
write.table(t(as.matrix(c(cont,seed,par.out,loglik.mif.est,loglik.mif.se))),
"mifOutput_TmeanB.csv", append = T,
col.names=F, row.names=F, sep = ",")
} else{
write.table(t(as.matrix(c(cont,seed,par.out,loglik.mif.est,loglik.mif.se))),
"mifOutput_TmeanB.csv", append = T, col.names=c("run","seed",
param.names, "loglik", "loglik.se"), row.names=F, sep = ",") }
}
}
}
}
|
7ec1d2f0c9ee81a26861a286bacfc1c3abe5a5cf | 422e5b55c4a07e0c489d1eb9ef708345f67d641f | /scripts/samnet_models.R | a1e22dc0bea45ac1b60b3825400b984c271c55bd | [] | no_license | dev-vw/samnet | 5ced6e3e379abce0f2c138bbdf61ec43796aeb1d | bc301f87c23a50ef0ea03920ecc5f1a0cd75d837 | refs/heads/master | 2021-04-05T23:41:35.070795 | 2018-03-20T20:49:19 | 2018-03-20T20:49:19 | 124,447,566 | 0 | 0 | null | 2018-03-08T21:00:14 | 2018-03-08T21:00:14 | null | UTF-8 | R | false | false | 2,716 | r | samnet_models.R | #### --------------------
#### PRELIMINARIES
#### --------------------
# import the statnet library
library(foreign)
library(tidyverse)
library(statnet)
require(foreign)
# sets the current working directory
setwd("/Users/vaniawang/Google Drive/grad school/PhD UCSB/projects/msm_seattle/raw data/")
# importing the .csv file and converting it to a tibble
msmdata <- as.tibble(read.dta('msm_data.dta'))
#### --------------------
#### DATA CLEANING
#### --------------------
# makes a new race variable that combines race___1 to 99, and then
# removes all race variables except "race" from the dataset
## msmdata <-
## mutate(msmdata, race = case_when(
## msmdata$race___1 == 'Checked' ~ 1,
## msmdata$race___2 == 'Checked' ~ 2,
## msmdata$race___3 == 'Checked' ~ 3,
## msmdata$race___4 == 'Checked' ~ 4,
## msmdata$race___5 == 'Checked' ~ 5,
## msmdata$race___6 == 'Checked' ~ 6,
## msmdata$race___88 == 'Checked' ~ 88,
## msmdata$race___99 == 'Checked' ~ 99,
## TRUE ~ 0)) %>% select(-matches('race___'))
# removes the date_today and agecheck variable
msmdata <- select(msmdata, -date_today)
# removes the agecheck variable
msmdata <- select(msmdata, -agecheck)
# removes the age_incorrect variable
msmdata <- select(msmdata, -age_incorrect)
# making subsets of data
## msmdata_het <- msmdata %>%
## select(-matches(race___))
#### --------------------
#### Descriptive Statistics
#### --------------------
# vector degree list of lifetime sexual partnerships with women,
f_rel.life <- msmdata$lifetime_women[!is.na(msmdata$lifetime_women)]
# number of msm who have ever had sex with women
f_rel.msm_num <- sum(!is.na(msmdata$lifetime_women))
# number of women msm have ever had sex with, assuming no duplicates
f_rel.f_num
#### --------------------
#### MODELS
#### --------------------
## -----
## Model 1: ERGM model for LIFETIME sexual relationships with women
## -----
# 146 is the number of men who have ever had sex with women in their
# lifetime
het.net <- network.initialize(272, directed = FALSE)
het.net %v% 'sex' <- c(rep(0, 146), rep(1, 126))
het.deg <- msmdata$lifetime_women[!is.na(msmdata$lifetime_women)]
het.mixmat <- c(0, 275)/2
het.edges <- sum(het.mixmat)
het.rel <- het.mixmat[1]
het.target.stats <- c(het.edges, het.rel)
het.fit <- ergm(
het.net ~ edges + nodematch('sex'),
target.stats = het.target.stats)
summary <- (het.fit)
het.sim1 <- simulate(het.fit)
## -----
## MODEL 2: ERGM model for LIFETIME sexual relationships with men
## -----
|
6ae2e539f21db3467dfbb3efac297a2b3f626625 | a6617bd28254e45d0f948c84d1d26d1e17a7689e | /inst/scripts/ROPOS2019.R | a344e50f63f001fdee62b510b928313cc151eeb4 | [
"MIT"
] | permissive | BigelowLab/fvcom | 37dba77b265e664920f9b68af699a445c5061e79 | 68566ac2c161bde1f549d6157dc69cee2fc8e704 | refs/heads/master | 2023-03-06T14:58:59.850621 | 2023-02-28T14:49:22 | 2023-02-28T14:49:22 | 204,050,423 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,164 | r | ROPOS2019.R | suppressPackageStartupMessages({
library(ncdf4)
library(readr)
library(dplyr)
library(fvcom)
library(sf)
})
#devtools::load_all("/mnt/ecocast/corecode/R/fvcom")
read_start <- function(filename = "ROPOS2019.geojson",
crs = NULL){
x <- sf::read_sf(filename)
if (!is.null(crs)) x <- sf::st_transform(x, crs)
x
}
BASE_URL <- 'http://www.smast.umassd.edu:8080/thredds/dodsC/models/fvcom/NECOFS/Archive/NECOFS_GOM'
URL <- file.path(BASE_URL,'2019/gom4_201906.nc')
X <- fvcom::FVCOMPhysics(URL)
P0 <- read_start("/mnt/ecocast/projects/fvcom/ROPOS2019/ROPOS2019.geojson",
crs = "+init=nad83:1802")
tstep = 60 * 15
tmax = 60 * 60 * 24 * 30
reverse = TRUE
verbose = FALSE
show_progress = TRUE
drag = c(0,0,0)
fixed_z = TRUE
clip_z = TRUE
pp <- particle_track(X, P0 = P0,
tstep = tstep, # 15 minutes
tmax = tmax, # 30 days
reverse = reverse,
verbose = verbose,
clip_z = clip_z,
show_progress = show_progress,
filename = "ROPOS2019-06-zclip-track.geojson")
X = 0
|
67926c9228a40df9a915014cd7abb85a56d316eb | 871b42e4ff1fa7efc21d8ccdd265e300cb283408 | /man/shaidyRegisterRepoAPI.Rd | 935d710bf1c467721421145f91c3e6f9d415d415 | [] | no_license | MD-Anderson-Bioinformatics/NGCHM-R | 2abef86ba0c4b8befaa803b03f37a495e9d86d6b | fdc3519bad51f1de2631d45d897fdcc2357edf90 | refs/heads/main | 2023-08-31T23:22:28.187163 | 2023-08-15T20:28:01 | 2023-08-15T20:28:01 | 76,884,228 | 8 | 5 | null | 2023-08-17T22:28:11 | 2016-12-19T17:51:24 | R | UTF-8 | R | false | true | 417 | rd | shaidyRegisterRepoAPI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shaidy.R
\name{shaidyRegisterRepoAPI}
\alias{shaidyRegisterRepoAPI}
\title{Set the methods for the repository API called api}
\usage{
shaidyRegisterRepoAPI(api, methods)
}
\arguments{
\item{api}{The name of a repository API}
\item{methods}{A list of repository methods}
}
\description{
Set the methods for the repository API called api
}
|
cc5063f698ba8520b5edef27c6d49ea7ee189562 | f210aaf6dc2e9e331bcc0a19e151587daa0cb5c5 | /working_files/shiny/shiny-jorgen-search.R | 032241db22b61e79f664e4da6659b6fbf3b65493 | [] | no_license | meakulpa/rubasic | fcbbd388c6a4f037c7fd3e80f43d8462e4815b92 | eeeb3aa4624c461c4f4746848d309b2a8ad2c1f7 | refs/heads/master | 2020-06-05T20:24:18.539452 | 2019-02-13T21:05:07 | 2019-02-13T21:05:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,216 | r | shiny-jorgen-search.R |
library(shiny)
library(spotifyr)
library(stringr)
library(DT)
ui <- shinyUI(fluidPage(
titlePanel("Search Spotify"),
sidebarLayout(
sidebarPanel(
textInput("artist", "1. Name an artist:"),
htmlOutput("artistImage")
),
mainPanel(
h3("Artists matching your search"),
tableOutput("artistTable")
)
)
))
server <- shinyServer(function(input, output) {
# Get access token
Sys.setenv(SPOTIFY_CLIENT_ID = 'a98864ad510b4af6851331638eec170f')
Sys.setenv(SPOTIFY_CLIENT_SECRET = '6445326414dd4bf381afbc779b182223')
access_token <- get_spotify_access_token()
artists <- reactive({
req(input$artist)
get_artists(artist_name = input$artist, access_token = access_token)
})
output$artistTable <- renderTable({
artists()$artist_name
},
colnames = FALSE)
output$artistImage <- renderUI({
image_url <- artists()$artist_img[1]
tags$img(src = image_url, height = 200, width = 200)
})
})
# Run the application
shinyApp(ui = ui, server = server)
|
1fea88414a93eab7b1d4a799c85f5c21f76a7518 | e37e15a714185d736f6ec2d5988d344f561bba44 | /R/positions_stat.R | 9464ef41e73c0f3c82d24b3a7193cc0a6e3f9c4d | [] | no_license | kovarsky/metagenome_old | add01ca8a005ecd182b1a7a2734853a1fa79d374 | 1b474ace40fa29d18df0c8de98bca82cbfe96101 | refs/heads/master | 2016-09-16T04:36:20.633171 | 2013-07-17T16:40:34 | 2013-07-17T16:40:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,885 | r | positions_stat.R | stat_table <- function(dir,prefix){
file <- paste(dir,prefix,'varpos_variants.txt',sep='')
data <- read.csv(file,header=F,sep='\t')
names(data) <- c('chrom','pos','A','T','G','C')
norm_func <- function(v){
return(sqrt(sum(v*v)))
}
data$norm <- apply(data[,3:6],1,norm_func)
data$sum <- apply(data[,3:6],1,sum)
#data$diff <- data$sum - data$max
#data$frac <- data$diff/data$sum
data$flag <- prefix
print (prefix)
print ('--done!--')
return(data)
}
filter_data <- function(data,covtresh=0.5,fractresh=0.1){
maxcov <- max(data$sum)
data <- data[-which(is.na(data$frac)),]
data <- data[which(data$frac > fractresh),]
data <- data[which((data$sum/maxcov) > covtresh),]
return(data)
}
merge_data <- function(data_list){
len <- length(data_list)
if (len < 2){
return(data_list[[1]])
}
else{
data <- data_list[[1]]
for (i in 2:len){
data <- rbind(data,data_list[[i]])
}
return(data)
}
}
read_all_tables <- function(dir,prefixes){
data_list <- list()
for (prefix in prefixes){
data_list[[prefix]] <- stat_table(dir,prefix)
}
return(data_list)
}
pos_processing <- function(data_list,covtresh,fractresh){
len <- length(data_list)
for (i in 1:len){
data_list[[i]] <- filter_data(data_list[[i]],covtresh,fractresh)
}
res <- merge_data(data_list)
}
comm_pos <- function(data1,data2){
label1 = data1$flag[1]
label2 = data2$flag[1]
len <- dim(data1)[1]
res <- data.frame(res=rep(0, len))
for (i in 3:6){
buff <- data1[,i] * data2[,i]
#print(buff)
res$res <- res$res + buff
#res <- cbind(res,data1[,i])
#res <- cbind(res,data2[,i])
#print(res)
}
norm <- data1$norm * data2$norm
res$res <- sqrt(res$res/norm)
#res$norm <- norm
res$pos <- data1$pos
res[,label1] <- data1$sum
res[,label2] <- data2$sum
res$chrom <- data1$chrom
ind <- which(res$res < 0.5)
print(length(ind))
res <- res[ind,]
ind <- which(res[,label1]>10 & res[,label2]>10)
print(length(ind))
return(res[ind,c("chrom","pos")])
}
whole_comm_pos <- function(data_list, name = '/data1/bio/runs-kovarskiy/additional_pileup_files/temp_pos.list'){
len <- length(data_list)
combs <- combn(len,2)
num <- dim(combs)[2]
for (i in 1:num){
buff <- comm_pos(data_list[[combs[1,i]]], data_list[[combs[2,i]]])
if (i > 1){
res <- merge(res,buff,by.x=c('chrom','pos'),by.y=c('chrom','pos'),all.x=T,all.y=T)
}
else{
res <- buff
}
}
write.table(x=res,file=name,sep='\t',col.names=F,row.names=F,quote=F)
return(res)
}
read_new_tables <- function(dir,prefix)
{
filename <- paste(dir,prefix,'newvarpos_variants.txt',sep='/')
data <- read.csv(filename,sep='\t',header=F)
names(data) <- c('chroms','pos','A','T','G','C','sum','diff','max_alt')
covfilename <- paste(dir,prefix,'common_coverage_summary.list',sep='/')
covdata <- read.csv(covfilename,sep='\t',header=F)
names(covdata) <- c('sum','times')
return(list(data,covdata))
}
read_cov_table <- function(dir,prefix)
{
covfilename <- paste(dir,prefix,'cover_summ.list',sep='/')
covdata <- read.csv(covfilename,sep='\t',header=F)
names(covdata) <- c('sum','times')
return(covdata)
}
plot_cov_stat <- function(strainnums){
len <- length(strainnums)
j = 0
for (i in strainnums){
print(i)
setnum <- as.character(strain_meta.data$set[i])
prefix <- strain_meta.data$str_subset[i]
strainname <- paste(strain_meta.data$short_descript[i],as.character(i),sep='_')
dir <- paste(CONSTDIR,setnum,sep='')
covdata <- read_cov_table(dir,prefix)
covdata$strain_num <- i
covdata$strainname <- strainname
if (j == 0){
res <- covdata
}
else{
res <- rbind(res,covdata)
}
j <- j + 1
}
return(res)
}
get_cdf <- function(datalist, max_alt_flag=F){
#maxes <- apply(data[,c('A','T','G','C')],1,max)
#data$diff <- data$sum - maxes
data <- datalist[[1]]
covdata <- datalist[[2]]
data$ind <- 1
sum_aggr <- aggregate(data$ind, by=list(data$sum),sum)
names(sum_aggr) <- c('sum','total_num')
if (max_alt_flag){
sum_diff_aggr <- aggregate(data$ind,by=list(data$max_alt,data$sum),sum)
}
else{
sum_diff_aggr <- aggregate(data$ind,by=list(data$diff,data$sum),sum)
}
names(sum_diff_aggr) <- c('diff','sum','num')
merged_data <- merge(sum_diff_aggr,covdata,by.x=c('sum'),by.y=c('sum'),all.x=T,all.y=F)
merged_data$freq <- merged_data$num/merged_data$times
merged_data$variab <- merged_data$diff/merged_data$sum
merged_data <- merged_data[which(merged_data$diff != 0),]
return(merged_data)
#ecdf_data$freq <- ecdf_data$num/ecdf_data$total_num
#return(data)
}
aggregation <- function(data, tresholds){
agg <- function(tresh){
temp <- data[data$variab > tresh,]
res <- aggregate(temp$freq,by=list(temp$sum),sum)
freqname<-paste('freq',as.character(tresh),sep='')
names(res) <- c('sum',freqname)
return(res)
}
len <- length(tresholds)
for (i in 1:len){
tresh <-tresholds[i]
print(i)
if (i==1){
res <- agg(tresh)
}
else{
res <- merge(res,agg(tresh),by.x=c('sum'),by.y=c('sum'),all.x=F,all.y=F)
}
}
return(res)
}
aggregation_by_diff <- function(data, tresholds){
agg <- function(tresh){
temp <- data[data$diff > tresh,]
res <- aggregate(temp$freq,by=list(temp$sum),sum)
numname<-paste('more',as.character(tresh),sep='')
names(res) <- c('sum',numname)
return(res)
}
len <- length(tresholds)
for (i in 1:len){
tresh <-tresholds[i]
print(i)
if (i==1){
res <- agg(tresh)
}
else{
res <- merge(res,agg(tresh),by.x=c('sum'),by.y=c('sum'),all.x=F,all.y=F)
}
}
return(res)
}
CONSTDIR <- '/data1/bio/runs-kovarskiy/metagenome_data/'
plot_position_stat <- function(strainnums, by.diffs = F, max_alt_flag=F){
len <- length(strainnums)
j = 0
for (i in strainnums){
print(i)
num <- which(strain_meta.data$set == i)
setnum <- as.character(strain_meta.data$set[num])
#prefix <- strain_meta.data$str_subset[num]
strainname <- paste(strain_meta.data$short_descript[num],num,sep='_')
#dir <- paste(CONSTDIR,setnum,sep='')
datalist <- read_new_tables(CONSTDIR,setnum)
merged_data <- get_cdf(datalist,max_alt_flag)
if (by.diffs){
agg <- aggregation_by_diff(merged_data,c(0,1,2,5))
}
else{
agg <- aggregation(merged_data,c(0.0,0.01,0.02,0.05))
}
agg$strain_num <- i
agg$strainname <- strainname
if (j == 0){
res <- agg
}
else{
res <- rbind(res,agg)
}
j <- j + 1
}
return(res)
}
cover_stat <- function(setnum){
path <- '/data1/bio/runs-kovarskiy/metagenome_data/'
statfilename <- paste(path, setnum, sep='','/total_sample_coverages.tsv')
statdata <- read.csv(statfilename, sep=' ', head=F)
namesfile <- paste('/data1/bio/runs-kovarskiy/metagenome_data',setnum,'pileups.list',sep='/')
snames <- read.csv(namesfile, sep='', header=F)
snames <- snames[,1]
names(statdata) <- c('names','covered_positions','sum_of_cover_depth')
statdata$names <- snames
statdata <- merge(x=statdata,y=data.sample_names2,by.x=c('names'),by.y=c('names'),all.x=T,all.y=F)
return(statdata)
}
cover_stats <- function(setnums){
len <- length(setnums)
for (i in 1:len){
data <- try(cover_stat(setnums[i]))
if (typeof(data)!='character'){
summn <- sum(as.numeric(data$sum_of_cover_depth))
summc <- sum(as.numeric(data$covered_positions))
temp <- data.frame(setnum=setnums[i],summn=summn,summc=summc)
print(temp)
if (i == 1){
res <- temp
}
else{
res <- rbind(res,temp)
}
}
}
return(res)
}
finstat_plot <- function(position_stat_data){
strain_num_ind <- which(names(position_stat_data)=='strain_num')
temp <- melt(position_stat_data[,-strain_num_ind],id.vars=c('sum','strainname'))
p <- ggplot(temp,aes(x=sum,y=value,group=variable,fill=variable))+geom_density(stat="identity",alpha=0.5,size=0.3)+facet_wrap(~strainname)+scale_y_continuous(limits=c(0,0.5))
return(p)
}
snp_rate_stat <- function(setnums, treshs){
len <- length(setnums)
for (i in 1:len){
setnum <- setnums[[i]]
snp_rate <- snp_rate_load(setnum, treshs)
if (i==1){
res <- snp_rate
}
else{
res <- rbind(res, snp_rate)
}
}
return(res)
}
snp_rate_load <- function(setnum, treshs=c(5)){
print(setnum)
snp_stat_file <- paste('/data1/bio/runs-kovarskiy/metagenome_data',setnum,'new_snp_stat2.tsv',sep='/')
namesfile <- paste('/data1/bio/runs-kovarskiy/metagenome_data',setnum,'pileups.list',sep='/')
snames <- read.csv(namesfile, sep='', header=F)
snames <- snames[,1]
#snames <- rep(snames, rep(length(treshs),length(snames)))
new_snp_stat <- read.csv(snp_stat_file, sep='', header=F)
names(new_snp_stat) <- c('names','varpos_cov','snps')
new_snp_stat$treshs <- treshs
cov_file <- paste('/data1/bio/runs-kovarskiy/metagenome_data',setnum,'total_sample_coverages.tsv',sep='/')
cov_stat <- read.csv(cov_file, sep='', header=F)
names(cov_stat) <- c('tcov','tnts')
cov_stat$names <- snames
res <- merge(new_snp_stat, cov_stat, by.x=c('names'), by.y=c('names'), all.x=F, all.y=F)
res$frac <- res$snps/res$tcov
res <- merge(res, data.sample_names2, by.x=c('names'), by.y=c('names'),all.x=F,all.y=F)
res$setnum <- setnum
return(res)
}
snp_rate_plot <- function(setnums,treshold, covtreshold=10^6){
cumul_cov <- get_cumul_cov(setnums)
res <- snp_rate_stat(setnums,c(0.005, 0.01, 0.02, 0.05))
rate_stat <- res
res <- res[which(res$treshs==treshold),]
res <- res[which(res$tcov>covtreshold),]
agg <- aggregate(res$frac,by=list(res$setnum),mean)
res$set <- res$setnum
res <- res[,-11]
res <- merge(res,cumul_cov,by.x=c("set"),by.y=c("set"),all.x=T,all.y=T)
#names(agg) <- c("set",'snpfrac')
#agg <- merge(agg,cumul_cov,by.x=c("set"),by.y=c("set"),all.x=T,all.y=T)
return(list(res, rate_stat))
}
get_cumul_cov <- function(setnums){
res <- data.frame(set=c(), possum=c(), ntsum=c(), samplnum=c())
for (setnum in setnums){
fname <- paste('/data1/bio/runs-kovarskiy/metagenome_data',setnum,'total_sample_coverages.tsv',sep='/')
data <- read.csv(fname, sep=' ', header=F)
possum <- sum(as.numeric(data[,2]),na.rm=T)
ntsum <- sum(as.numeric(data[,3]),na.rm=T)
if (is.na(ntsum)){
print(data)
}
samplnum <- length(which(!is.na(data[,3])))
temp <- data.frame(set=setnum, possum=possum, ntsum=ntsum, samplnum=samplnum)
res <- rbind(res, temp)
}
res <- merge(res,strain_meta.data[,c('set','length','short_descript')],by.x=c("set"),by.y=c("set"),all.x=F,all.y=F)
return(res)
}
#plot: ggplot(res,aes(x=flag,ymin=pos,ymax=pos+100,size=50,color=flag)) + geom_linerange() + coord_flip() + scale_size(range = c(1, 50)) + scale_alpha(range=c(0.1,0.5)) |
c747db7d7cb7ee1fbdb9be29dd8d70e8f4c185c6 | 78eb8c3af3ca1d5e88ba0a32fb85a6f13adba1b7 | /Review_xPassModel.R | 19192360aa33bd2336f63e8da4a4c18f0c1a48ea | [] | no_license | tabosch/ASAShootingApp | 473f4f114665749f03f9cba858af10e478498b96 | c17c6607909ae1b0d69efbf7195e747e641b41e4 | refs/heads/master | 2020-03-16T19:51:20.368727 | 2018-05-02T21:22:44 | 2018-05-02T21:22:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,083 | r | Review_xPassModel.R | # Review xPass model
library(dplyr)
library(gbm)
library(stringr)
library(ggplot2)
library(scales)
passes <- readRDS("IgnoreList/AllPassingData.rds")
smod <- readRDS("IgnoreList/xPassModel.rds")
smod16 <- readRDS("IgnoreList/xPassModel_2016.rds")
passes[["success.pred.16"]] <- predict(smod16, passes, type = "response", n.trees = 1000)
# Partial plots ####
plot(smod, i.var = "angle", return.grid = T, type = "response", continuous.resolution = 25) %>%
mutate(angle = -180*angle/pi) %>%
filter(abs(angle) <= 160) %>%
ggplot(aes(x = angle, y = y)) +
geom_line(size = 2) +
xlab("Angle (degrees)") + ylab("Completion probability") + ggtitle("Predicted pass success by angle") +
scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
labs(caption = "0 degrees implies forward; positive angles imply the passer's right") +
theme(plot.caption = element_text(hjust = 0.5))
xy.tab <- plot(smod, i.var = c("x", "y"), return.grid = T, type = "response", continuous.resolution = 12)
names(xy.tab) <- c("x", "y", "pred")
xy.tab %>%
ggplot(aes(x = x, y = y)) +
geom_tile(aes(fill = pred)) +
scale_fill_gradient2(low = "red", mid = "yellow", high = "forestgreen", midpoint = .7) +
xlab("X coordinate (yards from own goal)") + ylab("Y coordinate (yards from right sideline)") +
ggtitle("Predicted pass success by field position")
# Playerdiff
# Make table of indicators with observed/predicted rates
# Validation ####
# By position
pos.ae <- passes %>%
filter(year >= 2017) %>%
group_by(season.pos) %>%
summarize(Passes = n(),
Act = sum(success)/Passes,
Exp = sum(success.pred.16)/Passes,
`A-E` = Act - Exp)
pos.ae %>%
ggplot(aes(x = season.pos, y = `A-E`)) +
geom_bar(stat = "identity") +
geom_line(aes(y = 0)) +
xlab("Position") +
ggtitle("Actual vs. expected passing by player position")
# By gamestate
gs.ae <- passes %>%
filter(year >= 2017) %>%
group_by(Gamestate = pmax(-4, pmin(4, ifelse(home == 1, hscore - ascore, ascore - hscore)))) %>%
summarize(Passes = n(),
Act = sum(success)/Passes,
Exp = sum(success.pred.16)/Passes,
`A-E` = Act - Exp)
gs.ae %>%
ggplot(mapping = aes(Gamestate, `A-E`)) +
geom_line(size = 2) +
geom_line(aes(y = 0)) +
ggtitle("Actual vs. expected passing by gamestate")
# By coordinates
xy.ae <- passes %>%
filter(year >= 2017) %>%
group_by(x = cut(x, 4), y = cut(y, 4)) %>%
summarize(Passes = n(),
Act = sum(success)/Passes,
Exp = sum(success.pred.16)/Passes,
`A-E` = Act - Exp)
xy.ae %>%
ggplot(aes(x = x, y = y)) +
geom_tile(aes(fill = `A-E`)) +
scale_fill_gradient2(low = "red", mid = "forestgreen", high = "red", midpoint = 0) +
xlab("X coordinate (yards from own goal)") + ylab("Y coordinate (yards from right sideline)") +
ggtitle("Actual vs. expected passing by zone")
chi.test.stat <- sum((xy.ae$Act - xy.ae$Exp)^2*xy.ae$Passes/xy.ae$Exp)
(p.value <- pchisq(chi.test.stat, 16, lower.tail = F))
# By field zone
# By general direction/field zone |
e4bf3cd49dc1fcdaea9b346bc9eab153e1c3a621 | 3fde134c2cee356e5a432ae0a71a96423f76074c | /man/get.pb.umi.Rd | b757b9ecf3c67f637e3aba91ff7b12b2e8144c69 | [] | no_license | klprint/snCerFun | 7ec3e0d751bd1f4c849f7696843696b68cd1b1ed | 17ba9d9b1b1d34c03b4c207b30e4e23e65f7f4e5 | refs/heads/master | 2020-12-23T17:11:29.468163 | 2020-01-30T13:06:38 | 2020-01-30T13:07:15 | 237,214,006 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 718 | rd | get.pb.umi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{get.pb.umi}
\alias{get.pb.umi}
\title{Creates a pseudobulk using a grouped tibble}
\usage{
get.pb.umi(umi, df.grouped, cell.col = "cell_id", min.cells = 50,
n.cores = 1)
}
\arguments{
\item{umi}{UMI count matrix (genes x cells)}
\item{df.grouped}{Grouped tibble. The grouping should only be based on a single column}
\item{cell.col}{Column in the tibble, where the cell names are stored.}
\item{min.cells}{Minimal number of cells which should be merged to a pseudobulk}
\item{n.cores}{Number of cores to run in parallel.}
}
\value{
Matrix of pseudobulks
}
\description{
Creates a pseudobulk using a grouped tibble
}
|
deb63056a5b1e9fd2551699bda15462cc2019225 | 03bd83293b953b58002c3748370c3eacbcee880f | /legacy/printOneSolution.R | c225e61d481f3f0d5c6b1d69b92ed9120adc9d77 | [] | no_license | davgutavi/trlab-R-utilities | 34f0c25a95461aed482f393d7379dd82412edad7 | cf3edbcb4a1cea70c3a2bea4e0e6e28301e7b032 | refs/heads/master | 2023-07-21T01:31:09.827263 | 2023-07-13T12:10:29 | 2023-07-13T12:10:29 | 107,287,411 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,341 | r | printOneSolution.R | source("configuration.R")
source("SolUtilities.R")
require(lattice)
##***********************************************inputs
input <- "/Users/davgutavi/Desktop/soluciones_paper/elu/elu_msl_02.sol"
##***********************************************inputs
##***Lectura del fichero .sol
props <- read.properties(input)
##***Obtener información del dataset
datasetInfo <- getDataset(props$dataset)
show(paste("selected dataset =", datasetInfo$.attrs["name"]))
#****Obtener los paths de las listas de genes, condiciones y tiempos
paths <- getGSTtags(datasetInfo)
show(paths)
#****Obtener los genes, condiciones y tiempos en forma de vector
genesL <- as.vector(read.table(paths["genes"],sep = "\n")$V1)
samplesL <- as.vector(read.table(paths["samples"],sep = "\n")$V1)
timesL <- as.vector(read.table(paths["times"],sep = "\n")$V1)
#****Obtener valores del dataset
dataset <- getDatasetValues(datasetInfo)
#****Obtener los puntos [gen,condición,tiempo,expresión génica] de cada solución
solutions <-getTriclusters(props,dataset)
aux1 <- paste0(unlist(strsplit(input, "/")))
aux2 <- aux1[-length(aux1)]
aux3 <-paste0(aux2,collapse="/")
out1 <- paste0(aux3, "/graphs/")
out2 <- paste0(aux3, "/tri_02")
solution<-solutions[[1]]
outPath<-out2
####*****************************************************************************************************************************
####*****************************************************************************************************************************
####*****************************************************************************************************************************
####*****************************************************************************************************************************
w<-8
h<-8
fsizeXaxis<-0.7
fsizeYaxis<-0.7
fsizeBoxes<-1.0
color=TRUE
axis <-c(fsizeYaxis,fsizeXaxis)
boxes<-list (cex = fsizeBoxes)
gforctag <- "Genes for each Condition"
tforctag <- "Times for each Condition"
cforttag <- "Conditions for each Time"
eltag <- "expression levels"
timetag <- "times"
gentag <- "genes"
g <- solution$g
s <- solution$s
t <- solution$t
el <- solution$el
fg<-factor(g)
fs<-factor(s)
ft<-factor(t)
distance <- 2
left <- distance
div <- ceiling(length(levels(fg))/2)
right<-length(levels(fg))-distance
at<-c(left,div,right)
labels<-c(g[left],g[div],g[right])
genes <- c()
for (gi in levels(fg)){
genes<-append(genes, as.character(genesL[as.numeric(gi)+1]))
}
conditions <- c()
for (si in levels(fs)){
conditions<-append(conditions, as.character(samplesL[as.numeric(si)+1]))
}
times <- c()
for (ti in levels(ft)){
times<-append(times, as.character(timesL[as.numeric(ti)+1]))
}
out_gct <-paste0(outPath, "_gct.eps")
out_gtc <-paste0(outPath, "_gtc.eps")
out_tgc <-paste0(outPath, "_tgc.eps")
# x=genes, o=conditions p=times
xyplot(el ~ fg | ft,solution,
main= cforttag,
xlab= gentag,
ylab= eltag,
groups = s,
type = "a",
font = "arial",
scales = list(x = list(at=at,labels=genes),cex=axis),
layout = c(1, nlevels(ft)),
strip=strip.custom(factor.levels=times,par.strip.text = boxes))
dev.copy2eps(file = out_gct, width=w, height=h)
# x=genes, o=times p=conditions
xyplot(el ~ fg | fs, solution,
main=tforctag,
xlab= gentag,
ylab= eltag,
groups = t,
type = "a",
font = "arial",
scales = list(x = list(at=at,labels=genes),cex=axis),
layout = c(1, nlevels(fs)),
strip=strip.custom(factor.levels=conditions,par.strip.text = boxes)
)
dev.copy2eps(file = out_gtc, width=w, height=h)
# x=times, o=genes p=conditions
xyplot(el ~ ft | fs, solution,
main=gforctag,
xlab= timetag,
ylab= eltag,
groups = g,
type = "a",
font = "arial",
layout = c(1, nlevels(fs)),
scales = list(x = list(labels=times),cex=axis),
strip=strip.custom(factor.levels=conditions,par.strip.text = boxes)
)
dev.copy2eps(file = out_tgc, width=w, height=h) |
be843ab1fecd5b40cf03f236606cc45fdae541c7 | 7dfa69da69b9a064ae6ec41822751337c65fefad | /r/auto_r/templates/ALL_ssz_as_factor_recovered.r | 199fcc402d113e74fb8a7de28ccd9bd2d7070482 | [
"MIT"
] | permissive | CESEL/WarningsGuruPaper | 1c019743bc4f20306a4c3ec1eceef797008c7753 | f40ded3ccd3b818e9a69262c62e9aee38f54f02e | refs/heads/master | 2022-04-26T18:10:12.376211 | 2020-04-10T15:08:41 | 2020-04-10T15:08:41 | 254,658,006 | 0 | 0 | null | 2020-04-10T14:42:04 | 2020-04-10T14:42:04 | null | UTF-8 | R | false | false | 2,448 | r | ALL_ssz_as_factor_recovered.r | require("RPostgreSQL")
# Used to obtain deviance explained
require("modEvA")
# used for multi-colinearity | vif
require("car")
con <- dbConnect(dbDriver("PostgreSQL"), dbname="cas_exp", host="localhost")
newWarnings <- dbGetQuery(con, "SELECT new_warnings
from commit_warning_summary as w
where w.new_warnings > 0")
avgDevDelta <- dbGetQuery(con, "SELECT c.fix = 'True' as fix, c.contains_bug = 'TRUE' as contains_bug, repo_name, nd,
la, ld, @lt as lt, ndev, @age as age, exp, @rexp as rexp, jlint_warnings, new_jlint_warnings, findbugs_warnings,
new_findbugs_warnings, security_warnings, new_security_warnings,
build != 'BUILD' as build_failed, warnings > 0 as w_bool
from commits as c, commit_warning_recovered_summary as w
where c.repository_id = w.repo and c.commit_hash = w.commit")
#detach(avgDevDelta)
attach(avgDevDelta)
sink("{summary_path}/recovered/bugs/models_as_factor_all_repo_logged_recovered.txt")
summary(newWarnings)
summary(avgDevDelta)
print("model 1")
mb1 = glm(contains_bug ~
log2(1+nd) +
log2(1+la) +
log2(1+ld) +
log2(1+lt) +
fix +
log2(1+ndev) +
log2(1+age) +
log2(1+exp) +
log2(1+rexp) +
as.factor(repo_name), family=binomial(), control = list(maxit = 50))
summary(mb1)
vif(mb1)
print(round(exp(coef(mb1)),digits=2))
print(paste("d2 = ", Dsquared(mb1)))
# Add the new warning counts
print("model 2 - just warnings")
mb2 = glm(contains_bug ~
log2(1+new_security_warnings) +
log2(1+security_warnings) +
log2(1+new_findbugs_warnings) +
log2(1+new_jlint_warnings) +
log2(1+findbugs_warnings) +
log2(1+jlint_warnings) +
build_failed +
as.factor(repo_name), family=binomial(),
control = list(maxit = 50))
summary(mb2)
#vif(mb2)
print(round(exp(coef(mb2)),digits=2))
print(paste("d2 = ", Dsquared(mb2)))
# Combined model
print("combined model")
mb3 = glm(contains_bug ~
log2(1+nd) +
log2(1+la) +
log2(1+ld) +
log2(1+lt) +
fix +
log2(1+ndev) +
log2(1+age) +
log2(1+exp) +
log2(1+rexp) +
log2(1+new_security_warnings) +
log2(1+security_warnings) +
log2(1+new_findbugs_warnings) +
log2(1+new_jlint_warnings) +
log2(1+findbugs_warnings) +
log2(1+jlint_warnings) +
build_failed +
as.factor(repo_name), family=binomial(),
control = list(maxit = 50))
summary(mb3)
#vif(mb3)
print(round(exp(coef(mb3)),digits=2))
print(paste("d2 = ", Dsquared(mb3)))
print("original with combined")
md1 = anova(mb1, mb3)
print(md1)
summary(md1)
print("warnings with combined")
md2 = anova(mb2, mb3)
print(md2)
summary(md2)
sink()
|
d577ff00695c8d8eaeccba200ddd4ca3dd48ef9e | c758fb1c8ba3238f7e3a9b049a42765796652c53 | /make_concoct_cov.r | 94083014be88e344a7ff623dfb6cd6e52ac5d191 | [] | no_license | microgenomics/MAGnifico | 5032e75e223df4c402e69b71d1ff4b272d83e716 | e3e293f24c77e523dfa47267a634dfb5f4f4e4e6 | refs/heads/master | 2020-06-03T03:42:49.489797 | 2019-06-11T18:29:56 | 2019-06-11T18:29:56 | 191,423,525 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,489 | r | make_concoct_cov.r | #get directory name
#get directory names
dir <-commandArgs(trailingOnly = TRUE)
##rhizosphere_2017
##define sample list and number of samples
#samples <- c("G1-1","G1-2","G1-3","G2-1","G2-2","G2-3")
#sediments mar de cortes
#define sample list and number of samples
samples<-c("PRO1748_Plate1_C1", "PRO1748_Plate1_D1", "PRO1748_Plate1_B2", "PRO1748_Plate1_H2", "PRO1748_Plate1_B3", "PRO1748_Plate1_D3", "PRO1748_Plate1_F3", "PRO1748_Plate1_H3", "PRO1748_Plate1_D4", "PRO1748_Plate1_E4", "PRO1748_Plate1_A5", "PRO1748_Plate1_F5", "PRO1748_Plate2_A1", "PRO1748_Plate2_B1", "PRO1748_Plate2_C1", "PRO1748_Plate2_D1", "PRO1748_Plate2_E1", "PRO1748_Plate2_F1", "PRO1748_Plate2_G1", "PRO1748_Plate2_H1", "PRO1748_Plate2_A2", "PRO1748_Plate2_B2", "PRO1748_Plate2_C2", "PRO1748_Plate2_D2", "PRO1748_Plate2_E2", "PRO1748_Plate2_F2", "PRO1748_Plate2_G2", "PRO1748_Plate2_H2", "PRO1748_Plate2_A3", "PRO1748_Plate2_C3", "PRO1748_Plate2_D3", "PRO1748_Plate2_E3", "PRO1748_Plate2_F3", "PRO1748_Plate2_G3", "PRO1748_Plate2_H3", "PRO1748_Plate2_A4", "PRO1748_Plate2_B4", "PRO1748_Plate2_C4", "PRO1748_Plate2_D4", "PRO1748_Plate2_E4", "PRO1748_Plate2_F4", "PRO1748_Plate2_G4", "PRO1748_Plate2_H4", "PRO1748_Plate2_A5", "PRO1748_Plate2_B5", "PRO1748_Plate2_C5", "PRO1748_Plate2_D5", "PRO1748_Plate2_E5", "PRO1748_Plate2_F5", "PRO1748_Plate2_G5", "PRO1748_Plate2_H5", "PRO1748_Plate2_A6", "PRO1748_Plate2_B6", "PRO1748_Plate2_C6")
smpl_nb <- length(samples)
for (i in 1:smpl_nb )
{
#read table of coverage for each samples
path <- paste (dir,samples[i] , ".coverage", sep='' )
table_cov <- read.table (path , header = F)
#normalize read count to contig length and add a 10000 factor for workable values
table_cov$V2 <- table_cov$V3*10000/table_cov$V2
#make a subtable with values of interest
table_cov <- table_cov[,c(1,2)]
colnames(table_cov) <- c("contig",samples[i])
if (i == 1)
{
#prepare the general table
table_all <- table_cov
}
#rename sample table
cur_sample <- samples[i]
assign(paste (cur_sample , "_cov", sep = ""), table_cov)
}
for (i in 2:smpl_nb )
{
#add remaining samples to the general table
cur_sample <- paste (samples[i])
current <- get (paste (cur_sample , "_cov" , sep = ""))
table_all <- merge (table_all, current, by.table_all = contig, by.current = contig )
}
#write the output table
path <- paste (dir, "concoct_all.coverage", sep='' )
write.table(table_all[c(2:length(table_all$contig)),] , path , row.names = F , col.names = F , quote = F , sep = "\t")
|
76ee5c34c790335491f2bb3a3e0809c75991abd2 | 8a31a89ddd235833c7ab6e50b855810a499d41c7 | /transgen/mort_data.R | a0a1c7a1c022fb13e641c36c85b3f300950ab62f | [] | no_license | janejpark/niehs | 3030ce0c0672d1180d780cfc2e97b0e7ba2a7e5e | 875fae998d9125173d0118cb76d03783063b9e26 | refs/heads/master | 2021-01-20T15:49:46.075310 | 2020-04-04T22:42:50 | 2020-04-04T22:42:50 | 64,373,948 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,311 | r | mort_data.R | ## Mortality Data
## 2019 08 12
library(readxl)
library(tidyverse)
library(ggplot2)
library(RColorBrewer)
library(gridExtra)
setwd("~/Documents/NIEHS_LSU_UCD/niehs/transgen/DAVID/")
## Subset data by 00% and 56% samples at day 15
rawdata <- read_excel("~/Documents/NIEHS_LSU_UCD/Exp1-2/Phenotype/EmbryoMorts/190812_ars_morts.xlsx")
morts <- rawdata[rawdata$`Embryo Treatment`=="0" | rawdata$`Embryo Treatment`=="56",]
morts$trt <- paste(morts$`Parent Treatment`, morts$`Embryo Treatment`)
mortsub <- morts[morts$Day=="15",]
# also transferred total start values from day 0 to the mortsub df
# e.g.
# mortsub[mortsub$trt == "Exposed 56",]$`Total Start` <- morts[morts$trt=="Exposed 56" & morts$Day=="1",]$`Total Start`
## Can't use ANOVA on proportion or count data, need to use poisson regression (glm)
mortsub$`Embryo Treatment` <- factor(mortsub$`Embryo Treatment`)
mortsub$`Parent Treatment` <- factor(mortsub$`Parent Treatment`)
m.glm <- glm(`Percent_Dead` ~ `Parent Treatment` * `Embryo Treatment` + offset(log(`Total Start`)),
data=mortsub[mortsub$Gen=="1",])
#m.glm2 <- glm(`Percent_Dead` ~ `Parent Treatment` * `Embryo Treatment` + offset(log(`Total Start`)), family="binomial",
# data=mortsub[mortsub$Gen=="1",])
summary(m.glm)
anova(m.glm, m.glm2)
m <- lm(`Percent_Dead` ~ `Parent Treatment` * `Embryo Treatment` * Gen, data=mortsub[mortsub$Gen=="1",])
ma <- anova(m)
# Run the functions length, mean, and sd on the value of "change" for each group,
# broken down by parent and embryo treatments
library(plyr)
cdata <- ddply(mortsub, c("`Parent Treatment`", "`Embryo Treatment`", "Gen"), summarise,
N = length(`Percent_Dead`),
mean = mean(`Percent_Dead`),
sd = sd(`Percent_Dead`),
se = sd / sqrt(N)
)
cdata
## Line graphs of mortality between 00% and 56% embryonic treatment, control and exposed parent treatments
cdata$`Embryo Treatment` <- factor(cdata$`Embryo Treatment`)
pd <- position_dodge(0.1)
labels <- c(`1` = "F1", `2` = "F2")
md <- ggplot(cdata, aes(x=factor(`Embryo Treatment`), y=mean, group=`Parent Treatment`, color=`Parent Treatment`)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.2, position=pd, size=1) +
geom_line(position=pd, size=1) +
geom_point(position=pd, size=3, shape=21, fill="white") +
scale_colour_manual(values = c("#56B4E9", "#E69F00")) +
scale_x_discrete(labels=c("0" = "0%", "56" = "56%")) +
xlab("Embryo WAF Treatment (%)") +
ylab("Proportion Dead") +
facet_grid(. ~ Gen, labeller=labeller(Gen = labels)) +
ggtitle("Cumulative Mortality of ARS Embryos by Experimental Day 15") +
theme_light()
## Heart rate data
hearts <- read_excel("~/Documents/NIEHS_LSU_UCD/niehs/transgen/190814_ARS_hr.xlsx")
heart <- hearts[hearts$`WAF %`=="0" | hearts$`WAF %` == "0.56",]
hdata <- ddply(heart, c("`WAF %`", "Parent_Treatment", "Gen"), summarise,
N = length(BPM),
mean = mean(BPM),
sd = sd(BPM),
se = sd / sqrt(N)
)
hdata
hdata$`WAF %` <- factor(hdata$`WAF %`)
hdata$Parent_Treatment <- factor(hdata$Parent_Treatment)
## Line graphs for heart rates
pd <- position_dodge(0.1)
labels <- c(`0.00` = "0%", `0.56` = "56%")
hr <- ggplot(hdata, aes(x=`WAF %`, y=mean, group=Parent_Treatment, color=Parent_Treatment)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.2, position=pd, size=1) +
geom_line(position=pd, size=1) +
geom_point(position=pd, size=3, shape=21, fill="white") +
scale_colour_manual(values = c("#56B4E9", "#E69F00")) +
scale_x_discrete(labels=c("0" = "0%", "0.56" = "56%")) +
xlab("Embryo WAF Treatment (%)") +
ylab("Beats per minute (BPM)") +
facet_grid(. ~ Gen) +
ggtitle("Heart rates of ARS embryos") +
theme_light()
##glm and aov for heartrate data
l.f1 <- lm(formula=BPM ~ `WAF %` * `Parent_Treatment`, data=heart[heart$Gen == "F1",])
a.f1 <- anova(l.f1)
l.f2 <- lm(formula=BPM ~ `WAF %` * `Parent_Treatment`, data=heart[heart$Gen == "F2",])
a.f2 <- anova(l.f2)
l <- lm(formula=BPM ~ `WAF %` * `Parent_Treatment` * Gen, data=heart)
a <- anova(l)
glm(`Percent_Dead` ~ `Parent Treatment` * `Embryo Treatment` * Gen,
family="binomial",
data=mortsub)
require(gridExtra)
phen <- grid.arrange(md, hr, ncol=1)
|
4ea27b36ef1d24b9c0d93284269e5f52ff0a1d86 | 2618a34654330482c04f44a233daf3486914b184 | /plot2.R | e030c2216e8cbee9fafd941ede9e76a4d197d520 | [] | no_license | vgarzon/ExData_Plotting1 | 864ae421bb0b22dfd7dcda2ab6e42c25d3c0d83a | c614862169188727994651f0677101d14e5ef256 | refs/heads/master | 2021-01-15T21:02:06.175761 | 2016-01-29T17:56:26 | 2016-01-29T17:56:26 | 50,666,238 | 0 | 0 | null | 2016-01-29T14:09:39 | 2016-01-29T14:09:39 | null | UTF-8 | R | false | false | 898 | r | plot2.R | # Coursera Data Science Specialization
# Course 4: Exploratory data analysis
# Week 1 assignment - plot2.R
# 2016-01-29
library(lubridate)
# Read data file, mind header, separator and "NA"
hpc_dfr <- read.table("household_power_consumption.txt", header = TRUE,
sep = ";", na.strings = "?", stringsAsFactors = FALSE)
# Convert combined date, time strings to POSIXct and add column to data frame
hpc_dfr$DateTime <- dmy_hms(paste(hpc_dfr$Date, hpc_dfr$Time))
# Time interval
datInt <- interval(ymd("2007-02-01"), ymd("2007-02-03"))
# Subsetting data for plotting
hpc <- hpc_dfr[hpc_dfr$DateTime %within% datInt, ]
# Initialize PNG device, 480x480 pixels
png(filename = "plot2.png", width = 480, height = 480)
# Plot time series
plot(Global_active_power ~ DateTime, hpc, type = 'l',
ylab = "Global Active Power (kilowatts)", xlab = "")
# Close PNG device
dev.off()
|
6e450d84fbfdb2c32131796d0f62d9ec96fc578a | d8f5550dbbe4696c170d391e09d00738714c5dd0 | /man/attachContext.Rd | 245e267c1ba01ea7398149fb0a0f64e18b2b8ee6 | [] | no_license | dami82/mutSignatures | 19e044eb99ee8c65c4123c131fa978bd5bd9d93c | ad406018266f80a3048ae6219bf6c09cf6f69134 | refs/heads/master | 2023-01-24T00:39:46.026623 | 2023-01-18T21:22:55 | 2023-01-18T21:22:55 | 123,127,851 | 12 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,169 | rd | attachContext.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s03_all_functions.R
\name{attachContext}
\alias{attachContext}
\title{Attach Nucleotide Context.}
\usage{
attachContext(
mutData,
BSGenomeDb,
chr_colName = "chr",
start_colName = "start_position",
end_colName = "end_position",
nucl_contextN = 3,
context_colName = "context",
skip_seqName_check = FALSE
)
}
\arguments{
\item{mutData}{data.frame storing mutation data}
\item{BSGenomeDb}{a BSGenomeDb-class object, storing info about the genome of interest}
\item{chr_colName}{string, name of the column storing seqNames. Defaults to "chr"}
\item{start_colName}{string, name of the column storing start positions. Defaults to "start_position"}
\item{end_colName}{string, name of the column storing end positions. Defaults to "end_position"}
\item{nucl_contextN}{integer, the span of nucleotides to be retrieved around the variant. Defaults to 3}
\item{context_colName}{string, name of the column that will be storing the
nucleotide context. Defaults to "context"}
\item{skip_seqName_check}{logical, shall seqNames be checked to remove non-official chromosomes.
Defaults to FALSE}
}
\value{
a modified data.frame including the nucleotide context in a new column
}
\description{
Retrieve the nucleotide context around each DNA variant based on the
genomic coordinates of the variant and a reference BSGenome database.
}
\details{
This function is part of the user-interface set of tools included in mutSignatures. This is an exported function.
}
\references{
More information and examples about mutational signature analysis can be found here:
\enumerate{
\item \bold{GitHub Repo}: \url{https://github.com/dami82/mutSignatures/}
\item \bold{More info and examples} about the mutSignatures R library: \url{https://www.data-pulse.com/dev_site/mutsignatures/}
\item \bold{Sci Rep paper}, introducing mutS: \url{https://www.nature.com/articles/s41598-020-75062-0/}
\item \bold{Oncogene paper}, Mutational Signatures Operative in Bladder Cancer: \url{https://www.nature.com/articles/s41388-017-0099-6}
}
}
\author{
Damiano Fantini, \email{damiano.fantini@gmail.com}
}
|
ae9be7bbab9aba1a4128c9156836aa9c2815ef86 | 8ac6fd04fa2f9d62cf23cce35f2a3bbc787debbd | /server.R | c10d47c93749509cce40f3ed4e16db4762a4cd85 | [] | no_license | charliejhadley/idn_family-policies-timeline | dee918b9793c6a5c1aee15793dc84fa8a5cb5d5a | b16d201b5c00995aad707246ae8827af31ae9054 | refs/heads/master | 2021-07-21T18:51:32.735861 | 2017-10-31T10:41:41 | 2017-10-31T10:41:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,354 | r | server.R | ## =============================== License ========================================
## ================================================================================
## This work is distributed under the MIT license, included in the parent directory
## Copyright Owner: University of Oxford
## Date of Authorship: 2016
## Author: Martin John Hadley (orcid.org/0000-0002-3039-6849)
## Academic Contact: Mireia Borrell-Porta (orcid.org/0000-0003-2328-1258)
## Data Source: local file
## ================================================================================
library("plyr")
library("tidyverse")
library("shiny")
library("DT")
library("lubridate")
library("plotly")
library("shinyBS")
library("scales")
library("rfigshare")
library("readxl")
library("forcats")
## =========================== Beautification ===================================
## ==============================================================================
gantt_labeler <-
function(start_date = NA,
end_date = NA,
y_axis = NA,
color = NA) {
paste0(
"Policy Name: ",
y_axis,
"</br>",
"Enforcement Period: ",
as.Date(start_date),
" to ",
as.Date(end_date),
"</br>",
"Policy Type: ",
color
)
}
new_lines_to_p_tags <- function(text) {
gsub(pattern = "\n", replacement = "<br />", text)
}
## =========================== Shiny Server Fn ===================================
## ==============================================================================
source("data-processing.R", local = T)
# source("figshare.R", local = T)
source("long-colnames-replacements.R", local = T)
shinyServer(function(input, output, session) {
output$timeline_selected_cols_UI <- renderUI({
selectInput(
"timeline_selected_cols",
label = "Columns to show: ",
choices = long_colnames_replacements,
selected = initial_columns,
multiple = TRUE,
width = "100%"
)
})
output$timeline <- renderPlotly({
timeline_data$type.of.policy <-
gsub("allowances policy",
"</br>allowances policy",
timeline_data$type.of.policy)
cutoff_timeline_data <- timeline_data
cutoff_timeline_data$valid.from.b[cutoff_timeline_data$valid.from.b < as.Date("1997/01/01")] <-
as.Date("1997/01/01")
policy_separators <- cutoff_timeline_data %>%
filter(valid.from.b > as.Date("1997/01/01") &
valid.until.c > as.Date("1997/01/01"))
timeline_ggplot <- ggplot(
cutoff_timeline_data,
aes(
x = valid.from.b,
xend = valid.until.c,
y = name.of.policy,
yend = name.of.policy,
colour = type.of.policy
)
) +
geom_segment(
size = 4,
aes(
# x = Valid.from.b. + 60*60*24*10*3,
# xend = Valid.until.c. - 60*60*24*10*3,
x = valid.from.b,
xend = valid.until.c,
# Draw tooltipped geom_segments over everything, make almost invisible
y = name.of.policy,
yend = name.of.policy,
text = NULL
)
) +
geom_segment(
data = policy_separators,
size = 4,
aes(
# x = Valid.from.b. - 60*60*24*10*3,
# xend = Valid.from.b. + 60*60*24*10*3,
x = valid.from.b - 18,
xend = valid.from.b + 18,
y = name.of.policy,
yend = name.of.policy,
text = NULL
),
color = "black"
) +
geom_segment(
size = 4,
show.legend = F,
aes(
x = valid.from.b,
xend = valid.until.c,
y = name.of.policy,
yend = name.of.policy,
text = gantt_labeler(
start_date = valid.from.b,
end_date = valid.until.c,
y_axis = name.of.policy,
color = type.of.policy
),
alpha = 0.001 # Draw tooltipped geom_segments over everything, make almost invisible
)
) +
scale_x_date(
breaks = seq(as.Date("1997/01/01"), as.Date(paste0(
year(max(
cutoff_timeline_data$valid.until.c
)) + 1, "-01-01"
)), "years"),
labels = date_format("%Y"),
limits = c(as.Date("1997/01/01"), as.Date(
max(cutoff_timeline_data$valid.until.c)
))
) +
xlab("") + ylab("") + scale_colour_brewer(name = "Type of Policy",
type = "qual",
palette = "Dark2") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.margin = unit(c(0, 0, 1, 1), "cm"))
ggplotly(timeline_ggplot, tooltip = "text", source = "timeline")
})
output$timeline_selected_Policy_Table <- DT::renderDataTable({
event_data <- event_data("plotly_click", source = "timeline")
selected_Policy <-
levels(timeline_data$name.of.policy)[event_data$y]
data_to_show <-
timeline_data %>% filter(as.character(name.of.policy) == selected_Policy)
data_to_show <- data_to_show[, input$timeline_selected_cols]
colnames(data_to_show) <- mapvalues(
colnames(data_to_show),
from = long_colnames_replacements %>% as.character(),
to = long_colnames_replacements %>% names(),
warn_missing = F
)
data_to_show
}, extensions = c("FixedHeader", "Buttons"),
rownames = FALSE,
escape = FALSE,
class = 'cell-border stripe',
options = list(
autoWidth = FALSE,
scrollX = TRUE,
paging = FALSE,
dom = 'Bfrtip',
buttons = list(
list(
extend = "excel",
text = "Download current view of data",
filename = "Filtered Policies",
exportOptions = list(modifier = list(selected = FALSE))
)
),
defer = TRUE,
fixedHeader = list(header = TRUE),
columnDefs = list(list(width = "200px", targets = 0))
# fixedColumns = list(leftColumns = 2, rightColumns = 1)
))
output$plain_datatable_selected_cols_UI <- renderUI({
selectInput(
"plain_datatable_selected_cols",
label = "Columns to show: ",
choices = long_colnames_replacements,
multiple = TRUE,
selected = initial_columns,
width = "100%"
)
})
output$plain_datatable_selected_Policy_Table <-
DT::renderDataTable({
data_to_show <- timeline_data[, input$plain_datatable_selected_cols]
colnames(data_to_show) <- mapvalues(
colnames(data_to_show),
from = long_colnames_replacements %>% as.character(),
to = long_colnames_replacements %>% names(),
warn_missing = F
)
data_to_show
}, extensions = c("FixedHeader", "Buttons"),
rownames = FALSE,
filter = 'top',
escape = FALSE,
class = 'cell-border stripe',
options = list(
autoWidth = FALSE,
paging = FALSE,
scrollX = TRUE,
dom = 'Bfrtip',
fixedHeader = list(header = TRUE),
buttons = list(
list(
extend = "excel",
text = "Download current view of data",
filename = "Filtered Policies",
exportOptions = list(modifier = list(selected = FALSE))
)
),
columnDefs = list(list(width = "200px", targets = 0))
# fixedColumns = list(leftColumns = 2, rightColumns = 1)
))
output$timeline_selected_Policy_UI <- renderUI({
event_data <- event_data("plotly_click", source = "timeline")
if (is.null(event_data)) {
wellPanel("Select an event in the timeline to view more details about the policy.")
} else {
fluidRow(column(
uiOutput("timeline_selected_cols_UI"),
bsTooltip(
"timeline_selected_cols_UI",
"Add/remove columns to the table by typing/removing names from here",
"top",
options = list(container = "body")
),
uiOutput("type_of_details"),
DT::dataTableOutput("timeline_selected_Policy_Table", width = "100%"),
width = 12
))
}
})
# output$download_spreadsheet <- downloadHandler(
# filename = "policies.xlsx",
# # desired file name on client
# content = function(con) {
# file.copy("data/policies.xlsx", con)
# }
# )
#
}) |
82fd8f8995003eb1f65fb1b5131a08bcdf3f3aff | e6a13b677387eb24ad0bc4e3b4527445d5912b37 | /Reading data in R.R | 45a051b232ed71a511ea4f311ad409140d7fab29 | [] | no_license | Hrithik1/R-learning | c9af0d1c4c4d3e93eebc801cc9194f64cba61a7f | a03ff0358609aea3df9df0b2d9a242e0fc921bbc | refs/heads/main | 2023-04-12T10:55:25.972300 | 2021-05-14T16:55:35 | 2021-05-14T16:55:35 | 326,401,985 | 1 | 1 | null | 2021-01-06T13:58:14 | 2021-01-03T12:29:12 | R | UTF-8 | R | false | false | 83 | r | Reading data in R.R | x<-url("https://www.google.com","r")
y<-readLines(x)
head(y)
#read.table
#read.csv |
fa670e32a81fe244575e31616e02dd9d9feb11db | bff6f874ddadce8109260ac9c36a8e1f76bc5536 | /ndnd_functions_temp/cpgs_hd.R | b76ab4b5d53612cf99b32b5906069a16eb4d0cc9 | [] | no_license | ElliotSivel/NDND | b45d06e5b8f0ea9b81e8793ab6f203426b7e4b75 | 88ef12fe46ffb7ac3ead95dfbe4ab1c50aa7dacb | refs/heads/master | 2021-06-22T00:16:58.066969 | 2021-01-12T17:03:31 | 2021-01-12T17:03:31 | 171,261,910 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,438 | r | cpgs_hd.R | # library(RcppEigen)
# library(Rcpp)
# library(inline)
#creation de la fonction CPP
cpgsCPP='#include <Eigen/Cholesky>
using Eigen::Map;
using Eigen::MatrixXd;
using Eigen::VectorXd;
const Map<MatrixXd>A(as<Map<MatrixXd> >(AA));
const Map<VectorXd>b(as<Map<VectorXd> >(bb));
const Map<VectorXd>x0(as<Map<VectorXd> >(xx0));
int N=Rcpp::as<int>(NN);
int p=A.cols();
int m=A.rows();
int runup,discard=100;
// Check input arguments
if (m < (p+1)){
throw std::range_error("dimensions mismatch");
}
// Initialisation
MatrixXd X(N+runup+discard,p);
int n=0;
MatrixXd x(p,1);
MatrixXd y(p,1);
x=x0;
// Initialize variables for keeping track of sample mean, covariance
// and isotropic transform.
MatrixXd M(p,1);
M.setZero();
MatrixXd S2(p,p);
S2.setZero();
// outer products.
MatrixXd S(p,p);
MatrixXd S0(p,p);
S.setIdentity();
MatrixXd T1(p,p);
T1.setIdentity();
MatrixXd W(m,p);
W = A;
MatrixXd d(m,1);
MatrixXd delta0(p,1);
MatrixXd delta1(p,1);
MatrixXd z(m,1);
while (n < (N+runup+discard)){ //sampling loop
y=x;
// compute approximate stochastic transformation
if (n == runup-1){
T1=S.transpose().llt().matrixU();
W = A*T1;
}
y=T1.inverse()*y;
// choose p new components
int i=0;
for (int i=0;i<p;++i){
//Find points where the line with the (p-1) components x_i
//fixed intersects the bounding polytope.
VectorXd e(p);e.setZero();
e(i)= 1;
z = (W*e); //prevent any divisions by 0
d=(b - W*y);
d=d.cwiseQuotient(z);
double tmin=-9e9;
double tmax=9e9;
for (int j=0;j<m;++j){
if (z(j)<0 && tmin<d(j)) tmin=d(j);
if (z(j)>0 && tmax>d(j)) tmax=d(j);
}
y(i)+=(tmin+(tmax-tmin)*rand()/(double)RAND_MAX);
}
x=T1*y;
X.row(n)= x.col(0);
++n;
// Incremental mean and covariance updates
delta0 = x - M; // delta new point wrt old mean
M+= delta0/(double)n; // sample mean
delta1= x - M; // delta new point wrt new mean
if (n > 1){
S2 +=(n-1)/(double)(n*n)*(delta0*delta0.transpose())+(delta1*delta1.transpose());
S0 = S;
S = S2/(double)(n-1); // sample covariance
} else {
S.setIdentity();
}
}
return wrap(X.bottomRows(N));'
#compilation (à ne faire qu'une seule fois)
cpgs2<-cxxfunction(signature(NN="integer",AA="matrix",bb="vector",xx0="vector"),cpgsCPP,plugin="RcppEigen")
# #jeu de test
# A=matrix(c(1,0,0,1,-1,0,0,-1,1,1),ncol=2,byrow=TRUE)
# b=c(50,50,0,0,75)
# x0=c(25,10)
#
# #test de la fonction
# debut=Sys.time()
# res=cpgs2(150000,A,b,x0)
# end=Sys.time()
# duree=end-debut
# print(duree)
#
# #test de l'ancienne fonction
# debut=Sys.time()
# res=cpgs(150000,A,b,x0)
# end=Sys.time()
# duree=end-debut
# print(duree)
#
# Sys.getenv()
# Sys.setenv(PATH =
# "C:/Users/a22073/Documents/Rtools/bin/;
# C:/Users/a2207/Documents/R-3.6.0/bin/x64;
# C:/Program Files (x86)/Common Files/Oracle/Java/javapath;
# C:/WINDOWS/system32;
# C:/WINDOWS;
# C:/WINDOWS/System32/Wbem;
# C:/WINDOWS/System32/WindowsPowerShell/v1.0/;
# C:/Program Files/Intel/WiFi/bin/;
# C:/Program Files/Common Files/Intel/WirelessCommon/;
# C:/Program Files/Git/cmd;
# C:/Users/Administrator/AppData/Local/Microsoft/WindowsApps;
# C:/Users/Administrator/AppData/Local/Box/Box Edit/")
# Sys.setenv(BINPREF = "C:/Rtools/mingw_$(WIN)/bin/") |
3c5769bb1a06531af4d107e3c34f4e62b3aec9c9 | 770a778f89aa210f199c30180a6e0365d3baaf3b | /Data/generate_r.R | 57d5808f8632f3fc779b0e6da3e98ca17121d6ec | [] | no_license | alizeeguyot/METARET | 38e0ced2a09a220c9ab45d310bc614efc4dfe10d | d41ea476e57fdcc5ee7eb5f5ff58c81aea91b715 | refs/heads/master | 2023-06-06T05:18:00.633568 | 2021-06-27T08:03:18 | 2021-06-27T08:03:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,699 | r | generate_r.R | ## function that translates choices into each different task
## into an 'r' risk parameter of a CRRA x utility funtion
get_r <- function(bibkey, task, choice) {
if (bibkey == "Charness2019") {
if (task == "EG") {
if (choice == 1) { out <- -2.45 }
if (choice == 2) { out <- runif(1, min = -2.45, max = -0.16)}
if (choice == 3) { out <- runif(1, min = -0.16, max = 0.29)}
if (choice == 4) { out <- runif(1, min = 0.29, max = 0.50)}
if (choice == 5) { out <- runif(1, min = 0.50, max = 1)}
if (choice == 6) { out <- 1}
}
if (task == "HL") {
if (choice == 10) { out <- 2.71 }
if (choice == 9) { out <- runif(1, min = 1.95, max = 2.71) }
if (choice == 8) { out <- runif(1, min = 1.49, max = 1.95) }
if (choice == 7) { out <- runif(1, min = 1.15, max = 1.49) }
if (choice == 6) { out <- runif(1, min = 0.85, max = 1.15) }
if (choice == 5) { out <- runif(1, min = 0.59, max = 0.85) }
if (choice == 4) { out <- runif(1, min = 0.32, max = 0.59) }
if (choice == 3) { out <- runif(1, min = 0.03, max = 0.32) }
if (choice == 2) { out <- runif(1, min = -0.37, max = 0.03) }
if (choice == 1) { out <- -0.37 }
if (choice == 0) { out <- -0.37 }
}
## TODO: how to deal with the long left tail??
if (task == "IG") {
if (choice == 8) { out <- 1 }
if (choice == 0) { out <- -1 }
if (choice != 8 & choice != 0) { out <- (-0.405465 -log(8 - choice) + log(8 + 1.5*choice))/(-log(8-choice) + log(8 + 1.5*choice)) }
}
if (task != "HL" & task != "EG" & task != "IG") { out <- NA }
}
if (bibkey == "Crosetto2016") {
if (task == "EG") {
if (choice == 1) { out <- -1 }
if (choice == 2) { out <- runif(1, min = -1, max = 0.33)}
if (choice == 3) { out <- runif(1, min = 0.33, max = 0.62)}
if (choice == 4) { out <- runif(1, min = 0.62, max = 0.80)}
if (choice == 5) { out <- 1}
}
if (task == "HL") {
if (choice == 10) { out <- 2.71 }
if (choice == 9) { out <- runif(1, min = 1.95, max = 2.71) }
if (choice == 8) { out <- runif(1, min = 1.49, max = 1.95) }
if (choice == 7) { out <- runif(1, min = 1.15, max = 1.49) }
if (choice == 6) { out <- runif(1, min = 0.85, max = 1.15) }
if (choice == 5) { out <- runif(1, min = 0.59, max = 0.85) }
if (choice == 4) { out <- runif(1, min = 0.32, max = 0.59) }
if (choice == 3) { out <- runif(1, min = 0.03, max = 0.32) }
if (choice == 2) { out <- runif(1, min = -0.37, max = 0.03) }
if (choice == 1) { out <- -0.37 }
if (choice == 0) { out <- -0.37 }
}
if (task == "BART") { out <- choice/(100-choice) }
if (task == "IG") {
if (choice == 4) { out <- 1 }
if (choice != 4) { out <- (log(4-choice) - log(8+3*choice) + log(3))/(log(4-choice) +log(2) - log(8+3*choice)) }
}
}
if (bibkey == "Meta2016") {
if (task == "HL") {
if (choice == 10) { out <- 2.71 }
if (choice == 9) {out <- runif(1, min = 1.95, max = 2.71) }
if (choice == 8) { out <- runif(1, min = 1.49, max = 1.95) }
if (choice == 7) { out <- runif(1, min = 1.15, max = 1.49) }
if (choice == 6) { out <- runif(1, min = 0.85, max = 1.15) }
if (choice == 5) { out <- runif(1, min = 0.59, max = 0.85) }
if (choice == 4) { out <- runif(1, min = 0.32, max = 0.59) }
if (choice == 3) { out <- runif(1, min = 0.03, max = 0.32) }
if (choice == 2) { out <- runif(1, min = -0.37, max = 0.03) }
if (choice == 1) { out <- -0.37 }
if (choice == 0) { out <- -0.37 }
}
}
if (bibkey == "Menkhoff_Sakha_2017") {
if (task == "EG") {
if (choice == 1) { out <- -1 }
if (choice == 2) { out <- runif(1, min = -1, max = 0.33)}
if (choice == 3) { out <- runif(1, min = 0.33, max = 0.62)}
if (choice == 4) { out <- runif(1, min = 0.62, max = 0.80)}
if (choice == 5) { out <- 1}
}
## this is a bit iffy, to be changed
if (task == "EG_loss") {
if (choice == 1) { out <- -0.25 }
if (choice == 2) { out <- runif(1, min = -0.25, max = 0.60)}
if (choice == 3) { out <- runif(1, min = 0.60, max = 0.82)}
if (choice == 4) { out <- runif(1, min = 0.82, max = 1)}
if (choice == 5) { out <- 1}
}
if (task == "IG") {
if (choice == 100) { out <- 1 }
if (choice == 0) { out <- -1 }
if (choice != 100 & choice != 0) { out <- (log(1/(2*(100-choice))) + log(100 + 2*choice))/(-log(100-choice) + log(100 + 2*choice)) }
}
if (task == "CEPL") {
if (choice == 20) { out <- 1.52 }
if (choice == 19) { out <- runif(1, min = 1.36, max = 1.52) }
if (choice == 18) { out <- runif(1, min = 1.22, max = 1.36) }
if (choice == 17) { out <- runif(1, min = 1.1, max = 1.22) }
if (choice == 16) { out <- runif(1, min = 1, max = 1.1) }
if (choice == 15) { out <- runif(1, min = 0.91, max = 1) }
if (choice == 14) { out <- runif(1, min = 0.83, max = 0.91) }
if (choice == 13) { out <- runif(1, min = 0.76, max = 0.83) }
if (choice == 12) { out <- runif(1, min = 0.69, max = 0.76) }
if (choice == 11) { out <- runif(1, min = 0.63, max = 0.69) }
if (choice == 10) { out <- runif(1, min = 0.57, max = 0.63) }
if (choice == 9) { out <- runif(1, min = 0.52, max = 0.57) }
if (choice == 8) { out <- runif(1, min = 0.48, max = 0.52) }
if (choice == 7) { out <- runif(1, min = 0.43, max = 0.48) }
if (choice == 6) { out <- runif(1, min = 0.39, max = 0.43) }
if (choice == 5) { out <- runif(1, min = 0.34, max = 0.39) }
if (choice == 4) { out <- runif(1, min = 0.3, max = 0.34) }
if (choice == 3) { out <- runif(1, min = 0.26, max = 0.3) }
if (choice == 2) { out <- runif(1, min = 0.2, max = 0.26) }
if (choice == 1) { out <- runif(1, min = -1, max = 0.2) }
if (choice == 0) { out <- -1 }
}
}
if (bibkey == "Holzmeister2019") {
if (task == "HL") {
if (choice == 10) { out <- 2.71 }
if (choice == 9) { out <- runif(1, min = 1.95, max = 2.71) }
if (choice == 8) { out <- runif(1, min = 1.49, max = 1.95) }
if (choice == 7) { out <- runif(1, min = 1.15, max = 1.49) }
if (choice == 6) { out <- runif(1, min = 0.85, max = 1.15) }
if (choice == 5) { out <- runif(1, min = 0.59, max = 0.85) }
if (choice == 4) { out <- runif(1, min = 0.32, max = 0.59) }
if (choice == 3) { out <- runif(1, min = 0.03, max = 0.32) }
if (choice == 2) { out <- runif(1, min = -0.37, max = 0.03) }
if (choice == 1) { out <- -0.37 }
if (choice == 0) { out <- -0.37 }
}
if (task == "BRET") {
if (choice == 100) { out <- 10 }
if (choice != 100) { out <- choice/(100-choice) }
}
if (task == "EG") {
if (choice == 1) { out <- -1.97 }
if (choice == 2) { out <- runif(1, min = -1.97, max = 0)}
if (choice == 3) { out <- runif(1, min = 0, max = 0.39)}
if (choice == 4) { out <- runif(1, min = 0.39, max = 0.58)}
if (choice == 5) { out <- runif(1, min = 0.58, max = 1)}
if (choice == 6) { out <- 1}
}
if (task == "CEPL") {
if (choice == 9) { out <- 7.96 }
if (choice == 8) { out <- runif(1, min = 3.71, max = 7.96) }
if (choice == 7) { out <- runif(1, min = 2.06, max = 3.71) }
if (choice == 6) { out <- runif(1, min = 1, max = 2.06) }
if (choice == 5) { out <- runif(1, min = 0.07, max = 1) }
if (choice == 4) { out <- runif(1, min = -1, max = 0.07) }
if (choice == 3) { out <- runif(1, min = -2.93, max = -1) }
if (choice == 2) { out <- runif(1, min = -2.93) }
if (choice == 1) { out <- -2.93 }
}
}
if (bibkey == "Frey2017") {
if (task == "BART") {
out <- choice/(128-choice)
}
if (task == "HL"){
if (choice == 10) { out <- 2.71 }
if (choice == 9) { out <- runif(1, min = 1.95, max = 2.71) }
if (choice == 8) { out <- runif(1, min = 1.49, max = 1.95) }
if (choice == 7) { out <- runif(1, min = 1.15, max = 1.49) }
if (choice == 6) { out <- runif(1, min = 0.85, max = 1.15) }
if (choice == 5) { out <- runif(1, min = 0.59, max = 0.85) }
if (choice == 4) { out <- runif(1, min = 0.32, max = 0.59) }
if (choice == 3) { out <- runif(1, min = 0.03, max = 0.32) }
if (choice == 2) { out <- runif(1, min = -0.37, max = 0.03) }
if (choice == 1) { out <- -0.37 }
if (choice == 0) { out <- -0.37 }
}
}
out
}
|
f6f894031a3b15e63d0dd84af440c7b4cf6a0697 | 54d6ea7251681b5a49ab139a0bf332d06da5439a | /plot/EndOfApril-multipleCorrectionMade/scalable_single_item.R | 1aa08f41d011398f9cc4578e48fe6270a02d64fb | [] | no_license | wsgan001/MasterThesis-3 | d558b3dcf5b302ef3a4e1e3ffc412ffd67ca29cc | a3dc8797242e3f9b282a8621fced57981fc6bdae | refs/heads/master | 2020-04-07T00:31:18.669699 | 2017-06-03T00:41:58 | 2017-06-03T00:41:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,002 | r | scalable_single_item.R | tiff("scalable_single_item.png", width=9, height=9, units='in', res=120)
par(mfrow=c(3,3), cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
#BIBLE
#DATA
x <- c(0.01, 0.009, 0.008, 0.007, 0.006, 0.005, 0.004, 0.003, 0.002, 0.001)*100
zero <- c(28247, 23874, 27147, 28035, 30995, 37511, 43290, 55725, 77181, 176405)/1000
one <- c(26462, 19830, 18776, 22199, 23191, 25965, 31464, 36128, 51794, 133619)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(18, 176))
lines(x, one, col="red", type="o", pch=1)
title(main="BIBLE", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#protein
#DATA
x <- c(0.9999, 0.99988, 0.99987, 0.99986)*100
zero <- c(316549, 446706, 554299, 686114)/1000
one <- c(330404, 385173, 417496, 446156)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(316, 686))
lines(x, one, col="red", type="o", pch=1)
title(main="protein", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#Kosarak-70
#DATA
x <- c(0.003, 0.0028, 0.0026, 0.0024, 0.0022, 0.002)*100
zero <- c(15922, 16873, 18065, 19459, 21731, 24384)/1000
one <- c(13030, 13860, 14687, 17677, 17885, 18931)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(13, 24))
lines(x, one, col="red", type="o", pch=1)
title(main="Kosarak-70", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#LEVIATHAN
#DATA
x <- c(0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01)*100
zero <- c(3170, 2933, 3398, 4253, 3470, 3782, 4780, 5159, 6189, 10052)/1000
one <- c(2515, 2563, 2887, 2826, 2935, 3285, 3486, 3942, 4755, 7643)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(2, 10))
lines(x, one, col="red", type="o", pch=1)
title(main="LEVIATHAN", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#PubMed
#DATA
x <- c(0.05, 0.04, 0.03, 0.02, 0.01, 0.005)*100
zero <- c(9556, 10489, 10974, 12881, 20003, 30201)/1000
one <- c(7966, 8307, 8904, 10346, 14256, 21949)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(7, 30))
lines(x, one, col="red", type="o", pch=1)
title(main="PubMed", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#FIFA
#DATA
x <- c(0.14, 0.12, 0.1, 0.09, 0.08, 0.07, 0.06, 0.05)*100
zero <- c(26572, 28193, 35703, 44324, 48639, 57630, 72229, 117669)/1000
one <- c(22003, 23806, 28856, 33836, 41424, 50408, 65124, 105365)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(22, 117))
lines(x, one, col="red", type="o", pch=1)
title(main="FIFA", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#slen1
#DATA
x <- c(0.0017, 0.00165, 0.0016, 0.00155, 0.0015, 0.00145, 0.0014, 0.00135, 0.0013)*100
zero <- c(15077, 16317, 18549, 18766, 19779, 20306, 21739, 23757, 26922)/1000
one <- c(13078, 18296, 22978, 24689, 23803, 27311, 28748, 27514, 29418)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(13, 29))
lines(x, one, col="red", type="o", pch=1)
title(main="slen1", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
#slen2
#DATA
x <- c(0.0015, 0.00145, 0.0014, 0.00135, 0.0013, 0.00125, 0.0012, 0.00115, 0.0011)*100
zero <- c(13005, 13462, 15471, 20308, 19068, 20138, 23366, 26772, 27830)/1000
one <- c(13452, 14307, 16422, 20270, 22787, 23525, 24869, 29102, 32317)/1000
#PLOT
plot(x, zero, log = "xy", type="o", pch=0, xlab="Minsup(%)",ylab="Time (s logscale)", col="blue", xlim = rev(range(x)), ylim=c(13, 32))
lines(x, one, col="red", type="o", pch=1)
title(main="slen2", cex.lab=1.5, cex.axis=1.5, cex.main=1.5, cex.sub=1.5)
grid()
legend("bottomright", legend=c("reference", "spark_singleItem"), lty=c(1,1),lwd=c(2,2), col=c("blue", "red"), cex = 1)
dev.off()
|
a49a219680d89478bb412dc2c2b336eb1d2658af | 4f4ba6d34b2411350abc91cc44b7bd1875970584 | /R/data_capitales_prov.R | 2dd2aa84d154f1299fdb08af5428578ad6326627 | [
"LicenseRef-scancode-warranty-disclaimer",
"MIT",
"CC-BY-4.0"
] | permissive | perezp44/spanishRentidadesIGN | a2129e25e0251fc9d5ae97abc979d25c41fc3b59 | 0686da61ddaa47b80a6bf8801214c4539c9c05de | refs/heads/master | 2021-05-02T13:20:16.023342 | 2018-02-09T19:15:20 | 2018-02-09T19:15:20 | 120,757,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 696 | r | data_capitales_prov.R | #------------------------------ This file documents el df capitales_prov
#-- Simplemente tiene las 52 capitales de provincia
#-
#' Capitales de las 52 "Provincias" de Spain
#'
#' df con los 52 municipios Spain que son capitales de provincia (+ Ceuta y Melilla)
#'
#' @source \url{http://www.ign.es/web/ign/portal/rcc-nomenclator-nacional}
#'
#' @format A data frame with 52 rows y 4 variables:
#' \itemize{
#' \item INECodMuni: Código municipio más usado (5 digitos)
#' \item NOMBRE_ACTUAL: Nombre del municipio
#' \item COD_PROV: Código de la provincia.
#' \item PROVINCIA: Nombre de la provincia.
#' }
#'
#' @examples
#' \dontrun{
#' df <- capitales_prov
#' }
#'
"capitales_prov"
|
75c7060db002bf23cd7bb114667d7da20f69e073 | a9dec343915b76a49ad1a7e57a53f3690bc40f5a | /R/utils.R | e724dee952723ef03eb2498354b3c564c72e7741 | [] | no_license | LongBeachInnovationTeam/CLB-Business-Licensing-Trend-Analysis | 8d7f834c5eacf1bed97edfd274774cbe17cbcd19 | 589b5c9a9f9d8cf1067b40bd7fac3b8f4daaf71b | refs/heads/master | 2020-03-13T10:32:49.570402 | 2017-03-11T09:39:49 | 2017-03-11T09:39:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,680 | r | utils.R |
#' Merge history and forecast for plotting.
#'
#' @param m Prophet object.
#' @param fcst Data frame returned by prophet predict.
#'
#' @importFrom dplyr "%>%"
df_for_plotting <- function(m, fcst) {
# Make sure there is no y in fcst
fcst$y <- NULL
df <- m$history %>%
dplyr::select(ds, y) %>%
dplyr::full_join(fcst, by = "ds") %>%
dplyr::arrange(ds)
return(df)
}
#' Plot the prophet forecast.
#'
#' @param x Prophet object.
#' @param fcst Data frame returned by predict(m, df).
#' @param uncertainty Boolean indicating if the uncertainty interval for yhat
#' should be plotted. Must be present in fcst as yhat_lower and yhat_upper.
#' @param xlabel Optional label for x-axis
#' @param ylabel Optional label for y-axis
#' @param ... additional arguments
#'
#' @return A ggplot2 plot.
#'
#' @examples
#' \dontrun{
#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
#' y = sin(1:366/200) + rnorm(366)/10)
#' m <- prophet(history)
#' future <- make_future_dataframe(m, periods = 365)
#' forecast <- predict(m, future)
#' plot_forecast(m, forecast)
#' }
#'
#' @export
plot_forecast <- function(x, fcst, uncertainty = TRUE, xlabel = 'ds',
ylabel = 'y', ...) {
df <- df_for_plotting(x, fcst)
forecast.color <- "#0072B2"
gg <- ggplot2::ggplot(df, ggplot2::aes(x = ds, y = y)) +
ggplot2::labs(x = xlabel, y = ylabel)
if (exists('cap', where = df)) {
gg <- gg + ggplot2::geom_line(
ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)
}
if (uncertainty && exists('yhat_lower', where = df)) {
gg <- gg +
ggplot2::geom_ribbon(ggplot2::aes(ymin = yhat_lower, ymax = yhat_upper),
alpha = 0.2,
fill = forecast.color,
na.rm = TRUE)
}
gg <- gg +
ggplot2::geom_point(na.rm=TRUE) +
ggplot2::geom_line(ggplot2::aes(y = yhat), color = forecast.color,
na.rm = TRUE) +
ggplot2::theme(aspect.ratio = 3 / 5)
return(gg)
}
#' Plot the trend component of a prophet forecast.
#' Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
#' present, and holidays if present.
#'
#' @param m Prophet object.
#' @param fcst Data frame returned by predict(m, df).
#' @param uncertainty Boolean indicating if the uncertainty interval should be
#' plotted for the trend, from fcst columns trend_lower and trend_upper.
#' @param xlabel Optional label for x-axis
#' @param ylabel Optional label for y-axis
#'
#' @return A ggplot2 plot.
#'
#' @export
#' @importFrom dplyr "%>%"
prophet_plot_trend_component <- function(m, fcst, uncertainty = TRUE, xlabel = 'Day of year', ylabel = '') {
df <- df_for_plotting(m, fcst)
forecast.color <- "#0072B2"
# Plot the trend
gg.trend <- ggplot2::ggplot(df, ggplot2::aes(x = ds, y = trend)) +
ggplot2::geom_line(color = forecast.color, na.rm = TRUE)
if (exists('cap', where = df)) {
gg.trend <- gg.trend + ggplot2::geom_line(ggplot2::aes(y = cap),
linetype = 'dashed',
na.rm = TRUE)
}
if (uncertainty) {
gg.trend <- gg.trend +
ggplot2::geom_ribbon(ggplot2::aes(ymin = trend_lower,
ymax = trend_upper),
alpha = 0.2,
fill = forecast.color,
na.rm = TRUE)
}
gg.trend <- gg.trend +
ggplot2::labs(x = xlabel, y = ylabel)
return (gg.trend)
}
#' Plot the yearly component of a prophet forecast.
#' Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
#' present, and holidays if present.
#'
#' @param m Prophet object.
#' @param fcst Data frame returned by predict(m, df).
#' @param uncertainty Boolean indicating if the uncertainty interval should be
#' plotted for the trend, from fcst columns trend_lower and trend_upper.
#' @param xlabel Optional label for x-axis
#' @param ylabel Optional label for y-axis
#'
#' @return A ggplot2 plot.
#'
#' @export
#' @importFrom dplyr "%>%"
prophet_plot_yearly_component <- function(m, fcst, uncertainty = TRUE, xlabel = 'Day of year', ylabel = '') {
df <- df_for_plotting(m, fcst)
forecast.color <- "#0072B2"
# Plot yearly seasonality, if present
if ("yearly" %in% colnames(df)) {
# Drop year from the dates
df.s <- df %>%
dplyr::mutate(doy = strftime(ds, format = "2000-%m-%d")) %>%
dplyr::group_by(doy) %>%
dplyr::slice(1) %>%
dplyr::ungroup() %>%
dplyr::mutate(doy = zoo::as.Date(doy)) %>%
dplyr::arrange(doy)
gg.yearly <- ggplot2::ggplot(df.s, ggplot2::aes(x = doy, y = yearly,
group = 1)) +
ggplot2::geom_line(color = forecast.color, na.rm = TRUE) +
ggplot2::scale_x_date(labels = scales::date_format('%B %d')) +
ggplot2::labs(x = xlabel, y = ylabel)
if (uncertainty) {
gg.yearly <- gg.yearly +
ggplot2::geom_ribbon(ggplot2::aes(ymin = yearly_lower,
ymax = yearly_upper),
alpha = 0.2,
fill = forecast.color,
na.rm = TRUE)
}
return (gg.yearly)
}
}
export_df_as_csv <- function(df, filename) {
write_csv(df, filename, na = "")
}
#' @importFrom dplyr "%>%"
df_daily_count <- function(df, d) {
return (
df %>%
mutate(ds = lubridate::as_date(d)) %>%
group_by(ds) %>%
filter(!is.na(ds)) %>%
summarise(
y = log(n())
) %>%
select(ds, y)
)
} |
ecc9b852d19374db7161dbe6390b239a18688619 | 3ece7008ae659e4604da95e3936300eab19732f4 | /man/plotEIC.xsAnnotate.Rd | 0a41ffa2fc712d142172b1ac5272c4251e26bbe3 | [] | no_license | sneumann/CAMERA | 14600dee1054917c03c2a48ec24092d7ba5e3416 | 89bd1e8d7e720bae6532eef5328e602acb38e82a | refs/heads/master | 2023-04-12T19:41:01.256040 | 2022-02-08T08:48:02 | 2022-02-08T08:48:02 | 18,191,106 | 10 | 24 | null | 2021-09-27T08:09:11 | 2014-03-27T20:55:17 | R | UTF-8 | R | false | false | 960 | rd | plotEIC.xsAnnotate.Rd | \name{plotEICs-methods}
\docType{methods}
\alias{plotEICs.xsAnnotate}
\alias{plotEICs}
\alias{plotEICs,xsAnnotate-method}
\title{Plot extracted ion chromatograms from (multiple) Pseudospectra}
\description{
Batch plot a list of extracted ion chromatograms to the current
graphics device.
}
\section{Methods}{
\describe{
\item{object = "xsAnnotate"}{
\code{
plotEICs(object,
xraw,
pspec=1:length(object@pspectra),
maxlabel=0, sleep=0)}
}
}
}
\value{
None.
}
\arguments{
\item{object}{the \code{xsAnnotate} object}
\item{xraw}{\code{xcmsRaw} object underlying the the xsAnnotate}
\item{maxlabel}{How many m/z labels to print}
\item{sleep}{seconds to pause between plotting EICs}
\item{...}{other graphical parameters}
}
\author{Steffen Neumann, \email{sneumann@ipb-halle.de}}
\seealso{
\code{\link{xsAnnotate-class}},
\code{\link{png}},
\code{\link{pdf}},
\code{\link{postscript}},
}
\keyword{methods}
\keyword{hplot}
|
7da0d8aebe1df0e903a54a8468bd37e99344870b | a7458ddecee95867d027d83193fa0423575e171f | /plotMetabolites.R | c55a7dfc069586ed2854ba9d1b5dd6eff0e36253 | [] | no_license | riinbre-bioinfo/Ghonem_Metabolites | 5a3e20f3ee00ed7fb3f5b582a52d35fc62fff4c2 | fb43a47df101e4363936be9c8f7372a723630a5f | refs/heads/master | 2020-05-23T05:00:33.946983 | 2019-05-14T15:05:52 | 2019-05-14T15:05:52 | 186,644,305 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,800 | r | plotMetabolites.R | # script to create strip plots for the metabolite data, run ANOVA for the metabolite data and create bar plots for enzyme data
metabsStripPlot <- function(data_meta.df, data_log.df, xvar, shape, color, grouped = FALSE, shapes = FALSE, colors = FALSE, brewer = FALSE, legend = TRUE) {
metabNames = names(data_log.df)
data.df = cbind(data_meta.df, data_log.df)
stripPlot <- function(metab, data, xvar, shape, color, grouped, shapes, colors, brewer) {
p = ggplot(data, aes_(x = as.name(xvar), y = as.name(metab), shape = as.name(shape), color = as.name(color))) + theme_bw()
if (legend == FALSE) {
p = p + theme(legend.position = "none")
}
if (is.vector(shapes)) {
p = p + scale_shape_manual(values = shapes)
}
if (is.character(colors) && brewer == FALSE) {
p = p + scale_color_manual(values = colors)
} else if (is.character(colors) && brewer == TRUE) {
p = p + scale_color_brewer(palette = colors)
} else {
p = p + scale_color_brewer(palette="Dark2")
}
if (grouped == TRUE) {
p = p +
geom_boxplot(position = position_dodge(0.5)) +
geom_jitter(position = position_dodge(0.5), size = 3)
} else {
p = p +
geom_jitter(position = position_jitter(0.2), size = 3)
}
p = p +
stat_summary(fun.data = mean_se, geom = "errorbar", color = "black", width = 0.5, size = 1.2) +
stat_summary(fun.y=mean, geom = "point", color = "black", size = 4, pch = 1) +
theme(text = element_text(face = "bold", size = 18), axis.text = element_text(face = "bold", size = 14))
}
lapply(metabNames, stripPlot, data = data.df, xvar = xvar, shape = shape, color = color, grouped = grouped, shapes = shapes, colors = colors, brewer = brewer)
}
metabANOVA <- function(data_meta.df, data_log.df, fixed, random) { # run ANOVA and multicomp Tukey on all metabolites
metabNames = names(data_log.df)
data.df = cbind(data_meta.df, data_log.df)
runANOVA <- function(metab, data, fixed, random) {
form = as.formula(paste(metab, paste(fixed, random, sep = "+"), sep = "~")) # construct formula
data.aov = aov(form, data = data) # run anova
args = list("Tukey") # next commands pass "Treatment" to mcp
names(args) = fixed
cmp = do.call(mcp, args)
data.glht = glht(data.aov, linfct = cmp) # run glht
summary(data.glht)
}
summaries = lapply(metabNames, runANOVA, data = data.df, fixed = fixed, random = random) # return list of anova summaries
names(summaries) <- names(data_log.df)
summaries
}
enzymesBarPlot <- function(enzyme, data.df, group) {
df.summ <- data.df %>% group_by(get(group)) %>% summarize(Mean = mean(get(enzyme)), se = sd(get(enzyme))/sqrt(n()))
names(df.summ)[1] <- group
df.summ
# p <- ggplot(df.summ, aes(x = get(group), y = Mean, fill = get(group))) +
# geom_bar(stat = "identity") +
# stat_summary(fun.data = mean_se, geom = "errorbar", color = "black", width = 0.25)
# p <- ggplot(df.summ, aes(x = TreatCohort, y = Mean, ymin = Min, ymax = Max, fill = TreatCohort)) +
# geom_bar(stat = "identity")
# enz_summaries <- lapply(enzymes, function(x) tapply(data.df[, x], data.df[, group], mean))
# names(enz_summaries) <- enzymes
# means.df <- rownames_to_column(as.data.frame(enz_summaries), var = group)
# barPlot <- function(enzyme, data, xvar) {
# p = ggplot(data.df[, enzymes], aes_(x = as.name(xvar), y = as.name(enzyme))) + theme_bw()
# p = p + geom_bar(stat("identity"))
# p = p + stat_summary(fun.data = mean_se, geom = "errorbar", color = "black", width = 0.25)
# }
# lapply(enzymes, barPlot, data = data.df, xvar = "TreatCohort")
# means.df
} |
09386f9af5d77f52612eda89fd3f4d899122f933 | 29585dff702209dd446c0ab52ceea046c58e384e | /TreePar/R/Fderifuncshifth.R | efac414b4b210761ff62d7c2e1fa124650f099f8 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 464 | r | Fderifuncshifth.R | Fderifuncshifth<-function(time,t,lambda,mu,k) {
i <- inter(time,t)
if (time==0){
res<-0
} else {
tnew<-c(t[1:i],time)
res<-lambda[i]/(lambda[i]-mu[i])
if (i>1) {
for (j in 1:(i-1)) {
res<-res*exp((lambda[j]-mu[j])*(tnew[j+1]-tnew[j]))}}
# if (i<k){
# res<-res*(exp((lambda[i]-mu[i])*(tnew[i+1]-tnew[i]))-1)
# } else {
res<-res*((lambda[i]-mu[i])*exp((lambda[i]-mu[i])*(tnew[i+1]-tnew[i]))) }
#res<-res+Ffuncshifth(t[i],t,lambda,mu)
#}
res
} |
a4fb656d85b481004c804e01b776a4169cdb4e8f | c647ec9c91130d4ecf2d7f1cce97827a833516bd | /man/dem.Rd | 9a7075ea2a22d7d883378c9827fb9d8a3b3856ee | [
"MIT"
] | permissive | sysmedlab/ehR | 1f8e75ed9101bf1416e5cd2106de1cfb6fb978d5 | 41c219b88c76f2432aa61df2b959d235d71ed0a1 | refs/heads/master | 2021-09-08T02:01:13.250539 | 2018-02-23T21:58:27 | 2018-02-23T21:58:27 | 103,680,558 | 4 | 3 | MIT | 2018-03-05T15:56:34 | 2017-09-15T16:52:49 | R | UTF-8 | R | false | true | 464 | rd | dem.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dem_data.R
\docType{data}
\name{dem}
\alias{dem}
\title{Simulated EHR data - Demographics.}
\format{An object of class \code{data.table} (inherits from \code{data.frame}) with 400 rows and 9 columns.}
\usage{
data(dem)
}
\description{
Simulated EHR data (no PHI constraints) featuring demographics data for ~500 patients.
}
\details{
Maintained by: Clara Marquardt
}
\keyword{datasets}
|
4176af58a85909ab57c2554da2abd7ad3dbb7889 | 2da2406aff1f6318cba7453db555c7ed4d2ea0d3 | /inst/snippet/mvn-marginal03.R | ab6d560886777b242e9cb7cab6a7dace96c08c4d | [] | no_license | rpruim/fastR2 | 4efe9742f56fe7fcee0ede1c1ec1203abb312f34 | d0fe0464ea6a6258b2414e4fcd59166eaf3103f8 | refs/heads/main | 2022-05-05T23:24:55.024994 | 2022-03-15T23:06:08 | 2022-03-15T23:06:08 | 3,821,177 | 11 | 8 | null | null | null | null | UTF-8 | R | false | false | 364 | r | mvn-marginal03.R | # simulate 3 independent Norm(0,1) vars
Z1 <- rnorm(100000); Z2 <- rnorm(100000); Z3 <- rnorm(100000)
# create the X's from the Z's
X1 <- 1 + Z1; X2 <- 2 + Z1 + Z2; X3 <- 3 + Z1 + 2 * Z2 + Z3
data.frame( `E(X1)` = mean(X1), `E(X2)` = mean(X2),
`Var(X1)` = var(X1), `Var(X2)` = var(X2),
`Cov(X1, X2)` = cov(X1, X2), check.names = FALSE)
|
e8efe19c9ff802b900f3bb72cbe64a17bb793437 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/stream/vignettes/stream.R | 50879b7baebb0b75805b090f5d43d0c62f3a242e | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,013 | r | stream.R | ### R code from vignette source 'stream.Rnw'
### Encoding: UTF-8
###################################################
### code chunk number 1: stream.Rnw:138-139
###################################################
options(width = 75, digits = 3, prompt = 'R> ', scipen = 3)
###################################################
### code chunk number 2: stream.Rnw:654-657
###################################################
library("stream")
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2)
###################################################
### code chunk number 3: stream.Rnw:666-668
###################################################
dstream <- DSC_DStream(gridsize = .1, Cm = 1.2)
update(dstream, stream, n = 500)
###################################################
### code chunk number 4: initial_example
###################################################
km <- DSC_Kmeans(k = 3)
recluster(km, dstream)
plot(km, stream, type = "both")
###################################################
### code chunk number 5: stream.Rnw:884-889
###################################################
library("stream")
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 3, noise = .05, p = c(.5, .3, .1))
stream
###################################################
### code chunk number 6: stream.Rnw:910-912
###################################################
p <- get_points(stream, n = 5)
p
###################################################
### code chunk number 7: stream.Rnw:923-925
###################################################
p <- get_points(stream, n = 100, class = TRUE)
head(p, n = 10)
###################################################
### code chunk number 8: static
###################################################
plot(stream, n = 500)
###################################################
### code chunk number 9: static_pc
###################################################
plot(stream, n = 500, method = "pc")
###################################################
### code chunk number 10: moa1
###################################################
set.seed(1000)
stream <- DSD_Benchmark(1)
stream
###################################################
### code chunk number 11: stream.Rnw:985-989 (eval = FALSE)
###################################################
## for(i in 1:4) {
## plot(stream, 250, xlim = c(0, 1), ylim = c(0, 1))
## tmp <- get_points(stream, n = 1400)
## }
###################################################
### code chunk number 12: moa1
###################################################
plot(stream, 250, xlim = c(0, 1), ylim = c(0, 1))
arrows(.15, .85, .85, .15, col = rgb(.8, .8, .8, .6), lwd = 10)
arrows(.15, .15, .85, .85, col = rgb(.8, .8, .8, .6), lwd = 10)
tmp <- get_points(stream, n = 1400)
###################################################
### code chunk number 13: moa2
###################################################
plot(stream, 250, xlim = c(0, 1), ylim = c(0, 1))
arrows(.15, .85, .85, .15, col = rgb(.8, .8, .8, .6), lwd = 10)
arrows(.15, .15, .85, .85, col = rgb(.8, .8, .8, .6), lwd = 10)
tmp <- get_points(stream, n=1400)
###################################################
### code chunk number 14: moa3
###################################################
plot(stream, 250, xlim = c(0, 1), ylim = c(0, 1))
arrows(.15,.85,.85,.15, col=rgb(.8,.8,.8,.6), lwd=10)
arrows(.15,.15,.85,.85, col=rgb(.8,.8,.8,.6), lwd=10)
tmp <- get_points(stream, n=1400)
###################################################
### code chunk number 15: moa4
###################################################
plot(stream, 250, xlim=c(0,1), ylim=c(0,1))
arrows(.15,.85,.85,.15, col=rgb(.8,.8,.8,.6), lwd=10)
arrows(.15,.15,.85,.85, col=rgb(.8,.8,.8,.6), lwd=10)
###################################################
### code chunk number 16: stream.Rnw:1041-1044 (eval = FALSE)
###################################################
## reset_stream(stream)
## animate_data(stream, n = 10000, horizon = 100,
## xlim = c(0, 1), ylim = c(0, 1))
###################################################
### code chunk number 17: stream.Rnw:1050-1053 (eval = FALSE)
###################################################
## library("animation")
## animation::ani.options(interval = .1)
## ani.replay()
###################################################
### code chunk number 18: stream.Rnw:1060-1062 (eval = FALSE)
###################################################
## saveHTML(ani.replay())
## saveGIF(ani.replay())
###################################################
### code chunk number 19: stream.Rnw:1086-1089
###################################################
library("stream")
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 5)
###################################################
### code chunk number 20: stream.Rnw:1094-1095 (eval = FALSE)
###################################################
## write_stream(stream, "data.csv", n = 100, sep = ",")
###################################################
### code chunk number 21: stream.Rnw:1125-1129
###################################################
file <- system.file("examples", "kddcup10000.data.gz", package = "stream")
stream_file <- DSD_ReadCSV(gzfile(file),
take = c(1, 5, 6, 8:11, 13:20, 23:42), class = 42, k = 7)
stream_file
###################################################
### code chunk number 22: stream.Rnw:1141-1142
###################################################
get_points(stream_file, n = 5)
###################################################
### code chunk number 23: stream.Rnw:1149-1151
###################################################
stream_scaled <- DSD_ScaleStream(stream_file, center = TRUE, scale = TRUE)
get_points(stream_scaled, n = 5)
###################################################
### code chunk number 24: stream.Rnw:1182-1184
###################################################
data("EuStockMarkets", package = "datasets")
head(EuStockMarkets)
###################################################
### code chunk number 25: stream.Rnw:1191-1193
###################################################
replayer <- DSD_Memory(EuStockMarkets, k = NA)
replayer
###################################################
### code chunk number 26: stream.Rnw:1199-1201
###################################################
get_points(replayer, n = 5)
replayer
###################################################
### code chunk number 27: stream.Rnw:1209-1210 (eval = FALSE)
###################################################
## get_points(replayer, n = 2000)
###################################################
### code chunk number 28: stream.Rnw:1212-1214
###################################################
err <- try(get_points(replayer, n = 2000))
cat(err)
###################################################
### code chunk number 29: stream.Rnw:1228-1230
###################################################
reset_stream(replayer, pos = 100)
replayer
###################################################
### code chunk number 30: stream.Rnw:1536-1539
###################################################
library("stream")
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = .05)
###################################################
### code chunk number 31: stream.Rnw:1547-1549
###################################################
dstream <- DSC_DStream(gridsize = .1, Cm = 1.2)
dstream
###################################################
### code chunk number 32: stream.Rnw:1557-1559
###################################################
update(dstream, stream, n = 500)
dstream
###################################################
### code chunk number 33: stream.Rnw:1568-1569
###################################################
head(get_centers(dstream))
###################################################
### code chunk number 34: cluster
###################################################
plot(dstream, stream)
###################################################
### code chunk number 35: cluster-grid
###################################################
plot(dstream, stream, grid = TRUE)
###################################################
### code chunk number 36: stream.Rnw:1889-1893
###################################################
library("stream")
stream <- DSD_Gaussians(k = 3, d = 2, noise = .05)
dstream <- DSC_DStream(gridsize = .1)
update(dstream, stream, n = 2000)
###################################################
### code chunk number 37: stream.Rnw:1901-1902
###################################################
evaluate(dstream, stream, n = 100)
###################################################
### code chunk number 38: stream.Rnw:1913-1914
###################################################
evaluate(dstream, stream, measure = c("purity", "crand"), n = 500)
###################################################
### code chunk number 39: stream.Rnw:1955-1961
###################################################
set.seed(1000)
stream <- DSD_Benchmark(1)
dstream <- DSC_DStream(gridsize = .05, lambda = .01)
ev <- evaluate_cluster(dstream, stream,
measure = c("numMicroClusters", "purity"), n = 5000, horizon = 100)
head(ev)
###################################################
### code chunk number 40: evaluation
###################################################
plot(ev[ , "points"], ev[ , "purity"], type = "l",
ylab = "Avg. Purity", xlab = "Points")
###################################################
### code chunk number 41: stream.Rnw:1994-1999 (eval = FALSE)
###################################################
## set.seed(1000)
## stream <- DSD_Benchmark(1)
## dstream <- DSC_DStream(gridsize = .05, lambda = .01)
## r <- animate_cluster(dstream, stream, horizon = 100, n = 5000,
## measure = "purity", plot.args = list(xlim = c(0, 1), ylim = c(0, 1)))
###################################################
### code chunk number 42: stream.Rnw:2029-2036
###################################################
library("stream")
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = .05)
dstream <- DSC_DStream(gridsize = .05, Cm = 1.5)
update(dstream, stream, n = 1000)
dstream
###################################################
### code chunk number 43: recluster
###################################################
plot(dstream, stream, type = "both")
###################################################
### code chunk number 44: recluster2
###################################################
km <- DSC_Kmeans(k = 3, weighted = TRUE)
recluster(km, dstream)
km
plot(km, stream, type = "both")
###################################################
### code chunk number 45: stream.Rnw:2097-2098
###################################################
evaluate(km, stream, measure = c("purity", "crand", "SSQ"), n = 1000)
###################################################
### code chunk number 46: stream.Rnw:2103-2105
###################################################
evaluate(km, stream, c(measure = "purity", "crand", "SSQ"), n = 1000,
assign = "macro")
###################################################
### code chunk number 47: stream.Rnw:2128-2131
###################################################
points <- get_points(stream, n = 100)
assignment <- get_assignment(dstream, points, type = "macro")
assignment
###################################################
### code chunk number 48: silhouette
###################################################
assignment[is.na(assignment)] <- 0L
library("cluster")
plot(silhouette(assignment, dist = dist(points)))
###################################################
### code chunk number 49: data_bng
###################################################
set.seed(1000)
library("stream")
stream <- DSD_Memory(DSD_BarsAndGaussians(noise = .05), n = 1500)
stream
plot(stream)
###################################################
### code chunk number 50: stream.Rnw:2356-2364
###################################################
algorithms <- list(
'Sample' = DSC_TwoStage(micro = DSC_Sample(k = 100),
macro = DSC_Kmeans(k = 4)),
'Window' = DSC_TwoStage(micro = DSC_Window(horizon = 100),
macro = DSC_Kmeans(k = 4)),
'D-Stream' = DSC_DStream(gridsize = .7, Cm = 1.5),
'DBSTREAM' = DSC_DBSTREAM(r = .45)
)
###################################################
### code chunk number 51: stream.Rnw:2374-2378
###################################################
for(a in algorithms) {
reset_stream(stream)
update(a, stream, n = 1000)
}
###################################################
### code chunk number 52: stream.Rnw:2383-2384
###################################################
sapply(algorithms, nclusters, type = "micro")
###################################################
### code chunk number 53: microclusters
###################################################
op <- par(no.readonly = TRUE)
layout(mat = matrix(1:length(algorithms), ncol = 2))
for(a in algorithms) {
reset_stream(stream)
plot(a, stream, main = description(a), type = "micro")
}
par(op)
###################################################
### code chunk number 54: microclusters_assignment
###################################################
op <- par(no.readonly = TRUE)
layout(mat = matrix(1:length(algorithms), ncol = 2))
for(a in algorithms) {
reset_stream(stream)
plot(a, stream, main = description(a),
assignment = TRUE, weight = FALSE, type = "micro")
}
par(op)
###################################################
### code chunk number 55: stream.Rnw:2462-2469
###################################################
sapply(algorithms, FUN=function(a) {
reset_stream(stream, pos = 1001)
evaluate(a, stream,
measure = c("numMicroClusters", "purity"),
type = "micro",
n = 500)
})
###################################################
### code chunk number 56: macroclusters
###################################################
op <- par(no.readonly = TRUE)
layout(mat=matrix(1:length(algorithms), ncol = 2))
for(a in algorithms) {
reset_stream(stream)
plot(a, stream, main = description(a), type = "both")
}
par(op)
###################################################
### code chunk number 57: stream.Rnw:2513-2519
###################################################
sapply(algorithms, FUN = function(a) {
reset_stream(stream, pos = 1001)
evaluate(a, stream, measure = c("numMacroClusters", "purity",
"SSQ", "cRand", "silhouette"),
n = 500, assign = "micro", type = "macro")
})
###################################################
### code chunk number 58: stream.Rnw:2541-2543
###################################################
set.seed(0)
stream <- DSD_Memory(DSD_Benchmark(1), n = 5000)
###################################################
### code chunk number 59: stream.Rnw:2553-2561
###################################################
algorithms <- list(
'Sample' = DSC_TwoStage(micro = DSC_Sample(k = 100, biased = TRUE),
macro = DSC_Kmeans(k = 2)),
'Window' = DSC_TwoStage(micro = DSC_Window(horizon = 100, lambda = .01),
macro = DSC_Kmeans(k = 2)),
'D-Stream' = DSC_DStream(gridsize = .1, lambda = .01),
'DBSTREAM' = DSC_DBSTREAM(r = .05, lambda = .01)
)
###################################################
### code chunk number 60: stream.Rnw:2572-2577
###################################################
evaluation <- lapply(algorithms, FUN = function(a) {
reset_stream(stream)
evaluate_cluster(a, stream, horizon = 100, n = 5000, measure = "crand",
type = "macro", assign = "micro")
})
###################################################
### code chunk number 61: stream.Rnw:2593-2596
###################################################
Position <- evaluation[[1]][ , "points"]
cRand <- sapply(evaluation, FUN = function(x) x[ , "cRand"])
head(cRand)
###################################################
### code chunk number 62: dynamic
###################################################
matplot(Position, cRand, type = "l", lwd = 2)
legend("bottomleft", legend = names(evaluation),
col = 1:6, lty = 1:6, bty = "n", lwd = 2)
###################################################
### code chunk number 63: dynamic_box
###################################################
boxplot(cRand, las = 2, cex.axis = .8)
###################################################
### code chunk number 64: stream.Rnw:2654-2662 (eval = FALSE)
###################################################
## library("stream")
## con <- gzcon(
## url(paste("http://archive.ics.uci.edu/ml/machine-learning-databases/",
## "kddcup99-mld/kddcup.data.gz", sep="")))
##
## stream <- DSD_ReadCSV(con, take=c(1, 5, 6, 8:11, 13:20, 23:42),
## class=42, k=7)
## stream2 <- DSD_ScaleStream(stream, n=1000)
###################################################
### code chunk number 65: stream.Rnw:2668-2670 (eval = FALSE)
###################################################
## dstream <- DSC_DStream(gridsize = .5, gaptime = 10000L, lambda = .01)
## update(dstream, stream2, n = 4000000, verbose = TRUE)
|
ce983931c4bab5ec37816b88b1fea7fe19fc28d4 | b9f0c8a42d1da0aa40fa46197d3c4174648fe6e1 | /src/analyze.R | 9770a0cdae4f092c358497a7d1e7df738d08f61b | [
"BSD-2-Clause"
] | permissive | danlooo/correlation-workshop | e8f78a27e96bf7c65346a04d600cab1e81003e82 | a9040dfcf2c432d8ba79596d615600e19ad79d27 | refs/heads/main | 2023-03-28T14:36:28.264046 | 2021-03-26T15:45:25 | 2021-03-26T15:45:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 730 | r | analyze.R | #!/usr/bin/env Rscript
#
# Setup and configure drake
#
source(here::here("src/setup.R"))
cache <- drake::new_cache(".drake")
drake::drake_cache(here::here(".drake"))$unlock()
make(
# plan
plan = plan,
parallelism = "clustermq",
# allow multiple works to access global env at the same time
lock_envir = FALSE,
recoverable = TRUE,
recover = TRUE,
# caching
cache = cache,
garbage_collection = TRUE,
memory_strategy = "preclean",
# config
seed = 1337,
keep_going = TRUE,
jobs = jobs,
# output
verbose = 1,
format = "qs",
log_make = "log/drake.log"
)
rmarkdown::render(
input = "src/report.Rmd",
knit_root_dir = ".",
output_dir = ".",
output_file = "report.html",
)
|
2b3a2b72d023953c6f92945bea646afd13a28a39 | 38d127b3e63855d9897eb82f76f9def1fc381e6e | /R/getSubaccounts.R | 0905a84e6513cf231636824d1a7f0625a08473d9 | [] | no_license | erikpal/bRush | ed2ac601050d82eac15211dacc3047923df413a4 | 42f47fe87ee982e9207b2f9ec6577daa0a01e764 | refs/heads/master | 2023-05-11T17:36:19.160006 | 2023-05-02T15:57:42 | 2023-05-02T15:57:42 | 88,922,475 | 15 | 2 | null | null | null | null | UTF-8 | R | false | false | 834 | r | getSubaccounts.R | #' Get Sub-accounts
#'
#' Get details of sub-accounts for a provided account.
#' @param accountID Integer of the account ID to find sub-accounts for
#' @param recursive Boolean to search returned subaccounts for subaccounts
#' @param server The base url of a Canvas installation
#' @param ... Optional page options to pass to processRequest
#' @export
getSubaccounts <- function(accountID, recursive = FALSE, server = "test", ...) {
url <- loadURL(server)
url$path <- "api/v1/accounts/accountID/sub_accounts"
url$path <- sub("accountID", accountID, url$path)
if (recursive == TRUE) {
url$query <- list(recursive = TRUE)
}
##Pass the url to the request processor
results <- processRequest(url, ...)
return(results)
} |
08bb1d8e8c80acf9ac1ebcdbff1ad04604703bb7 | c242f642570f4124c986687a362d46684b058e76 | /R/multilevelPSA.demo.R | 952c854168a78677123fca23ff5024daac165c4d | [] | no_license | cityofsmiles87/Dissertation | dc07bb3afdf3e7fbe28b9b0030378dbacf7c5e83 | 8aa6bbad542f4e4a0f777587415e9f1d4736616f | refs/heads/master | 2020-09-14T14:25:53.132408 | 2014-12-16T14:53:29 | 2014-12-16T14:53:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,877 | r | multilevelPSA.demo.R | options(digits=3)
options(width=80)
install.packages('multilevelPSA', repos='http://cran.r-project.org')
require('multilevelPSA')
data(pisana)
data(pisa.psa.cols)
pisana$MathScore <- apply(pisana[,paste0('PV', 1:5, 'MATH')], 1, sum) / 5
pisana <- pisana[,!names(pisana) %in%
c(paste0('PV', 1:5, 'MATH'),paste0('PV', 1:5, 'SCIE'),paste0('PV', 1:5, 'READ'))]
mlpsa <- mlpsa.ctree(pisana[,c('CNT','PUBPRIV',pisa.psa.cols)],
formula=PUBPRIV ~ ., level2='CNT')
mlpsa.df <- getStrata(mlpsa, pisana, level2='CNT')
names(mlpsa.df)
mlpsa.lr <- mlpsa.logistic(pisana[,c('CNT','PUBPRIV',pisa.psa.cols)],
formula=PUBPRIV ~ ., level2='CNT')
mlpsa.lr.df <- getPropensityScores(mlpsa.lr, nStrata=5)
cv.bal <- covariate.balance(covariates=student[,pisa.psa.cols],
treatment=student$PUBPRIV,
level2=student$CNT,
strata=mlpsa.df$strata)
plot(cv.bal) + theme(axis.text.y=element_text(size=5))
ggsave('~/Dropbox/School/Dissertation/Figures/pisabalance.pdf', width=6, height=8.5)
mlpsa.df$PUBPRIV <- factor(as.character(mlpsa.df$PUBPRIV), levels=c('Public','Private'))
table(mlpsa.df$PUBPRIV)
results.psa.math <- mlpsa(response=mlpsa.df$MathScore,
treatment=mlpsa.df$PUBPRIV,
strata=mlpsa.df$strata,
level2=mlpsa.df$CNT)
summary(results.psa.math)
pdf('Figures/pisamlpsa.pdf', width=6, height=6)
plot(results.psa.math)
dev.off()
mlpsa.circ.plot(results.psa.math, level2.label=FALSE)
ggsave('Figures/PISACircPlot.pdf', width=7, height=7)
mlpsa.difference.plot(results.psa.math, sd=mean(mlpsa.df$MathScore, na.rm=TRUE), xlim=c(-.2, .05))
ggsave('Figures/pisadiffplot.pdf', width=8, height=3)
results.psa.lr.math <- mlpsa(response=pisana$MathScore,
treatment=pisana$PUBPRIV,
strata=mlpsa.lr.df$strata,
level2=mlpsa.lr.df$level2)
summary(results.psa.lr.math)
plot(results.psa.lr.math)
|
e0261fce2db31e01c6b1e7be958fa80305e1aeb1 | b83cfb6a045040319338cf5e16c0392887993ee0 | /RCode/ROracle.r | 65dff1360dd10dc769c420104cb8e78dbbf1e60f | [] | no_license | xiaojiezhou/OftenUsed | 75360c9318158e24045a646e97d61fbf9f02b44e | d016df176fa24b9763da0ce4aa408184f533273e | refs/heads/master | 2022-06-22T03:52:56.112580 | 2020-05-06T14:42:37 | 2020-05-06T14:42:37 | 260,552,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | r | ROracle.r |
# install.packages('ROracle')
require(ROracle)
exaDrv = dbDriver("Oracle")
exaCon = dbConnect(exaDrv, username="[AN_RT_WS95]", dbname="exa_uskrgprdh")
statsdat <- dbGetQuery(exaCon,
"
SELECT * FROM HHSPEND_MONTH_TEST_V2
where rownum <= 100
"
)
dbDisconnect(exaCon)
str(statsdat) |
309f4b10158a7980384b46910d9981f50f9f32a5 | f23b7b75a87ea3280c9e984a3b275a6fd83e135f | /Source/man/read.run.file.Rd | d8fc3ce0f5d41e7258e79e2d6b64cd579127ad14 | [] | no_license | HumanExposure/SHEDSHTRPackage | 1d830b5d8211e3e7ae000239cec7c17a44af9952 | c3d6c45557e77c29592218d7056e0d110e55e5ee | refs/heads/master | 2023-07-20T18:44:16.311968 | 2021-09-03T17:01:52 | 2021-09-03T17:01:52 | 113,353,498 | 4 | 3 | null | null | null | null | UTF-8 | R | false | true | 510 | rd | read.run.file.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ReadData.R
\name{read.run.file}
\alias{read.run.file}
\title{read.run.file}
\usage{
read.run.file(run.file = "run_test.txt")
}
\description{
Each SHEDS run has its own "run.file" that the user prepares before the run. This file contains all the settings and file references needed for the run. Read.run.file occurs at the start of each SHEDS run. The contents of the run.file are examined, and stored in the R object "specs".
}
|
368f1e9deea069c7c6b0f41543909f73766c9120 | 57aba6b27797866b9c6721d3d1237bba95052659 | /Data/PCA.R | 44c37fef94331b0d96ed2eb1e5c6b0a7af5f62a8 | [] | no_license | efwaring/autumnalTemps | 8c179b35ce2c9bd1fa2c464a5c4568549a2e5ea3 | 3c7021a6f304a7150330b49e454d4675fe69dedb | refs/heads/master | 2021-09-06T23:55:56.915291 | 2018-02-13T18:41:52 | 2018-02-13T18:41:52 | 59,041,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 883 | r | PCA.R | # PCA for growth chamber data
library(ggfortify)
allPCA <- read.csv("GCdata.csv")
allPCA = allPCA %>% mutate(climate=temp+phoper) %>% filter(climate!=35) %>%
select(spp, climate, nTreatment, aamb,dr,netqe,jmax,vcmax,Nitrogen)
allPCA$dr = allPCA$dr * -1
log.allPCA <- log(allPCA[c(4, 5, 6, 7, 8, 9)])
log.allPCA[33,]$dr <-0
log.allPCA[31,]$dr <-0
log.allPCA <- na.omit(log.allPCA)
log.allPCA$Rd <- log.allPCA$dr
log.allPCA$dr<-NULL
pr.allPCA <- prcomp(log.allPCA, scale=T, center=T)
plot(pr.allPCA)
print(pr.allPCA)
cumsum((pr.allPCA$sdev)^2) / sum(pr.allPCA$sdev^2)
autoplot(pr.allPCA, data = allPCA, fill='spp', shape="nTreatment",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size =4) +
scale_shape_manual(values=c(23,21,22))+
scale_fill_manual(values=c("black", NA)) +
theme(legend.position="none")
|
dadb1f8712078326271b59f90d1d4e5eeb427fef | 3e508d7cd0798f70f70242a837f99123397fc1c4 | /tests/test-sim3way.R | 3ae9f99f07d72cbd97d74daaa971e90897343876 | [] | no_license | jlivsey/Dvar | f0d142df9866a4232947d114891111097c4c5851 | 824cdcc822829d655b905bfeca54a40b82f16e36 | refs/heads/master | 2023-07-26T19:17:28.531305 | 2023-07-06T13:55:28 | 2023-07-06T13:55:28 | 237,249,747 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | test-sim3way.R | library(L1pack)
library(Dvar)
options(warn = -1)
#' Lets look at simulation for same dimension as Age x sex x blockGroup
#' First set the dimensionality and simulate histogram
mydim = c(6, 2, 10)
A <- array(sample(1:10, size = prod(mydim), replace = TRUE), mydim)
Nrep <- 2000
bpar <- 1/3
#' Run simulation with only total. I use only the total because the simulation
#' function is not setup to take no margins.
res <-
Sim3Way(
Nrep = Nrep,
intab = A,
bpar = bpar,
marPack = list(list(0,0,0))
)
#' Look at the first 10 estimates, the true values and the rmse
x <-
cbind(
c(A)[1:10],
round(res$param[1:10], 4),
round(res$rmse[1:10], 4)
)
colnames(x) <- c("true", "estimate", "rmse")
print(x)
#' Construct marPack with all possible margins and total. This is ugly at the
#' moment because the simulation function expects a list of lists.
eg <- expand.grid(0:mydim[1], 0:mydim[2], 0:mydim[3])
# which rows have all none zero entries (not valid margins)
allNotZero <- (eg[,1] != 0) & (eg[, 2] != 0) & (eg[, 3] != 0)
marPackMatrix <- eg[!allNotZero, ]
marPack = list()
for(i in 1:dim(marPackMatrix)[1]){
marPack[[i]] <- list()
marPack[[i]][[1]] <- marPackMatrix[i, 1]
marPack[[i]][[2]] <- marPackMatrix[i, 2]
marPack[[i]][[3]] <- marPackMatrix[i, 3]
}
#' Run simulation with full margins and total
resFull <-
Sim3Way(
Nrep = Nrep,
intab = A,
bpar = bpar,
marPack = marPack
)
#' Add full margins sim results to output table
x <-
cbind(
c(A)[1:10],
round(res$param[1:10], 4),
round(res$rmse[1:10], 4),
round(resFull$param[1:10], 4),
round(resFull$rmse[1:10], 4)
)
colnames(x) <- c("true",
"estimate-none",
"rmse-none",
"estimate-full",
"rmse-full")
print(x)
#' Interestingly some of the estimates are not better with the full margins.
#' Let's look at the average rmse of full vs no margins
mean(res$rmse)
mean(resFull$rmse)
#' Much Better!
hist(res$rmse, breaks = 30, main = "rmse with no margins")
hist(resFull$rmse, breaks = 30, main = "rmse with full margins")
#' Test to see if weight vector is working
weightVec <- rep(1, prod(mydim) + length(marPack))
|
73ffc25acbddb3ee43e0f80709f6221590b371be | 3df5e0d9cd46d3d86fe6879d85c96a3f8829fbd3 | /man/Scorecard_gen_code_sas.Rd | 447898d5f7a329f4808f05656aed1749e01fbcca | [] | no_license | gravesee/rubbish | d69e1de8a8c6be9084941eeb0b0d39c391a3fb74 | fce7aba82bbfead3a28bf16bc2972491d6a1991b | refs/heads/master | 2021-06-12T23:16:47.411331 | 2017-04-10T00:21:33 | 2017-04-10T00:21:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 942 | rd | Scorecard_gen_code_sas.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scorecard_class.R
\name{Scorecard_gen_code_sas}
\alias{Scorecard_gen_code_sas}
\title{Generate SAS code for Scorecard object}
\arguments{
\item{pfx}{character prefix to prepend to variable names
by the Scorecard model object. Defaults to 1.}
\item{method}{method used for calculating the reference level for adverse
action codes. Three possible choices:
\itemize{
\item{"min" }{Calculate difference from minimum of perf values - default}
\item{"max" }{Calculate difference from maximum of perf values}
\item{"neutral" }{Calculate difference from zero}#'
}}
}
\value{
a character vector of SAS code
}
\description{
generate SAS code represenation of the Scorecard object. The SAS
code that is genereated calculates the score, adverse action code distances,
and provides a set of macro assignments for assigning adverse action codes
to particular bin levels.
}
|
f8a68740b6fc37b66b72e15e9bfc432fd34df920 | cf63cb42a6b26550f9b6270434e68b64d383ba3e | /R_file/lab3.R | 2ebf3f8202045e1cffe7a97f4e1308233984b577 | [] | no_license | zyz293/Statistical_Methods_for_Data_Mining | b8f84ad3bfcf1711fabb6272d8391b58bdc0a7cf | 3eda7095c8bf61095b92b65e4386c73363e66d79 | refs/heads/master | 2020-04-21T11:49:22.774238 | 2015-07-02T22:07:19 | 2015-07-02T22:07:19 | 38,459,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,078 | r | lab3.R | setwd('C:/Users/DaTui/Desktop/winter_quarter/IEME304/lab/3')
LAB3 = read.csv("Lab3.csv")
CVInd = function(n,K) { #n is sample size; K is number of parts; returns K-length list of indices for each part
m = floor(n/K) #approximate size of each part
r = n-m*K
I = sample(n,n) #random reordering of the indices
Ind = list() #will be list of indices for all K parts
length(Ind) = K
for (k in 1:K) {
if (k <= r) kpart = ((m+1)*(k-1)+1):((m+1)*k)
else kpart = ((m+1)*r+m*(k-r-1)+1):((m+1)*r+m*(k-r))
Ind[[k]] = I[kpart] #indices for kth part of data
}
return (Ind)
}
n=79;K=10
Ind = CVInd(n=n,K=K)
y = LAB3[[1]]
yhat = y
for (k in 1:K) {
out = lm(y~.,data=LAB3[-Ind[[k]],])
yhat[Ind[[k]]] = cbind(1,as.matrix(LAB3[Ind[[k]], 2:9]))%*% as.matrix(out$coef)
}
CVSSE1 = sum((y-yhat)^2) #CV SSE
#now compare to a different shrinkage parameter
yhat = y
for (k in 1:K) {
out = lm(y~x1+x2,data=LAB3[-Ind[[k]],])
yhat[Ind[[k]]] = cbind(1,as.matrix(LAB3[Ind[[k]], 2:3]))%*% as.matrix(out$coef)
}
CVSSE2 = sum((y-yhat)^2) #CV SSE
c(CVSSE1,CVSSE2)
|
ce0850060d362d99bcce056b3d47c01355d35748 | 774c97f4ceeebbfa7db0efd09a07c318763c530b | /R/Gen_x_z.R | 21e25b3b70f6210f7cc63687902358b0b3d9f858 | [] | no_license | BIG-S2/GFPLVCM | 3c709126c81b4fe3dba3fc533d46c5860a96e40a | 47075a7e8dd9bcec21892f3278d8babbd9d35ca8 | refs/heads/master | 2020-05-03T12:03:59.954202 | 2019-03-30T21:54:38 | 2019-03-30T21:54:38 | 178,615,183 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,879 | r | Gen_x_z.R | Gen_x_z<-function(time_number, time_point, u_time, len_k){
## generate x and z for one subject
phi=function(t,k){
if(k==1) return(1) else return(sqrt(2)*cos((k-1)*pi*t) );
}
###################################################################
### generate the correlation relationship within the subject ######
###################################################################
# sigma_z_temp=diag( time_number )
# for(j in 1: time_number ){
# for(k in j: time_number ){
# sigma_z_temp[j, k] = exp(- abs ( time_point[j] - time_point[k] ) )
# sigma_z_temp[k, j]=sigma_z_temp[j, k]
# }
# }
sigma_z_temp = exp( -abs( outer(time_point, time_point, "-") ) )
z_temp_value = MASS::mvrnorm( n=1,rep(0,time_number), sigma_z_temp )
######################################
### generate x for a subject #########
######################################
kk=matrix(rep(c( 1:len_k ), u_time ), len_k, u_time );
u=matrix(rep(seq(0,1,length=u_time),len_k ), len_k, u_time, byrow=T);
phi_u=matrix(mapply(function(t,k)phi(t,k),u,kk), len_k, u_time);
M = MASS::mvrnorm( n=len_k,rep(0,time_number), sigma_z_temp ) ## len_k*length of S
x_temp_value=0
for(k in 1: len_k){
x_temp_value=x_temp_value + as.matrix( M[k, ] )%*%phi_u[k, ]*(-1)^(k+1)/k
}
#################################
### generate beta ###############
#################################
kk_s=matrix(rep(c( 1:len_k ), time_number ), len_k, time_number );
s=matrix(rep(time_point ,len_k ), len_k, time_number, byrow=T);
phi_s=matrix(mapply(function(t,k)phi(t,k),s,kk_s), len_k, time_number);
beta_temp_value=0
for(k in 1: len_k){
beta_temp_value=beta_temp_value + as.matrix( phi_s[k, ] )%*%phi_u[k, ]*(-1)^(k+1)/(k^2) ## s*u
}
list(z_temp_value=z_temp_value, x_temp_value=x_temp_value, beta_temp_value=beta_temp_value)
}
|
0c51c9606b972582e704df6f033f33a4809e9146 | 3098b0bd52cee12201a1e932e42a0be939526e87 | /cachematrix.R | b1d6006349a213e37acc1634c49c4fba267a598e | [] | no_license | pjs83/ProgrammingAssignment2 | e094c5cd97861962f1a0065b29ce6e75f752ae40 | c42e5b3d5fc87bff130844b0841584d545afae3d | refs/heads/master | 2020-05-31T00:09:45.134618 | 2014-09-13T18:50:26 | 2014-09-13T18:50:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | cachematrix.R | ## Matrix inversion using previously calculated inverse matrices if available to speed up the process and avoid recalculating.
## Creates a speial matrix object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
A <- NULL
set <- function(y) {
x <<- y
A <<- NULL
}
get <- function() x
setsolve <- function(solve) A <<- solve
getsolve <- function() A
list(set = set, get = get,
setsolve = setsolve,
getsolve = getsolve)
}
## Computes the inverse of the special matrix returned bz makeCacheMatrix. If the inverse has already been computed, then the function
## retrieves the inverse from the cache.
cacheSolve <- function(x, ...) {
A <- x$getsolve()
if(!is.null(A)) {
message("getting cached data")
return(A)
}
data <- x$get()
A <- solve(data, ...)
x$setsolve(A)
A
}
|
6150193427becf2545dde1801443d02bfa68214d | c7fddad890f1e87f6ca12685f5777aa39cf320f6 | /plot1.R | 969cfd765041277ec17916ddafdecd57ad24ed1c | [] | no_license | garydeinert/ExplData-Analysis-Course-Project-1 | 6c24acc5e7b10f26efc6c10c50f2d7ff0915e932 | dad8efb485ecbcf96c4e08751cf9180b49b80d39 | refs/heads/master | 2020-05-19T11:19:44.980615 | 2015-03-08T19:45:49 | 2015-03-08T19:45:49 | 31,861,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,346 | r | plot1.R |
## ======== set directory and filename here.
dir <- "C:/Users/Gary/Desktop/Data Science Specialzation/Exploratory Data Course Proj 1"
setwd(dir)
filenm <- "household_power_consumption.txt"
## ========= end set directory and filename
my.data <- as.data.frame(read.table(filenm, header=TRUE, sep = ";", na.strings = "?"))
my.data$Date <- as.Date(my.data$Date,"%d/%m/%Y")
my.data$Time <- strptime(my.data$Time,"%H:%M:%S")
## Date: Date in format dd/mm/yyyy, Time in format hh:mm:ss
## Global_active_power: household global minute-averaged active power (in kilowatt)
## Global_reactive_power: household global minute-averaged reactive power (in kilowatt)
## Voltage: minute-averaged voltage (in volt)
## Global_intensity: household global minute-averaged current intensity (in ampere)
## --- All sub-metering in watt-hours of active energy.
## Sub_metering_1: kitchen - dishwasher, oven, microwave
## Sub_metering_2: laundry room - washing-machine,dryer, refrigerator, light.
## Sub_metering_3: an electric water-heater and an air-conditioner.
my.subset <- my.data[my.data$Date=="2007-02-01" | my.data$Date == "2007-02-02",]
png(filename = "plot1.png", width = 480, height = 480)
hist(my.subset$Global_active_power,main="Global Active Power", col="Red",xlab = "Global Active Power (Kilowatts)")
dev.off()
|
607f3e8109d7f96cfc70d73617a0faca82b89e97 | 21bea3d65527823f2c05a0651902d0493e87fdec | /R/mathOps.R | 94399bbdba0c6586df938639db07ec18d1d00395 | [] | no_license | cran/fSeries | b75f581bf81a53a6121ade5325f3397c21382a9a | e7b134694da3344e8c8863330be35c6c1dd464b0 | refs/heads/master | 2021-03-12T22:56:17.873727 | 2009-05-25T00:00:00 | 2009-05-25T00:00:00 | 17,718,720 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,720 | r | mathOps.R |
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library 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 Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
# Copyrights (C)
# for this R-port:
# 1999 - 2007, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# METHOD: MATHEMATICAL OPERATIONS ON DATA:
# Ops.timeSeries Returns group 'Ops' for a 'timeSeries' object
# abs.timeSeries Returns abolute values of a 'timeSeries' object
# sqrt.timeSeries Returns sqrt values of a 'timeSeries' object
# exp.timeSeries Returns exponentials of a 'timeSeries' object
# log.timeSeries Returns logarithms of a 'timeSeries' object
# sign.timeSeries Returns the signs of a 'timeSeries' object
# diff.timeSeries Differences a 'timeSeries' object
# scale.timeSeries Centers and/or scales a 'timeSeries' object
# quantile.timeSeries Returns quantiles of an univariate 'timeSeries'
################################################################################
Ops.timeSeries =
function(e1, e2 = 1)
{ # A function implemented by Diethelm Wuertz
# Description:
# Uses group 'Ops' generic functions for 'timeSeries' objects
# Arguments:
# e1, e2 - two objects of class 'timeSeries'.
# Value:
# Returns an object of class 'timeSeries'.
# FUNCTION:
# Save:
s1 = e1
s2 = e2
# Which one is a 'timeSeries' object?
i1 = inherits(e1, "timeSeries")
i2 = inherits(e2, "timeSeries")
# Match positions and FinCenter?
if (i1 && i2) {
if (!identical(as.vector(e1@positions), as.vector(e2@positions)))
stop("positions slot must match")
if (!identical(e1@FinCenter, e2@FinCenter))
stop("FinCenter slot must match")
}
# Extract Data Slot:
if (i1) e1 = e1@Data
if (i2) e2 = e2@Data
# Compute:
s = NextMethod(.Generic)
# Make timeSeries:
if ( i1) { s1@Data = s; s = s1 }
if (!i1 && i2) { s2@Data = s; s = s2 }
if ( i1 && !i2) s@units = s1@units
if (!i1 && i2) s@units = s2@units
if ( i1 && i2) s@units = paste(s1@units, "_", s2@units, sep = "")
colnames(s@Data) = s@units
df = data.frame()
if (i1) {
if (dim(s1@recordIDs)[1] > 0)
df = s1@recordIDs
}
if (i2) {
if (dim(s2@recordIDs)[1] > 0)
df = s2@recordIDs
}
if (i1 & i2) {
if (dim(s1@recordIDs)[1] > 0 & dim(s2@recordIDs)[1] > 0)
df = data.frame(s1@recordIDs, s2@recordIDs)
}
s@recordIDs = df
# Return Value:
s
}
# ------------------------------------------------------------------------------
abs.timeSeries =
function(x)
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns absolute values of a 'timeSeries' object
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'.
# Note:
# abs is .Primitive
# FUNCTION:
# Absolute Values:
x@Data = abs(x@Data)
# Return Value:
x
}
# ------------------------------------------------------------------------------
sqrt.timeSeries =
function(x)
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns logarithmic values of a 'timeSeries' object
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'.
# Note:
# sqrt is .Primitive
# FUNCTION:
# Absolute Values:
x@Data = sqrt(x@Data)
# Return Value:
x
}
# ------------------------------------------------------------------------------
exp.timeSeries =
function(x)
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns exponential values of a 'timeSeries' object
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'.
# Note:
# exp is .Primitive
# FUNCTION:
# Absolute Values:
x@Data = exp(x@Data)
# Return Value:
x
}
# ------------------------------------------------------------------------------
log.timeSeries =
function(x, base = exp(1))
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns logarithmic values of a 'timeSeries' object
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'.
# Note:
# log is .Primitive
# FUNCTION:
# Absolute Values:
x@Data = log(x@Data, base = base)
# Return Value:
x
}
# ------------------------------------------------------------------------------
sign.timeSeries =
function(x)
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns the signs of a 'timeSeries' object
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'.
# Note:
# sign is .Primitive
# FUNCTION:
# Which sign ?
x@Data = sign(x@Data)
# Return Value;
x
}
# ------------------------------------------------------------------------------
diff.timeSeries =
function(x, lag = 1, diff = 1, trim = FALSE, pad = NA, ...)
{ # A function implemented by Diethelm Wuertz
# Description:
# Difference 'timeSeries' objects.
# Arguments:
# x - a 'timeSeries' object.
# lag - an integer indicating which lag to use.
# By default 1.
# diff - an integer indicating the order of the difference.
# By default 1.
# trim - a logical. Should NAs at the beginning of the
# series be removed?
# pad - a umeric value with which NAs should be replaced
# at the beginning of the series.
# Value:
# Returns a differenced object of class 'timeSeries'.
# FUNCTION:
# Convert:
y = as.matrix(x)
# Check NAs:
# if (any(is.na(y))) stop("NAs are not allowed in time series")
# Difference:
z = diff(y, lag = lag, difference = diff)
# Trim:
if (!trim) {
diffNums = dim(y)[1] - dim(z)[1]
zpad = matrix(0*y[1:diffNums, ] + pad, nrow = diffNums)
rownames(zpad) = rownames(y)[1:diffNums]
z = rbind(zpad, z)
}
# Record IDs:
df = x@recordIDs
if (trim) {
if (sum(dim(df)) > 0) {
TRIM = dim(df)[1] - dim(z)[1]
df = df[-(1:TRIM), ]
}
}
# Return Value:
timeSeries(data = z, charvec = rownames(z), units = colnames(z),
format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter,
recordIDs = df, title = x@title, documentation = x@documentation)
}
# ------------------------------------------------------------------------------
scale.timeSeries =
function(x, center = TRUE, scale = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Centers and/or scales a 'timeSeries' object.
# Arguments:
# FUNCTION:
# Scale:
x@Data = scale(x = x@Data, center = center, scale = scale)
# Return Value:
x
}
# ------------------------------------------------------------------------------
quantile.timeSeries =
function(x, ...)
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns quantiles of an univariate 'timeSeries
# Arguments:
# FUNCTION:
# Check:
stopifnot(NCOL(x) == 1)
# Quantiles:
ans = quantile(x = as.vector(x), ...)
# Return Value:
ans
}
################################################################################ |
ad29ab583d57c46115c768679d1e6c2746d19da2 | be236873f4cd08b8e4a2b0b7a228385da2645abe | /run_analysis.R | 58dcc54f313e9b9b1dcb92616d6ea66d0aa1fe60 | [] | no_license | gitbeckywong/Coursera-3-Project | f17585b4b22a31a68672b83818924587406b3c12 | 6b2bdf2339d077da614ddb742d18c3f44b1c1fb7 | refs/heads/master | 2020-06-09T04:47:06.838179 | 2014-10-24T06:24:03 | 2014-10-24T06:24:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,374 | r | run_analysis.R | ## Load the "dplyr" package. (It is assumed that the "dplyr" package has already been installed.)
library(dplyr)
## Before proceeding, ensure all folders and data files are contained in the working folder.
## Specifically, the folders "test" and "train" should be in the working folder.
## Read all required files into R. Use relative file paths as needed.
## Note - "Inertial Signals" folder is ignored, since it contains no mean or std data.
features <- tbl_df(read.table("features.txt", sep = "", header = FALSE, stringsAsFactors = FALSE))
subject_test <- tbl_df(read.table("./test/subject_test.txt", sep = "", header = FALSE, col.names = "Subject"))
X_test <- tbl_df(read.table("./test/X_test.txt", sep = "", header = FALSE, col.names = features[[2]]))
Y_test <- tbl_df(read.table("./test/Y_test.txt", sep = "", header = FALSE, col.names = "Activity"))
subject_train <- tbl_df(read.table("./train/subject_train.txt", sep = "", header = FALSE, col.names = "Subject"))
X_train <- tbl_df(read.table("./train/X_train.txt", sep = "", header = FALSE, col.names = features[[2]]))
Y_train <- tbl_df(read.table("./train/Y_train.txt", sep = "", header = FALSE, col.names = "Activity"))
activity_labels <- tbl_df(read.table("activity_labels.txt", sep ="", header = FALSE))
## Step 1:
## Use cbind and rbind to "merge" all files into one complete data set.
test_all <- tbl_df(cbind(subject_test, Y_test, X_test))
train_all <- tbl_df(cbind(subject_train, Y_train, X_train))
completedata <- tbl_df(rbind(test_all, train_all))
## Step 2:
## Use select and contains to extract subject, activity, and any mean or std measurements,
## as well as any other variables that may contain the string "mean" or "std".
meanstddata_withFreq <- select(completedata, Subject, Activity, contains("mean"), contains("std"))
## Remove any columns with "meanFreq" or "angle" from this dataset, as they are not what the question is asking for.
meanstddata <- select(meanstddata_withFreq, -contains("meanFreq"), -contains("angle"))
## Step 3:
## Convert Activity column from integers to factors, then change the levels to the activity_labels.
fact <- as.factor(meanstddata$Activity)
levels(fact) <- activity_labels[[2]]
meanstddata$Activity <- fact
## Step 4:
## Rename all variables to be more descriptive.
## Due to variables' lengths, I have used camelCase for easier understanding.
## Use gsub() function to more easily edit the variable names (rather than having to rename each one individually).
tablenames <- names(meanstddata)
newnames <- gsub("\\.","",tablenames)
newnames <- gsub("BodyBody","Body",newnames)
newnames <- gsub("mean","Mean",newnames)
newnames <- gsub("std","Std",newnames)
newnames <- gsub("Acc","Acceleration",newnames)
newnames <- gsub("Gyro","Gyroscope",newnames)
newnames <- gsub("Mag","Magnitude",newnames)
newnames <- gsub("tBody","TimeBody",newnames)
newnames <- gsub("tGravity","TimeGravity",newnames)
newnames <- gsub("fBody","FrequencyBody",newnames)
names(meanstddata) <- newnames
## Step 5: Create a new independent tidy data set with the average of each variable for each activity and each subject.
## (Chaining used to simplify code.)
finaltidydata <-
meanstddata %>%
group_by(Subject, Activity) %>%
summarise_each(funs(mean))
## Create a .txt file containg the final tidy data.
write.table(finaltidydata, file = "tidydata.txt", row.name = FALSE) |
d9bb0de74830281bf794e3bc272e7844372024a8 | 762d865864484193149b4e0902304064324c6ac0 | /DevelopingDataProductsFinalProject/server.R | 02701458f671184743124f1a658edbd2a4a975a6 | [] | no_license | sairampraneeth/Developing-Data-Products-Final-Project | 8c70fa19b147795f10671821ca38f5f4d7d85813 | 43864c1cf3ad4d2eff392deac554cab70a3ba9c7 | refs/heads/master | 2021-01-10T23:39:23.327578 | 2016-10-10T04:31:43 | 2016-10-10T04:31:43 | 70,410,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,874 | r | server.R | library(shiny)
data(mtcars)
Dset <- mtcars
PredictionModel <- lm(mpg ~ .,data = Dset)
checker <- TRUE
shinyServer(
function(input,output){
printEvent <- eventReactive(input$button1,{
test <- data.frame(cyl = input$id1,
disp = input$id2,
hp = input$id3,
drat = input$id4,
wt = input$id5,
qsec = input$id6,
vs = switch(input$id7,V = 0,S = 1),
am = switch(input$id8,A = 0,M = 1),
gear = input$id9,
carb = input$id10
)
predmod <<- PredictionModel
print(paste("The Predicted Mileage is",predict(predmod,test),".The current prediction model has a variability coverage of ",round(summary(predmod)$r.squared * 100,2),"Percent"))
})
observeEvent(input$button2,{
test <- data.frame(cyl = input$id1,
disp = input$id2,
hp = input$id3,
drat = input$id4,
wt = input$id5,
qsec = input$id6,
vs = switch(input$id7,V = 0,S = 1),
am = switch(input$id8,A = 0,M = 1),
gear = input$id9,
carb = input$id10
)
predmod <<- PredictionModel
pred <- predict(predmod,test)
newRecord <- data.frame(mpg = pred,test)
Dset <<- rbind(Dset,newRecord)
PredictionModel <<- lm(mpg ~ .,data = Dset)
})
output$predictedvalue <- renderText({
printEvent()
})
actionDone <- eventReactive(input$button2,{
print(paste("New Record Added!! Total Number of Records = ",nrow(Dset)))
})
output$ActionDone <- renderText({
actionDone()
})
}
) |
568db051443a0a7129f7664793e9d9d83ed7b5c5 | 387b669bd2f9a5b9695d529b8f3f4e71ef4d4f35 | /lab/course1/Robust sumaries.R | b3b4ab7159fba7989be4acb76f5d4dcc5c4b673d | [] | no_license | chadchouGitHub/GenomeNote | c6bda81db246ee4d487b2be6ccdeb3c71e92d5ac | 383565129447440d3a4319821951a26b27872d8d | refs/heads/master | 2020-04-04T18:21:40.816236 | 2015-05-11T23:41:36 | 2015-05-11T23:41:36 | 30,615,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,865 | r | Robust sumaries.R | # Robust summaries and log transformation
# All these function are in the base package.
set.seed(1)
x=c(rnorm(100,0,1)) ##real distribution
x[23] <- 100 ##mistake made in 23th measurement
boxplot(x)
mean(x)
sd(x)
#> mean(x)
#[1] 1.108142 ## it is random number from 0 to 1, how the mean is bigger than 1. Because one outliner "100"
# is big enough to mess up the mean.
#> sd(x)
#[1] 10.02938 # So does the SD.
#----The Median Absolute Deviance-- mad()------------------------------#
# The median absolute deviace (MAD) is a robust summary for the standard deviation.
#defined by computing the differences between each point and the median and taking
# the median of their absolute values:
# 1.4826median{|Xi−median(Xi)|} The "1.4826 " just a scale factor that guarantees an unbiased. ????
mad(x)
# [1] 0.8857141 Not sure how much close to real mean, but make much more sense.
#--------------------------Spearman correlation---------------------------#
# This is compare to correlation.
set.seed(1)
x=c(rnorm(100,0,1)) ##real distribution
x[23] <- 100 ##mistake made in 23th measurement
y=c(rnorm(100,0,1)) ##real distribution
y[23] <- 84 ##similar mistake made in 23th measurement
library(rafalib)
mypar(1,1)
plot(x,y,main=paste0("correlation=",round(cor(x,y),3)),pch=21,bg=1,xlim=c(-3,100),ylim=c(-3,100))
abline(0,1)
# The correlation=0.99 is caculate from cor(x,y) But real cor is not 0.99
set.seed(1)
x1=c(rnorm(100,0,1)) ##real distribution
y1=c(rnorm(100,0,1)) ##real distribution
library(rafalib)
mypar(1,1)
plot(x1,y1,main=paste0("correlation=",round(cor(x1,y1),3)),pch=21,bg=1,xlim=c(-3,3),ylim=c(-3,3))
abline(0,1)
# The real cor is 0.001, if there is no outline
# Below use spearman cor to compare to cor including outliner.
# To apply spearman cor the cor() need to assign method="spearman"
mypar(1,2)
plot(x,y,main=paste0("correlation=",round(cor(x,y),3)),pch=21,bg=1,xlim=c(-3,100),ylim=c(-3,100))
plot(rank(x),rank(y),main=paste0("correlation=",round(cor(x,y,method="spearman"),3)),pch=21,bg=1,xlim=c(-3,100),ylim=c(-3,100))
abline(0,1)
# The real cor w/o outliner is 0.001 when outline (100,84) point in this 100 points (all between 0 to 1)
# the cor become 0.99, but the spearman cor is 0.066 much more sense.
hist(ratios) # Where is this data set?????
#log(x/y)=log(x)−log(y)=−(log(y)−log(x))=log(y/x)
x <- 2^(-5:5) ##this 1/32,1/16,1/8,...,1,2,...,32
mypar2(1,2)
plot(x)
abline(h=1)
plot(log2(x))
abline(h=0)
#------------------------------------------------------
x=2^seq(1,5)
y=c(rev(1/x),1,x)
Names=c(paste0("1/",rev(x)),1,x)
mypar(1,1)
plot(seq(along=y),y,xlab="",ylab="",type="n",xaxt="n")
text(seq(along=y),y,Names,cex=1.5)
abline(h=1)
#------------------------------------------------------
plot(seq(along=y),y,xlab="",ylab="",type="n",log="y",xaxt="n")
text(seq(along=y),y,Names,cex=1.5)
abline(h=1)
|
1328aba9f03508de495306e22f10a2383b90ff47 | b7b0b1dc4b412fa31026b5807f9d3fa1ef73f3ea | /man/funkmeans03A.Rd | 08aa1dd410abc5494574eee8ebdcdd9a71c6fce6 | [
"MIT"
] | permissive | kisungyou/T4cluster | efc268fb199ee0ce085ccd4872a04bd215986045 | cf15db529209023c0409a70ebfc1b721d8b785b0 | refs/heads/master | 2023-07-31T18:15:41.016081 | 2021-09-23T04:42:04 | 2021-09-23T04:42:04 | 295,601,655 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,550 | rd | funkmeans03A.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functional_kmeans03A.R
\name{funkmeans03A}
\alias{funkmeans03A}
\title{Functional K-Means Clustering by Abraham et al. (2003)}
\usage{
funkmeans03A(fdobj, k = 2, ...)
}
\arguments{
\item{fdobj}{a \code{'fd'} functional data object of \eqn{N} curves by the \pkg{fda} package.}
\item{k}{the number of clusters (default: 2).}
\item{...}{extra parameters including \describe{
\item{maxiter}{the maximum number of iterations (default: 10).}
\item{nstart}{the number of random initializations (default: 5).}
}}
}
\value{
a named list of S3 class \code{T4cluster} containing
\describe{
\item{cluster}{a length-\eqn{N} vector of class labels (from \eqn{1:k}).}
\item{mean}{a \code{'fd'} object of \eqn{k} mean curves.}
\item{algorithm}{name of the algorithm.}
}
}
\description{
Given \eqn{N} curves \eqn{\gamma_1 (t), \gamma_2 (t), \ldots, \gamma_N (t) : I \rightarrow \mathbf{R}},
perform \eqn{k}-means clustering on the coefficients from the functional data expanded by
B-spline basis. Note that in the original paper, authors used B-splines as the choice of basis
due to nice properties. However, we allow other types of basis as well for convenience.
}
\examples{
# -------------------------------------------------------------
# two types of curves
#
# type 1 : sin(x) + perturbation; 20 OF THESE ON [0, 2*PI]
# type 2 : cos(x) + perturbation; 20 OF THESE ON [0, 2*PI]
# type 3 : sin(x) + cos(0.5x) ; 20 OF THESE ON [0, 2*PI]
# -------------------------------------------------------------
## PREPARE : USE 'fda' PACKAGE
# Generate Raw Data
datx = seq(from=0, to=2*pi, length.out=100)
daty = array(0,c(100, 60))
for (i in 1:20){
daty[,i] = sin(datx) + rnorm(100, sd=0.5)
daty[,i+20] = cos(datx) + rnorm(100, sd=0.5)
daty[,i+40] = sin(datx) + cos(0.5*datx) + rnorm(100, sd=0.5)
}
# Wrap as 'fd' object
mybasis <- fda::create.bspline.basis(c(0,2*pi), nbasis=10)
myfdobj <- fda::smooth.basis(datx, daty, mybasis)$fd
## RUN THE ALGORITHM WITH K=2,3,4
fk2 = funkmeans03A(myfdobj, k=2)
fk3 = funkmeans03A(myfdobj, k=3)
fk4 = funkmeans03A(myfdobj, k=4)
## FUNCTIONAL PCA FOR VISUALIZATION
embed = fda::pca.fd(myfdobj, nharm=2)$score
## VISUALIZE
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(embed, col=fk2$cluster, pch=19, main="K=2")
plot(embed, col=fk3$cluster, pch=19, main="K=3")
plot(embed, col=fk4$cluster, pch=19, main="K=4")
par(opar)
}
\references{
\insertRef{abraham_unsupervised_2003}{T4cluster}
}
\concept{functional}
|
386aee9e8a3d0e1f9ef282c832534725811c97d9 | d71f89f90448ca3dae2efe3cfd2212e59d41eec4 | /R/Chunks.R | 14d8bc18e02ea85b4e9999c67231c4638b9dd41e | [] | no_license | warnbergg/regone | ce2b5f9db882b40c025fc8a669e9d65f5404e3a8 | 851be8e37259b1b0f599038e08ae14c27365b454 | refs/heads/master | 2023-08-22T02:18:39.488804 | 2021-10-13T14:11:07 | 2021-10-13T14:11:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 626 | r | Chunks.R | #' Chunks
#'
#' Splitting vector into specified n.chunks.
#' @param vec Any vector. Vector to split into chunks. No default.
#' @param n.chunks Integer vector of length 1. Number of chunks to split the vector into. No default.
#' @param exclude Character vector. Names of elements to remove from vector before splitting. Particularly relevant for use in package regone. Defaults to c("residuals", "predicted").
#' @export
Chunks <- function(vec, n.chunks, exclude = c("residuals", "predicted")) {
vec <- vec[!vec %in% exclude]
chunks <- split(vec, cut(seq_along(vec), n.chunks, labels = FALSE))
return (chunks)
}
|
204a3c0ad05d3279c25de83c6bf6d389af323be5 | 72450ba3ff4cf6e5d28252a4af1d1d17ea07a7e1 | /R/dataVision.R | c3b0de9707ae1b0169798441a36e58f5b8947fb9 | [
"MIT"
] | permissive | aikinman/dataVision | a214287848635520520b790b82505e527dd0fd37 | ddfbb0ef9d41924be02d503f9d5c6c1ba7338bea | refs/heads/main | 2023-01-19T17:25:42.552921 | 2020-11-19T05:47:16 | 2020-11-19T05:47:16 | 314,026,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 587 | r | dataVision.R | #' dataVision
#'
#' @param x
#' @param y
#' @param df
#' @param verbose
#' @param na.rm
#'
#' @return a plot visualizing your x and y data from your specified dataset
#' @export
#'
#' @examples
#' plotFunction('Sepal.Width', 'Petal.Width', iris)
#'
dataVision <- function(x, y, df, verbose = TRUE, na.rm =FALSE) {
if(verbose) message("Plotting your data...stay tuned!")
if(x == class(logical) | x == class(integer) | y == class(logical) | y == class(integer)) warning('x and y must be either class = factor or numeric to use this function!')
plot(df[,y]~df[,x], pch=1, cex=0.8)
}
|
21631ebc6ddcf7f518bd8652316589ff2c2fbf47 | b1d31f04054b32b1dbca0d8ce7406dd63b4130f1 | /R/utils.R | d33e0e55e5d795bbd9dacfd8fa85e107d2200257 | [] | no_license | lianos/GenomicCache | 15d711415e47c1080ffadb1a558a60d3e3bc24d9 | dc89e1e7b29d5702e00e50ec5926b74012a68886 | refs/heads/master | 2020-04-17T15:15:23.388012 | 2019-01-02T19:06:01 | 2019-01-02T19:06:01 | 816,175 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,598 | r | utils.R | ##' Checks that a directory exists and will create it if not.
##'
##' If the directory does not exist, and the caller does not want to create it
##' an error will be thrown
##'
##' @export
##' @author Steve Lianoglou \email{slianoglou@@gmail.com}
##'
##' @param path The path to the directory to check.
##' @param create A logical indicating whether or not the directory should be
##' created if it doesn't exist
##' @param verbose Let us know what's going on
##'
##' @return \code{TRUE} if everything is kosher, otherwise an error is thrown.
checkOrCreateDirectory <- function(path, create=FALSE, verbose=TRUE,
raise.error=TRUE) {
if (!dir.exists(path)) {
if (!create) {
if (raise.error) {
stop("Directory", path, "does not exist", sep=" ")
} else {
return(FALSE)
}
} else {
if (verbose) cat("Creating directory", path, "...\n")
if (!dir.create(path)) {
if (raise.error) {
stop("Error! Check permissions? Parent directory exists?")
} else {
return(FALSE)
}
}
}
}
path
}
##' Convenience method to sets \code{NA}'s in a logical vector to \code{FALSE}.
##'
##' @export
##' @author Steve Lianoglou \email{slianoglou@@gmail.com}
##'
##' @param the.logical A logical vector/Rle
##' @return A \code{logical} with \code{NA} values set to \code{FALSE}
na.logical <- function(the.logical) {
the.logical <- as.logical(the.logical)
the.logical[is.na(the.logical)] <- FALSE
the.logical
}
##' Convert NA values in vectors and data.frames to a default value
##'
##' @param wut The thing to convert
##' @param to The value convert NA to
##' @return The same type as \code{wut}
convert.na <- function(wut, to=".defaults.") {
if (is.character(to) && to[1] == ".defaults.") {
to <- list(logical=FALSE, numeric=0, integer=0L, character="",
factor="")
}
if (is.vector(wut) || is.factor(wut)) {
wut.type <- is(wut)[1]
if (is.list(to)) {
if (!wut.type %in% names(to)) {
stop("Unknown default conversion value for", wut.type, sep=" ")
}
to <- to[[wut.type]]
}
if (wut.type == 'factor') {
levels(wut) <- c(levels(wut), to)
}
wut[is.na(wut)] <- to
} else if (inherits(wut, 'data.frame') || inherits(wut, 'DataFrame')) {
cols <- 1:ncol(wut)
if (is(wut, 'data.table')) {
## Don't change key columns
cols <- setdiff(cols, which(colnames(wut) %in% key(wut)))
}
for (idx in cols) {
wut[[idx]] <- convert.na(wut[[idx]], to=to)
}
}
wut
}
filterByChr <- function(grl, which.chr=NULL) {
if (!is.null(which.chr)) {
keep <- sapply(grl, function(g) {
length(g) > 0 && all(as.logical(seqnames(g) == which.chr))
})
grl <- grl[keep]
}
grl
}
checkVerbose <- function(...) {
verbose <- list(...)$verbose
if (is.null(verbose)) verbose <- options()$verbose
verbose
}
## Loads one item from the rda file. if what is null, it will
## load the first item
load.it <- function(rda.file, what=NULL) {
if (!file.exists(rda.file)) {
stop("Can't find data file ", rda.file)
}
if (substring(rda.file, nchar(rda.file) - 3L) == '.rds') {
return(readRDS(rda.file))
}
e <- new.env()
vars <- load(rda.file, e)
if (length(vars) == 0L) {
stop("No objects found in ", rda.file)
}
if (is.null(what)) {
what <- vars[1]
}
if (!what %in% vars) {
stop("Object `", what, "` not found in ", rda.file)
}
get(what, e, inherits=FALSE)
}
##' Returns the bioconductor annoation package name for the given genome.
##'
##' @param from A character string naming the genome, ie. hg18, mm9, etc.
##' The function also checks to see if it is the name of the package itself.
##' @param package Passed through to the \code{\link{annotationPackage}}
##' function.
getAnnoPackageName <- function(from, package=NULL) {
is.anno.package <- length(grep('^org\\..*\\.db$', from) == 1L)
if (is.anno.package) {
## this is probably the package name itself
if (!require(from, character.only=TRUE)) {
stop("Unknown package: ", from)
}
from
} else {
## probably the genome
annotationPackage(from, package=package)
}
}
dir.exists <- function(path) {
path <- as.character(path)
sapply(file.info(path)$isdir, isTRUE)
}
assert.dir.exists <- function(path) {
if (!dir.exists(path)) stop("Can't access directory: ", path)
}
##' Returns an object of type \code{type} from a list, this is most useful
##' when \code{the.list} has one object of \code{type} in it.
##'
##' Primarily used to get arguments out of function calls with \code{(...)}
##' assumes tha
##'
##' If this object isn't found, or other error, returns \code{NULL}
takeFromListByType <- function(the.list, type, multi=FALSE, index=FALSE) {
take <- which(sapply(the.list, function(arg) inherits(arg, type)))
if (length(take) == 0L) {
return(NULL)
}
if (length(take) > 1) {
if (is.logical(multi[1])) {
if (!multi) {
warning("Multiple objects of type ", type, " found.")
take <- '..NOTHING..'
}
} else if (is.numeric(multi)) {
if (any(multi > length(take)) || any(multi < 0L)) {
warning("multi take subscript(s) out of bounds")
take <- '..NOTHING..'
} else {
take <- take[multi]
}
} else {
warning("Illegal type of multi argument: ", is(multi)[1])
take <- '..NOTHING..'
}
}
if (index) {
ret.val <- take
} else {
ret.val <- if (length(take) > 1) the.list[take] else the.list[[take]]
}
ret.val
}
|
c98de8feb3e162686abe321b097a0c2f91976400 | ec4e37d127bbe0b7ce3a3aad97730f2c5658616d | /R/get_restricted.R | b7f3a2dae5a4c5e3baa8bd52af94061b361fc424 | [] | no_license | EmilNejst/Rcivecm | 7d5f02f5a54c9ae107c31ab18a80127108972811 | fb7cb123b732673b8f989d2175c6bbe095098af2 | refs/heads/master | 2021-01-22T10:57:05.921639 | 2017-02-26T16:53:55 | 2017-02-26T16:53:55 | 82,055,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 611 | r | get_restricted.R | #' @title Get exogenous variables restricted to the cointegration vector
#'
#' @param data is an xts object with the naming conventions of
#' Rcivecm.
#'
#' @return an xts object with the exogenous variables that
#' are restricted to the cointegration space
get_restricted <- function(data) {
variable_names <- colnames(data)
if(sum(str_sub(variable_names,1,2) == 'xr_')==0) {
restricted_names = NULL
} else {
restricted_names <- variable_names[(str_sub(variable_names,1,2) == 'xr_')]
restricted_variables <- data[,restricted_names]
}
return(restricted_variables)
}
|
ffefdda02b3915ce7e273b8cfeb241b49ec64377 | e23185c2b0e2f37beb090832feafaf647cd8f19d | /scripts/TidyAustenWellsBrontes.R | 5e3a658db25ce30bdb7eb58933bbe497819ffc4a | [] | no_license | Jonlittle27/Text-Mining-Projects | 9a9122f2be02e718091fce10c5b298409cc662e5 | 5eca661c2809c1390cc4d018b8bd0c17739d6305 | refs/heads/master | 2022-11-24T19:28:19.361906 | 2020-07-28T18:22:23 | 2020-07-28T18:22:23 | 271,325,521 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 586 | r | TidyAustenWellsBrontes.R | ##Libraries
library(gutenbergr)
library(tidyverse)
library(tidytext)
#Gutenberg book downloads
##Import Austen Books
ja_books <- gutenberg_download(c(161,1342,141,158,121,105))
##Import Wells Books
hgw_books <- gutenberg_download(c(35,36,159,5230))
##Import Bronte Books
bs_books <- gutenberg_download(c(767,768,969,1260,9182))
#Basic text tidying
##Tidy Austen Books
ja_tidy <- ja_books %>%
unnest_tokens(word,text)
##Tidy Wells Books
hgw_tidy <- hgw_books %>%
unnest_tokens(word,text)
##Tidy Bronte Sisters Books
bs_tidy <- bs_books %>%
unnest_tokens(word,text)
|
9eb3d75a9b4885e8d4f9cf65c78cdbb5eec677d0 | fe487c83debd4b17e05641122c82c0b771d4b11d | /man/lines3DDataFrame.Rd | 7ac9876a366e4e3f8f3763ba043d7aa047292bf9 | [] | no_license | CloudComputer/geomod3D | 5a21c673048cfef147d8bc68c901ea599f49b638 | fd00f8f3188977a07028b08e1e6a7f055322bc60 | refs/heads/master | 2021-08-14T05:59:02.322040 | 2017-11-14T17:41:02 | 2017-11-14T17:41:02 | 114,229,121 | 1 | 0 | null | 2017-12-14T09:20:03 | 2017-12-14T09:20:02 | null | UTF-8 | R | false | true | 2,562 | rd | lines3DDataFrame.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lines3DDataFrame.R
\name{lines3DDataFrame}
\alias{lines3DDataFrame}
\alias{lines3DDataFrame}
\title{Drillhole database}
\usage{
lines3DDataFrame(lines_list = NULL, df = NULL, collar = NULL,
assay = NULL, survey = NULL, holeid = "HOLEID", from = "FROM",
to = "TO", X = "X", Y = "Y", Z = "Z")
lines3DDataFrame(lines_list = NULL, df = NULL, collar = NULL,
assay = NULL, survey = NULL, holeid = "HOLEID", from = "FROM",
to = "TO", X = "X", Y = "Y", Z = "Z")
}
\arguments{
\item{lines_list}{A \code{list} containing the line segments' coordinates.}
\item{df}{A \code{data.frame} with the line segments' attributes.}
\item{collar, assay, survey}{Data frames with drillhole data.}
\item{holeid, from, to, X, Y, Z}{Column names from which to draw the parameters.}
}
\description{
Drillhole database represented as line segments. Extends the
\code{spatial3DdataFrame} class.
Drillhole database represented as line segments.
}
\details{
The \code{"HOLEID"} column is used by this object's methods. Do not
remove or rename it.
There are two ways to build a \code{lines3DDataFrame} object:
either providing the data in the format that is seen in the final object
(less common) or by providing collar and assay data (more common).
In the first mode, both \code{lines_list} and \code{df} must be given.
\code{lines_list} is a \code{list} where each entry is a numeric vector
of length 6 containing the X, Y, and Z coordinates for both ends of a
line segment. \code{df} is a data frame containing the attributes for
each segment.
In the second mode, both \code{collar} and \code{assay} must be given.
\code{collar} contains the X, Y, and Z coordinates for each drillhole's
collar. \code{assay} contains the lithological description, chemical
analysis, etc. in intervals delimimted by the \code{from} and \code{to}
columns. \code{survey} contains the dipmeter measurements for each interval.
If it is missing, the holes are assumed vertical. The \code{holeid} column
must be present in all three data frames.
}
\section{Slots}{
\describe{
\item{\code{coords}}{A \code{list} containing the coordinates for each data unit.}
\item{\code{data}}{A \code{data.frame} with each data unit's attributes.}
\item{\code{bbox}}{A \code{matrix} containing the coordinates of two opposing
edges of the object's bounding box.}
}}
\seealso{
\code{\link{lines3DDataFrame-init}},
\code{\link{spatial3DDataFrame-class}}
\code{\link{spatial3DDataFrame-class}},
\code{\link{lines3DDataFrame-class}}
}
|
885a3de210ed0a67e4f45402b654a75c394c28c1 | 44adaf73180ccb431abe3f0464968d3d3832c8f9 | /R/sim-fmri-2covar.R | 980296edd663b20173a2f0f17795fd55e78ea6aa | [] | no_license | bioimaginggroup/adaptsmoFMRI | 7f3870ff27ccdeb9c3a1a50a5a1c42bb683906ae | 60c0272cccd2d2cf3ee2cefda3049699dd5b0b01 | refs/heads/master | 2021-06-05T15:42:41.654811 | 2021-04-20T09:59:08 | 2021-04-20T09:59:08 | 147,805,013 | 1 | 0 | null | 2018-09-07T09:51:43 | 2018-09-07T09:51:43 | null | UTF-8 | R | false | false | 3,758 | r | sim-fmri-2covar.R | #' This function returns the synthetic spatiotemporal data set resembling functional MR Images (fMRI) data.
#'
#' The returned data is simulated on a 20 x 20 grid.
#'
#' @name sim.fmri2COVAR
#' @aliases sim.fmri2COVAR
#' @title Simulate FMRI Data
#' @usage sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)
#' @param hrf haemodynamic response function, needs to be a vector of length \code{T}.
#' @param beta.Var1 scalar, defines the height of the activated area, in form of a cylinder of the first grid.
#' @param beta.Var2 scalar, defines the height of the activated area, in form of a cylinder of the second grid.
#' @author Max Hughes
#' @import Matrix
#' @note This function is solely for two covariates.
#' @examples
#' # non-transformed hr-function
#' T <- 180
#' seq.length <- T*3
#' index <- seq(3, T*3, by = 3)
#' vis <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
#' vis <- as.matrix(vis[index])
#' aud <- rep(c(-0.5, 0.5), each=45, times=ceiling(T/30*1.5))
#' aud <- as.matrix(aud[index])
#' hrf <- cbind(vis,aud)
#' # define height of activation area
#' beta.Var1 <- beta.Var2 <- 3
#' # use function to obtain fmri data
#' data <- sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)$fmri
sim.fmri2COVAR <- function(hrf, beta.Var1, beta.Var2){
if(any(is.na(hrf)))
stop("\nNAs in hr function.\n")
## For model with two covariates
p <- dim(hrf)[2]
if(p!=2)
stop("Haemodynamic response function needs to be a matrix with column dimension of 2.")
I <- 400
## covariate(s) (for now only one: visual)
Z <- as.matrix(hrf) # note: Z is only for one pixel
T <- dim(Z)[1]
Z.Var1 <- as.matrix(Z[,1])
Z.Var2 <- as.matrix(Z[,2])
IZ.Var1 <- kronecker(as(diag(1, nrow=I, ncol=I), "sparseMatrix"), Z.Var1)
IZ.Var2 <- kronecker(as(diag(1, nrow=I, ncol=I), "sparseMatrix"), Z.Var2)
## generate sigma^2
sigma.sq <- numeric(I)
for(i in 1:I)
sigma.sq[i] <- 25 + 2*rnorm(1)
## generate epsilon
eps <- matrix(nrow=T, ncol=I)
for(i in 1:I)
eps[,i] <- rnorm(T, mean=0, sd=sqrt(sigma.sq[i])) # Variance or sd???
eps <- as.vector(eps)
## generate beta grid for Variable 1
beta.sim.Var1 <- matrix(0, nrow=I/20, ncol=I/20)
beta.sim.Var1[8:14,8:14] <- beta.Var1
beta.sim.Var1[8,8] <- 0
beta.sim.Var1[9,8] <- 0
beta.sim.Var1[8,9] <- 0
beta.sim.Var1[13,8] <- 0
beta.sim.Var1[14,9] <- 0
beta.sim.Var1[14,8] <- 0
beta.sim.Var1[9,14] <- 0
beta.sim.Var1[8,13] <- 0
beta.sim.Var1[8,14] <- 0
beta.sim.Var1[14,13] <- 0
beta.sim.Var1[13,14] <- 0
beta.sim.Var1[14,14] <- 0
beta.sim.Var1 <- as.vector(beta.sim.Var1)
## generate beta grid for Variable 2
beta.sim.Var2 <- matrix(0, nrow=I/20, ncol=I/20)
beta.sim.Var2[8:14,8:14] <- beta.Var2
beta.sim.Var2[8,8] <- 0
beta.sim.Var2[9,8] <- 0
beta.sim.Var2[8,9] <- 0
beta.sim.Var2[13,8] <- 0
beta.sim.Var2[14,9] <- 0
beta.sim.Var2[14,8] <- 0
beta.sim.Var2[9,14] <- 0
beta.sim.Var2[8,13] <- 0
beta.sim.Var2[8,14] <- 0
beta.sim.Var2[14,13] <- 0
beta.sim.Var2[13,14] <- 0
beta.sim.Var2[14,14] <- 0
beta.sim.Var2 <- as.vector(beta.sim.Var2)
## generate y
y <- numeric(T*I)
y <- IZ.Var1%*%beta.sim.Var1+IZ.Var2%*%beta.sim.Var2+eps
## convert back to to an array of dim (nrow x ncol x T), so that it coincides with
## the dim of real data
y <- t(matrix(nrow=T, ncol=I, data=y))
y <- array(y, dim=c(20,20,T))
# interchange the first two subscripts on a 3-way array y
y <- aperm(y, c(2,1,3))
# same procededure for epsilon
eps <- t(matrix(nrow=T, ncol=I, data=eps))
eps <- array(eps, dim=c(20,20,T))
eps <- aperm(eps, c(2,1,3))
return(list("fmri"=y, "hrf"=Z, "coeff1"=beta.sim.Var1, "coeff2"=beta.sim.Var2,
"resid"=eps, "sigma"=sigma.sq))
}
|
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