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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d00fe34b591160c30a5e62021b65b004b9180378
|
697c87e65f7c3a12fdea8141c2859f9da044841f
|
/R/aimerMethods.R
|
3fde8c248ad4e05cc8459748c60c7c2d650cdc69
|
[] |
no_license
|
Lei-D/aimer
|
944278394d280cacba3d109205c8802c2043f1d5
|
b7650af34f9661e1dbfffe9e2b40ecb719d49bea
|
refs/heads/master
| 2021-07-19T11:54:29.280976
| 2017-10-27T15:50:59
| 2017-10-27T15:50:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,028
|
r
|
aimerMethods.R
|
#'predicts new values for an aimer model.
#'
#'@param object required, a model generated by the aimer function.
#'@param newdata required, a new data matrix to predict with.
#'@param ... additional arguments, currently ignored.
#'
#'@return predicted value vector of length nrow(newdata).
#'
#'@export
predict.aimer <- function(object, newdata, ...){
newdata = sweep(as.matrix(newdata), 2, object$meanx)
indeces = object$beta != 0
newdata[,indeces] %*% object$beta[indeces] + object$meany
}
#'predicts new values for an aimerCV model.
#'
#'@param object required, a model generated by the aimerCV function.
#'@param newdata required, a new data matrix to predict with.
#'@param ... additional arguments, currently ignored.
#'
#'@return predicted value vector of length nrow(newdata).
#'
#'@export
predict.aimerCV <- function(object, newdata, ...){
predict.aimer(object, newdata)
}
#'returns the fitted values of an aimer model.
#'
#'@param object required, an aimer model.
#'@param ... additional arguments, currently ignored.
#'
#'@return fitted values of the original data (vector of length n).
#'
#'@export
fitted.aimer <- function(object, ...){
object$fitted
}
#'returns the fitted values of an aimerCV model.
#'
#'@param object required, an aimerCV model.
#'@param ... additional arguments, currently ignored.
#'
#'@return fitted values of the original data (vector of length n).
#'
#'@export
fitted.aimerCV <- function(object, ...){
object$fitted
}
#'returns the residuals of an aimer model.
#'
#'@param object required, an aimer model.
#'@param ... additional arguments, currently ignored.
#'
#'@return residuals of the original data (vector of length n).
#'
#'@export
residuals.aimer <- function(object, ...){
object$residuals
}
#'returns the residuals of an aimerCV model.
#'
#'@param object required, an aimerCV model.
#'@param ... additional arguments, currently ignored.
#'
#'@return residuals of the original data (vector of length n).
#'
#'@export
residuals.aimerCV <- function(object, ...){
object$residuals
}
#'returns the coefficients of an aimer model.
#'
#'@param object required, an aimer model.
#'@param ... additional arguments, currently ignored.
#'
#'@return coefficient vector for the model
#'
#'@export
coef.aimer <- function(object, ...){
object$beta
}
#'returns the coefficients of an aimerCV model.
#'
#'@param object required, an aimerCV model.
#'@param ... additional arguments, currently ignored.
#'
#'@return coefficient vector for the model
#'
#'@export
coef.aimerCV <- function(object, ...){
object$beta
}
#'plots the residuals in terms of the fitted values for an aimer model
#'
#'@param x required, an aimer model.
#'@param ... additional arguments, passed to `plot`
#'
#'@return void.
#'
#'@export
plot.aimer <- function(x, ...){
plot(x$residuals ~ x$fitted, xlab = "fitted values", ylab = "residuals", ...)
}
#'creates a heatmap of each value of ncomps tested in an aimerCV model
#'
#'@param x required, an aimerCV model.
#'@param ... additional arguments, currently ignored
#'
#'@return void.
#'
#'@export
#'@importFrom graphics par plot
plot.aimerCV <- function(x, ...){
original = par(ask = TRUE)
# if (!requireNamespace("ggplot2", quietly = TRUE)){
# stop("This function requires the installation of the ggplot2 package.")
# }
MyDF = data.frame(expand.grid(x$ncomps, x$nCovs))
best = match(x$nCov.select.best, x$nCovs.select)
getColor <- function(coord){
x$mse[best, coord[1], coord[2]]
}
MyDF$col = apply(expand.grid(1:length(x$ncomps), 1:length(x$nCovs)),
MARGIN = 1, FUN = getColor)
p = ggplot2::ggplot(MyDF, ggplot2::aes_string(x = 'Var1', y = 'Var2', fill = 'col')) +
ggplot2::geom_tile() +
ggplot2::scale_fill_continuous(low = 'blue', high = 'red',
guide = ggplot2::guide_legend(title = 'MSE')) +
ggplot2::xlab("ncomps") +
ggplot2::ylab("nCovs") +
ggplot2::ggtitle("For Optimal nCov.select")
print(p)
# image(x = x$ncomps, y = x$nCovs,
# z = x$mse[x$nCovs.select == x$nCov.select.best,,],
# xlab = "ncomps", ylab = "ncovs", main = "for optimal value of nCovs.select")
MyDF = data.frame(expand.grid(x$nCovs.select, x$ncomps))
best = match(x$nCov.best, x$nCovs)
getColor <- function(coord){
x$mse[coord[1], coord[2], best]
}
MyDF$col = apply(expand.grid(1:length(x$nCovs.select), 1:length(x$ncomps)),
MARGIN = 1, FUN = getColor)
p = ggplot2::ggplot(MyDF, ggplot2::aes_string(x = 'Var1', y = 'Var2', fill = 'col')) +
ggplot2::geom_tile() +
ggplot2::scale_fill_continuous(low = 'blue', high = 'red',
guide = ggplot2::guide_legend(title = 'MSE')) +
ggplot2::xlab("nCovs.select") +
ggplot2::ylab("ncomps") + ggplot2::ggtitle("For Optimal nCovs")
print(p)
# image(x = x$nCovs.select, y = x$ncomps,
# z = x$mse[,,x$nCovs == x$nCov.best],
# xlab = "nCovs.select", ylab = "ncomps", main = "for optimal value of nCovs")
MyDF = data.frame(expand.grid(x$nCovs.select, x$nCovs))
best = match(x$ncomp.best, x$ncomps)
getColor <- function(coord){
x$mse[coord[1], best, coord[2]]
}
MyDF$col = apply(expand.grid(1:length(x$nCovs.select), 1:length(x$nCovs)),
MARGIN = 1, FUN = getColor)
p = ggplot2::ggplot(MyDF, ggplot2::aes_string(x = 'Var1', y = 'Var2', fill = 'col')) +
ggplot2::geom_tile() +
ggplot2::scale_fill_continuous(low = 'blue', high = 'red',
guide = ggplot2::guide_legend(title = 'MSE')) +
ggplot2::xlab("nCovs.select") +
ggplot2::ylab("nCovs") + ggplot2::ggtitle("For Optimal ncomps")
print(p)
# image(x = x$nCovs.select, y = x$nCovs,
# z = x$mse[,x$ncomps == x$ncomp.best,],
# xlab = "nCovs.select", ylab = "nCovs", main = "for optimal value of ncomps")
par(original)
}
|
709ccc2d97653a5ba92d9d4f299fc11c86ccf960
|
4a8209a0137e308f4a96ad0baede74f79803b384
|
/R/Project.R
|
7381a0471260149c1e05b942a45ff357677ab962
|
[
"MIT"
] |
permissive
|
david-yunbae/Project
|
52e96297b6f3a0457727a6da4fcca5337a034dab
|
2a04bd5088c1cf149623e1cde5f445a439ab91f1
|
refs/heads/main
| 2023-08-28T18:15:50.252789
| 2021-10-28T23:02:20
| 2021-10-28T23:02:20
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,386
|
r
|
Project.R
|
packages <- c("tidyverse", "knitr", "rmarkdown","roxygen2","testthat","usethis","devtools","ggplot2","ggrepel","stats","kableExtra","bookdown","shiny","patchwork","dplyr","broom")
lapply(packages, library, character.only=TRUE)
#' Assumption
#'
#' @description This function outputs the 3 graphs of assumption for the linear regression model.
#' @param x text string; This should be the data file name of the test sample.
#'
#' @return 3 graphs of linearity, constant variance and normal distribution of the residuals
#' @export
#'
#' @examples assumption(x=project-csv)
assumption <- function(x,testtype){
dat <- readr::read_csv(x,show_col_types = FALSE)
#Prepare assumption for lm
D <- lm(height ~ weight,dat)
a <- ggplot2::ggplot(dat,aes(x=height,y=weight))+ geom_point() + stat_smooth(method="lm", col="red") +ggtitle("I) Y vs X")
b <- ggplot2::ggplot(dat)+geom_point(mapping=aes(x=D$fitted.values ,y=D$residuals)) + geom_hline(yintercept=0,lwd=2)+ggtitle("II) Residual plot")+ylab("Residuals")+xlab("Fitted values")
c <- ggplot2::ggplot(dat)+geom_histogram(mapping=aes(x=D$residuals),bins=40) +ggtitle("III) Distribution is normal")+xlab("Residuals")
#Prepare assumption for ttest
d <- ggplot2::ggplot(dat, aes(sample=height, group=gender, colour=gender))+geom_qq()+geom_qq_line()+xlab("theoretical")+ylab("sample")
e <- dat %>% group_by(gender) %>% summarise(n=n(),mu=mean(height),sd=sd(height))
#Preapre assumption for chitest
datm <- dat %>% filter(gender=="Male") %>% select(phys)
datf <- dat %>% filter(gender=="Female") %>% select(phys)
datmn <- datm %>% filter(phys=="None") %>% count()
datmm <- datm %>% filter(phys=="Moderate") %>% count()
datmi <- datm %>% filter(phys=="Intense") %>% count()
datfn <- datf %>% filter(phys=="None") %>% count()
datfm <- datf %>% filter(phys=="Moderate") %>% count()
datfi <- datf %>% filter(phys=="Intense") %>% count()
table <- dplyr::tibble(Male=c(datmn[[1]],datmm[[1]],datmi[[1]]),Female=c(datfn[[1]],datfm[[1]],datfi[[1]]))
cat("STAGE II>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Assumptions >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> \n")
Sys.sleep(2)
if (testtype=="lm"){
cat("Assumptions: \n \nI Linearity\nII Constant variance\nIII Residuals normally distributed\n \n")
print((a+b)/c)
} else if (testtype=="ttest"){
cat("Assumptions: \n \nI Normality\nII Equal variance (slarger/ssmaller < 2) - Actual value: ", max(e$sd)/min(e$sd),"\n \n")
print(d)
} else if (testtype=="chitest"){
cat("Assumptions: \n \nI Normal approximation: All entries must be greater than 5.\n \n")
print(table)
}
}
assumptiongraph <- function(x,testtype){
dat <- readr::read_csv(x,show_col_types = FALSE)
#Prepare assumption for lm
D <- lm(height ~ weight,dat)
a <- ggplot2::ggplot(dat,aes(x=height,y=weight))+ geom_point() + stat_smooth(method="lm", col="red") +ggtitle("I) Y vs X")
b <- ggplot2::ggplot(dat)+geom_point(mapping=aes(x=D$fitted.values ,y=D$residuals)) + geom_hline(yintercept=0,lwd=2)+ggtitle("II) Residual plot")+ylab("Residuals")+xlab("Fitted values")
c <- ggplot2::ggplot(dat)+geom_histogram(mapping=aes(x=D$residuals),bins=40) +ggtitle("III) Distribution is normal")+xlab("Residuals")
#Prepare assumption for ttest
d <- ggplot2::ggplot(dat, aes(sample=height, group=gender, colour=gender))+geom_qq()+geom_qq_line()+xlab("theoretical")+ylab("sample")
e <- dat %>% dplyr::group_by(gender) %>% dplyr::summarise(n=n(),mu=mean(height),sd=sd(height))
#Preapre assumption for chitest
datm <- dat %>% dplyr::filter(gender=="Male") %>% dplyr::select(phys)
datf <- dat %>% dplyr::filter(gender=="Female") %>% dplyr::select(phys)
datmn <- datm %>% dplyr::filter(phys=="None") %>% dplyr::count()
datmm <- datm %>% dplyr::filter(phys=="Moderate") %>% dplyr::count()
datmi <- datm %>% dplyr::filter(phys=="Intense") %>% dplyr::count()
datfn <- datf %>% dplyr::filter(phys=="None") %>% dplyr::count()
datfm <- datf %>% dplyr::filter(phys=="Moderate") %>% dplyr::count()
datfi <- datf %>% dplyr::filter(phys=="Intense") %>% dplyr::count()
table <- dplyr::tibble(Male=c(datmn[[1]],datmm[[1]],datmi[[1]]),Female=c(datfn[[1]],datfm[[1]],datfi[[1]]))
if (testtype=="lm"){
(a+b)/c
} else if (testtype=="ttest"){
d
} else if (testtype=="chitest"){
table
}
}
wrapperlm <- function(x){
dat <- readr::read_csv(x,show_col_types = FALSE)
B <- lm(height~weight,dat)$coefficients[[2]]
df <- lm(height~weight,dat)$df.residual
t <- qt(0.025,df,lower.tail=TRUE)
p <- broom::glance(lm(height~weight,dat))$p.value[[1]]
SE <- coef(summary(lm(height~weight,dat)))[,"Std. Error"][[2]]
out <- list(type="lm",param=x,beta=B,CI=t*SE,t_value=t,degree_of_freedom=df,p_value=p)
out$CI <- list(min=B-t*SE,max=B+t*SE)
class(out) <- "myr"
out
}
wrapperttest <- function(x){
dat <- readr::read_csv(x,show_col_types = FALSE)
datm <- dat %>% filter(gender=="Male") %>% select(height)
datf <- dat %>% filter(gender=="Female") %>% select(height)
test <- t.test(datm,datf,var.equal=TRUE)
out <- list(type="ttest",param=x,CI=t,t_value=test[[1]][[1]],degree_of_freedom=test[[2]][[1]],p_value=test[[3]][[1]])
out$CI <- list(min=broom::glance(test)$conf.low,max=broom::glance(test)$conf.high)
class(out) <- "myr"
out
}
wrapperchitest <- function(x){
dat <- readr::read_csv(x,show_col_types = FALSE)
datm <- dat %>% filter(gender=="Male") %>% select(phys)
datf <- dat %>% filter(gender=="Female") %>% select(phys)
datmn <- datm %>% filter(phys=="None") %>% count()
datmm <- datm %>% filter(phys=="Moderate") %>% count()
datmi <- datm %>% filter(phys=="Intense") %>% count()
datfn <- datf %>% filter(phys=="None") %>% count()
datfm <- datf %>% filter(phys=="Moderate") %>% count()
datfi <- datf %>% filter(phys=="Intense") %>% count()
table <- dplyr::tibble(Male=c(datmn[[1]],datmm[[1]],datmi[[1]]),Female=c(datfn[[1]],datfm[[1]],datfi[[1]]))
test <- chisq.test(table,correct=FALSE)
out <- list(type="chitest",param=x,t_value=test[[1]][[1]],degree_of_freedom=test[[2]][[1]],p_value=test[[3]][[1]])
class(out) <- "myr"
out
}
printer.myr <- function(x){
if(x$type=="lm"){
cat(">>>>>>>>>>>>>>>>>>>>>>>>>>>> Relevant test: Linear regression <<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n \n")
Sys.sleep(4)
cat("STAGE I>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Hypothesis >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \nH0 : B = 0 against H1 : B != 0\n \n")
} else if(x$type=="ttest"){
cat(">>>>>>>>>>>>>>>>>>>>>>>>>>>> Relevant test: t test <<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n \n")
Sys.sleep(4)
cat("STAGE I>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Hypothesis >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \nH0 : mu1 = mu2 against H1 : mu1 != mu2\n \n")
} else if(x$type=="chitest"){
cat(">>>>>>>>>>>>>>>>>>>>>>>>>>>> Relevant test: chi test <<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n \n")
Sys.sleep(4)
cat("STAGE I>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Hypothesis >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \nH0 : the two variables are independent against each other. H1 : not H0.\n \n")
}
Sys.sleep(2)
assumption(x$param,x$type)
Sys.sleep(4)
cat("STAGE III>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Results >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \n")
if(x$type=="lm"){
cat("I beta: ", x$beta, "\nII CI: (", x$CI$min, ",", x$CI$max, ")\nIII degree of freedom: ", x$degree_of_freedom, "\nIV critical value : ", x$t_value, "\nV p value: ", x$p_value, "\n \n")
} else if(x$type=="ttest"){
cat("I CI: (", x$CI$min, ",", x$CI$max, ")\nII degree of freedom: ", x$degree_of_freedom, "\nIII critical value : ", x$t_value, "\nIV p value: ", x$p_value, "\n \n")
} else if(x$type=="chitest"){
cat("I degree of freedom: ", x$degree_of_freedom, "\nII critical value : ", x$t_value, "\nIII p value: ", x$p_value, "\n \n")
}
Sys.sleep(5)
cat("STAGE IV>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Decision >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \n")
Sys.sleep(3)
if (x$p_value < 0.05){
cat("REJECT: p-value = ", x$p_value, " < 0.05\n \n")
} else {
cat("DO NOT REJECT: p-value = ", x$p_value, " > 0.05\n \n")
}
Sys.sleep(4)
cat("STAGE V>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Conclusion >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n \n")
if (x$type=="lm"){
if (x$p_value <0.05){
cat("There is a relationship between height and weight: As the P-value is very small, we have very strong evidence to reject H0. I.E. very strong evidence that the slope parameter is significant and there is a relationship between the height and weight of the sample population.")
} else if(x$p_value >0.05){
cat("There isn't any relationship between height and weight: As the P-value is large, we have no evidence to reject H0. I.E. no evidence that the slope parameter is significant and there isn't any relationship between the height and weight of the sample population.")
}
} else if(x$type=="ttest"){
if (x$p_value <0.05){
cat("The mean height of male and female are NOT the same: As the P-value is very small, we have very strong evidence to reject H0. I.E. very strong evidence that the mean height of male is not the same as the mean height of female.")
} else if(x$p_value >0.05){
cat("The mean height of male and female are the same: As the P-value is large, we have no evidence to reject H0. I.E. no evidence that the mean height of male is not the same as the mean height of female.")
}
} else if(x$type=="chitest"){
if (x$p_value <0.05){
cat("Gender affects the amount of physical activity: As the P-value is very small, we have very strong evidence to reject H0. I.E. very strong evidence that the two variables are dependent against each other. Gender affects the physical activity.")
} else if(x$p_value >0.05){
cat("Gender does NOT affect the amount of physical activity: As the P-value is large, we have no evidence to reject H0. I.E. no evidence that the two variables are dependent against each other. The two variables are independent against each other and there is no association between gender and the amount of physical acitivity.")
}
}
}
printer <- function(x){
UseMethod("printer")
}
|
a4c1087ed21cedafa72cbdf3cb2f34bbc217e292
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/RSDA/examples/sym.kmeans.Rd.R
|
9c7b4f3784107559ca6124db9dc3f65181bb39b2
|
[] |
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
| 187
|
r
|
sym.kmeans.Rd.R
|
library(RSDA)
### Name: sym.kmeans
### Title: Symbolic k-Means
### Aliases: sym.kmeans
### Keywords: Kmeans Symbolic
### ** Examples
data(oils)
sk<-sym.kmeans(oils,k=3)
sk$cluster
|
5e5d33320b97da7c500becab519de981d051ca41
|
4642e25457ecfdaf564ec83133084715de71818a
|
/DQ Functions/summarise-changes.R
|
0ba1cc9a09f4da48251ff2d1671c66dc4e96038d
|
[] |
no_license
|
DasOakster/wilko-data-quality
|
4639743b654e79c7db982f1ad19549f94a680d41
|
c65642bc3a4fd87055e2edeced1b7092dc5a4486
|
refs/heads/master
| 2021-09-13T03:01:59.193182
| 2018-04-12T14:26:08
| 2018-04-12T14:26:08
| 123,016,410
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,372
|
r
|
summarise-changes.R
|
count.changes <- function(psa1) {
library(dplyr)
psa1.dir <- paste("D:/OneDrive/Work Files/Wilko/Data Cleanse/",psa1,"/Uploads/",sep="")
update.age.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Age.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.assembly.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Assembly.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.brand.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Brand.csv",sep = ""),header = TRUE)
update.capacity.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Capacity.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.coverage.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Coverage.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.model.no.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Model.Number.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.size.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Size.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.pack.qty.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Pack.Qty.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.colour.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Colour.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.material.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Material.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.web.description.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Web.Description.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.washable.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Washable.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.brand.data <- update.brand.data[,c("PSA_1","PSA_2","Article","Web.Description","Web.Description","Brand","Title.Brand")]
colnames(update.brand.data) <- c("PSA_1","PSA_2","Article","Web.Description","Old.Value","New.Value","Change")
update.brand.data$Change <- "Update"
a <<- rbind(update.age.data,update.assembly.data,update.capacity.data,update.coverage.data,update.model.no.data,
update.size.data,update.pack.qty.data,update.colour.data,update.material.data,update.web.description.data,
update.washable.data,update.brand.data)
b <<- a[a$Change != "Keep",]
change.file <<- data.frame(count(b,b$Change))
write.csv(change.file,paste(psa1.dir,"Total_Changes.csv",sep = ""))
}
|
9bb382b36a186f9e452d22d612270717130b7121
|
0d3f737aa66620cd3c9cdd5d09b11340873ff099
|
/run_analysis.R
|
7726929f8956756d307d1d1403f809685e38ba6b
|
[] |
no_license
|
tjdurrant/Data_Cleaning_Assignment
|
c6a4076f83a309d1b033e569c4c86676e5b69442
|
9610cb2ea4b641f2fa05750445948f297cfbbc63
|
refs/heads/master
| 2021-01-22T22:09:16.622064
| 2017-03-19T23:21:42
| 2017-03-19T23:21:42
| 85,513,158
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,002
|
r
|
run_analysis.R
|
library(dplyr)
#Read in features and activites data
features <- read.table("./features.txt")
activity_labels <- read.table("./activity_labels.txt")
#Read in training data
x_train <- read.table("./x_train.txt")
y_train <- read.table("./y_train.txt")
subject_train <- read.table("./subject_train.txt")
#Read in test data
x_test <- read.table("./x_test.txt")
y_test <- read.table("./y_test.txt")
subject_test <- read.table("./subject_test.txt")
#rename variables
y_train <- rename(y_train, activityid = V1)
y_test <- rename(y_test, activityid = V1)
subject_train <- rename(subject_train, subjectID = V1)
subject_test <- rename(subject_test, subjectID = V1)
#add acivity variable
y_train$activity <- factor(y_train$activityid, levels=activity_labels$V1,
labels=activity_labels$V2)
y_test$activity <- factor(y_test$activityid, levels=activity_labels$V1,
labels=activity_labels$V2)
#rename X variables
names(x_train) <- features[,2]
names(x_test) <- features[,2]
#add Train or Test variable
x_train$datatype <- "Train"
x_test$datatype <- "Test"
#cbind y and subject
y_train <- cbind(y_train, subject_train)
y_test <- cbind(y_test, subject_test)
#cbind y and x
tidy_data_train <- cbind(y_train, x_train)
tidy_data_test <- cbind(y_test, x_test)
#rbind train and test
tidy_data <- rbind(tidy_data_train, tidy_data_test)
head(tidy_data$activity, n = 100)
#grep mean and std variables
formatted_tidy_data <- grep("activity|subject|mean|std", names(tidy_data), value = TRUE)
formatted_tidy_data
tidy_data <- tidy_data[,formatted_tidy_data]
tidy_data
write.table(tidy_data, "./tidy_data.txt", row.name=FALSE)
#group_by subject, activity. average remaining variables.
activity_subject_data <- tidy_data %>%
group_by(subjectID, activity) %>%
summarise_each(funs(overallmean = mean))
write.table(activity_subject_data, "./activity_subject_data.txt", row.name=FALSE)
#########################################################
|
c34a74d648ae975bfa23a3d6b2d9e4c7e5b553a5
|
78a12d66cf2c127fdd2c368b7b251972cbc0330d
|
/code.R
|
624fe0790d0e9759928b0508d8677b8908df0af1
|
[] |
no_license
|
jqyangDaz/Daz
|
b3d3095cd0a48dcca060cef0c6b4391ef3f72206
|
43de7196431cdd0ae1d694d4a228c0c45ba96c24
|
refs/heads/master
| 2020-03-16T21:41:02.617593
| 2018-05-11T08:10:56
| 2018-05-11T08:10:56
| 133,009,907
| 1
| 0
| null | null | null | null |
GB18030
|
R
| false
| false
| 7,116
|
r
|
code.R
|
library(plotrix)
library(grid)
library(vcd)
library(tseries)
#导入所需的包
homi<-read.csv('e:/database.csv')
#录入数据
nrow(homi)
#查看数据行数
length(homi)
#查看数据列数
View(homi)
#预览数据
attach(homi)
bar1<-table(Month)
#获取Month数据的的分类频数
barplot(bar1)
#绘制条形图
pie1<-table(Year)
pie(pie1,main='年份和凶杀案数量的饼图')
#绘制饼图
lals_pct <- paste(names(pie1), " ", round(pie1/sum(pie1), 2)*100, "%", sep="")
#计算百分比
lals_pct
bar2<-table(Year)
barplot(bar2,main = 'bar of year',col='pink')
#绘制条形图
pie2<-table(Victim.Sex)
#获取频数
lals_pct1 <- paste(names(pie2), " ", round(pie2/sum(pie2), 2)*100, "%", sep="")
#计算百分比
pie3D(pie2,labels=lals_pct,main = 'pie of sex',explode=0.1)
#绘制3D饼图
pie3<-table(Victim.Race)
#获取频数
lals_pct2 <- paste(round(pie3/sum(pie3),2)*100, "%", sep="")
#计算百分比
pie(pie3,main ="pie of Vicitim's race",labels = lals_pct2,
col=c("purple", "violetred1", "green3","cornsilk", "cyan"))
#绘制饼图
legend(0.8,1.25,names(pie3),fill = c("purple", "violetred1", "green3","cornsilk", "cyan"))
#添加标注
pie4<-table(Perpetrator.Sex)
#获取频数
lals_pct3 <- paste(names(pie4), " ", round(pie4/sum(pie4),2)*100, "%", sep="")
#计算百分比
pie3D(pie4,labels = lals_pct3,explode = 0.1)
#绘制3D饼图
pie5<-table(Perpetrator.Race)
#获取频数
lals_pct4 <- paste(names(pie5), " ", round(pie5/sum(pie5),2)*100, "%", sep="")
#计算百分比
pie(pie5,labels = lals_pct4,main = "pie of Perpetrator's Race")
#绘制饼图
bar2<-table(Relationship)
#获取频数
barplot(bar2,main="Hist of Relationship",col='purple')
#绘制条形图
summary(Victim.Age)
#查看数据概要
Victimage<-c(Victim.Age)
#转换为向量
V<-Victimage[Victimage<998]
#排除年龄为998的数据
summary(V)
#查看数据概要
boxplot(V,main = 'box of age',ylab='years')
#绘制箱线图
summary(Perpetrator.Age)
P<-na.omit(Perpetrator.Age)
#删除缺失值
summary(P)
boxplot(P,main = 'box of age',ylab='years')
chisq.test(Victim.Count,Perpetrator.Count)
#卡放拟合优度检验
chisq.test(Year,Weapon)
pie6<-table(Weapon)
lals_pct5 <- paste(names(pie6), " ", round(pie6/sum(pie6),2)*100, "%", sep="")
pie3D(pie6,explode=0.1,labels=lals_pct5)
barplot(pie6,col=c('blue','red','orange','purple','yellow'),main = 'Bar of Weapon')
w1<-subset(homi,Year==1980,select=c(Year,Weapon))
#提取各年的武器数据
w2<-subset(homi,Year==1990,select=c(Year,Weapon))
w3<-subset(homi,Year==2000,select=c(Year,Weapon))
w4<-subset(homi,Year==2010,select=c(Year,Weapon))
bar3<-table(w1$Weapon)
bar4<-table(w2$Weapon)
bar5<-table(w3$Weapon)
bar6<-table(w4$Weapon)
par(mfrow=c(2,2))
#绘制图时使用2*2格式
barplot(bar3,main = '1980')
barplot(bar4,main = '1990')
barplot(bar5,main = '2000')
barplot(bar6,main = '2010')
#绘制条形图
fan1<-table(Crime.Solved)
lals_pct6 <- paste(names(pie7), " ", round(pie7/sum(pie7),2)*100, "%", sep="")
fan.plot(pie7,labels = lals_pct6,col=c('pink','blue'),radius=0.8)
r1<-subset(homi,Year==1980,select=c(Year,Crime.Solved))
r2<-subset(homi,Year==1990,select=c(Year,Crime.Solved))
r3<-subset(homi,Year==2000,select=c(Year,Crime.Solved))
r4<-subset(homi,Year==2010,select=c(Year,Crime.Solved))
#提取案件是否结案的数据
fan2<-table(r1$Crime.Solved)
fan3<-table(r2$Crime.Solved)
fan4<-table(r3$Crime.Solved)
fan5<-table(r4$Crime.Solved)
par(mfrow=c(2,2))
lals_pct7 <- paste(names(fan2), " ", round(fan2/sum(fan2),2)*100, "%", sep="")
lals_pct8 <- paste(names(fan3), " ", round(fan3/sum(fan3),2)*100, "%", sep="")
lals_pct9 <- paste(names(fan4), " ", round(fan4/sum(fan4),2)*100, "%", sep="")
lals_pct10 <- paste(names(fan5), " ", round(fan5/sum(fan5),2)*100, "%", sep="")
pie(fan2,col=c('pink','blue'),labels=lals_pct7,radius=0.8,main='1980')
pie(fan3,col=c('pink','blue'),labels=lals_pct8,radius=0.8,main='1990')
pie(fan4,col=c('pink','blue'),labels=lals_pct9,radius=0.8,main='2000')
pie(fan5,col=c('pink','blue'),labels=lals_pct10,radius=0.8,main = '2010')
Y<-as.data.frame(table(Year))
#提取数据并强制转化为数据框类型
Y<-subset(Y,select = c(counts))
tscounts<- ts(Y,start = c(1980))
#将数据转化为时间序列
plot.ts(tscounts)
#绘制时间序列图
tscountsdiff<-diff(tscounts,differences=1)
#将序列进行一阶差分
plot.ts(tscountsdiff)
#绘制一阶差分后的图
acf(tscountsdiff,lag.max = 20)
pacf(tscountsdiff,lag.max = 20)
#绘制acf和pacf图
counts<-arima(tscountsdiff,order=c(1,0,0))
#构建AR模型
a<-subset(homi,Victim.Age<998,select = c(Year,Victim.Age,Perpetrator.Age))
detach(homi)
attach(a)
par(mfrow=c(1,2))
plot(Victim.Age~Year,main='Year vs Vicitim.Age')
plot(Perpetrator.Age~Year,main='Year vs Perpetrator.Age')
lm.1<-lm(Perpetrator.Count~Perpetrator.Age,data=homi)
#回归模型的拟合
summary(lm.1)
#回归方程结果的查看
sex<-subset(homi,Perpetrator.Sex!='Unknown',select=c(Perpetrator.Count,Perpetrator.Sex,Perpetrator.Age))
detach(homi)
attach(sex)
as.factor(Perpetrator.Sex)
#将性别转化为因子变量
lm.2<-lm(Perpetrator.Count~-1+Perpetrator.Sex,data=sex)
#没有截距项的回归拟合
summary(lm.2)
lm.3<-lm(Perpetrator.Count~-1+Perpetrator.Sex+Perpetrator.Age,data=sex)
#没有截距项的多元线性回归
summary(lm.3)
|
37f6cb4cde2cba89d93364960ad0366321fe2a7e
|
99e62a6f0161cc9de03bf066ae7d27e9cf25c179
|
/sc_preprocess.R
|
e4e3da05a63da525aaec9e249b4fa17d501f6fda
|
[] |
no_license
|
ardadurmaz/trajectory-sc
|
09431f8e1037a3548916b1d5df8e04854422a56e
|
6164659b7b415115a4aacf90991ca38e8b754e84
|
refs/heads/master
| 2020-04-17T15:11:18.370858
| 2019-01-20T17:30:12
| 2019-01-20T17:30:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,503
|
r
|
sc_preprocess.R
|
library(Matrix)
library(cellrangerRkit)
library(ggplot2)
library(scran)
library(scater)
library(edgeR)
library(limma)
## Read Data ##
temp.path <- '~/SingleCellData/single_cell_analysis/Marusyk_Joint3Aggr_ALK_redo'
genome <- 'GRCh38'
count <- load_cellranger_matrix(temp.path)
aggr.data <- read.csv('~/SingleCellData/single_cell_analysis/Marusyk_Joint3Aggr_ALK_redo/outs/aggregation_csv.csv')
targets <- as.character(aggr.data$library_id[as.numeric(gsub('^.+-', replacement = '', colnames(count)))])
count.mat <- as.matrix(count)
## Map Gene Symbols ##
ensembl <- useMart('ENSEMBL_MART_ENSEMBL', dataset = 'hsapiens_gene_ensembl')
mapping <- getBM(mart = ensembl,
attributes = c('ensembl_gene_id', 'hgnc_symbol'),
filters = 'ensembl_gene_id',
values = rownames(count.mat))
mapping <- na.omit(mapping[mapping$ensembl_gene_id != '' & mapping$hgnc_symbol != '',])
mapping <- mapping[match(rownames(count.mat), table = mapping$ensembl_gene_id),]
rownames(mapping) <- 1:nrow(mapping)
idx <- is.na(mapping$ensembl_gene_id) | is.na(mapping$hgnc_symbol)
count.mat <- count.mat[!idx,]
mapping <- mapping[!idx,]
rownames(mapping) <- 1:nrow(mapping)
idx <- order(Matrix::rowMeans(count.mat), decreasing = TRUE)
count.mat <- count.mat[idx,]
mapping <- mapping[idx,]
idx <- duplicated(mapping$hgnc_symbol)
count.mat <- count.mat[!idx,]
mapping <- mapping[!idx,]
rownames(count.mat) <- mapping$hgnc_symbol
## Filter ##
sc.data <- SingleCellExperiment(assays = list(counts = as(count.mat, 'dgCMatrix')))
sc.data <- calculateQCMetrics(sc.data,
use_spikes = FALSE,
detection_limit = 0)
plotExprsFreqVsMean(sc.data)
keep.total <- sc.data$total_counts > 6e3 ## Remove cells based on total counts
keep.n <- sc.data$total_features_by_counts > 1000 ## Remove cells based on number of genes expressed
sc.data <- sc.data[,keep.total & keep.n]
n.exprs <- nexprs(sc.data,
byrow = TRUE,
detection_limit = 0)
keep_feature <- n.exprs > 250
sc.data <- sc.data[keep_feature,]
## Normalize ##
targets <- as.character(aggr.data$library_id[as.numeric(gsub('^.+-',
replacement = '',
colnames(sc.data)))])
sc.data <- computeSumFactors(sc.data,
cluster = targets)
saveRDS(sc.data, file = '~/sc_trajectory/data/processedData.rds')
|
6eaf6ec142fe7b5d8cac6583dacbf196c1bc0673
|
76f353cb3366684c4708b36662c44f86204381d2
|
/script/DANMPUT.R
|
17c8d38314255d2aa07db99449ca094521a6815e
|
[] |
no_license
|
barbarian1803/saham_analisis
|
cdd431499348db370072eaec71490a6bbb2ff590
|
b5c33108faf16e8b4fcda9e0ef9f9a8a9b08c16e
|
refs/heads/master
| 2021-01-24T08:09:07.733311
| 2016-11-21T05:38:17
| 2016-11-21T05:38:17
| 70,199,529
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 184
|
r
|
DANMPUT.R
|
DANMPUT <- read.table("RD/DANMPUT.csv",header=FALSE,quote="",sep="\t")
colnames(DANMPUT) <- c("date","price")
summary(DANMPUT$price)
DANMPUT[duplicated(DANMPUT$date),]
rm(DANMPUT_rev)
|
c0453a013dbea734608437d85644e4eadf80a419
|
cef3b5e2588a7377281a8f627a552350059ca68b
|
/cran/paws.game.development/man/gamelift_start_matchmaking.Rd
|
2d58a972f016a45ef5bbaf276e751ebfe5e7d7e7
|
[
"Apache-2.0"
] |
permissive
|
sanchezvivi/paws
|
b1dc786a9229e0105f0f128d5516c46673cb1cb5
|
2f5d3f15bf991dcaa6a4870ed314eb7c4b096d05
|
refs/heads/main
| 2023-02-16T11:18:31.772786
| 2021-01-17T23:50:41
| 2021-01-17T23:50:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,588
|
rd
|
gamelift_start_matchmaking.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gamelift_operations.R
\name{gamelift_start_matchmaking}
\alias{gamelift_start_matchmaking}
\title{Uses FlexMatch to create a game match for a group of players based on
custom matchmaking rules}
\usage{
gamelift_start_matchmaking(TicketId, ConfigurationName, Players)
}
\arguments{
\item{TicketId}{A unique identifier for a matchmaking ticket. If no ticket ID is
specified here, Amazon GameLift will generate one in the form of a UUID.
Use this identifier to track the matchmaking ticket status and retrieve
match results.}
\item{ConfigurationName}{[required] Name of the matchmaking configuration to use for this request.
Matchmaking configurations must exist in the same Region as this
request. You can use either the configuration name or ARN value.}
\item{Players}{[required] Information on each player to be matched. This information must include
a player ID, and may contain player attributes and latency data to be
used in the matchmaking process. After a successful match, \code{Player}
objects contain the name of the team the player is assigned to.}
}
\description{
Uses FlexMatch to create a game match for a group of players based on
custom matchmaking rules. If you're also using GameLift hosting, a new
game session is started for the matched players. Each matchmaking
request identifies one or more players to find a match for, and
specifies the type of match to build, including the team configuration
and the rules for an acceptable match. When a matchmaking request
identifies a group of players who want to play together, FlexMatch finds
additional players to fill the match. Match type, rules, and other
features are defined in a \code{MatchmakingConfiguration}.
To start matchmaking, provide a unique ticket ID, specify a matchmaking
configuration, and include the players to be matched. For each player,
you must also include the player attribute values that are required by
the matchmaking configuration (in the rule set). If successful, a
matchmaking ticket is returned with status set to \code{QUEUED}.
Track the status of the ticket to respond as needed. If you're also
using GameLift hosting, a successfully completed ticket contains game
session connection information. Ticket status updates are tracked using
event notification through Amazon Simple Notification Service (SNS),
which is defined in the matchmaking configuration.
\strong{Learn more}
\href{https://docs.aws.amazon.com/gamelift/latest/flexmatchguide/match-client.html}{Add FlexMatch to a Game Client}
\href{https://docs.aws.amazon.com/gamelift/latest/flexmatchguide/match-notification.html}{Set Up FlexMatch Event Notification}
\href{https://docs.aws.amazon.com/gamelift/latest/flexmatchguide/match-tasks.html}{FlexMatch Integration Roadmap}
\href{https://docs.aws.amazon.com/gamelift/latest/flexmatchguide/gamelift-match.html}{How GameLift FlexMatch Works}
\strong{Related operations}
\itemize{
\item StartMatchmaking
\item DescribeMatchmaking
\item StopMatchmaking
\item AcceptMatch
\item StartMatchBackfill
}
}
\section{Request syntax}{
\preformatted{svc$start_matchmaking(
TicketId = "string",
ConfigurationName = "string",
Players = list(
list(
PlayerId = "string",
PlayerAttributes = list(
list(
S = "string",
N = 123.0,
SL = list(
"string"
),
SDM = list(
123.0
)
)
),
Team = "string",
LatencyInMs = list(
123
)
)
)
)
}
}
\keyword{internal}
|
453571b12e34505bee05f29aa647616ba55e6ac1
|
7b00b082bb032398858c863965607bf47c2e6513
|
/artigo_pairs_def.R
|
427436c5972b347d847c53129c138a8173444a69
|
[] |
no_license
|
lemuelemos/Pairs_trading
|
b9f79978caebc2891767d4b10e6924a762269b89
|
95b52fc80cac9c092ca2671964fa4478ad02ac3c
|
refs/heads/master
| 2021-04-27T11:26:28.688792
| 2019-04-01T03:01:40
| 2019-04-01T03:01:40
| 122,562,684
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,068
|
r
|
artigo_pairs_def.R
|
library(doParallel)
library(partialCI)
library(readxl)
library(xts)
library(stringr)
library(dplyr)
library(plyr)
library(timeSeries)
library(Rcpp)
##### Import data and cleaning NA's
#source('cpp_codes.R')
sourceCpp("cpp_codes.cpp")
ibrx_2007_2018 <- read_excel("ibrx last price 2007 até 2018_2.xlsx") #### Reading the data
ibrx_2007_2018$Dates <- as.Date(ibrx_2007_2018$Dates) ## Setting the format of the dates column
ibrx_2007_2018 <- as.data.frame(ibrx_2007_2018) ## Transform in data_frame to easy handling
ibrx_2007_2018[,2:ncol(ibrx_2007_2018)] <- apply(ibrx_2007_2018[,2:ncol(ibrx_2007_2018)],2,as.numeric) ## Transform the data form char type to numeric type
ibrx_2007_2018 <- xts(ibrx_2007_2018[,-1],order.by = ibrx_2007_2018$Dates) ## transform in xts to easy handling with time series
ibrx_2007_2018_integridade <- apply(ibrx_2007_2018,2,
function(x) sum(is.na(x))/nrow(ibrx_2007_2018))*100 ## Calculate the percentage of missing data
ibrx_2007_2018_70 <- ibrx_2007_2018[,names(ibrx_2007_2018_integridade[which(ibrx_2007_2018_integridade<10)])] ## Taking the coluns eith more than 90% of integrite
ibrx_2008_2017_70 <- ibrx_2007_2018_70["2008/2017"] ## subsetting to eliminate more missing data.
ibrx_2008_2017_70 <- na.spline(ibrx_2008_2017_70) ## remove "NA's" spline method Missing values (NAs) are replaced by linear interpolation via approx or cubic spline interpolation via spline, respectively.
rm(list=c("ibrx_2007_2018","ibrx_2007_2018_70")) ## remove objects that will not be use
Nomes <- colnames(ibrx_2008_2017_70) ## Taking the names of equity's
Nomes <- str_sub(Nomes, 1,6)
colnames(ibrx_2008_2017_70) <- Nomes
#ibrx_2008_2017_70 <- ibrx_2008_2017_70[,1:20]
### Setting the window of estimation
estimation_method <- "fixed"
dir.create(file.path(getwd(), "resultados"))
###
resultados_por_tr <- list(NULL)
threshold <- matrix(c(1,1,0.5,0),2,2)
formation_windown <- c(503,1007)
names(formation_windown) <- c("2Y","4Y")
trading_days <- c(90,180)
rolling_window <- c(63,126)
for(pp in 1:length(formation_windown)){
ret_port <- as.list(NULL)
pairs_est <- list(NULL)
pairs_est_tested <- list(NULL)
trading_return <- as.list(NULL)
select_port <- as.list(NULL)
retornos <- as.list(NULL)
time_window <- as.list(NULL)
ret_aux <- as.list(NULL)
trades <- list(NULL)
returns <- list(NULL)
window_test <- seq(1,nrow(ibrx_2008_2017_70),by=(rolling_window[pp]))
resultados_por_tr <- list(NULL)
for(kk in 1:nrow(threshold)){
tr <- threshold[kk,]
for(p in seq_along(window_test)){
test_period <- window(ibrx_2008_2017_70,
start=time(ibrx_2008_2017_70)[window_test[p]],
end=if(is.na(time(ibrx_2008_2017_70)[window_test[p]+formation_windown[pp]])){break}
else{time(ibrx_2008_2017_70)[window_test[p]+formation_windown[pp]]})
time_window[[p]] <- time(test_period)
test_period_est <- as.data.frame(test_period)
test_period_est <- test_period_est[,1:20]
### Estimating pairs
cat("\r",paste0("Estimating the pairs from portfolio "
,p,". Period from ",
min(time_window[[p]]), " to ",max(time_window[[p]])))
no_cores <- detectCores()
cl <- makeCluster(no_cores)
clusterExport(cl, "test_period_est")
clusterEvalQ(cl, library(partialCI))
pares <- parLapply(cl,test_period_est,
function(x) apply(test_period_est,2,
function(y) if(x!=y){try(fit.pci(x,y))}))
stopCluster(cl)
pares <- unlist(pares, recursive = F)
pares <- pares[!sapply(pares,is.null)]
pares <- pares[!sapply(pares, function(x) is.na(x$rho.se))]
names(pares) <- gsub("TIET11vs","TIET11 vs",
paste0(str_sub(names(pares), 1,6),"vs ",
str_sub(names(pares), 8,13)))
pares <- pares[!sapply(pares,is.null)] ### Retirando os valores vazios
pares <- pares[!sapply(pares, function(x) is.na(x$rho.se))] ### Retirando os pares com problemas de estimação
#saveRDS(pares,file=paste0(getwd(),"/resultados/pair_",
#min(time_window[[p]]),"_to_",
#max(time_window[[p]]),"_portfolio",p,"_fmw_",
#names(formation_windown)[pp],"_tr_(",tr[1],",",tr[2],")"))
pairs_est[[p]] <- pares
#### Taking the pairs with R square greater than 0.5
cat("\r",paste0("Taking the pais with R2>0.5. Portfolio ",p))
paresR <- pares[sapply(pares,function(x) x$pvmr > 0.5)]
paresR <- paresR[sapply(paresR,function(x) x$rho > 0.5)]
#rm(pares)
### Testing partial Cointegration
cat("\r",paste0("Testing for partial coitegration. Portfolio ",p))
cl <- makeCluster(no_cores)
clusterExport(cl, "paresR")
clusterEvalQ(cl, library(partialCI))
paresRtested <- paresR[parSapply(cl,paresR,
FUN = function(x) which.hypothesis.pcitest(test.pci(x))=="PCI")]
stopCluster(cl)
#rm(paresR)
#saveRDS(paresRtested,file=paste0(getwd(),"/resultados/pairRtested_",
#min(time_window[[p]]),"_to_",
#max(time_window[[p]]),"_portfolio",p,"_fmw_",
#names(formation_windown)[pp],"_tr_(",tr[1],",",tr[2],")"))
pairs_est_tested[[p]] <- paresRtested
### Estimation of ocult states
cat("\r",paste0("Estimation of ocult states. Portfolio ",p))
paresRtestedM <- lapply(paresRtested, function(x) statehistory.pci(x))
betas_formation <- ldply(paresRtested, function(x) x$beta)
colnames(betas_formation) <- c("Pares","betas")
#rm(paresRtested)
############### Normalizando O M
cat("\r",paste0("Normalizing the M. Portfolio",p))
Zm_fornation <- lapply(paresRtestedM, function(x) x$M/sd(x$M))
Zm_fornation <- as.data.frame(Zm_fornation)
colnames(Zm_fornation) <- gsub("\\."," ",names(Zm_fornation))
#rm(paresRtestedM)
### sign of operations
cat("\r",paste0("Sign for operations - threshold[",tr[1],",",tr[2],"]. Portolio ",p))
sinal <- matrix(data = rep(0,ncol(Zm_fornation)*nrow(Zm_fornation)),ncol = ncol(Zm_fornation),nrow = nrow(Zm_fornation))
sinal[1,1:ncol(sinal)] <- "Fora"
sinal <- sncalc(ncol(Zm_fornation),nrow(Zm_fornation),as.matrix(Zm_fornation), tr=tr, sinal=sinal)
sinal<- as.data.frame(sinal)
colnames(sinal) <- names(Zm_fornation)
sinal %>% mutate_if(is.factor,as.character) -> sinal
#as.xts(sinal, order.by = time(test_period))
############# Return Calc
cat("\n",paste0("Return Calc. Portfolio",p))
parestrade <- list(NULL)
for(j in 1:length(sinal)){
parestrade[[j]] <- cbind(test_period[,str_sub(names(sinal)[j],end=6)],
test_period[,str_sub(names(sinal)[j],start=-6)])
names(parestrade)[j] <- names(sinal)[j]
colnames(parestrade[[j]]) <- cbind(str_sub(names(sinal)[j],end=6),
str_sub(names(sinal)[j],start=-6))
}
invest <- data.frame(matrix(data = rep(1,ncol(Zm_fornation)*nrow(Zm_fornation)),ncol = ncol(Zm_fornation),nrow = nrow(Zm_fornation)))
retorno <- data.frame(matrix(data = rep(0,ncol(Zm_fornation)*nrow(Zm_fornation)),ncol = ncol(Zm_fornation),nrow = nrow(Zm_fornation)))
ttf <- data.frame(matrix(data = rep(0,ncol(Zm_fornation)*nrow(Zm_fornation)),ncol = ncol(Zm_fornation),nrow = nrow(Zm_fornation)))
results <- NULL
par_est <- data.frame(NULL)
for(j in 1:length(parestrade)){
par_est <- parestrade[[j]]
results <- returcalc(as.matrix(sinal[,j]),
as.matrix(par_est),betas = betas_formation$betas[j],invest = invest[,j])
invest[,j] <- results[[1]]
retorno[,j] <- results[[2]]
ttf[,j] <- results[[2]]
}
colnames(invest) <- names(parestrade)
colnames(retorno) <- names(parestrade)
colnames(ttf) <- names(parestrade)
################ Cáculo dos Retornos Totais, Desvios Padrões e Sharpe.
cat("\r",paste0("Calculating return and sharpe. Portfolio ",p))
portret <- as.data.frame(matrix(data = rep(0,ncol(Zm_fornation)*3),ncol = ncol(Zm_fornation),nrow = 3))
for(f in 1:length(invest)){
portret[1,f] <- ((invest[nrow(invest),f]/invest[1,f])-1)*100
portret[2,f] <- sd(invest[,f])
portret[3,f] <- portret[1,f]/portret[2,f]
colnames(portret)[f] <- names(parestrade)[f]
}
portret <- t(portret) ## Retornos Totais
colnames(portret) <- c("Retorno Total","Desvio Padrão","Sharpe")
ret_port[[p]] <- portret ## Retornos Totais
names(ret_port)[p] <- paste0("Return Formation Period ",p)
#####################################################
############### Periodo de Trading ##################
#####################################################
if(estimation_method == "fixed"){
source("trading_period_fixed_window.R")
} else {source("trading_period_rolling_window.R")}
}
#### Salvando Dados Importantes
source('res_data_est.R')
saveRDS(pairs_est,file = paste0(getwd(),"/resultados/pairs_fmw_",
names(formation_windown)[pp],"_tr(",tr[1],",",tr[2],")"))
saveRDS(pairs_est_tested,file = paste0(getwd(),"/resultados/pairs_tested_fmw_",
names(formation_windown)[pp],"_tr(",tr[1],",",tr[2],")"))
}
}
|
ee2499ee634b668899d58ee3276c10a51944c3f3
|
a520b1ed88904ce8090687cec3284111a871ac7a
|
/StatisticalRethinking/Exercise_Ch5.R
|
878a86601afcbfb5d29f5508f45824260b681a63
|
[] |
no_license
|
EijiGorilla/R-Statistics
|
b59cb499cb24a383043f58f415a5b4d03bee863a
|
e900a786dcf80b21eeab29fefb9bed409d4907d0
|
refs/heads/master
| 2020-04-18T11:52:19.386254
| 2019-01-25T08:50:24
| 2019-01-25T08:50:24
| 167,515,532
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 22,059
|
r
|
Exercise_Ch5.R
|
library(rethinking)
data("WaffleDivorce")
d=WaffleDivorce
head(d)
op=par(mfrow=c(2,2))
d$MedianAgeMarriage.s=(d$MedianAgeMarriage-mean(d$MedianAgeMarriage))/sd(d$MedianAgeMarriage)
plot(Divorce~MedianAgeMarriage.s,data=d,col=col.alpha(rangi2,0.5))
#fit model
m5.1=map(
alist(
Divorce~dnorm(mu,sigma),
mu<-a+b*MedianAgeMarriage.s,
a~dnorm(10,10),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5.1,corr=TRUE)
# MAP and 89% PI
MAM.seq=seq(from=-3,to=3.5,length.out = 30)
post=extract.samples(m5.1)
mu=link(m5.1,data=data.frame(MedianAgeMarriage.s=MAM.seq))
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
# plot
plot(Divorce~MedianAgeMarriage.s,data=d,col=rangi2)
abline(m5.1)
lines(MAM.seq,mu.mean)
shade(mu.PI,MAM.seq)
precis(m5.1)
d$Marriage.s=(d$Marriage-mean(d$Marriage))/sd(d$Marriage)
m5.2=map(
alist(
Divorce~dnorm(mu,sigma),
mu<-a+b*Marriage.s,
a~dnorm(10,10),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
plot(Divorce~Marriage.s,data=d,col=rangi2)
MA.seq=seq(from=-3,to=3,length.out=40)
post=extract.samples(m5.2)
mu=sapply(MA.seq,function(Marriage) post$a+post$b*Marriage)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
abline(m5.2)
shade(mu.PI,MA.seq)
precis(m5.2)
head(d)
m5.3=map(
alist(
Divorce~dnorm(mu,sigma),
mu<-a+bR*Marriage.s+bA*MedianAgeMarriage.s,
a~dnorm(10,10),
bR~dnorm(0,1),
bA~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5.3,corr=TRUE)
library(rethinking)
plot(precis(m5.3))
op=par(mfrow=c(2,2))
# Predictor residuals
m5.4=map(
alist(
Marriage.s~dnorm(mu,sigma),
mu<-a+b*MedianAgeMarriage.s,
a~dnorm(0,10),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
m5.4
mu=coef(m5.4)["a"]+coef(m5.4)["b"]*d$MedianAgeMarriage.s
m.resid=d$Marriage.s-mu
plot(Marriage.s~MedianAgeMarriage.s,d,col=rangi2)
abline(m5.4)
for(i in 1:length(m.resid)){
x=d$MedianAgeMarriage.s[i]
y=d$Marriage.s[i]
lines(c(x,x),c(mu[i],y),lwd=5,col=col.alpha("black",0.7))
}
library(rethinking)
plot(Marriage.s~MedianAgeMarriage.s,d,col=rangi2)
abline(m5.4)
for(i in 1:length(m.resid)){
x=d$MedianAgeMarriage.s[i]
y=d$Marriage.s[i]
lines(c(x,x),c(mu[i],y),lwd=0.5,col=col.alpha("black",0.7))
}
op=par(mfrow=c(2,2))
plot(Divorce~m.resid,d,col=rangi2)
abline(v=0)
m5.41=map(
alist(
MedianAgeMarriage.s~dnorm(mu,sigma),
mu<-a+b*Marriage.s,
a~dnorm(0,10),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
m5.41
mu=coef(m5.41)["a"]+coef(m5.41)["b"]*d$Marriage.s
m.resid=d$MedianAgeMarriage.s-mu
plot(MedianAgeMarriage.s~Marriage.s,d,col=rangi2)
abline(m5.41)
for(i in 1:length(m.resid)){
x=d$Marriage.s[i]
y=d$MedianAgeMarriage.s[i]
lines(c(x,x),c(mu[i],y),col=col.alpha("black",0.4))
}
plot(d$Divorce~m.resid,col=rangi2)
abline(v=0,l)
?abline
# Counterfactual plots
m5.3=map(
alist(
Divorce~dnorm(mu,sigma),
mu<-a+bR*Marriage.s+bA*MedianAgeMarriage.s,
a~dnorm(10,10),
bR~dnorm(0,1),
bA~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
A.ave=mean(d$MedianAgeMarriage.s)
R.seq=seq(from=-3,to=3,length.out = 30)
post=extract.samples(m5.3)
head(post)
pred.data=data.frame(Marriage.s=R.seq,MedianAgeMarriage.s=A.ave)
# Compute counterfactual mean divorce
mu=link(m5.3,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
# compute counterfactual divorce outcome
R.sim=sim(m5.3,data=pred.data,n=1e4)
R.sim.mean=apply(R.sim,2,mean)
R.sim.PI=apply(R.sim,2,PI,prob=0.89)
plot(Divorce~Marriage.s,data=d,type="n")
mtext("MedianAgeMarriage.s=0")
lines(R.seq,mu.mean)
shade(mu.PI,R.seq)
shade(R.sim.PI,R.seq)
plot(Divorce~MedianAgeMarriage.s,d)
R.ave=mean(d$Marriage.s)
A.seq=seq(from=-3,to=3,length.out=50)
pred.data=data.frame(MedianAgeMarriage.s=A.seq,Marriage.s=R.ave)
# compute counterfactual mean median age at marriage
mu=link(m5.3,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
# compute counterfactual actual median age at marriage
A.sim=sim(m5.3,data=pred.data,n=1e4)
A.sim.PI=apply(A.sim,2,PI,prob=0.89)
plot(Divorce~MedianAgeMarriage.s,data=d,type="n")
mtext("Marriage.s=0")
lines(A.seq,mu.mean)
shade(mu.PI,A.seq)
shade(A.sim.PI,A.seq)
# Posterior prediction plots
# call link to compute predicted mean for each observation of Divorce
mu=link(m5.3)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
# plot obs. vs pred. for Divorce
plot(mu.mean~d$Divorce,col=rangi2,ylim=range(mu.PI),xlab="Observed Divorce",ylab="Predicted Divorce")
abline(a=0,b=1,col=col.alpha("black",0.4))
for(i in 1:nrow(d)){
lines(rep(d$Divorce[i],2),c(mu.PI[1,i],mu.PI[2,i]),col=col.alpha("black",0.7))
}
identify(x=d$Divorce,y=mu.mean,labels=d$Loc,cex=0.8)
# compute residulas
div.resid=d$Divorce-mu.mean
# simulate for computing 89% predicted values for acutual divorce
div.sim=sim(m5.3,n=1e4)
sim.PI=apply(div.sim,2,PI,prob=0.89)
# plot predicted residuals by states
div.resid
o=order(div.resid)
dotchart(div.resid[o],labels=d$Loc[o],cex=0.6)
abline(v=0,col=col.alpha("black",0.4))
for(i in 1:nrow(d)){
j=o[i]
lines(d$Divorce[j]-c(mu.PI[1,j],mu.PI[2,j]),rep(i,2),col=col.alpha("black",0.4))
points(d$Divorce[j]-c(sim.PI[1,j],sim.PI[2,j]),rep(i,2),pch=3,col=col.alpha("black",0.4),cex=0.6)
}
#
data("milk")
d=milk
dcc=d[complete.cases(d),]
head(dcc)
m5.5=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+b*neocortex.perc,
a~dnorm(45,100),
b~dnorm(0,1),
sigma~dunif(0,1)
),data=dcc)
precis(m5.5,digits=3)
plot(kcal.per.g~neocortex.perc,dcc,col=rangi2)
np.seq=0:100
pred.data=data.frame(neocortex.perc=np.seq)
mu=link(m5.5,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
lines(np.seq,mu.mean)
shade(mu.PI,np.seq)
str(dcc)
plot(kcal.per.g~log(mass),dcc,col=rangi2)
dcc$log.mass=log(dcc$mass)
m5.6=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+b*log.mass,
a~dnorm(45,100),
b~dnorm(0,1),
sigma~dunif(0,1)
),data=dcc)
precis(m5.6)
mas.seq=seq(from=-3,to=5,length.out = 50)
pred.data=data.frame(log.mass=mas.seq)
mu=link(m5.6,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
lines(mas.seq,mu.mean)
shade(mu.PI,mas.seq)
#
m5.7=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+bn*neocortex.perc+bm*log.mass,
a~dnorm(45,100),
bn~dnorm(0,1),
bm~dnorm(0,1),
sigma~dunif(0,1)
),data=dcc)
plot(precis(m5.7,corr=TRUE))
# counterfactual plots: holding body mass constant at its mean
mass.ave=mean(dcc$log.mass)
neo.seq=seq(from=50,to=80,length.out=50)
pred.data=data.frame(neocortex.perc=neo.seq,log.mass=mass.ave)
mu=link(m5.7,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
plot(kcal.per.g~neocortex.perc,data=dcc,col=rangi2)
lines(neo.seq,mu.mean)
shade(mu.PI,neo.seq)
# counterfactual plots: holding neocortex (brain mass) constant at its mean
neo.ave=mean(dcc$neocortex.perc)
plot(kcal.per.g~log.mass,dcc)
log.mass.seq=seq(from=-3,to=5,length.out=50)
pred.data=data.frame(neocortex.perc=neo.ave,log.mass=log.mass.seq)
mu=link(m5.7,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
lines(log.mass.seq,mu.mean)
shade(mu.PI,log.mass.seq)
plot(neocortex.perc~log.mass,dcc,col=rangi2)
#
N=100
height=rnorm(N,10,2)
leg_prop=runif(N,0.4,0.5)
leg_left=leg_prop*height+rnorm(N,0,0.02)
leg_right=leg_prop*height+rnorm(N,0,0.02)
d=data.frame(height,leg_left,leg_right)
m5.8=map(
alist(
height~dnorm(mu,sigma),
mu<-a+bl*leg_left+br*leg_right,
a~dnorm(0,100),
bl~dnorm(2,10),
br~dnorm(2,10),
sigma~dunif(0,10)
),data=d)
precis(m5.8)
precis_plot(precis(m5.8))
post=extract.samples(m5.8)
head(post)
plot(bl~br,post,col=col.alpha(rangi2,0.1),pch=16)
sum_blbr=post$bl+post$br
dens(sum_blbr,col=rangi2,lwd=2)
m5.9=map(
alist(
height~dnorm(mu,sigma),
mu<-a+bl*leg_left,
a~dnorm(10,100),
bl~dnorm(2,10),
sigma~dunif(0,10)
),data=d)
precis(m5.9)
precis_plot(precis(m5.9))
#
data("milk")
d=milk
m5.10=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+bf*perc.fat,
a~dnorm(0.6,10),
bf~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
m5.11=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+bl*perc.lactose,
a~dnorm(0.6,10),
bl~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5.10,digits=3)
precis_plot(precis(m5.10))
precis(m5.11,digits=3)
precis_plot(precis(m5.11))
head(d)
plot(kcal.per.g~perc.fat,d,col=rangi2)
p.fat.seq=seq(from=-10,to=60,length.out = 100)
pred.data=data.frame(perc.fat=p.fat.seq)
mu=link(m5.10,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
lines(p.fat.seq,mu.mean)
shade(mu.PI,p.fat.seq)
m5.12=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+bl*perc.fat+br*perc.lactose,
a~dnorm(0.6,10),
bl~dnorm(0,1),
br~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis_plot(precis(m5.12))
pairs(~kcal.per.g+perc.fat+perc.lactose,data=d,col=rangi2)
cor(d$perc.fat,d$perc.lactose)
# number of plants
N=100
#simulate initial heights
h0=rnorm(N,10,2)
treatment=rep(0:1,each=N/2)
fungus=rbinom(N,size=1,prob=0.5-treatment*0.4)
h1=h0+rnorm(N,5-3*fungus)
d=data.frame(h0=h0,h1=h1,treatment=treatment,fungus=fungus)
m5.14=map(
alist(
h1~dnorm(mu,sigma),
mu<-a+bh*h0+bt*treatment,
a~dnorm(10,100),
c(bh,bt)~dnorm(0,10),
sigma~dunif(0,10)
),data=d)
precis(m5.14)
#
data("Howell1")
d=Howell1
str(d)
m5.15=map(
alist(
height~dnorm(mu,sigma),
mu<-a+bm*male,
a~dnorm(178,100),
bm~dnorm(0,10),
sigma~dunif(0,50)
),data=d)
precis(m5.15)
post=extract.samples(m5.15)
head(post)
mu.male=post$a+post$bm
PI(mu.male)
m5.15b=map(
alist(
height~dnorm(mu,sigma),
mu<-af*(1-male)+am*male,
af~dnorm(178,100),
am~dnorm(178,100),
sigma~dunif(0,150)
),data=d)
precis(m5.15b)
#5.4.2
library(rethinking)
data("milk")
d=milk
head(d)
unique(d$clade)
# 4 categories for clade: Ape, NWM, OWM, and S
# Create dummy variables for NWM, OWM and S. Ape=reference variable
d$clade.NWM=ifelse(d$clade=="New World Monkey",1,0)
d$clade.OWM=ifelse(d$clade=="Old World Monkey",1,0)
d$clade.S=ifelse(d$clade=="Strepsirrhine",1,0)
# model
m5.16=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a+b.NWM*clade.NWM+b.OWM*clade.OWM+b.S*clade.S,
a~dnorm(0.6,10),
c(b.NWM,b.OWM,b.S)~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5.16)
post=extract.samples(m5.16)
head(post)
mu.ape=post$a
mu.NWM=post$a+post$b.NWM
mu.OWM=post$a+post$b.OWM
mu.S=post$a+post$b.S
precis(data.frame(mu.ape,mu.NWM,mu.OWM,mu.S))
diff.NWM.OWM=post$b.NWM-post$b.OWM
quantile(diff.NWM.OWM,prob=c(0.025,0.5,0.975))
d$clade.id=coerce_index(d$clade)
unique(d$clade.id)
head(d)
d1=d[complete.cases(d),]
m5.16_alt=map(
alist(
kcal.per.g~dnorm(mu,sigma),
mu<-a[clade_id],
a[clade_id]~dnorm(0.6,1),
sigma~dunif(0,10)
),data=d)
# 5M1 (Spurious association: one predictor influences or highly correlates with outcome and another predictor)
N=100
x_real=rnorm(N)
x_spur=rnorm(N,x_real)
y=rnorm(N,x_real)
d=data.frame(y,x_real,x_spur)
pairs(d)
m.spur=map(
alist(
y~dnorm(mu,sigma),
mu<-a+b1*x_real+b2*x_spur,
a~dnorm(0,10),
c(b1,b2)~dnorm(0,1),
sigma~dunif(0,10)
),data=d
)
precis(m.spur)
precis_plot(precis(m.spur))
op=par(mfrow=c(2,2))
plot(y~x_real,d,col=rangi2)
# Counterfactual plots
# holding x_spur constant at its mean
x_spur.ave=mean(d$x_spur)
x_real.seq=seq(from=-3,to=4,length.out = 50)
pred.data=data.frame(x_real=x_real.seq,x_spur=x_spur.ave)
mu=link(m.spur,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m.spur,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
lines(x_real.seq,mu.mean)
shade(mu.PI,x_real.seq)
shade(sim.y.PI,x_real.seq)
abline(a=0,b=1)
mtext("Holding x_spur constat at its mean")
# holding x_real constant at its mean
x_real.ave=mean(d$x_real)
x_spur.seq=seq(from=-4,to=4,length.out=50)
pred.data=data.frame(x_real=x_real.ave,x_spur=x_spur.seq)
mu=link(m.spur,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m.spur,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(y~x_spur,data=d,col=rangi2)
lines(x_spur.seq,mu.mean)
shade(mu.PI,x_spur.seq)
shade(sim.y.PI,x_spur.seq)
# 5M2 Masked relatioinship: Predictors are highly correlated with one another, and one is positivily correlated while
# another negatively correlated with an outcome variable
# InCorrect method: (i.e. in the masked relationship, fitting a bivariate mode (model with x1 or x2 with y separately
N=100
rho=0.7
x_pos=rnorm(N)
x_neg=rnorm(N,rho*x_pos,sqrt(1-rho^2))
y=rnorm(N,x_pos-x_neg)
d=data.frame(y,x_pos,x_neg)
m.mask=map(
alist(
y~dnorm(mu,sigma),
mu<-a+b*x_pos,
a~dnorm(0,10),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis_plot(precis(m.mask))
post=extract.samples(m.mask)
x_pos.seq=seq(from=-4,to=4,length.out=50)
mu=sapply(x_pos.seq,function(x_pos) post$a+post$b)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sapply(x_pos.seq,function(x_pos) rnorm(
n=nrow(post),
mean=post$a+post$b*x_pos,
sd=post$sigma
))
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(y~x_pos,d,col=rangi2)
lines(x_pos.seq,mu.mean)
shade(mu.PI,x_pos.seq)
shade(sim.y.PI,x_pos.seq)
# Correct method: (i.e. usnig both variables captures a good relationship between the predictors and y)
N=100
rho=0.7
x_pos=rnorm(N)
x_neg=rnorm(N,rho*x_pos,sqrt(1-rho^2))
y=rnorm(N,x_pos-x_neg)
d=data.frame(y,x_pos,x_neg)
pairs(d)
m.mask=map(
alist(
y~dnorm(mu,sigma),
mu<-a+b1*x_pos+b2*x_neg,
a~dnorm(0,10),
c(b1,b2)~dnorm(0,1),
sigma~dunif(0,10)
),data=d
)
precis_plot(precis(m.mask))
op=par(mfrow=c(2,2))
# plot using both variables]
mu=link(m.mask)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m.mask)
sim.y.PI=apply(mu,2,PI,prob=0.89)
plot(mu.mean~d$y,ylab="Predicted",xlab="Observed",col=rangi2)
abline(a=0,b=1,lty=2)
# plot x_post (x) and y (y) with holding x_neg constat at its mean
x_neg.ave=mean(d$x_neg)
x_pos.seq=seq(from=-4,to=4,length.out = 50)
pred.data=data.frame(x_pos=x_pos.seq,x_neg=x_neg.ave)
mu=link(m.mask,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m.mask,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(y~x_pos,data=d,col=rangi2)
lines(x_pos.seq,mu.mean)
shade(mu.PI,x_pos.seq)
shade(sim.y.PI,x_pos.seq)
# plot x_neg with holding x_pos constant at its mean
x_pos.ave=mean(d$x_pos)
x_neg.seq=seq(from=-4,to=4,length.out=50)
pred.data=data.frame(x_pos=x_pos.ave,x_neg=x_neg.seq)
mu=link(m.mask,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m.mask,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(y~x_neg,data=d,col=rangi2)
lines(x_neg.seq,mu.mean)
shade(mu.PI,x_neg.seq)
shade(sim.y.PI,x_neg.seq)
#5M4
data("WaffleDivorce")
d=WaffleDivorce
head(d)
unique(d$Location)
str(d)
d$pct_LDS <- c(0.75, 4.53, 6.18, 1, 2.01, 2.82, 0.43, 0.55, 0.38,
0.75, 0.82, 5.18, 26.35, 0.44, 0.66, 0.87, 1.25, 0.77, 0.64, 0.81,
0.72, 0.39, 0.44, 0.58, 0.72, 1.14, 4.78, 1.29, 0.61, 0.37, 3.34,
0.41, 0.82, 1.48, 0.52, 1.2, 3.85, 0.4, 0.37, 0.83, 1.27, 0.75,
1.21, 67.97, 0.74, 1.13, 3.99, 0.92, 0.44, 11.5 )
# standardize predctors
d$Marriage.s=(d$Marriage-mean(d$Marriage))/sd(d$Marriage)
d$MedianAge.s=(d$MedianAgeMarriage-mean(d$MedianAgeMarriage))/sd(d$MedianAgeMarriage)
d$pct_LDS.s=(d$pct_LDS-mean(d$pct_LDS))/sd(d$pct_LDS)
# fit model
m5M4=map(
alist(
Divorce~dnorm(mu,sigma),
mu<-a+b1*Marriage.s+b2*MedianAge.s+b3*pct_LDS.s,
a~dnorm(0,10),
c(b1,b2,b3)~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis_plot(precis(m5M4))
str(d)
names(d)
d1=d[,c(7,15,16,17)]
head(d1)
pairs(d1)
#5H1
data("foxes")
d=foxes
head(d)
str(d)
pairs(d)
# weight ~ area
d$area.c=d$area-mean(d$area)
m5h1=map(
alist(
weight~dnorm(mu,sigma),
mu<-a+b*area,
a~dnorm(4,2),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
post=extract.samples(m5h1)
area.seq=seq(from=1,to=6,length.out = 50)
mu=sapply(area.seq,function(area) post$a+post$b)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
plot(weight~area,d,col=rangi2)
lines(area.seq,mu.mean)
shade(mu.PI,area.seq)
precis_plot(precis(m5h1));mtext("Weight~area")
# weight ~ groupsize
str(d)
plot(weight~groupsize,d,col=rangi2)
m5h1=map(
alist(
weight~dnorm(mu,sigma),
mu<-a+b*groupsize,
a~dnorm(4,2),
b~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5h1)
post=extract.samples(m5h1)
gsize.seq=seq(from=2,to=8,length.out = 50)
mu=sapply(gsize.seq,function(groupsize) post$a+post$b)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
lines(gsize.seq,mu.mean)
shade(mu.PI,gsize.seq)
precis_plot(precis(m5h1));mtext("Weight~groupsize")
#5H2
pairs(d[,c(3:5)])
m5h2=map(
alist(
weight~dnorm(mu,sigma),
mu<-a+b1*groupsize+b2*area,
a~dnorm(4,2),
c(b1,b2)~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis_plot(precis(m5h2))
# holding groupsize constant at its mean
gsize.ave=mean(d$groupsize)
area.seq=seq(from=1,to=6,length.out=50)
pred.data=data.frame(groupsize=gsize.ave,area=area.seq)
mu=link(m5h2,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h2,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
# plot
plot(weight~area,d,col=rangi2)
lines(area.seq,mu.mean)
shade(mu.PI,area.seq)
shade(sim.y.PI,area.seq)
mtext("Holding groupsize constant")
#holiding area constant at its mean
area.ave=mean(d$area)
gsize.seq=seq(from=1,to=9,length.out = 50)
pred.data=data.frame(groupsize=gsize.seq,area=area.ave)
mu=link(m5h2,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h2,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
# plot
plot(weight~groupsize,d,col=rangi2)
lines(gsize.seq,mu.mean)
shade(mu.PI,gsize.seq)
shade(sim.y.PI,gsize.seq)
mtext("holding area constat")
#5H3
pairs(d)
m5h3.1=map(
alist(
weight~dnorm(mu,sigma),
mu<-a+b1*avgfood+b2*groupsize,
a~dnorm(4,2),
c(b1,b2)~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5h3.1,corr=TRUE)
precis_plot(precis(m5h3.1))
# holding avgfood at constant
avgfood.ave=mean(d$avgfood)
gsize.seq=seq(from=1,to=9,length.out = 50)
pred.data=data.frame(avgfood=avgfood.ave,groupsize=gsize.seq)
mu=link(m5h3.1,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h3.1,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(weight~groupsize,d,col=rangi2);mtext("Holding avgfood constant")
lines(gsize.seq,mu.mean)
shade(mu.PI,gsize.seq)
shade(sim.y.PI,gsize.seq)
# Holding groupsize constant
gsize.ave=mean(d$groupsize)
avgfood.seq=seq(from=0.2,to=1.4,length.out = 50)
pred.data=data.frame(avgfood=avgfood.seq,groupsize=gsize.ave)
mu=link(m5h3.1,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h3.1,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(weight~avgfood,d,col=rangi2);mtext("holding groupsize constant")
lines(avgfood.seq,mu.mean)
shade(mu.PI,avgfood.seq)
shade(sim.y.PI,avgfood.seq)
# posterior prediction
mu=link(m5h3.1)
mu.mean=apply(mu,2,mean) #predicted
plot(mu.mean~d$weight,col=rangi2,xlab="Observed",ylab="Predicted");mtext("Obs vs Pred Weight")
abline(a=0,b=1)
##
m5h3.2=map(
alist(
weight~dnorm(mu,sigma),
mu<-a+b1*avgfood+b2*groupsize+b3*area,
a~dnorm(4,2),
c(b1,b2,b3)~dnorm(0,1),
sigma~dunif(0,10)
),data=d)
precis(m5h3.2,corr=TRUE)
precis_plot(precis(m5h3.2,corr=TRUE))
# Holding avgfood and groupsize constant
avgfood.ave=mean(d$avgfood)
gsize.ave=mean(d$groupsize)
area.seq=seq(from=1,to=6,length.out=50)
pred.data=data.frame(avgfood=avgfood.ave,groupsize=gsize.ave,area=area.seq)
mu=link(m5h3.2,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h3.2,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(weight~area,d,col=rangi2);mtext("Holiding avgfood & groupsize constant",cex=0.8)
lines(area.seq,mu.mean)
shade(mu.PI,area.seq)
shade(sim.y.PI,area.seq)
# Holding avgfood and area constant
avgfood.ave=mean(d$avgfood)
area.ave=mean(d$area)
gsize.seq=seq(from=1,to=9,length.out = 50)
pred.data=data.frame(avgfood=avgfood.ave,groupsize=gsize.seq,area=area.ave)
mu=link(m5h3.2,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h3.2,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(weight~groupsize,d,col=rangi2);mtext("holding avgfood & area constant",cex=0.8)
lines(gsize.seq,mu.mean)
shade(mu.PI,gsize.seq)
shade(sim.y.PI,gsize.seq)
# Holding groupsize and area constant
gsize.ave=mean(d$groupsize)
area.ave=mean(d$area)
avgfood.seq=seq(from=0.2,to=1.4,length.out = 50)
pred.data=data.frame(avgfood=avgfood.seq,groupsize=gsize.ave,area=area.ave)
mu=link(m5h3.2,data=pred.data)
mu.mean=apply(mu,2,mean)
mu.PI=apply(mu,2,PI,prob=0.89)
sim.y=sim(m5h3.2,data=pred.data)
sim.y.PI=apply(sim.y,2,PI,prob=0.89)
plot(weight~avgfood,d,col=rangi2);mtext("holding groupsize & area constant",cex=0.8)
lines(avgfood.seq,mu.mean)
shade(mu.PI,avgfood.seq)
shade(sim.y.PI,avgfood.seq)
# obs vs predicted plot
mu=link(m5h3.2)
mu.mean=apply(mu,2,mean)
plot(mu.mean~d$weight,col=rangi2,xlab="Observed",ylab="Predicted");mtext("Obs vs Pred Weight")
abline(a=0,b=1)
|
9e7df1607075a39ffd944df777f1f3f635649e3c
|
9802eec0484e38be543320d3b40cf6993ac27e47
|
/tests/testthat.R
|
a20dd2811727f84397cf0dc71ce80d4a5a094d4b
|
[] |
no_license
|
ijlyttle/datadict
|
31e861930f5ade672ceca6501e49952a2981ba85
|
34abba8bd50e3db0f42e01a059dbe7dc4047b9ef
|
refs/heads/master
| 2021-01-22T16:25:19.994574
| 2014-08-11T13:08:22
| 2014-08-11T13:08:22
| 22,684,355
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 59
|
r
|
testthat.R
|
library(testthat)
library(datadict)
test_check("datadict")
|
eee75dcd86db61a01ca8108fb61d59e06a8d0b0b
|
2c058880d02be07bd9ee7a9e273752ff930075f8
|
/cachematrix.R
|
616cd8f96f26841a9cf9b959145c7f7c0adc7e94
|
[] |
no_license
|
preisacher/ExData_Plotting1
|
9ce612087070a83193ca8276de2e417719c1cc39
|
a50d49f52900b5460c5a953724b709ad4eb07a4c
|
refs/heads/master
| 2020-12-25T03:00:49.840150
| 2016-01-26T13:45:57
| 2016-01-26T13:45:57
| 42,243,338
| 0
| 0
| null | 2015-09-10T12:34:22
| 2015-09-10T12:34:22
| null |
UTF-8
|
R
| false
| false
| 884
|
r
|
cachematrix.R
|
## These functions are very closely immitating the examples we
## were given using vectors. All I really did was to change from
## using Vectors to a Matrix.
##
## The makeCacheMatrix returns a list of functions that can be used to return
## its inverse ... solve.
makeCacheMatrix <- function(x = matrix()) {
m<-NULL
set<-function(y){
x <<- y
m <<- NULL
}
get <- function() x
setmatrix <- function(solve) m <<- solve
getmatrix <- function() m
list(set = set,
get = get,
setmatrix = setmatrix,
getmatrix = getmatrix)
}
## This function computes the invers of the matrix that is returned by
## the makeCacheMatrix function
cacheSolve <- function(x, ...) {
m<-x$getmatrix()
if(!is.null(m)){
message("getting cached data")
return(m)
}
matrix<-x$get()
m<-solve(matrix, ...)
x$setmatrix(m)
## Return the inverse of the matrix
m
}
|
0938fb7d7514875d826e229be67b19534b5a87ea
|
e4a073e4785f3415e46907c302da09680cd064ff
|
/cutting_floor/email.R
|
fe81fcb40b80ca15789e19d518cff4a8515371bb
|
[] |
no_license
|
sandeepgangarapu/COVID-DAILY-STATSLETTER
|
e15b280a1da3bea32ab18968728106bf1bbaf6b4
|
5cfd065e31e9309f1ed33b7f319128ab3458a2eb
|
refs/heads/master
| 2021-05-17T06:48:37.217485
| 2020-04-03T11:42:16
| 2020-04-03T11:42:16
| 250,682,036
| 0
| 2
| null | 2020-04-05T08:19:04
| 2020-03-28T00:39:54
|
Python
|
UTF-8
|
R
| false
| false
| 607
|
r
|
email.R
|
library(gmailr)
library(googlesheets4)
email_list = read_sheet("https://docs.google.com/spreadsheets/d/1HI0llUuuVHRTSnMacgnIVzQmYgZGUckQ2cRMhPTA7QM/")
setwd("G:\\My Drive\\Projects\\COVID")
#gm_auth_configure(path = "G:\\My Drive\\Projects\\COVID\\credentials.json")
html_msg <- gm_mime() %>%
gm_bcc(email_list$email_id) %>%
gm_from("sandeepgangarapu.iitkgp@gmail.com") %>%
gm_subject("Latest COVID stats - Automatically Generated Email") %>%
gm_text_body("If you see a bug, Please reply to this email with the issue.") %>% gm_attach_file("newsletter_generator.pdf")
gm_send_message(html_msg)
|
afadcd52356a814b133a03023f1d2b0d6878fdda
|
d0a7960cae8a536457425921ae8913516a24a824
|
/scripts/res_themes.R
|
04dd86d87c0465d4c34f223b075bc7dc0424b3e5
|
[
"MIT"
] |
permissive
|
dampierch/herv
|
6ab3cb82c2b103d0335c51c4cd84eb80656b6e29
|
9f1ce0e676977b6c8d25fdf446c0807826b80bea
|
refs/heads/master
| 2023-03-09T02:01:52.970776
| 2021-02-23T19:40:00
| 2021-02-23T19:40:00
| 284,400,012
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,153
|
r
|
res_themes.R
|
## to be sourced in results.R
ggp_theme_default <- theme(
panel.background=element_rect(fill="white"),
panel.grid.major=element_line(color="white"),
panel.grid.minor=element_line(color="white"),
plot.margin=margin(t=1, r=1, b=1, l=1, unit="lines"),
plot.title=element_text(size=12, face="bold", hjust=0.5),
plot.subtitle=element_text(size=10, face="plain", hjust=0.5),
axis.title.x=element_text(size=10, face="plain"),
axis.title.y=element_text(size=10, face="plain"),
axis.text.x=element_text(size=9, face="plain"),
axis.text.y=element_text(size=9, face="plain"),
# axis.ticks.x=element_blank(),
# axis.ticks.y=element_blank(),
axis.line.x.bottom=element_line(),
axis.line.y.left=element_line(),
legend.key=element_rect(fill="white"),
legend.position="top",
legend.title=element_blank(),
strip.background=element_rect(fill="black"),
strip.text=element_text(colour="white")
)
ggp_theme_col <- ggp_theme_default +
theme(
plot.margin=margin(t=0.5, r=0.5, b=0.5, l=0.5, unit="lines"),
plot.title=element_text(size=12, face="plain", hjust=0.5),
axis.text.x=element_text(size=10, face="plain", angle=45, hjust=1),
axis.text.y=element_text(size=10, face="plain"),
axis.line.x.bottom=element_blank(),
axis.line.y.left=element_blank(),
legend.position="none"
)
ggp_theme_vol <- ggp_theme_default +
theme(
plot.margin=margin(t=0.25, r=0.25, b=0.25, l=0.25, unit="lines"),
legend.key=element_blank(),
legend.key.size=unit(0.001, "cm"),
legend.key.width=unit(0.001, "cm"),
legend.position=c(0.18, 0.9),
legend.spacing.x=unit(0.001, "cm"),
legend.spacing.y=unit(0.001, "cm"),
legend.text=element_text(size=8, margin=margin(t=0.001))
)
ggp_theme_box <- ggp_theme_default +
theme(
plot.margin=margin(t=0.5, r=1, b=0.5, l=1, unit="lines"),
axis.text.x=element_text(size=9, face="plain", angle=45, hjust=1),
axis.text.y=element_text(size=10, face="plain"),
legend.position="none",
legend.key=element_blank(),
legend.key.size=unit(0.75, "cm"),
legend.key.width=unit(0.75, "cm"),
legend.spacing.x=unit(0.5, "cm"),
legend.spacing.y=unit(0.5, "cm"),
legend.text=element_text(size=9, margin=margin(t=0.1))
)
ggp_theme_heat <- ggp_theme_default +
theme(
# axis.title.x=element_text(size=10, face="plain"),
# axis.title.y=element_text(size=10, face="plain"),
axis.text.x=element_text(size=9, face="plain"),
axis.text.y=element_text(size=9, face="plain"),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
# axis.text.x=element_blank(),
# axis.text.y=element_blank(),
axis.ticks.x=element_blank(),
axis.ticks.y=element_blank(),
axis.line.x.bottom=element_blank(),
axis.line.y.left=element_blank(),
legend.position="top",
legend.title=element_text(size=9, face="plain")
)
ggp_theme_charac <- ggp_theme_default +
theme(
panel.border=element_rect(colour="black", size=1.0, fill=NA),
plot.margin=margin(t=0.5, r=0.5, b=0.5, l=0.5, unit="lines"),
axis.text.x=element_text(size=8, face="plain"),
axis.text.y=element_text(size=8, face="plain"),
strip.text=element_text(colour="white"),
axis.line.x.bottom=element_blank(),
axis.line.y.left=element_blank(),
legend.position="none"
)
draw_key_default <- function(data, params, size) {
## this function is here in case GeomBar$draw_key must be manipulated and
## then reset once occasion for altered parameters done
## https://stackoverflow.com/questions/11366964/is-there-a-way-to-change-the-spacing-between-legend-items-in-ggplot2
if (is.null(data$size)) {
data$size <- 0.5
}
lwd <- min(data$size, min(size) / 4)
grid::rectGrob(
width = grid::unit(1, "npc") - grid::unit(lwd, "mm"),
height = grid::unit(1, "npc") - grid::unit(lwd, "mm"),
gp = grid::gpar(
col = data$colour %||% NA,
fill = alpha(data$fill %||% "grey20", data$alpha),
lty = data$linetype %||% 1,
lwd = lwd * .pt,
linejoin = params$linejoin %||% "mitre",
lineend = if (identical(params$linejoin, "round")) "round" else "square"
)
)
}
draw_key_large <- function(data, params, size) {
## this function is here in case GeomBar$draw_key must be reset to allow for
## larger spacing between legend keys; not used in this script
## https://stackoverflow.com/questions/11366964/is-there-a-way-to-change-the-spacing-between-legend-items-in-ggplot2
lwd <- min(data$size, min(size) / 4)
grid::rectGrob(
width = grid::unit(0.6, "npc"),
height = grid::unit(0.6, "npc"),
gp = grid::gpar(
col = data$colour,
fill = alpha(data$fill, data$alpha),
lty = data$linetype,
lwd = lwd * .pt,
linejoin = "mitre"
)
)
}
|
51522325be929343b492c194fccd8d7949c8dda8
|
3156a7a4033706a8db408fa52d8694d41cc36078
|
/R/multigraph.R
|
6f54c7a18c5cc6968c9cfc896ac338d5631b3585
|
[] |
no_license
|
cran/netCoin
|
d38db4ec39ea0049c475749c3f3b53a88e2a8a51
|
b81ebd9501cf53c5ef2770a715a6e072ef265ac6
|
refs/heads/master
| 2023-04-02T22:45:46.863944
| 2023-03-23T21:40:02
| 2023-03-23T21:40:02
| 77,029,038
| 3
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 812
|
r
|
multigraph.R
|
multigraphCreate <- function(..., mode = c("default","parallel","frame"),
mfrow = c(1,2),
frame = 0, speed = 50, loop = FALSE, lineplots = NULL,
dir = NULL, show = FALSE){
diraux <- NULL
if(!is.null(dir) && !identical(show,TRUE)){
diraux <- dir
}
mode <- substr(mode[1],1,1)
if(mode=="p"){
obj <- rd3_multigraph(..., mfrow=mfrow, dir=diraux)
}else if(mode=="f"){
obj <- evolNetwork_rd3(..., frame, speed, loop, lineplots, diraux)
}else{
obj <- rd3_multigraph(..., dir=diraux)
}
if(identical(show,TRUE)){
if(is.null(dir)){
plot(obj)
}else{
plot(obj,dir=dir)
}
}
class(obj) <- c("mGraph",class(obj))
return(obj)
}
multiPages <- rd3_multiPages
addImage <- rd3_addImage
addDescription <- rd3_addDescription
|
d0ab966e7a657eaf632a9cede4ece1c6f4f5fd14
|
dcb76ed15a952660c4546916f8ef9e111f496884
|
/R/machine-ls.R
|
0971b6226912587c637300f81ef221be52354b3a
|
[] |
no_license
|
DavisVaughan/machinegun
|
b8bea1d56d278a6a28cc4daf37e67ddd9426c6fd
|
128898cead1c4dfc23785aa0a7ad45bc5817a3dd
|
refs/heads/master
| 2020-03-26T10:16:07.139337
| 2018-08-16T02:32:36
| 2018-08-16T02:32:36
| 144,789,195
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 112
|
r
|
machine-ls.R
|
#' List all docker-machine instances
#'
#' @export
machine_ls <- function(...) {
machine_command("ls", ...)
}
|
5950f52a372220f54d585f3d8b50b1c2ab223cd8
|
7c47b764ee111e5ef3f0b3e3aa4b284dd6cda770
|
/man/create_spp_occurence_raster_list.Rd
|
8bae1cdeaa96439786fd73419e086126531f4e4f
|
[
"MIT"
] |
permissive
|
Vizzuality/vspt
|
845476a6fe1556015b59392003d4966e4d963051
|
c5d927ea031519733af4d60516d239a1b0c2b438
|
refs/heads/master
| 2021-07-12T13:25:19.277790
| 2020-10-28T15:34:19
| 2020-10-28T15:34:19
| 216,042,807
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 700
|
rd
|
create_spp_occurence_raster_list.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_spp_occurence_raster_list.R
\name{create_spp_occurence_raster_list}
\alias{create_spp_occurence_raster_list}
\title{Create spp presence-absence raster(s)}
\usage{
create_spp_occurence_raster_list(spp_occurence_sfc_list, r, land_mask)
}
\arguments{
\item{r}{Raster object to use as base for adding presence-absence}
\item{land_mask}{sf::sf polygon land mask}
\item{spp_occurence_sf_list}{List of sf::sfc objects with spp occurence}
}
\value{
A list of raster::raster objects representing presence (1) and absence (0) of a species, and NA values for no data.
}
\description{
Create spp presence-absence raster(s)
}
|
bb289de82567ae04fc8421575ae05b9d1c8881ef
|
f5ad30ec784cccd03371242a63b99f265bf98dc5
|
/R/sprintf-ca-function.R
|
74c0cd96b744ee564e44c59f8bbe32eb4a69bf9f
|
[] |
no_license
|
claus-e-andersen/clanTools
|
cf93ec1b81b38277f90eaab7901a8f82b2ffa847
|
dbbca750aba52b08c99d43a2a49a05810e2d0630
|
refs/heads/master
| 2021-07-06T09:22:18.006123
| 2020-10-17T04:20:21
| 2020-10-17T04:20:21
| 22,555,505
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,804
|
r
|
sprintf-ca-function.R
|
#' @title Print number to fit certain format. An alternative is used if this requires too many characters.
#' @description
#' This is a variation of sprintf, that checks if the formated result is too long.
#' This can happen, for example, with electrometer output, where normal results are
#' 0.004 pC and then an overflow gives a charge of 6e47 C, which produces an output
#' that ruins txtplot table output.
#' @usage sprintf.ca("\%.5f", c(1.23, 2.33, 1e99), max.chars=7)
#' @name sprintf.ca
#' @author Claus E. Andersen
#' @return formated number.
#' @param format is the main format (e.g. "\%.5f") which is used if the output does not require too many characters.
#' @param number to print (e.g. 1.334 or 1.4e99).
#' @param format.alt is the alternative format (e.g. "\%.4e") which is used for output that require too many characters.
#' @param max.char is the maximum number of characters (e.g. 10) that the main format needs to fit (otherwise the alternative format will be used).
#' @export sprintf.ca
sprintf.ca <- function(format,number,format.alt="%.4e",max.chars=10){
# This is a variation of sprintf, that checks if the formated
# results is too long. This can happen, for example, with electrometer
# output, where normal results are 0.004 pC and then an overflow
# gives a charge of 6e47 C, which produces an output that ruins
# txtplot table output.
# Created: January 12, 2019
# Claus E. Andersen
# Sample calls:
# x <- 3.4557e-3
# sprintf.ca("%.5f", round(x,0))
# x <- 6.6e42
# sprintf.ca("%.5f", round(x,0))
# x <- 6.6e42
# sprintf.ca("%.5f", round(x,0),max.chars=9999)
y.main <- sprintf(format, number)
y.alt <- sprintf(format.alt, number)
res <- y.main
ok <-nchar(y.main)>max.chars
if(sum(ok)>0){res[ok]<-y.alt[ok]}
res
}# sprintf.ca
|
312a0903d5e2ec8d659a09fe65735b2788bbb61f
|
28d02ce963df64a6802d003f40a5fb4a1e987b5f
|
/10.27_PseudotimeClassification/virtualtrend.R
|
02685fe1cd071c520bd8385a026eae1883ec299d
|
[] |
no_license
|
Zhouzhiling/R
|
b31586a13a0e3e2557bb519ef73d0fc9b6e5c87b
|
6d46af4117b6c8f5b013d0c02b517a84cfb756d8
|
refs/heads/master
| 2021-05-16T10:19:46.904628
| 2017-10-27T15:57:28
| 2017-10-27T15:57:28
| 104,708,872
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,945
|
r
|
virtualtrend.R
|
rm(list=ls())
src <- read.table(file.choose(),head=T,sep="\t")
src
src <- src[,order(src[1,])]
#setwd("C:/Users/37908/Desktop/")
#jpeg(file="1027.jpg",width=20000,height=10000)
#plot(src[1,])
#dev.off()
src_trim <-as.matrix(src[,-rep(1:26)])
low_quant <- ceiling(ncol(src_trim) / 3)
high_quant <- ceiling(ncol(src_trim) / 3) * 2
low_quant_num <- src_trim[1,low_quant]
high_quant_num <- src_trim[1,high_quant]
persutime <- src_trim[1,]
src_trim <- src_trim[-1,]
gene_name <- row.names(src_trim)
flag <- matrix(0,nrow=nrow(src_trim),ncol=3)
count1 <- 0
count2 <- 0
count3 <- 0
#归类求各个时期个个基因表达量的和
for(j in 1:length(persutime))
{
if(persutime[j] < low_quant_num)
for(i in 1:nrow(src_trim))
{
if(persutime[j] < low_quant_num)
{
flag[i,1] = flag[i,1]+src_trim[i,j]
count1 <- count1 + 1
}
}
else if(persutime[j] < high_quant_num)
{
for(i in 1:nrow(src_trim))
{
flag[i,2] = flag[i,2]+src_trim[i,j]
count2 <- count2 + 1
}
}
else
for(i in 1:nrow(src_trim))
{
{
flag[i,3] = flag[i,3]+src_trim[i,j]
count3 <- count3 + 1
}
}
}
row.names(flag) <- gene_name
#把全为0的删掉先
tmp_flag <- matrix(0,nrow=nrow(flag),ncol=3)
tmp_gene_frame <- matrix("",nrow=nrow(flag),ncol=1)
count_no_zero <- 0
#is.numeric(flag)
for(k in 1:nrow(flag))
{
if((flag[k,1]!=0)||(flag[k,2]!=0)||(flag[k,3]!=0))
{
tmp_flag[count_no_zero,] <- flag[k,]
tmp_gene_frame[count_no_zero,1] <- gene_name[k]
count_no_zero <- count_no_zero + 1
}
}
#基因三阶段数量
flag_no_zero <- tmp_flag[rep(1:19157),]
#基因名称
tmp_gene_frame <- tmp_gene_frame[rep(1:19157),1]
row.names(flag_no_zero) <- tmp_gene_frame
#得到和之后求平均值
flag_avg <- matrix(0,nrow=nrow(flag_no_zero),ncol=3)
for(i in 1:nrow(flag_no_zero))
{
{
flag_avg[i,1] = flag_no_zero[i,1] * 1000000 / count1
flag_avg[i,2] = flag_no_zero[i,2] * 1000000 / count2
flag_avg[i,3] = flag_no_zero[i,3] * 1000000 / count3
}
}
row.names(flag_avg) <- tmp_gene_frame
#取样看个例子w
setwd("C:/Users/37908/Desktop/")
jpeg(file="output.jpg",width=2500,height=500)
par(mfrow=c(3,5))
flag_avg_mat <- data.frame(flag_avg)
for(i in 1:15)
plot(x=c(0,4,19),y=flag_avg_mat[i,], type="l",xlab="time",ylab="amount",main=row.names(flag_avg_mat)[i],cex.main=2)
dev.off()
#导出成表格，备用查询具体细节
write.csv(flag_avg,file="C:/Users/37908/Desktop/classify.csv",row.names=TRUE)
#?write.csv
class <- matrix("",nrow=nrow(flag_avg) , ncol=9 )
# d means decrease
# k means keep stable
# i means increase
dd_num <- 0
di_num <- 0
dk_num <- 0
id_num <- 0
ii_num <- 0
ik_num <- 0
kd_num <- 0
ki_num <- 0
kk_num <- 0
#确认是否是数字类型
#is.numeric(flag_avg)
error = 0.1
for(i in 1:nrow(flag_avg))
# for(i in 1:8)
{
# 从第一到第二阶段降
if(flag_avg[i,2] < flag_avg[i,1]*(1-error))
{
if(flag_avg[i,2] > flag_avg[i,3]*(1+error) )
{
dd_num <- dd_num + 1
#第一类,降降
class[dd_num,1] <- tmp_gene_frame[i]
}
if(flag_avg[i,3] > flag_avg[i,2]*(1+error) )
{
di_num <- di_num + 1
#第二类，降升
class[di_num,2] <- tmp_gene_frame[i]
}
if(flag_avg[i,3]<= flag_avg[i,2]*(1+error) && flag_avg[i,3]>=flag_avg[i,2]*(1-error))
{
dk_num <- dk_num + 1
#第三类，降平
class[dk_num,3] <- tmp_gene_frame[i]
}
}
# 从第一到第二阶段升
if(flag_avg[i,2] > flag_avg[i,1]*(1+error))
{
if(flag_avg[i,3] < flag_avg[i,2]*(1-error) )
{
id_num <- id_num + 1
#第四类,升降
class[id_num,4] <- tmp_gene_frame[i]
}
if(flag_avg[i,3] > flag_avg[i,2]*(1+error) )
{
ii_num <- ii_num + 1
#第五类，升升
class[ii_num,5] <- tmp_gene_frame[i]
}
if(flag_avg[i,3]<= flag_avg[i,2]*(1+error) && flag_avg[i,3]>=flag_avg[i,2]*(1-error))
{
ik_num <- ik_num + 1
#第六类，升平
class[ik_num,6] <- tmp_gene_frame[i]
}
}
#从第一到第二阶段平
if(flag_avg[i,2] <= flag_avg[i,1]*(1+error) && flag_avg[i,2] >= flag_avg[i,1]*(1-error))
{
if(flag_avg[i,3] < flag_avg[i,2]*(1-error) )
{
kd_num <- kd_num + 1
#第七类,平降
class[kd_num,7] <- tmp_gene_frame[i]
}
if(flag_avg[i,3] > flag_avg[i,2]*(1+error) )
{
ki_num <- ki_num + 1
#第八类，平升
class[ki_num,8] <- tmp_gene_frame[i]
}
if(flag_avg[i,3]<= flag_avg[i,2]*(1+error) && flag_avg[i,3]>=flag_avg[i,2]*(1-error))
{
kk_num <- kk_num + 1
#第九类，平平
class[kk_num,9] <- tmp_gene_frame[i]
}
}
}
|
c149b7aeabe98d312a4945a97e2c51b25813619a
|
1cc4e9886e3bc4614c8105d48b7e98e209948cf5
|
/Rscripts/functions.R
|
0971e222cbdadbd8e7d1077b4eb09ad3ea81c777
|
[] |
no_license
|
mikaells/Find-pBGCs
|
55108bb349346e856f31ca424dc4d7773d75fb15
|
f59e7b25bdc94592735733076634ecf3b491770b
|
refs/heads/master
| 2023-07-09T07:43:39.796862
| 2021-08-16T18:55:02
| 2021-08-16T18:55:02
| 299,373,323
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,515
|
r
|
functions.R
|
color.gradient <- function(x, colors=c("grey90","red"), colsteps=100) {
return( colorRampPalette(colors) (colsteps) [ findInterval(x, seq(min(x),max(x), length.out=colsteps)) ] )
}
layout.by.attr <- function(graph, wc, cluster.strength=1,layout=layout.auto) {
g <- graph.edgelist(get.edgelist(graph)) # create a lightweight copy of graph w/o the attributes.
E(g)$weight <- 1
attr <- cbind(id=1:vcount(g), val=wc)
g <- g + vertices(unique(attr[,2])) + igraph::edges(unlist(t(attr)), weight=cluster.strength)
l <- layout(g, weights=E(g)$weight)[1:vcount(g),]
return(l)
}
spreadClus=function(L,transTab, doKleb=T, minSize=16) {
#find major clusters
dbclus=dbscan::dbscan(L, eps = 1.5)
#save them with main table
translationTable2$dbcan=dbclus$cluster
#have a look
#plot(L, col=0)#translationTable2$dbcan+1)
#make a new layout to overwrite
l2=L
#klebsiella is to close to escherichia
#finding indeces of both
klebIndx=which(translationTable2$dbcan==7)
entIndx=which(translationTable2$mainNames=="Escherichia")
#finding cluster centers
klebCent=apply(l[klebIndx,],MARGIN = 2,mean)
eschCent=apply(l[which(translationTable2$mainNames=="Escherichia"),], MARGIN = 2,mean)
#estimate position of kleb relative to esch
xx=(klebCent[1]-eschCent[1])
yy=(klebCent[2]-eschCent[2])
#dd=sqrt(xx^2+yy^2)
#move klebsiella away from esch
#if relative position is positive move more positive and vice versa
l2[klebIndx,1]=l[klebIndx,1] +c(ifelse(xx>1,2,-2))
l2[klebIndx,2]=l[klebIndx,2] +c(ifelse(yy>1,2,-2))
#plot again
plot(L, col=1)#translationTable2$dbcan+1,pch =16, cex =.1)
j=3
#spreading out clusters
#for all clusters in graph, do
for(j in unique(translationTable2$dbcan)) {
if(j==0) next #if cluster is just the non-clustered
#find index of cluster members and calculate centroid
dbIndx=which(translationTable2$dbcan==j)
dbCent=c(mean(l[dbIndx,1]),mean(l[dbIndx,2]))
#if cluster is too small, next
if(length(dbIndx)<minSize) next
#for all cluster members, do
for(i in (dbIndx)){
#find relative position and distance to center
xx=(l[i,][1]-dbCent[1])
yy=(l[i,][2]-dbCent[2])
dd=sqrt(xx^2+yy^2)
#to each point, add max three but less if cluster is small
#to pull away from center
#l2[i,]=l[i,]+(3-(3/(1*(length(dbIndx))^(1/10))))*c(xx,yy)
l2[i,]=l[i,]+(0.2*sqrt(length(dbIndx)))*c(xx,yy)
points(x = l2[i,1],y=l2[i,2], pch=16,cex=0.1,col=translationTable2$dbcan[i]+1)
}
}
return(l2)
}
# triangle vertex shape
mytriangle <- function(coords, v=NULL, params) {
vertex.color <- params("vertex", "color")
if (length(vertex.color) != 1 && !is.null(v)) {
vertex.color <- vertex.color[v]
}
vertex.size <- 1/200 * params("vertex", "size")
if (length(vertex.size) != 1 && !is.null(v)) {
vertex.size <- vertex.size[v]
}
symbols(x=coords[,1], y=coords[,2], bg=vertex.color,
stars=cbind(vertex.size, vertex.size, vertex.size),
add=TRUE, inches=FALSE)
}
# clips as a circle
add_shape("triangle", clip=shapes("circle")$clip,
plot=mytriangle)
# generic star vertex shape, with a parameter for number of rays
mystar <- function(coords, v=NULL, params) {
vertex.color <- params("vertex", "color")
if (length(vertex.color) != 1 && !is.null(v)) {
vertex.color <- vertex.color[v]
}
vertex.size <- 1/200 * params("vertex", "size")
if (length(vertex.size) != 1 && !is.null(v)) {
vertex.size <- vertex.size[v]
}
norays <- params("vertex", "norays")
if (length(norays) != 1 && !is.null(v)) {
norays <- norays[v]
}
mapply(coords[,1], coords[,2], vertex.color, vertex.size, norays,
FUN=function(x, y, bg, size, nor) {
symbols(x=x, y=y, bg=bg,
stars=matrix(c(size,size/2), nrow=1, ncol=nor*2),
add=TRUE, inches=FALSE)
})
}
# no clipping, edges will be below the vertices anyway
add_shape("star", clip=shape_noclip,
plot=mystar, parameters=list(vertex.norays=5))
# plot(1:60, 4-(3/(1*((1:60))^(1/10))))
# plot(1:60, 0.3*sqrt(1:60))
color.gradient <- function(x, colors=c("grey90","red"), colsteps=100) {
return( colorRampPalette(colors) (colsteps) [ findInterval(x, seq(min(x),max(x), length.out=colsteps)) ] )
}
|
286b1c08937ceab6355320d2442a97456e92294e
|
0137840c620668a93987f291f2d527793dae28f6
|
/scripts/tutorial.R
|
003b9e91322f0b9522a5dc6cffeb75bbca49b663
|
[] |
no_license
|
justinmillar/trevor-strava
|
9271c87246585b49d497bd55b8e2b1fedc07e53f
|
58dfe478d53d96f9dfa3fcf124844f0d510ee41a
|
refs/heads/master
| 2021-05-05T07:07:53.019964
| 2018-01-26T21:38:51
| 2018-01-26T21:38:51
| 118,854,663
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,657
|
r
|
tutorial.R
|
library(strava)
library(tidyverse)
data <- process_data("data/")
# Facets ----
p1 <- plot_facets(data)
ggsave("plots/facets01.png", p1, width = 20, height = 20, units = "cm")
# Activity map ----
p2 <- plot_map(data, lon_min = -87, lon_max = -82, lat_min = 42, lat_max = 45.5)
ggsave("plots/map01.png", p2, width = 20, height = 15, units = "cm", dpi = 600)
# Elevation profile ----
p3 <- plot_elevations(data)
ggsave("plots/elevations01.png", p3, width = 20, height = 20, units = "cm")
# Ridges ----
library(ggridges)
library(lubridate)
library(tidyverse)
# Process the data
# data <- process_data(<gpx file path>)
# Summarise data
data <- data %>%
group_by(id) %>%
summarise(start = min(time), end = max(time)) %>%
mutate(start_time = as.POSIXct(strftime(start, format = "%H:%M:%S"), format = "%H:%M:%S"),
end_time = as.POSIXct(strftime(end, format = "%H:%M:%S"), format = "%H:%M:%S"),
duration = end_time - start_time,
wday = wday(start, week_start = 1))
# Function for processing an activity on a minute-by-minute basis; active = 1, not active = 0
compute_day_curve <- function(df_row) {
start <- as.numeric(data[df_row, "start_time"])
end <- as.numeric(data[df_row, "end_time"])
wday <- as.character(data[df_row, "wday"])
result <- data.frame(time = seq(as.POSIXct("00:00:00", format = "%H:%M:%S"),
as.POSIXct("23:59:58", format = "%H:%M:%S"), by = 60)) %>%
mutate(time_end = lead(time, default = as.POSIXct("23:59:59", format = "%H:%M:%S")),
active = ifelse(time > start & time_end < end, 1, 0), wday = wday)
result
}
# Process all activities
plot_data <- 1:nrow(data) %>%
map_df(~compute_day_curve(.x), .id = "id") %>%
filter(!is.na(active), active > 0) %>%
mutate(wday = as.factor(wday))
plot_data$wday <- factor(plot_data$wday, levels = rev(levels(plot_data$wday)))
# Create plot
p <- ggplot() +
geom_density_ridges(aes(x = time, y = wday), plot_data, size = 0.5) +
theme_ridges() +
scale_y_discrete(expand = c(0.01, 0), labels = c("Sun", "Sat", "Fri", "Thu", "Wed", "Tue", "Mon")) +
scale_x_datetime(expand = c(0, 0), date_labels = "%I:%M %p") +
theme(panel.grid = element_blank(), plot.margin = unit(rep(1, 4), "cm")) +
xlab(NULL) + ylab(NULL)
# Save plot
ggsave("plots/ridges01.png", p, width = 24, height = 20, units = "cm")
# Calendar ----
library(ggart)
library(ggthemes)
library(ggTimeSeries)
library(lubridate)
library(strava)
library(tidyverse)
library(viridis)
data <- process_data("data/")
# Summarise data
summary <- data %>%
mutate(time = lubridate::date(data$time),
year = strftime(data$time, format = "%Y"),
date_without_month = strftime(data$time, format = "%j"),
month = strftime(data$time, format = "%m"),
day_of_month = strftime(data$time, format = "%d"),
year_month = strftime(data$time, format = "%Y-%m")) %>%
group_by(time, year, date_without_month, month, day_of_month, year_month) %>%
summarise(total_dist = sum(dist_to_prev), total_time = sum(time_diff_to_prev)) %>%
mutate(speed = (total_dist) / (total_time /60^2)) %>%
mutate(pace = (total_time / 60) / (total_dist)) %>%
mutate(type = "day") %>%
ungroup %>%
mutate(id = as.numeric(row.names(.)))
# Generate plot data
time_min <- "2015-04-18"
time_max <- today()
max_dist <- 70
daily_data <- summary %>%
group_by(time) %>%
summarise(dist = sum(total_dist)) %>%
ungroup() %>%
mutate(time = lubridate::date(time)) %>%
filter(complete.cases(.), time > time_min, time < time_max) %>%
mutate(dist_scaled = ifelse(dist > max_dist, max_dist, dist))
# Create plot
p <- ggplot_calendar_heatmap(daily_data, "time", "dist_scaled",
dayBorderSize = 0.5, dayBorderColour = "white",
monthBorderSize = 0.75, monthBorderColour = "transparent",
monthBorderLineEnd = "round") +
xlab(NULL) +
ylab(NULL) +
scale_fill_continuous(name = "km", low = "#DAE580", high = "#236327", na.value = "#EFEDE0") +
facet_wrap(~Year, ncol = 1) +
theme_tufte() +
theme(strip.text = element_text(), axis.ticks = element_blank(), legend.position = "bottom")
# Save plot
ggsave("plots/calendar01.png", p, width = 30, height = 30, units = "cm", dpi = 300)
# Circles ----
library(packcircles)
cir_data <- summary %>%
select(id, year, total_dist, speed)
packing <- circleProgressiveLayout(cir_data$total_dist)
cir_data <- cbind(cir_data, packing)
plot(cir_data$radius, cir_data$total_dist)
dat.gg <- circleLayoutVertices(packing, npoints=100)s
ggplot() +
geom_polygon(data = dat.gg, aes(x, y, fill = ))
|
258450d4384da9d9ac34e5c568fe2d3f0f00367e
|
6f9ab236999fff566b0ed76f6fc2146d63e3f7f1
|
/rotations/man/plot.Rd
|
861277a4d7ca2c382073a30aabf92ca5ed2f01c3
|
[
"MIT"
] |
permissive
|
stanfill/rotationsC
|
d4733140b6e40c61b2d9312474c1a8786f1974fb
|
66722f095a68d81e506c29cfac7d4a8a69e664cf
|
refs/heads/master
| 2022-07-22T01:17:54.917073
| 2022-06-24T21:27:24
| 2022-06-24T21:27:24
| 9,582,475
| 0
| 3
| null | 2021-03-11T21:43:23
| 2013-04-21T16:51:07
|
C++
|
UTF-8
|
R
| false
| true
| 2,835
|
rd
|
plot.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot}
\alias{plot}
\alias{plot.SO3}
\alias{plot.Q4}
\title{Visualizing random rotations}
\usage{
\method{plot}{SO3}(
x,
center = mean(x),
col = 1,
to_range = FALSE,
show_estimates = NULL,
label_points = NULL,
mean_regions = NULL,
median_regions = NULL,
alp = NULL,
m = 300,
interactive = FALSE,
...
)
\method{plot}{Q4}(
x,
center = mean(x),
col = 1,
to_range = FALSE,
show_estimates = NULL,
label_points = NULL,
mean_regions = NULL,
median_regions = NULL,
alp = NULL,
m = 300,
interactive = FALSE,
...
)
}
\arguments{
\item{x}{n rotations in \code{SO3} or \code{Q4} format.}
\item{center}{rotation about which to center the observations.}
\item{col}{integer or vector comprised of 1, 2, 3 indicating which column(s)
to display. If \code{length(col)>1} then each eyeball is labelled with the
corresponding axis.}
\item{to_range}{logical; if \code{TRUE} only part of the globe relevant to
the data is displayed}
\item{show_estimates}{character vector to specify which of the four
estimates of the principal direction to show. Possibilities are "all",
"proj.mean", "proj.median", "geom.mean", "geom.median".}
\item{label_points}{vector of labels.}
\item{mean_regions}{character vector to specify which of the three confidence
regions to show for the projected mean. Possibilities are "all",
"trans.theory","trans.bootstrap, "direct.theory", "direct.bootstrap".}
\item{median_regions}{character vector to specify which of the three
confidence regions to show for the projected median. Possibilities are
"all", "theory", "bootstrap."}
\item{alp}{alpha level to be used for confidence regions. See
\code{\link{region}} for more details.}
\item{m}{number of bootstrap replicates to use in bootstrap confidence
regions.}
\item{interactive}{deprecated; \code{sphereplot} was set to be removed from CRAN
and was going to take this package down with it}
\item{...}{parameters passed onto the points layer.}
}
\value{
A visualization of rotation data.
}
\description{
This function produces a static three-dimensional globe onto
which one of the columns of the provided sample of rotations is projected.
The data are centered around a user-specified rotation matrix. The static
plot uses \code{ggplot2}. Interactive plots are no longer supported.
}
\examples{
r <- rvmises(200, kappa = 1.0)
Rs <- genR(r)
plot(Rs, center = mean(Rs), show_estimates = "proj.mean", shape = 4)
\donttest{
# Z is computed internally and contains information on depth
plot(
Rs,
center = mean(Rs),
show_estimates = c("proj.mean", "geom.mean"),
label_points = sample(LETTERS, 200, replace = TRUE)
) +
aes(size = Z, alpha = Z) +
scale_size(limits = c(-1, 1), range = c(0.5, 2.5))
}
}
|
08f9c592f8d465d692cd625850d8818e065c5cfb
|
1204d0e3c990b77ae922038254354c3b660b0cab
|
/Graphs/2D_and.R
|
92c34dce36dd5f767b682b162847667892137ad8
|
[] |
no_license
|
HussamHallak/Soundex_Arabic_Names
|
56174b370c84566245ef3397338d2a5bd6f11812
|
142cf7cd4672c8a84eec09c382fcaf22d1a530b6
|
refs/heads/main
| 2023-02-04T19:07:54.994422
| 2020-12-27T00:42:15
| 2020-12-27T00:42:15
| 324,659,651
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 868
|
r
|
2D_and.R
|
# Create a grouped barplot and add a legend
Libindic_Soundex <- c(P=0.961, R=0.802, TNR=0.999, F=0.874, ACC=0.996, BA=0.901)
Jellyfish_Soundex <- c(P=0.963, R=0.833, TNR=0.999, F=0.894, ACC=0.997, BA=0.917)
Jellyfish_Match_Rating <- c(P=0.988, R=0.657, TNR=0.999, F=0.789, ACC=0.995, BA=0.828)
Fuzzy_NYSIIS <- c(P=0.982, R=0.429, TNR=1.0, F=0.597, ACC=0.991, BA=0.714)
Fuzzy_DMetaphone <- c(P=0.934, R=0.811, TNR=0.999, F=0.868, ACC=0.996, BA=0.905)
all <- rbind(Libindic_Soundex, Jellyfish_Soundex, Jellyfish_Match_Rating, Fuzzy_NYSIIS, Fuzzy_DMetaphone)
barplot(all, beside = TRUE, main = "Metrics based on both directions names matching using phonetic algorithms", xlab = "Metric", ylim = c(0,1), ylab = "Value", col = c("dodgerblue3", "black", "orange", "skyblue1", "red"), legend.text = rownames(all), xlim = c(0,55), args.legend = list(cex=0.8,x = "topright"))
|
ef4c261c544b8fe70aec91ce21bb2f2a42758291
|
5a9d99c0266ced580e9e02bf3839acb2fb8ef49d
|
/man/add_starting_trees_to_xml.Rd
|
dbcaee9925b17e702a8dd7b3bf1571cf99ff310a
|
[] |
no_license
|
emvolz-phylodynamics/sarscov2Rutils
|
1555fe2512057f86ec12f5d192b912121c7b4781
|
6747443599fb391a2f21c5de019d11058dec7e86
|
refs/heads/sarscov2Rutils
| 2023-04-18T23:54:43.415890
| 2020-11-05T12:02:56
| 2020-11-05T12:02:56
| 266,338,998
| 12
| 5
| null | 2021-02-02T19:39:22
| 2020-05-23T13:09:29
|
R
|
UTF-8
|
R
| false
| true
| 1,037
|
rd
|
add_starting_trees_to_xml.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/starttree1.R
\name{add_starting_trees_to_xml}
\alias{add_starting_trees_to_xml}
\title{Make starting trees, insert into beast xml and create ML tree plot}
\usage{
add_starting_trees_to_xml(
xmlfn,
fastafn,
plotout = NULL,
regionDemes = c("Il"),
ntres = 1,
ncpu = 4
)
}
\arguments{
\item{xmlfn}{File name of beast xml}
\item{fastafn}{File name of sequence data (needed for ML tree estimation)}
\item{plotout}{Output file name for ML tree plot. Set NULL to not plot}
\item{regionDemes}{regions to colour in the output ML tree}
\item{ntres}{integer how many start trees to produce? a distinct xml is made for each}
\item{ncpu}{Number of CPUs to use}
}
\value{
Some treedater trees. New XMLs are written to disk
}
\description{
This will generate multiple starting trees by ML & treedater.
Each tree is produced by a different random resolution of polytomies in the ML tree
Sequence names must have sample times included (see prep_tip_labels)
}
|
4c7903ff5a122868df61324446e9a61649b85f47
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/hash/examples/del.Rd.R
|
d650b7bf4fcea782d94af6346fa264dc9889a334
|
[] |
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
| 315
|
r
|
del.Rd.R
|
library(hash)
### Name: del
### Title: Remove key-value pair(s) from a hash
### Aliases: del del-methods del,ANY,hash-method delete delete-methods
### delete,ANY,hash-method
### Keywords: methods data manip
### ** Examples
h <- hash( letters, 1:26 )
h # 26 elements
del( "a", h )
h # 25 elements
|
26cf5ba907ef3adec892dbb3b624b4c324d513fc
|
bbb9d380e3e7af973f69661b0e7693b2fea73da6
|
/man/occupations.Rd
|
7b8a8abdcd4edd8b19e27dd875a139493c1d3597
|
[] |
no_license
|
cran/skillsengineeR
|
55c7fa9a46e3bb588a927ddc6630bd10b039902c
|
a8eacbc9ab7b28f8f62ff974369d8e6ad4425b2d
|
refs/heads/master
| 2023-03-19T06:57:25.246922
| 2021-03-04T08:50:06
| 2021-03-04T08:50:06
| 344,525,243
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 655
|
rd
|
occupations.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_functions.R
\name{occupations}
\alias{occupations}
\title{Pull the full list of occupations}
\usage{
occupations(token, handle_status = "warn", response_raw = FALSE)
}
\arguments{
\item{token}{Authorization token obtained from \code{get_access_token}}
\item{handle_status}{How to handle bad HTTP status. Set as either \code{warn} or \code{error}}
\item{response_raw}{Logical value whether to return the API response as
raw, unparsed text. Defaults to \code{FALSE}}
}
\value{
A data.frame; occupations relevant to query
}
\description{
Pull the full list of occupations
}
|
3f06a9035cb1cb6aeb8377bf67a2e6cf03705754
|
bbc76ad786e92e25ec734ab20d6bcc0e51c1bd9d
|
/man/dtw.Rd
|
5ab1f10fd82aa2056af869104bc61c13f46152bc
|
[] |
no_license
|
akinori-ito/DTW
|
fda888b339c4318069e3cad150545a7f908b2bff
|
6fcc339763fda4bb8837ecfdefda0203f165932e
|
refs/heads/master
| 2020-04-02T03:43:05.556146
| 2016-06-07T00:38:53
| 2016-06-07T00:38:53
| 60,339,033
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 585
|
rd
|
dtw.Rd
|
\name{dtw}
\alias{dtw}
\title{Dynamic Time Warping}
\usage{
dtw(x,y,window=100)
}
\description{
dtw calculates correspondence of rows of two matrices x and y using dynamic time warping algorithm.
}
\details{
Variables x and y should be matrices having the same length of columns. The variable window denotes the calculation window of DTW; if window is large, the result becomes optimum and calculation becomes slower.
Return value is a list.
xsize: number of rows of x.
ysize: number of rows of y.
opt: a two-column matrix that is correspondences of rows of x and y.
}
\examples{
}
|
e71831d208ffcc3ee0936b7488c5b7506b29bad1
|
28f02ab412dcfed615601f747c6d28e654c444f1
|
/technical_analysis-master/michele/code_quantmod/04_miller01.R
|
84126287d0f78e8221caf2f63159fdf3e991f142
|
[
"MIT"
] |
permissive
|
karansoneja/DataScience
|
a7b2a09cd4ca20cd8d51d7fed4f2196ce2d0de84
|
883343eebbb27664e55a89cc9d42bc842ed1a3f7
|
refs/heads/master
| 2022-12-27T12:25:04.192150
| 2020-09-30T17:19:59
| 2020-09-30T17:19:59
| 215,329,005
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,228
|
r
|
04_miller01.R
|
## Miller's code (https://ntguardian.wordpress.com/2017/04/03/introduction-stock-market-data-r-2/)
# library(quantmod)
library(IKTrading)
## ===== get data ==========
start <- as.Date("2010-01-01")
end <- as.Date("2016-10-01")
# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the quantmod function getSymbols, and pass a string as a first argument to identify the desired ticker symbol, pass "yahoo" to src for Yahoo! Finance, and from and to specify date ranges
# The default behavior for getSymbols is to load data directly into the global environment, with the object being named after the loaded ticker symbol. This feature may become deprecated in the future, but we exploit it now.
getSymbols("AAPL", src="yahoo", from = start, to = end)
## ===== some charting with SMA's =========
candleChart(AAPL, up.col = "black",
dn.col = "red", theme = "white",
subset = "2016-01-04/")
AAPL_sma_20 <- SMA(
Cl(AAPL), # The closing price of AAPL, obtained by quantmod's Cl() function
n = 20 # The number of days in the moving average window
)
AAPL_sma_50 <- SMA(
Cl(AAPL),
n = 50
)
AAPL_sma_200 <- SMA(
Cl(AAPL),
n = 200
)
zoomChart("2016") # Zoom into the year 2016 in the chart
addTA(AAPL_sma_20, on = 1, col = "red") # on = 1 plots the SMA with price
addTA(AAPL_sma_50, on = 1, col = "blue")
addTA(AAPL_sma_200, on = 1, col = "green")
## ======== 1. create indicators by comparing SMA crossing ========
AAPL_trade <- AAPL
AAPL_trade$`20d` <- AAPL_sma_20
AAPL_trade$`50d` <- AAPL_sma_50
regime_val <- sigComparison("", data = AAPL_trade,
columns = c("20d", "50d"), relationship = "gt") -
sigComparison("", data = AAPL_trade,
columns = c("20d", "50d"), relationship = "lt")
plot(regime_val["2016"], main = "Regime", ylim = c(-2, 2))
plot(regime_val, main = "Regime", ylim = c(-2, 2))
## regime and main series in the same chart
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white", subset = "2016-01-04/")
addTA(regime_val, col = "blue", yrange = c(-2, 2))
addLines(h = 0, col = "black", on = 3)
addSMA(n = c(20, 50), on = 1, col = c("red", "blue"))
# zoomChart("2016")
## count how many bullish and how many bearish days there were (i.e. buy vs sell)
table(as.vector(regime_val))
## generate signals out of the regimes -- actual triggering of mkt operations
sig <- diff(regime_val) / 2
plot(sig, main = "Signal", ylim = c(-2, 2))
table(sig)
## ====== computing profitability =====
# The Cl function from quantmod pulls the closing price from the object
# holding a stock's data
# Buy prices
Cl(AAPL)[which(sig == 1)]
# Sell prices
Cl(AAPL)[sig == -1]
# Since these are of the same dimension, computing profit is easy
as.vector(Cl(AAPL)[sig == 1])[-1] - Cl(AAPL)[sig == -1][-table(sig)[["1"]]]
candleChart(AAPL, up.col = "black", dn.col = "red", theme = "white")
addTA(regime_val, col = "blue", yrange = c(-2, 2))
addLines(h = 0, col = "black", on = 3)
addSMA(n = c(20, 50), on = 1, col = c("red", "blue"))
zoomChart("2014-05/2014-07")
## ########################################################## ##
## ============ 2. ADJUSTING ALL DATA ========================
getSymbols(Symbols = symbols, src = "yahoo", from = start, to = end)
## Quickly define adjusted versions of each of these
`%s%` <- function(x, y) {paste(x, y)}
`%s0%` <- function(x, y) {paste0(x, y)}
for (s in symbols) {
eval(parse(text = s %s0% "_adj <- adjustOHLC(" %s0% s %s0% ")"))
}
symbols_adj <- paste(symbols, "adj", sep = "_")
## here uses quantstrat to manage portfolios
## =============== some trading rule indicators =============
## Indicators are used to construct signals
add.indicator(strategy = strategy_st, name = "SMA", # SMA is a function
arguments = list(x = quote(Cl(mktdata)), # args of SMA
n = 20),
label = "fastMA")
add.indicator(strategy = strategy_st, name = "SMA",
arguments = list(x = quote(Cl(mktdata)),
n = 50),
label = "slowMA")
## ============= Next comes trading signals =======
add.signal(strategy = strategy_st, name = "sigComparison", # Remember me?
arguments = list(columns = c("fastMA", "slowMA"),
relationship = "gt"),
label = "bull")
add.signal(strategy = strategy_st, name = "sigComparison",
arguments = list(columns = c("fastMA", "slowMA"),
relationship = "lt"),
label = "bear")
## ============== rules that generate trades ============
## BUY
add.rule(strategy = strategy_st, name = "ruleSignal", # Almost always this one
arguments = list(sigcol = "bull", # Signal (see above) that triggers
sigval = TRUE,
ordertype = "market",
orderside = "long",
replace = FALSE,
prefer = "Open",
osFUN = osMaxDollar,
# The next parameter, which is a parameter passed to
# osMaxDollar, will ensure that trades are about 10%
# of portfolio equity
maxSize = quote(floor(getEndEq(account_st,
Date = timestamp) * .1)),
tradeSize = quote(floor(getEndEq(account_st,
Date = timestamp) * .1))),
type = "enter", path.dep = TRUE, label = "buy")
## SELL
add.rule(strategy = strategy_st, name = "ruleSignal",
arguments = list(sigcol = "bear",
sigval = TRUE,
orderqty = "all",
ordertype = "market",
orderside = "long",
replace = FALSE,
prefer = "Open"),
type = "exit", path.dep = TRUE, label = "sell")
## ======================= Execution =======
## Execution of the strategy
applyStrategy(strategy_st, portfolios = portfolio_st)
## Update profile
updatePortf(portfolio_st)
dateRange <- time(getPortfolio(portfolio_st)$summary)[-1]
updateAcct(portfolio_st, dateRange)
updateEndEq(account_st)
## And some statistics
tStats <- tradeStats(Portfolios = portfolio_st, use="trades",
inclZeroDays = FALSE)
tStats[, 4:ncol(tStats)] <- round(tStats[, 4:ncol(tStats)], 2)
print(data.frame(t(tStats[, -c(1,2)])))
final_acct <- getAccount(account_st)
plot(final_acct$summary$End.Eq["2010/2016"], main = "Portfolio Equity")
## ======================================================== ##
## ========= 3. ENLARGE PORTFOLIO ============================
start = "2018-01-01"
end = "2018-07-01"
# Get new symbols
symbols = c("AAPL", "MSFT", "GOOG", "FB", "TWTR", "NFLX", "AMZN", "YHOO",
"SNY", "NTDOY", "IBM", "HPQ")
quantmod::getSymbols(Symbols = symbols, from = start, to = end)
# Quickly define adjusted versions of each of these
`%s%` <- function(x, y) {paste(x, y)}
`%s0%` <- function(x, y) {paste0(x, y)}
for (s in symbols) {
eval(parse(text = s %s0% "_adj <- adjustOHLC(" %s0% s %s0% ")"))
}
initDate = "1990-01-01"
symbols_adj <- paste(symbols, "adj", sep = "_")
currency("USD")
stock(symbols_adj,currency= "USD" ,multiplier = 1)
init
strategy_st_2 <- portfolio_st_2 <- account_st_2 <- "SMAC-20-50_v2"
rm.strat(portfolio_st_2)
rm.strat(strategy_st_2)
initPortf(portfolio_st_2, symbols = symbols_adj,
initDate = initDate, currency = "USD")
initAcct(account_st_2, portfolios = portfolio_st_2,
initDate = initDate, currency = "USD",
initEq = 1000000)
initOrders(portfolio_st_2, store = TRUE)
strategy(strategy_st_2, store = TRUE)
add.indicator(strategy = strategy_st_2, name = "SMA",
arguments = list(x = quote(Cl(mktdata)),
n = 20),
label = "fastMA")
add.indicator(strategy = strategy_st_2, name = "SMA",
arguments = list(x = quote(Cl(mktdata)),
n = 50),
label = "slowMA")
# Next comes trading signals
add.signal(strategy = strategy_st_2, name = "sigComparison", # Remember me?
arguments = list(columns = c("fastMA", "slowMA"),
relationship = "gt"),
label = "bull")
add.signal(strategy = strategy_st_2, name = "sigComparison",
arguments = list(columns = c("fastMA", "slowMA"),
relationship = "lt"),
label = "bear")
# Finally, rules that generate trades
add.rule(strategy = strategy_st_2, name = "ruleSignal",
arguments = list(sigcol = "bull",
sigval = TRUE,
ordertype = "market",
orderside = "long",
replace = FALSE,
prefer = "Open",
osFUN = osMaxDollar,
maxSize = quote(floor(getEndEq(account_st_2,
Date = timestamp) * .1)),
tradeSize = quote(floor(getEndEq(account_st_2,
Date = timestamp) * .1))),
type = "enter", path.dep = TRUE, label = "buy")
add.rule(strategy = strategy_st_2, name = "ruleSignal",
arguments = list(sigcol = "bear",
sigval = TRUE,
orderqty = "all",
ordertype = "market",
orderside = "long",
replace = FALSE,
prefer = "Open"),
type = "exit", path.dep = TRUE, label = "sell")
applyStrategy(strategy_st_2, portfolios = portfolio_st_2)
# Now for analytics
updatePortf(portfolio_st_2)
## =====
dateRange <- time(getPortfolio(portfolio_st_2)$summary)[-1]
updateAcct(account_st_2, dateRange)
updateEndEq(account_st_2)
tStats2 <- tradeStats(Portfolios = portfolio_st_2, use="trades",
inclZeroDays = FALSE)
tStats2[, 4:ncol(tStats2)] <- round(tStats2[, 4:ncol(tStats2)], 2)
print(data.frame(t(tStats2[, -c(1,2)])))
final_acct2 <- getAccount(account_st_2)
plot(final_acct2$summary$End.Eq["2010/2016"], main = "Portfolio Equity")
## ============ 4. BENCHMARKING ===========
# one should always buy an index fund that merely reflects the composition of the market.
# SPY is an exchange-traded fund (a mutual fund that is traded on the market like a stock)
# whose value effectively represents the value of the stocks in the S&P 500 stock index.
# By buying and holding SPY, we are effectively trying to match our returns with the market
# rather than beat it.
getSymbols("SPY", from = start, to = end)
# A crude estimate of end portfolio value from buying and holding SPY
1000000 * (SPY$SPY.Adjusted["2016-09-30"][[1]] / SPY$SPY.Adjusted[[1]])
plot(final_acct2$summary$End.Eq["2010/2016"] / 1000000,
main = "Portfolio Equity", ylim = c(0.8, 2.5))
lines(SPY$SPY.Adjusted / SPY$SPY.Adjusted[[1]], col = "blue")
|
9ec281bd3c50c693bb9eba082b954c1aca4ff41e
|
d8be1dd889f673d8636c02139c4f5fbfa4200fcd
|
/tests/testthat/test_bind_schema_to_entity_request.R
|
4576a21487c6d909d1b6ae8c0207c3efe07fa229
|
[] |
no_license
|
thomasyu888/synr-sdk-client
|
c03a23ff852a819f3dab1ee103f8caa7f7d16ec0
|
a090a5e062ca41ee68ee719ac30b041a0b2a9809
|
refs/heads/main
| 2023-02-26T03:10:25.802797
| 2021-02-02T15:10:28
| 2021-02-02T15:10:28
| 333,743,854
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 874
|
r
|
test_bind_schema_to_entity_request.R
|
# Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test BindSchemaToEntityRequest")
model.instance <- BindSchemaToEntityRequest$new()
test_that("entityId", {
# tests for the property `entityId` (character)
# The ID of the the entity.
# uncomment below to test the property
#expect_equal(model.instance$`entityId`, "EXPECTED_RESULT")
})
test_that("schema$id", {
# tests for the property `schema$id` (character)
# The $id of the JSON schema to bind to the entity. Note: If the $id includes a semantic version then entity will be bound to that specific version. If the $id excludes the semantic version then the entity will be bound to the latest version of that schema.
# uncomment below to test the property
#expect_equal(model.instance$`schema$id`, "EXPECTED_RESULT")
})
|
edf8d812654d5e4ff45af08c2c1e54c77dc5f399
|
43937991b4969b915f03c247d13cf00a5d81ae3e
|
/R/plot_calls.R
|
16b397d19da61a21abef103a41c3cd8d5e7edcde
|
[] |
no_license
|
mjwestgate/circleplot
|
51f2e84acd590553e611ece0b2eb59c817bc34a0
|
52d705dc050f74ec75a5a528ed981a9c5a1c772f
|
refs/heads/master
| 2020-12-09T23:34:15.721674
| 2017-07-04T08:50:57
| 2017-07-04T08:50:57
| 24,923,479
| 10
| 0
| null | 2016-09-09T03:35:04
| 2014-10-08T03:47:28
|
R
|
UTF-8
|
R
| false
| false
| 5,001
|
r
|
plot_calls.R
|
# plot functions - called by user
# function to draw a figure, if supplied with a data.frame or matrix
circleplot<-function(
input, # a distance matrix (class 'dist') or square matrix (class matrix)
cluster=TRUE, # should points be rearranged using hclust? Defaults to TRUE
reduce=FALSE, # should nodes with no connections be removed?
draw=TRUE, # should the figure be drawn?
add=FALSE, # should this figure be added to an existing plot?
style="classic", # "pie" or "clock" are current alternatives
plot.control, # a list containing plot attributes. See ?circleplot
...
)
{
# catch errors
if(any(c("classic", "pie", "clock")==style)==FALSE){
warning(paste("style = '", style, "' not recognised: switched to style = 'classic'", sep=""))
style<-"classic"}
# test whether the object given was calculated by circleplot
check.names<-function(x){
if(length(x)==3){
test<-names(x)==c("locations", "plot.control", "line.data")
if(length(test)==0){return(FALSE)
}else{all(test)}
}else{FALSE}}
add.existing.plot<-class(input)=="list" & check.names(input)
# if input was calculated by circleplot, extract relevant information
if(add.existing.plot){
plot.options<-input$plot.control
circleplot.object<-input$locations
line.object<-input$line.data
style<-plot.options$style
# if not, calculate (and plot) node and edge locations as usual
}else{
dataset<-check.inputs(input, reduce)
plot.options<-set.plot.attributes(dataset, plot.control, reduce, style) # set plot attributes/defaults
circleplot.object<-calc.circleplot(dataset, plot.options, cluster, style) # get line and point attributes
# calculate inter-point distances
# allows setting of pc.scale (to calculate curvature of lines relative to origin)
point.distance<-dist(circleplot.object$points[, c("x", "y")])
scale.distance<-point.distance-min(point.distance)
scale.distance<-((scale.distance/max(scale.distance))*
plot.options$line.curvature[2])+ plot.options$line.curvature[1]
scale.distance<-as.matrix(scale.distance)
# loop to calculate and draw lines
line.object <-lapply(circleplot.object$lines,
function(a, add, circleplot.object, scale.distance, plot.options){
if(nrow(a)>0){ # this may not be sufficient
line.list<-split(a, c(1:nrow(a)))
line.list<-lapply(line.list, function(x, plot.object, distance, options){
calc.lines(x, plot.object, distance, options)},
plot.object=circleplot.object, distance=scale.distance, options= plot.options)
}
}, add=add, circleplot.object= circleplot.object,
scale.distance= scale.distance, plot.options= plot.options)
}
# set plot window attributes
if(draw & class(input)=="list" & check.names(input)==FALSE){
par(mfrow=panel.dims(length(circleplot.object$lines)))}
# DRAW
if(draw){
# this has to run within lapply, in case lists are supplied to circleplot
# if(is.null(line.object[[1]])==FALSE){
invisible(lapply(line.object, function(a, add, circleplot.object, plot.options){
if(add==FALSE){
do.call(par, circleplot.object$par)
do.call(plot, circleplot.object$plot)}
# draw these lines
if(is.null(a)==FALSE){
invisible(lapply(a,
FUN=function(z, asymmetric, arrow.attr){
draw.curves(z)
if(asymmetric)draw.arrows(z, arrow.attr)},
asymmetric=attr(circleplot.object, "asymmetric"), arrow.attr=plot.options$arrows))
}
# add points or polygons, depending on style
switch(style,
"classic"={do.call(points,
as.list(circleplot.object$points[, -which(colnames(circleplot.object$points)=="labels")]))},
"pie"={invisible(lapply(circleplot.object$polygons, function(x){do.call(polygon, x)}))},
"clock"={
invisible(lapply(circleplot.object$nodes, function(x){do.call(lines, x)}))
do.call(lines, circleplot.object$border)}
)
# label points
label.suppress.test<-is.logical(plot.options$point.labels) & length(plot.options$point.labels)==1
if(label.suppress.test==FALSE){
labels.list<-split(circleplot.object$labels, 1:nrow(circleplot.object$labels))
invisible(lapply(labels.list, FUN=function(x){do.call(text, x)}))}
}, add=add, circleplot.object= circleplot.object, plot.options= plot.options))
if(class(input)=="list" & add.existing.plot==FALSE)par(mfrow=c(1, 1))
} # end if(draw)
# return information as needed
return(invisible(list(locations= circleplot.object, plot.control=plot.options, line.data= line.object)))
}
# simple code to get pretty point colours
point.attr<-function(distance.matrix)
{
if(length(attr(distance.matrix, "Labels"))==0){
attr(distance.matrix, "Labels")<-paste("V", c(1:attr(distance.matrix, "Size")), sep="")}
labels<-as.character(attr(distance.matrix, "Labels"))
color.hex<-c(RColorBrewer::brewer.pal(8, "Dark2"),
brewer.pal(9, "Set1"),
brewer.pal(8, "Set2")
)[1:length(labels)]
point.attributes<-data.frame(
labels= labels,
pch=19,
col=color.hex,
cex=3,
stringsAsFactors=FALSE)
return(point.attributes)
}
|
e8782ff1256b0c96bac3f19070a5222c371bf053
|
c5344271e392c529d9245ce7fdfe723678edd580
|
/Simulations/Fig_4_Analysis.R
|
6c2a6f7cdca696b4e25686bb43316f07ff0da5f2
|
[] |
no_license
|
njacobs627/Pan99_IVGs_Spatial_Structure
|
41cfb0a61e174e7ccd2f4d166b31432ffdf756a7
|
a1d82fb50b0ebe4105476e4a20b328ecd0e4c6ae
|
refs/heads/master
| 2020-05-23T07:22:00.328932
| 2019-06-08T13:51:18
| 2019-06-08T13:51:18
| 186,676,634
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 22,919
|
r
|
Fig_4_Analysis.R
|
require(tidyverse)
require(dplyr)
Proj.Home = "/Users/Nate/Box Sync/Lowen_Lab/Data_Notebook/Documentation" #For Nate's Macbook Pro
Data.Path = file.path(Proj.Home,"Simulation_Test_Results")
setwd(Data.Path)
# Analyze ----
Fig4 = read.csv(file = file.path(Data.Path,"Fig4_Data_SHORT.csv"))
Fig4.Peak = Fig4 %>%
group_by(Sim,Spread_Frac,Pp,Virus_D) %>%
dplyr::summarise(Max_Productive = max(Productive),
Max_Virions = max(Virions),
Max_Semi = max(Infected - Productive))
Fig4.Peak
Fig4.Hours = Fig4 %>%
filter((t * 4) %in% (1:(96 * 4)))
write.csv(Fig4.Peak,file=file.path(Data.Path,"Fig4_Peak_Data.csv"),row.names=FALSE)
write.csv(Fig4.Hours,file=file.path(Data.Path,"Fig4_Hours_Data.csv"),row.names=FALSE)
# Visualize ----
Fig4.Peak = read.csv(file = file.path(Data.Path,"Fig4_Peak_Data.csv")) %>%
mutate(Virus_D = log10(Virus_D / 3600)) %>%
filter(Virus_D < 5,
Spread_Frac %in% c(0,0.25)) %>%
mutate(Spread_Frac = recode(Spread_Frac, "0.25" = "25% Local Spread","0" = "Diffusion Only"))
Fig4.Hours = read.csv(file=file.path(Data.Path,"Fig4_Hours_Data.csv")) %>%
mutate(Virus_D = log10(Virus_D / 3600)) %>%
filter(Virus_D < 5,
Spread_Frac %in% c(0,0.25)) %>%
mutate(Spread_Frac = recode(Spread_Frac, "0.25" = "25% Local Spread","0" = "Diffusion Only"))
# Plot Stock ----
Plot.Stock = ggplot() +
scale_color_manual(values = c("Diffusion Only" = "midnightblue","25% Local Spread" = "steelblue1"), guide = FALSE) +
scale_fill_manual(values = c("Diffusion Only" = "midnightblue","25% Local Spread" = "steelblue1"), guide = FALSE) +
scale_y_continuous(limits = c(0,105)) +
geom_vline(xintercept = log10((5.825)), lty = 2) +
coord_cartesian(xlim = c(-0.5,4)) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
# Raw r0 ----
Early.Time = 12
df4 = Fig4.Hours %>%
filter(t == Early.Time) %>%
mutate(Slope = pmax(log10(Productive),0)) %>%
dplyr::select(Pp,Virus_D,Spread_Frac,Sim,Slope) %>%
group_by(Pp,Virus_D,Spread_Frac) %>%
dplyr::summarise(r0 = mean(Slope)) %>%
ungroup %>%
mutate(Pp = Pp + 0.005) %>%
mutate(Pp = round(Pp,2))
df4
Plot.Stock +
geom_point(data = df4,
aes(x = Virus_D,
y = r0,
color = Spread_Frac)) +
geom_smooth(data = df4,
aes(x = Virus_D,
y = r0,
linetype = factor(Pp),
color = Spread_Frac,
fill = Spread_Frac,
group = interaction(Spread_Frac,Pp)),
lwd = 1.2,
alpha = 0.3) +
labs(y = TeX("\\textbf{$\\log_1_0$ Cells Infected in 12 Hours}"),
x = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}"),
linetype = TeX("\\textbf{$\\P_P$}"),
size = TeX("\\textbf{$\\P_P$}"),
color = NULL,
fill = NULL) +
scale_linetype_manual(values = c("1" = 1,"0.58" = 1212)) +
scale_y_continuous(limits = c(0,4)) +
scale_color_manual(values = c("Diffusion Only" = "midnightblue","25% Local Spread" = "steelblue1")) +
scale_fill_manual(values = c("Diffusion Only" = "midnightblue","25% Local Spread" = "steelblue1"))
ggsave('Figures/4A_r0_Raw.pdf',
width = 7,
height = 5,
unit = "in")
#r0 Cost (log10 Productive cells in 12 hours) ----
Early.Time = 12
df1 = Fig4.Hours %>%
filter(t == Early.Time,
Pp == 1) %>%
mutate(Slope = log10(Productive) / t) %>%
group_by(Virus_D,Spread_Frac) %>%
dplyr::summarise(Intact_r0 = mean(Slope))
df2 = Fig4.Hours %>%
filter(t == Early.Time,
Pp < 1) %>%
mutate(Slope = pmax(log10(Productive) / t,0)) %>%
dplyr::select(Pp,Virus_D,Spread_Frac,Sim,Slope)
df3 = right_join(df1,df2) %>%
mutate(Cost = (1 - (Slope / Intact_r0)) * 100) %>%
group_by(Virus_D,Spread_Frac) %>%
dplyr::summarise(Cost = mean(Cost))
Plot.Stock +
geom_point(data = df3,
aes(x = Virus_D,
y = Cost,
color = Spread_Frac,
group = Spread_Frac)) +
geom_smooth(data = df3,
aes(x = Virus_D,
y = Cost,
group = Spread_Frac,
color = Spread_Frac,
fill = Spread_Frac),
lty = 1212,
alpha = 0.3) +
labs(y = "% Reduction in Initial Growth Rate",
x = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}"))
ggsave('Figures/4B_r0_Reduction.pdf',
width = 6,
height = 5,
unit = "in")
#Time to 100 Cells ----
Target_Cells = 100
df1 = Fig4.Hours %>%
filter(Pp == 1,
Productive >= Target_Cells) %>%
group_by(Spread_Frac,Virus_D,Sim) %>%
dplyr::summarise(To_Target_Cells = min(t)) %>%
ungroup() %>%
group_by(Spread_Frac,Virus_D) %>%
dplyr::summarise(Intact_To_Target_Cells = mean(To_Target_Cells))
df2 = Fig4.Hours %>%
filter(Pp < 1,
Productive >= Target_Cells) %>%
group_by(Spread_Frac,Virus_D,Sim) %>%
dplyr::summarise(To_Target_Cells = min(t)) %>%
dplyr::select(Spread_Frac,Virus_D,Sim,To_Target_Cells)
df3 = right_join(df1,df2) %>%
mutate(Target_Cell_Cost = (To_Target_Cells - Intact_To_Target_Cells) / Intact_To_Target_Cells * 100) %>%
group_by(Virus_D,Spread_Frac) %>%
dplyr::summarise(Target_Cell_Cost = mean(Target_Cell_Cost))
Plot.Stock +
geom_point(data = df3,
aes(x = Virus_D,
y = Target_Cell_Cost,
color = Spread_Frac,
group = Spread_Frac)) +
geom_smooth(data = df3,
aes(x = Virus_D,
y = Target_Cell_Cost,
group = Spread_Frac,
color = Spread_Frac,
fill = Spread_Frac),
lty = 1212,
alpha = 0.3) +
scale_y_continuous(limits = c(0,600)) +
labs(y = "% Increase in Time to Infect 100 Cells",
x = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}"))
ggsave('Figures/4C_Increase_in_Time_to_100_Cells.pdf',
width = 6,
height = 5,
unit = "in")
#Time to 1e5 Virions ----
Target_Virions = 1e5
df1 = Fig4.Hours %>%
filter(Pp == 1,
Virions >= Target_Virions) %>%
group_by(Spread_Frac,Virus_D,Sim) %>%
dplyr::summarise(To_Target_Virions = min(t)) %>%
ungroup() %>%
group_by(Spread_Frac,Virus_D) %>%
dplyr::summarise(Intact_To_Target_Virions = mean(To_Target_Virions))
df2 = Fig4.Hours %>%
filter(Pp < 1,
Virions >= Target_Virions) %>%
group_by(Spread_Frac,Virus_D,Sim) %>%
dplyr::summarise(To_Target_Virions = min(t)) %>%
dplyr::select(Spread_Frac,Virus_D,Sim,To_Target_Virions)
df3 = right_join(df1,df2) %>%
mutate(Target_Virion_Cost = (To_Target_Virions - Intact_To_Target_Virions) / Intact_To_Target_Virions * 100) %>%
group_by(Virus_D,Spread_Frac) %>%
dplyr::summarise(Target_Virion_Cost = mean(Target_Virion_Cost))
Plot.Stock +
geom_point(data = df3,
aes(x = Virus_D,
y = Target_Virion_Cost,
color = Spread_Frac,
group = Spread_Frac)) +
geom_smooth(data = df3,
aes(x = Virus_D,
y = Target_Virion_Cost,
group = Spread_Frac,
color = Spread_Frac,
fill = Spread_Frac),
lty = 1212,
alpha = 0.3) +
scale_y_continuous(limits = c(0,400)) +
labs(y = TeX("\\textbf{% Increase in Time to Yield 10^5 Virions}"),
x = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}"))
ggsave('Figures/4D_Increase_in_Time_to_1e5_Virions.pdf',
width = 6,
height = 5,
unit = "in")
# Supplement ----
# Representative Time Course ----
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 1,Sim == 1,Spread_Frac == "25% Local Spread"),
aes(x = t,
y = Productive,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_y_continuous(limits = c(0,10000),labels = comma) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{Productively Infected Cells}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2C_Time_Course_PP1_Local_Spread.pdf',
width = 5,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 0.575,Sim == 1,Spread_Frac == "25% Local Spread"),
aes(x = t,
y = Productive,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_y_continuous(limits = c(0,10000),labels = comma) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{Productively Infected Cells}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2G_Time_Course_PP_058_Local_Spread.pdf',
width = 6,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 1,Sim == 1,Spread_Frac == "Diffusion Only"),
aes(x = t,
y = Productive,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_y_continuous(limits = c(0,10000),labels = comma) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{Productively Infected Cells}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2A_Time_Course_PP1_No_Spread.pdf',
width = 5,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 0.575,Sim == 1,Spread_Frac == "Diffusion Only"),
aes(x = t,
y = Productive,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_y_continuous(limits = c(0,10000),labels = comma) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{Productively Infected Cells}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2E_Time_Course_PP_058_No_Spread.pdf',
width = 6,
height = 5,
unit = "in")
# Virion Time Course ----
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 1,Sim == 1,Spread_Frac == "25% Local Spread"),
aes(x = t,
y = Virions %>% log10,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{$\\log_1_0$ Virions}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2D_Virion_Time_Course_PP1_Local_Spread.pdf',
width = 5,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 0.575,Sim == 1,Spread_Frac == "25% Local Spread"),
aes(x = t,
y = Virions %>% log10,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{$\\log_1_0$ Virions}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2H_Virion_Time_Course_PP_058_Local_Spread.pdf',
width = 6,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 1,Sim == 1,Spread_Frac == "Diffusion Only"),
aes(x = t,
y = Virions %>% log10,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{$\\log_1_0$ Virions}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2B_Virion_Time_Course_PP1_No_Spread.pdf',
width = 5,
height = 5,
unit = "in")
ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 0.575,Sim == 1,Spread_Frac == "Diffusion Only"),
aes(x = t,
y = Virions %>% log10,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis(guide = FALSE) +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(x = TeX("\\textbf{Time Post-Inoculation (Hours)}"),
y = TeX("\\textbf{$\\log_1_0$ Virions}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
ggsave('Figures/Supp2F_Virion_Time_Course_PP_058_No_Spread.pdf',
width = 6,
height = 5,
unit = "in")
D_Legend = ggplot() +
geom_line(data = Fig4.Hours %>% filter(Pp == 0.575,Sim == 1,Spread_Frac == "Diffusion Only"),
aes(x = t,
y = Virions %>% log10,
color = Virus_D,
group = Virus_D),
lwd = 1.2) +
scale_color_viridis() +
scale_x_continuous(limits = c(0,96),breaks = c(0,24,48,72)) +
labs(color = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}")) +
theme(text=element_text(size = 13,face="bold"),
strip.text.x=element_text(size=rel(1.5),margin=margin(0,0,3,0)),
strip.text.y=element_text(size=rel(1.5),margin=margin(0,0,0,0),angle=0),
strip.background = element_blank(),
plot.title = element_text(size=rel(1.5)),
axis.text.x=element_text(angle = 0,vjust=0.75,size=rel(1.5),color = "black"),
axis.text.y=element_text(size=rel(1.5),color = "black"),
axis.line.x = element_line(size=0.5),
axis.line.y = element_line(size=0.5),
axis.ticks.x = element_line(size=0.5),
axis.ticks.y = element_line(size = 0.5),
axis.title.y = element_text(size=rel(1.2),color = "black"),
axis.title.x = element_text(size=rel(1.2)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
plot(g_legend(D_Legend))
ggsave('Figures/Supp2_D_Legend.pdf',
width = 4,
height = 4,
unit = "in")
# Max # Semi-Infected Cells ----
df1 = Fig4.Peak %>%
group_by(Virus_D,Spread_Frac) %>%
dplyr::summarise(Max_Semi = mean(Max_Semi))
Plot.Stock +
geom_point(data = df1,
aes(x = Virus_D,
y = Max_Semi,
color = Spread_Frac)) +
geom_smooth(data = df1,
aes(x = Virus_D,
y = Max_Semi,
group = Spread_Frac,
color = Spread_Frac,
fill = Spread_Frac),
lty = 1212,
alpha = 0.3) +
scale_y_continuous(limits = c(-100,3200), breaks = c(0,1000,2000,3000)) +
labs(y = TeX("\\textbf{Peak # Cells with IVGs}"),
x = TeX("\\textbf{Diffusion Coefficient ($\\log_1_0$ $\\mu$m^2/s)}"))
ggsave('Figures/6B_Max_Semi.pdf',
width = 6,
height = 5,
unit = "in")
|
72c25df60fe1fb7031a8b3670b78f97eeb918236
|
44692f59da65ef2a635b7c721a2f2e9a8ada38bd
|
/R/MS_SSM_plots.R
|
1d7ea2e78a780a0951ab95c2d3028b2fa319c8ea
|
[] |
no_license
|
vtrijoulet/MS_SSM
|
d35a807a7305c8b2ea504ad9149fc2104e1050ae
|
672535011415f0e2fe1825b76b809501028f9642
|
refs/heads/master
| 2020-04-22T21:11:46.074173
| 2019-09-30T11:56:13
| 2019-09-30T11:56:13
| 170,665,801
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 15,513
|
r
|
MS_SSM_plots.R
|
years<-min(data$year1):data$lastyear
Y1<-min(data$year1)
sp.names<-attr(data,"sp_names")
z.stat <- 1.96
name.folder<-paste(paste(sp.names,collapse ="_"),"_",Y1,"-",data$lastyear,"_rec",data$recruit_model,
"_M",data$M_model,"_err-rec",data$process_rec,"_err-survi",data$process_survival,
"_err-M",data$process_M,"_pred",data$predation_on,"_Type",data$functional_response+1,
"_sizepref",data$gamma_pref_estim,"_consrate",data$cons_rate_estim,"_flagdiet",data$flag_nll_diet,sep="")[1]
mypath<-getwd()
new.path<-paste(mypath,"Results",name.folder,sep="/")
dir.create(new.path,recursive=TRUE)
setwd(new.path)
#### Calculate CI ####
names.rep.all <- names(sd.rep$value)
names.rep <- unique(names.rep.all)
se <- list()
k=0
for (i in 1:length(names.rep)){
k=k+1
dim <- dim(rep[[names.rep[i]]])
idx <- as.vector(array(k:(k+prod(dim)-1),dim=dim))
se[[names.rep[i]]] <- array(sd.rep$sd[idx], dim=dim)
k=k+prod(dim)-1
}
# #### Calculate mean mortality rates and corresponding CI ####
#
# ## Function to estimate se for mean of adereported objects
# require(numDeriv)
# se.ADrep <- function (object, sp, FUN){
# phi <- function(x) FUN(x)
# ix <- obj$env$ADreportIndex()[[object]][,,sp]
# covx <- sd.rep$cov[ix, ix]
# x <- rep[[object]][,,sp]
# J <- jacobian(phi, x) ## Derivative of phi at x
# covPhix <- J %*% covx %*% t(J) ## Covariance of phi(x)
# return(sqrt(diag(covPhix))) # se around phi(x)
# }
#
# ## F
# mean.F<-array(dim=c(data$Y,data$sp))
# se.mean.F<-array(0,dim=c(data$Y,data$sp))
# mean.FAA<-array(dim=c(data$max_A,data$sp))
# se.mean.FAA<-array(0,dim=c(data$max_A,data$sp))
# for (i in 1:data$sp){
# mean.F[,i]<-apply(rep$F[,data$min_A_catch[i]:data$max_A_catch[i],i],1,mean)
# se.mean.F[,i] <- se.ADrep("F",i,rowMeans)
# mean.FAA[data$min_A_catch[i]:data$max_A_catch[i],i]<-apply(rep$F[,data$min_A_catch[i]:data$max_A_catch[i],i],2,mean)
# se.mean.FAA[,i] <- se.ADrep("F",i,colMeans)
# }
#
# ## M
# mean.MAA<-array(dim=c(data$max_A,data$sp))
# se.mean.MAA<-array(0,dim=c(data$max_A,data$sp))
# for (i in 1:data$sp){
# mean.MAA[1:data$Aplus[i],i]<-apply(rep$MAA[,1:data$Aplus[i],i],2,mean)
# se.mean.MAA[,i] <- se.ADrep("MAA",i,colMeans)
# }
#
# ## P
# if (data$predation_on==1){
# mean.PAA<-array(dim=c(data$max_A,data$sp))
# se.mean.PAA<-array(0,dim=c(data$max_A,data$sp))
# mean.PAA.y<-array(dim=c(data$Y,data$sp))
# se.mean.PAA.y<-array(dim=c(data$Y,data$sp))
# for (i in 1:data$sp){
# mean.PAA[,i]<-apply(rep$PAA[,,i],2,mean)
# se.mean.PAA[,i]<-se.ADrep("PAA",i,colMeans)
# mean.PAA.y[,i]<-apply(rep$PAA[,1:data$Aplus[i],i],1,mean)
# se.mean.PAA.y[,i]<-se.ADrep("PAA",i,rowMeans)
# }
# }
#
# ## Z
# mean.Z<-array(dim=c(data$max_A,data$sp))
# se.mean.Z<-array(dim=c(data$max_A,data$sp))
# mean.Z.y<-array(dim=c(data$Y,data$sp))
# se.mean.Z.y<-array(dim=c(data$Y,data$sp))
# for (i in 1:data$sp){
# mean.Z[,i]<-apply(rep$Z[,,i],2,mean)
# se.mean.Z[,i]<-se.ADrep("Z",i,colMeans)
# mean.Z.y[,i]<-apply(rep$Z[,1:data$Aplus[i],i],1,mean)
# se.mean.Z.y[,i]<-se.ADrep("Z",i,rowMeans)
# }
#### Plot functions ####
plot.time <- function(pred, obs=NULL, se=0, legend=0, xaxt="n", xlab="", ylab="", main="", ylim=c(min(na.omit(c((pred-z.stat*se),obs))),max(na.omit(c((pred+z.stat*se),obs)))) ){
plot(pred~years, ylim=ylim, ylab=ylab, type="l", main=main, xlab=xlab, xaxt=xaxt)
if (!is.null(obs)) points(obs~years, pch=16, col="red")
if (!is.null(se)) polygon(x=c(years,rev(years)), y=c(pred-z.stat*se,rev(pred+z.stat*se)), col=rgb(0,0,0,alpha=0.3), border = NA)
if (legend==1 & i==1) legend("topleft",legend=c("Observed","Predicted"),pch=c(16,NA),lty=c(NA,1),col=c("red", "black"),bty="n", cex=0.8)
}
plot.age <- function(pred, age, se=0, legend=0, xaxt="n", xlab="", ylab="", main="", ylim=c(min(na.omit((pred-z.stat*se))),max(na.omit((pred+z.stat*se)))) ){
plot(y=pred, x=age, ylim=ylim, ylab=ylab, type="l", xaxt=xaxt, main=main, xlab=xlab, xlim=c(1, data$max_A))
if (!is.null(se)) polygon(x=c(age,rev(age)), y=c(pred-z.stat*se,rev(pred+z.stat*se)), col=rgb(0,0,0,alpha=0.3), border = NA)
#if (legend==1 & i==1) legend("topright",legend=c("Observed","Predicted"),pch=c(16,NA),lty=c(NA,1),col=c("red", "black"),bty="n")
}
#### Start pdf output file ####
pdf(file="Figures_fit.pdf")
obs_aggr_Cw<-data$obs_aggr_Cw
for (i in 1:data$sp)
for (t in 1:data$Y)
if (data$obs_aggr_Cw[t,i]==-999)
obs_aggr_Cw[t,i]<-NA
par(mfrow=c(data$sp,1), oma=c(3,4,1,1), mar=c(0,0,0,0),xpd=NA)
for (i in 1:data$sp){
plot.time(pred=rep$aggr_Cw[,i], obs=obs_aggr_Cw[,i], se=se$aggr_Cw[,i], legend=1, ylab=paste0(sp.names[i]," total catch (tons)"))
if(i==data$sp) axis(1)
}
obs_aggr_I<-data$obs_aggr_I
for (j in 1:data$n_surv[i])
for (i in 1:data$sp)
for (t in 1:data$Y)
if (data$obs_aggr_I[t,i,j]==-999)
obs_aggr_I[t,i,j]<-NA
par(mfrow=c(data$sp,data$n_surv_max), oma=c(3,4,2,1), mar=c(0,1,0,1),xpd=NA)
if (data$n_surv_max>4) {par(mfrow=c(data$sp,3), oma=c(3,4,2,1), mar=c(0,1,0,1),xpd=NA)
} else {par(mfrow=c(data$sp,data$n_surv_max), oma=c(3,4,2,1), mar=c(0,1,0,1),xpd=NA)}
for (i in 1:data$sp){
for (j in 1:data$n_surv[i]){
plot.time(pred=rep$aggr_I[,i,j], obs=obs_aggr_I[,i,j], se=se$aggr_I[,i,j], legend=1)
if (i==1) mtext(paste0("Survey ",j),side=3, line=0.5, cex=0.7)
if(i==data$sp) axis(1)
if(j==1) mtext(paste0(sp.names[i]," total abundance indices"), side=2, line=3, cex=0.6)
}
}
for (j in 1:data$sp){
if (length(data$min_A_catch[j]:data$max_A_catch[j])>4)
par(mfrow=c(ceiling(length(data$min_A_catch[j]:data$max_A_catch[j])/3),3), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
else
par(mfrow=c(ceiling(length(data$min_A_catch[j]:data$max_A_catch[j])/2),2), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
for (i in data$min_A_catch[j]:data$max_A_catch[j]){
plot.time(pred=rep$prop_Caa[,i,j], obs=data$obs_prop_Caa[,i,j], xaxt="s", legend=1, main=paste0("Age ",i), ylab=paste(sp.names[j],"age comp catch",sep=" "))
}
#for (n in 1:(12-length(data$min_A_catch[j]:data$max_A_catch[j])))
#plot.new()
}
#par(mfrow=c(4,3), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
for (j in 1:data$sp){
for (k in 1:data$n_surv[j]){
if (length(data$min_A_surv[j,k]:data$max_A_surv[j,k])>4)
par(mfrow=c(ceiling(length(data$min_A_surv[j,k]:data$max_A_surv[j,k])/3),3), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
else
par(mfrow=c(ceiling(length(data$min_A_surv[j,k]:data$max_A_surv[j,k])/2),2), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
if (length(data$min_A_surv[j,k]:data$max_A_surv[j,k])==1) par(mfrow=c(1,1), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
for (i in data$min_A_surv[j,k]:data$max_A_surv[j,k]){
plot.time(pred=rep$prop_Iaa[,i,j,k], obs=data$obs_prop_Iaa[,i,j,k], xaxt="s", legend=1, main=paste0("Age ",i), ylab=paste(sp.names[j]," age comp survey",k,sep=" "))
}
#for (n in 1:(12-length(data$min_A_surv[j,k]:data$max_A_surv[j,k])))
#plot.new()
}
}
# Plot SSB
par(mfrow=c(data$sp,1), oma=c(3,4,1,1), mar=c(0,0,0,0),xpd=NA)
for (i in 1:data$sp){
plot.time(pred=rep$SSB[,i], se=se$SSB[,i], ylab=paste0(sp.names[i], " SSB"))
if(i==data$sp) axis(1)
}
#plot recruits
#par(mfrow=c(data$sp,1), oma=c(3,4,1,1), mar=c(0,0,0,0),xpd=NA)
for (i in 1:data$sp){
plot.time(pred=rep$NAA[,1,i], se=se$recruits[,i], ylab=paste0(sp.names[i], " recruitment"))
if(i==data$sp) axis(1)
}
par(mfrow=c(data$sp,1), oma=c(2,2,1,1), mar=c(2,2,2,2),xpd=NA)
for (i in 1:data$sp){
plot(rep$NAA[2:data$Y,1,i]~rep$SSB[1:(data$Y-1),i], type="l", xlab=paste0(sp.names[i]," SSB"), ylab=paste0(sp.names[i]," recruitment"))
text(y=rep$NAA[2:data$Y,1,i], x=rep$SSB[1:(data$Y-1),i], label=years[-data$Y], col="red")
}
# Plot s_surv
par(mfrow=c(data$sp,data$n_surv_max), oma=c(3,4,2,1), mar=c(0,1,0,1),xpd=NA)
for (i in 1:data$sp){
for (j in 1:data$n_surv[i]){
plot.age(pred=rep$s_surv[data$min_A_surv[i,j]:data$max_A_surv[i,j],i,j], age=data$min_A_surv[i,j]:data$max_A_surv[i,j])
if (i==1) mtext(paste0("Survey ",j),side=3, line=0.5, cex=0.7)
if (j==1) mtext(paste0("Survey selectivity on ", sp.names[i]), side=2, line=3, cex=0.6)
if (i==data$sp) axis(1)
}
}
# Plot s_F
par(mfrow=c(data$sp,1), oma=c(3,4,1,1), mar=c(0,0,0,0),xpd=NA)
for (i in 1:data$sp){
plot.age(pred=rep$s_F[data$min_A_catch[i]:data$max_A_catch[i],i], age=data$min_A_catch[i]:data$max_A_catch[i], ylab=paste0("Fishing selectivity on ", sp.names[i]))
if (i==data$sp) axis(1)
}
# Plot F
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot.time(pred=rep$mean_Fy[,i], se=se$mean_Fy[,i], ylab=paste0(sp.names[i], " mean fishing mortality"), legend=0)
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot.age(pred=rep$mean_FAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], se=se$mean_FAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], age=data$min_Fbar_idx[i]:data$max_Fbar_idx[i], ylab=paste0("Mean fishing mortality"))
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.age(pred=rep$F[1,1:data$Aplus[i],i], age=1:data$Aplus[i], ylab="Fishing mortality in all years", ylim=c(0,max(rep$F[,,i])))
for(t in 2:data$Y){
lines(rep$F[t,1:data$Aplus[i],i],col=t)
}
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.time(pred=rep$F[,1,i], ylim=c(min(rep$F[,,i]),max(rep$F[,,i])), ylab=paste0("Fishing mortality at all ages on ", sp.names[i]))
for(a in 2:data$Aplus[i]){
lines(rep$F[,a,i]~years,col=a)
}
if (i==1) legend("topleft", lty=1, col=1:data$max_A, legend=1:data$max_A, bty="n", cex=0.7, ncol=2)
if (i==data$sp) axis(1)
}
#plot M
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.age(pred=rep$MAA[1,1:data$Aplus[i],i], age=1:data$Aplus[i], ylab="Natural mortality in all years", ylim=c(min(rep$MAA),max(rep$MAA)))
for(t in 2:data$Y){
lines(rep$MAA[t,1:data$Aplus[i],i],col=t)
}
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot.time(pred=rep$mean_My[,i], se=se$mean_My[,i], ylab=paste0(sp.names[i], " mean natural mortality"), legend=0)
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot.age(pred=rep$mean_MAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], se=se$mean_MAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], age=data$min_Fbar_idx[i]:data$max_Fbar_idx[i], ylab="Mean natural mortality")
}
#plot PAA
#par(mfrow=c(data$sp,1))
if (data$predation_on==1){
for (i in 1:data$sp){
plot.age(pred=rep$mean_PAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], se=se$mean_PAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], age=data$min_Fbar_idx[i]:data$max_Fbar_idx[i], ylab="Mean predation mortality")
if (i==data$sp) axis(1)
}
for (i in 1:data$sp){
plot.time(pred=rep$mean_Py[,i], se=se$mean_Py[,i], ylab=paste0("Mean predation mortality on ", sp.names[i]))
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.age(pred=rep$PAA[1,1:data$Aplus[i],i], age=1:data$Aplus[i], ylab="Predation mortality in all years", ylim=c(0,max(rep$PAA[,,i])))
for(t in 2:data$Y){
lines(rep$PAA[t,1:data$Aplus[i],i],col=t)
}
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.time(pred=rep$PAA[,1,i], ylim=c(min(rep$PAA[,,i]),max(rep$PAA[,,i])), ylab=paste0("Predation mortality at all ages on ", sp.names[i]))
for(a in 2:data$Aplus[i]){
lines(rep$PAA[,a,i]~years,col=a)
}
if (i==1) legend("topleft", lty=1, col=1:data$max_A, legend=1:data$max_A, bty="n", cex=0.7, ncol=2)
if (i==data$sp) axis(1)
}
}
#plot Z
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot.age(pred=rep$mean_ZAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], se=se$mean_ZAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], age=data$min_Fbar_idx[i]:data$max_Fbar_idx[i], ylab="Mean total mortality")
if (i==data$sp) axis(1)
}
for (i in 1:data$sp){
plot.time(pred=rep$mean_Zy[,i], se=se$mean_Zy[,i], ylab=paste0("Mean total mortality on ", sp.names[i]))
if (i==data$sp) axis(1)
}
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp) {
plot.age(pred=rep$Z[1,1:data$Aplus[i],i], age=1:data$Aplus[i], ylab="Total mortality in all years", ylim=c(min(rep$Z),max(rep$Z)))
for(t in 2:data$Y){
lines(rep$Z[t,1:data$Aplus[i],i],col=t)
}
if (i==data$sp) axis(1)
}
#Area plot mortalities
col<-c("deepskyblue3","midnightblue","darkolivegreen3")
if (data$predation_on==0) {
rep$mean_PAA=rep$mean_MAA
rep$mean_PAA[]=0
}
M=rep$mean_PAA+rep$mean_MAA
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
age=(data$min_Fbar_idx[i]:data$max_Fbar_idx[i])
plot(rep$mean_ZAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i]~age,xaxt="n", type="n", xlab="",ylab=paste0("Total mortality on ", sp.names[i]),ylim=c(0,max(rep$mean_ZAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i])))
polygon(x=c(data$min_Fbar_idx[i]:data$max_Fbar_idx[i],rev(data$min_Fbar_idx[i]:data$max_Fbar_idx[i])), c(rep$mean_PAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], rev(rep(0,length(data$min_Fbar_idx[i]:data$max_Fbar_idx[i])))),col=col[1],border=NA)
polygon(x=c(data$min_Fbar_idx[i]:data$max_Fbar_idx[i],rev(data$min_Fbar_idx[i]:data$max_Fbar_idx[i])), c(M[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], rev(rep$mean_PAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i])),col=col[2],border=NA)
polygon(x=c(data$min_Fbar_idx[i]:data$max_Fbar_idx[i],rev(data$min_Fbar_idx[i]:data$max_Fbar_idx[i])), c(rep$mean_ZAA[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i], rev(M[data$min_Fbar_idx[i]:data$max_Fbar_idx[i],i])),col=col[3],border=NA)
if (i==1) if (data$predation==1) legend("topleft", legend=rev(c("P", "M", "F")), fill=rev(col), border=NA, bty="n") else legend("topleft", legend=rev(c("M", "F")), fill=rev(col), border=NA, bty="n")
if (i==data$sp) axis(1)
}
if (data$predation_on==0) {
rep$mean_Py=rep$mean_My
rep$mean_Py[]=0
}
My=rep$mean_Py+rep$mean_My
#par(mfrow=c(data$sp,1))
for (i in 1:data$sp){
plot(rep$mean_Zy[,i]~years,xaxt="n", type="n", xlab="",ylab=paste0("Total mortality on ", sp.names[i]),ylim=c(0,max(rep$mean_Zy[,i])))
polygon(x=c(years,rev(years)), c(rep$mean_Py[,i], rev(rep(0,data$Y))),col=col[1],border=NA)
polygon(x=c(years,rev(years)), c(My[,i], rev(rep$mean_Py[,i])),col=col[2],border=NA)
polygon(x=c(years,rev(years)), c(rep$mean_Zy[,i], rev(My[,i])),col=col[3],border=NA)
if (i==1) if (data$predation==1) legend("topleft", legend=rev(c("P", "M", "F")), fill=rev(col), border=NA, bty="n") else legend("topleft", legend=rev(c("M", "F")), fill=rev(col), border=NA, bty="n")
if (i==data$sp) axis(1)
}
# # Plot cons_rate
# if (data$cons_rate_estim==1){
# par(mfrow=c(data$sp,data$n_pred))
# for (j in 1:data$n_pred)
# for (i in 1:data$sp){
# plot(rep$cons_rate[i,,j],main=sp.names[i],ylab="Consumption rate (kg/predator/year)",xlab=paste("Age",sp.names[j], "predator",sep=" "),type="l")
# }
# }
dev.off() ## end of pdf file
#### Save estimates #####
# outputs<-cbind(sd.rep$value,sd.rep$sd)
# colnames(outputs)<-c("Estimate","Std.Error")
# fixed.param<-cbind(sd.rep$par.fixed)
# #random.param<-cbind(sd.rep$par.random)
# write.csv(outputs,"sd_rep.csv")
# write.csv(fixed.param,"fixed_par.csv")
# write.csv(rep,"rep.csv")
# sink("outtext.txt")
# lapply(rep, print)
# sink()
save(data,file="data.RData")
save(init,file="init.RData")
save(rep,file="rep.RData")
save(sd.rep,file="sd.rep.RData")
save(map,file="map.RData")
save(opt,file="opt.RData")
#save.image("MS_SSM_workspace.RData")
|
1eb1e4be825f17e3a1aa009b6f5ca5bd7c2c4dc6
|
787a278be967f58fced754b31722033c127b600e
|
/6_three_sp_consortia.R
|
adada694efe21852f2bcf22a05cd51ff525b75bb
|
[
"MIT"
] |
permissive
|
djbajic/structure-function-bacilli
|
17cd654ba4c8409a244b0bd140fcd10a8cddb701
|
16769943b58d20926c9cb0e8486126847cb5b4fa
|
refs/heads/master
| 2020-09-01T01:20:52.291192
| 2019-11-11T03:32:31
| 2019-11-11T03:32:31
| 218,840,107
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 891
|
r
|
6_three_sp_consortia.R
|
source('functions.R')
map <- fread('data/stepwise_map_PA.csv')
strfun <- fread('data/structure_function_PA.csv')
strains <- c( 'C', 'E', 'M', 'P', 'S', 'T')
# .. epistasis in pairs as a function of single background strains (3-body)
d <- map[bg %in% strains | is.na(bg)]
d[, bg := ifelse(is.na(bg), '-', bg)]
d[, bg := ordered(bg, c('-', sort(c( 'C', 'E', 'M', 'P', 'S', 'T'))))]
# .. strength of interactions in 3-body
k <- cbind(do.call(rbind, strsplit(d$pair, '-')), as.character(d$bg))
k <- t(apply(k, 1, sort))
k <- apply(k, 1, paste, collapse = '-')
k <- strfun[match(k, V2)]
d <- cbind(d, k[, c('Vj.1.V2', 'Vj.1.V2.se',
'Vj.2.V2', 'Vj.2.V2.se',
'Vj.real.V2', 'se.real.V2')])
d[, eps.2 := (Vj.2.V2 - Vj.1.V2)]
d[, eps.2.se := sqrt(Vj.2.V2.se^2 + Vj.1.V2.se^2)]
write.csv(d, file='data/three_sp_consortia.csv', row.names=FALSE)
|
f227191a4c89510c8e1e01db1c646ab0017f957d
|
4c44593583577dc94b74f6102b23e90b11d62de9
|
/BIEN Scripts/ext.clade.sister.r
|
81155a417c6b98053ad2c0e7c0e5131f91867bd8
|
[] |
no_license
|
redakc/kerkhofflab
|
7334bfd89de5877b37e52bc9b07a321b45156b5f
|
425ed6100137d48c5cbc07db75af4ca2cc8df52c
|
refs/heads/master
| 2020-04-05T22:49:01.500457
| 2017-03-27T18:45:49
| 2017-03-27T18:45:49
| 52,403,262
| 0
| 0
| null | 2017-03-27T18:45:50
| 2016-02-24T00:58:13
|
R
|
UTF-8
|
R
| false
| false
| 412
|
r
|
ext.clade.sister.r
|
#Function to extract clade that includes all desired tips plus the
#clade sister to the mrca of those tips
ext.clade.sister=function(phy, tips){
require(ape)
require(geiger)
require(phytools)
mrca.focal=getMRCA(phy, tips)
mrca.sister=getSisters(phy, mrca.focal)
mrca.both=getMRCA(phy, c(tips(phy,mrca.focal), tips(phy, mrca.sister)))
ext.clade=extract.clade(phy, mrca.both)
return(ext.clade)
}
|
70231340a9a4d134fd2b7bdee4988c315aba9105
|
328eaeda3c51826f13d93859ce1b58e298e34f08
|
/Scripts/Data Organization.R
|
ba42075c733b9b1cc29e08d065ceb390d24c3c76
|
[] |
no_license
|
s-gibson/ASA-NFL-book
|
237ce5ad62f9dba1774bc8e8836d81de8a55b914
|
d0519c816d93aa7ec4460ce4be3207692f6af8e6
|
refs/heads/master
| 2021-01-25T09:38:01.826688
| 2017-08-30T12:13:36
| 2017-08-30T12:13:36
| 93,863,763
| 8
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,750
|
r
|
Data Organization.R
|
########################
## ASA NFL DFS book ##
## Data Oganization ##
## Stewart Gibson ##
## 6/10/17 ##
########################
## Import data of weekly scores, O/U's
OU.2016 <- read.csv("data/2016_DonBest_VegasData_NFL_Week.csv")
OU.2012_2015 <- read.csv("data/2012_2015_Final_DonbestData_NFL.csv")
## Keep only regular season games
OU.2016 <- OU.2016[which(OU.2016$Regular.Season == 1),]
# Create variables for year and week
OU.2016$Year <- 2016
OU.2012_2015$Year <- substr(OU.2012_2015$Date, start = 7, stop = 10)
OU.2016$Week <- NA
OU.2012_2015$Week <- NA
OU.2016$Information <- as.character(OU.2016$Information)
OU.2012_2015$Information <- as.character(OU.2012_2015$Information)
for (i in c(1:17)) {
OU.2016$Week[grep(paste('WEEK ', i, " ", sep = ''), OU.2016$Information)] <- i
OU.2012_2015$Week[grep(paste('WEEK ', i, " ", sep = ''), OU.2012_2015$Information)] <- i
}
rm(i)
# Create data frame of only 2015 data
OU.2015 <- OU.2012_2015[which(OU.2012_2015$Year == 2015),]
# Remove Preseaston/Regular Season/Postseason columns from OU.2016
OU.2016 <- OU.2016[,c(1:11,15:17)]
# Import players' weekly fantasy points data
Fantasy.2016 <- read.csv("data/2016_NFL_Fantasy_Points.csv")
Fantasy.2016$First.Name <- as.character(Fantasy.2016$First.Name)
Fantasy.2016$Last.Name <- as.character(Fantasy.2016$Last.Name)
# Remove [space] in front of players' first and last names
Fantasy.2016$First.Name <- gsub(" ", "", Fantasy.2016$First.Name)
Fantasy.2016$Last.Name <- gsub(" ", "", Fantasy.2016$Last.Name)
# Change class of Team Name (Home, Away) columns (OU.2015, OU.2016) to "character"
OU.2015$Home.Team <- as.character(OU.2015$Home.Team)
OU.2015$Away.Team <- as.character(OU.2015$Away.Team)
OU.2016$Home.Team <- as.character(OU.2016$Home.Team)
OU.2016$Away.Team <- as.character(OU.2016$Away.Team)
# Manually fill in missing Home Teams from OU.2016
OU.2016$Home.Team[which(is.na(OU.2016$Home.Team))] <- c("Atlanta Falcons", "Arizona Cardinals",
"Arizona Cardinals", "Atlanta Falcons",
"Arizona Cardinals", "Arizona Cardinals",
"Atlanta Falcons", "Arizona Cardinals",
"Atlanta Falcons", "Arizona Cardinals",
"Atlanta Falcons", "Atlanta Falcons",
"Arizona Cardinals", "Arizona Cardinals",
"Atlanta Falcons", "Atlanta Falcons")
# Replace elongated teams names in OU.2015_2016 with abbreviations (i.e. "Arizona Cardinals" ->
# "ari")
full.names <- unique(OU.2016$Home.Team)[order(unique(OU.2016$Home.Team))][c(1:19, 21, 20, 22:27, 29, 28, 30:32)]
for (i in c(1:32)) {
OU.2016$Home.Team[which(OU.2016$Home.Team ==
full.names[i])] <- as.character(levels(Fantasy.2016$Team)[i])
OU.2016$Away.Team[which(OU.2016$Away.Team ==
full.names[i])] <- as.character(levels(Fantasy.2016$Team)[i])
OU.2015$Home.Team[which(OU.2015$Home.Team ==
full.names[i])] <- as.character(levels(Fantasy.2016$Team)[i])
OU.2015$Away.Team[which(OU.2015$Away.Team ==
full.names[i])] <- as.character(levels(Fantasy.2016$Team)[i])
}
rm(i, full.names)
## NOTE: Still need to do some work on "St. Louis Rams". Wait to see if 2015 player fantasy data
## notes Rams' players as "stl" or "lar".
## Change Christine Michael's information in weeks 1-10 to reflect SEA games. Substitute
## Russell Wilson's Team, H/A, Oppt info
Fantasy.2016$Team[which(Fantasy.2016$First.Name == "Christine" &
Fantasy.2016$Week <= 10)] <- 'sea'
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Christine" &
Fantasy.2016$Week <= 10)] <-
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Russell" &
Fantasy.2016$Last.Name == "Wilson" &
Fantasy.2016$Week <= 10)]
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Christine" &
Fantasy.2016$Week <= 10)] <-
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Russell" &
Fantasy.2016$Last.Name == "Wilson" &
Fantasy.2016$Week <= 10)]
## Change Josh Huff's information in weeks 1-8 to reflect PHI games. Substitute Carson Wentz'
## Team, H/A, Oppt info
Fantasy.2016$Team[which(Fantasy.2016$First.Name == "Josh" &
Fantasy.2016$Last.Name == "Huff" &
Fantasy.2016$Week <= 8)] <- 'phi'
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Josh" &
Fantasy.2016$Last.Name == "Huff" &
Fantasy.2016$Week <= 8)] <-
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Carson" &
Fantasy.2016$Last.Name == "Wentz" &
Fantasy.2016$Week <= 8)]
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Josh" &
Fantasy.2016$Last.Name == "Huff" &
Fantasy.2016$Week <= 8)] <-
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Carson" &
Fantasy.2016$Last.Name == "Wentz" &
Fantasy.2016$Week <= 8)]
## Change Griff Whalen's team to 'sdg'
Fantasy.2016$Team[which(Fantasy.2016$Last.Name == 'Whalen')] <- 'sdg'
## Change Ronnie Hillman's information in weeks 1-11 to reflect MIN games. Substitute with Matt
## Asiata's Team, H/A, Oppt information.
Fantasy.2016$Team[which(Fantasy.2016$First.Name == "Ronnie" &
Fantasy.2016$Last.Name == "Hillman" &
Fantasy.2016$Week <= 11)] <- 'min'
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Ronnie" &
Fantasy.2016$Last.Name == "Hillman" &
Fantasy.2016$Week <= 11)] <-
Fantasy.2016$h.a[which(Fantasy.2016$First.Name == "Matt" &
Fantasy.2016$Last.Name == "Asiata" &
Fantasy.2016$Week <= 11 &
Fantasy.2016$Week >= 7)]
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Ronnie" &
Fantasy.2016$Last.Name == "Hillman" &
Fantasy.2016$Week <= 11)] <-
Fantasy.2016$Oppt[which(Fantasy.2016$First.Name == "Matt" &
Fantasy.2016$Last.Name == "Asiata" &
Fantasy.2016$Week <= 11 &
Fantasy.2016$Week >= 7)]
## Remove John Phillips - Wk 5
Fantasy.2016 <- Fantasy.2016[-1958,]
## REMOVE THIS CODE ONCE MASON PROVIDES REST OF WEEK 14 SCORES
## Remove Wk 14 Fantasy information
Fantasy.2016 <- Fantasy.2016[which(Fantasy.2016$Week != 14),]
## Add columns to Fantasy.2016 that show players' teams' actual points and actual spread
Fantasy.2016$Actual.Points <- NA
Fantasy.2016$Actual.Spread <- NA
Fantasy.2016$Team <- as.character(Fantasy.2016$Team)
for (i in c(1:nrow(Fantasy.2016))) {
if (Fantasy.2016$h.a[i] == 'h') {
Fantasy.2016$Actual.Points[i] <- OU.2016$Home.Team.Score[which(
OU.2016$Home.Team == Fantasy.2016$Team[i] &
OU.2016$Week == Fantasy.2016$Week[i])]
Fantasy.2016$Actual.Spread[i] <- OU.2016$Actual.Spread..Relative.to.Home.Team.[which(
OU.2016$Home.Team == Fantasy.2016$Team[i] &
OU.2016$Week == Fantasy.2016$Week[i])]
} else {
Fantasy.2016$Actual.Points[i] <- OU.2016$Away.Team.Score[which(
OU.2016$Away.Team == Fantasy.2016$Team[i] &
OU.2016$Week == Fantasy.2016$Week[i])]
Fantasy.2016$Actual.Spread[i] <- -1*OU.2016$Actual.Spread..Relative.to.Home.Team.[which(
OU.2016$Away.Team == Fantasy.2016$Team[i] &
OU.2016$Week == Fantasy.2016$Week[i])]
}
}
rm(i)
## Create columns that store players' names as their full name (First Last) and as their first
## initial + last name
Fantasy.2016$First.Last <- paste(Fantasy.2016$First.Name, Fantasy.2016$Last.Name)
Fantasy.2016$Initial.Last <- paste(substr(Fantasy.2016$First.Last, start = 1, stop = 1),
". ", Fantasy.2016$Last.Name, sep = "")
## Import and clean injury data
Injuries.2016 <- read.csv("data/merged_injuries_2016.csv")
## Convert Team column to character, change names of teams to match team-naming scheme of other
## datasets.
Injuries.2016$Team <- as.character(Injuries.2016$Team)
Injuries.2016$Team[which(Injuries.2016$Team == "clt")] <- "ind"
Injuries.2016$Team[which(Injuries.2016$Team == "crd")] <- "ari"
Injuries.2016$Team[which(Injuries.2016$Team == "htx")] <- "hou"
Injuries.2016$Team[which(Injuries.2016$Team == "jax")] <- "jac"
Injuries.2016$Team[which(Injuries.2016$Team == "oti")] <- "ten"
Injuries.2016$Team[which(Injuries.2016$Team == "rai")] <- "oak"
Injuries.2016$Team[which(Injuries.2016$Team == "ram")] <- "lar"
Injuries.2016$Team[which(Injuries.2016$Team == "rav")] <- "bal"
## Add a column for the average fantasy points scored by each player who was injured in 2016
Injuries.2016$Avg.FP <- NA
for (i in 1:nrow(Injuries.2016)) {
Injuries.2016$Avg.FP[i] <- max(0,mean(Fantasy.2016$DK.points[which(
Fantasy.2016$First.Last == Injuries.2016$Player[i]
)], na.rm = T))
}
## Establish a threshold of average fantasy points required for a player to be a significant injury.
## Only keep players whose average fantasy point total is above that threshold.
Inj.threshold <- 10
Injuries.2016 <- Injuries.2016[which(Injuries.2016$Avg.FP >= Inj.threshold),]
## Save data environment for future use
rm(i, Inj.threshold)
save.image("data/clean_data.RData")
|
0795b5816f7caf177dcfbcd449aa34691e8a0d7d
|
ba65e258d288b8ccd8011313e6b4c0522f802e93
|
/man/logtransform.Rd
|
9ce618e836a47e75ef8efbbc511e8c9feea0d7c2
|
[] |
no_license
|
cran/doebioresearch
|
3572acba107f48228ea6db867d9c8639e29829ce
|
fd141323443a017ae973f373c9be3e9c7f25d0ce
|
refs/heads/master
| 2022-11-18T14:03:19.248583
| 2020-07-08T11:20:03
| 2020-07-08T11:20:03
| 278,226,971
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 933
|
rd
|
logtransform.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logtransform.R
\name{logtransform}
\alias{logtransform}
\title{Log transformation of the numeric vector}
\usage{
logtransform(numeric.vector)
}
\arguments{
\item{numeric.vector}{data vector to be transformed}
}
\value{
A list of
\itemize{
\item \code{Ratio}- A ratio of maximum and minimum values of the data
\item \code{LogTransformedVector} - A vector of the transformed data
\item \code{Comment} - A comment about zero being present in data or not
}
}
\description{
The function carries out log with base 10 transformation of each values of vector.
If one of values of a vector is 0, 1 is added to each observation.
Log transformation is carried out for the data when variance is proportional to square of the mean and treatment effects are multiplicative in nature.
}
\examples{
vector<-c(100,0,120,1000,52,30,60)
logtransform(vector)
}
|
c32f5f2865dd05ab590cc974b0569f488864fc95
|
e2d06410af5af94eff00bffd230fb5c689daaba1
|
/R/users.R
|
8fcfd3383b75aac9712bcc34ff24564c7d56d5a3
|
[] |
no_license
|
MarkEdmondson1234/gtmR
|
b99855895768c2b7dd3eee33881a89adcdc20770
|
644684285808ecf02a59fa31830802d4b1435d28
|
refs/heads/master
| 2021-01-10T16:22:00.092511
| 2020-12-05T21:32:24
| 2020-12-05T21:32:24
| 55,138,326
| 6
| 3
| null | 2020-12-05T21:32:25
| 2016-03-31T09:37:49
|
R
|
UTF-8
|
R
| false
| false
| 717
|
r
|
users.R
|
#' GTM users list
#'
#' Downloads all users that have access to the account
#'
#' @param accountId Add your GTM account ID
#' @export
#'
gtm_list_users <- function(accountId){
acc_url <- "https://www.googleapis.com/tagmanager/v2/accounts"
user_url <- paste(acc_url, "/",accountId,"/user_permissions", sep = "")
f_perm <- gar_api_generator(user_url,
"GET")
permissions_list <- f_perm()
permissions_list$content$userAccess$accountAccess$permission <- unlist(permissions_list$content$userAccess$accountAccess$permission)
permissions_list$content$userAccess$accountAccess <- permissions_list$content$userAccess$accountAccess$permission
as.data.frame(permissions_list$content)
}
|
892db8af0dcfb43f65106aa161b1d11a37e6902f
|
e8c17cc82dd543dc61d633aacd1fde248868db99
|
/scripts/R/session2/simple_database_queries.R
|
2bd84bcc8d049f679a41766d0ba9defb9c10e956
|
[] |
no_license
|
H4estu/COVID19-Data-Exploration
|
7d19eb30229b71c6118117769fb7ce1909f985c2
|
f549e629fe15f61ac0c4e83406af4ed860604f31
|
refs/heads/master
| 2022-03-08T03:42:53.456581
| 2022-02-23T19:06:46
| 2022-02-23T19:06:46
| 250,889,937
| 0
| 2
| null | 2020-06-01T22:21:42
| 2020-03-28T20:44:37
|
HTML
|
UTF-8
|
R
| false
| false
| 913
|
r
|
simple_database_queries.R
|
library(data.table)
library(sf)
library(RPostgreSQL)
library(magrittr)
# -------- Access the COVID-19 Database --------- #
source(file.path(git.path,'Code/config_files/db_config.R'))
con <- db_connect.fn()
# ----------------------------------------------- #
report_data <- dbGetQuery(con, 'SELECT * FROM covid_data.dummy_table') %>% data.table
select_example <- dbGetQuery(con, "SELECT * FROM covid_data.dummy_table WHERE deaths > 100") %>% data.table
update_example <- dbGetQuery(con, "UPDATE covid_data.dummy_table SET combined_key = 'This is an update', recovered=1 WHERE country_region = 'Mainland China'") %>% data.table
insert_example <- dbExecute(con, "INSERT INTO covid_data.dummy_table (confirmed, deaths, recovered) VALUES (1000, 0, 100)")
# need permission to do this
delete_example <- dbGetQuery(con, "DELETE FROM covid_data.dummy_table where recovered = 100") %>% data.table
dbDisconnect(con)
|
ddb1b14993d8748f81f64216c173630e8f476b1e
|
9fed66e4b6496d9f2428c8cb0054d255b3bb3c90
|
/plot2.R
|
bccc425720f30a263da3521d807160590374cde4
|
[] |
no_license
|
catblue/ExData_Plotting1
|
6312dad8f9d9a6c9b8fc8e414c06994e5e86411a
|
2bac4fe1ca9c6c1616f3e8ab986d574ab4e9a356
|
refs/heads/master
| 2020-12-26T02:11:11.058585
| 2014-05-11T17:13:45
| 2014-05-11T17:13:45
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,542
|
r
|
plot2.R
|
#downloading and unziping data
if(!file.exists("./data/household_power_consumption.txt")){
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip?accessType=DOWNLOAD"
download.file(fileUrl, destfile="./exdata-data-household_power_consumption.zip", method="curl")
unzip("exdata-data-household_power_consumption.zip",exdir="./data")
}
#Fast data loading
if(!"data.table" %in% installed.packages()) install.packages("data.table")
library(data.table)
options(warn = -1) #fread() doesn't react properly on arguments and sends warnings
energyTemp <- fread("data/household_power_consumption.txt", header=T, sep=";", na.strings = "?",
stringsAsFactors=F, colClasses = c(rep("character",2), rep("numeric",7)))
options(warn = 0)
#subseting dataframe
setkey(energyTemp,Date)
eng <- data.frame(energyTemp[(Date == "1/2/2007") | (Date == "2/2/2007")]) #a bit risky but it works!
#some cleaning
eng$Time <- strptime(paste(eng$Date,eng$Time, sep=" "),format="%d/%m/%Y %H:%M:%S") #just better to have full time available
eng$Date <- as.Date(eng$Date,format="%d/%m/%Y")
energyTemp <- NULL
#----------- Plot generation ------------
#Plot2 - Global Active Power - scaterplot
if(!file.exists("./figure")){dir.create("./figure")}
png(filename="./figure/plot2.png", width = 480, height = 480, units = "px")
x <- eng$Time; y <- eng$Global_active_power
plot(x,y, main="", ylab="Global Active Power (kilowatts)", xlab="", type="l" )
dev.off()
|
b0c7138849192921ae2ef20802f546afe70c1e0d
|
2d590228a8b39b5bf747825d3fe303e430124066
|
/man/sensitivity.Rd
|
89e11a8baf099f24162dc36da6b4e1a160ab31f5
|
[] |
no_license
|
cran/gripp
|
b33cbb359f68bdeedb9dcef9476923ed68f56c7e
|
8fedae6b2871b53e9a61af45a107720390a7f741
|
refs/heads/master
| 2020-06-23T10:12:44.184493
| 2019-08-24T04:30:02
| 2019-08-24T04:30:02
| 198,593,773
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,511
|
rd
|
sensitivity.Rd
|
\name{sensitivity}
\alias{sensitivity}
\title{Sensitivity matrix calculator}
\usage{sensitivity(parm_s)}
\arguments{
\item{parm_s}{Set of values to be considered as parameters for the Direct Problem solution}
}
\value{A matrix with the derivative of the function that represents the Direct
Problem for each parameter.}
\description{
This sotfware will calculate the sensitivity matrix for the Direct Problem.
First order derivatives are calculated using central difference approximation.
{\if{html}{\figure{Inverse_Problem_R_Solver_Sens.png}{options: width="100\%" alt="Figure: Inverse_Problem_R_Solver_Sens.png"}}}
}
\examples{
#
# Configure the name of the folder where each file with the parameter to be changed.
# If the folder is 'wd' then it will not be changed.
# You can change it to another location where your parameter files are.
auxi <- system.file(package = "gripp")
folder_name <- c(auxi,auxi)
#
# Configure the name of each file to be changed in the Direct Problem Solver.
file_name <- c('f1.R','f1.R')
#
# Configure the name of each parameter to be changed.
parm_name <- c('A','B')
#
# Configure alternative method using line number to enter each parameter
# When line_number is zero, then the input method uses keywords.
line_number <-c(0,0)
#
# Configure each parameter type, where:
# 1 means a numeric variable informed as a string, such as parm <- "1.38"
# 2 means a numeric vector informed as a string of numbers separated with a space character.
# For example, parm <- "1.25 3.4". You must tell which one will be the variable considered.
parm_type <- c(1,1)
#
# Informe the position of the variable to be considered in the problem in the vector.
# If the parameter is a numeric variable, then its position is zero.
parm_vector <- c(0,0)
#
# Configure the smallest value for each parameter
parm_min <- c(0,0)
#
# Configure the larger value for each parameter
parm_max <- c(2,5)
#
# Configure the name of the folder where the command must be called.
# If the folder is 'wd' then it will not be changed.
# You can change it to another location where your parameter files are.
command_folder <- auxi
#
# This variable means that the Direct Problem is solved using R or outside
# if this is FALSE, the results are to be read from a file.
# When it is TRUE, results will be passed inside R using the variable "results"
isitR <- TRUE
#
# Configure the command to be used to call the Direct Problem Solver.
# if this is FALSE, the results are to be read from a file.
# When it is TRUE, results will be passed inside R using the variable "result"
command <- 'f1.R'
#
# Parameter positive and negative percentual difference to be used to calculate the derivative
# ppdif must me a number between 0 and 100
# parameter_pos <- parm + (ppdif/100)*(parm_max-parm_min)
# parameter_neg <- parm - (ppdif/100)*(parm_max-parm_min)
ppdif <- 1
#
# Configure a string to be used to attrib values in the Direct Problem file.
attrib_str <- '<-'
#
# Configure the name of the file with the results obtained by the Direct Problem.
# It must be a single column of values.
# The results are changed at each run by the Direct Problem Solver.
result <- 'result.dat'
#
# Configure the name of the folder where the results can be found after each run.
# If the folder is 'wd' then it will not be changed.
# You can change it to another location where your result file is.
result_folder <- tempdir()
#
sensitivity(c(1,3))
}
|
80f4968cfe695048d1bf4e32b493250958de963b
|
70f7231b9f8c041c21abc4abaedfaa5b2a6bd107
|
/depth_split.R
|
323c8e1542d4a6736c696e7a3b1666ff6b7ba0e9
|
[] |
no_license
|
Kikiliuz/depth_split
|
07f41f9a84c6f177e567b82d0b5660020bf0d275
|
4ae58b5335cc4709e24914cb75e30294b7212aa5
|
refs/heads/master
| 2020-03-21T05:42:34.265900
| 2018-06-21T13:44:50
| 2018-06-21T13:44:50
| 138,173,631
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 637
|
r
|
depth_split.R
|
library('dplyr')
set.seed(1)
x<-round(runif(min=100,max=10000,n=1231))
y<-runif(min=0,max=1,n=1231)
data<-data.frame(cbind(x,y))
depth_split = function(data, k, variable) {
n = nrow(data) # 获取data的行数
data = arrange(data, variable) # 对data进行排序
depth_list=rep(0, n) # 创建新行，初始化为0
for(i in c(1:n)) {
depth_list[i] =i/k # 对每行的depth列，赋值组号
}
data$depth = depth_list # 创建新列
data$split=cut(depth_list,k) #等宽分组
return(data) # 返回
}
a<-depth_split(data=data,k=10,variable=y)
summarise(group_by(a,split),length(x),min(y),max(y))#检查排序
|
c1f97beb7e622887a55a3afd7d7926ef52591500
|
f76dcb5082462f759a63ff46d41c4acc2dbe5a93
|
/man/rws_read_init.Rd
|
8d6ed90adf3f213229d78f078a2880e03ee79497
|
[
"MIT"
] |
permissive
|
poissonconsulting/readwritesqlite
|
ba7ae2d6c814eb4880f94ee3e0ee77793a12436a
|
db961138ad98b957b70b3e4f257ab8c9c317b8e2
|
refs/heads/main
| 2022-11-02T22:30:43.073748
| 2022-10-16T23:09:15
| 2022-10-16T23:09:15
| 158,617,726
| 39
| 1
|
NOASSERTION
| 2022-09-28T12:29:35
| 2018-11-21T23:26:36
|
R
|
UTF-8
|
R
| false
| true
| 568
|
rd
|
rws_read_init.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/init.R
\name{rws_read_init}
\alias{rws_read_init}
\alias{rws_read_sqlite_init}
\title{Read Initialization Data table from a SQLite Database}
\usage{
rws_read_init(conn)
}
\arguments{
\item{conn}{A \linkS4class{SQLiteConnection} to a database.}
}
\value{
A data frame of the init table
}
\description{
The table is created if it doesn't exist.
}
\examples{
conn <- rws_connect()
rws_read_init(conn)
rws_write(rws_data, exists = FALSE, conn = conn)
rws_read_init(conn)
rws_disconnect(conn)
}
|
cf019fc12b77515d05074adcab187c4af9078b45
|
e6b97f595b589e38238287d617605753b20511cf
|
/workflow.R
|
2ffb9a74aa28acbe63161c55dc1ddbc57d104fdb
|
[] |
no_license
|
peggylind/SyntheticDataSet
|
c3bd0f8cce17fe32f4341ac49fd5c98680c3d010
|
b2cdf2581d64504e9af4d5af2c2c199ea4908d57
|
refs/heads/master
| 2020-04-05T22:17:47.762325
| 2019-07-18T00:36:56
| 2019-07-18T00:36:56
| 157,251,390
| 0
| 0
| null | 2018-11-12T17:34:16
| 2018-11-12T17:34:15
| null |
UTF-8
|
R
| false
| false
| 1,167
|
r
|
workflow.R
|
# Workflow controller for SAM creation
source("BaseScripts/basesam.R")
#set working directory to where all the data folders can be found
housingDataDirectory <- "~/University Of Houston/Price, Daniel M - Social Network Hypergraphs/HCAD/2015/"
censusDataDirectory <- "~/University Of Houston/Price, Daniel M - Social Network Hypergraphs/Census/2014/"
sam <- createBaseSAM(housingdir = housingDataDirectory,
censusdir = censusDataDirectory,
censusFromRDS = TRUE,
HCAD_parcelsFromRDS = TRUE,
numberOfCores = 1)
# do some sanity checks and more columns
if (sanityChecks(sam)) {
# Add column to add level to data
sam <- one_of(sam)
# TODO move into HCAD preprocessing - adds cooordinates to the model based on the geometry column
sam <- add_lat_long(sam)
# This converts all the columns in to the model to the appropriate class (either character or numeric)
sam <- convertColumnTypes(sam)
} else {
print("Did not pass sanity checks!")
}
#add extension columns - NHANES etc.
#Save the final result
saveRDS(sam, paste0("complete_sam", Sys.Date(), ".RDS"))
|
121f60b8bc82be2a14ed46868b42dd206a6fe88c
|
a04716b6fd12d346a07b26c85d3cb3ea995def54
|
/a1.R
|
e167e22260636ba5614089c0cddebe570b345c28
|
[] |
no_license
|
mjherz/kt1
|
991fcfedffbda25a2788148d7aaec3a0071337f4
|
e6a2592aa60df06596b03ccff22e5850e1bc1632
|
refs/heads/master
| 2016-09-05T12:43:45.241676
| 2013-06-25T18:46:36
| 2013-06-25T18:46:36
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,441
|
r
|
a1.R
|
set.seed(6001)
train1<-read.csv("train.csv")
test1<-read.csv("test.csv")
gcm<-read.csv("genderclassmodel.csv")
gm<-read.csv("gendermodel.csv")
names(train1)
dim(train1)
sapply(train1,class)
train1$pclass<-as.factor(train1$pclass)
train1$survfact<-as.factor(train1$survived)
#develop cabin group
train1$cabgr<-as.factor(substr(train1$cabin,1,1))
table(train1$cabgr)
sum(train1$cabgr=="")
#missing values
sum(is.na(train1))
which(is.na(train1),arr.ind=TRUE)
#missing age,cabgr
train1$cabgr[train1$cabgr==""]<-NA
train1$cabgr<-as.factor(train1$cabgr)
train1$survdoub<-as.double(train1$survived)
sum(is.na(train1$cabgr))
which(is.na(train1),arr.ind=TRUE)
#missing two values in embarked; insert NA
train1$embarked[train1$embarked==""]<-NA
library(randomForest)
#for first, exclude name, ticket, cabin because of number of factors
#include cabgr (first digit of cabin), and age, but only for complete records
train2<-train1[-which(is.na(train1),arr.ind=TRUE)[,1],]
which(is.na(train2),arr.ind=TRUE)
sum(is.na(train2))
dim(train2)
dim(train1)
form1<-as.formula("survfact~embarked+age+sex+sibsp+parch+pclass+fare+cabgr")
rf1<-randomForest(form1,data=train2,prox=TRUE)
importance(rf1)
train3<-cbind(train2[[2]],train2[[4]],train2[[5]],train2[[6]],train2[[7]],train2[[9]],train2[[11]],train2[[13]])
rfcv1<-rfcv(train3,train2$survfact,cv.fold=10)
rfcv1$error.cv
#exclude cabgr (first digit of cabin), and age because of missing records
rm(train2,train3,form1)
train2<-data.frame(as.factor(train1[[2]]))
train2<-cbind(train2,as.factor(train1[[4]]),as.integer(train1[[6]]),as.integer(train1[[7]]),as.numeric(train1[[9]]),as.factor(train1[[11]]))
colnames(train2)<-c(names(train1)[[2]],names(train1)[[4]],names(train1)[[6]],names(train1)[[7]],names(train1)[[9]],names(train1)[[11]])
library(caret)
library(RWeka)
library(gbm)
library(party)
library(glmnet)
library(C50)
library(e1071)
fitcontrol<-trainControl(method="repeatedcv",number=10,repeats=10)
fitresults1<-data.frame(matrix(ncol=6))
#standardize all errors-- kappa or rmse
cripper1<-train(train2,train1$survfact,method="JRip",trControl=fitcontrol)
fitresults1[1,1]<-"jrip"
fitresults1[1,2:6]<-cripper1$results
form1<-as.formula("survfact~embarked+sex+sibsp+parch+pclass+fare")
crf1<-train(form1,data=train1,method="rf",trControl=fitcontrol)
fitresults1[2,1]<-"rf"
fitresults1[2,2:6]<-crf1$results
cctree1<-train(train2,train1$survfact,method="ctree",trControl=fitcontrol)
fitresults1[3,1]<-"ctree"
fitresults1[3,2:6]<-cctree1$results
#standardize errors
ccforest1<-train(train2,train1$survfact,method="cforest",trControl=fitcontrol)
fitresults1[4,1]<-"cforest"
fitresults1[4,2:6]<-ccforest1$results
#works, but what are the errors
cj48tree1<-train(train2,train1$survfact,method="J48",trControl=fitcontrol)
fitresults1[5,1]<-"j48"
fitresults1[5,2:6]<-cj48tree1$results
#standardize errors
cgbm1<-train(train2,train1$survfact,method="gbm",distribution="bernoulli",trControl=fitcontrol)
fitresults1[6,1]<-"gbm"
fitresults1[6,2:6]<-cgbm1$results
#standardize errors
form1<-as.formula("survived~embarked+sex+sibsp+parch+pclass+fare")
cglmnet1<-train(form1,data=train1,method="glmnet",family="binomial",trControl=fitcontrol)
fitresults1[7,1]<-"glmnet"
fitresults1[7,2:6]<-cglmnet1$results
#works, but not certain of teh type of errors
form1<-as.formula("survfact~embarked+sex+sibsp+parch+pclass+fare")
c50rules1<-train(form1,data=train1,method="C5.0Rules")
fitresults1[8,1]<-"c50rules"
fitresults1[8,2:6]<-c50rules1$results
#standardize errors
cPART1<-train(train2,train1$survfact,method="PART",trControl=fitcontrol)
fitresults1[9,1]<-"part"
fitresults1[9,2:6]<-cPART1$results
form1<-as.formula("survived~embarked+sex+sibsp+parch+pclass+fare")
cglmstepaic1<-train(form1,data=train1,method="glmStepAIC",family="binomial",trControl=fitcontrol)
fitresults1[10,1]<-"glmstepaic"
fitresults1[10,2:6]<-cglmstepaic1$results
csvm1<-train(form1,data=train1,method="svmLinear",family="binomial",trControl=fitcontrol)
fitresults1[11,1]<-"svm"
fitresults1[11,2:6]<-csvm1$results
#can use varImp on some
test2<-data.frame(as.factor(test1[[1]]))
test2<-cbind(test2,as.factor(test1[[3]]),as.integer(test1[[5]]),as.integer(test1[[6]]),as.numeric(test1[[8]]),as.factor(test1[[10]]))
colnames(test2)<-c(names(test1)[[1]],names(test1)[[3]],names(test1)[[5]],names(test1)[[6]],names(test1)[[8]],names(test1)[[10]])
test2$embarked[test2$embarked==""]<-NA
restest3<-data.frame(matrix(ncol=2,nrow=dim(test1)[1]))
restest3$survpred<-as.integer(0)
restest3$agreement<-as.integer(0)
#153 does not have a fare; impute for now
test2$fare[153]<-mean(test1$fare[-c(153)])
for (i in 1:dim(test2)[1]) {
j=0
if(predict(cripper1,test2[i,])==1) {j=j+1}
if(predict(crf1,test2[i,])==1) {j=j+1}
# if(predict(cctree1,test2[i,])==1) {j=j+1}
# if(predict(ccforest1,test2[i,])==1) {j=j+1}
if(predict(cj48tree1,test2[i,])==1) {j=j+1}
if(predict(cgbm1,test2[i,])==1) {j=j+1}
if(predict(cglmnet1,test2[i,])==1) {j=j+1}
if(predict(c50rules1,test2[i,])==1) {j=j+1}
if(predict(cPART1,test2[i,])==1) {j=j+1}
if(predict(cglmstepaic1,test2[i,])==1) {j=j+1}
if(predict(csvm1,test2[i,])==1) {j=j+1}
restest3$agreement[i]<-j
if(j>=6) {restest3$survpred[i]<-1}
}
sum(restest3$survpred==1)/dim(restest3)[1]
sum(train1$survived==1)/dim(train1)[1]
survpred<-data.frame((matrix(nrow=dim(test1)[1])))
survpred[[1]]<-restest3$survpred
write.csv(survpred, file = "survpred1.csv",row.names=FALSE)
#confusion matrix
|
80cf90b140d1c5cee4a463861963a0b4e8e8fb9a
|
40b8c18de5170436038973ed1478e9817a4270f8
|
/scripts/03_linear_regression_training_data.R
|
dba08d10bd85e0efd31bab4ef7388945004ef0e3
|
[] |
no_license
|
desval/ResazurinMIC
|
a97eb7e12a8407e8af27cfb7de77d3fc60c269fe
|
19b6aac10a24280eec77139c36e7192e280468c5
|
refs/heads/master
| 2021-09-15T16:56:18.775159
| 2017-07-10T10:46:04
| 2017-07-10T10:46:04
| 79,265,781
| 0
| 0
| null | 2017-01-17T19:54:13
| 2017-01-17T19:54:13
| null |
UTF-8
|
R
| false
| false
| 4,807
|
r
|
03_linear_regression_training_data.R
|
# Description
## ----------------------------------------------------------------------------------------------------------------------------
# In this file we fit a linear regression to the training data and plot the model
# Load dependencies and set options
## ----------------------------------------------------------------------------------------------------------------------------
rm(list=ls())
packages <- c("ggplot2", "RColorBrewer")
to.install <- setdiff(packages, rownames(installed.packages()))
if (length(to.install) > 0) {
install.packages(to.install)
}
lapply(packages, library, character.only = TRUE)
# load and subset data (to calculate the number of evaluable strains)
## ----------------------------------------------------------------------------------------------------------------------------
alldata <- read.csv("output/tables/alldata.csv", stringsAsFactors = F)
#above limit of detection (resazurin method):
alldata <- subset(alldata, alldata$quality != "above limit of detection")
#above or below limit of detection (Etest method):
alldata$above <- grepl(">",alldata$MIC)
length(alldata[alldata$above==T,]$above)
alldata$below <- grepl("<",alldata$MIC)
length(alldata[alldata$below==T,]$below)
cleandata <- subset(alldata, alldata$above!=TRUE & alldata$below!=TRUE)
# cleandata_t <- subset(cleandata, cleandata$run == "training")
# cleandata_v <- subset(cleandata, cleandata$run == "validation")
# cleandata_r <- subset(cleandata, cleandata$run == "reference")
training <- subset(cleandata,cleandata$run == "training")
validation <- subset(cleandata,cleandata$run == "validation")
# estimate linear regression model
##-----------------------------------------------------------------------------------------------------------------------
etest <- as.numeric(training$MIC)
esti <- as.numeric(training$Estimate)
print(summary(linreg <- lm(log(etest)~(esti))))
esti_fit_log <- linreg$fitted.values
esti_fit <- exp(linreg$fitted.values)
prediction <- data.frame(esti_fit)
# save parameters and parameters variance~covariance matrix in a named list, we will use it to bootstrap
vcov.list <- list(list("Estimates"=linreg, "Matrix"=list(vcov(linreg), "Summary"=summary(linreg))))
saveRDS(vcov.list, "output/tables/lm_parameters_variance_covariance_matrix_list.rds")
# save pearson correlation
pearson <- cor(log(etest),esti,method="pearson")
saveRDS(pearson, "output/tables/pearson.rds")
# Plot regression and save it as rds object, so that we can combine it with plots from
# other scripts
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# get fitted values
df4 <- as.data.frame(cbind(esti_fit,training))
# set plot options
par(mar = c(5,5,5,5))
par(oma = c(1,1,1,1))
options(scipen = 100)
# function for formatting axis label decimals
fmt_dcimals <- function(decimals=0){
function(x) as.character(round(x,decimals))
}
# colorvector <- c( "#D95F02", "#7570B3", "#E7298A" ,"#66A61E", "#1F78B4", "#A6761D", "#666666")
colorvector <- brewer.pal(9,"Set1")[c(3,2,1,4,5,9,8)]
#colorvector <- brewer.pal(8,"Set2")
dfplot <- df4
dfplot$antibiotic <- gsub("Penicillin","Penicillin", dfplot$antibiotic ) # cosmetic changes
p1<-ggplot(dfplot, aes (x = exp(Estimate), y = as.numeric(MIC))) +
#geom_text(data = data.frame(), aes(0.05, 0.0005, label = ""),cex=16, fontface="bold")+
geom_point(size=2,alpha=0.6,aes(colour = factor(antibiotic)))+ #labs(aes(colour=antibiotic))+
geom_smooth(method = "lm",color="steelblue2",show.legend = F,se = F,level=0.95)+
scale_y_continuous("Etest MIC (mg/L)",trans='log10' , limits = c(0.00001, 5000),
breaks=c(0.00001,0.0001,0.001, 0.01,0.1,1,10,100,1000), labels = fmt_dcimals(5))+
scale_x_continuous(expression("EC"[50]*" (mg/L)"), trans='log10' , limits = c(0.00001, 5000),
breaks=c(0.00001,0.0001,0.001, 0.01,0.1,1,10,100,1000), labels = fmt_dcimals(5)) +
scale_color_manual(values=colorvector) + geom_abline(intercept = 0.0000001, slope = 1, color="black", lty=2)+
theme_bw(18)+theme(legend.justification=c(-0.01,1.01),
legend.position=c(0,1),
legend.text = element_text(colour="black"),
legend.title = element_blank(),
axis.line = element_line(colour = "black"),
legend.key = element_rect(colour = "white")) +
guides(colour = guide_legend(override.aes = list(size=8)))
p1
regr_plot <- list("regression" = p1)
saveRDS(regr_plot, "output/figures/regression_plot.RDS")
# file end
## -----------------------------------------------------------------------------------------------------------
|
d97f26345dd556b3af2c247fe54e40cf00da083e
|
749df9b8dd733b3c63dbea598540ffc7a97d8c84
|
/Supplementaryscript2.r
|
bef7758c7c2745d94323df0e7bb0bb71db9b5964
|
[] |
no_license
|
thalescherubino/RTqPCR
|
4845dd030d4f28c2a64e552935fd52da663c1d7c
|
ca205737b4b9edf56b4f6a7f040934895d014649
|
refs/heads/main
| 2023-04-08T05:40:42.966678
| 2021-04-23T16:29:41
| 2021-04-23T16:29:41
| 360,313,870
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,454
|
r
|
Supplementaryscript2.r
|
# Supplementaryscript2.r
############################
#Traditional method-DeltaCT#
############################
#first calculate the mean between both technical replicates
deltaCtData <- RTqPCRdata[seq(1,nrow(RTqPCRdata),2),]
deltaCtData$ct <- colMeans(matrix(RTqPCRdata$ct,nrow=2),na.rm=T)
#Opt - Wat
target.opt <- deltaCtData$ct[(which(deltaCtData$treat == "opt" & deltaCtData$gene == TARGET))]
refa.opt <- deltaCtData$ct[(which(deltaCtData$treat == "opt" & deltaCtData$gene == REFA))]
refb.opt <- deltaCtData$ct[(which(deltaCtData$treat == "opt" & deltaCtData$gene == REFB))]
target.wat <- deltaCtData$ct[(which(deltaCtData$treat == "wat" & deltaCtData$gene == TARGET))]
refa.wat <- deltaCtData$ct[(which(deltaCtData$treat == "wat" & deltaCtData$gene == REFA))]
refb.wat <- deltaCtData$ct[(which(deltaCtData$treat == "wat" & deltaCtData$gene == REFB))]
opt <- target.opt - 0.5*(refa.opt + refb.opt)
wat <- target.wat - 0.5*(refa.wat + refb.wat)
diff.opt.wat <- opt - wat
t.test.opt.wat <- t.test(opt, wat)
#aca - cat
target.aca <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == TARGET))]
refa.aca <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFA))]
refb.aca <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFB))]
target.cat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == TARGET))]
refa.cat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFA))]
refb.cat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFB))]
aca <- target.aca - 0.5*(refa.aca + refb.aca)
cat <- target.cat -0.5*(refa.cat + refb.cat)
diff.aca.cat <- aca - cat
t.test.aca.cat <- t.test(aca, cat)
#aca.opt - aca.wat
target.aca.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == TARGET & deltaCtData$treat == "opt"))]
refa.aca.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFA & deltaCtData$treat == "opt"))]
refb.aca.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFB & deltaCtData$treat == "opt"))]
target.aca.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == TARGET & deltaCtData$treat == "wat"))]
refa.aca.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFA & deltaCtData$treat == "wat"))]
refb.aca.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "aca" & deltaCtData$gene == REFB & deltaCtData$treat == "wat"))]
aca.opt <- target.aca.opt - 0.5*(refa.aca.opt + refb.aca.opt)
aca.wat <- target.aca.wat -0.5*(refa.aca.wat + refb.aca.wat)
diff.aca.opt.wat <- aca.opt - aca.wat
t.test.aca.opt.wat <- t.test(aca.opt, aca.wat)
#cat.opt - cat.wat
target.cat.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == TARGET & deltaCtData$treat == "opt"))]
refa.cat.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFA & deltaCtData$treat == "opt"))]
refb.cat.opt <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFB & deltaCtData$treat == "opt"))]
target.cat.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == TARGET & deltaCtData$treat == "wat"))]
refa.cat.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFA & deltaCtData$treat == "wat"))]
refb.cat.wat <- deltaCtData$ct[(which(deltaCtData$cultivar == "cat" & deltaCtData$gene == REFB & deltaCtData$treat == "wat"))]
cat.opt <- target.cat.opt - 0.5*(refa.cat.opt + refb.cat.opt)
cat.wat <- target.cat.wat -0.5*(refa.cat.wat + refb.cat.wat)
diff.cat.opt.wat <- cat.opt - cat.wat
t.test.cat.opt.wat <- t.test(cat.opt, cat.wat)
#create a vector with the mean of the -diff (or delta delta Ct)
diff.expression.means <- c(mean(-diff.opt.wat),
mean(-diff.aca.cat),
mean(-diff.aca.opt.wat),
mean(-diff.cat.opt.wat))
names(diff.expression.means) <- c("opt - wat", "aca - cat", "aca.opt - aca.wat", "cat.opt - cat.wat ")
lower.conf.int <- c(-t.test.opt.wat$conf.int[1],
-t.test.aca.cat$conf.int[1],
-t.test.aca.opt.wat$conf.int[1],
-t.test.cat.opt.wat$conf.int[1])
upper.conf.int <- c(-t.test.opt.wat$conf.int[2],
-t.test.aca.cat$conf.int[2],
-t.test.aca.opt.wat$conf.int[2],
-t.test.cat.opt.wat$conf.int[2])
pdf("deltaDeltaCtLog2FC.pdf", h=8,w=8)
barplot <- barplot(diff.expression.means,beside=T, ylim=c(-5,5),las=1,yaxt='n',cex.names=1.1)
title(ylab=expression("Log"[2]*"FC"),line=2)
axis(2, at =c(seq(-15,15,1)),las =2,cex=.5)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=-1,lty=3,lwd=2,col="red")
arrows(x0 = barplot, y0 = lower.conf.int, x1 = barplot,y1=upper.conf.int,code=3,angle=90,length=0.05,col="#964841",lwd=2)
dev.off()
FC.diff.expression.means <- 2^diff.expression.means
FC.lower.conf.int <- 2^upper.conf.int
FC.upper.conf.int <- 2^lower.conf.int
pdf("deltaDeltaCtFC.pdf", h=8,w=8)
barplot <- barplot(FC.diff.expression.means,beside=T, ylim=c(0,5),las=1,yaxt='n',cex.names=1)
title(ylab=expression("FC"),line=2)
axis(2, at =c(seq(0,10,1)),las =2,cex=.5)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=.5,lty=3,lwd=2,col="blue")
arrows(x0 = barplot, y0 = FC.lower.conf.int, x1 = barplot,y1=FC.upper.conf.int,code=3,angle=90,length=0.05,col="#964841",lwd=2)
dev.off()
############################################
#make a single plot with Delta Ct and model#
############################################
names <- c("opt - wat", "aca - cat", "aca.opt - aca.wat", "cat.opt - cat.wat ")
pdf("LMM.versus.DeltaCt.pdf",h=9,w=9)
par(mfrow = c(2,2))
#log2FC lMM model
barplot <- barplot(confidence.intervals[,1],beside=T, ylim=c(
if(min(confidence.intervals[,2])>=-5){-5}else{min(confidence.intervals[,2])*1.2},
if(max(confidence.intervals[,3])<=5){5}else{max(confidence.intervals[,3])*1.2}
),las=1,yaxt='n',names="",main="LMM model",cex.main=2)
text( x= barplot,y=if(min(confidence.intervals[,2])>=-5){-6.5}else{min(confidence.intervals[,2])*1.4},
labels = names,srt=30,xpd=T)
axis(2, at =c(seq(-100,100,1)),las =2,cex=.5)
title(ylab=expression("Log"[2]*"FC"),line=2,cex.lab=1.5)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=-1,lty=3,lwd=2,col="red")
arrows(x0 = barplot, y0 = confidence.intervals[,2], x1 = barplot,y1=confidence.intervals[,3],code=3,angle=90,length=0.05,col="#964841",lwd=2)
#FC lMM model
barplot <- barplot(FCCI[,1],beside=T, ylim=c(0,
if(max(FCCI[,3]) <= 5){5}else
{max(FCCI[,3])*1.20
}),
las=1,yaxt='n',cex.names=1,main="LMM model",names="",cex.main=2)
title(ylab=expression("FC"),line=2,cex.lab=1.5)
axis(2, at =c(seq(0,100)),las =2,cex=.5)
text( x= barplot,y=if(max(FCCI[,3]) <= 5 ){-.7}else if(max(FCCI[,3]) <= 15){- 1.4}else{-3.5},
labels = names,srt=30,xpd=T)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=.5,lty=3,lwd=2,col="blue")
arrows(x0 = barplot, y0 = FCCI[,2], x1 = barplot,y1=FCCI[,3],code=3,angle=90,length=0.05,col="#964841",lwd=2)
FC.strapolator=FALSE
# Delta Delta Ct Log2FC
barplot <- barplot(diff.expression.means,beside=T, ylim=c(
if(min(upper.conf.int)>=-5){-5}else{min(upper.conf.int)*1.2},
if(max(lower.conf.int)<=5){5}else{max(upper.conf.int)*1.2}
),las=1,yaxt='n',cex.names=1.1,main=expression(paste(Delta,bold("Ct method"))),names="",cex.main=2)
text( x= barplot,y=if(min(upper.conf.int)>=-5){-6.5}else{min(upper.conf.int)*1.4},
labels = names,srt=30,xpd=T)
title(ylab=expression("Log"[2]*"FC"),line=2,cex.lab=1.5)
axis(2, at =c(seq(-100,100,1)),las =2,cex=.5)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=-1,lty=3,lwd=2,col="red")
arrows(x0 = barplot, y0 = lower.conf.int, x1 = barplot,y1=upper.conf.int,code=3,angle=90,length=0.05,col="#964841",lwd=2)
#Ct methdo FC
barplot <- barplot(FC.diff.expression.means,beside=T, ylim=c(0,
if(max(FC.upper.conf.int) <= 5){5}else{max(FC.upper.conf.int)*1.2})
,las=1,yaxt='n',cex.names=1,main=expression(paste(Delta,bold("Ct method"))),names="",cex.main=2)
text( x= barplot,y=if(max(FC.upper.conf.int) <= 5 ){-.7}else if(max(FC.upper.conf.int) <= 15){- 1.4}else{-3.5},
labels = names,srt=30,xpd=T)
title(ylab=expression("FC"),line=2,cex.lab=1.5)
axis(2, at =c(seq(0,100,1)),las =2,cex=.5)
abline(h=0)
abline(h=1,lty=3,lwd=2,col="red")
abline(h=.5,lty=3,lwd=2,col="blue")
arrows(x0 = barplot, y0 = FC.lower.conf.int, x1 = barplot,y1=FC.upper.conf.int,code=3,angle=90,length=0.05,col="#964841",lwd=2)
dev.off()
|
5c6641a06f79b0be82b10516160e6f9171238bda
|
09847d5e16938d783a1a8a37b576d5840dd7e5f7
|
/tests/predict.R
|
063999b0337b332bb914c32a4e5c1231642d6e69
|
[] |
no_license
|
cran/aster
|
b6dc91ebf539df904d55f4cacd584a6ea5ff27a1
|
61fc3cd89e7f6776279333b534652356c7059f89
|
refs/heads/master
| 2021-07-04T00:20:22.476008
| 2021-06-13T03:40:32
| 2021-06-13T03:40:32
| 17,694,509
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,986
|
r
|
predict.R
|
library(aster)
# needed because of the change in R function "sample" in R-devel
suppressWarnings(RNGversion("3.5.2"))
set.seed(42)
nind <- 25
vars <- c("l2", "l3", "f2", "f3", "h2", "h3")
pred <- c(0, 1, 1, 2, 3, 4)
fam <- c(1, 1, 1, 1, 3, 3)
length(pred) == length(fam)
nnode <- length(pred)
theta <- matrix(0, nind, nnode)
root <- matrix(1, nind, nnode)
x <- raster(theta, pred, fam, root)
dimnames(x) <- list(NULL, vars)
data <- as.data.frame(x)
site <- factor(sample(LETTERS[1:4], nind, replace = TRUE))
foo <- rnorm(nind)
data <- data.frame(x, site = site, foo = foo, root = 1)
redata <- reshape(data, varying = list(vars),
direction = "long", timevar = "varb", times = as.factor(vars),
v.names = "resp")
out <- aster(resp ~ foo + site + varb, pred, fam, varb, id, root,
data = redata)
sout1 <- summary(out, show.graph = TRUE)
##### redo with aster.default and predict.aster
out2 <- aster(x, root, pred, fam, modmat = out$modmat)
sout2 <- summary(out2)
foo <- match(sort(unique(site)), site)
modmat.pred <- out$modmat[foo, , ]
origin.pred <- out$origin[foo, ]
pout1 <- predict(out2, modmat = modmat.pred, parm.type = "canon")
##### case 1: model = "unco", obj = "unco", parm = "cano" ####
fred <- predict(out2, modmat = modmat.pred, parm.type = "canon",
se.fit = TRUE)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out2$fisher) %*%
t(fred$gradient))))
sally <- matrix(modmat.pred, ncol = length(out2$coef))
all.equal(fred$gradient, sally)
all.equal(fred$fit, as.numeric(origin.pred) + as.numeric(sally %*% out$coef))
##### case 1a: same but with amat
node.names <- dimnames(out$modmat)[[2]]
site.names <- levels(site)
amat <- array(0, c(dim(modmat.pred)[1:2], length(site.names)))
for (i in seq(along = site.names))
amat[i, grep("h", node.names), i] <- 1
alfie <- predict(out2, modmat = modmat.pred, parm.type = "canon",
se.fit = TRUE, amat = amat)
amatmat <- matrix(amat, ncol = dim(amat)[3])
all.equal(alfie$fit, as.numeric(t(amatmat) %*% fred$fit))
all.equal(alfie$gradient, t(amatmat) %*% fred$gradient)
all.equal(alfie$se.fit, sqrt(diag(alfie$gradient %*% solve(out2$fisher) %*%
t(alfie$gradient))))
##### case 2: model = "cond", obj = "cond", parm = "cano" ####
##### no test -- same code as case 1
##### case 3: model = "unco", obj = "cond", parm = "cano" ####
out3 <- aster(x, root, pred, fam, modmat = out$modmat, type = "cond")
sout3 <- summary(out3)
fred <- predict(out3, modmat = modmat.pred, parm.type = "canon",
se.fit = TRUE)
nind <- dim(modmat.pred)[1]
nnode <- dim(modmat.pred)[2]
ncoef <- dim(modmat.pred)[3]
aster:::setfam(fam.default())
beta.hat <- out3$coef
theta.hat <- as.numeric(sally %*% beta.hat)
phi.hat <- .C(aster:::C_aster_theta2phi,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta.hat),
phi = double(nind * nnode))$phi
all.equal(fred$fit, phi.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out3$fisher) %*%
t(fred$gradient))))
my.gradient <- 0 * fred$gradient
epsilon <- 1e-9
for (k in 1:ncoef) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
theta.epsilon <- as.numeric(sally %*% beta.epsilon)
phi.epsilon <- .C(aster:::C_aster_theta2phi,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta.epsilon),
phi = double(nind * nnode))$phi
my.gradient[ , k] <- (phi.epsilon - phi.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
alfie <- predict(out3, modmat = modmat.pred, parm.type = "canon",
se.fit = TRUE, amat = amat)
all.equal(alfie$fit, as.numeric(t(amatmat) %*% fred$fit))
all.equal(alfie$gradient, t(amatmat) %*% fred$gradient)
all.equal(alfie$se.fit, sqrt(diag(alfie$gradient %*% solve(out3$fisher) %*%
t(alfie$gradient))))
##### case 4: model = "cond", obj = "unco", parm = "cano" ####
fred <- predict(out2, modmat = modmat.pred, parm.type = "canon",
model.type = "cond", se.fit = TRUE)
aster:::setfam(fam.default())
beta.hat <- out2$coef
phi.hat <- as.numeric(origin.pred) + as.numeric(sally %*% beta.hat)
theta.hat <- .C(aster:::C_aster_phi2theta,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
phi = as.double(phi.hat),
theta = double(nind * nnode))$theta
all.equal(fred$fit, theta.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out2$fisher) %*%
t(fred$gradient))))
my.gradient <- 0 * fred$gradient
epsilon <- 1e-9
for (k in 1:ncoef) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
phi.epsilon <- as.numeric(origin.pred) + as.numeric(sally %*% beta.epsilon)
theta.epsilon <- .C(aster:::C_aster_phi2theta,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
phi = as.double(phi.epsilon),
theta = double(nind * nnode))$theta
my.gradient[ , k] <- (theta.epsilon - theta.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
alfie <- predict(out2, modmat = modmat.pred, parm.type = "canon",
model.type = "cond", se.fit = TRUE, amat = amat)
all.equal(alfie$fit, as.numeric(t(amatmat) %*% fred$fit))
all.equal(alfie$gradient, t(amatmat) %*% fred$gradient)
all.equal(alfie$se.fit, sqrt(diag(alfie$gradient %*% solve(out2$fisher) %*%
t(alfie$gradient))))
##### case 5: model = "cond", obj = "cond", parm = "mean" ####
root.pred <- matrix(1, nind, nnode)
fred <- predict(out3, modmat = modmat.pred, parm.type = "mean",
model.type = "cond", root = root.pred, x = root.pred)
aster:::setfam(fam.default())
beta.hat <- out3$coef
theta.hat <- as.numeric(sally %*% beta.hat)
xi.hat <- .C(aster:::C_aster_theta2ctau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta.hat),
ctau = double(nind * nnode))$ctau
all.equal(fred, xi.hat)
fred <- predict(out3, modmat = modmat.pred, parm.type = "mean",
model.type = "cond", root = root.pred, x = root.pred, se.fit = TRUE)
all.equal(fred$fit, xi.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out3$fisher) %*%
t(fred$gradient))))
aster:::setfam(fam.default())
my.gradient <- 0 * fred$gradient
epsilon <- 1e-9
for (k in 1:ncoef) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
theta.epsilon <- as.numeric(sally %*% beta.epsilon)
xi.epsilon <- .C(aster:::C_aster_theta2ctau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta.epsilon),
ctau = double(nind * nnode))$ctau
my.gradient[ , k] <- (xi.epsilon - xi.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
##### case 6: model = "unco", obj = "unco", parm = "mean" ####
fred <- predict(out2, modmat = modmat.pred, parm.type = "mean",
root = root.pred)
beta.hat <- out2$coef
beta2tau <- function(beta) {
phi <- origin.pred + matrix(sally %*% beta, nrow = nind)
theta <- .C(aster:::C_aster_phi2theta,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
phi = as.double(phi),
theta = matrix(as.double(0), nind, nnode))$theta
ctau <- .C(aster:::C_aster_theta2ctau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta),
ctau = double(nind * nnode))$ctau
tau <- .C(aster:::C_aster_ctau2tau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
root = as.double(root.pred),
ctau = as.double(ctau),
tau = double(nind * nnode))$tau
return(tau)
}
aster:::setfam(fam.default())
tau.hat <- beta2tau(beta.hat)
all.equal(fred, tau.hat)
fred <- predict(out2, modmat = modmat.pred, parm.type = "mean",
root = root.pred, se.fit = TRUE)
all.equal(fred$fit, tau.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out2$fisher) %*%
t(fred$gradient))))
aster:::setfam(fam.default())
my.gradient <- 0 * fred$gradient
for (k in 1:length(beta.hat)) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
tau.epsilon <- beta2tau(beta.epsilon)
my.gradient[ , k] <- (tau.epsilon - tau.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
##### case 7: model = "cond", obj = "unco", parm = "mean" ####
fred <- predict(out2, modmat = modmat.pred, parm.type = "mean",
model.type = "cond", root = root.pred, x = root.pred)
beta.hat <- out2$coef
beta2xi <- function(beta) {
phi <- origin.pred + matrix(sally %*% beta, nrow = nind)
theta <- .C(aster:::C_aster_phi2theta,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
phi = as.double(phi),
theta = matrix(as.double(0), nind, nnode))$theta
ctau <- .C(aster:::C_aster_theta2ctau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta),
ctau = double(nind * nnode))$ctau
return(ctau)
}
aster:::setfam(fam.default())
xi.hat <- beta2xi(beta.hat)
all.equal(fred, xi.hat)
fred <- predict(out2, modmat = modmat.pred, parm.type = "mean",
model.type = "cond", root = root.pred, x = root.pred, se.fit = TRUE)
all.equal(fred$fit, xi.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out2$fisher) %*%
t(fred$gradient))))
aster:::setfam(fam.default())
my.gradient <- 0 * fred$gradient
for (k in 1:ncoef) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
xi.epsilon <- beta2xi(beta.epsilon)
my.gradient[ , k] <- (xi.epsilon - xi.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
##### case 8: model = "unco", obj = "cond", parm = "mean" ####
fred <- predict(out3, modmat = modmat.pred, root = root.pred)
beta.hat <- out3$coef
beta2tau <- function(beta) {
theta <- matrix(sally %*% beta, nrow = nind)
ctau <- .C(aster:::C_aster_theta2ctau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
theta = as.double(theta),
ctau = double(nind * nnode))$ctau
tau <- .C(aster:::C_aster_ctau2tau,
nind = as.integer(nind),
nnode = as.integer(nnode),
pred = as.integer(pred),
fam = as.integer(fam),
root = as.double(root.pred),
ctau = as.double(ctau),
tau = double(nind * nnode))$tau
return(tau)
}
aster:::setfam(fam.default())
tau.hat <- beta2tau(beta.hat)
all.equal(fred, tau.hat)
fred <- predict(out3, modmat = modmat.pred, root = root.pred, se.fit = TRUE)
all.equal(fred$fit, tau.hat)
all.equal(fred$se.fit, sqrt(diag(fred$gradient %*% solve(out3$fisher) %*%
t(fred$gradient))))
aster:::setfam(fam.default())
my.gradient <- 0 * fred$gradient
for (k in 1:ncoef) {
beta.epsilon <- beta.hat
beta.epsilon[k] <- beta.hat[k] + epsilon
tau.epsilon <- beta2tau(beta.epsilon)
my.gradient[ , k] <- (tau.epsilon - tau.hat) / epsilon
}
all.equal(fred$gradient, my.gradient, tolerance = sqrt(epsilon))
##### HOORAY !!!!! ##### That's it for aster.predict #####
##### now for aster.predict.formula #####
##### case 1: newdata missing
pout2 <- predict(out)
newdata <- data.frame(site = factor(LETTERS[1:4]))
for (v in vars)
newdata[[v]] <- 1
newdata$root <- 1
newdata$foo <- modmat.pred[ , "l2", "foo"]
renewdata <- reshape(newdata, varying = list(vars),
direction = "long", timevar = "varb", times = as.factor(vars),
v.names = "resp")
louise <- predict(out, newdata = renewdata, varvar = varb, idvar = id,
root = root, se.fit = TRUE)
all.equal(louise$modmat, modmat.pred)
fred <- predict(out2, modmat = modmat.pred, root = root.pred, se.fit = TRUE)
all.equal(louise$fit, fred$fit)
all.equal(louise$se.fit, fred$se.fit)
foo <- new.env(parent = emptyenv())
bar <- suppressWarnings(try(load("predict.rda", foo), silent = TRUE))
if (inherits(bar, "try-error")) {
save(sout1, sout2, sout3, pout1, pout2, file = "predict.rda")
} else {
print(all.equal(sout1, foo$sout1))
print(all.equal(sout2, foo$sout2))
print(all.equal(sout3, foo$sout3))
print(all.equal(pout1, foo$pout1))
print(all.equal(pout2, foo$pout2))
}
##### test for global variables #####
saves <- c("out", "renewdata", "out2", "modmat.pred", "root.pred", "louise",
"fred")
rm(list = setdiff(ls(), saves))
ls()
louise.too <- predict(out, newdata = renewdata, varvar = varb, idvar = id,
root = root, se.fit = TRUE)
identical(louise, louise.too)
fred.too <- predict(out2, modmat = modmat.pred, root = root.pred,
se.fit = TRUE)
identical(fred, fred.too)
##### test of newcoef #####
fake <- out2
beta.new <- fake$coefficients + rnorm(length(fake$coefficients)) * 0.1
fake$coefficients <- beta.new
fred.fake <- predict(fake, modmat = modmat.pred, root = root.pred,
se.fit = TRUE)
fred.new <- predict(out2, modmat = modmat.pred, root = root.pred,
se.fit = TRUE, newcoef = beta.new)
identical(fred.fake, fred.new)
|
6c4a97f7f670a2e4a5aa03decb6a0a8c8bdcd7d4
|
325902f26f8df4914f3931d6a3a5c5af12b975b1
|
/R scripts/Protein_examples_zoom.R
|
98add3d4ae4e9ab296e4dbbb99e713dcc412aef3
|
[] |
no_license
|
Rappsilber-Laboratory/ProteomeHD
|
5e9ca415a0dac31ef46972eeff018547b9ee8aeb
|
2ee6d87110b9d4932af0d106927eb289dfbce321
|
refs/heads/master
| 2020-04-28T22:22:08.913323
| 2019-11-05T12:24:45
| 2019-11-05T12:24:45
| 175,614,695
| 8
| 0
| null | 2019-03-14T12:10:33
| 2019-03-14T12:10:32
| null |
UTF-8
|
R
| false
| false
| 7,099
|
r
|
Protein_examples_zoom.R
|
## In this script we analyse the distribution of four example proteins in the coregulation map: An uncharacterised microprotein,
## an uncharacterised protein without coregulation partners above threshold and two multifunctional proteins
# Load the required libraries
library(data.table); library(ggplot2); library(gridExtra); library(grid)
#### Prepare the data ####
# Load coregulation scores and annotate arbitrary threshold
tC <- fread("coregulation_scores.csv")
n_coreg <- floor( tC[,.N] * 0.005 ) # We arbitrarily define the highest scoring 0.5% of protein pairs as "co-regulated". Assign those.
score_cut_off <- tC[ order(-coregulation_score) # What score cut-off does that correspond to?
][ n_coreg , coregulation_score ]
tC[ coregulation_score >= score_cut_off , coregulated := TRUE ] # Assign the term
# Load the tSNE map coordinates
SNE <- fread("tSNE_coordinates_and_annotation.csv")
# Add Gene names to the SNE map
ProHD <- fread("ProteomeHD_v1_1.csv")
ProHD <- ProHD[, .(Majority_protein_IDs, Gene_names)]
SNE <- merge(SNE, ProHD, by.x = "ProteinID", by.y = "Majority_protein_IDs")
# A function to "weight" the tSNE map by the treeClust score for a target protein
make_custom_SNE <- function(x){
d1 <- tC[ Protein_1 == x , .(Target = Protein_1, Partner = Protein_2, coregulated) ]
d2 <- tC[ Protein_2 == x , .(Target = Protein_2, Partner = Protein_1, coregulated) ]
d3 <- rbind(d1, d2)
d4 <- merge(SNE, d3, by.x = "ProteinID", by.y = "Partner", all.x = TRUE)
return( d4[, .(ProteinID, coregulated) ] )
}
#### Get coregulation partners for the selected proteins of interest ####
# Use the custom function to assign coregulation partners
TMEM256 <- make_custom_SNE( "Q8N2U0" )
HEATR5B <- make_custom_SNE( "Q9P2D3;Q9P2D3-3" )
DDX3X <- make_custom_SNE( "O00571;O00571-2" )
PHB <- make_custom_SNE( "P35232" )
# Modify colnames
names(TMEM256)[ names(TMEM256) == "coregulated" ] <- "coreg_with_TMEM256"
names(HEATR5B)[ names(HEATR5B) == "coregulated" ] <- "coreg_with_HEATR5B"
names(DDX3X)[ names(DDX3X) == "coregulated" ] <- "coreg_with_DDX3X"
names(PHB)[ names(PHB) == "coregulated" ] <- "coreg_with_PHB"
# Merge into one table, together with tSNE coordinates
SNE <- merge( SNE, TMEM256 )
SNE <- merge( SNE, HEATR5B )
SNE <- merge( SNE, DDX3X )
SNE <- merge( SNE, PHB )
#### Uncharacterised proteins: TMEM256 and HEATR5B ####
# Set TMEM256 zoom region
TMEM256_zoom_x <- c( 5, 11 )
TMEM256_zoom_y <- c( -48, -42 )
# Set HEATR5B zoom region
HEATR5B_zoom_x <- c( 35, 37)
HEATR5B_zoom_y <- c( 3, 6)
# Global map with the zoomed regions annotated
p1 <- ggplot(SNE, aes( x = tSNE_x_dim, y = tSNE_y_dim ))+
geom_point(shape = 16, size = 0.1, colour = "grey50")+
geom_point(data = SNE[ coreg_with_TMEM256 == TRUE ], shape = 16, size = 0.2, alpha = 0.5, colour = "royalblue4")+
geom_point(data = SNE[ coreg_with_HEATR5B == TRUE ], shape = 16, size = 0.2, alpha = 0.5, colour = "red")+
annotate("rect", xmin = TMEM256_zoom_x[1], xmax = TMEM256_zoom_x[2], ymin = TMEM256_zoom_y[1], ymax = TMEM256_zoom_y[2], colour="black", fill=NA, size=0.25)+
annotate("rect", xmin = HEATR5B_zoom_x[1], xmax = HEATR5B_zoom_x[2], ymin = HEATR5B_zoom_y[1], ymax = HEATR5B_zoom_y[2], colour="black", fill=NA, size=0.25)+
theme(panel.background=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(),
panel.border=element_rect(fill=NA, colour="black", size=0.25), panel.grid.major=element_blank(),
axis.title=element_blank(), legend.position = "none", plot.margin = unit( c(0.5, 0.5, 0.5, 0.5), "mm"))
p1
## Map zoom for TMEM256
# In this zoom I want to show the enrichment for GO term "mitochondrion inner membrane" (GO:0005743). Download list of UniProt IDs
# associated with this term from QuickGO and load here
mito_IMM <- fread("QuickGO_mito_IMM.tsv")
# Simplify protein IDs for assigning subcellular location
SNE[, SimpleID := gsub(";.+", "", ProteinID) ][, SimpleID := gsub("-.+", "", SimpleID) ]
pTMEM256 <- ggplot(SNE, aes( x = tSNE_x_dim, y = tSNE_y_dim))+
geom_point(size = 0.6, shape = 16, colour = "grey50" )+
geom_point(data = SNE[ coreg_with_TMEM256 == TRUE], shape = 16, size = 0.6, colour = "royalblue4")+
geom_point(data = SNE[ SimpleID %in% mito_IMM$`GENE PRODUCT ID` ], shape = 21, size = 3, colour = "darkorange")+
geom_point(data = SNE[ Gene_names == "TMEM256" ], shape=16, size = 2, colour = "royalblue4")+
geom_text( data = SNE[ Gene_names == "TMEM256" ], aes(label = Gene_names), size = 3, colour="royalblue4")+
xlim( TMEM256_zoom_x )+
ylim( TMEM256_zoom_y )+
theme(panel.background=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(),
panel.border=element_rect(fill=NA, colour="black", size=0.25), panel.grid.major=element_blank(),
axis.title=element_blank(), legend.position = "top", plot.margin = unit( c(0.5, 0.5, 0.5, 0.5), "mm"))
pTMEM256
# Map zoom for HEATR5B
pHEATR5B <- ggplot(SNE, aes( x = tSNE_x_dim, y = tSNE_y_dim))+
geom_point(size = 0.6, shape = 16, colour = "grey50" )+
geom_point(data = SNE[ coreg_with_HEATR5B == TRUE], shape = 16, size = 0.6, colour = "red")+
geom_point(data = SNE[ Gene_names == "HEATR5B" ], shape=16, size = 2, colour = "red")+
geom_text( aes(label = Gene_names), size = 3 , hjust = -0.1)+
xlim( HEATR5B_zoom_x )+
ylim( HEATR5B_zoom_y )+
theme(panel.background=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(),
panel.border=element_rect(fill=NA, colour="black", size=0.25), panel.grid.major=element_blank(),
axis.title=element_blank(), legend.position = "top", plot.margin = unit( c(0.5, 0.5, 0.5, 0.5), "mm"))
pHEATR5B
# Output combined plot
p1b <- arrangeGrob(pHEATR5B, pTMEM256)
pUnc <- arrangeGrob(p1, p1b, nrow = 1)
grid.draw(pUnc)
ggsave("TMEM256_HEATR5B_plot.pdf", pUnc, width=9, height=4.5, units=c("cm"))
#### Multifunctional proteins: DDX3X AND prohibitin ####
# Global map with the co-regulated proteins shown
p2 <- ggplot(SNE, aes( x = tSNE_x_dim, y = tSNE_y_dim ))+
geom_point(shape = 16, size = 0.1, colour = "grey50")+
geom_point(data = SNE[ coreg_with_DDX3X == TRUE ], shape = 16, size = 0.2, alpha = 0.5, colour = "orangered")+
geom_point(data = SNE[ coreg_with_PHB == TRUE ], shape = 16, size = 0.2, alpha = 0.5, colour = "deepskyblue1")+
theme(panel.background=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(),
panel.border=element_rect(fill=NA, colour="black", size=0.25), panel.grid.major=element_blank(),
axis.title=element_blank(), legend.position = "none", plot.margin = unit( c(0.5, 0.5, 0.5, 0.5), "mm"))
p2
# Save plot
ggsave("DDX3X_PHB.pdf", p2, width=4.5, height=4.5, units=c("cm"))
|
32161ff44f3983f7a36ff985cefdbb4e7c1ce135
|
104e81350a792f38d9522857cfbad03836ef7683
|
/Week1/htmlDownload.R
|
ed292b98f6ace500fae02bd44930f705fcdd6c3f
|
[] |
no_license
|
jtantongco/Coursera_GetCleanData
|
bcf184d13e0e245b204b784871330f616b3086b4
|
080ff4bd328942d0d0d68043b52cb5fc48fd653a
|
refs/heads/master
| 2021-01-01T15:36:18.433559
| 2014-08-24T09:22:21
| 2014-08-24T09:22:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 287
|
r
|
htmlDownload.R
|
library(XML)
fileUrl <- "http://espn.go.com/nfl/team/_/name/bal/baltimore-ravens"
doc <- htmlTreeParse(fileUrl, useInternal=TRUE)
scores <- xpathSApply(doc, "//li[@class='score']", xmlValue) #seems to return empty
teams <- xpathSApply(doc,"//li[@class='team-name']", xmlValue) #is good
|
38ba394aa46c2540bd30a5189a91dfe1f69920e5
|
afe39a330e68856413be87018519f7119dde6508
|
/R/create_resource.R
|
48cb36254855e2251a580e1d4dbfcbeeb5f5df8b
|
[] |
no_license
|
jchrom/trelloR
|
4a142222c34d480b25b7f3fd75614c6af1cf66eb
|
cca04eb70bf5060a7c7f858fa9911bd8c68e2089
|
refs/heads/master
| 2023-08-31T06:33:57.940993
| 2023-08-27T18:09:07
| 2023-08-27T18:09:07
| 34,352,839
| 40
| 12
| null | 2017-01-21T19:47:02
| 2015-04-21T21:20:56
|
R
|
UTF-8
|
R
| false
| false
| 2,484
|
r
|
create_resource.R
|
#' Create Resources
#'
#' Create resources via Trello API.
#'
#' See [Trello API reference](https://developer.atlassian.com/cloud/trello/rest)
#' for more info about what elements can be included in POST request body.
#'
#' @param resource Model name, eg. `"card"`.
#' @param id Model id.
#' @param path Path.
#' @param body A named list.
#' @param token An object of class `"Trello_API_token"`, a path or `NULL`.
#'
#' * If a `Token`, it is passed as is.
#' * If `NULL` and a cache file called `".httr-oauth"` exists, the newest token
#' is read from it. If the file is not found, an error is thrown.
#' * If a character vector of length 1, it will be used as an alternative path
#' to the cache file.
#'
#' @param verbose Whether to pass [httr::verbose()] to [httr::RETRY()].
#' @param on.error Behavior when HTTP status >= 300, defaults to `"stop"`.
#' @param handle Passed to [httr::RETRY()].
#' @param encode,response Deprecated.
#'
#' @family functions to create resources
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' # Get token with write access
#' key = Sys.getenv("MY_TRELLO_KEY")
#' secret = Sys.getenv("MY_TRELLO_SECRET")
#'
#' token = get_token("my_app", key = key, secret = secret,
#' scope = c("read", "write"))
#'
#' # Get board ID
#' url = "Your board URL"
#' bid = get_id_board(url, token)
#'
#' # Get lists on that board, extract ID of the first one
#' lid = get_board_lists(bid, token)$id[1]
#'
#' # Content for the new card
#' payload = list(
#' idList = lid,
#' name = "A new card",
#' desc = "#This card has been created by trelloR",
#' pos = "bottom"
#' )
#'
#' # Create card and store the response (to capture the ID
#' # of the newly created resource)
#' r = create_resource("card", body = payload, token = token)
#'
#' # Get ID of the new card
#' r$id
#' }
create_resource = function(resource, id = NULL, path = NULL,
body = list(name = "New"), token = NULL,
on.error = c("stop", "warn", "message"),
verbose = FALSE, handle = NULL,
encode, response) {
warn_for_argument(encode)
warn_for_argument(response)
url = httr::modify_url(
url = "https://api.trello.com",
path = c(1, paste0(resource, "s"), id, path)
)
trello_api_verb("POST", url = url, times = 1L, handle = handle,
token = token, verbose = verbose, body = body,
on.error = on.error)
}
|
17289ae45f927b9fc86c7967a20242ecdcb704bd
|
de8336641d9f4ee5295a9e42fff7b4f742281338
|
/tests/testthat/test-pick_UScode.R
|
f114cfecf5ca8f883688c165ed88b8ebbc260e00
|
[
"MIT"
] |
permissive
|
sjmarks/Birdr
|
44b8e2abe13ccacdf32073e757121f4e3853ebe5
|
2183cbb28272d6b96f0635c21987fd6d06e471c6
|
refs/heads/master
| 2022-06-07T03:04:35.841992
| 2020-05-04T04:14:29
| 2020-05-04T04:14:29
| 260,333,483
| 2
| 2
|
MIT
| 2020-05-04T04:14:30
| 2020-04-30T22:40:53
|
R
|
UTF-8
|
R
| false
| false
| 286
|
r
|
test-pick_UScode.R
|
test_that("pickUSCode returns proper name and code", {
correct_result <- tidyr::tibble(code = "US-CA-079", name = "San Luis Obispo")
my_result <- pick_UScode(state = "California", county = "San Luis Obispo", ebirdkey = "rqksong3qcbm")
expect_equal(my_result, correct_result)
})
|
3c5a60b9d187b32c6037ebeea5ef336cb962c8b7
|
53e56475273a36deeb2d1f133947f531e4e3871a
|
/proj_1/sampleMultinomial.R
|
8fc3017cee09f6168b8a512ed0f3f3ff58f3b428
|
[] |
no_license
|
Marvedog/Tma4300-Kode
|
34af409101da8471be4853f4ec07ed7d986a23bb
|
d8af15888f6078030a9834fecddb30fec7b3fc9d
|
refs/heads/master
| 2021-05-12T06:52:51.580650
| 2018-02-09T09:01:25
| 2018-02-09T09:01:25
| 117,229,525
| 0
| 1
| null | 2018-02-02T07:48:04
| 2018-01-12T10:45:57
|
R
|
UTF-8
|
R
| false
| false
| 641
|
r
|
sampleMultinomial.R
|
# -------------- Multinomial sampling -----------------#
# Input:
# p : Sorted list of probabilities summing to unity.
# N : Number of draws
# Output:
# out: vector of draws corresponding to each interval
sampleMultinomial <- function(p, N) {
len_p <- length(p)
# Compute cumulative sum
cumSum <- matrix(0,len_p, 1)
cumSum[1] <- p[1]
for (i in 2:len_p) {
cumSum[i] <- cumSum[i-1] + p[i]
}
u <- runif(N)
# Increment output draws
out <- matrix(0, len_p, 1)
for (i in 1:N) {
for (j in 1:len_p) {
if (u[i] < cumSum[j]) {
out[j] <- out[j] + 1
break
}
}
}
return (out)
}
|
623e52504b377e12623dc0325c265163f598f73b
|
9c4fd75b5fa36683a7f72598d5b7ffb3e155fa74
|
/man/dput_levels.Rd
|
7a9c6bdf5a40d17ca612a47c8a7919e50376a577
|
[] |
no_license
|
gridl/thinkr
|
3c291d6ec16c83c32f596064493f4178977f7714
|
24f85da8c65e77b0f36cd80b7d40389e0ba7b92a
|
refs/heads/master
| 2020-03-23T03:17:16.909851
| 2018-06-21T04:15:31
| 2018-06-21T04:15:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 402
|
rd
|
dput_levels.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/levels_to_vec.R
\name{dput_levels}
\alias{dput_levels}
\title{return R instruction to create levels}
\usage{
dput_levels(vec)
}
\arguments{
\item{vec}{a factor or character vector}
}
\value{
a R instruction
}
\description{
return R instruction to create levels
}
\examples{
dput_levels(iris$Species)
}
|
b001b7f7bd00b59a34c1d09f2ca8e184ee209281
|
50291a5d7e7652f8dcfbfb718906f4ba0b8964e7
|
/ui.R
|
d83c90417c1a28dc44a4dc38546d54532c49755a
|
[] |
no_license
|
bborgesr/reactnb
|
e9f94f00951299519f7968d61e48a75d345d7506
|
0083b79c560ecc1dd6b38221fe0bbcc75ad48b72
|
refs/heads/master
| 2021-08-22T16:53:56.972462
| 2017-11-30T17:56:32
| 2017-11-30T17:56:32
| 119,948,057
| 1
| 0
| null | 2018-02-02T07:28:43
| 2018-02-02T07:28:43
| null |
UTF-8
|
R
| false
| false
| 682
|
r
|
ui.R
|
shinyUI(basicPage(
tags$link(rel='stylesheet', type='text/css', href='lib/jqueryui/css/ui-lightness/jquery-ui-1.10.3.custom.css'),
tags$script(src='lib/jqueryui/js/jquery-ui-1.10.3.custom.js'),
tags$script(src='lib/jquery.ui.touch-punch.min.js'),
tags$script(src='reactnb.js'),
tags$link(rel='stylesheet', type='text/css', href='reactnb.css'),
tags$link(rel='stylesheet', type='text/css', href='reactnb-hi.css'),
tags$div(id='output'),
tags$div(class='command_container',
'Type an R expression to observe:',
tags$input(type='command', id='command', class='reactnb-command',
autocomplete='off', autocorrect='off')
),
tags$div(id="trash")
))
|
63d5e1962e11107df6c82eec599dbacb6856038c
|
f7a0f3cbeefdc01fc0f172a47359c0c4610c95a7
|
/code_active/sim_analysis_looping_info_1.R
|
93157ed8c287ec91e07d04bb3d727e609354f40a
|
[] |
no_license
|
EESI/exploring_thematic_structure
|
65e77efbb56fea646a9f165eaa94f955f68259ff
|
06f7ea096c31dbb63b09fc117ee22411e52ab60e
|
refs/heads/master
| 2020-08-25T03:30:04.253394
| 2019-10-23T03:02:18
| 2019-10-23T03:02:18
| 216,955,082
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 30,832
|
r
|
sim_analysis_looping_info_1.R
|
jsd <- function(p,q){
m <- .5*(p+q)
sqrt(.5*(sum(p*log(p/m)) + sum(q*log(q/m))))
}
entropy <- function(x){
x <- x[x!=0]
f <- x/sum(x)
-sum(f * log2(f))
}
norm10 <- function(x) (x-min(x))/(max(x)-min(x))
bcd <- function(x,y) sum(abs(x-y)/sum(x+y))
N_OTU <- 500
N_SAMP <- 100
SC_P <- c(.1,.25,.50,.75)
SC_N <- 10
SC_SAMP_P <- .75
N_SC <- 5
param_subset_idx <- with(param_grid,which(n_otu %in% N_OTU & n_samp %in% N_SAMP & sc_p %in% SC_P & sc_n %in% SC_N & sc_samp_p %in% SC_SAMP_P))
fns_subset <- paste0('data/dat_',param_subset_idx,'.rds')
LIST_SUBSET <- vector(mode='list',length=length(fns_subset))
for (i in seq_along(fns_subset)) LIST_SUBSET[[i]] <- readRDS(file.path(sim_dir,fns_subset[i]))
param_values <- param_grid[param_subset_idx,]
param_values$idx <- as.integer(rownames(param_values))
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(15)))
P_MIN <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
E_MIN <- E_MEAN <- P_MEAN <- P_MIN
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {P_MIN[p,m] <- P_MEAN[p,m] <- E_MEAN[p,m] <- E_MIN[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,paste0('otu',OTUS[sc,])])
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2[,paste0('otu',OTUS[sc,])]
TOP_SC <- TOP_SC/rowSums(TOP_SC)
TOP_SC <- TOP_SC[,names(which(!(OTU_SC==0)))]
OTU_SC <- OTU_SC[names(which(!(OTU_SC==0)))]
apply(TOP_SC,1,function(p) jsd(p,OTU_SC))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
NSCORE_MIN <- matrix(NA,nrow(SCORE),ncol(SCORE),dimnames=dimnames(SCORE))
for (i in 1:NROW(NSCORE_MIN)){
for (j in 1:NCOL(NSCORE_MIN)){
NSCORE_MIN[i,j] <- SCORE[i,j]/min(SCORE[i,-j])
}
}
NSCORE_MEAN <- matrix(NA,nrow(SCORE),ncol(SCORE),dimnames=dimnames(SCORE))
for (i in 1:NROW(NSCORE_MEAN)){
for (j in 1:NCOL(NSCORE_MEAN)){
NSCORE_MEAN[i,j] <- SCORE[i,j]/median(SCORE[i,-j]) ### using median
}
}
NSCORE_MIN_THRES <- 1*(NSCORE_MIN<1)
nscore_min_prop <- 1-mean(colSums(NSCORE_MIN_THRES)==0)
nscore_min_entr <- sum(apply(NSCORE_MIN_THRES,1,function(x) entropy(table(which(x==1)))))
NSCORE_MEAN_THRES <- 1*(NSCORE_MEAN<1)
nscore_mean_prop <- 1-mean(colSums(NSCORE_MEAN_THRES)==0)
nscore_mean_entr <- sum(apply(NSCORE_MEAN_THRES,1,function(x) entropy(table(which(x==1)))))
P_MIN[p,m] <- nscore_min_prop
E_MIN[p,m] <- nscore_min_entr
P_MEAN[p,m] <- nscore_mean_prop
E_MEAN[p,m] <- nscore_mean_entr
}
}
P <- param_values %>%
left_join(data.frame(P_MIN,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(E_MEAN,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.pmin',model),
model=gsub('\\.y','\\.emean',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(emean,pmin,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_x_reverse() +
scale_colour_brewer(type='qual',palette=2)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(25)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
#GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,paste0('otu',OTUS[sc,])])
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2[,paste0('otu',OTUS[sc,])]
TOP_SC <- TOP_SC/rowSums(TOP_SC)
TOP_SC <- TOP_SC[,names(which(!(OTU_SC==0)))]
OTU_SC <- OTU_SC[names(which(!(OTU_SC==0)))]
apply(TOP_SC,1,function(p) jsd(p,OTU_SC))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (all(col_thres != 0)){
break
}
}
TH[p,m] <- thres
S[p,m] <- sum((rowSums(1*(SCORE<thres))))/ncol(SCORE)
}
}
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.threshold',model),
model=gsub('\\.y','\\.score',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(25)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- min(BETA2)
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2
TOP_SC <- TOP_SC/rowSums(TOP_SC)
TOP_SC <- TOP_SC[,names(which(!(OTU_SC==0)))]
OTU_SC <- OTU_SC[names(which(!(OTU_SC==0)))]
apply(TOP_SC,1,function(p) jsd(p,OTU_SC))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (all(col_thres != 0)){
break
}
}
TH[p,m] <- thres
S[p,m] <- sum((rowSums(1*(SCORE<thres))))/ncol(SCORE)
}
}
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.threshold',model),
model=gsub('\\.y','\\.score',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(15)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- min(BETA2)
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2
#TOP_SC <- TOP_SC/rowSums(TOP_SC)
TOP_SC <- TOP_SC[,names(which(!(OTU_SC==0)))]
OTU_SC <- OTU_SC[names(which(!(OTU_SC==0)))]
apply(TOP_SC,1,function(p) jsd(p,OTU_SC))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) != 1)/K
}
}
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.threshold',model),
model=gsub('\\.y','\\.score',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
xlim(0,1) + ylim(0,1) +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(15)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- NA
OTU_SC <- dense_rank(dplyr::desc(OTU_SC))
OTU_SC[is.na(OTU_SC)] <- length(OTU_SC)
names(OTU_SC) <- colnames(OTU)
OTU_SC <- OTU_SC[colnames(BETA2)]
TOP_SC <- t(apply(BETA2,1,function(x) dense_rank(dplyr::desc(x))))
dimnames(TOP_SC) <- dimnames(BETA2)
apply(TOP_SC,1,function(p) cor(p,OTU_SC,method='kendall'))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) != 1)/K
}
}
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.threshold',model),
model=gsub('\\.y','\\.score',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
scale_x_reverse() +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(15)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,paste0('otu',OTUS[sc,])])
OTU_SC <- dense_rank(dplyr::desc(OTU_SC))
names(OTU_SC) <- paste0('otu',OTUS[sc,])
TOP_SC <- t(apply(BETA2,1,function(x) dense_rank(dplyr::desc(x))))
dimnames(TOP_SC) <- dimnames(BETA2)
TOP_SC <- TOP_SC[,paste0('otu',OTUS[sc,])]
apply(TOP_SC,1,function(p) cor(p,OTU_SC,method='kendall'))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) != 1)/K
}
}
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
mutate(model=gsub('\\.x','\\.threshold',model),
model=gsub('\\.y','\\.score',model)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) + geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
scale_x_reverse() +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(15)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- MAX50 <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
q_rank <- -qnormalize(1:N_OTU)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,paste0('otu',OTUS[sc,])])
OTU_SC <- q_rank[dense_rank(dplyr::desc(OTU_SC))]
names(OTU_SC) <- paste0('otu',OTUS[sc,])
TOP_SC <- t(apply(BETA2,1,function(x) q_rank[dense_rank(dplyr::desc(x))]))
dimnames(TOP_SC) <- dimnames(BETA2)
TOP_SC <- TOP_SC[,paste0('otu',OTUS[sc,])]
apply(TOP_SC,1,function(p) sqrt(sum(p - OTU_SC)^2))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
MAX50[p,m] <- median(apply(SCORE,2,max))
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) != 1)/K
}
}
colnames(MAX50) <- paste0(colnames(MAX50),'.max50')
colnames(TH) <- paste0(colnames(TH),'.threshold')
colnames(S) <- paste0(colnames(S),'.score')
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(P)),by='idx') %>%
left_join(data.frame(MAX50,idx=1:nrow(P)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) +
geom_point(size=5) + facet_grid(sc_p~sc_m) +
geom_vline(aes(xintercept=max50,colour=model),size=1,alpha=.7) +
scale_colour_brewer(type='qual',palette=2) +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(50)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- MAX50 <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- 0
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2
TOP_SC[TOP_SC<1e-300] <- 1e-300
apply(TOP_SC,1,function(p) sum(ifelse(OTU_SC==0,0,OTU_SC*log(OTU_SC/p))))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
MAX50[p,m] <- median(apply(SCORE,2,min))
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) > 1)/K
}
}
colnames(MAX50) <- paste0(colnames(MAX50),'.max50')
colnames(TH) <- paste0(colnames(TH),'.threshold')
colnames(S) <- paste0(colnames(S),'.score')
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(TH)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(S)),by='idx') %>%
left_join(data.frame(MAX50,idx=1:nrow(MAX50)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) +
geom_vline(aes(xintercept=max50,colour=model),size=1,alpha=.5,linetype=2) +
geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(50)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- MAX50 <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
#OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- 0
OTU_SC <- OTU_SC/sum(OTU_SC)
TOP_SC <- BETA2
TOP_SC[TOP_SC<1e-300] <- 1e-300
apply(TOP_SC,1,function(p) sum(ifelse(OTU_SC==0,0,OTU_SC*log(OTU_SC/p))))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
MAX50[p,m] <- median(apply(SCORE,2,min))
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) > 1)/K
}
}
colnames(MAX50) <- paste0(colnames(MAX50),'.max50')
colnames(TH) <- paste0(colnames(TH),'.threshold')
colnames(S) <- paste0(colnames(S),'.score')
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(TH)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(S)),by='idx') %>%
left_join(data.frame(MAX50,idx=1:nrow(MAX50)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) +
geom_vline(aes(xintercept=max50,colour=model),size=1,alpha=.5,linetype=2) +
geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
theme(aspect.ratio=1)
model_param_values <- with(LIST_SUBSET[[1]]$model_params,which(model %in% c('unsup') & K %in% c(50)))
S <- matrix(0.0,nrow(param_values),length(model_param_values),
dimnames=list(1:nrow(param_values),LIST_SUBSET[[1]]$model_params[model_param_values,'ps']))
TH <- MAX50 <- S
for (p in seq_along(param_values$idx)){
for (m in seq_along(model_param_values)){
MM <- LIST_SUBSET[[p]]
M <- MM$out[[model_param_values[m]]]
if (class(M$data$fit) == 'try-error') {S[p,m] <- TH[p,m] <- NA; next}
K <- M$data$fit$settings$dim$K
VOCAB <- M$data$fit$vocab
OTUS <- M$data$sim$sc_otus
SAMPS <- M$data$sim$samples
LOGBETA2 <- M$data$fit$beta$logbeta[[1]]
LOGBETA2[LOGBETA2==-Inf] <- min(LOGBETA2[LOGBETA2>-Inf])
BETA2 <- exp(LOGBETA2)
colnames(BETA2) <- VOCAB
OTU <- t(otu_table(M$data$ps))
META <- sample_data(M$data$ps)
SIG <- M$results$sig
sc_df <- data.frame(sc=paste0('sc',1:(3*N_SC)),
sc_idx=paste0('sc',rep(1:5,3)),
sc_type=rep(c('1','0','b'),each=N_SC))
G1 <- rownames(OTU)[rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2))]
G2 <- rownames(OTU)[!(rownames(OTU) %in% paste0('s',1:(nrow(OTU)/2)))]
GB <- rownames(OTU)
G <- list(G1=G1,G2=G2,GB=GB)
SCORE <- sapply(1:nrow(OTUS), function(sc) {
#GROUP <- G[[ifelse(sc <= 5, 1, ifelse(sc >= 10, 3, 2))]]
GROUP <- rownames(OTU)[rownames(OTU) %in% paste0('s',SAMPS[[sc]])]
OTU_SC <- colSums(OTU[GROUP,])
OTU_SC <- OTU_SC[colnames(BETA2)]
OTU_SC <- OTU_SC/sum(OTU_SC)
OTU_SC[!(names(OTU_SC) %in% paste0('otu',OTUS[sc,]))] <- 0
TOP_SC <- BETA2
TOP_SC[TOP_SC<1e-300] <- 1e-300
apply(TOP_SC,1,function(p) bcd(p,OTU_SC))
})
dimnames(SCORE) <- list(paste0('T',1:nrow(SCORE)),paste0('sc',1:ncol(SCORE)))
THRES <- seq(min(SCORE),max(SCORE),by=.01)
for (thres in THRES){
col_thres <- colSums(1*(SCORE<thres))
if (sum(col_thres) >= K){
thres <- thres_last
break
}
thres_last <- thres
}
MAX50[p,m] <- median(apply(SCORE,2,min))
TH[p,m] <- thres
S[p,m] <- sum(rowSums(1*(SCORE<thres)) > 1)/K
}
}
colnames(MAX50) <- paste0(colnames(MAX50),'.max50')
colnames(TH) <- paste0(colnames(TH),'.threshold')
colnames(S) <- paste0(colnames(S),'.score')
P <- param_values %>%
left_join(data.frame(TH,idx=1:nrow(TH)),by='idx') %>%
left_join(data.frame(S,idx=1:nrow(S)),by='idx') %>%
left_join(data.frame(MAX50,idx=1:nrow(MAX50)),by='idx') %>%
gather(model,score,-(n_otu:idx)) %>%
separate(model,c('model','stat'),sep='\\.') %>%
spread(stat,score)
P %>%
ggplot(aes(threshold,score,shape=model,colour=model)) +
geom_vline(aes(xintercept=max50,colour=model),size=1,alpha=.5,linetype=2) +
geom_point(size=5) + facet_grid(sc_p~sc_m) +
scale_colour_brewer(type='qual',palette=2) +
theme(aspect.ratio=1)
|
5f142a713c53ea4a45d0ff3b11740b18bc6cd06d
|
79aa26d2d18b78dce81bb799bedd74d0ac0c47d9
|
/man/select2.Rd
|
feb18f7845b33ae038d1fa18458d4ca5db1d69dc
|
[] |
no_license
|
MadsQE/minidplyr
|
729fe7d5409959a21d7fb08cb5381d6d7ab2606f
|
dac9172150bccd7e796eadffaba1a2ab382e3e3f
|
refs/heads/main
| 2023-07-16T21:13:46.823292
| 2021-08-26T09:34:53
| 2021-08-26T09:34:53
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 373
|
rd
|
select2.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/select.R
\name{select2}
\alias{select2}
\title{Select some columns}
\usage{
select2(df, names_or_ind)
}
\arguments{
\item{df}{A data frame}
\item{names_or_ind}{A vector of column names or indices}
}
\value{
An other data frame, with the subsetted columns.
}
\description{
Select some columns
}
|
82edd76f78571c515f71a3f5f42cda08d79a104e
|
447418d950c976e5dd9ead3722399f927e29fb7c
|
/Factors.R
|
6c2b6d9f332a0f392b549aeb1d9f53ee509fc89b
|
[] |
no_license
|
archits14/datasciencecoursera
|
9678c4ade7410dc7f11554ab5753aa1c57647c0a
|
7b08290838f390f377608170f0600c76ba96f4f8
|
refs/heads/master
| 2022-12-14T07:57:49.002406
| 2020-09-05T03:24:25
| 2020-09-05T03:24:25
| 291,470,543
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 178
|
r
|
Factors.R
|
temps <- c('cold','med','hot','hot','hot','cold','med')
summary(temps)
fact.temp <- factor(temps, ordered = TRUE, levels = c('cold','med','hot'))
fact.temp
summary(fact.temp)
|
20548dbb89198b87b644e2f7923604fbd009265a
|
94da0f2f5d7fce4f84d4a540dd0edfed09b264e4
|
/R/sumZero.R
|
ae7c1704d04f97aa87e9d8d723d64d0734bef29a
|
[] |
no_license
|
karirogg/SirKR
|
6f02cd2f80cf29ef38b600cbfd54f79ba315a6ef
|
f88039ef5c7e918a08d2d20d02d64df26ffe3059
|
refs/heads/master
| 2021-05-29T15:49:41.948932
| 2015-04-11T00:02:46
| 2015-04-11T00:02:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 812
|
r
|
sumZero.R
|
#' Sum of Vector Elements
#'
#' Takes in a numeric vector and returns the sum of it. If at least one entry is numeric, then the function returns 0. If no entry is a value, then it returns NA.
#' @param x A numeric vector to find the sum of
#' @return The sum of the vector x
#' @examples
#' x <- c(NA,2)
#' sumZero(x)
#' returns: 0
#' @export
sumZero <- function(x) {
if(!(class(x) %in% c("numeric", "logical","integer"))) {
stop("The input value is not able to convert to a numeric vector")
}
returnValue = 0
if(all(is.na(x)) == TRUE) {
returnValue = sum(x, na.rm = F)
} else if(is.na(x[1]) & !is.na(x[2]) | !is.na(x[1]) & is.na(x[2])) {
returnValue = 0
} else {
returnValue = sum(x, na.rm = T)
}
returnValue
}
|
581ba27e784b1b41178c61d555e590d6b7da6cf3
|
4d6a64ab3bc0813e6888d58eeff8a809739f826b
|
/FishBase/R/fb_ration.R
|
7f66b730d034167a7b103c9637f4f03c71da5f50
|
[
"Apache-2.0"
] |
permissive
|
cornejotux/FishBaseR
|
2d54075078238c89887ed8356b878c6f1cb7b900
|
72fbabbda485583fbcf06fb5502593e0f6e7fe7a
|
refs/heads/master
| 2022-12-25T18:03:43.153346
| 2020-09-29T20:13:50
| 2020-09-29T20:13:50
| 299,727,130
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,443
|
r
|
fb_ration.R
|
fb_ration <-
function(idFB = NA, Genus = NA, Species = NA, server = 'http://www.fishbase.tw/')
{
require("XML")
require("stringr")
require('RCurl')
if (is.na(idFB))
{
ids <- fb_ids(Genus=Genus, Species=Species, server = server)
idFB <- ids$idFB
StockCode <- ids$StockCode
}
if(!is.na(idFB))
{
url <- paste(server, "TrophicEco/RationList.php?ID=", idFB, sep = "")
s <- readHTMLTable(url, as.data.frame=TRUE) ## I got the file as a XML class
if (is.null(s$dataTable))
{
diet <- c(as.character(idFB), as.character(Genus), as.character(Species), t(rep(NA, 7)))
names(diet) <- c('idFB', 'Genus', 'Species', 'Weight', 'Ration', 'K1', 'EvacRate', 'Temperature', 'Salinity', 'FoodI')
}
if (!is.null(s$dataTable))
{
diet <- cbind(as.character(idFB), as.character(Genus), as.character(Species), s$dataTable)
names(diet) <- c('idFB', 'Genus', 'Species', 'Weight', 'Ration', 'K1', 'EvacRate', 'Temperature', 'Salinity', 'FoodI')
diet$idFB <- as.character(diet$idFB)
diet$Genus <- as.character(diet$Genus)
diet$Species <- as.character(diet$Species)
diet$Weight <- as.character(diet$Weight)
diet$Ration <- as.character(diet$Ration)
diet$K1 <- as.character(diet$K1)
diet$EvacRate <- as.character(diet$EvacRate)
diet$Temperature <- as.character(diet$Temperature)
diet$Salinity <- as.character(diet$Salinity)
diet$FoodI <- as.character(diet$FoodI)
}
}
return(diet)
}
|
fdd7796b725ecd7c5fdcaa5581783410bf2f3eae
|
47bb25ddbf692279a80e28ecaadeffbfd28973d5
|
/tests/testthat/test-advent.R
|
8717155e334ef55f02b7034791b574fa09d79234
|
[] |
no_license
|
MatMoore/adventofcode2019
|
d7c28cd7c9650d8ba9472506b4d71a5e82035964
|
95db2b43dd9b3647b93ef0e75d337a1e1df1ecae
|
refs/heads/master
| 2020-09-22T14:33:08.996773
| 2019-12-06T22:00:47
| 2019-12-06T22:00:47
| 225,240,509
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,574
|
r
|
test-advent.R
|
context("day 1")
test_that("day 1 part 1 returns the correct answer", {
answer <- day_01_part1()
expect_equal(answer, 3456641)
})
test_that("day 1 part 2 returns the correct answer", {
answer <- day_01_part2()
expect_equal(answer, 5182078)
})
context("day 2")
test_that("day 2 part 1 returns the correct answer", {
answer <- day_02_part1()
expect_equal(answer, 3790645)
})
test_that("day 2 part 2 returns the correct answer", {
answer <- day_02_part2()
expect_equal(answer, 6577)
})
context("day 3")
test_that("can parse a wire direction", {
expect_equal(parse_direction("L1"), "L")
expect_equal(parse_direction("R0"), "R")
expect_equal(parse_direction("U123"), "U")
expect_equal(parse_direction("D4"), "D")
})
test_that("can parse a wire length", {
expect_equal(parse_length("L1"), 1)
expect_equal(parse_length("R0"), 0)
expect_equal(parse_length("U123"), 123)
expect_equal(parse_length("D4"), 4)
})
test_that("can parse a wire", {
input <- "R8"
path <- parse_wire(input)
expect_equal(path$direction, "R")
expect_equal(path$wire_length, 8)
})
test_that("can parse a wire path", {
input <- c("R8", "U5", "L5", "D3")
path <- parse_wire_path(input)
expect_equal(path[[1]]$direction, "R")
expect_equal(path[[1]]$wire_length, 8)
expect_equal(path[[2]]$direction, "U")
expect_equal(path[[2]]$wire_length, 5)
expect_equal(path[[3]]$direction, "L")
expect_equal(path[[3]]$wire_length, 5)
expect_equal(path[[4]]$direction, "D")
expect_equal(path[[4]]$wire_length, 3)
})
test_that("can place central node on a grid", {
path <- parse_wire_path(c("R8", "U5", "L5", "D3"))
dims = wire_grid_dimensions(list(path))
expect_equal(dims$cx, 1)
expect_equal(dims$cy, 6)
})
test_that("can calculate required grid size", {
path <- parse_wire_path(c("R8", "U5", "L5", "D3"))
dims = wire_grid_dimensions(list(path))
expect_equal(dims$width, 9)
expect_equal(dims$height, 6)
})
test_that("can put central node in the centre of a grid", {
# | . . . .
# | . . . .
# | . o - +
# | . . . |
# + - - - +
path <- parse_wire_path(c("R2", "D2", "L4", "U4"))
dims = wire_grid_dimensions(list(path))
expect_equal(dims$cx, 3)
expect_equal(dims$cy, 3)
expect_equal(dims$width, 5)
expect_equal(dims$height, 5)
})
test_that("can draw a wire path", {
path <- parse_wire_path(c("R2", "D2", "L4", "U4"))
dims = wire_grid_dimensions(list(path))
grid <- matrix(0, nrow=dims$height, ncol=dims$width)
grid <- draw_wire_path(matrix=grid, row=dims$cy, col=dims$cx, path=path)
expected <- rbind(
c(12,0,0,0,0),
c(11,0,0,0,0),
c(10,0,0,1,2),
c(9, 0,0,0,3),
c(8, 7,6,5,4)
)
expect_equal(grid, expected)
})
test_that("can identify wire crossings", {
path1 <- parse_wire_path(c("R8", "U5", "L5", "D3"))
path2 <- parse_wire_path(c("U7", "R6", "D4", "L4"))
dims <- wire_grid_dimensions(list(path1, path2))
grid <- matrix(0, nrow=dims$height, ncol=dims$width)
grid2 <- draw_wire_path(matrix=grid, row=dims$cy, col=dims$cx, path=path1)
grid3 <- draw_wire_path(matrix=grid, row=dims$cy, col=dims$cx, path=path2)
grid2[grid2!=0] <- 1
grid3[grid3!=0] <- 1
expected <- rbind(
c(1,1,1,1,1,1,1,0,0),
c(1,0,0,0,0,0,1,0,0),
c(1,0,0,1,1,1,2,1,1),
c(1,0,0,1,0,0,1,0,1),
c(1,0,1,2,1,1,1,0,1),
c(1,0,0,1,0,0,0,0,1),
c(1,0,0,0,0,0,0,0,1),
c(0,1,1,1,1,1,1,1,1)
)
expect_equal(grid2 + grid3, expected)
})
test_that("can find the manhattan distance", {
expect_equal(manhattan_distance(c(1,1), c(2,2)), 2)
expect_equal(manhattan_distance(c(2,2), c(1,1)), 2)
expect_equal(manhattan_distance(c(2,2), c(3,1)), 2)
expect_equal(manhattan_distance(c(2,2), c(2,5)), 3)
})
test_that("can find intersections", {
path1 <- parse_wire_path(c("R8", "U5", "L5", "D3"))
path2 <- parse_wire_path(c("U7", "R6", "D4", "L4"))
dims <- wire_grid_dimensions(list(path1, path2))
grid <- matrix(0, nrow=dims$height, ncol=dims$width)
grid2 <- draw_wire_path(matrix=grid, row=dims$cy, col=dims$cx, path=path1)
grid3 <- draw_wire_path(matrix=grid, row=dims$cy, col=dims$cx, path=path2)
grid2[grid2!=0] <- 1
grid3[grid3!=0] <- 1
intersections <- find_intersections(grid2 + grid3)
expected <- rbind(
c(row=5, col=4),
c(row=3, col=7)
)
expect_equal(intersections, expected)
})
test_that("can find the closest intersection", {
path1 <- parse_wire_path(c("R8", "U5", "L5", "D3"))
path2 <- parse_wire_path(c("U7", "R6", "D4", "L4"))
closest <- closest_intersection(path1, path2)
expect_equal(closest, 6)
})
test_that("example1 has a distance of 159", {
input1 <- c("R75", "D30", "R83", "U83", "L12", "D49", "R71", "U7", "L72")
path1 <- parse_wire_path(input1)
input2 <- c("U62", "R66", "U55", "R34", "D71", "R55", "D58", "R83")
path2 <- parse_wire_path(input2)
answer <- closest_intersection(path1, path2)
expect_equal(answer, 159)
})
test_that("example1 takes 610 steps", {
input1 <- c("R75", "D30", "R83", "U83", "L12", "D49", "R71", "U7", "L72")
path1 <- parse_wire_path(input1)
input2 <- c("U62", "R66", "U55", "R34", "D71", "R55", "D58", "R83")
path2 <- parse_wire_path(input2)
answer <- shortest_steps(path1, path2)
expect_equal(answer, 610)
})
# These are commented because they are slow to run
#test_that("day 3 part 1 returns the incorrect answer", {
# answer <- day03_part1()
# print(answer)
#
# expect_equal(answer, 273)
#})
#test_that("day 3 part 1 returns the incorrect answer", {
# answer <- day03_part2()
# print(answer)
#
# expect_equal(answer, 15622)
#})
|
4fe6f29010b516c529ecc9fca961e0a47471158b
|
0fc8ad550144e4e96c9aae06f83e2be7aaef5a48
|
/R/SmallNR.R
|
b631ce64079c7d0c78b8f9e55c350adc9854599e
|
[] |
no_license
|
nroming/NRmisc
|
f3a3072bfbe257b65f00005948d68d37635624f8
|
41b375758997310288a99a562f5bd89a01cb844e
|
refs/heads/master
| 2021-01-22T02:39:57.560916
| 2015-03-05T10:55:49
| 2015-03-05T10:55:49
| 25,294,408
| 0
| 2
| null | 2015-02-03T15:52:10
| 2014-10-16T09:03:22
|
R
|
UTF-8
|
R
| false
| false
| 490
|
r
|
SmallNR.R
|
#' Return n-smallest value of a vector of numbers
#'
#' @author Niklas Roming
#' @param x A vector (numeric or integer)
#' @param n Which value to return (e.g. n=2 => second-smallest)
#' @return The n-smallest value of x
#' @export
SmallNR <- function(x, n){
# this function returns the second smallest value of a vector
# http://stackoverflow.com/questions/2453326/fastest-way-to-find-second-third-
# highest-lowest-value-in-vector-or-column
y <- unlist(sort(x)[n])
return(y)
}
|
1af80c4abeb254be90f906eab515ac24a9046cf1
|
ee547df8ce469ca54c111a61edb66b3c6123b588
|
/man/SVMModel.Rd
|
08a851ba76aa33db4166b861e1dba0a2100628df
|
[] |
no_license
|
brian-j-smith/MachineShop
|
b4d6ee3d3e900ac0733ea0a663c54ba74cc17800
|
3599d9af6f58faff2f423520193d3029637f7bc5
|
refs/heads/master
| 2023-04-07T00:49:41.675201
| 2023-03-21T14:23:23
| 2023-03-21T14:23:23
| 150,329,909
| 63
| 12
| null | 2018-11-18T23:55:38
| 2018-09-25T21:12:07
|
R
|
UTF-8
|
R
| false
| true
| 3,518
|
rd
|
SVMModel.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ML_SVMModel.R
\name{SVMModel}
\alias{SVMModel}
\alias{SVMANOVAModel}
\alias{SVMBesselModel}
\alias{SVMLaplaceModel}
\alias{SVMLinearModel}
\alias{SVMPolyModel}
\alias{SVMRadialModel}
\alias{SVMSplineModel}
\alias{SVMTanhModel}
\title{Support Vector Machine Models}
\usage{
SVMModel(
scaled = TRUE,
type = character(),
kernel = c("rbfdot", "polydot", "vanilladot", "tanhdot", "laplacedot", "besseldot",
"anovadot", "splinedot"),
kpar = "automatic",
C = 1,
nu = 0.2,
epsilon = 0.1,
prob.model = FALSE,
cache = 40,
tol = 0.001,
shrinking = TRUE
)
SVMANOVAModel(sigma = 1, degree = 1, ...)
SVMBesselModel(sigma = 1, order = 1, degree = 1, ...)
SVMLaplaceModel(sigma = numeric(), ...)
SVMLinearModel(...)
SVMPolyModel(degree = 1, scale = 1, offset = 1, ...)
SVMRadialModel(sigma = numeric(), ...)
SVMSplineModel(...)
SVMTanhModel(scale = 1, offset = 1, ...)
}
\arguments{
\item{scaled}{logical vector indicating the variables to be scaled.}
\item{type}{type of support vector machine.}
\item{kernel}{kernel function used in training and predicting.}
\item{kpar}{list of hyper-parameters (kernel parameters).}
\item{C}{cost of constraints violation defined as the regularization term in
the Lagrange formulation.}
\item{nu}{parameter needed for nu-svc, one-svc, and nu-svr.}
\item{epsilon}{parameter in the insensitive-loss function used for eps-svr,
nu-svr and eps-bsvm.}
\item{prob.model}{logical indicating whether to calculate the scaling
parameter of the Laplacian distribution fitted on the residuals of numeric
response variables. Ignored in the case of a factor response variable.}
\item{cache}{cache memory in MB.}
\item{tol}{tolerance of termination criterion.}
\item{shrinking}{whether to use the shrinking-heuristics.}
\item{sigma}{inverse kernel width used by the ANOVA, Bessel, and Laplacian
kernels.}
\item{degree}{degree of the ANOVA, Bessel, and polynomial kernel functions.}
\item{...}{arguments passed to \code{SVMModel} from the other constructors.}
\item{order}{order of the Bessel function to be used as a kernel.}
\item{scale}{scaling parameter of the polynomial and hyperbolic tangent
kernels as a convenient way of normalizing patterns without the need to
modify the data itself.}
\item{offset}{offset used in polynomial and hyperbolic tangent kernels.}
}
\value{
\code{MLModel} class object.
}
\description{
Fits the well known C-svc, nu-svc, (classification) one-class-svc (novelty)
eps-svr, nu-svr (regression) formulations along with native multi-class
classification formulations and the bound-constraint SVM formulations.
}
\details{
\describe{
\item{Response types:}{\code{factor}, \code{numeric}}
\item{\link[=TunedModel]{Automatic tuning} of grid parameters:}{
\itemize{
\item SVMANOVAModel: \code{C}, \code{degree}
\item SVMBesselModel: \code{C}, \code{order}, \code{degree}
\item SVMLaplaceModel: \code{C}, \code{sigma}
\item SVMLinearModel: \code{C}
\item SVMPolyModel: \code{C}, \code{degree}, \code{scale}
\item SVMRadialModel: \code{C}, \code{sigma}
}
}
}
Arguments \code{kernel} and \code{kpar} are automatically set by the
kernel-specific constructor functions.
Default values and further model details can be found in the source link
below.
}
\examples{
fit(sale_amount ~ ., data = ICHomes, model = SVMRadialModel)
}
\seealso{
\code{\link[kernlab]{ksvm}}, \code{\link{fit}},
\code{\link{resample}}
}
|
7eae27afc03859369c64a562ea1c95aed2e1630f
|
79457aaae83a0b3914a38874c10907440e0dfc61
|
/man/gdalcubes_options.Rd
|
3753ecc5b24dc7169976e380dc6125c8204054d9
|
[] |
permissive
|
appelmar/gdalcubes
|
be9786b36fbe4e25a5c0245968634f57a40752ad
|
2134f769454e147660e7a73c61afa14219de20b4
|
refs/heads/master
| 2023-08-07T20:56:02.442579
| 2023-07-25T06:36:46
| 2023-07-25T06:36:46
| 148,130,790
| 74
| 7
|
MIT
| 2023-03-23T19:56:08
| 2018-09-10T09:25:01
|
C++
|
UTF-8
|
R
| false
| true
| 3,083
|
rd
|
gdalcubes_options.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/config.R
\name{gdalcubes_options}
\alias{gdalcubes_options}
\title{Set or read global options of the gdalcubes package}
\usage{
gdalcubes_options(
...,
parallel,
ncdf_compression_level,
debug,
cache,
ncdf_write_bounds,
use_overview_images,
show_progress,
default_chunksize,
streaming_dir,
log_file,
threads
)
}
\arguments{
\item{...}{not used}
\item{parallel}{number of parallel workers used to process data cubes or TRUE to use the number of available cores automatically}
\item{ncdf_compression_level}{integer; compression level for created netCDF files, 0=no compression, 1=fast compression, 9=small compression}
\item{debug}{logical; print debug messages}
\item{cache}{logical; TRUE if temporary data cubes should be cached to support fast reprocessing of the same cubes}
\item{ncdf_write_bounds}{logical; write dimension bounds as additional variables in netCDF files}
\item{use_overview_images}{logical; if FALSE, all images are read on original resolution and existing overviews will be ignored}
\item{show_progress}{logical; if TRUE, a progress bar will be shown for actual computations}
\item{default_chunksize}{length-three vector with chunk size in t, y, x directions or a function taking a data cube size and returning a suggested chunk size}
\item{streaming_dir}{directory where temporary binary files for process streaming will be written to}
\item{log_file}{character, if empty string or NULL, diagnostic messages will be printed to the console, otherwise to the provided file}
\item{threads}{number of threads used to process data cubes (deprecated)}
}
\description{
Set global package options to change the default behavior of gdalcubes. These include how many parallel processes are used
to process data cubes, how created netCDF files are compressed, and whether or not debug messages should be printed.
}
\details{
Data cubes can be processed in parallel where the number of chunks in a cube is distributed among parallel
worker processes. The actual number of used workers can be lower if a data cube as less chunks. If parallel
is TRUE, the number of available cores is used. Setting parallel = FALSE can be used to disable parallel processing.
Notice that since version 0.6.0, separate processes are being used instead of parallel threads to avoid
possible R session crashes due to some multithreading issues.
Caching has no effect on disk or memory consumption,
it simply tries to reuse existing temporary files where possible.
For example, changing only parameters to \code{plot} will void
reprocessing the same data cube if cache is TRUE.
The streaming directory can be used to control the performance of user-defined functions,
if disk IO is a bottleneck. Ideally, this can be set to a directory on a shared memory device.
Passing no arguments will return the current options as a list.
}
\examples{
gdalcubes_options(parallel=4) # set the number
gdalcubes_options() # print current options
gdalcubes_options(parallel=FALSE) # reset
}
|
7359924195940ed7e5e7ef88fcb7e92e512daf60
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/netgsa/examples/NetGSA.Rd.R
|
4a55b66b2865863e633000fa57df9f37261cc1e3
|
[] |
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
| 357
|
r
|
NetGSA.Rd.R
|
library(netgsa)
### Name: NetGSA
### Title: Network-based Gene Set Analysis
### Aliases: NetGSA
### ** Examples
set.seed(1)
## NetGSA with directed networks
## NetGSA with undirected networks
data(netgsaex2)
A = netgsaex2$A
B = netgsaex2$B
x = netgsaex2$x
y = netgsaex2$y
# -Not-run-
# fit = NetGSA(A, x, y, B, lklMethod="REML", directed=FALSE)
|
026150d9880f35f653a89fae7a4750f8ebf7abda
|
ee2553a8d0160ce6b0410007cdda86e71fd5ad3d
|
/JHigh HW 3/HW 3 Problem 4/HW 4 Problem 4.R
|
11558cbef4784f74e35bd0ae928ee035df77a19b
|
[] |
no_license
|
joehigh/EN.553.732
|
bd4b958acbcbc7cf91b5c0d120add6ca2994b6c9
|
fc277a288418c3ad577f28c6733e56b3e07fd416
|
refs/heads/master
| 2022-04-27T01:41:00.268173
| 2020-04-29T09:24:44
| 2020-04-29T09:24:44
| 255,237,001
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,167
|
r
|
HW 4 Problem 4.R
|
#Problem4
#part a
glucose = read.table("glucose.dat", header = FALSE);
data=as.matrix(glucose)
data=as.numeric(data)
hist(data,breaks=seq(50,200,5),freq=FALSE,main="Problem 4 Part a")
lines(density(data))
#part c
y=data
set.seed(123)
n=length(y)
iter=10000
a=1
b=1
mu0=120
tau0.sq=200
sigma0.sq=1000
nu0=10
x=matrix(0,iter, n)
p=numeric(iter)
theta1=numeric(iter)
theta2=numeric(iter)
sigma1.sq=numeric(iter)
sigma2.sq=numeric(iter)
p[1]=rbeta(1, a, b)
x[1,]=rbinom(n,1,p[1])
theta1[1]=rnorm(1,mu0,sqrt(tau0.sq))
theta2[1]=rnorm(1,mu0,sqrt(tau0.sq))
sigma1.sq[1]=1/rgamma(1, nu0/2, nu0*sigma0.sq/2)
sigma2.sq[1]=1/rgamma(1, nu0/2, nu0*sigma0.sq/2)
for (i in 2:iter)
{
for (j in 1:n)
{
y1=dnorm(y[j], theta1[i-1], sqrt(sigma1.sq[i-1]))
y2=dnorm(y[j], theta2[i-1], sqrt(sigma2.sq[i-1]))
x[i,j]=rbinom(1,1,(p[i-1]*y1)/(p[i-1]*y1+(1-p[i-1])*y2))
}
c=sum(x[i,])
p[i]=rbeta(1,a+c,b+n-c)
y_1.bar=mean(y[x[i,]==1])
mu_n=(mu0/tau0.sq+c*y_1.bar/sigma1.sq[i-1])/(1/tau0.sq+c/sigma1.sq[i-1])
tau2_n=1/(1/tau0.sq+c/sigma1.sq[i-1])
theta1[i]=rnorm(1, mu_n, sqrt(tau2_n))
nu_n=nu0+c
s2_n=sum((y[x[i,] == 1]-theta1[i])^2)/c
sigma2_n=(nu0*sigma0.sq+c*s2_n)/nu_n
sigma1.sq[i]=1/rgamma(1,nu_n/2,nu_n*sigma2_n/2)
y_2.bar=mean(y[x[i,] == 0])
mu_n=(mu0/tau0.sq+(n-c)*y_2.bar/sigma2.sq[i-1])/(1/tau0.sq+(n-c)/sigma2.sq[i-1])
tau2_n=1/(1/tau0.sq+(n-c)/sigma2.sq[i-1])
theta2[i]=rnorm(1, mu_n, sqrt(tau2_n))
nu_n=nu0+(n-c)
s2_n=sum((y[x[i,] == 0]-theta2[i])^2)/(n-c)
sigma2_n=(nu0*sigma0.sq+(n-c)*s2_n)/nu_n
sigma2.sq[i]=1/rgamma(1,nu_n/2,nu_n*sigma2_n/2)
}
theta_1s=rep(0,iter)
theta_2s=rep(0,iter)
for (i in 1:iter)
{
theta_1s[i]=min(theta1[i], theta2[i])
theta_2s[i]=max(theta1[i], theta2[i])
}
acf(theta_1s,main="Problem 4 Part c, theta1 ")
acf(theta_2s,main="Problem 4 Part c, theta2 ")
effectiveSize(theta_1s)
effectiveSize(theta_2s)
#part d
x_1=rbinom(length(p), 1, p)
y_1=numeric(iter)
for (i in 1:iter)
{
if (x_1[i] == 1)
{
y_1[i]=rnorm(1, theta1[i], sqrt(sigma1.sq[i]))
}
else
{
y_1[i]=rnorm(1, theta2[i], sqrt(sigma2.sq[i]))
}
}
hist(y_1,breaks=seq(0,300,5),freq=FALSE,main="Problem 4 Part d")
lines(density(y))
|
bec35d2c7f7335148b8eb60880d63bf84884fcc7
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/Devore7/examples/ex07.58.Rd.R
|
a0e5200c0d1e07d27b90a9fc173e1b20f8fd2014
|
[] |
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
| 160
|
r
|
ex07.58.Rd.R
|
library(Devore7)
### Name: ex07.58
### Title: R Data set: ex07.58
### Aliases: ex07.58
### Keywords: datasets
### ** Examples
data(ex07.58)
str(ex07.58)
|
94819142fd6c9795a92bd2ddfc7542259883d733
|
060c6a303098ef689c43ea0feff68c68272477e9
|
/kinomeAnalysis/kinome_baseline_data_munging.R
|
12fd6dbdfd110b218cba0a1ef5b52d55405c2070
|
[] |
no_license
|
Sage-Bionetworks/Synodos_NF2
|
e1c004191de438d6efa2d565f7d1c1e36a90efaa
|
1506b57c74469439e81fe8afbc6de9add681c57c
|
refs/heads/master
| 2022-12-20T16:20:09.620615
| 2022-12-14T22:53:52
| 2022-12-14T22:53:52
| 20,036,276
| 2
| 0
| null | 2014-05-28T18:09:16
| 2014-05-21T20:29:18
|
R
|
UTF-8
|
R
| false
| false
| 4,448
|
r
|
kinome_baseline_data_munging.R
|
library(synapseClient)
library("gdata")
library("tidyr")
library("dplyr")
library("reshape2")
require("parallel")
library("plyr")
library("doMC")
library("gdata")
registerDoMC(4)
synapseLogin()
#schwannoma baseline data
baseline_schwannoma_human_synid <- "syn4214458"
baseline_schwannoma_human <- synGet(baseline_schwannoma_human_synid)
baseline_schwannoma_human <- read.xls(xls=baseline_schwannoma_human@filePath,sheet="trimmed")
rownames(baseline_schwannoma_human) <- baseline_schwannoma_human$Gene
baseline_schwannoma_human <- baseline_schwannoma_human[,grepl('LFQ',colnames(baseline_schwannoma_human))]
baseline_schwannoma_human$Gene <- toupper(rownames(baseline_schwannoma_human))
baseline_schwannoma_mouse_synid <- "syn4214457"
baseline_schwannoma_mouse <- synGet(baseline_schwannoma_mouse_synid)
baseline_schwannoma_mouse <- read.xls(xls=baseline_schwannoma_mouse@filePath,sheet="trimmed")
rownames(baseline_schwannoma_mouse) <- baseline_schwannoma_mouse$Gene
baseline_schwannoma_mouse <- baseline_schwannoma_mouse[,grepl('LFQ',colnames(baseline_schwannoma_mouse))]
baseline_schwannoma_mouse$Gene <- toupper(rownames(baseline_schwannoma_mouse))
#vestibular schwannoma data
baseline_vest_schwannoma_synid <- "syn4942532"
baseline_vest_schwannoma <- synGet(baseline_vest_schwannoma_synid)
baseline_vest_schwannoma <- read.xls(xls=baseline_vest_schwannoma@filePath,sheet="Kinase LFQ")
rownames(baseline_vest_schwannoma) <- baseline_vest_schwannoma$Gene.names
baseline_vest_schwannoma <- baseline_vest_schwannoma[,grepl('LFQ',colnames(baseline_vest_schwannoma))]
baseline_vest_schwannoma$Gene <- toupper(rownames(baseline_vest_schwannoma))
View(baseline_vest_schwannoma)
#meningioma baseline data
baseline_meningioma_synid <- "syn3104723"
baseline_meningioma <- synGet(baseline_meningioma_synid)
baseline_meningioma <- read.xls(xls=baseline_meningioma@filePath,sheet="Kinases with LFQ in all")
rownames(baseline_meningioma) <- baseline_meningioma$Gene.names
baseline_meningioma <- baseline_meningioma[,grepl('LFQ',colnames(baseline_meningioma))]
baseline_meningioma$Gene <- toupper(rownames(baseline_meningioma))
kinome_baseline_LFQ_data <- Reduce(function(x,y) merge(x,y,by="Gene", all=T), list(baseline_meningioma, baseline_schwannoma_human,baseline_schwannoma_mouse, baseline_vest_schwannoma))
colnames(kinome_baseline_LFQ_data) <- gsub('LFQ.intensity.', '',colnames(kinome_baseline_LFQ_data))
#upload to synapse
outfile <- "Synodos_kinome_baseline_LFQ_data.tsv"
write.table(kinome_baseline_LFQ_data, file=outfile, sep="\t", row.names=F, col.names=T, quote=F)
synStore(File(outfile, parentId = "syn4259360"),
used = c(baseline_meningioma_synid, baseline_schwannoma_human_synid, baseline_schwannoma_mouse_synid,
baseline_vest_schwannoma_synid),
executed = "https://github.com/Sage-Bionetworks/Synodos_NF2/blob/master/kinomeAnalysis/kinome_baseline_data_munging.R")
unlink(outfile)
###
#process new baseline data - iTRAQ based
###
source("kinomeAnalysis/kinome_data_functions.R")
#new baseline triplicate data - protein level
meningioma_baseline_triplicates_proteins_synid <- 'syn5575201'
schwannoma_baseline_triplicates_proteins_synid <- 'syn5575204'
meningioma_baseline_triplicates_proteins <- get_new_kinome_proteinData(meningioma_baseline_triplicates_proteins_synid)
schwannoma_baseline_triplicates_proteins <- get_new_kinome_proteinData(schwannoma_baseline_triplicates_proteins_synid)
Kinome_baseline_iTRAQ_data <- rbind(meningioma_baseline_triplicates_proteins,schwannoma_baseline_triplicates_proteins)
Kinome_baseline_iTRAQ_data <- Kinome_baseline_iTRAQ_data %>% mutate(condition = sample) %>%
separate(sample, into=c('cellLine', 'referenceSample'), sep="/") %>%
separate(cellLine, into=c('cellLine', 'replicate'), sep="_") %>%
separate(referenceSample, into=c('referenceSample', 'refSampleReplicate'), sep="_") %>%
mutate(log2ratio = log2(ratio))
View(Kinome_baseline_iTRAQ_data)
#upload to synapse
outfile <- "Synodos_kinome_baseline_iTRAQ_data.tsv"
write.table(Kinome_baseline_iTRAQ_data, file=outfile, sep="\t", row.names=F, col.names=T, quote=F)
synStore(File(outfile, parentId = "syn4259360"),
used = c(meningioma_baseline_triplicates_proteins_synid,
schwannoma_baseline_triplicates_proteins_synid),
executed = "https://github.com/Sage-Bionetworks/Synodos_NF2/blob/master/kinomeAnalysis/kinome_baseline_data_munging.R")
unlink(outfile)
|
7cb4806da6638c720a9dc80285c58ee74060e83f
|
75778fec111d3c10b1dca261dd7dddd47c3a4bc8
|
/script_data_analysis.R
|
b591f73e2c54cc36f1e1dd21413c49989a59861b
|
[] |
no_license
|
veren4/Protein_Prediction_II_plots
|
6ed5591b13580422b340e038c852f7e48db487cd
|
9e59d9b77baa408c2cc490ff45098f18197faaf1
|
refs/heads/master
| 2022-11-23T06:50:37.937935
| 2019-11-28T11:28:32
| 2019-11-28T11:28:32
| 279,658,912
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,136
|
r
|
script_data_analysis.R
|
#
# density plot of positions of NLSs/ NESs in the protein sequences
#
# in fasta file: find out absolute lengths of proteins
library(tidyverse)
my_protein_sequences = seqinr::read.fasta(file = "C:/Users/Verena/1_Studium/03_Aufbaustudium_Informatik/Protein Prediction II/Exercise/data/ns/nes_nls.fasta",
seqtype = "AA",
as.string = T)
my_proteins = data.frame(protein_ID = character(),
aa_sequence = character(),
sequence_length = integer(),
stringsAsFactors = F)
for(i in 1:length(my_protein_sequences)){
my_proteins[i, "protein_ID"] = attributes(my_protein_sequences[[i]])$name
my_proteins[i, "aa_sequence"] = my_protein_sequences[[i]][1]
my_proteins[i, "sequence_length"] = seqinr::getLength(my_protein_sequences[[i]])
}
# in excel file: extract start + end of NLS/ NES and wether its an NLS or NES
locations = read_tsv(file = "C:/Users/Verena/1_Studium/03_Aufbaustudium_Informatik/Protein Prediction II/Exercise/data/ns/nes_nls.tab",
col_names = F) %>%
dplyr::rename(protein_ID = X1,
start = X2,
end = X3,
kind = X4)
my_proteins = dplyr::full_join(my_proteins, locations, by = c("protein_ID" = "protein_ID"))
rm(my_protein_sequences, i, locations)
# Theoretisch ist es nicht gründlich genug, davon auszugehen, dass die Sequenzen
# in den beiden Dokumenten gleich orientiert sind.
my_proteins = dplyr::mutate(my_proteins,
perc_start = round((start/sequence_length)*100, digits = 0),
perc_end = round((end/sequence_length)*100, digits = 0))
occupied_percentage_boxes_NLS = c()
occupied_percentage_boxes_NES = c()
for(i in 1:nrow(my_proteins)){
temp = my_proteins[i, "perc_start"]:my_proteins[i, "perc_end"]
if(my_proteins[i, "kind"] == "NLS"){
occupied_percentage_boxes_NLS = c(occupied_percentage_boxes_NLS, temp)
}else{
occupied_percentage_boxes_NES = c(occupied_percentage_boxes_NES, temp)
}
}
# Für diesen Plot könnte ich die Prozentpunkte auch mit mehr Nachkommastellen berechnen.
my_proteins$protein_ID = factor(my_proteins$protein_ID)
my_proteins$kind = factor(my_proteins$kind, levels = c("NLS", "NES"))
# backup = my_proteins
# my_proteins = backup
my_proteins_kinder = my_proteins %>%
select(protein_ID, kind) %>%
group_by(protein_ID) %>%
summarise(signal_cases = length(unique(kind)))
my_proteins = left_join(my_proteins, my_proteins_kinder, by = c("protein_ID" = "protein_ID"))
my_proteins = my_proteins %>%
mutate(final_kind_sorter = case_when(signal_cases == 2 ~ "both",
kind == "NES" ~ "NES",
kind == "NLS" ~ "NLS"))
start_sorter = select(my_proteins, protein_ID, perc_start) %>%
group_by(protein_ID) %>%
summarise(first_start = min(perc_start))
my_proteins = left_join(my_proteins, start_sorter, by = c("protein_ID" = "protein_ID"))
my_proteins$final_kind_sorter = factor(my_proteins$final_kind_sorter, levels = c("NLS", "both", "NES"))
# sort by first_start within the 3 kind groups --------------------------------
NLS_orderer = dplyr::filter(my_proteins, final_kind_sorter == "NLS") %>%
arrange(first_start)
########### here lieth the problem!!!!!!!! ##################
# Du musst innerhalb der facet sortieren, und dabei berücksichtigen, dass später
# NxS eines Proteins innerhalb einer Zeile sind.
# ggplot2 ---------------------------------------------------------------------
gantt = ggplot(data = my_proteins, mapping = aes(x = perc_start, y = protein_ID,
xend = perc_end, yend = protein_ID)) +
facet_grid(rows = vars(final_kind_sorter)) +
geom_segment(mapping = aes(color = kind)) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.key = element_blank(),
panel.background = element_blank(),
text = element_text(size=14)) +
ylab("protein sequence") +
xlab("relative protein sequence position [%]") +
scale_color_manual(values = c("#37c837", "#ff7f0e"),
name = element_blank())
gantt
# plot separately -------------------------------------------------------------
NLS_plot_input = filter(my_proteins, final_kind_sorter == "NLS")
NLS_plot_input$perc_start = factor(NLS_plot_input$perc_start, levels = order(NLS_plot_input$first_start))
NLS_gantt = ggplot(data = NLS_plot_input, mapping = aes(x = perc_start, y = protein_ID,
xend = perc_end, yend = protein_ID)) +
geom_segment(mapping = aes(color = kind)) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.key = element_blank(),
panel.background = element_blank(),
text = element_text(size=14)) +
ylab("protein sequence") +
xlab("relative protein sequence position [%]") +
scale_color_manual(values = c("#37c837", "#ff7f0e"),
name = element_blank())
NLS_gantt
|
e576868601a4dd8b191f700bc3b84a42622bc402
|
18e8822e6cce16631058ecfd14906bbb1580aa66
|
/R/BITFAM_scATAC.R
|
536e2ce15f87955d9762087b92ab584f7a490118
|
[] |
no_license
|
jaleesr/BITFAM
|
fe443d9b0bc23016526e483e918cfe38bd069913
|
b604014c40329b3f737d4a152d44114a60c518b1
|
refs/heads/master
| 2023-02-21T11:10:50.972711
| 2023-02-13T13:50:50
| 2023-02-13T13:50:50
| 310,433,311
| 27
| 18
| null | null | null | null |
UTF-8
|
R
| false
| false
| 340
|
r
|
BITFAM_scATAC.R
|
#' Extract the genes that have scATAC-seq peaks on their promoter regions
#'
#' @param scATAC_obj A preprocessed Seurat object of scATAC-seq data.
#' @return the genes that have scATAC-seq peaks on their promoter regions
#' @export
#' @import rstan
#' @import Seurat
BITFAM_scATAC <- function(scATAC_obj){
return(rownames(scATAC_obj))
}
|
312d64ea0082a5d031500741fdb5457733e9d543
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/bamlss/examples/boost.Rd.R
|
a17b2ba42017f1f0414018c6214b03e6a653c4db
|
[] |
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
| 2,784
|
r
|
boost.Rd.R
|
library(bamlss)
### Name: boost
### Title: Boosting BAMLSS
### Aliases: boost boostm boost.summary boost.plot print.boost.summary
### plot.boost.summary boost.frame
### Keywords: regression
### ** Examples
## Not run:
##D ## Simulate data.
##D set.seed(123)
##D d <- GAMart()
##D
##D ## Estimate model.
##D f <- num ~ x1 + x2 + x3 + lon + lat +
##D s(x1) + s(x2) + s(x3) + s(lon) + s(lat) + te(lon,lat)
##D
##D b <- bamlss(f, data = d, optimizer = boost,
##D sampler = FALSE, scale.d = TRUE, nu = 0.01,
##D maxit = 1000, plot = FALSE)
##D
##D ## Plot estimated effects.
##D plot(b)
##D
##D ## Print and plot the boosting summary.
##D boost.summary(b, plot = FALSE)
##D boost.plot(b, which = 1)
##D boost.plot(b, which = 2)
##D boost.plot(b, which = 3, name = "mu.s.te(lon,lat).")
##D
##D ## Extract estimated parameters for certain
##D ## boosting iterations.
##D parameters(b, mstop = 1)
##D parameters(b, mstop = 100)
##D
##D ## Also works with predict().
##D head(do.call("cbind", predict(b, mstop = 1)))
##D head(do.call("cbind", predict(b, mstop = 100)))
##D
##D ## Another example using the modified liklihood
##D ## bootsing algorithm.
##D f <- list(
##D num ~ x1 + x2 + x3 + lon + lat +
##D s(x1) + s(x2) + s(x3) + s(lon) + s(lat) + te(lon,lat),
##D sigma ~ x1 + x2 + x3 + lon + lat +
##D s(x1) + s(x2) + s(x3) + s(lon) + s(lat) + te(lon,lat)
##D )
##D
##D b <- bamlss(f, data = d, optimizer = boostm,
##D sampler = FALSE, scale.d = TRUE, nu = 0.05,
##D maxit = 400, stop.criterion = "AIC", force.stop = FALSE)
##D
##D ## Plot estimated effects.
##D plot(b)
##D
##D ## Plot AIC and log-lik contributions.
##D boost.plot(b, "AIC")
##D boost.plot(b, "loglik.contrib")
##D
##D ## Out-of-sample selection of model terms.
##D set.seed(123)
##D d <- GAMart(n = 5000)
##D
##D ## Split data into training and testing
##D i <- sample(1:2, size = nrow(d), replace = TRUE)
##D d.test <- subset(d, i == 1)
##D d.train <- subset(d, i == 2)
##D
##D ## Model formula
##D f <- list(
##D num ~ s(x1) + s(x2) + s(x3),
##D sigma ~ s(x1) + s(x2) + s(x3)
##D )
##D
##D ## Create model frame for out-of-sample selection.
##D sm <- boost.frame(f, train = d.train, test = d.test, family = "gaussian")
##D
##D ## Out-of-sample selection function.
##D sfun <- function(parameters) {
##D sm$parameters <- parameters
##D p <- predict(sm, type = "parameter")
##D -1 * sum(sm$family$d(d.test$num, p, log = TRUE))
##D }
##D
##D ## Start boosting with out-of-sample negative
##D ## log-likelihood selection of model terms.
##D b <- bamlss(f, data = d.train, sampler = FALSE, optimizer = boost,
##D selectfun = sfun, always = "best")
##D
##D ## Plot curve of negative out-of-sample log-likelihood.
##D boost.plot(b, which = "user")
## End(Not run)
|
a3e3b07172666b137d96f2e19211870f68d2a0cd
|
d69f4337b05a04eafa938a070eb824ef0cb517e3
|
/bin/plotAME.R
|
fe36766cbafec4dd41738be6e31bf27931b38bdf
|
[] |
no_license
|
biomystery/atacMotif
|
2d7b0212c0bc4c4f6f38f49b09e4a063bf3dc384
|
e68d54bf00036a648ea6da62c19c3ab308e834f8
|
refs/heads/master
| 2022-02-24T01:07:02.357139
| 2019-10-09T06:59:25
| 2019-10-09T06:59:25
| 138,637,073
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 19,498
|
r
|
plotAME.R
|
source("./bin/aux_funs.R")
tfclass <- readRDS("./db/tfclass.rds")
# input parameters --------------------------------------------------------
ame_path <- "./test/ame_2kbg_all.res.txt"
th <- 0.1
# ame’s output ------------------------------------------------------------
ame_res <- read.table(ame_path,
header = T,
stringsAsFactors = F)
# load jaspar_to_TFclass dic---------------------------------------------------------
# filter adj_p.value <1e-5
ame_res.anno <- ame_res%>%
filter(adj_p.value<=th)
# jaspa dic
dic$jasparTOensembl <- unique(ame_res.anno$motif_ID)
names(dic$jasparTOensembl) <- unique(ame_res.anno$motif_ID)
if(F){
# grab jaspar info from web
for(i in 1:length(dic.jasparTOensembl)) {
print(i);
dic.jasparTOensembl[i]=getEnsemblFromJaspar(dic.jasparTOensembl[i])}
# tfclass dic
dic.ensemblTOsubfamily <- tfclass$merge%>%
separate_rows(tf.id,sep = ";") %>%
distinct(tf.id,.keep_all = T)%>%
column_to_rownames("tf.id")
# jaspa to tfclass
dic.jasparTOtfclass <-data.frame(ensembl.id = dic.jasparTOensembl)%>%
rownames_to_column("motif_ID")%>%
separate_rows(ensembl.id,sep = ";") %>%
right_join(dic.ensemblTOsubfamily%>%rownames_to_column("ensembl.id"))%>%
filter(!is.na(motif_ID))
saveRDS(list(jasparTOensembl=dic.jasparTOensembl,
ensemblTOtfclass=dic.ensemblTOsubfamily,
jasparTOtfclass=dic.jasparTOtfclass),
file = "./db/dic_jaspar_tfclass.rds")
}
dic<- readRDS("./db/dic_jaspar_tfclass.rds")
names(dic)
# add annotation -----------------------------------------------------------------
tmp <- sapply(unique(ame_res[ame_res$adj_p.value<=th,"motif_ID"]),function(a)
(dic$jasparTOtfclass%>%
filter(row_number()==grep(a,dic$jasparTOtfclass$jaspar.id)[1])),simplify = F)
tmp<-do.call(rbind,tmp)
# 17 out of 430 are not in the dic.
all(unique(ame_res$motif_ID) %in% tmp$jaspar.id)
sum(!unique(ame_res$motif_ID) %in% tmp$jaspar.id)
length(unique(ame_res$motif_ID))
unique(ame_res$motif_ID) [!unique(ame_res$motif_ID) %in% tmp$jaspar.id]
pd.ame_res <- ame_res%>%
filter(adj_p.value<=th)
pd.ame_res<-cbind(pd.ame_res,tmp[pd.ame_res$motif_ID,-1])%>%
#unite(tf,1:2)%>%
mutate(tf=motif_alt_ID)%>%
mutate(subfamily.name=ifelse(is.na(subfamily.name),as.character(family.name),subfamily.name))%>%
unite(family,family.id,family.name)%>%
unite(subfamily,subfamily.id,subfamily.name)%>%
mutate(p.value=-log10(p.value),
adj_p.value=-log10(adj_p.value),
E.value=-log10(E.value),
log2FE = log2((X.TP+0.1)/(X.FP+0.1)))
res.sum <- data.frame(pd.ame_res[,c("sample","FASTA_max")]%>%distinct(),
nmotifs=sapply(unique(pd.ame_res$sample),
function(x) nrow(pd.ame_res%>% filter(sample==x))),
nmotifs.FE=sapply(unique(pd.ame_res$sample),
function(x) nrow(pd.ame_res%>%
filter(log2FE>1)%>%
filter(sample==x)))
)
res.sum%>% mutate(frac=round(nmotifs/FASTA_max,2))
write.table(data.frame(pd.ame_res[,c("sample","FASTA_max")]%>%distinct(),
nmotifs=sapply(unique(pd.ame_res$sample), function(x) nrow(pd.ame_res%>% filter(sample==x)))),
file = "n.csv",sep = ',',quote = F,row.names = F)
ggplot(pd.ame_res%>%filter(log2FE>1),aes(sample,tf))+
geom_tile(aes(fill=log2FE))+
theme(axis.text.y = element_blank())
#+scale_fill_gradientn(colours = wes_palette("Zissou1", 21, type = "continuous"))
# thresholding ------------------------------------------------------------
pd <- pd.ame_res%>%
filter(log2FE>0)%>%
filter(adj_p.value > 10)%>%
dplyr::select(sample,tf,log2FE)%>%
spread(key=sample,value = log2FE)%>%
column_to_rownames("tf")
pd.2 <- pd; pd.2[is.na(pd)]<-0
# hcluster ----------------------------------------------------------------
require(cluster)
distfunc <- function(x) daisy(x,metric="gower")
d <- distfunc(pd.2)
dend <- as.dendrogram(hclust(d))
dend <- as.dendrogram(hclust(dist(pd.2)))
plot(dend)
pheatmap(pd[order.dendrogram(dend),],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21),
show_rownames = F)
# ranking -----------------------------------------------------------------
# order by column
rord <- pd %>%
rownames_to_column("tf")%>%
arrange(desc(alpha_1),desc(alpha_2),
desc(beta_1),desc(beta_2),
desc(delta_1),desc(delta_2),
desc(endothelial_1),desc(endothelial_2),
desc(exocrine),desc(gamma),
desc(glial),desc(immune),desc(stellate))
pheatmap(pd[rord$tf,],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = F,fontsize_row = 6)
require(heatmaply)
heatmaply(pd[rord$tf,],scale="none",
Rowv = NULL,Colv = NULL,
colors = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21])
# customized ranking ------------------------------------------------------------------
custRank_all <- function(input.pd=pd.2,na.val=0){
custRank <- function(pd.2,v="alpha_1"){
y <- pd.2[,v]
idx.nna <- which(y!=na.val); idx.na <- which(y==na.val)
idx.idx <- order(apply(pd.2[idx.nna,],1,mean),decreasing = T)
res.list <- list()
res.list$ordered <- pd.2[idx.nna[idx.idx],]
res.list$un_ordered <- pd.2[idx.na,]
res.list
}
tmp.pd <- input.pd;final.pd <- input.pd[-(1:nrow(input.pd)),]
for(x in colnames(input.pd)){
tmp.res <- custRank(pd.2 = tmp.pd,v=x)
tmp.pd <- tmp.res$un_ordered
final.pd<- rbind(final.pd,tmp.res$ordered)
}
final.pd[-1,]
}
pd.3 <- pd; pd.3[is.na(pd)]<- na.value
final.pd <- custRank_all(input.pd = pd.3,na.val = na.value)
pheatmap(pd[rownames(final.pd),],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = F,fontsize_row = 6)
# digital customized ranking ----------------------------------------------
bks <- seq(min(pd, na.rm = T), max(pd,na.rm = T), length.out = 15 + 1)
mat = as.matrix(pd)
pd.3 <- matrix(as.numeric(cut(as.vector(mat), breaks = bks, include.lowest = T)),
nrow(mat), ncol(mat), dimnames = list(rownames(mat), colnames(mat)))
pd.3[is.na(pd.3)]<- 0
final.pd <- custRank_all(input.pd = pd.3)
pheatmap(pd[rownames(final.pd),],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = F,fontsize_row = 6)
# kmeans ----------------------------------------------
getClusts <- function(pd.2,...){
require(NbClust)
nb <- NbClust(pd.2,method="complete",...)
require(factoextra)
fviz_nbclust(nb) + theme_minimal()
ords <-(sapply(1:max(nb$Best.partition), function(x) which(nb$Best.partition==x)))
brks <- cumsum(lapply(ords, length))
ords <- unlist(ords)
list(ords=ords,brks=brks)
}
res <- getClusts(pd.2=pd.2,min.nc=10)
pheatmap(pd[res$ords,],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],gaps_row = res$brks,
show_rownames = F,fontsize_row = 6)
heatmaply(pd[res$ords,],scale="none",
Rowv = NULL,Colv = NULL,gaps_row=brks,
colors = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21])
# row-wise scale ---------------------------------------------------------
pd.3 <- t(scale(t(pd.2)))
pd.3[pd.3>1.96] <- 1.96; pd.3[pd.3 < -1.96] <- -1.96
res <- getClusts(pd.2=pd.3,min.nc=10)
pheatmap(pd.3[res$ords,],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(21),gaps_row = res$brks,
show_rownames = F,fontsize_row = 6)
heatmaply(pd.3[res$ords,],scale="none",
Rowv = NULL,Colv = NULL,gaps_row=pd.3$brks,
colors = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(21))
pd.3[is.na(pd)]<- -1.96
# by supfamily ------------------------------------------------------
pd.sub <- pd.ame_res%>%
filter(log2FE>0)%>%
filter(adj_p.value > 10)%>%
dplyr::select(sample,subfamily,log2FE)%>%
group_by(sample,subfamily)%>%
summarise(mlog2FE=max(log2FE))%>%
spread(key=sample,value = mlog2FE)%>%
as.data.frame()%>%
column_to_rownames("subfamily")
# order by level
rord <- pd.sub %>%
rownames_to_column("tf")%>%
arrange(desc(alpha_1),desc(alpha_2),
desc(beta_1),desc(beta_2),
desc(delta_1),desc(delta_2),
desc(endothelial_1),desc(endothelial_2),
desc(exocrine),desc(gamma),
desc(glial),desc(immune),desc(stellate))
pheatmap(pd.sub[rord$tf,],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = F,fontsize_row = 6)
heatmaply(pd.sub[rord$tf,],scale="none",
Rowv = NULL,Colv = NULL,gaps_row=brks,
colors = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21])
# hclust
pd.sub <- pd.sub[apply(pd.sub, 1, function(x) sum(is.na(x))<ncol(pd.sub)),]
pd.sub.2 <- pd.sub;pd.sub.2[is.na(pd.sub.2)] <- 0
dend <- as.dendrogram(hclust(dist(pd.sub.2)))
pheatmap(pd.sub[order.dendrogram(dend),],scale = "none",cluster_rows = F,cluster_cols = T,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"GnBu"))(11),
show_rownames = T,fontsize_row = 6)
# nbClust
ords <- getClusts(pd.sub.2,min.nc=5)
pheatmap(pd.sub[ords$ords,],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],gaps_row = ords$brks,
show_rownames = T,fontsize_row = 6)
# cust
final.pd <- custRank_all(input.pd = pd.sub.2)
pheatmap(pd.sub[rownames(final.pd),],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = T,fontsize_row = 5,fontsize_col = 6)
heatmaply(pd.sub[rownames(final.pd),],scale="none",
Rowv = NULL,Colv = NULL,gaps_row=brks,
colors = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21])
# by family -----------------------------------------------------------
pd.fa <- pd.ame_res%>%
filter(log2FE>0)%>%
filter(adj_p.value > 10)%>%
dplyr::select(sample,family,log2FE)%>%
group_by(sample,family)%>%
summarise(mlog2FE=max(log2FE))%>%
spread(key=sample,value = mlog2FE)%>%
as.data.frame()%>%
column_to_rownames("family")
pd.fa <- pd.fa[apply(pd.fa, 1, function(x) sum(is.na(x))<ncol(pd.fa)),]
pd.fa.2 <- pd.fa;pd.fa.2[is.na(pd.fa)] <- 0
dend <- as.dendrogram(hclust(dist(pd.fa.2)))
pheatmap(pd.fa[order.dendrogram(dend),],scale = "none",cluster_rows = F,cluster_cols = T,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"GnBu"))(11),
show_rownames = T,fontsize_row = 8)
na.value <- -ncol(pd.fa)
pd.fa.2 <- pd.fa;pd.fa.2[is.na(pd.fa)] <- na.value
final.pd <- custRank_all(input.pd = pd.fa.2,na.val = na.value)
final.pd <- final.pd[-grep("NA",rownames(final.pd)),]
heatmaply(pd.fa[rownames(final.pd),],scale="none",
Rowv = NULL,Colv = NULL,gaps_row=brks,
colors = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21])
pheatmap(pd.fa[rownames(final.pd),],scale = "none",cluster_rows = F,cluster_cols = F,na_col = "grey",
color = colorRampPalette(brewer.pal(9,"Blues"))(21)[7:21],
show_rownames = T,fontsize_row = 6)
# save data -------------------------------------------------------------------
# promoter reads cnt
promoter <- read.table('./test/Islet_123.promoter_peak_counts.txt',header = T,row.names = 1)
apply(promoter, 2, sum)
calCpm <- function(x) x/sum(x)*100000
promoter.cpm <- apply(promoter[,-1], 2,calCpm)
require(reshape)
long = melt(promoter, id.vars= "celltypes")
ggplot(long, aes (value)) +
geom_density(aes(color = X2))
plot(density(promoter[,"glial"]))
# add ensemble id
# ensemble -> gene symbol
require(biomaRt)
human<- useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
name.dic <- getBM(attributes =c("ensembl_gene_id", "external_gene_name"),
filters = "external_gene_name",
rownames(promoter.cpm),
mart = human)
promoter.cpm.2<- right_join(name.dic,promoter.cpm%>%as.data.frame()%>%rownames_to_column("external_gene_name"))
idx <- (promoter.cpm.2$ensembl_gene_id%in% dat$tfclass$ensembl.id[!is.na(dat$tfclass$ensembl.id)] |
promoter.cpm.2$external_gene_name %in% as.character(dat$tfclass$tf.symbol)[!is.na(as.character(dat$tfclass$tf.symbol))])
promoter.cpm.2 <- promoter.cpm.2[idx,]
tmp <- right_join(promoter.cpm.2,dat$tfclass,by=c("ensembl_gene_id"="ensembl.id"),)
tmp <- tmp[!is.na(tmp$external_gene_name),]
dat <- list()
dat$motif <- pd.ame_res
dat$tfclass <- dic$merged
dat$promoter.cpm <- promoter.cpm.2
saveRDS(dat,file = "appDat.rds")
# export csv
pd.tmp <- pd.ame_res%>%
filter(log2FE>0)%>%
filter(adj_p.value > 10)%>%
dplyr::select(sample,motif_ID,motif_alt_ID,ensembl.id,subfamily,family,log2FE)%>%
spread(key=sample,value = log2FE)
write.csv(pd.tmp,"log2FE_res.csv",quote = F)
write.csv(tmp,"")
# TOP5 TFs ----------------------------------------------
pd.ame_res.top5<- pd.ame_res.hsap%>%
as.tibble()%>%
gather(key="sample",value = "log10_adj_p.value",1:15)%>%
group_by(sample)%>%
top_n(5,log10_adj_p.value)%>%
spread(sample,log10_adj_p.value)
# print
pd.ame_res.top5<-pd.ame_res.top5[,c(colnames(pd.ame_res.top5)[1:7],paste0("sample_",1:15))]%>%
arrange(sample_1,sample_2,sample_3,sample_4,sample_5,sample_6,sample_7,sample_8,sample_9,sample_10,sample_11,sample_12,sample_13,sample_14,sample_15)%>%
print(n=28)
# ensemble -> gene symbol
require(biomaRt)
human<- useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
name.dic <- getBM(attributes =c("ensembl_gene_id", "external_gene_name"),
filters = "ensembl_gene_id",
pd.ame_res.top5$ensembl.id,
mart = human)
pd.ame_res.top5<- right_join(name.dic,pd.ame_res.top5,by=c("ensembl_gene_id"="ensembl.id"))
# Gene expression connection ----------------------------------------------
require(RSQLite)
sqlitePath <- "./data/zhang-rnaseq-db.sqlite"
mydb <- dbConnect(SQLite(), sqlitePath)
query <- sprintf("SELECT * FROM %s", "samples")
metadata.samples <- dbGetQuery(mydb, query)
query <- sprintf("SELECT gene_id,description FROM %s", "genes_no_mt_tpm_rescale")
genes <- dbGetQuery(mydb, query)
rownames(genes) <- genes$gene_id
getExp <- function(gg){
data.frame(TPM= as.numeric(dbGetQuery(mydb,'SELECT * FROM genes_no_mt_tpm_rescale WHERE "gene_id" = :g',
params=list(g=gg))[metadata.samples$id]),
metadata.samples,Gene=gg)%>%dplyr::select(id,TPM)%>%spread(key = id,value = TPM)
}
exp.dat <- sapply(pd.ame_res.top5$external_gene_name,
getExp,simplify = F)
all.equal(names(exp.dat),
pd.ame_res.top5$external_gene_name)
exp.dat<-do.call(rbind,exp.dat)
exp.dat <- exp.dat[,c(9:10,1:8,11:18)]
pd.ame_res.top5<- right_join(pd.ame_res.top5,exp.dat%>%
rownames_to_column("external_gene_name"))
# save
write.csv(pd.ame_res.top5,file = "./data/zhang_top5_enrich_motif.csv")
# heatmap
p1 <- ggplot(pd.ame_res.top5%>%
unite(gene,external_gene_name,subfamily)%>%
dplyr::select("gene",starts_with("sample"))%>%
mutate(gene=factor(gene,levels = gene)) %>%
gather("sample","log10_adj_p.value",2:16)%>%
mutate(sample=factor(sample,levels = paste0("sample_",1:15))),
aes(sample,gene,fill=log10_adj_p.value))%+% geom_tile()%+%
scale_fill_gradientn(colours =rev(brewer.pal(7,"YlGnBu")),na.value="NA")+
theme(plot.background=element_blank(),
axis.text=element_text(face="bold"),
panel.grid = element_line(colour = "black"))+
theme_bw()
ggplot(pd.ame_res.top5%>%
unite(gene,external_gene_name,subfamily)%>%
dplyr::select("gene",
starts_with("Me"),
starts_with("AEC"),
starts_with("VEC"))%>%
mutate(gene=factor(gene,levels = gene)) %>%
gather("sample","logTPM",2:18),
aes(sample,gene,fill=logTPM))+ geom_tile()+
geom_tile(colour="white",size=0.25)+
scale_fill_gradientn(colours =rev(brewer.pal(7,"YlGnBu")),na.value="NA")
theme(plot.background=element_blank(),
axis.text=element_text(face="bold"),
panel.grid = element_line(colour = "black"))+
theme_bw()
# heatmap expression
pd <- pd.ame_res.top5%>%
unite(gene,external_gene_name,subfamily)%>%
dplyr::select("gene",starts_with("sample"))%>%
column_to_rownames("gene")
b <- simplot(pd)
pd.2.dup <-right_join(pd.ame_res.top5[,1:2],
pd.ame_res.hsap%>%
filter(ensembl.id %in% pd.ame_res.top5$ensembl_gene_id),
by=c('ensembl_gene_id'="ensembl.id"))%>%
unite(gene,external_gene_name,subfamily)%>%
filter(duplicated(gene))
pd.2.dup <- pd.2.dup[c()]
pd.2 <- right_join(pd.ame_res.top5[,1:2],
pd.ame_res.hsap%>%
filter(ensembl.id %in% pd.ame_res.top5$ensembl_gene_id),
by=c('ensembl_gene_id'="ensembl.id"))%>%
unite(gene,external_gene_name,subfamily)%>%
filter(!duplicated(gene))%>%
dplyr::select("gene",starts_with("sample"))%>%
column_to_rownames("gene")
pd.2[pd.2.dup$gene[5],1:15] <-pd.2.dup[5,3:17]
pd.2 <- pd.2[rownames(pd),paste0("sample_",1:15)]
b.2 <- simplot(pd.2)
b.3 <- simplot(apply(pd.2[rownames(pd),paste0("sample_",1:15)],2,normalise))
pd <- pd.ame_res.top5%>%
unite(gene,external_gene_name,subfamily)%>%
dplyr::select("gene",
starts_with("Me"),
starts_with("AEC"),
starts_with("VEC"))%>%
column_to_rownames("gene")
a<- simplot(pd)
a2<- simplot(t(apply(pd, 1,normalise)))
pd.2 <- pd %>%rownames_to_column("gene")%>%
gather(key=sample,value = TPM,2:(ncol(pd)+1))%>%
separate(sample,into =c("Cell","Day","Rep")) %>%
group_by(gene,Cell,Day)%>%
summarise(avgTPM=mean(TPM))%>%
unite(sample,Cell,Day)%>%
spread(key = sample,value = avgTPM)%>%
ungroup()%>%
mutate(gene=factor(gene,levels = rownames(pd)))%>%
arrange(gene)%>%as.data.frame()%>%
column_to_rownames("gene")
pd.2 <- pd.2[,c(5,1:4,6:9)]
c<- simplot(pd.2)
c2<- simplot(t(apply(pd.2, 1, normalise)))
require(gridExtra)
grid.arrange(b+theme(axis.text.x = element_blank()),
a+theme(axis.text = element_blank()),ncol=2)
grid.arrange(b+theme(axis.text.x = element_blank()),
a2+theme(axis.text = element_blank()),ncol=2)
grid.arrange(b+theme(axis.text.x = element_blank()),
c+theme(axis.text = element_blank()),ncol=2)
grid.arrange(b+theme(axis.text.x = element_blank()),
b.3+theme(axis.text = element_blank()),
c2+theme(axis.text = element_blank()),ncol=3)
grid.arrange(b+theme(axis.text.x = element_blank()),
b.2+theme(axis.text = element_blank()),
c+theme(axis.text = element_blank()),ncol=3)
|
c7b3fb11b7d158cf807bfa1824220d8a0e786490
|
ba0d52a9447cc2cedcaacafd8349fc50a32363b5
|
/R/prepBoxplotHealthData.R
|
591ef4c4c3ae0449e86e21cfd8bd5626e302bff3
|
[
"CC0-1.0"
] |
permissive
|
robschick/tangled
|
49590a754531b8e50294abb4d86fcd9cc85d037c
|
e4c0e49fa87802dd39fba01dc4fba5cef25e7b31
|
refs/heads/master
| 2023-04-07T19:24:43.838552
| 2022-05-04T19:11:30
| 2022-05-04T19:11:30
| 33,547,111
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,416
|
r
|
prepBoxplotHealthData.R
|
#' Prepare data for making the boxplots of health during entanglement
#'
#' The goal of this is to make the box plots of the health during the entanglement window.
#' This uses two data frames: (tangRepro and tangNonRepro) which are stored
#' in the package structure. The function also used the unimpacted reference
#' cases that are returned from the `returnUnimpactedHealth.R` helper
#' function. This function returns two data frames: \code{dfLongRepro}, and
#' \code{dfLongNonRepro}
#'
#' @param \code{tangRepro} data frame with information on the temporal
#' extend of windows for entnanglement events of
#' reproductively active animals
#' @param \code{tangNonRepro} data frame with information on the temporal
#' extend of windows for entnanglement events of
#' \emph{non}-reproductively active animals
#' @return \code{dfLongRepro} a melted data frame of the health of
#' reproductively active animals during entanglement windows
#' @return \code{dfLongNonRepro} a melted data frame of the health of
#' \emph{non}-reproductively active animals during entanglement windows
#' @export
#' @examples
#' \dontrun{
#' prepBoxplotHealthData(tangRepro, tangNonRepro, anomFlag = TRUE, thold = 67)
#' }
prepBoxplotHealthData <- function(tangRepro, tangNonRepro, anomFlag = FALSE, thold = 67){
tmp <- returnUnimpactedHealth(anomFlag = anomFlag)
nonrepvec <- tmp$nonrep
repvec <- tmp$rep
nonrepvec1980 <- tmp$nonrep1980
repvec1980 <- tmp$rep1980
nonrepvec1990 <- tmp$nonrep1990
repvec1990 <- tmp$rep1990
nonrepvec2000 <- tmp$rep2000
repvec2000 <- tmp$nonrep2000
useAnom <- anomFlag
for(z in 1:2){
ifelse(z == 1, repro <- TRUE, repro <- FALSE)
if (repro) {
tSub <- tangRepro
} else {
tSub <- tangNonRepro
}
if (useAnom){
healthmean <- anom
}
dfSum <- data.frame(egno = rep(NA, times = nrow(tSub)),
EntanglementId = rep(NA, times = nrow(tSub)),
eventNo = rep(NA, times = nrow(tSub)),
nMonths = rep(NA, times = nrow(tSub)),
hAnom = rep(NA, times = nrow(tSub)),
lthold = rep(NA, times = nrow(tSub)),
gearInj = rep(NA, times = nrow(tSub)),
startDate = rep(NA, times = nrow(tSub)),
endDate = rep(NA, times = nrow(tSub)))
idFac <- factor(unique(tSub$EGNo))
for(i in 1:length(tSub$EGNo)){
# i <- 32 #, EGNo == 1130 is a good test animal; i <- 390 is another (EGNo = 1102) (Both for Non-Repro)
# i = 4 # EGNo == 1014 for Repro
ind <- tSub$EGNo[i]
ent_id <- tSub$EntanglementId[i]
if(ind == 1045) next()
eventNo <- tSub$EventNo[i]
if(!ind %in% ID){next()}
htest <- healthmean[which(ID == ind),]
ti <- tSub[tSub$EGNo == ind,]
# Assemble the vector of integers for the duration of different gear injury combinations
# asking for the start and end of the health window during which I'll calculate health
# also for the date of first severe entanglement & the the 12 month recovery date
evnum <- nrow(ti)
s <- match(t(tSub[i, 'swindmonyr']), myName)
sDate <- tSub[i, 'StartDateWindow', drop = TRUE]
e <- match(t(tSub[i, 'ewindmonyr']), myName)
eDate <- tSub[i, 'EndDateWindow', drop = TRUE]
sev <- match(t(tSub[i, 'fsevmonyr']), myName)
sev <- sev[is.finite(sev)]
gstat <- tSub[i, 'gearInj']
hind <- htest[s:e]
lthold <- length(which(hind < thold))
dfSum[i, 'egno'] <- ind
dfSum[i, 'EntanglementId'] <- ent_id
dfSum[i, 'eventNo'] <- eventNo
dfSum[i, 'hAnom'] <- median(hind, na.rm = TRUE)
dfSum[i, 'lthold'] <- lthold
dfSum[i, 'gearInj'] <- gstat
dfSum[i, 'nMonths'] <- length(s:e)
dfSum[i, 'startDate'] <- lubridate::year(sDate)
dfSum[i, 'endDate'] <- lubridate::year(eDate)
}
dfSum$gearnogear <- 0
sev1idx <- which(dfSum$gearInj == 1)
mod1idx <- which(dfSum$gearInj == 2)
min1idx <- which(dfSum$gearInj == 4)
dfSum[sev1idx, 'gearnogear'] <- 1
dfSum[mod1idx, 'gearnogear'] <- 1
dfSum[min1idx, 'gearnogear'] <- 1
dfSum$variable <- 'impacted'
# add in the unimpacted animals - using data coming from the splitpopHealth.R function
# ONly adding them in once to avoid the double data, i.e. adding the same unimpacted data for both
# the rep and the non-rep, when we really only want them once.
if (repro) {
uvec <- repvec
dfuvec <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec,
lthold = 9999,
gearInj = 0,
startDate = '01-0000',
endDate = '01-0000',
gearnogear = 0,
variable = 'unimpacted')
# 1980
uvec1980 <- repvec1980
dfuvec1980 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec1980,
lthold = 9999,
gearInj = 0,
startDate = 1980,
endDate = 1989,
gearnogear = 0,
variable = 'unimpacted')
# 1990
uvec1990 <- repvec1990
dfuvec1990 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec1990,
lthold = 9999,
gearInj = 0,
startDate = 1990,
endDate = 1999,
gearnogear = 0,
variable = 'unimpacted')
# 2000
uvec2000 <- repvec2000
dfuvec2000 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec2000,
lthold = 9999,
gearInj = 0,
startDate = 2000,
endDate = 2009,
gearnogear = 0,
variable = 'unimpacted')
dfLong <- rbind(dfSum, dfuvec, dfuvec1980, dfuvec1990, dfuvec2000)
} else {
uvec <- nonrepvec
dfurvec <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec,
lthold = 9999,
gearInj = 0,
startDate = '01-0000',
endDate = '01-0000',
gearnogear = 0,
variable = 'unimpacted')
# 1980
uvec1980 <- nonrepvec1980
dfurvec1980 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec1980,
lthold = 9999,
gearInj = 0,
startDate = 1980,
endDate = 1989,
gearnogear = 0,
variable = 'unimpacted')
# 1990
uvec1990 <- nonrepvec1990
dfurvec1990 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec1990,
lthold = 9999,
gearInj = 0,
startDate = 1990,
endDate = 1999,
gearnogear = 0,
variable = 'unimpacted')
# 2000
uvec2000 <- nonrepvec2000
dfurvec2000 <- data.frame(egno = 9999,
EntanglementId = 0,
eventNo = 0,
nMonths = 0,
hAnom = uvec2000,
lthold = 9999,
gearInj = 0,
startDate = 2000,
endDate = 2009,
gearnogear = 0,
variable = 'unimpacted')
dfLong <- rbind(dfSum, dfurvec, dfurvec1980, dfurvec1990, dfurvec2000)
}
dfLong$group <- 1
mod0idx <- which(dfLong$gearInj == 5)
min0idx <- which(dfLong$gearInj == 6)
unimpactedidx <- which(dfLong$variable == 'unimpacted')
dfLong[c(mod1idx, mod0idx), 'group'] <- 2
dfLong[c(min1idx, min0idx), 'group'] <- 3
dfLong[unimpactedidx, 'group'] <- 4
dfLong$group <- factor(dfLong$group, levels = c(4, 3, 2, 1) )
# Put an identifier column
if (repro) {
dfLongRepro <- dfLong
dfLongRepro$status <- 'RepFem'
} else {
dfLongNonRepro <- dfLong
dfLongNonRepro$status <- 'NonRepFem'
}
} # end loop over the 'repro' variable
list(dfLongRepro = dfLongRepro, dfLongNonRepro = dfLongNonRepro)
}
|
d0f182d7d28df9cf4c8b8e3d4abb480cb1ecfff3
|
35ae390a7e9df0e77e3a247588424efc50cc1220
|
/plot5.R
|
3f66f6e6fd2754bff4e6d38e0fc1a6889ff2b0b4
|
[] |
no_license
|
tomashaber/ExData_Plotting2
|
b27ab507953b2bdcf839d6d091519cd8af354d37
|
45530013fd608609e6398f36ea2b1d1d4f0f3c7b
|
refs/heads/master
| 2021-01-10T02:39:12.454755
| 2015-11-22T20:47:22
| 2015-11-22T20:47:22
| 46,672,432
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 701
|
r
|
plot5.R
|
library(ggplot2)
library(dplyr)
wd <- getwd()
setwd("2")
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
v <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case=TRUE)
vSCC <- SCC[v,]$SCC
vNEI <- NEI[NEI$SCC %in% vSCC,]
data <- filter(vNEI, vNEI$fips=="24510")
png("plot5.png", width=480, height=480, units="px")
print(ggplot(data,aes(factor(year),Emissions))
+ geom_bar(stat="identity",fill="red",width=0.9)
+ theme_bw()
+ guides(fill=FALSE)
+ labs(x="year", y=expression("Total PM2,5 Emission (10^5 Tons)"))
+ labs(title=expression("PM2.5 Motor Vehicle Source Emissions in Baltimore from 1999-2008")))
dev.off()
setwd(wd)
|
7963efa405f6168f029bd29157d0f2db4b106237
|
9aafde089eb3d8bba05aec912e61fbd9fb84bd49
|
/codeml_files/newick_trees_processed/7896_0/rinput.R
|
37bb1153bd7289eddc308772f2c26435eaaa0ffd
|
[] |
no_license
|
DaniBoo/cyanobacteria_project
|
6a816bb0ccf285842b61bfd3612c176f5877a1fb
|
be08ff723284b0c38f9c758d3e250c664bbfbf3b
|
refs/heads/master
| 2021-01-25T05:28:00.686474
| 2013-03-23T15:09:39
| 2013-03-23T15:09:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 135
|
r
|
rinput.R
|
library(ape)
testtree <- read.tree("7896_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="7896_0_unrooted.txt")
|
0b277d8b7671e0d05988444ae511cc6af4e9e3eb
|
231e93f8115c71e8a4ef643ca51524f041f3f57d
|
/man/bold_stats.Rd
|
24c896be1678e57f1317cbe01be011ec3594f3f8
|
[
"MIT"
] |
permissive
|
GhostsOfHiroshima/bold
|
88c7d6551f3ef978e89986f945438e0fc1c9dcfb
|
82de5cfc8971e0b59eb661eab76f7f96f9858f78
|
refs/heads/master
| 2020-06-11T14:00:14.434375
| 2019-06-26T02:56:00
| 2019-06-26T02:56:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,663
|
rd
|
bold_stats.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bold_stats.R
\name{bold_stats}
\alias{bold_stats}
\title{Get BOLD stats}
\usage{
bold_stats(taxon = NULL, ids = NULL, bin = NULL, container = NULL,
institutions = NULL, researchers = NULL, geo = NULL,
dataType = "drill_down", response = FALSE, ...)
}
\arguments{
\item{taxon}{(character) Returns all records containing matching taxa. Taxa
includes the ranks of phylum, class, order, family, subfamily, genus,
and species.}
\item{ids}{(character) Returns all records containing matching IDs. IDs
include Sample IDs, Process IDs, Museum IDs and Field IDs.}
\item{bin}{(character) Returns all records contained in matching BINs. A
BIN is defined by a Barcode Index Number URI.}
\item{container}{(character) Returns all records contained in matching
projects or datasets. Containers include project codes and dataset codes}
\item{institutions}{(character) Returns all records stored in matching
institutions. Institutions are the Specimen Storing Site.}
\item{researchers}{(character) Returns all records containing matching
researcher names. Researchers include collectors and specimen identifiers.}
\item{geo}{(character) Returns all records collected in matching geographic
sites. Geographic sites includes countries and province/states.}
\item{dataType}{(character) one of "overview" or "drill_down" (default).
"drill_down": a detailed summary of information which provides record
counts by [BINs, Country, Storing Institution, Species]. "overview":
the total counts of [BINs, Countries, Storing Institutions, Orders,
Families, Genus, Species]}
\item{response}{(logical) Note that response is the object that returns
from the Curl call, useful for debugging, and getting detailed info on
the API call.}
\item{...}{Further args passed on to \code{\link[crul]{HttpClient}}, main
purpose being curl debugging}
}
\description{
Get BOLD stats
}
\examples{
\dontrun{
x <- bold_stats(taxon='Osmia')
x$total_records
x$records_with_species_name
x$bins
x$countries
x$depositories
x$order
x$family
x$genus
x$species
# just get all counts
lapply(Filter(is.list, x), "[[", "count")
res <- bold_stats(taxon='Osmia', response=TRUE)
res$url
res$status_code
res$response_headers
# More than 1 can be given for all search parameters
bold_stats(taxon=c('Coelioxys','Osmia'))
## curl debugging
### These examples below take a long time, so you can set a timeout so that
### it stops by X sec
bold_stats(taxon='Osmia', verbose = TRUE)
# bold_stats(geo='Costa Rica', timeout_ms = 6)
}
}
\references{
\url{http://v4.boldsystems.org/index.php/resources/api?type=webservices}
}
|
ea43da1a58f6e7a167c472346f0601653e222693
|
e2baae0f2cfe109900b67fbdfc0080eb2aa854b5
|
/man/khclust_euc.Rd
|
3d63bb058a3b4e04df4f7d909384dafa1435cca9
|
[] |
no_license
|
HBPMedical/CCC
|
5eb3411c420e13a14529858b1b848208ca4416d7
|
32a4b64e07e60c229c86f33286aa43742ba6f23c
|
refs/heads/master
| 2021-09-07T19:07:00.290850
| 2018-02-27T14:35:46
| 2018-02-27T14:35:46
| 69,594,420
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 883
|
rd
|
khclust_euc.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{khclust_euc}
\alias{khclust_euc}
\title{Title Gap statistics for hclust Euclidean}
\usage{
khclust_euc(x, K.max, B, verbose, plot.num.clus)
}
\arguments{
\item{x}{data matrix}
\item{K.max}{positive integer specifying the number of clusters, less than the number of observations.}
\item{B}{integer, number of Monte Carlo (“bootstrap”) samples}
\item{verbose}{integer or logical, determining if “progress” output should be printed. The default prints
one bit per bootstrap sample}
\item{plot.num.clus}{if TRUE (default) the gap statistic plot will be printed}
}
\value{
the clusGap function output
}
\description{
Title Gap statistics for hclust Euclidean
}
\examples{
# khclust_euc(subx,K.max=10, B=60, verbose = FALSE, plot.num.clus=TRUE )
}
|
865887324e48c77dd0f66476cea169a51e71366d
|
34dd9a3a2313d0b4484ea3bf32e65175cdd718eb
|
/man/GSA.read.gmt.Rd
|
c52078044c44448138b1a24ea5868e3405e7d927
|
[] |
no_license
|
cran/GSA
|
6afc50cd79249bb0051596d7ef82a2e7513b97af
|
43634ce04a75fcc52023419eb6225524bacf71e0
|
refs/heads/master
| 2022-05-13T06:39:05.299449
| 2022-03-19T13:42:01
| 2022-03-19T13:42:01
| 17,679,483
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,656
|
rd
|
GSA.read.gmt.Rd
|
\name{GSA.read.gmt}
\alias{GSA.read.gmt}
\title{Read in a gene set collection from a .gmt file}
\description{
Read in a gene set collection from a .gmt file
}
\usage{
GSA.read.gmt(filename)
}
\arguments{
\item{filename}{The name of a file to read data values from. Should be
a tab-separated text file, with one row per gene set. Column 1 has gene set
names (identifiers), column 2 has gene set descriptions, remaining columns are gene ids for genes in that
geneset}.
}
\details{This function reads in a geneset collection from a .gmt text file,
and creates an R object that can be used as input into GSA.
We use UniGene symbols for our gene set names in our .gmt files and expression datasets,
to match the two.
However the user is free to use other identifiers, as long as the same ones
are used in the gene set collections and expression datasets.
}
\value{
A list with components
\item{genesets}{List of gene names (identifiers) in each gene set},
\item{geneset.names}{Vector of gene set names (identifiers)},
\item{geneset.descriptions}{Vector of gene set descriptions}
}
\references{Efron, B. and Tibshirani, R.
On testing the significance of sets of genes. Stanford tech report rep 2006.
http://www-stat.stanford.edu/~tibs/ftp/GSA.pdf
}
\author{Robert Tibshirani}
\examples{
# read in functional pathways gene set file from Broad institute GSEA website
# http://www.broad.mit.edu/gsea/msigdb/msigdb_index.html
# You have to register first and then download the file C2.gmt from
# their site
#GSA.read.gmt(C2.gmt)
}
\keyword{univar}% at least one, from doc/KEYWORDS
\keyword{survival}
\keyword{ts}
\keyword{nonparametric}
|
c7d394c6398719f97b6e6feb8fcbdef2209ab4fc
|
3124eae2c2cc624306b83f945f0f0730841798ce
|
/man/itakura.dist.Rd
|
57caebc88cd639902ea6d8dae5c69d8713fe69b4
|
[] |
no_license
|
cran/seewave
|
7841703a7f1cf237ce67e84f0f5b1dba877b1dff
|
1f0b3d5688151141129368c17f826ccedcb4ad96
|
refs/heads/master
| 2023-08-08T11:45:55.093227
| 2023-07-16T05:50:02
| 2023-07-16T06:34:59
| 17,699,546
| 18
| 12
| null | 2023-01-27T12:57:14
| 2014-03-13T06:15:47
|
R
|
UTF-8
|
R
| false
| false
| 2,399
|
rd
|
itakura.dist.Rd
|
\name{itakura.dist}
\alias{itakura.dist}
\title{Itakuro-Saito distance}
\description{Compare two distributions (e.g. two frequency spectra) by
computing the Itakuro-Saito distance}
\usage{itakura.dist(spec1, spec2, scale=FALSE)}
\arguments{
\item{spec1}{any distribution, especially a spectrum obtained with \code{\link{spec}} or \code{\link{meanspec}} (not in dB). This can be either a two-column matrix (col1 = frequency, col2 = amplitude) or a vector (amplitude).}
\item{spec2}{any distribution, especially a spectrum obtained with
\code{\link{spec}} or \code{\link{meanspec}} (not in dB). This can be
either a two-column matrix (col1 = frequency, col2 = amplitude) or a
vector (amplitude).}
\item{scale}{a logical, if \code{TRUE} the distance is scaled by dividing the
distance by the length of \code{spec1} (or \code{spec2}).}
}
\details{The Itakura-Saito (I-S) distance is a
non-symmetric measure of the difference between two probability
distributions. It is here adapted for frequency spectra. The distance
is asymmetric, ie computing the I-S distance between spec1 and spec2 is
not the same as computing it between spec2 and spec1. A symmetry can be
obtained by calculating the mean between the two directions.\cr
The distance is obtained following:\cr
\deqn{D_{I-S}(spec1 \Vert spec2) = \sum{\frac{spec1}{spec2} -
log(\frac{spec1}{spec2}) - 1}}{%
D(spec1 || spec2) = sum(spec1/spec2 - log(spec1/spec2) - 1)}
}
\value{The function returns a list of three items:
\item{D1}{The I-S distance of 'spec2' with respect to 'spec1'
(\emph{i.e.} D(spec1 || spec2))}
\item{D2}{The I-S distance of 'spec1' with respect to 'spec2'
(\emph{i.e.} D(spec2 || spec1))}
\item{D}{The symmetric distance (\emph{i.e.} D = 0.5*(D1+D2))}
If \code{scale = TRUE} the distance is divided by the length of \code{spec1}
(or \code{spec2}).
}
\note{The function works for both Hz and (htk-)mel scales.}
\author{Jerome Sueur, improved by Laurent Lellouch}
\seealso{\code{\link{kl.dist}}, \code{\link{ks.dist}}, \code{\link{logspec.dist}}, \code{\link{simspec}}, \code{\link{diffspec}}}
\examples{
# Comparison of two spectra
data(tico)
tico1 <- spec(tico, at=0.65, plot=FALSE)
tico2 <- spec(tico, at=1.1, plot=FALSE)
itakura.dist(tico1, tico2)
itakura.dist(tico1, tico2, scale=TRUE)
}
\keyword{distribution}
\keyword{ts}
|
1024d7186c5da32c9916729369d608d93ebbb518
|
f884d7bcd31c81582960a7d8f820e3d3f4cf6953
|
/man/formatSettings.Rd
|
aa055446faf970e99612b2044615e309f75709e0
|
[
"MIT"
] |
permissive
|
rtmtemp/kwb.monitoring
|
bc716dfb18f489dc4d1040f39359fc781d362ff2
|
6aefc1d076c4cfa8ffbdd1abf8f143f7deb8b9e5
|
refs/heads/master
| 2022-11-06T23:51:03.604207
| 2019-02-14T01:04:28
| 2019-02-14T01:04:28
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 552
|
rd
|
formatSettings.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/info.R
\name{formatSettings}
\alias{formatSettings}
\title{Format Settings}
\usage{
formatSettings(settings, settingNames = names(settings),
do.stop = FALSE)
}
\arguments{
\item{settings}{list of settings}
\item{settingNames}{names of the settings, by default:
\code{names(settings)}}
\item{do.stop}{passed to \code{kwb.monitoring:::get_H_threshold},
\code{kwb.monitoring:::get_Q_threshold},
\code{kwb.monitoring:::get_V_threshold}}
}
\description{
Format Settings
}
|
e7ad0d3d60a6a427f7e38abe2b57bf2d05bae028
|
2407dd0c4ecba24555a73ca84640330fa3e754e4
|
/R/gonogo.R
|
0cfec4e3b84f89acce74285634d08c4d53919c3b
|
[] |
no_license
|
brian-lau/Rexpneuro
|
c6f7cf7cc8ede7f41145b4d9b389620aa8e30103
|
0caf9b72c2108ce50f7b7e1ebd8d43aea3a6f949
|
refs/heads/master
| 2023-07-03T00:44:58.757667
| 2020-11-23T17:06:31
| 2020-11-23T17:06:31
| 308,354,517
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,956
|
r
|
gonogo.R
|
#' @export
read_eventide <- function(fname = NULL,
name = NULL,
basedir = getwd(),
start_date = "30012017", # daymonthyear
end_date = "30012021", # daymonthyear
min_trials = 1,
include_tracker = FALSE,
include_spike = FALSE,
...
) {
library(magrittr)
library(dplyr)
if (is.null(names) & is.null(fname)) stop("Specify subject name or filename.")
if (!is.null(fname)) {
fnames <- fname
name <- stringr::str_split(fnames,"_")[[1]][1]
} else {
fnames <- list.files(path = basedir,
pattern = glob2rx(paste0(name, "_", "GNG", "*.txt")),
ignore.case = TRUE)
}
d <- purrr::map_chr(stringr::str_split(fnames,"_"), 3)
t <- purrr::map_chr(stringr::str_split(fnames,"_"), 4)
t <- purrr::map_chr(stringr::str_split(t,".txt"), 1)
d <- as.POSIXct(paste(d, t),
"%d%m%Y %H-%M", tz = "Europe/Paris")
## Eventide trial data
# Sort by ascending experiment date
ind <- order(d)
fnames <- fnames[ind]
d <- d[ind]
start_date <- as.POSIXlt(start_date, format = "%d%m%Y", tz = "Europe/Paris")
end_date <- as.POSIXlt(end_date, format = "%d%m%Y", tz = "Europe/Paris")
ind <- (start_date <= d) & (d <= end_date)
fnames <- fnames[ind]
d <- d[ind]
## Eventide tracker data
if (include_tracker) {
td <- parse_tracker_filenames(basedir = basedir)
#dt <- difftime(d, td$date, units = "secs")
# Find index for matching tracker file by time difference
dt <- t(matrix(unlist(purrr::map(d, ~(abs(difftime(.x, td$date, units = "secs"))))), ncol = length(d)))
mind <- which(dt==matrixStats::rowMins(dt),arr.ind=T)
#map_int(d, ~which.min(abs(difftime(.x, td$date, units = "secs"))))
dt <- dt[mind]
td$fnames <- td$fnames[mind[,2]]
td$date <- td$date[mind[,2]]
td$dt <- dt
}
if (!any(ind)) {
out <- list(
call = match.call(),
name = name,
info = NULL,
trial_data = NULL
)
} else {
# Read session data
dat <- purrr::map(paste(basedir, fnames, sep = .Platform$file.sep), read_eventide_single, ...)
trial_data = purrr::map_dfr(dat, "trial_data", .id = "session")
info <- tibble::tibble(session = unique(trial_data$session),
version = purrr::map_chr(dat, "version"),
date = unlist(purrr::map(dat, "date") %>% purrr::reduce(c)),
fname_eventide = fnames
)
if (include_tracker) {
dat_tracker <- purrr::map(paste(basedir, td$fnames, sep = .Platform$file.sep), read_eventide_tracker, ...)
tracker_data <- purrr::map_dfr(dat_tracker, "tracker_data", .id = "session")
info %<>% tibble::add_column(date_tracker = td$date,
fname_tracker = td$fnames,
dt = dt
)
# Remove extra tracker data (from session terminating before trial_data written)
tracker_data %<>% group_by(session, counter_total_trials) %>%
nest() %>% semi_join(trial_data, by = c("session", "counter_total_trials"))
# Convert to trial time
if ("define_trial_onset_time_absolute" %in% names(trial_data)) {
tracker_data$define_trial_onset_time_absolute = trial_data$define_trial_onset_time_absolute
f <- function(df, t0) {
df$t <- df$t - t0
return(df)
}
tracker_data %<>% mutate(data = map(data, ~f(.x, define_trial_onset_time_absolute)))
}
} else {
tracker_data <- NULL
}
out <- list(
call = match.call(),
name = name,
info = info,
trial_data = trial_data,
tracker_data = tracker_data
)
#temp = trial_data %>% nest_join(tracker_data)
}
class(out) <- "GNGeventide"
return(out)
}
#' @export
summary.GNGeventide <- function(obj,
skim_func = NULL, # see skimr::skim_with
summarise_durations = FALSE
) {
library(dplyr)
library(skimr)
cat("Call: ")
print(obj$call)
cat("Name:\t\t", obj$name, "\n")
cat("# sessions:\t", nrow(obj$info), "\n")
cat("# trials:\t", nrow(obj$trial_data), "\n")
if (is.null(skim_func)) {
skim_func <- skim_with(
base = sfl(missing = n_missing),
numeric = sfl(mean = mean,
std = sd,
med = median,
mad = mad,
hist = function(x) inline_hist(x, 20)),
append = F
)
}
x <- obj$trial_data %>%
group_by(condition) %>%
skim_func(is_correct, is_abort)
print(x, include_summary = FALSE, width = NULL)
x <- obj$trial_data %>%
group_by(block) %>%
skim_func(is_correct, is_abort)
print(x, include_summary = FALSE, width = NULL)
cat("\nFilter by correct trials")
x <- obj$trial_data %>%
group_by(block) %>%
filter(is_correct & (condition != "nogo")) %>%
skim_func(counter_total_trials, counter_trials_in_block, rt, mt)
print(x, include_summary = FALSE, width = NULL)
if (summarise_durations) {
ind = stringr::str_detect(names(obj$trial_data), "^measured")
x <- obj$trial_data %>%
skim_func(names(obj$trial_data)[stringr::str_detect(names(obj$trial_data), "^measured")])
print(x, include_summary = FALSE, width = NULL)
}
}
#' @export
read_eventide_single <- function(fname,
remove_measured = FALSE,
zero_trial_start_time = TRUE
) {
library(magrittr)
library(dplyr)
library(readr)
# Parse filename
## Parse header
x <- stringr::str_replace_all(readLines(fname, n = 8), ";", "")
hdr_txt <- stringr::str_split(
stringr::str_replace_all(x, stringr::fixed(" "), ""),
":", simplify = TRUE)
ind <- hdr_txt[,2] != ""
hdr_txt <- hdr_txt[ind, ]
out <- list(
name = hdr_txt[5,2],
date = as.POSIXct(paste0(hdr_txt[3,2], " ", hdr_txt[4,2], ":", hdr_txt[4,3]),
"%d/%m/%Y %H:%M", tz = "Europe/Paris"),
version = hdr_txt[1,2]
)
## Read data
ct <- readr::cols(
.default = col_double(),
"Counter Total Trials" = col_integer(),
"Counter Trials In Block" = col_integer(),
"Blocked Mode" = col_logical(),
"Condition Name" = col_character(),
"Trial Result Str" = col_character(),
"Is Correct Trial" = col_logical(),
"Is Incorrect Trial" = col_logical(),
"Is Abort Trial" = col_logical(),
"Is Repeat Trial" = col_logical()
)
df <- readr::read_csv2(fname, col_names = TRUE, col_types = ct, skip = 8, locale(decimal_mark = ","))
df %<>% janitor::remove_empty(which = "cols") %>%
janitor::clean_names()
# Movement time
df$mt <- df$tt - df$rt
# Fill in cue set index for sessions with only one
if (!("cue_set_index" %in% colnames(df))) {
df %<>% tibble::add_column(cue_set_index = 0, .after = "block_index")
}
cnames <- colnames(df)
df %<>% rename(cueset = cue_set_index) %>%
mutate(cueset = factor(cueset, levels = c(0,1), labels = c("old", "new")))
# convert time to seconds
msec_to_sec <- function(x, na.rm = FALSE) (x/1000)
tvars <- c("rt", "rt2", "tt", "mt", "reward_delay")
tvars <- c(tvars, cnames[stringr::str_ends(cnames, "_duration")],
cnames[stringr::str_ends(cnames, "_time")])
df %<>% mutate_at(which(cnames %in% tvars), msec_to_sec) %>%
tibble::add_column(direction = contra_ipsi_tar(df$tar_x, tolower(out$name)),
.after = "cueset") %>%
tibble::add_column(block = as.factor(df$block_index),
.after = "block_index") %>%
relocate(mt, .after = rt2)
# set factor levels of trial_result_str, condition_name, direction
levels(df$block) <- c("con", "mix")
df$condition_name[df$condition_name=="Go"] = "go"
df$condition_name[df$condition_name=="Go control"] = "go_con"
df$condition_name[df$condition_name=="Nogo"] = "nogo"
df %<>% tibble::add_column(condition = factor(df$condition_name,
levels = c("go_con", "go", "nogo")),
.after = "condition_name")
df$condition_name[df$condition_name=="go_con"] = "go"
df %<>% rename(gng = condition_name) %>%
mutate(gng = factor(gng, levels = c("nogo", "go")))
df %<>% rename(is_correct = is_correct_trial, is_incorrect = is_incorrect_trial,
is_abort = is_abort_trial, is_repeat = is_repeat_trial)
df$trial_result_str[df$trial_result_str=="target touch"] = "target_touch"
df$trial_result_str[df$trial_result_str=="fixation holding"] = "fixation_holding"
df$trial_result_str[df$trial_result_str=="Late retouch"] = "err_late_retouch"
df$trial_result_str[df$trial_result_str=="cue touch aborted"] = "err_cue_touch_aborted"
df$trial_result_str[df$trial_result_str=="Else-where touch"] = "err_elsewhere_touch"
df$trial_result_str[df$trial_result_str=="Overall too late"] = "err_overall_too_late"
df$trial_result_str[df$trial_result_str=="Anticipation"] = "err_anticipation"
df$trial_result_str[df$trial_result_str=="Early target release"] = "err_early_target_release"
df %<>% tibble::add_column(event = factor(df$trial_result_str,
levels = c("fixation_holding",
"target_touch",
"err_anticipation",
"err_cue_touch_aborted",
"err_late_retouch",
"err_elsewhere_touch",
"err_early_target_release",
"err_overall_too_late"
)),
.after = "trial_result_str")
df$cue_duration_programmed <- df$cue_duration
df$cue_duration <- df$measured_cue_duration
if (remove_measured) df %<>% select(-starts_with("measured"))
if (zero_trial_start_time) {
df %<>% mutate(define_trial_onset_time_absolute = define_trial_onset_time, .before = define_trial_onset_time)
df %<>% mutate(across(ends_with("_onset_time"), ~purrr::map2_dbl(.x, define_trial_onset_time, ~.x - .y)))
}
out$trial_data <- df
class(out) <- "GNGeventide_single"
return(out)
}
#' @export
read_eventide_tracker <- function(fname, Fs = 100) {
library(dplyr)
library(tidyr)
library(purrr)
library(readr)
# Parse filename
## Parse header
x <- stringr::str_replace_all(readLines(fname, n = 3), ";", "")
hdr_txt <- stringr::str_split(
stringr::str_replace_all(x, stringr::fixed(" "), ""),
":", simplify = TRUE, n = 2)
ind <- hdr_txt[,2] != ""
hdr_txt <- hdr_txt[ind, ]
out <- list(
date = as.POSIXct(hdr_txt[1,2], "%Y.%d.%m%H:%M", tz = "Europe/Paris"),
version = hdr_txt[2,2]
)
## Read data
ct <- readr::cols_only(
`User Field` = col_integer(),
`Current Event` = col_character(),
`EventIDE TimeStamp` = col_double(),
`Gaze CVX` = col_double(), # degrees
`Gaze CVY` = col_double(),
#`Gaze X` = col_double(), # pixels
#`Gaze Y` = col_double(),
Pressure = col_double(),
#`Is Touch` = col_logical(),
X10 = col_logical()
)
df <- readr::read_csv2(fname, col_names = TRUE, col_types = ct, skip = 3, locale(decimal_mark = ","))
df %<>% janitor::remove_empty(which = "cols") %>%
rename("counter_total_trials" = "User Field",
"state" = "Current Event",
"t" = "EventIDE TimeStamp",
"x" = "Gaze CVX",
"y" = "Gaze CVY",
"pressure" = "Pressure")
df %<>% filter((state == "Fixation") |
(state == "Cue") |
(state == "Target>Holding Fixation ROI") |
(state == "Target>Waiting") |
(state == "Target>Target touch") |
(state == "Eval") |
(state == "Correct>Delay") |
(state == "Abort")) %>%
mutate(x = ifelse(pressure==0, NA, x), y = ifelse(pressure==0, NA, y))
lev <- c("Fixation", "Cue", "Target>Holding Fixation ROI", "Target>Waiting",
"Target>Target touch", "Eval", "Correct>Delay", "Abort")
lev <- lev[lev %in% unique(df$state)]
df$state <- factor(df$state, levels = lev)
## Linearly interpolate to regular grid
# Create a regular grid
myseq <- function(from, to, by) tibble(t = seq(from, to, by))
df2 <- df %>% group_by(counter_total_trials) %>%
summarise(start = min(t), end = max(t), .groups = "drop") %>%
group_by(counter_total_trials) %>%
mutate(t_r = map2(start, end, ~myseq(start, end, 1000/Fs))) %>% # times in msec
select(-start,-end)
# Join with original data
df2 %<>% full_join(df %>% group_by(counter_total_trials) %>% nest(), by = "counter_total_trials")
# Interpolate
myapprox <- function(x, y, xout, method = "linear") {
tibble(r = approx(x, y, xout, ties = min, na.rm = FALSE, method = method)$y)
}
df2 %<>% mutate(state = map2(data, t_r, ~myapprox(.x$t, as.integer(.x$state), .y$t, method = "constant")),
x = map2(data, t_r, ~myapprox(.x$t, .x$x, .y$t)),
y = map2(data, t_r, ~myapprox(.x$t, .x$y, .y$t)),
pressure = map2(data, t_r, ~myapprox(.x$t, .x$pressure, .y$t))) %>%
select(-data) %>%
unnest(cols = c(t_r, state, x, y, pressure), names_sep = "_") %>%
rename(t = t_r_t, state = state_r, x = x_r, y = y_r, pressure = pressure_r) %>%
mutate(pressure = ifelse(is.na(x), 0, pressure), t = t/1000)
df2$state <- factor(df2$state, labels = lev)
out$Fs <- Fs
out$tracker_data <- df2 %>% ungroup()
class(out) <- "GNGeventide_tracker"
return(out)
}
contra_ipsi_tar <- function(x, subject) {
# Contra/Ipsi relative to arm used
dir = x
if (subject == "tess") {
dir[x < 0] = "ipsi"
dir[x > 0] = "contra"
} else if (subject == "chanel") {
dir[x < 0] = "ipsi"
dir[x > 0] = "contra"
} else if (subject == "flocky") {
dir[x > 0] = "ipsi"
dir[x < 0] = "contra"
}
dir = factor(dir, levels = c("ipsi", "contra"))
return(dir)
}
#' @export
parse_tracker_filenames <- function(basedir = getwd()) {
fnames <- list.files(path = basedir, pattern =
glob2rx(paste0("TrackerLog--ELOTouchTracker--", "*.txt")))
d <- purrr::map_chr(stringr::str_split(fnames,"--"), 3)
t <- purrr::map_chr(stringr::str_split(fnames,"--"), 4)
t <- purrr::map_chr(stringr::str_split(t,".txt"), 1)
d <- as.POSIXct(paste(d, t),
"%Y-%d-%m %H-%M", tz = "Europe/Paris")
# Sort by ascending experiment date
ind <- order(d)
fnames <- fnames[ind]
d <- d[ind]
return(list(fnames = fnames, date = d))
}
|
2327910847afe871f74f18386c2304382a3b4cbb
|
487a34c5ace2b1a60229c5403335de734616561e
|
/1d2-basicstats.R
|
9eb57d44dfb473d21d7e2d1708b617ea0d7f9575
|
[] |
no_license
|
hhenoida/dataanalytics
|
39d261a288f90c97effc0358d49fd2ffb8566578
|
c563272f7890a0731fbb9e24e5ff6309ea8586ee
|
refs/heads/master
| 2020-03-31T19:50:12.135620
| 2018-10-29T12:40:39
| 2018-10-29T12:40:39
| 152,513,808
| 259
| 15
| null | null | null | null |
UTF-8
|
R
| false
| false
| 620
|
r
|
1d2-basicstats.R
|
# Basic Stats
x = ceiling(rnorm(10000, mean=60, sd=20))
mean(x)
median(x)
#there is no mode function for mode stats
table(x)
sort(table(x), decreasing=T)
#mode
library(modeest)
mlv(x,method='shorth')
#quantile
quantile(x)
quantile(x,seq(.1,1,by=.1)) #decile
quantile(x,seq(.01,1,by=.01)) #percentile
library(e1071) # load e1071
plot(density(x)) #density plot
e1071::skewness(x) # apply the skewness
kurtosis(x)
sd(x); var(x)
cov(women$weight, women$height)
cor(women$height, women$height)
stem(x)
#Freq Table
library(fdth) #fast way of creating FT
ftable1 = fdt(x)
ftable1
|
e0ee1e3f4b7b94112dbde3108138803580450774
|
7f02263a680124a9b6fed6e709013034be0dc2e8
|
/SciDataEpi2020/functions/plot_plus.r
|
996d8e1545e100e85fbcc929c1474de820715f1f
|
[] |
no_license
|
Hindrance/EpiSciData2020
|
a8fa07e67a240a81d76391b614175593369e2810
|
b271bb99bd793992fea7f41fe46ef1a981e33e61
|
refs/heads/master
| 2022-11-10T14:53:32.093301
| 2020-06-23T11:21:55
| 2020-06-23T11:21:55
| 266,233,813
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,609
|
r
|
plot_plus.r
|
#############################################################
# PLOTS: POINTS PLUS DENSITIES - by Juliette (and Vince :D)
#############################################################
# __
# _.-~ )
# _..--~~~~,' ,-/ _
# .-'. . . .' ,-',' ,' )
# ,'. . . _ ,--~,-'__..-' ,'
# ,'. . . (@)' ---~~~~ ,'
# /. . . . '~~ ,-'
# /. . . . . ,-'
# ; . . . . - . ,'
# : . . . . _ /
# . . . . . `-.:
# . . . ./ - . )
# . . . | _____..---.._/ _____
#~---~~~~----~~~~ ~~
#
# DENSITY PLOT DOLPHIN ######################################
# datax = x values
# datay = y values
# classes = class factors
# colours = ordered vector of colours (as a HEX string)
# a = plot vertical adjust
plot_plus <- function(datax, datay, classes, colours, a=10,pch=16,cex=1,ylim, xlim, bw=c(diff(range(datax))/20,diff(range(datay)/20)),...){
if(class(classes)!="factor"){
class.vector <- factor(classes, levels=unique(classes))} else {
class.vector <- classes
}
class.colours <- colours
# Create a plot range - used for all densities
if(missing(xlim)){x.range <- range(datax)+c((-(range(datax)[2]-range(datax)[1])/10),(range(datax)[2]-range(datax)[1])/10)} else {
x.range <- range(xlim)+c((-(range(datax)[2]-range(datax)[1])/10),(range(datax)[2]-range(datax)[1])/10)
}
if(missing(ylim)){y.range <- range(datay)+c((-(range(datay)[2]-range(datay)[1])/10),(range(datay)[2]-range(datay)[1])/10)} else {
y.range <- range(ylim)+c((-(range(datay)[2]-range(datay)[1])/10),(range(datay)[2]-range(datay)[1])/10)
}
# Set up plot window
# par(mar=c(5,5,5,5),xpd=T,cex=cex)
par(xpd=T,cex=cex)
plot(datax,datay,pch=NA,
yaxs="i",ylim=y.range,
xaxs="i",xlim=x.range,...)
# For each class defined in class list.
for(i in levels(class.vector)){
# What data belongs to our class i?
i.vector <- which(class.vector == i)
# What colour belongs to our class i?
i.colour <- class.colours[which(levels(class.vector) == i)]
# Let's plot our class i data
if(pch < 21){points(datax[i.vector],datay[i.vector],col=i.colour,pch=pch)}
if(pch >= 21){points(datax[i.vector],datay[i.vector],bg=i.colour,pch=pch, col="black")}
# Let's find the density of our class y along the x-axis
x.density <- density(datax[i.vector],from = min(x.range),to = max(x.range), bw=bw[1])
x.density$x <- c(min(x.density$x), x.density$x, max(x.density$x))
x.density$y <- c(0, x.density$y, 0)
x.density$y <- x.density$y/length(i.vector)
# Let's find the density of our class y along the y-axis (here we rename x and y because of reasons)
y.density <- density(datay[i.vector],from = min(y.range),to = max(y.range), bw=bw[2])
y.density$x <- c(min(y.density$x), y.density$x, max(y.density$x))
y.density$y <- c(0, y.density$y, 0)
y.density$y <- y.density$y/length(i.vector)
names(y.density)[1:2] <- c("y","x")
# We need a coefficient to make the density appear on our plot scale - this is scaled to the plot window by "a".
x.coeff <- ((y.range[2]-y.range[1])/a)/max(x.density$y)
y.coeff <- ((x.range[2]-x.range[1])/a)/max(y.density$x)
# Let's produce some density plots on the x-axis (axis 3) - adding the y axis coefficient.
polygon(x.density$x,x.density$y*x.coeff+max(y.range),col=paste(i.colour,"80",sep=""), border=i.colour)
# Let's produce some density plots on the y-axis (axis 4) - adding the x axis coefficient.
polygon(y.density$x*y.coeff+max(x.range),y.density$y,col=paste(i.colour,"80",sep=""), border=i.colour)
# Plot population distribution values?? - to be continued.. .need to find ways to machine learn / parse population between density distib's
# x.density$y[which.max(x.density$y)]
}
par(xpd=F)
box()
}
example.data.function <- function( class.no = 6, n.factors=2,
class.length.m = 50,
class.length.sd = 10,
norm.m.range = c(-50,50),
norm.sd.range = c(0,30),
linear.range = c(-50,50)
) {
# Example data
# class.no = number of classes (clusters)
# class.length.m = number of data in each class
# class.length.sd = s deviation of data
# norm.m.range = lower and upper limits of normal distribution means
# norm.sd.range = lower and upper limits of normal distribution SDs
# linear.range = lower and upper limits of linear function
name.vector <- c(LETTERS,letters,0:9)
multi.factor.set <- t(matrix(rnorm(class.length.m*class.no*n.factors, runif(n.factors, 0, 1), runif(n.factors, 0, 0)), n.factors, class.length.m*class.no))
multi.factor.set <- matrix(rnorm(class.length.m*n.factors, runif(n.factors, 0, 1), runif(n.factors, 0, 0)), n.factors, class.length.m)
class.vector <- character()
class.values.x <- numeric()
class.values.y <- numeric()
class.colours <- character()
for(i in 1:class.no){
class.name <- paste(name.vector[round(runif(6,1,length(name.vector)))], collapse="")
class.length <- round(rnorm(1,class.length.m, class.length.sd))
class.vector <- c(class.vector, rep(class.name, class.length))
linearx <- seq(runif(1, linear.range[1], linear.range[2]),runif(1, linear.range[1], linear.range[2]), length=class.length)
lineary <- seq(runif(1, linear.range[1], linear.range[2]),runif(1, linear.range[1], linear.range[2]), length=class.length)
class.values.x <- c(class.values.x, (rnorm(class.length,runif(1,norm.m.range[1], norm.m.range[2]),runif(1,norm.sd.range[1], norm.sd.range[2])))+linearx)
class.values.y <- c(class.values.y, (rnorm(class.length,runif(1,norm.m.range[1], norm.m.range[2]),runif(1,norm.sd.range[1], norm.sd.range[2])))+lineary)
c.temp <- sample(colours(),1)
c.temp <- sprintf("#%02X%02X%02X",col2rgb(c.temp)[1], col2rgb(c.temp)[2], col2rgb(c.temp)[3])
class.colours <- c(class.colours, rep(c.temp,class.length))
}
class.vector <- as.factor(class.vector)
output <- data.frame(class.vector, class.values.x, class.values.y, class.colours, stringsAsFactors=F)
output
}
if(no.example.plx == F){
#################### EXAMPLE DATA #########################
plot.example.data <- function(){
temp.data <- example.data.function(5, 50, 10, c(-50, 50), c(0, 30), c(-50, 50))
plot_plus(temp.data$class.values.x, temp.data$class.values.y, classes = temp.data$class.vector,colours=unique(temp.data$class.colours), pch=21, cex=1.2)
}
plot.example.data()
#################### EXAMPLE DATA #########################
}
|
bf4204c4eeaceb96894c8cd6933899f93d83f7d6
|
c981caf103a3540f7964e6c41a56ca34d67732c4
|
/R/mice.impute.eap.R
|
162326db22d762e9d3ab3ff88d0136c95af7e900
|
[] |
no_license
|
alexanderrobitzsch/miceadds
|
8285b8c98c2563c2c04209d74af6432ce94340ee
|
faab4efffa36230335bfb1603078da2253d29566
|
refs/heads/master
| 2023-03-07T02:53:26.480028
| 2023-03-01T16:26:31
| 2023-03-01T16:26:31
| 95,305,394
| 17
| 2
| null | 2018-05-31T11:41:51
| 2017-06-24T15:16:57
|
R
|
UTF-8
|
R
| false
| false
| 415
|
r
|
mice.impute.eap.R
|
## File Name: mice.impute.eap.R
## File Version: 2.07
mice.impute.eap <- function (y, ry, x, eap, ...)
{
pos <- parent.frame(n=1)
res <- mice_imputation_get_states(pos=pos)
vname <- res$vname
newstate <- res$newstate
M.scale <- eap[[ vname ]][[ "M" ]]
SE.scale <- eap[[ vname ]][[ "SE" ]]
N <- length(M.scale)
ximp <- stats::rnorm( N, mean=M.scale, sd=SE.scale )
return(ximp)
}
|
c7878052270c641731c821593ef8bdced6675f93
|
0a906cf8b1b7da2aea87de958e3662870df49727
|
/grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610386845-test.R
|
72fd159b80b0d971b048ad0e03215b200edd1218
|
[] |
no_license
|
akhikolla/updated-only-Issues
|
a85c887f0e1aae8a8dc358717d55b21678d04660
|
7d74489dfc7ddfec3955ae7891f15e920cad2e0c
|
refs/heads/master
| 2023-04-13T08:22:15.699449
| 2021-04-21T16:25:35
| 2021-04-21T16:25:35
| 360,232,775
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 597
|
r
|
1610386845-test.R
|
testlist <- list(a = -774646785L, b = 385877056L, x = c(-623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623247288L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -636853204L, -1L, -1L, 278592L, 751971372L))
result <- do.call(grattan:::anyOutside,testlist)
str(result)
|
2d06eb0cf5a285db35ba08d3c9f9ba8ec5dd3719
|
8c4c1dd8db80357e6d0fed7adafe9a4a371c2801
|
/plot3.R
|
5c242439e09ef484895cce3691ac147703735b73
|
[] |
no_license
|
tj---/ExData_Plotting1
|
694b50bf42721088a904e673b9296eab70695ba0
|
c402485d9a8c4b8343560d965af2b0b090c0b4da
|
refs/heads/master
| 2021-01-22T18:42:55.226052
| 2015-06-04T20:24:24
| 2015-06-04T20:24:24
| 36,820,921
| 0
| 0
| null | 2015-06-03T18:00:33
| 2015-06-03T18:00:31
| null |
UTF-8
|
R
| false
| false
| 480
|
r
|
plot3.R
|
source("util.R")
data <- load_data() # Load the relevant data from the file
png(file = "plot3.png", width = 480, height = 480)
plot(data$DateTime, data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering")
lines(data$DateTime, data$Sub_metering_2, col = "red")
lines(data$DateTime, data$Sub_metering_3, col = "blue")
legend("topright", lty = c(1, 1, 1), col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
dev.off()
|
7a5b8863f39d34673b1ca152d584e82619025bc7
|
80fbecdc50f0f580336f32ee3160a8ebf16b83ff
|
/run_analysis.R
|
d5fded9ec1ec5172fb677cd7eecc5d6c8b79a491
|
[] |
no_license
|
hpirespt/Getting-and-Cleaning-Data
|
b22a6089fe58e30d03ac47e3308ff3ec436cd1b1
|
b257e40f26369916beb6bfcf27b50e38d2a3be8c
|
refs/heads/master
| 2022-04-15T00:44:33.517701
| 2020-04-11T14:21:09
| 2020-04-11T14:21:09
| 254,882,900
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,279
|
r
|
run_analysis.R
|
run_analysis<-function(){
library(dplyr)
#path definitions
pathfeatures<-".\\data\\UCI HAR Dataset\\features.txt"
pathactivities<-".\\data\\UCI HAR Dataset\\activity_labels.txt"
pathtestX<-".\\data\\UCI HAR Dataset\\test\\X_test.txt"
pathtesty<-".\\data\\UCI HAR Dataset\\test\\y_test.txt"
pathsubject_test<-".\\data\\UCI HAR Dataset\\test\\subject_test.txt"
pathtestX<-".\\data\\UCI HAR Dataset\\test\\X_test.txt"
pathtesty<-".\\data\\UCI HAR Dataset\\test\\y_test.txt"
pathsubject_test<-".\\data\\UCI HAR Dataset\\test\\subject_test.txt"
pathtrainX<-".\\data\\UCI HAR Dataset\\train\\X_train.txt"
pathtrainy<-".\\data\\UCI HAR Dataset\\train\\y_train.txt"
pathsubject_train<-".\\data\\UCI HAR Dataset\\train\\subject_train.txt"
######################################################################################
#read data
features<-read.table(pathfeatures, sep="")
activities<-read.table(pathactivities, sep="")
test_X_data<- read.table(pathtestX, sep = "")
test_y_data<- read.table(pathtesty, sep = "")
subject_test<- read.table(pathsubject_test, sep = "")
train_X_data<- read.table(pathtrainX, sep = "")
train_y_data<- read.table(pathtrainy, sep = "")
subject_train<- read.table(pathsubject_train, sep = "")
#######################################################################################
#add headers with names to test_X_data
colnames(test_X_data)<-features[,2]
colnames(train_X_data)<-features[,2]
#######################################################################################
#Bind subject and Y_test with Cbind and give names to columns: subject, acactivitieslabels (Because it reprsents the activities by numbers)
Y_subBind_test<-data.frame(cbind(subject_test,test_y_data))
colnames(Y_subBind_test)<-c("subject","activitieslabels")
Y_subBind_train<-data.frame(cbind(subject_train,train_y_data))
colnames(Y_subBind_train)<-c("subject","activitieslabels")
#######################################################################################
#Bind Y_subbind with Test_X data:
Comp_Test <- cbind(Y_subBind_test, test_X_data)
Comp_Train <- cbind(Y_subBind_train, train_X_data)
#######################################################################################
#Bind both dataframes
Final_dataset<-rbind(Comp_Test,Comp_Train)
#Replace activities labels for the names presents in activities.txt
Final_dataset$activitieslabels<-factor(Final_dataset$activitieslabels)
levels(Final_dataset$activitieslabels) <- activities$V2
#######################################################################################
#get mean columns
get_means<-grep("mean()" , names(Final_dataset), value=TRUE)
get_meanfreq<-grep("meanFreq()" , names(Final_dataset), value=TRUE)
get_cols_mean<-setdiff(get_means, get_meanfreq)
#get std columns
get_cols_std<-grep("std()" , names(Final_dataset), value=TRUE)
######################################################################################
#Tidy Datase: Subsetting Final_dataset to only have subject, activities, mean and std columns
#Sort columns by sybject and activities
Tidy_dataset<-Final_dataset[,c("subject","activitieslabels",get_cols_mean,get_cols_std)]
Tidy_dataset<-arrange(Tidy_dataset, subject, activitieslabels)
#####################################################################################
#start creating Tidy dataset 2: average of each variable for each activity and each subject
#using dplyr functions
Tidy_dataset_2<-group_by(Tidy_dataset,subject,activitieslabels)
Tidy_dataset_2<-summarise_each(Tidy_dataset_2, funs = mean)
#####################################################################################
# Writing Tidy_dataset_2 txt file
write.table(Tidy_dataset_2,"Tidy_dataset_2.txt",row.name=FALSE )
#Print Tidy Dataset
Tidy_dataset
#####################################################################################
}
|
ff27a56b2bb45cdc2ed34799304a405dbe00d7a2
|
439a1a4a95ea2a915c20b12aa49d083d28be5e72
|
/visualization/phylogenetic_analysis.R
|
9d70dae12ca54855371dfc4dce0307b39e61d837
|
[] |
no_license
|
davidgllund/ARG_analysis_scripts
|
611b4b1efa8976a4b5ef37b7b8d67a1c4dbacb63
|
7f1388ab8fa48951534813c850ae62222deebd5a
|
refs/heads/master
| 2023-05-26T23:36:37.365107
| 2021-06-01T12:48:36
| 2021-06-01T12:48:36
| 337,497,994
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,939
|
r
|
phylogenetic_analysis.R
|
# Import packages
library("ggplot2")
library("ggtree")
# Import files with the ids of the tips in the tree, the phylum corresponding to each tip, and the names of the tips
ids<-read.table('headers.txt')
phylum<-read.table("phylum.txt")
names<-read.table("names.txt")
# Combine input files to a data.frame containing all metadata of each tip
tip_metadata<-data.frame(Label=ids, Phylum=phylum, Gene=names)
colnames(tip_metadata) <- c("Label", "Phylum", "Gene")
# Re-order the phylum information for the plot
tip_metadata$Phylum<-factor(tip_metadata$Phylum, levels = c("Mobile", "Actinobacteria", "Bacteroidetes", "Firmicutes", "Proteobacteria", "Miscellaneous", "Metagenome"))
# Add labels to tree outgroup
tip_metadata<-rbind(tip_metadata, c("KsgA", NA, "KsgA"))
tip_metadata<-rbind(tip_metadata, c("APH2IIIa", NA, "APH(2'')-IIIa"))
tip_metadata<-rbind(tip_metadata, c("APH2IIa", NA, "APH(2'')-IIa"))
tip_metadata<-rbind(tip_metadata, c("APH2Ie", NA, "APH(2'')-Ie"))
# Define colors and shapes of tips
phylum_cols<-c("#000000","#8da0cb","#66c2a5","#fc8d62","#e78ac3","#C8C8C8","#FFFFFF")
phylum_shape<-c(21,21,21,21,21,21,21,1)
# Import phylogenetic tree in newwick format and plot with ggtree
tree <- read.tree("tree.txt")
p <- ggtree(tree, layout='circular')
p <- p %<+% tip_metadata +
geom_tippoint(aes(fill=Phylum, shape = Phylum), size=4.5, color="black", stroke=0.7) +
scale_fill_manual(values=phylum_cols) +
scale_shape_manual(values = phylum_shape) +
theme(legend.position = c(0.9, 0.62), legend.title=element_text(size=24), legend.text=element_text(size=22)) +
guides(shape=guide_legend(override.aes = list(size = 6))) +
geom_tiplab(aes(subset=!is.na(Gene), label=Gene), align=T, fontface='bold', size=7) +
geom_tiplab(aes(subset=is.na(Gene), label=Gene), align=F)
# Plot tree and save into pdf
pdf("tree_annotated.pdf",height = 25, width = 25)
plot(p)
dev.off()
|
f40af827692e9ca06ffa8200f43d5a7e565f6f4b
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/homals/examples/roskam.Rd.R
|
02f0d5e43c2a8cd5890554f51451c51eada5d962
|
[] |
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
| 151
|
r
|
roskam.Rd.R
|
library(homals)
### Name: roskam
### Title: Roskam dataset
### Aliases: roskam
### Keywords: datasets
### ** Examples
data(roskam)
roskam
|
ffc4c5049094d1363bd378f35a3c953a306cddfa
|
06efc31a33d3ea3cd3b645c582bacda5373badd4
|
/CeTrAn/other_codes/F1000master.R
|
40f389f19284d25c8ec8552dfab6e5b9352baaf6
|
[
"MIT"
] |
permissive
|
Marlouck/CeTrAn
|
081b4d1d42417f67fab1c085a1d0c78068ffc95d
|
e4883361de3fa21ba9f6f2a33f38850953f4fe57
|
refs/heads/master
| 2020-04-17T18:09:14.563943
| 2019-01-10T09:23:57
| 2019-01-10T09:23:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,426
|
r
|
F1000master.R
|
### code for figure 4 in the F1000 paper
REBOOT =F#if true: all data are recalculated
only_one_additional_group=F #if true, groups selected such that only the group given will be analysed with the 5 initial groups.
#Additional_group = "data entered by the user!"
#indicate analysis variables:
g_duration_slider = 10 #default=10, min0 max20
g_bin_size= 1 #default=1, min0 max20
g_supress_paints=TRUE
g_treshold = 8 # min0 max20
#indicate which analysis is done
g_general= TRUE
g_roundarena= TRUE
g_stripes_deviation=TRUE
g_occupancy= TRUE
g_angledev= TRUE
g_outputtext= TRUE
g_log= TRUE
g_thigmo= TRUE
g_pca= FALSE
g_individual= FALSE
g_open_pdf=FALSE
outputfile = "temp/output"
##NEED TO BE CHANGED WHEN MOVING TO A NEW ENVIRONMENT
#define folders where files are read/written
g_inputdir = "~/Desktop/F1000/Buridan_data_reworked/uploads"
g_outputdir="~/Desktop/F1000/Buridan_data_reworked/output"
rgghome= "~/Gits/CeTrAn/CeTrAn"
##
setwd(g_inputdir)
#uploads.csv is modified after the data is uploaded via the F1000 code
importdata= read.csv("uploads.csv")
#import the previous analysis
if (REBOOT) {onlycolname=structure(list(id = logical(0), group = logical(0), date = logical(0),
timeofday = logical(0), length_experiment = logical(0), median_speed = logical(0),
distance_traveled_mm__permin = logical(0), turning_angle = logical(0),
meander = logical(0), activitytime_permin_ST = logical(0),
act_bouts_ST = logical(0), pause_duration_ST = logical(0),
numb_pause_permin_ST = logical(0), activitytime_permin_TT = logical(0),
act_bouts_TT = logical(0), pause_length_TT = logical(0),
numb_pauses_permin_TT = logical(0), centrophobism_moving = logical(0),
centrophobism_sitting = logical(0), number_of_walks_permin = logical(0),
stripe_deviation = logical(0), UID = logical(0)), .Names = c("id",
"group", "date", "timeofday", "length_experiment", "median_speed",
"distance_traveled_mm__permin", "turning_angle", "meander", "activitytime_permin_ST",
"act_bouts_ST", "pause_duration_ST", "numb_pause_permin_ST",
"activitytime_permin_TT", "act_bouts_TT", "pause_length_TT",
"numb_pauses_permin_TT", "centrophobism_moving", "centrophobism_sitting",
"number_of_walks_permin", "stripe_deviation", "UID"), class = "data.frame", row.names = integer(0))
setwd(g_outputdir)
write.csv(onlycolname,"analysed_data.csv")
}
analyseresults=read.csv(paste(g_outputdir,"analysed_data.csv", sep="/"))
analyseresults=analyseresults[,-1]
analyseresults$UID= as.factor(analyseresults$UID)
analysed = levels(analyseresults$UID)
# check for new data to analyse, analyse it and add it to the result table
for (i in c(1:nrow(importdata))){
UID=importdata[i,1]
if (!UID %in% analysed){
message("YES")
if (exists("combf_table")){analyseresults=combf_table}
groupname=importdata[i,8]
g_filetablename = paste (groupname, "/grouping.csv",sep="")
g_filetable= read.csv(paste(g_inputdir,g_filetablename, sep="/"),sep = ",", header=FALSE)
g_filetable[,1]= paste(groupname,g_filetable[,1], sep="/")
setwd(rgghome)
source("CeTrAn_norgg_xml.r")
setwd(g_outputdir)
f_table$UID = UID
combf_table= rbind( f_table,analyseresults)
#write.csv(f_table,"analysed_data.csv")
}
setwd(g_outputdir)
### if new data write the new analysis, if no new data, just write the data into the combf_table variable
if (exists("combf_table")){
write.csv(combf_table,"analysed_data.csv")
}else {combf_table=analyseresults}
}
#now the result table is used to perform the PCA:
data=combf_table
setwd(rgghome)
if (only_one_additional_group){
source(other_codes/f1000_only_one_group.r)
}
source ("other_codes/PCA_stablef1000.R")# you need to get line 7 out
##not in use anymore: old way to add colors
##this value "abc" is deciding the color of the plot. it could eventually be chosen by the user, that is why I put the plotting code apart: if we make this value changeable, we do not have to do the PCA analysis again.
#abc= c(2,rep(1,length(levels(PCA_res$group))-1)) #last group in red, all the other in black
#abc=c (1:600) # color used per default, only 6 different colors used in loop
source ("other_codes/plotting_PCA_f1000.R") # you will need to change *pdf("test.pdf")* (l.9) with the appropriate output, probably *png("pca_plot.png")*
##there you should get an image.
## users should be able to see the image and download the "allrawdata.csv" file.
|
e8db6fb2cfe01d30355d0fcd8127286b61deb8d1
|
4faa70a753a7192dd0eaff0191e2c21bb2997185
|
/wgmeans.R
|
43cc11c5e241f529fbaf0cbd982d3a81647c1c97
|
[] |
no_license
|
SaptarshiC98/-WG--means
|
7b85909c69754ef889692cb761e57086af3f3de9
|
0c9910947f4aa4e0804a2c65e100f60e7c8abac8
|
refs/heads/master
| 2021-09-23T23:30:50.254010
| 2018-09-29T04:25:28
| 2018-09-29T04:25:28
| 108,992,157
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,456
|
r
|
wgmeans.R
|
sq.euc.dist= function(x1, x2) sum((x1 - x2) ^ 2)
wt.euc.dist.sq=function(x1,x2,w){
p=(x1-x2)^2
p=w*p
return(sum(p))
}
vec.wt.euc.dist.sq=function(x1,x2,w){
p=(x1-x2)^2
p=w*p
return(p)
}
library(nortest)
sq.euc.dist= function(x1, x2) sum((x1 - x2) ^ 2)
k.means= function(X,M,w,tmax){
X=as.matrix(X)
N =(dim(X))[1]
k=dim(M)[1]
t=0
label=numeric(N)
wdist=numeric(k)
repeat{
t=t+1
for(i in 1:N){
for(j in 1:k){
wdist[j]=wt.euc.dist.sq(X[i,],M[j,],w)
}
label[i]=which.min(wdist)
}
for(i in 1:k){
I=which(label==i)
M[i,]=colMeans(X[I,])
}
if(t>tmax){
break
}
}
return(list(label,M))
}
proj=function(X,v,w){
X=as.matrix(X)
n=dim(X)[1]
pro=numeric(dim(X)[1])
norm.v.sq=sum(v*v*w)
for(i in 1:n){
pro[i]=sum(X[i,]*v*w)/norm.v.sq
}
# hist(pro)
pro
}
split=function(c,X,w){
X=as.matrix(X)
p=prcomp(X)
# A=diag(w)
# B=A%*%B
# B=B%*%A
# cat(A)
s=p$rotation[,1]
lambda=p$sdev[1]^2
m=sqrt(2*lambda/pi)*s
rm(p)
return(list(c+m,c-m))
}
split.2=function(X,c,alpha,w){
X=as.matrix(X)
flag=0
N=dim(X)[1]
d=dim(X)[2]
spl=split(c,X,w)
c1=spl[[1]]
c2=spl[[2]]
c1=as.matrix(c1)
c2=as.matrix(c2)
M=cbind(c1,c2)
M=t(M)
p=k.means(X,M,w,50)
M=p[[2]]
c1=M[1,]
c2=M[2,]
v=c1-c2
projected=proj(X,v,w)
test=ad.test(projected)
if(test$p.value<alpha){
flag=1
return(list(flag,c1,c2))
}else{
flag=0
return(list(flag,c))
}
}
wgmeans=function(X,beta,alpha,tmax){
n=dim(X)[1]
d=dim(X)[2]
for(i in 1:d){
X[,i]=X[,i]-mean(X[,i])
X[,i]=X[,i]/sd(X[,i])
}
M=as.matrix(colMeans(X))
M=t(M)
weight=rep(1/d,d)
label=rep(1,n)
#dist=numeric(c)
t=0
D=numeric(d)
flag=0
#plot(X)
repeat{
t=t+1
flag=0
#update centres and clusternumbers
if(is.vector(M)==TRUE){
M=as.matrix(M)
M=t(M)
}
c=dim(M)[1]
counter=1
c1=c
new.mat=matrix(rep(0,2*c *d),ncol=d)
cat(c1)
for(i in 1 : c){
I=which(label==i)
## if(length(I)<8){
## break
## }
s=split.2(X[I,],M[i,],alpha,weight^beta)
if(s[[1]]==1){
new.mat[counter,]=s[[2]]
new.mat[(counter+1),]=s[[3]]
counter=counter+2
}else if(s[[1]]==0){
new.mat[counter,]=s[[2]]
counter=counter+1
}
}
rm(M)
M=matrix(rep(0,d * (counter-1)),ncol=d)
for(i in 1 : (counter-1)){
M[i,]=new.mat[i,]
}
rm(new.mat)
c=dim(M)[1]
# if(c1==c){
# break
# }
## p=k.means(X,M,weight^beta,200)
## M=p[[2]]
## points(M,col=3,pch=19)
cat(c)
dist=numeric(c)
for(iter in 1:30){ #update membership
for(i in 1 : n){
for(j in 1 : c){
dist[j]=wt.euc.dist.sq(X[i,],M[j,],weight^beta)
}
label[i]=which.min(dist)
}
#update centres
for(i in 1:c){
I=which(label==i)
M[i,]=colMeans(X[I,])
}
#update weights
for(j in 1:d){
D[j]=0
}
for(i in 1:c){
I=which(label==i)
for(k in I){
D=D+vec.wt.euc.dist.sq(X[k,],M[i,],rep(1,d))
}
}
for(i in 1:d){
if(D[i]!=0){
D[i]=1/D[i]
D[i]=D[i]^(1/(beta-1))
}
}
s=sum(D)
weight=D/s
}
# cat(weight)
# cat('\n')
## cat(weight)
#check to discard features
## max=max(weight)
## max=max*.1
## truth=(weight<max)
## if(sum(truth)>0){
##I=which(weight<max)
## weight(I)=0
## }
if(t>tmax){
break
cat('here1')
}
for(i in 1:c){
I=which(label==i)
if(length(I)<8){
flag=1
}
}
if(flag==1){
break
cat('here2')
}
if(c1==c){
cat('here3')
break
}
}
return(list(label,M,weight,t))
}
|
f2b28d086d320fe8832badefc0f85b398ceaf2ee
|
43e6a86a838a2f4bb21059190640551dd1fc4dc9
|
/R/plotMethy.R
|
ebd74b1319290932867a1f276eb510047e2aab97
|
[] |
no_license
|
jianhong/RRBSeq
|
3dc6e2e0bacc0d8bbda49d4a6ea907d21b522f63
|
e7f648897a588b705aaef559402e0eb2e2f28a63
|
refs/heads/master
| 2021-01-25T08:42:39.259480
| 2017-03-30T14:16:32
| 2017-03-30T14:16:32
| 35,629,158
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,386
|
r
|
plotMethy.R
|
plotMethy <- function(data, txdb, orgdb, range, gene,
meanCutoff=c(totalC=0, methyC=0, ratio=0.05),
...){
if(missing(txdb) || missing(orgdb) || missing(data)){
stop("data, txdb and orgdb are required")
}
if(missing(range) && missing(gene)){
stop("range and gene both are missing.")
}
if(any(!c("totalC", "methyC", "ratio") %in% names(meanCutoff)) ||
!inherits(meanCutoff, c("numeric", "integer"))){
stop("meanCutoff must be a numeric vector with names totalC, methyC, ratio")
}
if(missing(range)){
org <- gsub(".db", "", deparse(substitute(orgdb)))
eg <- mget(gene, get(paste0(org, "SYMBOL2EG")), ifnotfound = NA)
if(is.na(eg[[1]][1])){
eg <- mget(gene, get(paste0(org, "ALIAS2EG")), ifnotfound = NA)
}
if(is.na(eg[[1]][1])){
stop("can not retrieve location info by ", gene)
}
eg <- eg[[1]][1]
start <- mget(eg, get(paste0(org, "CHRLOC")))[[1]][1]
end <- mget(eg, get(paste0(org, "CHRLOCEND")))[[1]][1]
chr <- mget(eg, get(paste0(org, "CHR")))[[1]][1]
strand <- ifelse(start>0, "+", "-")
range <- GRanges(chr, IRanges(abs(start)-2000, abs(end)+1000),
strand=strand)
seqlevelsStyle(range) <- "UCSC"
}
stopifnot(inherits(data, c("GRanges", "RRBSeqDataSet")))
if(class(data)=="GRanges"){
mc <- mcols(data)
total.C <- grepl("^totalC", colnames(mc))
methy.C <- grepl("^methyC", colnames(mc))
ratio <- grepl("ratio", colnames(mc))
if(sum(total.C)<1 || sum(methy.C)<1 || sum(ratio)<1){
stop("Input could be output of compareMethylation which will generate",
"a GRanges object with metadata which colnames contains",
"totalC, methyC and ratio.")
}
total.C <- as.data.frame(mc[, total.C, drop=FALSE])
methy.C <- as.data.frame(mc[, methy.C, drop=FALSE])
ratio <- as.data.frame(mc[, ratio, drop=FALSE])
sampleNames <- gsub("^methyC.", "", colnames(methy.C))
}else{
## class(data)=="RRBSeqDataSet"
raw <- data
data <- as(raw, "GRanges")
total.C <- totalC(raw)
methy.C <- methyC(raw)
ratio <- ratio(raw)
sampleNames <- sampleNames(raw)
}
keep <- rowMeans(methy.C) > meanCutoff['methyC'] &
rowMeans(total.C) > meanCutoff['totalC'] &
rowMeans(ratio) > meanCutoff['ratio']
data <- reCenterPeaks(data[keep], width=1)
total.C <- total.C[keep, , drop=FALSE]
methy.C <- methy.C[keep, , drop=FALSE]
ratio <- ratio[keep, , drop=FALSE]
if(length(unique(sampleNames))!=ncol(methy.C)){
stop("Can not get sample names")
}
args <- as.list(match.call(expand.dots=FALSE))$`...`
if(length(args$type)!=1){
if(length(sampleNames)==1){
type <- "circle"
}else{
type <- "pie.stack"
}
}else{
type <- args$type
}
if(length(args$legend)==0){
legend <- NULL
}
data.gr <- rep(data, length(sampleNames))
color.set <- as.list(as.data.frame(rbind(rainbow(length(sampleNames)),
"#FFFFFFFF"),
stringsAsFactors=FALSE))
names(color.set) <- sampleNames
if(type %in% c("pie", "pie.stack")){
type <- "pie.stack"
unMethyC <- total.C - methy.C
if(any(unMethyC < 0)){
stop("Some total counts are smaller than methylated counts")
}
colnames(unMethyC) <- colnames(methy.C)
mc <- lapply(as.list(as.data.frame(rbind(methy.C, unMethyC))),
function(.ele) matrix(.ele, ncol=2, byrow=FALSE))
mcols(data.gr) <- do.call(rbind, mc)
colnames(mcols(data.gr)) <- c("methylated", "unmethylated")
data.gr$stack.factor <- rep(sampleNames, each=length(data))
data.gr$color <- color.set[data.gr$stack.factor]
legend <- list(labels=sampleNames, col="gray80",
fill=sapply(color.set, `[`, 1))
}else{
data.gr <- split(data.gr, rep(sampleNames, each=length(data)))
data.gr <- mapply(function(.ele, id){
mcols(.ele) <- methy.C[, id, drop=FALSE]
.ele$color <- color.set[[id]][1]
.ele
}, data.gr, 1:length(data.gr))
}
## get transcripts from ranges
method <- ifelse(length(args$method)>0, args$method, "BH")
if(length(args$features)==0){
suppressMessages(trs <- geneModelFromTxdb(txdb, orgdb, gr=range))
features <- sapply(trs, function(.ele) .ele$dat)
features <- unlist(GRangesList(features))
features$featureLayerID <- paste(features$symbol,
features$transcript,
sep=":")
features$fill <- as.numeric(factor(features$transcript))
features$height <- ifelse(grepl("utr", features$feature), 0.02, 0.04)
}
args$SNP.gr <- data.gr
args$features <- features
args$ranges <- range
args$type <- type
args$legend <- legend
if(length(args$ylab)==0 && class(data.gr)=="GRanges") args$ylab <- "methylation"
args <- as.list(args)
do.call(lolliplot, args = args)
}
|
ecce800734040ab9f42831e9f9e55ea96dde411a
|
5b430612262f3de20da50ee86c29dc71d4477edb
|
/IDU_Incidence_Maps/app.R
|
8e6c329c84e8f15a5fb5c3e89bfc45a708b98398
|
[] |
no_license
|
ErichDenk/Team_AMIE_670_Project
|
9d9ea7c788d5467a04992e65685c4a722287a636
|
1b7a8a79b8c803e2ffb1c95bb688aec01d3a5938
|
refs/heads/master
| 2020-04-20T17:12:48.138619
| 2019-04-30T00:56:42
| 2019-04-30T00:56:42
| 168,982,286
| 2
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,045
|
r
|
app.R
|
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
require(urbnmapr)
require(tidyverse)
require(shiny)
require(viridis)
require(here)
incidenceData <- read.csv(here("Active-data-sets/incidenceDat.csv")) %>%
select(., state_name = State, Year = Year1, NewDiag = "New.Diagnoses.State.Cases") %>%
mutate(state_name = as.character(state_name))
mapCreateData <- left_join(incidenceData, states, by = "state_name")
theme_map <- function(...) {
theme_minimal() +
theme(
text = element_text(family = "Arial", color = "#22211d"),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.title = element_text(size=10),
legend.text=element_text(size=8),
legend.position = "bottom",
# panel.grid.minor = element_line(color = "#ebebe5", size = 0.2),
panel.grid.major = element_line(color = "#ebebe5", size = 0.2),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "#f5f5f2", color = NA),
panel.background = element_rect(fill = "#f5f5f2", color = NA),
legend.background = element_rect(fill = "#f5f5f2", color = NA),
panel.border = element_blank(),
...
)
}
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("HIV Incidence due to Injection Drug Use"),
# Sidebar with a drop down list for which year to look at
sidebarLayout(
sidebarPanel(
selectInput(inputId = "year", choices = unique(incidenceData$Year), label = "Select Year", selected = 2008)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("map")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$map <- renderPlot({
mapCreateData %>%
filter(., Year == input$year) %>%
# first layer plots states
ggplot(data = states, mapping = aes(long, lat, group=group)) +
# center the map view on the US
coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
# black boarder and grey fill for all states
geom_polygon(color = "black", fill = NA) +
# outline for counties, I commented it out because it looked too busy
# geom_polygon(data = counties, fill = NA, color = "white") +
# adding the IDU as the density fill per county
geom_polygon(data = mapCreateData, aes(fill = log(NewDiag))) +
#change gradient for scale bar -- I wanted darker color to be higher IDU density.
scale_fill_viridis(option = "magma", direction = -1,
name = "IDU Incidence",
guide = guide_colorbar(
direction = "horizontal",
barheight = unit(2, units = "mm"),
barwidth = unit(50, units = "mm"),
draw.ulim = F,
title.position = 'top',
title.hjust = 0.5,
label.hjust = 0.5)) +
#scale_fill_gradientn( colors = c("white", "blue"),
# guide = guide_colorbar(title.position = "top"))+
# re plot the black boarder lines
geom_polygon(color = "black", fill = NA)
})
}
# Run the application
shinyApp(ui = ui, server = server)
|
297db95253655b0ae42e632bd42b54717c3e7a26
|
573edac85effdda60291c96f568af4bcf36833a5
|
/man/compute_fixed_coef.Rd
|
8daacbf88cae558df14169b49067fd1c29ba83f5
|
[] |
no_license
|
L-Ippel/SEMA
|
d71835566c17df707896bcd9ef32960c71b2c43a
|
1d0e3a48c855df704cad18c7ab6bb73d08bd4efa
|
refs/heads/master
| 2021-06-03T18:26:07.776301
| 2018-08-06T11:32:00
| 2018-08-06T11:32:00
| 38,366,963
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 824
|
rd
|
compute_fixed_coef.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Mstep.R
\name{compute_fixed_coef}
\alias{compute_fixed_coef}
\title{compute_fixed_coef computes the coefficients of the fixed effects,
see from raudenbush and bryk (2002) Hierarchial linear models, 2nd edition,
EQUATION 14.10.}
\usage{
compute_fixed_coef(x_inv, xy, t1)
}
\arguments{
\item{x_inv}{The inverted matrix of the fixed effects variables.}
\item{xy}{The product of fixed effects variables and dependent variable.}
\item{t1}{A vector with Complete Data Sufficient Statistics of the
fixed effects.}
}
\value{
A vector with fixed effects coefficients.
}
\description{
compute_fixed_coef computes the coefficients of the fixed effects,
see from raudenbush and bryk (2002) Hierarchial linear models, 2nd edition,
EQUATION 14.10.
}
|
c36e1d4889fdda0db66fcfbbe2a0b4d6425cba71
|
7bb21189354bf72b2e8aeeb9f0e4340e69ed2913
|
/man/plot.Planes.Rd
|
b57a6bc745e1a17eb4f26aa02f567fde81b733fc
|
[] |
no_license
|
elvanceyhan/pcds
|
16371849188f98138933afd2e68a46167f674923
|
00331843a0670e7cd9a62b7bca70df06d4629212
|
refs/heads/master
| 2023-07-02T10:03:48.702073
| 2023-06-16T15:50:46
| 2023-06-16T15:50:46
| 218,353,699
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,621
|
rd
|
plot.Planes.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ClassFunctions.R
\name{plot.Planes}
\alias{plot.Planes}
\title{Plot a \code{Planes} \code{object}}
\usage{
\method{plot}{Planes}(
x,
x.grid.size = 10,
y.grid.size = 10,
xlab = "x",
ylab = "y",
zlab = "z",
phi = 40,
theta = 40,
...
)
}
\arguments{
\item{x}{Object of class \code{Planes}.}
\item{x.grid.size, y.grid.size}{the size of the grids
for the \eqn{x} and \eqn{y} axes, default is 10 for both}
\item{xlab, ylab, zlab}{Titles for the \eqn{x}, \eqn{y},
and \eqn{z} axes,
respectively (default is \code{xlab="x"}, \code{ylab="y"},
and \code{zlab="z"}).}
\item{theta, phi}{The angles defining the viewing direction,
default is 40 for both.
\code{theta} gives the azimuthal direction and
\code{phi} the colatitude. see \code{\link[graphics]{persp}}.}
\item{\dots}{Additional parameters for \code{plot}.}
}
\value{
None
}
\description{
Plots the plane together with the defining 3D \code{points}.
}
\examples{
\dontrun{
P<-c(1,10,3); Q<-c(1,1,3); C<-c(3,9,12)
pts<-rbind(P,Q,C)
xr<-range(pts[,1]); yr<-range(pts[,2])
xf<-(xr[2]-xr[1])*.1
#how far to go at the lower and upper ends in the x-coordinate
yf<-(yr[2]-yr[1])*.1
#how far to go at the lower and upper ends in the y-coordinate
x<-seq(xr[1]-xf,xr[2]+xf,l=5) #try also l=10, 20 or 100
y<-seq(yr[1]-yf,yr[2]+yf,l=5) #try also l=10, 20 or 100
plPQC<-Plane(P,Q,C,x,y)
plPQC
plot(plPQC,theta = 225, phi = 30, expand = 0.7,
facets = FALSE, scale = TRUE)
}
}
\seealso{
\code{\link{print.Planes}},
\code{\link{summary.Planes}},
and \code{\link{print.summary.Planes}}
}
|
aa88882fa026ac3669c58c0342b8e2c12e9a75d2
|
fd84077ffbb3f1662c0d4d1c06116a55893b6201
|
/tests/testthat/test-main.R
|
8848e2182de860ecf4a2137092be322f7d496ffb
|
[] |
no_license
|
hamedbh/popstats
|
c9e46849e83934129b0d6b98bbb4ebb8a8da05d0
|
18da0cd52e7dfe710f245cca4545d430c837f3da
|
refs/heads/master
| 2020-08-12T00:47:12.069260
| 2019-10-19T07:34:11
| 2019-10-19T07:34:11
| 214,659,002
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,447
|
r
|
test-main.R
|
context("Results with non-numeric values")
test_that("Character type throw errors", {
expect_error(pop_var(letters))
expect_error(pop_sd(letters))
})
test_that("Factor type gives warning", {
expect_warning(pop_var(factor(1:5)))
expect_warning(pop_sd(factor(1:5)))
})
obj_pop_var_factor <- suppressWarnings(pop_var(factor(1:5)))
obj_pop_sd_factor <- suppressWarnings(pop_sd(factor(1:5)))
test_that("Factor type returns NA", {
expect_true(is.na(obj_pop_var_factor))
expect_true(is.na(obj_pop_sd_factor))
})
context("Confirming results on multiple data structures")
obj_small_vector <- 1:10
obj_small_matrix <- matrix(obj_small_vector,
ncol = 2)
obj_small_df <- data.frame(x1 = obj_small_vector,
x2 = obj_small_vector)
test_that("Variance results", {
expect_equal(pop_var(obj_small_vector),
8.25)
expect_equal(pop_var(obj_small_matrix),
8.25)
expect_equal(sapply(obj_small_df, pop_var),
c(x1 = 8.25, x2 = 8.25))
})
obj_sd_one_to_ten <- sqrt(sum((1:10 - mean(1:10))^2)/length(1:10))
test_that("Standard deviation results", {
expect_equal(pop_sd(obj_small_vector),
obj_sd_one_to_ten)
expect_equal(pop_sd(obj_small_matrix),
obj_sd_one_to_ten)
expect_equal(sapply(obj_small_df, pop_sd),
c(x1 = obj_sd_one_to_ten, x2 = obj_sd_one_to_ten))
})
|
3fbfa16c51aa88b7ab28d930049c3f79c958c666
|
f30cc1c33978ca5a708a7e0a493403ea88550160
|
/R/hxsurf.R
|
a7f9cc045fb90c27b31bf819036434de4abf7d17
|
[] |
no_license
|
natverse/nat
|
044384a04a17fd0c9d895e14979ce43e43a283ba
|
1d161fa463086a2d03e7db3d2a55cf4d653dcc1b
|
refs/heads/master
| 2023-08-30T21:34:36.623787
| 2023-08-25T07:23:44
| 2023-08-26T19:02:50
| 15,578,625
| 35
| 10
| null | 2023-01-28T19:03:03
| 2014-01-02T07:54:01
|
R
|
UTF-8
|
R
| false
| false
| 30,998
|
r
|
hxsurf.R
|
#' Read Amira surface (aka HxSurface or HyperSurface) files into hxsurf object
#'
#' @details Note that when \code{RegionChoice="both"} or
#' \code{RegionChoice=c("Inner", "Outer")} both polygons in inner and outer
#' regions will be added to named regions. To understand the significance of
#' this, consider two adjacent regions, A and B, with a shared surface. For
#' the polygons in both A and B, Amira will have a patch with (say)
#' InnerRegion A and OuterRegion B. This avoids duplication in the file.
#' However, it might be convenient to add these polygons to both regions when
#' we read them into R, so that regions A and B in our R object are both
#' closed surfaces. To achieve this when \code{RegionChoice="both"},
#' \code{read.hxsurf} adds these polygons to region B (as well as region A)
#' but swaps the order of the vertices defining the polygon to ensure that the
#' surface directionality is correct.
#'
#' As a rule of thumb, stick with \code{RegionChoice="both"}. If you get more
#' regions than you wanted, then try switching to \code{RegionChoice="Inner"}
#' or \code{RegionChoice="Outer"}.
#'
#' Note that the support for reading Amira's binary mesh format (HxSurface
#' binary) is less mature and in particular only a few multi region mesh files
#' have been tested. Finally there is no support to read meshes from the newer
#' "Amira Binary Surface format" although such files can be read into a list
#' using the \code{read.amiramesh} function.
#'
#' @param filename Character vector defining path to file
#' @param RegionNames Character vector specifying which regions should be read
#' from file. Default value of \code{NULL} => all regions.
#' @param RegionChoice Whether the \emph{Inner} or \emph{Outer} material, or
#' \emph{both} (default), should define the material of the patch. See
#' details.
#' @param FallbackRegionCol Colour to set regions when no colour is defined
#' @param Verbose Print status messages during parsing when \code{TRUE}
#' @return A list with S3 class hxsurf with elements \itemize{
#'
#' \item{Vertices}{ A data.frame with columns \code{X, Y, Z, PointNo}}
#'
#' \item{Regions}{ A list with 3 column data.frames specifying triplets of
#' vertices for each region (with reference to \code{PointNo} column in
#' \code{Vertices} element)}
#'
#' \item{RegionList}{ Character vector of region names (should match names of
#' \code{Regions} element)}
#'
#' \item{RegionColourList}{ Character vector specifying default colour to plot
#' each region in R's \code{\link{rgb}} format}
#'
#' }
#' @export
#' @seealso \code{\link{plot3d.hxsurf}, \link{rgb}}
#' @aliases hxsurf
#' @family amira
#' @family hxsurf
#' @examples
#' \dontrun{
#' read.hxsurf("my.surf", RegionChoice="both")
#' }
read.hxsurf<-function(filename,RegionNames=NULL,RegionChoice="both",
FallbackRegionCol="grey",Verbose=FALSE){
# Check for header confirming file type
firstLine=readLines(filename,n=1)
if(!any(grep("#\\s+hypersurface\\s+[0-9.]+\\s+ascii",firstLine,ignore.case=T,perl=T))){
if(!any(grep("#\\s+hypersurface\\s+[0-9.]+\\s+binary",firstLine,ignore.case=T,perl=T))){
stop(filename," does not appear to be an Amira HyperSurface file!")
}
res = tryCatch(
read.hxsurf.bin(
filename = filename,
FallbackRegionCol = FallbackRegionCol,
Verbose = Verbose
),
error = function(e)
stop(
"Support for reading binary Amira HyperSurface is still limited.\n",
"See https://github.com/natverse/nat/issues/429. Detailed error message",
as.character(e)
)
)
return(res)
}
initialcaps<-function(x) {substr(x,1,1)=toupper(substr(x,1,1)); x}
RegionChoice=match.arg(initialcaps(RegionChoice), c("Inner", "Outer", "Both"),
several.ok = TRUE)
if(RegionChoice[1]=="Both") RegionChoice=c("Inner", "Outer")
t=readLines(filename)
nLines=length(t)
if(Verbose) cat(nLines,"lines of text to parse\n")
# Find the start of the Vertices
dataStart=grep("^\\s*Vertices\\s*",t)[1]
if(Verbose) cat("Data start line =",dataStart,"\n")
headerLines=t[seq(dataStart-1)]
getfield=function(fName,textLines=headerLines,pos=2) unlist(strsplit(trimws(textLines[grep(fName,textLines)]),"\\s+",perl=TRUE))[pos]
nVertices=as.numeric(getfield("Vertices",t[dataStart],2))
if(Verbose) cat("nVertices =",nVertices,"\n")
d=list()
d$Vertices=read.table(filename,skip=dataStart,nrows=nVertices,col.names=c("X","Y","Z"),colClasses=rep("numeric",3))
d$Regions <- list()
d$Vertices$PointNo=seq(nrow(d$Vertices))
if(Verbose) cat("Finished processing Vertices\n")
# Now read in Triangles that define patches:
linesSkipped=dataStart+nVertices-1
remainingLines=t[(dataStart+nVertices):nLines]
PatchDefLine=grep("^\\s*Patches\\s*",remainingLines,perl=TRUE)
if(Verbose) cat("PatchDefLine =",PatchDefLine,"\n")
nPatches=as.numeric(getfield("Patches",remainingLines[PatchDefLine],2))
if(Verbose) cat("nPatches =",nPatches,"\n")
PatchStarts=grep("^\\s*{",remainingLines[PatchDefLine:length(remainingLines)],perl=TRUE)+PatchDefLine-1
if(length(PatchStarts)>nPatches) PatchStarts=PatchStarts[1:nPatches]
PatchEnds=grep("^\\s*}",remainingLines[PatchDefLine:length(remainingLines)],perl=TRUE)+PatchDefLine-1
if(length(PatchEnds)>nPatches) PatchEnds=PatchEnds[1:nPatches]
TriangleDeflines<-grep("Triangles",remainingLines)
if(length(TriangleDeflines)!=nPatches)
stop("Incorrect number of Triangle definition lines in",filename,"\n")
for(i in 1:nPatches){
if(Verbose) cat("TriangleDefline =",TriangleDeflines[i],"\n")
PatchHeader<-remainingLines[PatchStarts[i]:TriangleDeflines[i]]
# remove any opening braces - these would cause a problem if on same line
PatchHeader=sub("^\\s*\\{\\s*","",PatchHeader)
# convert all whitespace to single spaces
PatchHeader=gsub("\\s+"," ",PatchHeader)
if(Verbose) cat("PatchHeader is",length(PatchHeader),"lines long\n")
# note use of RegionChoice to switch naming between inner and outer
for(RegChoice in RegionChoice) {
RegionName=getfield(paste(RegChoice,"Region",sep=""),PatchHeader,2)
nTriangles=as.numeric(getfield("Triangles",PatchHeader,2))
if(nTriangles<0 || nTriangles>100000) stop("Bad triangle number: ", nTriangles)
if(Verbose) cat("nTriangles =",nTriangles,"for patch =",i,"\n")
# Check if we want to load in this region
if( is.null(RegionNames) || RegionName%in%RegionNames ){
# Ensure we do not try to add no triangles, or the exterior region
if(nTriangles == 0 || RegionName == "Exterior") next
thispatch=read.table(filename,skip=linesSkipped+TriangleDeflines[i],nrows=nTriangles,
quote='',colClasses='integer',blank.lines.skip=FALSE,
fill=FALSE,comment.char="",
col.names=c("V1","V2","V3"))
if(getfield(paste(RegChoice,"Region",sep=""),PatchHeader,1) == "OuterRegion") {
thispatch <- thispatch[, c(1,3,2)]
if(Verbose) message("Permuting vertices for ", RegionName, "...")
colnames(thispatch) <- c("V1","V2","V3")
}
# scan no quicker in these circs, problem is repeated file access
# specifying text directly also does not help dues to very slow textConnection
# check if we have already loaded a patch in this name
if(RegionName%in%names(d$Regions)){
# add to the old patch
if(Verbose) cat("Adding to patch name",RegionName,"\n")
d[['Regions']][[RegionName]]=rbind(d[['Regions']][[RegionName]],thispatch)
} else {
# new patch
if(Verbose) cat("Making new patch name",RegionName,"\n")
d[['Regions']][[RegionName]]=thispatch
}
}
}
}
d$RegionList=names(d$Regions)
# Handle colours for regions
d$RegionColourList <- vector(length=length(d$RegionList))
closeBraces <- grep("}", headerLines)
for(regionName in d$RegionList) {
# Find section in headerLines corresponding to this region
headerSecStart <- grep(paste0("^\\s*", regionName, "(\\s+ \\{){0,1}"), headerLines)[1]
headerSecEnd <- closeBraces[closeBraces > headerSecStart][1]
# Extract colour information
colorLine <- grep("Color", headerLines[headerSecStart:headerSecEnd], value=T)
if(length(colorLine) > 0) {
rgbValues <- strsplit(regmatches(colorLine, gregexpr("[0-9]$|[0-9][^\\.]|[0-9]\\.[0-9]+", colorLine, perl=T))[[1]], " ")
# clean up any trailing commas
rgbValues <- gsub("[,}]","", rgbValues)
color <- rgb(rgbValues[[1]], rgbValues[[2]], rgbValues[[3]])
} else {
color <- FallbackRegionCol
}
d$RegionColourList[which(d$RegionList == regionName)] <- color
}
class(d) <- c('hxsurf',class(d))
return(d)
}
read.hxsurf.bin <- function(filename, return.raw=FALSE, FallbackRegionCol='grey', Verbose=FALSE) {
con=file(filename, open='rb')
on.exit(close(con))
vertex_regex='^Vertices \\d+$'
# read header
h <- character()
line <- readLines(con, n=1)
while(!isTRUE(grepl(vertex_regex, line))) {
h=c(h, line)
line <- readLines(con, n=1)
}
params=.ParseAmirameshParameters(h)
materials=names(params$Parameters$Materials)
# read data blocks
data_regex='^\\s*(\\w+)\\s+(\\d+)$'
parse_data_line <- function(line) {
tryCatch({
res=stringr::str_match(line, data_regex)
n=suppressWarnings(as.integer(res[,3]))
checkmate::assert_int(n)
label=checkmate::assert_character(res[,2])
names(n)=label
n
}, error=function(e) {NA_integer_})
}
data <- list(header=h, params=params)
curpatch=NA_integer_
while(TRUE) {
if(length(line)<1) break
if(is.finite(curpatch)) {
if(length(data[['PatchInfo']])<curpatch)
data[['PatchInfo']][[curpatch]]=line
else
data[['PatchInfo']][[curpatch]]=c(data[['PatchInfo']][[curpatch]], line)
} else {
data[['header']]=c(data[["header"]], line)
}
# is this a closing bracket at the end of a section
firstchar=substr(trimws(line), 1, 1)
if(isTRUE(firstchar=='}') && is.finite(curpatch)) curpatch=curpatch+1
n=parse_data_line(line)
if(is.na(n) || n==0) {
line <- readLines(con, 1)
next
}
label=names(n)
if(label=='Vertices') {
chunk=readBin(con, what='numeric', n=n*3, size=4, endian = 'big')
data[['Vertices']]=matrix(chunk, ncol=3, byrow = T)
} else if (label=='Triangles') {
npatches=length(data[['Patches']])
chunk=readBin(con, what='integer', n=n*3, size=4, endian = 'big')
data[['Patches']][[npatches+1]]=matrix(chunk, ncol=3, byrow = T)
} else if(label=='Patches') {
curpatch=1
if(is.null(data[['Patches']])) data[['Patches']]=list()
if(is.null(data[['PatchInfo']])) data[['PatchInfo']]=list()
} else {
stop("Error parsing binary hxsurf file!")
}
line <- readLines(con, 1)
}
if(return.raw)
data
else parse.hxsurf.bin(data, FallbackRegionCol=FallbackRegionCol, Verbose=Verbose)
}
# FIXME: Ideally this would be harmonised with the code for read.hxsurf to
# avoid duplication
parse.hxsurf.bin <- function(data, FallbackRegionCol, Verbose) {
materials=data$params$Parameters$Materials
d=list()
d[['Vertices']]=as.data.frame(xyzmatrix(data$Vertices))
d[['Vertices']][,'PointNo']=seq_len(nrow(d[['Vertices']]))
d$Regions=list()
for(p in seq_along(data$Patches)) {
thispatch=as.data.frame(data$Patches[[p]])
pi=.ParseAmirameshParameters(data$PatchInfo[[p]])
RegionName <- pi$InnerRegion
if(RegionName%in%names(d$Regions)){
# add to the old patch
if(Verbose) cat("Adding to patch name",RegionName,"\n")
d[['Regions']][[RegionName]]=rbind(d[['Regions']][[RegionName]],thispatch)
} else {
# new patch
if(Verbose) cat("Making new patch name",RegionName,"\n")
d[['Regions']][[RegionName]]=thispatch
}
}
d$RegionList=names(d$Regions)
d$RegionColourList <- vector(length=length(d$RegionList))
for(regionName in d$RegionList) {
rgbValues <- materials[[regionName]][['Color']]
if(isTRUE(length(rgbValues)==3)) {
color <- rgb(rgbValues[[1]], rgbValues[[2]], rgbValues[[3]])
} else {
color <- FallbackRegionCol
}
d$RegionColourList[which(d$RegionList == regionName)] <- color
}
class(d) <- c('hxsurf',class(d))
return(d)
}
#' Write Amira surface (aka HxSurface or HyperSurface) into .surf file.
#'
#' @param surf hxsurf object to write to file.
#' @param filename character vector defining path to file.
#' @return \code{NULL} or integer status from \code{\link{close}}.
#' @export
#' @seealso \code{\link{plot3d.hxsurf}},\code{\link{read.hxsurf}}, \code{\link{rgb}}
#' @family amira
#' @family hxsurf
write.hxsurf <- function(surf, filename) {
fc <- file(filename, open="at")
cat("# HyperSurface 0.1 ASCII\n\n", file=fc)
cat("Parameters {\n", file=fc)
cat(" Materials {\n", file=fc)
cat(" Exterior {\n Id 1\n }\n", file=fc)
regionData <- cbind(surf$RegionList, surf$RegionColourList)
for (i in 1:nrow(regionData)) {
cat(" ", regionData[i, 1], " {\n", sep="", file=fc)
cat(" Id ", i+1, ",\n", sep="", file=fc)
cat(" Color ", paste(zapsmall(col2rgb(regionData[i, 2])/255), collapse=" "), "\n", sep="", file=fc)
cat(" }\n", file=fc)
}
cat(" }\n", file=fc)
cat(" BoundaryIds {\n Name \"BoundaryConditions\"\n }\n", file=fc)
cat("}\n\n", file=fc)
cat("Vertices ", nrow(surf$Vertices), "\n", sep="", file=fc)
apply(surf$Vertices[, 1:3], 1, function(x) cat(" ", sprintf(x[1], fmt="%.6f"), " ", sprintf(x[2], fmt="%.6f"), " ", sprintf(x[3], fmt="%.6f"), "\n", sep="", file=fc))
cat("NBranchingPoints 0\nNVerticesOnCurves 0\nBoundaryCurves 0\n", file=fc)
cat("Patches ", length(surf$Regions), "\n", sep="", file=fc)
for(i in 1:length(surf$Regions)) {
region <- surf$Regions[[i]]
cat("{\n", file=fc)
cat("InnerRegion ", names(surf$Regions[i]), "\n", sep="", file=fc)
cat("OuterRegion Exterior\n", file=fc)
cat("BoundaryId 0\n", file=fc)
cat("BranchingPoints 0\n\n", file=fc)
cat("Triangles ", nrow(region), "\n", sep="", file=fc)
apply(region, 1, function(x) cat(" ", paste(x, collapse=" "), "\n", sep="", file=fc))
cat("}\n", file=fc)
}
close(fc)
}
#' Plot amira surface objects in 3D using rgl
#'
#' @param x An hxsurf surface object
#' @param materials Character vector or \code{\link[base]{regex}} naming
#' materials to plot (defaults to all materials in x). See
#' \code{\link{subset.hxsurf}}.
#' @param col Character vector specifying colors for the materials, or a
#' function that will be called with the number of materials to plot. When
#' \code{NULL} (default) will use material colours defined in Amira (if
#' available), or \code{rainbow} otherwise.
#' @param ... Additional arguments passed to \code{triangles3d}
#' @inheritParams plot3d.neuronlist
#' @export
#' @seealso \code{\link{read.hxsurf}}
#' @family hxsurf
#' @examples
#' plot3d(kcs20)
#' plot3d(MBL.surf)
#'
#' \donttest{
#' # plot only vertical lobe
#' nclear3d()
#' plot3d(MBL.surf, materials="VL", alpha=0.3)
#'
#' # everything except vertical lobe
#' nclear3d()
#' plot3d(MBL.surf, alpha=0.3,
#' materials=grep("VL", MBL.surf$RegionList, value = TRUE, invert = TRUE))
#' }
plot3d.hxsurf<-function(x, materials=NULL, col=NULL, gridlines = FALSE, ...,
plotengine = getOption('nat.plotengine')){
plotengine <- check_plotengine(plotengine)
if (plotengine == 'rgl'){
# skip so that the scene is updated only once per hxsurf object
skip <- par3d(skipRedraw = TRUE)
on.exit(par3d(skip))
}
if (plotengine == 'plotly') {
psh <- openplotlyscene()$plotlyscenehandle
params=list(...)
opacity <- if("alpha" %in% names(params)) params$alpha else 1
}
materials=subset(x, subset = materials, rval='names')
if(is.null(col)) {
if(length(x$RegionColourList)){
col=x$RegionColourList[match(materials,x$RegionList)]
} else col=rainbow
}
if(is.function(col)) col=col(length(materials))
if(is.factor(col)) col=rainbow(nlevels(col))[as.integer(col)]
if(length(col)==1 && length(materials)>1) col=rep(col,length(materials))
names(col)=materials
rlist=list()
for(mat in materials) {
# get order triangle vertices
tri=as.integer(t(x$Regions[[mat]]))
if (plotengine == 'rgl'){
rlist[[mat]]=triangles3d(x[['Vertices']]$X[tri],x[['Vertices']]$Y[tri],
x[['Vertices']]$Z[tri],col=col[mat], ...)
} else {
tmpx <- as.mesh3d.hxsurf(x, Regions = mat)
psh <- psh %>%
plotly::add_trace(x = tmpx$vb[1,],
y = tmpx$vb[2,],
z = tmpx$vb[3,],
i = tmpx$it[1,]-1,
j = tmpx$it[2,]-1,
k = tmpx$it[3,]-1,
type = "mesh3d", opacity = opacity,
hovertext=mat,
hoverinfo="x+y+z+text",
facecolor = rep(col[mat],
length(tmpx$it[1,])))
}
}
if (plotengine == 'rgl'){
invisible(rlist)
} else {
psh <- psh %>% plotly::layout(showlegend = FALSE, scene=list(camera=.plotly3d$camera))
if(gridlines == FALSE){
psh <- psh %>% plotly::layout(scene = list(xaxis=.plotly3d$xaxis,
yaxis=.plotly3d$yaxis,
zaxis=.plotly3d$zaxis))
}
assign("plotlyscenehandle", psh, envir=.plotly3d)
psh
}
}
#' Convert an object to an rgl mesh3d
#'
#' Note that this provides a link to the Rvcg package
#' @param x Object to convert to mesh3d
#' @param ... Additional arguments for methods
#' @param Regions Character vector or regions to select from \code{hxsurf}
#' object
#' @param material rgl materials such as \code{color}
#' @param drop Whether to drop unused vertices (default TRUE)
#' @export
#' @rdname as.mesh3d
#' @seealso \code{\link[rgl]{as.mesh3d}}, \code{\link[rgl]{tmesh3d}},
#' \code{\link{as.hxsurf}}, \code{\link{read.hxsurf}}
#' @family hxsurf
as.mesh3d.hxsurf<-function(x, Regions=NULL, material=NULL, drop=TRUE, ...){
if(is.null(Regions)) {
Regions=x$RegionList
}
x=subset(x, Regions, drop=drop)
if(length(Regions)==1 && is.null(material)){
# find colour
material=list(color=x$RegionColourList[match(Regions,x$RegionList)])
}
verts=t(data.matrix(x$Vertices[,1:3]))
inds=t(data.matrix(do.call(rbind, x$Regions)))
tmesh3d(vertices=verts, indices=inds, homogeneous = FALSE, material = material, ...)
}
#' @description \code{as.mesh3d.boundingbox} converts a nat
#' \code{\link{boundingbox}} object into an rgl compatible \code{mesh3d}
#' object.
#' @rdname as.mesh3d
#' @export
#' @examples
#' bb=boundingbox(kcs20)
#' mbb=as.mesh3d(bb)
#' \donttest{
#' plot3d(kcs20)
#' # simple plot
#' plot3d(bb)
#' shade3d(mbb, col='red', alpha=0.3)
#'
#' }
as.mesh3d.boundingbox <- function(x, ...) {
centroid=colMeans(x)
size=diff(x)/2
mat=scaleMatrix(size[1], size[2], size[3])%*%translationMatrix(centroid[1], centroid[2], centroid[3])
cube3d(mat)
}
#' Convert an object to a nat hxsurf object
#'
#' @details \code{hxsurf} objects are based on the format of Amira's surface
#' objects (see \code{\link{read.hxsurf}}). They have the ability to include
#' multiple distinct regions. However, at the moment the only method that we
#' provide converts \code{mesh3d} objects, which can only include one region.
#' @param x A surface object
#' @param ... Additional arguments passed to methods
#'
#' @return A new surface object of class \code{hxsurf} (see
#' \code{\link{read.hxsurf}}) for details.
#' @export
#' @family hxsurf
#' @seealso \code{\link{as.mesh3d}}
#' @examples
#' tet=tetrahedron3d(col='red')
#' teth=as.hxsurf(tet)
#' \donttest{
#' plot3d(teth)
#' }
as.hxsurf <- function(x, ...) UseMethod('as.hxsurf')
#' @param region The default name for the surface region
#' @param col The surface colour (default value of NULL implies the colour
#' specified in mesh3d object or \code{grey} when the \code{mesh3d} object has
#' no colour.)
#' @export
#' @rdname as.hxsurf
as.hxsurf.mesh3d <- function(x, region="Interior", col=NULL, ...) {
if (is.null(x$it))
stop("This method only works for triangular mesh3d objects!")
h=list()
h$Vertices=data.frame(xyzmatrix(x))
colnames(h$Vertices)=c("X","Y","Z")
h$Vertices$PointNo=1:nrow(h$Vertices)
h$Regions[[region]]=data.frame(t(x$it))
colnames(h$Regions[[region]])=c("V1","V2","V3")
h$RegionList=names(h$Regions)
if(is.null(col)) col=x$material$col
h$RegionColourList <- if(!is.null(col)) col else 'grey'
class(h)=c("hxsurf","list")
h
}
#' Subset hxsurf object to specified regions
#'
#' @param x A dotprops object
#' @param subset Character vector specifying regions to keep. Interpreted as
#' \code{\link[base]{regex}} if of length 1 and no fixed match.
#' @param drop Whether to drop unused vertices after subsetting (default:
#' \code{TRUE})
#' @param rval Whether to return a new \code{hxsurf} object or just the names of
#' the matching regions
#' @param ... Additional parameters (currently ignored)
#' @return subsetted hxsurf object
#' @export
#' @family hxsurf
#' @examples
#' # plot only vertical lobe
#' vertical_lobe=subset(MBL.surf, "VL")
#' \donttest{
#' plot3d(vertical_lobe, alpha=0.3)
#' plot3d(kcs20)
#'
#' # there is also a shortcut for this
#' nclear3d()
#' plot3d(MBL.surf, subset = "VL", alpha=0.3)
#' }
subset.hxsurf<-function(x, subset=NULL, drop=TRUE, rval=c("hxsurf","names"), ...){
rval=match.arg(rval)
if(!is.null(subset)){
tokeep=integer(0)
if(is.character(subset)){
tokeep=match(subset,x$RegionList)
if(is.na(tokeep[1]) && length(subset)==1){
# try as regex
tokeep=grep(subset,x$RegionList)
}
}
if(!length(tokeep) || any(is.na(tokeep)))
stop("Invalid subset! See ?subset.hxsurf")
if(rval=='names') return(x$RegionList[tokeep])
x$Regions=x$Regions[tokeep]
x$RegionList=x$RegionList[tokeep]
x$RegionColourList=x$RegionColourList[tokeep]
} else if(rval=='names') return(x$RegionList)
if(drop){
# see if we need to drop any vertices
vertstokeep=sort(unique(unlist(x$Regions)))
# a vector where each position is the old vertex id and the value is the
# new one i.e. newid=vert_table[oldid]
vert_table=match(seq_len(nrow(x$Vertices)), vertstokeep)
# convert all vertex ids from old to new sequence
for(r in x$RegionList){
for(i in seq_len(ncol(x$Regions[[r]]))){
x$Regions[[r]][[i]]=vert_table[x$Regions[[r]][[i]]]
}
}
# drop unused vertices
x$Vertices=x$Vertices[vertstokeep, ]
}
x
}
#' Subset methods for different nat objects
#'
#' These methods enable subsets of some nat objects including neurons and
#' neuronlists to be obtained. See the help for each individual method for
#' details.
#'
#' @name subset
#' @seealso \code{\link{subset.neuron}}, \code{\link{subset.dotprops}},
#' \code{\link{subset.hxsurf}}, \code{\link{subset.neuronlist}}
NULL
#' Find which points of an object are inside a surface
#'
#' @details Note that \code{hxsurf} surface objects will be converted to
#' \code{mesh3d} before being passed to \code{Rvcg::vcgClostKD}, so if you are
#' testing repeatedly against the same surface, it may make sense to
#' pre-convert.
#'
#' \code{pointsinside} depends on the face normals for each face pointing out
#' of the object (see example). The face normals are defined by the order of
#' the three vertices making up a triangular face. You can flip the face
#' normal for a face by permuting the vertices (i.e. 1,2,3 -> 1,3,2). If you
#' find for a given surface that points are outside when you expect them to be
#' inside then the face normals are probably all the wrong way round. You can
#' invert them yourself or use the \code{Morpho::invertFaces} function to fix
#' this.
#'
#' The \code{rval} argument determines the return value. These options should
#' be fairly clear, but the difference between \code{logical} and
#' \code{consistent_logical} needs some explanation. The \code{logical} method
#' now does a pre-test to remove any points that are not in the 3D bounding
#' box (cuboid) enclosing the surf object. This often results in a significant
#' speed-up by rejecting distant points and has the additional benefit of
#' rejecting distant points that sometimes are erroneously declared inside the
#' mesh (see below). Regrettably it is not yet possible to extend this
#' approach when distances are being returned, which means there will be a
#' discrepancy between the results of \code{rval="logical"} and looking for
#' points with distance >=0. If you want to ensure consistency between these
#' approaches, use \code{rval="consistent_logical"}.
#'
#' If you find that some points but not all points are not behaving as you
#' would expect, then it may be that some faces are not coherently oriented.
#' The \code{Rvcg::\link[Rvcg]{vcgClean}} function can sometimes be used to
#' correct the orientation of the faces. Fixing more problematic cases may be
#' possible by generating a new surface using
#' \code{alphashape3d::\link[alphashape3d]{ashape3d}} (see examples).
#'
#' @param x an object with 3D points.
#' @param surf The reference surface - either a \code{mesh3d} object or any
#' object that can be converted using \code{as.mesh3d} including \code{hxsurf}
#' and \code{ashape3d} objects.
#' @param ... additional arguments for methods, eventually passed to
#' \code{\link{as.mesh3d}}.
#' @export
#' @examples
#' # check if the vertices in these neurons are inside the mushroom body calyx
#' # surface object
#' inout=pointsinside(kcs20, surf=subset(MBL.surf, "MB_CA_L"))
#' table(inout)
#' # you can also check if points are inside a bounding box
#' mbcalbb=boundingbox(subset(MBL.surf, "MB_CA_L"))
#' inout2=pointsinside(kcs20, mbcalbb)
#' # compare those two
#' table(inout, inout2)
#' pts=xyzmatrix(kcs20)
#' # nb that colour expressions maps combinations of two logicals onto 1:4
#' plot(pts[,1:2], col=1+inout+inout2*2)
#' # the colours are defined by
#' palette()[1:4]
#'
#' # be a bit more lenient and include points less than 5 microns from surface
#' MBCAL=subset(MBL.surf, "MB_CA_L")
#' inout5=pointsinside(kcs20, surf=MBCAL, rval='distance') > -5
#' table(inout5)
#' \donttest{
#' # show which points are in or out
#' # Hmm seems like there are a few red points in the vertical lobe
#' # that are well outside the calyx
#' points3d(xyzmatrix(kcs20), col=ifelse(inout5, 'red', 'black'))
#' plot3d(MBL.surf, alpha=.3)
#'
#' # Let's try to make an alphashape for the mesh to clean it up
#' library(alphashape3d)
#' MBCAL.as=ashape3d(xyzmatrix(MBCAL), alpha = 10)
#' # Plotting the points, we can see that is much better behaved
#' points3d(xyzmatrix(kcs20),
#' col=ifelse(pointsinside(kcs20, MBCAL.as), 'red', 'black'))
#' }
#'
#' \dontrun{
#' # Show the face normals for a surface
#' if(require('Morpho')) {
#' # convert to a mesh3d object used by rgl and Morpho packge
#' MBCAL.mesh=as.mesh3d(subset(MBL.surf, "MB_CA_L"))
#' fn=facenormals(MBCAL.mesh)
#' wire3d(MBCAL.mesh)
#' # show that the normals point out of the object
#' plotNormals(fn, long=5, col='red')
#'
#' # invert the faces of the mesh and show that normals point in
#' MBCAL.inv=invertFaces(MBCAL.mesh)
#' plotNormals(facenormals(MBCAL.inv), long=5, col='cyan')
#' }
#' }
pointsinside<-function(x, surf, ...) UseMethod('pointsinside')
#' @export
#' @param rval what to return.
#' @return A vector of logical values or distances (positive inside, negative
#' outside) equal to the number of points in x or the \code{mesh3d} object
#' returned by \code{Rvcg::vcgClostKD}.
#' @rdname pointsinside
pointsinside.default<-function(x, surf, ..., rval=c('logical','distance',
'mesh3d', 'consistent_logical')) {
rval=match.arg(rval)
if(rval=='logical') {
# use optimised contains_points approach
return(contains_points(surf, x, ...))
}
if(inherits(surf, "boundingbox")) {
stop("Only logical return values are currently possible ",
"with boundingbox objects!")
}
if(!requireNamespace('Rvcg', quietly = TRUE))
stop("Please install suggested library Rvcg to use pointsinside")
if(!inherits(surf,'mesh3d')) {
surf=as.mesh3d(surf, ...)
}
pts=xyzmatrix(x)
rmesh=Rvcg::vcgClostKD(pts, surf, sign = TRUE)
switch(rval,
consistent_logical = rmesh$quality >= 0,
distance = rmesh$quality,
mesh3d = rmesh
)
}
contains_points <- function(obj, points, ...) UseMethod("contains_points")
contains_points.boundingbox <- function(obj, points, ...) {
xyz=xyzmatrix(points)
xyz[,1] >= obj[1,1] & xyz[,2] >= obj[1,2] & xyz[,3] >= obj[1,3] &
xyz[,1] <= obj[2,1] & xyz[,2] <= obj[2,2] & xyz[,3] <= obj[2,3]
}
contains_points.mesh3d <- function(obj, points, ...) {
xyz=xyzmatrix(points)
inbb=contains_points(boundingbox(obj), xyz, ...)
# nb must call the original logical method to avoid infinite recursion
iosurf=pointsinside(xyz[inbb,,drop=F], surf = obj, ..., rval='consistent_logical')
res=inbb
res[inbb]=iosurf
res
}
contains_points.hxsurf<-contains_points.mesh3d
contains_points.ashape3d<-function(obj, points, ...) {
alphashape3d::inashape3d(obj, points=xyzmatrix(points), ...)
}
# Concatenate two HyperSurface objects
#
# @param x hxsurf
# @param y another hxsurf
# @return new hxsurf
# @examples
# h1 = as.hxsurf(icosahedron3d(), 'a')
# h2 = as.hxsurf(tetrahedron3d()+1, 'b')
# h3=c(h1, h2)
concat_hxsurfs <- function(x, y) {
nx <- x
nx$Vertices <- rbind(x$Vertices, y$Vertices)
nx$Vertices[,4] <- 1:length(nx$Vertices[,4])
for (reg in y$RegionList) {
nx$Regions[[reg]] <- nrow(x$Vertices) + y$Regions[[reg]]
}
nx$RegionList <- c(x$RegionList, y$RegionList)
nx$RegionColourList <- c(x$RegionColourList, y$RegionColourList)
nx
}
#' Concatenate HyperSurface objects
#'
#' @param ... multiple hxsurf objects
#' @return new hxsurf
#' @export
#' @rdname hxsurf-ops
#' @examples
#' h1 = as.hxsurf(icosahedron3d(), 'a')
#' h2 = as.hxsurf(tetrahedron3d()+1, 'b')
#' h3 = as.hxsurf(icosahedron3d()+3, 'c')
#' hc = c(h1, h2, h3)
`c.hxsurf` <- function(...) {
items <- list(...)
if (length(items) == 1) {
return(items[[1]])
} else {
hxs <- items[[1]]
for (i in 2:length(items)) {
hxs <- concat_hxsurfs(hxs, items[[i]])
}
hxs
}
}
|
3fb514477eb39cc62fe0edacda199229c82097fd
|
efeba9f5aff2e7afbf96a57e0baf62a8fb1a3b94
|
/Part2/Stage3-Structured data/ex.3-4/practice1.R
|
6462171e7e8b8e1ec01b715f0310cf45c653e86f
|
[] |
no_license
|
psm9619/R_Data_Analysis
|
b1db04295607b5b0811eb2151ce5378a812b2aa3
|
b6b8186a582174533ab41a68aeab77bdcf0ea854
|
refs/heads/master
| 2020-05-29T13:27:26.350660
| 2019-10-10T01:07:53
| 2019-10-10T01:07:53
| 189,161,472
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 966
|
r
|
practice1.R
|
library(reshape)
library(ggplot2)
library(dplyr)
data1 <- read.csv("연도별요양기관별보험청구건수_2001_2013.csv")
data2 <- read.csv("연도별요양기관별보험청구건수_2001_2013_세로.csv")
data2
row.names(data2) <- data2[,1]
data2
data2[1] <- NULL
func <- function(x) {
x/100000
}
data3 <- apply(data2, 2, func)
plot (data3[,1], xlab="", ylab="",
ylim=c(0,6000), axes=F, col="violet", type="o", lwd=2,
main=paste("연도별 요양 기관별 보험청구 건수 (단위: 십만건)","\n",
"출처:건강보험심사평가원"))
axis(1, at = 1:10, label=row.names(data3), las=2)
axis(2, las=1)
col=c("red","orange","yellow","green", "blue", "purple", "brown", "grey","lightblue")
for (i in 1:10){
lines(data3[,i], col=col[i], type="o", lwd=2)
}
abline(h=seq(0,6000,500), v=seq(1,100,1), lty=3,lwd=0.2)
legend(1,6000, col, cex=0.8, col=col, lty=1, lwd=2, bg='white')
|
eeb17a82aa5df82b78992448ae73539c028732b9
|
1a6f7dc7e39fd02f390ad6f058874367a1119742
|
/R/occurrencesLessThan.R
|
b42a91d3a053a45b53954de76e827d3f496af64a
|
[] |
no_license
|
cran/inverseRegex
|
f881d95cec7bdcd073a3779f876dec0dc78657d6
|
69f0cd3ec678a6babd4878ffc3e73ceb0f2e37b6
|
refs/heads/master
| 2022-11-09T02:02:21.757705
| 2022-10-23T15:25:07
| 2022-10-23T15:25:07
| 210,402,220
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,210
|
r
|
occurrencesLessThan.R
|
##' Identifies Infrequent inverseRegex Patterns in an R Object.
##'
##' Calls \code{inverseRegex} on the input object and identifies values that
##' occur infrequently.
##'
##' @param x Object to analyse for infrequent regex patterns.
##' @param fraction Fraction of the R object size; regex patterns that occur less
##' (or equal) often than this will be identified. For a vector this fraction will
##' be multiplied by the length of the object; for a matrix it will be multiplied by
##' the total number of entries; and for a data frame or tibble it will be multiplied
##' by the number of rows. Defaults to \code{0.05}.
##' @param n Alternative to the \code{fraction} argument which allows a literal
##' number of occurrences to be searched for. Defaults to NULL, in which case
##' \code{fraction} will be used.
##' @param ... Other arguments to be passed to \code{inverseRegex}.
##'
##' @return A collection of logical values with \code{TRUE} indicating entries with
##' an infrequent regex pattern. The class of the return value will depend on the
##' input object; matrices, data frames, and tibbles will be returned in kind; all
##' others are returned as vectors.
##'
##' @details This function is essentially a wrapper around calling \code{table()} on
##' the return value of \code{inverseRegex}. It can be used to identify the indices
##' of values that consist of a regex pattern different to others in the R object.
##'
##' @note NA values are not considered and will need to be identified separately.
##'
##' @examples
##' occurrencesLessThan(c(LETTERS, 1))
##'
##' x <- iris
##' x$Species <- as.character(x$Species)
##' x[27, 'Species'] <- 'set0sa'
##' apply(occurrencesLessThan(x), 2, which)
##'
##' @seealso inverseRegex, regex
##'
##' @author Jasper Watson
##'
##' @export
##'
##
occurrencesLessThan <- function(x,
fraction = 0.05,
n = NULL,
...
){
UseMethod('occurrencesLessThan')
}
##' @export
occurrencesLessThan.default <- function(...)
stop('Input class not supported')
vectorOption <- function(x, fraction = 0.05, n = NULL, ...){
out <- rep(FALSE, length(x))
if (is.null(n))
n <- fraction * length(x)
y <- inverseRegex(x, ...)
tab <- table(y)
tmp <- y %in% names(tab)[tab <= n]
if (length(tmp) > 0)
out <- tmp
out
}
##' @export
occurrencesLessThan.character <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.logical <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.integer <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.numeric <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.Date <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.POSIXct <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.factor <- function(x, fraction = 0.05, n = NULL, ...)
vectorOption(x, fraction, n, ...)
##' @export
occurrencesLessThan.matrix <- function(x, fraction = 0.05, n = NULL, ...){
out <- matrix(FALSE, nrow = nrow(x), ncol = ncol(x), dimnames = dimnames(x))
if (is.null(n))
n <- nrow(x) * ncol(x) * fraction
y <- inverseRegex(as.vector(x), ...)
tab <- table(y)
tmp <- y %in% names(tab)[tab <= n]
if (length(tmp) > 0)
out[] <- tmp
out
}
##' @export
occurrencesLessThan.data.frame <- function(x, fraction = 0.05, n = NULL, ...){
if (is.null(n))
n <- nrow(x) * fraction
out <- as.data.frame(matrix(FALSE, nrow = nrow(x), ncol = ncol(x),
dimnames = dimnames(x)),
stringsAsFactors = FALSE)
for (jj in 1:ncol(x) ){
y <- as.vector(unlist(inverseRegex(x[, jj, drop = FALSE], ...)))
tab <- table(y)
tmp <- y %in% names(tab)[tab <= n]
if (length(tmp) > 0)
out[, jj] <- tmp
}
out
}
##' @export
occurrencesLessThan.tbl_df <- function(x, fraction = 0.05, n = NULL, ...){
if (!requireNamespace('tibble', quietly = TRUE))
stop('Package tibble not available. Install or provide input as a data.frame.')
if (is.null(n))
n <- nrow(x) * fraction
out <- tibble::as_tibble(matrix(FALSE, nrow = nrow(x), ncol = ncol(x),
dimnames = dimnames(x)),
stringsAsFactors = FALSE)
for (jj in 1:ncol(x) ){
y <- as.vector(unlist(inverseRegex(x[, jj, drop = FALSE], ...)))
tab <- table(y)
tmp <- y %in% names(tab)[tab <= n]
if (length(tmp) > 0)
out[, jj] <- tmp
}
out
}
|
1e8b1d2d9d3a37fdeb392411f0762d0adf265806
|
908b54c4546885f4a165606117b3f1e06d2bc1dc
|
/tests/testthat/test_z_transform.R
|
c3daa8c238d7c77dc0b3f2b0cc8006f0a822bd02
|
[
"MIT"
] |
permissive
|
jeremymcrae/cifer
|
406de54640189e4d0135bceecedf06ed7a59915e
|
328114b73c76183d98e7a222bbcb281b66ad9de3
|
refs/heads/master
| 2021-01-17T07:18:46.317264
| 2016-07-08T12:52:45
| 2016-07-08T12:52:45
| 23,546,083
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,516
|
r
|
test_z_transform.R
|
library(cifer)
library(testthat)
context("Z-transform data")
test_that("get_l2r_z_scores output is correct", {
# construct a dataframe for two samples, one of which will have a Z
# transformed mean close to -0.707106, the other close to 0.707106, and
# trio members with Z transformed values close to 2.12.
pop = data.frame(a=c(1,2,3,4,5), b=c(3,4,5,6,7))
z_scores = get_l2r_z_scores(mom_data=c(5,6,7,8,9), dad_data=c(5,6,7,8,9),
child_data=c(5,6,7,8,9), pop)
# check that the Z transformed values are close to their expected values.
# Note that I don't include the full precision, since the values have 22
# significant figures, so it's easier to check if they are close to the
# predicted values.
expect_true(z_scores$population[1] - -0.707 < 0.01)
expect_true(z_scores$population[2] - 0.707 < 0.01)
expect_true(z_scores$dad - 2.12 < 0.01)
expect_true(z_scores$mom - 2.12 < 0.01)
expect_true(z_scores$child - 2.12 < 0.01)
# and check that the function copes with parents lacking probe data
z_scores = get_l2r_z_scores(mom_data=NULL, dad_data=c(5,6,7,8,9),
child_data=c(5,6,7,8,9), pop)
expect_identical(z_scores$mom, NA)
# and check that the function copes with parents lacking probe data
z_scores = get_l2r_z_scores(mom_data=NULL, dad_data=c(5,6,7,8,9),
child_data=c(5,6,7,8,9), population=c(1,2,3,4,5))
a = 0.632455532033676
expect_equal(z_scores$population, c(-2*a, -a, 0, a, 2*a))
})
|
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