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require("vcfR")
source("../../code/tools.r")
# read reference data from Snipper input file
nsam = 2000 # samples for simulating p-values
require("parallel")
options(mc.cores = 3)
##################
a = read.table("1000G_SampleListWithLocations.txt", header = FALSE)
inds = a[,1:3]
dat = getData("1000G_AIMsetKidd.vcf.gz", inds)
test.sample.numeric = dat$sample
test.samLoc = dat$samLoc
test.samLocFine = dat$samLocFine
names(test.samLoc) = rownames(test.sample.numeric)
names(test.samLocFine) = rownames(test.sample.numeric)
training.indsUsed = !is.element(test.samLocFine, c("ASW", "ACB", "MXL", "PUR", "CLM"))
training.sample.numeric = test.sample.numeric[training.indsUsed,]
training.samLoc = test.samLoc[training.indsUsed]
training.samLocFine = test.samLocFine[training.indsUsed]
names(training.samLoc) = rownames(training.sample.numeric)
names(training.samLocFine) = rownames(training.sample.numeric)
# select the three individuals treated in the paper
test.sample.numeric = test.sample.numeric[c("NA19719", "NA19720", "NA20278"),]
test.samLoc = test.samLoc[c("NA19719", "NA19720", "NA20278")]
test.samLocFine = test.samLocFine[c("NA19719", "NA19720", "NA20278")]
# Compute predictions as in Snipper, based on superpops
training.freqs = getfreqs(training.sample.numeric, training.samLoc)
training.refcounts = get.refallelecounts(training.sample.numeric, training.samLoc)
training.allcounts = get.allallelecounts(training.sample.numeric, training.samLoc)
predictions = matrix(0, ncol = nrow(training.refcounts), nrow = nrow(test.sample.numeric))
colnames(predictions) = rownames(training.refcounts)
rownames(predictions) = rownames(test.sample.numeric)
for(i in 1:nrow(test.sample.numeric)) {
predictions[i,] = prediction.naiveBayes(test.sample.numeric[i,], training.refcounts, training.allcounts)
}
# Compute admixture, recent-admixture and p-value, based on superpops
ia = get.admixtureproportions.multi(test.sample.numeric, training.freqs, tol=1e-6, verbose = TRUE)
pia = get.recentadmixtureproportions.multi(test.sample.numeric, training.freqs, tol=1e-6, verbose = TRUE)
loglik.ia = loglik.pia = test.sample.numeric
p = test.sample.numeric[,1]
res = mclapply(1:nrow(test.sample.numeric), function(i){
cat("\n\nIndividual ", i, "; Calculate p-value for ", rownames(test.sample.numeric)[i], " from ", as.vector(test.samLoc[i]), ".\n", sep="")
res = simulate.p.value(test.sample.numeric[i,], training.freqs, nsam, tol=1e-06, verbose = TRUE)
cat("p-value = ", res$p, ".\n", sep="")
res
})
for(i in 1:nrow(test.sample.numeric)) {
loglik.ia[i,] = get.loglik.admixture(test.sample.numeric[i,], training.freqs, ia[i,], sum = FALSE)
loglik.pia[i,] = get.loglik.recentadmixture(test.sample.numeric[i,], training.freqs, pia[i,], sum=FALSE)
p[i] = res[[i]]$p
}
delta.loglik = loglik.pia - loglik.ia
save(training.freqs, predictions, ia, loglik.ia, pia, loglik.pia, delta.loglik, test.sample.numeric, training.samLoc, file = "analysis_Kidd")
for(i in 1:nrow(test.sample.numeric)) {
cat("\n\nAnalysis for individual ", names(p)[i], ".\n", sep="")
cat("ia ", round(as.matrix(t(ia[i,])),3), "\n")
cat("pia ", round(as.matrix(t(pia[i,])),3), "\n")
cat("Heterozygous sites ", sum(test.sample.numeric[i,]==1), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i, test.sample.numeric[i,]==1]),3), ".\n\n", sep="")
cat("Homozygous sites ", sum(test.sample.numeric[i,]!=1), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i, test.sample.numeric[i,]!=1]),3), ".\n\n", sep="")
cat("Sum of sites ", sum(test.sample.numeric[i,]<10), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i,]),3), ".\n\n", sep="")
cat("p-value ", 2*p[i], ".\n\n", sep="")
}
|
/data/1000G/analysis_Kidd.r
|
no_license
|
pfaffelh/recent-admixture
|
R
| false
| false
| 3,766
|
r
|
require("vcfR")
source("../../code/tools.r")
# read reference data from Snipper input file
nsam = 2000 # samples for simulating p-values
require("parallel")
options(mc.cores = 3)
##################
a = read.table("1000G_SampleListWithLocations.txt", header = FALSE)
inds = a[,1:3]
dat = getData("1000G_AIMsetKidd.vcf.gz", inds)
test.sample.numeric = dat$sample
test.samLoc = dat$samLoc
test.samLocFine = dat$samLocFine
names(test.samLoc) = rownames(test.sample.numeric)
names(test.samLocFine) = rownames(test.sample.numeric)
training.indsUsed = !is.element(test.samLocFine, c("ASW", "ACB", "MXL", "PUR", "CLM"))
training.sample.numeric = test.sample.numeric[training.indsUsed,]
training.samLoc = test.samLoc[training.indsUsed]
training.samLocFine = test.samLocFine[training.indsUsed]
names(training.samLoc) = rownames(training.sample.numeric)
names(training.samLocFine) = rownames(training.sample.numeric)
# select the three individuals treated in the paper
test.sample.numeric = test.sample.numeric[c("NA19719", "NA19720", "NA20278"),]
test.samLoc = test.samLoc[c("NA19719", "NA19720", "NA20278")]
test.samLocFine = test.samLocFine[c("NA19719", "NA19720", "NA20278")]
# Compute predictions as in Snipper, based on superpops
training.freqs = getfreqs(training.sample.numeric, training.samLoc)
training.refcounts = get.refallelecounts(training.sample.numeric, training.samLoc)
training.allcounts = get.allallelecounts(training.sample.numeric, training.samLoc)
predictions = matrix(0, ncol = nrow(training.refcounts), nrow = nrow(test.sample.numeric))
colnames(predictions) = rownames(training.refcounts)
rownames(predictions) = rownames(test.sample.numeric)
for(i in 1:nrow(test.sample.numeric)) {
predictions[i,] = prediction.naiveBayes(test.sample.numeric[i,], training.refcounts, training.allcounts)
}
# Compute admixture, recent-admixture and p-value, based on superpops
ia = get.admixtureproportions.multi(test.sample.numeric, training.freqs, tol=1e-6, verbose = TRUE)
pia = get.recentadmixtureproportions.multi(test.sample.numeric, training.freqs, tol=1e-6, verbose = TRUE)
loglik.ia = loglik.pia = test.sample.numeric
p = test.sample.numeric[,1]
res = mclapply(1:nrow(test.sample.numeric), function(i){
cat("\n\nIndividual ", i, "; Calculate p-value for ", rownames(test.sample.numeric)[i], " from ", as.vector(test.samLoc[i]), ".\n", sep="")
res = simulate.p.value(test.sample.numeric[i,], training.freqs, nsam, tol=1e-06, verbose = TRUE)
cat("p-value = ", res$p, ".\n", sep="")
res
})
for(i in 1:nrow(test.sample.numeric)) {
loglik.ia[i,] = get.loglik.admixture(test.sample.numeric[i,], training.freqs, ia[i,], sum = FALSE)
loglik.pia[i,] = get.loglik.recentadmixture(test.sample.numeric[i,], training.freqs, pia[i,], sum=FALSE)
p[i] = res[[i]]$p
}
delta.loglik = loglik.pia - loglik.ia
save(training.freqs, predictions, ia, loglik.ia, pia, loglik.pia, delta.loglik, test.sample.numeric, training.samLoc, file = "analysis_Kidd")
for(i in 1:nrow(test.sample.numeric)) {
cat("\n\nAnalysis for individual ", names(p)[i], ".\n", sep="")
cat("ia ", round(as.matrix(t(ia[i,])),3), "\n")
cat("pia ", round(as.matrix(t(pia[i,])),3), "\n")
cat("Heterozygous sites ", sum(test.sample.numeric[i,]==1), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i, test.sample.numeric[i,]==1]),3), ".\n\n", sep="")
cat("Homozygous sites ", sum(test.sample.numeric[i,]!=1), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i, test.sample.numeric[i,]!=1]),3), ".\n\n", sep="")
cat("Sum of sites ", sum(test.sample.numeric[i,]<10), ".\n", sep="")
cat("Contribution to delta ell ", round(sum(delta.loglik[i,]),3), ".\n\n", sep="")
cat("p-value ", 2*p[i], ".\n\n", sep="")
}
|
new.function <- function(a) {
for(i in 1:a) {
b <- i^2
print(b)
}
}
new.function(6)
|
/functionTest.R
|
no_license
|
CodyMcMahon/lets_see_if_I_can_learn_R
|
R
| false
| false
| 91
|
r
|
new.function <- function(a) {
for(i in 1:a) {
b <- i^2
print(b)
}
}
new.function(6)
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Lasso/NSCLC.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.3,family="gaussian",standardize=TRUE)
sink('./NSCLC_041.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/Lasso/NSCLC/NSCLC_041.R
|
no_license
|
esbgkannan/QSMART
|
R
| false
| false
| 341
|
r
|
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/Lasso/NSCLC.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.3,family="gaussian",standardize=TRUE)
sink('./NSCLC_041.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geo_dirlist.R
\name{munge_dirlist}
\alias{munge_dirlist}
\title{Returns supplementary file names from Entrez GEO}
\usage{
munge_dirlist(x)
}
\arguments{
\item{x}{a character vector of files at Entrez GEO ftp site.}
}
\value{
a data frame with columns date, size and suppfile.
Date is the supplementary file date, size is filesize in bytes.
Suppfile is the supplementary file name.
}
\description{
Replaces GEOquery package getDirlisting function, seems to be faster.
}
\examples{
\dontrun{
## Download supplementary file name
url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81555/suppl/"
r <- httr::GET(url)
cont <- httr::content(r, as = "text", encoding = "UTF-8")
suppl_filename <- munge_dirlist(cont)
## Download series matrix file name
url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81555/miniml/"
r <- httr::GET(url)
cont <- httr::content(r, as = "text", encoding = "UTF-8")
miniml_filename <- munge_dirlist(cont)
}
}
|
/man/munge_dirlist.Rd
|
no_license
|
tpall/entrezquery
|
R
| false
| true
| 1,028
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geo_dirlist.R
\name{munge_dirlist}
\alias{munge_dirlist}
\title{Returns supplementary file names from Entrez GEO}
\usage{
munge_dirlist(x)
}
\arguments{
\item{x}{a character vector of files at Entrez GEO ftp site.}
}
\value{
a data frame with columns date, size and suppfile.
Date is the supplementary file date, size is filesize in bytes.
Suppfile is the supplementary file name.
}
\description{
Replaces GEOquery package getDirlisting function, seems to be faster.
}
\examples{
\dontrun{
## Download supplementary file name
url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81555/suppl/"
r <- httr::GET(url)
cont <- httr::content(r, as = "text", encoding = "UTF-8")
suppl_filename <- munge_dirlist(cont)
## Download series matrix file name
url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81555/miniml/"
r <- httr::GET(url)
cont <- httr::content(r, as = "text", encoding = "UTF-8")
miniml_filename <- munge_dirlist(cont)
}
}
|
library(data.table)
data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
data$Date <- as.Date(strptime(data$Date, "%d/%m/%Y"))
data <- subset(data, data$Date %in% as.Date(c("2007-02-01", "2007-02-02")))
png("plot1.png", width = 480, height = 480)
hist(as.numeric(as.character(data$Global_active_power)), col = "red", xlab = "Global Active Power (kilowatts)", main = "Global Active Power")
dev.off()
|
/plot1.R
|
no_license
|
Brainteaser9/ExData_Plotting1
|
R
| false
| false
| 425
|
r
|
library(data.table)
data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
data$Date <- as.Date(strptime(data$Date, "%d/%m/%Y"))
data <- subset(data, data$Date %in% as.Date(c("2007-02-01", "2007-02-02")))
png("plot1.png", width = 480, height = 480)
hist(as.numeric(as.character(data$Global_active_power)), col = "red", xlab = "Global Active Power (kilowatts)", main = "Global Active Power")
dev.off()
|
library(class)
library(caret)
require(mlbench)
library(e1071)
library(base)
require(base)
data(Sonar)
head(Sonar)
summary(Sonar)
str(Sonar)
cat("number of rows and columns are:", nrow(Sonar), ncol(Sonar))
###############
#Lets check how many ???? classes and ????
#classes Sonar data contain?and check whether Sonar data contains any NA in its columns.
#################
base::table(Sonar$Class)
apply(Sonar,2,function (x) sum(is.na(x)))
##############
# Take samples from our data to split Sonar into training
# and test sets
##########################################
SEED<-123
set.seed(SEED)
data<-Sonar[base::sample(nrow(Sonar)),] #shuffle the data
bound<-floor(0.7*nrow(data))
df_train<-data[1:bound,]
df_test <-data[(bound + 1):nrow(data), ]
###################################33
# Let's examine if the train and test samples have properly splitted with the almost the same portion of Class labels
##################################
cat("number of training and test samples are ", nrow(df_train), nrow(df_test))
cat("number of training classes: \n", base::table(df_train$Class)/nrow(df_train))
cat("\n")
cat("number of test classes: \n", base::table(df_test$Class)/nrow(df_test))
#####################3
# To simplify our job, we can create the following data frames
#####################
X_train <- subset(df_train, select=-Class)
y_train <- df_train$Class
X_test <- subset(df_test, select=-Class) # exclude Class for prediction
y_test <- df_test$Class
############33
#Training a model on data
####################
#Now, we are going to use knn function from class library with k=3???3???=
############
model_knn <- knn(train=X_train,
test=X_test,
cl=y_train, # class labels
k=3)
model_knn
####################3
#we can see how many classes have been correctly or incorrectly
#classified by comparing to the true labels as follows
#####################
conf_mat <- base::table(y_test, model_knn)
conf_mat
##################
#T o compute the accuracy, we sum up all the correctly classified
#observations (located in diagonal) and divide it by the total number of classes
###########
cat("Test accuracy: ", sum(diag(conf_mat))/sum(conf_mat))
########################
# To assess whether ????=3 is a good choice and see whether ????=3 leads to overfitt
# ing /underfitting the data, we could use knn.cv which does the leave-one-out
#cross-validations for training set (i.e., it singles out a training sample one at a
#time and tries to view it as a new example and see what class label it assigns).
#################################################################################
knn_loocv <- knn.cv(train=X_train, cl=y_train, k=3)
knn_loocv
######################
#Lets create a confusion matrix to compute the accuracy of the training
#labels y_train and the cross-validated predictions knn_loocv, same as the above.
#What can you find from comparing the LOOCV accuracy and the test accuracy above?
#########################################
conf_mat_cv <- base::table(y_train, knn_loocv)
conf_mat_cv
cat("LOOCV accuracy: ", sum(diag(conf_mat_cv)) / sum(conf_mat_cv))
###########################
#The difference between the cross-validated accuracy and the test accuracy shows
#that, k????=3 leads to overfitting. Perhaps we should change ???? to lessen
# the overfitting.
########################
##################
#Improve the performance of the model
#########################
###############3
#Cross Validation
###############
SEED <- 2016
set.seed(SEED)
# create the training data 70% of the overall Sonar data.
in_train <- createDataPartition(Sonar$Class, p=0.7, list=FALSE) # create training indices
ndf_train <- Sonar[in_train, ]
ndf_test <- Sonar[-in_train, ]
|
/Machine Learning.R
|
no_license
|
DAMARISKILANGO/DAMSY_RCODES
|
R
| false
| false
| 3,888
|
r
|
library(class)
library(caret)
require(mlbench)
library(e1071)
library(base)
require(base)
data(Sonar)
head(Sonar)
summary(Sonar)
str(Sonar)
cat("number of rows and columns are:", nrow(Sonar), ncol(Sonar))
###############
#Lets check how many ???? classes and ????
#classes Sonar data contain?and check whether Sonar data contains any NA in its columns.
#################
base::table(Sonar$Class)
apply(Sonar,2,function (x) sum(is.na(x)))
##############
# Take samples from our data to split Sonar into training
# and test sets
##########################################
SEED<-123
set.seed(SEED)
data<-Sonar[base::sample(nrow(Sonar)),] #shuffle the data
bound<-floor(0.7*nrow(data))
df_train<-data[1:bound,]
df_test <-data[(bound + 1):nrow(data), ]
###################################33
# Let's examine if the train and test samples have properly splitted with the almost the same portion of Class labels
##################################
cat("number of training and test samples are ", nrow(df_train), nrow(df_test))
cat("number of training classes: \n", base::table(df_train$Class)/nrow(df_train))
cat("\n")
cat("number of test classes: \n", base::table(df_test$Class)/nrow(df_test))
#####################3
# To simplify our job, we can create the following data frames
#####################
X_train <- subset(df_train, select=-Class)
y_train <- df_train$Class
X_test <- subset(df_test, select=-Class) # exclude Class for prediction
y_test <- df_test$Class
############33
#Training a model on data
####################
#Now, we are going to use knn function from class library with k=3???3???=
############
model_knn <- knn(train=X_train,
test=X_test,
cl=y_train, # class labels
k=3)
model_knn
####################3
#we can see how many classes have been correctly or incorrectly
#classified by comparing to the true labels as follows
#####################
conf_mat <- base::table(y_test, model_knn)
conf_mat
##################
#T o compute the accuracy, we sum up all the correctly classified
#observations (located in diagonal) and divide it by the total number of classes
###########
cat("Test accuracy: ", sum(diag(conf_mat))/sum(conf_mat))
########################
# To assess whether ????=3 is a good choice and see whether ????=3 leads to overfitt
# ing /underfitting the data, we could use knn.cv which does the leave-one-out
#cross-validations for training set (i.e., it singles out a training sample one at a
#time and tries to view it as a new example and see what class label it assigns).
#################################################################################
knn_loocv <- knn.cv(train=X_train, cl=y_train, k=3)
knn_loocv
######################
#Lets create a confusion matrix to compute the accuracy of the training
#labels y_train and the cross-validated predictions knn_loocv, same as the above.
#What can you find from comparing the LOOCV accuracy and the test accuracy above?
#########################################
conf_mat_cv <- base::table(y_train, knn_loocv)
conf_mat_cv
cat("LOOCV accuracy: ", sum(diag(conf_mat_cv)) / sum(conf_mat_cv))
###########################
#The difference between the cross-validated accuracy and the test accuracy shows
#that, k????=3 leads to overfitting. Perhaps we should change ???? to lessen
# the overfitting.
########################
##################
#Improve the performance of the model
#########################
###############3
#Cross Validation
###############
SEED <- 2016
set.seed(SEED)
# create the training data 70% of the overall Sonar data.
in_train <- createDataPartition(Sonar$Class, p=0.7, list=FALSE) # create training indices
ndf_train <- Sonar[in_train, ]
ndf_test <- Sonar[-in_train, ]
|
#' @title Induced model of learner.
#'
#' @description
#' Result from \code{\link{train}}.
#'
#' It internally stores the underlying fitted model,
#' the subset used for training, features used for training, levels of factors in the
#' data set and computation time that was spent for training.
#'
#' Object members: See arguments.
#'
#' The constructor \code{makeWrappedModel} is mainly for internal use.
#'
#' @template arg_learner
#' @param learner.model [any]\cr
#' Underlying model.
#' @template arg_taskdesc
#' @param subset [\code{integer}]\cr
#' Subset used for training.
#' @param features [\code{character}]\cr
#' Features used for training.
#' @param factor.levels [named \code{list} of \code{character}]\cr
#' Levels of factor variables (features and potentially target) in training data.
#' Named by variable name, non-factors do not occur in the list.
#' @param time [\code{numeric(1)}]\cr
#' Computation time for model fit in seconds.
#' @template ret_wmodel
#' @export
#' @aliases WrappedModel
makeWrappedModel = function(learner, learner.model, task.desc, subset, features, factor.levels, time) {
UseMethod("makeWrappedModel")
}
#' @export
makeWrappedModel.Learner = function(learner, learner.model, task.desc, subset, features, factor.levels, time) {
if (is.error(learner.model)) {
learner.model = as.character(learner.model)
time = NA_real_
cl = c("FailureModel", "WrappedModel")
} else {
cl = "WrappedModel"
}
makeS3Obj(cl,
learner = learner,
learner.model = learner.model,
task.desc = task.desc,
subset = subset,
features = features,
factor.levels = factor.levels,
time = time
)
}
#' @export
print.WrappedModel = function(x, ...) {
cat(
"Model for id = ", x$learner$id, " class = ", getClass1(x$learner), "\n",
"Trained on obs: ", length(x$subset), "\n",
"Used features: ", length(x$features), "\n",
"Hyperparameters: ", getHyperParsString(x$learner), "\n",
sep = ""
)
}
#' Get underlying R model of learner integrated into mlr.
#'
#' @param model [\code{\link{WrappedModel}}]\cr
#' The model, returned by e.g., \code{\link{train}}.
#' @return [any]. A fitted model, depending the learner / wrapped package. E.g., a
#' model of class \code{\link[rpart]{rpart}} for learner \dQuote{classif.rpart}.
#' @export
getLearnerModel = function(model) {
UseMethod("getLearnerModel")
}
#'@export
getLearnerModel.WrappedModel = function(model) {
model$learner.model
}
#' @title Is the model a FailureModel?
#'
#' @description
#' Such a model is created when one sets the corresponding option in \code{\link{configureMlr}}.
#'
#' For complex wrappers this getter returns \code{TRUE} if ANY model contained in it failed.
#'
#' @template arg_wrappedmod
#' @return [\code{logical(1)}].
#' @export
isFailureModel = function(model) {
UseMethod("isFailureModel")
}
#' @export
# by default the model is never a failure. if a failure happens we have the derived class FailureModel
isFailureModel.WrappedModel = function(model) {
return(FALSE)
}
#' @title Return error message of FailureModel.
#'
#' @description
#' Such a model is created when one sets the corresponding option in \code{\link{configureMlr}}.
#' If no failure occurred, \code{NA} is returned.
#'
#' For complex wrappers this getter returns the first error message encountered in ANY model that failed.
#'
#' @template arg_wrappedmod
#' @return [\code{character(1)}].
#' @export
getFailureModelMsg = function(model) {
UseMethod("getFailureModelMsg")
}
#' @export
getFailureModelMsg.WrappedModel = function(model) {
return(NA_character_)
}
|
/R/WrappedModel.R
|
no_license
|
dickoa/mlr
|
R
| false
| false
| 3,625
|
r
|
#' @title Induced model of learner.
#'
#' @description
#' Result from \code{\link{train}}.
#'
#' It internally stores the underlying fitted model,
#' the subset used for training, features used for training, levels of factors in the
#' data set and computation time that was spent for training.
#'
#' Object members: See arguments.
#'
#' The constructor \code{makeWrappedModel} is mainly for internal use.
#'
#' @template arg_learner
#' @param learner.model [any]\cr
#' Underlying model.
#' @template arg_taskdesc
#' @param subset [\code{integer}]\cr
#' Subset used for training.
#' @param features [\code{character}]\cr
#' Features used for training.
#' @param factor.levels [named \code{list} of \code{character}]\cr
#' Levels of factor variables (features and potentially target) in training data.
#' Named by variable name, non-factors do not occur in the list.
#' @param time [\code{numeric(1)}]\cr
#' Computation time for model fit in seconds.
#' @template ret_wmodel
#' @export
#' @aliases WrappedModel
makeWrappedModel = function(learner, learner.model, task.desc, subset, features, factor.levels, time) {
UseMethod("makeWrappedModel")
}
#' @export
makeWrappedModel.Learner = function(learner, learner.model, task.desc, subset, features, factor.levels, time) {
if (is.error(learner.model)) {
learner.model = as.character(learner.model)
time = NA_real_
cl = c("FailureModel", "WrappedModel")
} else {
cl = "WrappedModel"
}
makeS3Obj(cl,
learner = learner,
learner.model = learner.model,
task.desc = task.desc,
subset = subset,
features = features,
factor.levels = factor.levels,
time = time
)
}
#' @export
print.WrappedModel = function(x, ...) {
cat(
"Model for id = ", x$learner$id, " class = ", getClass1(x$learner), "\n",
"Trained on obs: ", length(x$subset), "\n",
"Used features: ", length(x$features), "\n",
"Hyperparameters: ", getHyperParsString(x$learner), "\n",
sep = ""
)
}
#' Get underlying R model of learner integrated into mlr.
#'
#' @param model [\code{\link{WrappedModel}}]\cr
#' The model, returned by e.g., \code{\link{train}}.
#' @return [any]. A fitted model, depending the learner / wrapped package. E.g., a
#' model of class \code{\link[rpart]{rpart}} for learner \dQuote{classif.rpart}.
#' @export
getLearnerModel = function(model) {
UseMethod("getLearnerModel")
}
#'@export
getLearnerModel.WrappedModel = function(model) {
model$learner.model
}
#' @title Is the model a FailureModel?
#'
#' @description
#' Such a model is created when one sets the corresponding option in \code{\link{configureMlr}}.
#'
#' For complex wrappers this getter returns \code{TRUE} if ANY model contained in it failed.
#'
#' @template arg_wrappedmod
#' @return [\code{logical(1)}].
#' @export
isFailureModel = function(model) {
UseMethod("isFailureModel")
}
#' @export
# by default the model is never a failure. if a failure happens we have the derived class FailureModel
isFailureModel.WrappedModel = function(model) {
return(FALSE)
}
#' @title Return error message of FailureModel.
#'
#' @description
#' Such a model is created when one sets the corresponding option in \code{\link{configureMlr}}.
#' If no failure occurred, \code{NA} is returned.
#'
#' For complex wrappers this getter returns the first error message encountered in ANY model that failed.
#'
#' @template arg_wrappedmod
#' @return [\code{character(1)}].
#' @export
getFailureModelMsg = function(model) {
UseMethod("getFailureModelMsg")
}
#' @export
getFailureModelMsg.WrappedModel = function(model) {
return(NA_character_)
}
|
\name{ppi}
\alias{ppi}
\docType{data}
\title{
Pathway information and Protein-Protein interactions from String DB V10
}
\description{}
\usage{data("ppi")}
\format{
The format is:
List of 2
$ pathwaydat: chr [1:87, 1:3] "Apoptosis" "Apoptosis" "Apoptosis" "Apoptosis" ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:3] "Pathway_v20150123" "Protein" "gene"
$ ppi :List of 12
..$ Apoptosis : chr [1:70, 1:3] "BIRC2" "BIRC2" "BIRC2" "BIRC2" ...
..$ Cell cycle : chr [1:42, 1:3] "CDKN1B" "CDKN1B" "CDKN1B" "CDKN1B" ...
..$ DNA damage response : chr [1:90, 1:3] "XRCC1" "XRCC1" "XRCC1" "XRCC1" ...
..$ EMT : chr [1:30, 1:3] "SERPINE1" "SERPINE1" "SERPINE1" "SERPINE1" ...
..$ Hormone receptor : chr [1:6, 1:3] "ESR1" "ESR1" "PGR" "PGR" ...
..$ Hormone signaling (Breast): chr [1:2, 1:3] "BCL2" "GATA3" "GATA3" "BCL2" ...
..$ PI3K/AKT : chr [1:74, 1:3] "TSC2" "TSC2" "TSC2" "TSC2" ...
..$ RAS/MAPK : chr [1:84, 1:3] "MAPK1" "MAPK1" "MAPK1" "MAPK1" ...
..$ RTK : chr [1:20, 1:3] "ERBB3" "ERBB3" "ERBB3" "ERBB3" ...
..$ TSC/mTOR : chr [1:18, 1:3] "RPS6KB1" "RPS6KB1" "RPS6KB1" "RPS6KB1" ...
..$ Breast reactive : chr [1:28, 1:3] "GAPDH" "GAPDH" "GAPDH" "GAPDH" ...
..$ Core reactive : chr [1:10, 1:3] "CDH1" "CDH1" "CDH1" "CAV1" ...
}
|
/man/ppi.Rd
|
no_license
|
MinJinHa/PRECISE
|
R
| false
| false
| 1,449
|
rd
|
\name{ppi}
\alias{ppi}
\docType{data}
\title{
Pathway information and Protein-Protein interactions from String DB V10
}
\description{}
\usage{data("ppi")}
\format{
The format is:
List of 2
$ pathwaydat: chr [1:87, 1:3] "Apoptosis" "Apoptosis" "Apoptosis" "Apoptosis" ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:3] "Pathway_v20150123" "Protein" "gene"
$ ppi :List of 12
..$ Apoptosis : chr [1:70, 1:3] "BIRC2" "BIRC2" "BIRC2" "BIRC2" ...
..$ Cell cycle : chr [1:42, 1:3] "CDKN1B" "CDKN1B" "CDKN1B" "CDKN1B" ...
..$ DNA damage response : chr [1:90, 1:3] "XRCC1" "XRCC1" "XRCC1" "XRCC1" ...
..$ EMT : chr [1:30, 1:3] "SERPINE1" "SERPINE1" "SERPINE1" "SERPINE1" ...
..$ Hormone receptor : chr [1:6, 1:3] "ESR1" "ESR1" "PGR" "PGR" ...
..$ Hormone signaling (Breast): chr [1:2, 1:3] "BCL2" "GATA3" "GATA3" "BCL2" ...
..$ PI3K/AKT : chr [1:74, 1:3] "TSC2" "TSC2" "TSC2" "TSC2" ...
..$ RAS/MAPK : chr [1:84, 1:3] "MAPK1" "MAPK1" "MAPK1" "MAPK1" ...
..$ RTK : chr [1:20, 1:3] "ERBB3" "ERBB3" "ERBB3" "ERBB3" ...
..$ TSC/mTOR : chr [1:18, 1:3] "RPS6KB1" "RPS6KB1" "RPS6KB1" "RPS6KB1" ...
..$ Breast reactive : chr [1:28, 1:3] "GAPDH" "GAPDH" "GAPDH" "GAPDH" ...
..$ Core reactive : chr [1:10, 1:3] "CDH1" "CDH1" "CDH1" "CAV1" ...
}
|
#######################################
### EcoVirtual -Internal Functions ####
#######################################
##############################################
### Island Biogeography and Neutral Theory ###
##############################################
## fuction rich used in 'simHub1' 'simHub2' simHub3'
rich <- function(x)length(unique(x))
## function animaIls used in 'archip' function
animaIsl=function(riq.tempo, ar.isl, locxy, sprain, col_riq=col_riq, S=S)
{
Nspp=S
nsppobs=max(riq.tempo)
maxt=dim(riq.tempo)[1]
nIsl<-length(ar.isl)
comp.max<-max(ar.isl)
tempo=length(riq.tempo)
col_spp=rainbow(max(riq.tempo))
col_func1=colorRamp(c("white", "green4"))
col_func2=colorRamp(c("green4", "royalblue"))
col_riq1=rgb(col_func1(seq(0,1, length.out=nsppobs)), maxColorValue=255)
col_riq2=rgb(col_func2(seq(0,1, length.out=Nspp-nsppobs)), maxColorValue=255)
col_riq=c(col_riq1,col_riq2)
## aqui inicia o grafico
layout(matrix(data=c(2,1), nrow=2, ncol=1), widths=c(1,1), heights=c(5,1))
old<-par(mar=c(2,2,1,3))
image(x=1:Nspp, y=1, matrix(data=1:Nspp, nrow=Nspp,ncol=1),col=col_riq, ylab="",xlab=paste("cycle", 1:length(maxt)), xaxt="n", yaxt="n", main="Richness")
axis(3, at=c(1.5,Nspp),tick=FALSE, labels=c("0", Nspp), mgp=c(0,0,0))
polygon(x=c(1.5,1.5,Nspp,Nspp), y=c(0.6,1.4,1.4,0.6), lwd=2)
plot(0:comp.max, 0:comp.max, usr=c(0,comp.max,0,comp.max), type="n", yaxt="n", xaxt="n", xlab="", ylab="", bty="n", main="Passive Sampling and Area ",mar=c(0,2,3,2), oma=c(0,0,0,0))
segments(x0=c(0,0,comp.max,0), y0=c(0,0,0,comp.max), x1=c(0,rep(comp.max,3)), y1=c(comp.max,0,comp.max,comp.max))
segments(x0=c(rep(0,nIsl), ar.isl), y0=c(ar.isl,rep(0,nIsl)), x1=c(ar.isl,ar.isl), y1=c(ar.isl,ar.isl))
for (i in 2:maxt)
{
lxy=locxy[[i]]
nspp=riq.tempo[i,]
for(f in nIsl:1)
{
vert=ar.isl[f]
polygon(x=c(0,vert, vert,0),y=c(0,0,vert,vert), col=col_riq[nspp[f]] )
}
points(lxy[,1],lxy[,2], col=col_spp[sprain[[i]]], pch=16)
Sys.sleep(.1)
}
par(old)
}
## grColExt used in 'animaColExt' and 'bioGeoIsl'
grColExt=function(E , I , P, area)
{
# Following code avoids spurious NOTE by R CMD check:
x <- NULL; rm(x);
S = I*P/(I+E) ; T = I*E/(I+E)
nIsl=length(E)
corIsl=rainbow(nIsl)
curve(I[1]-I[1]*x/P[1],0,P[1],bty="n",xlab="Number of Species", ylab="Rate",xaxt="n",yaxt="n", font.lab=2,lwd=2,ylim=c(0,1), main="Island Biogeography", col=corIsl[1])
curve((E[1]/P[1])*x,0,P,lwd=2,add=TRUE, col=corIsl[1], lty=2)
legend("top", legend=c("Colonization", "Extinction"), bty="n",lty=c(1,2))
abline(v=0)
abline(h=0)
mtext("St",side=1,at=P,font=2, line=1)
linhas=seq(0,1.5, length.out=nIsl)
for(i in 1:nIsl)
{
curve(I[i]-I[i]*x/P,0,P,lwd=2,add=TRUE, col=corIsl[i], lty=1)
curve((E[i]/P)*x,0,P,lwd=2,add=TRUE, col=corIsl[i], lty=2)
mtext(paste("S", i, sep=""),side=1,at=S[i], cex=0.8,font=2,col=corIsl[i], line=linhas[i])
mtext(paste("T", i, sep=""),side=2,at=T[i],cex=0.8,font=2,las=1,col=corIsl[i], line=linhas[i])
points(S[i],T[i],col=corIsl[i],pch=16,cex=1)
if(length(unique(area))>1)
{
siz_ar=2 +(area/max(area))
points(S[i],T[i],col=corIsl[i],cex=siz_ar[i])
}
segments(S[i],T[i],S[i],0,lty=3,col=corIsl[i])
segments(S[i],T[i],0,T[i],lty=3,col=corIsl[i])
Sys.sleep(0.1)
}
}
#grColExt(E = .5 , I = .5 , P = 100, area=1:10)
## animaRandWalk used in 'randWalk'
animaRandWalk = function(rwData, time=2, sleep=0.1)
{
xplus=max(time)*0.1
ymax=max(apply(rwData, 2, max))[1]
plot(time, rwData[,which.max(apply(rwData, 2, max))[1]], xlab="Steps", ylab="Distance from the edge",cex.axis=1.2, cex.lab=1.2,ylim=c(-.1* ymax,ymax), main="Random Walk", cex.main=1.5, type="n", xlim=c(0,max(time)))
polygon(x=c(-xplus, -xplus, max(time)+xplus, max(time)+xplus), y=c(ymax*-0.15,0,0,ymax*-0.15), col="gray")
text(max(time)/2, -0.05* ymax, labels="Absorption Surface", col="red", cex=1.5)
n=dim(rwData)[2]
ncolors= rainbow(n)
for(i in 2:length(time))
{
for(j in 1:n)
{
lines(time[1:i], rwData[1:i,j], col=ncolors[j], lty=j )
}
Sys.sleep(sleep)
}
}
## animaGame used in 'extGame'
animaGame = function(xGame, total, sleep=0.01)
{
xmax=length(xGame)
xseq=1:xmax
if(xmax>1e3){sleep=0}
if(xmax>1e4)
{
indx=ceiling(seq(1,xmax, len=1000))
xGame=xGame[indx]
xseq=xseq[indx]
}
plot(0:xmax, seq(0,total, len=xmax+1), xlab="Cycle", ylab="Money amount",cex.axis=1.2, cex.lab=1.2, ylim=c(-.1* total,total+total*0.1), main="Zero Sum Game", cex.main=1.5, type="n", cex.sub=0.9)
abline(h=total/2, lty=2, col="red")
cores= c("blue","black")
for(i in 2:xmax)
{
lines(xseq[1:i], xGame[1:i], col=cores[1], lty=2)
lines(xseq[1:i], total - xGame[1:i], col=cores[2], lty=3)
Sys.sleep(sleep)
}
polygon(x=c(-.2* xmax, -.2* xmax, xmax+ 0.1*xmax, xmax+ 0.1*xmax), y=c(-.2*total,0,0,-.2* total), col="gray")
polygon(x=c(-.2*xmax, -.2*xmax, xmax+ 0.1*xmax, xmax+ 0.1*xmax), y=c(total,total+total*.5,total +total*.5,total), col="gray")
text(xmax/2, - 0.05* total, labels="Loser", col="red", cex=1.5)
text(xmax/2, total + 0.05* total, labels="Winner", col="green", cex=1.5)
}
## animaHub used in 'simHub1', 'simHub2', 'simHub3'
animaHub=function(dadoHub, sleep=0.1)
{
maxsp=max(dadoHub)[1]
uniqsp=unique(as.numeric(dadoHub))
nind=dim(dadoHub)[1]
nsim=dim(dadoHub)[2]
ciclo=as.numeric(colnames(dadoHub))
pb = tkProgressBar(title = "Simulation Progress", max = nsim)
riq=apply(dadoHub, 2, rich)
## definindo o tamanho do retangulo
lado<-round(sqrt(nind))
lado2<-ceiling(nind/lado)
lastLine=lado*lado2 - nind
cormix=sample(rainbow(maxsp+10))
cor=c("#FFFFFF", cormix)
mcor<-c("#FFFFFF00","#000000")
spcol<-c(rep(0, lastLine),dadoHub[,1])
############ escala das especies da metapopulacao ########
layout(matrix(data=c(2,1), nrow=2, ncol=1), widths=c(1,1), heights=c(5,1))
old<-par(mar=c(2,2,1,2))
image(x=1:maxsp, y=1, matrix(data=1:maxsp, nrow=maxsp,ncol=1),col=rainbow(maxsp), ylab="",xlab="", xaxt="n", yaxt="n", main="Metacommunity Species colors", cex.main=0.8)
axis(3, at = c(1,maxsp), labels = c(1, maxsp), tick = FALSE, mgp=c(1,0,0), cex.axis=0.8)
hmat=matrix(spcol,ncol=lado, nrow=lado2)
par(mar=c(2,2,2,2))
image(hmat, col=cor[sort(unique(as.numeric(hmat)))], xaxt="n", yaxt="n")
grid(nx=lado2, ny=lado)
for (i in 2:nsim)
{
mvf=dadoHub[,i-1]!=dadoHub[,i]
matm<-matrix(c(rep(FALSE, lastLine),mvf ),ncol=lado, nrow=lado2)
image(matm,col=mcor, add=TRUE)
Sys.sleep(sleep)
spcol<-c(rep(0, lastLine),dadoHub[,i] )
cores=cor[sort(unique(spcol)+1)]
scol<-sort(unique(spcol))
lcol<-length(scol)
mcol<-match(spcol, scol)
hmat=(matrix(mcol,ncol=lado, nrow=lado2))
image(hmat, col=cores, add=TRUE)
grid(nx=lado2, ny=lado)
setTkProgressBar(pb, value = i, label = paste("Simulation #", ciclo[i], sep=""))
}
close(pb)
}
###############################
### Two Species Competition ###
###############################
### Meta competition 'animaMetaComp' used in 'metaComp'
animaMetaComp=function(dados)
{
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
op=par(mar=c(1,2,2,2))
layout(matrix(c(2,1), ncol=1, nrow=2), heights=c(5,1),widths=c(1,1))
plot(1:10,1:10,xaxt="n", yaxt="n", xlab="", ylab="", cex=0.8,type="n", , bty="n")
legend(0.5,11.8,ncol=4, legend=c("not available", "empty", "sup. competitor", "inf. competitor"), pch=c(15,22,15,15), title="Patches legend", col=c("red","black", "blue", "green"),bty="n")
image(0:ln, 0:cl, dados[,,1], col=c("red", "white","blue" ,"green") , breaks=c(-0.9,-0.001,0.1,1.5,2.9),main="Metapopulations Competition", xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.5)
for(i in 2:nsim)
{
par(new=TRUE)
image(0:ln, 0:cl, dados[,,i], col=c("red", "white","blue" ,"green") , breaks=c(-0.1,-0.001,0.1,1.9,2.9), xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.1)
}
}
#######################
### Metapopulations ###
#######################
## animaMeta2 used in 'metaPop', 'metaEr', 'metaCi', 'metaCier'
animaMeta2=function(dados)
{
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
image(0:ln, 0:cl, dados[,,1], col=c("white", "green") , breaks=c(0,0.99,5),main="Metapopulation Dynamics", sub=paste("Initial configuration from", nsim," simulations", sep=""), xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.5)
conta12=dados[,,1]+ (2*dados[,,2])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9),main="Metapopulation Dynamics", sub=paste("red= extinction; light green= colonization; dark green = permanence \n maximum time = ", nsim, sep=""), xlab="", ylab="")
for(i in 3:nsim)
{
conta12=dados[,,(i-1)]+ (2*dados[,,i])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9), xlab="", ylab="", add=TRUE)
Sys.sleep(.1)
}
}
## grFim used in 'metaPop', 'metaEr', 'metaCi', 'metaCier'
grFim=function(dados)
{
op=par(mfrow=c(2,2))
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
image(0:ln, 0:cl, dados[,,1], col=c("white", "green") , breaks=c(0,0.99,5),main="Metapopulation Dynamics", sub=paste("time = 1/", nsim, sep=""), xlab="", ylab="")
grid(ln,cl)
for(ts in c(4,2,1))
{
sim=round(nsim/ts)
conta12=dados[,,(sim-1)]+ (2*dados[,,sim])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9),main="Metapopulation Dynamics", sub=paste("red= extinction; light green= colonization;\n dark green = permanence \t time = ", sim, "/", nsim, sep=""), xlab="", ylab="")
}
par(op)
}
##############################
### Multispecies Functions ###
##############################
###############################
#Trade-off Multispecies Graphic
###
gr.toff=function(rq, fsp1,pe,add=FALSE,...)
{
# rq <- as.numeric(tclvalue(rqVar))
# fsp1 <- as.numeric(tclvalue(fsp1Var))
# pe <- as.numeric(tclvalue(peVar))
rank=1:rq
ci= pe/(1-fsp1)^(2*rank-1)
px= fsp1*(1-fsp1)^(rank-1)
if(add==FALSE)
{
toff<-dev.new( width=5, height=5)
}
old<-par(mar=c(3,3,3,3))
plot(ci~rank,col="red",ylim=c(0,max(ci)*1.1), type="b", ann=FALSE, axes=FALSE)
axis(4, cex.axis=0.8)#, yaxp=c(0,3,3))
par(new=TRUE)
plot(px~rank, ylim=c(0,fsp1),type="b", bty="n", ann=FALSE, cex.axis=0.8)#yaxt="n", xaxp=c(0,10,5))
#axis(2, cex.axis=0.8)#, yaxp=c(0,0.2,4))
mtext("Species competitive rank", 1, 2, cex=0.9)
mtext("Abundance", 2, 2, cex=0.9)
mtext("Colonization rate", 4, 2, cex=0.9)
mtext("Trade-off Species Rank ", 3, 0, cex=1.2)
par(old)
}
###############################
## animaCena used in 'regNicho'
animaCena=function(dados)
{
nt=dim(dados)[3]
dev.new()
op=par(mfrow=c(5,5), mar=c(0.1,0.1,0.1,0.1))
for(i in 1:nt)
{
image(dados[,,i], main="", bty="n",xaxt='n',yaxt='n', col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
}
dev.new()
par(mfrow=c(2,2))
image(dados[,,1], main= paste("Patch occupancy\n \t time=", 1 ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,round(nt/3)], main= paste("Patch occupancy\n \t time=", round(nt/3) ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,round(2*nt/3)], main= paste("Patch occupancy\n \ttime=", round(2*nt/3) ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,nt], main= paste("Patch occupancy\n \t time=", nt ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
par(op)
}
|
/R/intEcoVirtual.R
|
no_license
|
piklprado/EcoVirtual
|
R
| false
| false
| 12,823
|
r
|
#######################################
### EcoVirtual -Internal Functions ####
#######################################
##############################################
### Island Biogeography and Neutral Theory ###
##############################################
## fuction rich used in 'simHub1' 'simHub2' simHub3'
rich <- function(x)length(unique(x))
## function animaIls used in 'archip' function
animaIsl=function(riq.tempo, ar.isl, locxy, sprain, col_riq=col_riq, S=S)
{
Nspp=S
nsppobs=max(riq.tempo)
maxt=dim(riq.tempo)[1]
nIsl<-length(ar.isl)
comp.max<-max(ar.isl)
tempo=length(riq.tempo)
col_spp=rainbow(max(riq.tempo))
col_func1=colorRamp(c("white", "green4"))
col_func2=colorRamp(c("green4", "royalblue"))
col_riq1=rgb(col_func1(seq(0,1, length.out=nsppobs)), maxColorValue=255)
col_riq2=rgb(col_func2(seq(0,1, length.out=Nspp-nsppobs)), maxColorValue=255)
col_riq=c(col_riq1,col_riq2)
## aqui inicia o grafico
layout(matrix(data=c(2,1), nrow=2, ncol=1), widths=c(1,1), heights=c(5,1))
old<-par(mar=c(2,2,1,3))
image(x=1:Nspp, y=1, matrix(data=1:Nspp, nrow=Nspp,ncol=1),col=col_riq, ylab="",xlab=paste("cycle", 1:length(maxt)), xaxt="n", yaxt="n", main="Richness")
axis(3, at=c(1.5,Nspp),tick=FALSE, labels=c("0", Nspp), mgp=c(0,0,0))
polygon(x=c(1.5,1.5,Nspp,Nspp), y=c(0.6,1.4,1.4,0.6), lwd=2)
plot(0:comp.max, 0:comp.max, usr=c(0,comp.max,0,comp.max), type="n", yaxt="n", xaxt="n", xlab="", ylab="", bty="n", main="Passive Sampling and Area ",mar=c(0,2,3,2), oma=c(0,0,0,0))
segments(x0=c(0,0,comp.max,0), y0=c(0,0,0,comp.max), x1=c(0,rep(comp.max,3)), y1=c(comp.max,0,comp.max,comp.max))
segments(x0=c(rep(0,nIsl), ar.isl), y0=c(ar.isl,rep(0,nIsl)), x1=c(ar.isl,ar.isl), y1=c(ar.isl,ar.isl))
for (i in 2:maxt)
{
lxy=locxy[[i]]
nspp=riq.tempo[i,]
for(f in nIsl:1)
{
vert=ar.isl[f]
polygon(x=c(0,vert, vert,0),y=c(0,0,vert,vert), col=col_riq[nspp[f]] )
}
points(lxy[,1],lxy[,2], col=col_spp[sprain[[i]]], pch=16)
Sys.sleep(.1)
}
par(old)
}
## grColExt used in 'animaColExt' and 'bioGeoIsl'
grColExt=function(E , I , P, area)
{
# Following code avoids spurious NOTE by R CMD check:
x <- NULL; rm(x);
S = I*P/(I+E) ; T = I*E/(I+E)
nIsl=length(E)
corIsl=rainbow(nIsl)
curve(I[1]-I[1]*x/P[1],0,P[1],bty="n",xlab="Number of Species", ylab="Rate",xaxt="n",yaxt="n", font.lab=2,lwd=2,ylim=c(0,1), main="Island Biogeography", col=corIsl[1])
curve((E[1]/P[1])*x,0,P,lwd=2,add=TRUE, col=corIsl[1], lty=2)
legend("top", legend=c("Colonization", "Extinction"), bty="n",lty=c(1,2))
abline(v=0)
abline(h=0)
mtext("St",side=1,at=P,font=2, line=1)
linhas=seq(0,1.5, length.out=nIsl)
for(i in 1:nIsl)
{
curve(I[i]-I[i]*x/P,0,P,lwd=2,add=TRUE, col=corIsl[i], lty=1)
curve((E[i]/P)*x,0,P,lwd=2,add=TRUE, col=corIsl[i], lty=2)
mtext(paste("S", i, sep=""),side=1,at=S[i], cex=0.8,font=2,col=corIsl[i], line=linhas[i])
mtext(paste("T", i, sep=""),side=2,at=T[i],cex=0.8,font=2,las=1,col=corIsl[i], line=linhas[i])
points(S[i],T[i],col=corIsl[i],pch=16,cex=1)
if(length(unique(area))>1)
{
siz_ar=2 +(area/max(area))
points(S[i],T[i],col=corIsl[i],cex=siz_ar[i])
}
segments(S[i],T[i],S[i],0,lty=3,col=corIsl[i])
segments(S[i],T[i],0,T[i],lty=3,col=corIsl[i])
Sys.sleep(0.1)
}
}
#grColExt(E = .5 , I = .5 , P = 100, area=1:10)
## animaRandWalk used in 'randWalk'
animaRandWalk = function(rwData, time=2, sleep=0.1)
{
xplus=max(time)*0.1
ymax=max(apply(rwData, 2, max))[1]
plot(time, rwData[,which.max(apply(rwData, 2, max))[1]], xlab="Steps", ylab="Distance from the edge",cex.axis=1.2, cex.lab=1.2,ylim=c(-.1* ymax,ymax), main="Random Walk", cex.main=1.5, type="n", xlim=c(0,max(time)))
polygon(x=c(-xplus, -xplus, max(time)+xplus, max(time)+xplus), y=c(ymax*-0.15,0,0,ymax*-0.15), col="gray")
text(max(time)/2, -0.05* ymax, labels="Absorption Surface", col="red", cex=1.5)
n=dim(rwData)[2]
ncolors= rainbow(n)
for(i in 2:length(time))
{
for(j in 1:n)
{
lines(time[1:i], rwData[1:i,j], col=ncolors[j], lty=j )
}
Sys.sleep(sleep)
}
}
## animaGame used in 'extGame'
animaGame = function(xGame, total, sleep=0.01)
{
xmax=length(xGame)
xseq=1:xmax
if(xmax>1e3){sleep=0}
if(xmax>1e4)
{
indx=ceiling(seq(1,xmax, len=1000))
xGame=xGame[indx]
xseq=xseq[indx]
}
plot(0:xmax, seq(0,total, len=xmax+1), xlab="Cycle", ylab="Money amount",cex.axis=1.2, cex.lab=1.2, ylim=c(-.1* total,total+total*0.1), main="Zero Sum Game", cex.main=1.5, type="n", cex.sub=0.9)
abline(h=total/2, lty=2, col="red")
cores= c("blue","black")
for(i in 2:xmax)
{
lines(xseq[1:i], xGame[1:i], col=cores[1], lty=2)
lines(xseq[1:i], total - xGame[1:i], col=cores[2], lty=3)
Sys.sleep(sleep)
}
polygon(x=c(-.2* xmax, -.2* xmax, xmax+ 0.1*xmax, xmax+ 0.1*xmax), y=c(-.2*total,0,0,-.2* total), col="gray")
polygon(x=c(-.2*xmax, -.2*xmax, xmax+ 0.1*xmax, xmax+ 0.1*xmax), y=c(total,total+total*.5,total +total*.5,total), col="gray")
text(xmax/2, - 0.05* total, labels="Loser", col="red", cex=1.5)
text(xmax/2, total + 0.05* total, labels="Winner", col="green", cex=1.5)
}
## animaHub used in 'simHub1', 'simHub2', 'simHub3'
animaHub=function(dadoHub, sleep=0.1)
{
maxsp=max(dadoHub)[1]
uniqsp=unique(as.numeric(dadoHub))
nind=dim(dadoHub)[1]
nsim=dim(dadoHub)[2]
ciclo=as.numeric(colnames(dadoHub))
pb = tkProgressBar(title = "Simulation Progress", max = nsim)
riq=apply(dadoHub, 2, rich)
## definindo o tamanho do retangulo
lado<-round(sqrt(nind))
lado2<-ceiling(nind/lado)
lastLine=lado*lado2 - nind
cormix=sample(rainbow(maxsp+10))
cor=c("#FFFFFF", cormix)
mcor<-c("#FFFFFF00","#000000")
spcol<-c(rep(0, lastLine),dadoHub[,1])
############ escala das especies da metapopulacao ########
layout(matrix(data=c(2,1), nrow=2, ncol=1), widths=c(1,1), heights=c(5,1))
old<-par(mar=c(2,2,1,2))
image(x=1:maxsp, y=1, matrix(data=1:maxsp, nrow=maxsp,ncol=1),col=rainbow(maxsp), ylab="",xlab="", xaxt="n", yaxt="n", main="Metacommunity Species colors", cex.main=0.8)
axis(3, at = c(1,maxsp), labels = c(1, maxsp), tick = FALSE, mgp=c(1,0,0), cex.axis=0.8)
hmat=matrix(spcol,ncol=lado, nrow=lado2)
par(mar=c(2,2,2,2))
image(hmat, col=cor[sort(unique(as.numeric(hmat)))], xaxt="n", yaxt="n")
grid(nx=lado2, ny=lado)
for (i in 2:nsim)
{
mvf=dadoHub[,i-1]!=dadoHub[,i]
matm<-matrix(c(rep(FALSE, lastLine),mvf ),ncol=lado, nrow=lado2)
image(matm,col=mcor, add=TRUE)
Sys.sleep(sleep)
spcol<-c(rep(0, lastLine),dadoHub[,i] )
cores=cor[sort(unique(spcol)+1)]
scol<-sort(unique(spcol))
lcol<-length(scol)
mcol<-match(spcol, scol)
hmat=(matrix(mcol,ncol=lado, nrow=lado2))
image(hmat, col=cores, add=TRUE)
grid(nx=lado2, ny=lado)
setTkProgressBar(pb, value = i, label = paste("Simulation #", ciclo[i], sep=""))
}
close(pb)
}
###############################
### Two Species Competition ###
###############################
### Meta competition 'animaMetaComp' used in 'metaComp'
animaMetaComp=function(dados)
{
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
op=par(mar=c(1,2,2,2))
layout(matrix(c(2,1), ncol=1, nrow=2), heights=c(5,1),widths=c(1,1))
plot(1:10,1:10,xaxt="n", yaxt="n", xlab="", ylab="", cex=0.8,type="n", , bty="n")
legend(0.5,11.8,ncol=4, legend=c("not available", "empty", "sup. competitor", "inf. competitor"), pch=c(15,22,15,15), title="Patches legend", col=c("red","black", "blue", "green"),bty="n")
image(0:ln, 0:cl, dados[,,1], col=c("red", "white","blue" ,"green") , breaks=c(-0.9,-0.001,0.1,1.5,2.9),main="Metapopulations Competition", xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.5)
for(i in 2:nsim)
{
par(new=TRUE)
image(0:ln, 0:cl, dados[,,i], col=c("red", "white","blue" ,"green") , breaks=c(-0.1,-0.001,0.1,1.9,2.9), xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.1)
}
}
#######################
### Metapopulations ###
#######################
## animaMeta2 used in 'metaPop', 'metaEr', 'metaCi', 'metaCier'
animaMeta2=function(dados)
{
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
image(0:ln, 0:cl, dados[,,1], col=c("white", "green") , breaks=c(0,0.99,5),main="Metapopulation Dynamics", sub=paste("Initial configuration from", nsim," simulations", sep=""), xlab="", ylab="")
grid(ln,cl)
Sys.sleep(.5)
conta12=dados[,,1]+ (2*dados[,,2])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9),main="Metapopulation Dynamics", sub=paste("red= extinction; light green= colonization; dark green = permanence \n maximum time = ", nsim, sep=""), xlab="", ylab="")
for(i in 3:nsim)
{
conta12=dados[,,(i-1)]+ (2*dados[,,i])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9), xlab="", ylab="", add=TRUE)
Sys.sleep(.1)
}
}
## grFim used in 'metaPop', 'metaEr', 'metaCi', 'metaCier'
grFim=function(dados)
{
op=par(mfrow=c(2,2))
nsim=dim(dados)[3]
ln=dim(dados)[1]
cl=dim(dados)[2]
image(0:ln, 0:cl, dados[,,1], col=c("white", "green") , breaks=c(0,0.99,5),main="Metapopulation Dynamics", sub=paste("time = 1/", nsim, sep=""), xlab="", ylab="")
grid(ln,cl)
for(ts in c(4,2,1))
{
sim=round(nsim/ts)
conta12=dados[,,(sim-1)]+ (2*dados[,,sim])
image(0:ln, 0:cl, conta12, col=c("white","red","lightgreen", "darkgreen") , breaks=c(0,0.9,1.9,2.9,3.9),main="Metapopulation Dynamics", sub=paste("red= extinction; light green= colonization;\n dark green = permanence \t time = ", sim, "/", nsim, sep=""), xlab="", ylab="")
}
par(op)
}
##############################
### Multispecies Functions ###
##############################
###############################
#Trade-off Multispecies Graphic
###
gr.toff=function(rq, fsp1,pe,add=FALSE,...)
{
# rq <- as.numeric(tclvalue(rqVar))
# fsp1 <- as.numeric(tclvalue(fsp1Var))
# pe <- as.numeric(tclvalue(peVar))
rank=1:rq
ci= pe/(1-fsp1)^(2*rank-1)
px= fsp1*(1-fsp1)^(rank-1)
if(add==FALSE)
{
toff<-dev.new( width=5, height=5)
}
old<-par(mar=c(3,3,3,3))
plot(ci~rank,col="red",ylim=c(0,max(ci)*1.1), type="b", ann=FALSE, axes=FALSE)
axis(4, cex.axis=0.8)#, yaxp=c(0,3,3))
par(new=TRUE)
plot(px~rank, ylim=c(0,fsp1),type="b", bty="n", ann=FALSE, cex.axis=0.8)#yaxt="n", xaxp=c(0,10,5))
#axis(2, cex.axis=0.8)#, yaxp=c(0,0.2,4))
mtext("Species competitive rank", 1, 2, cex=0.9)
mtext("Abundance", 2, 2, cex=0.9)
mtext("Colonization rate", 4, 2, cex=0.9)
mtext("Trade-off Species Rank ", 3, 0, cex=1.2)
par(old)
}
###############################
## animaCena used in 'regNicho'
animaCena=function(dados)
{
nt=dim(dados)[3]
dev.new()
op=par(mfrow=c(5,5), mar=c(0.1,0.1,0.1,0.1))
for(i in 1:nt)
{
image(dados[,,i], main="", bty="n",xaxt='n',yaxt='n', col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
}
dev.new()
par(mfrow=c(2,2))
image(dados[,,1], main= paste("Patch occupancy\n \t time=", 1 ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,round(nt/3)], main= paste("Patch occupancy\n \t time=", round(nt/3) ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,round(2*nt/3)], main= paste("Patch occupancy\n \ttime=", round(2*nt/3) ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
image(dados[,,nt], main= paste("Patch occupancy\n \t time=", nt ), bty="n",xaxt='n',yaxt='n',col=c("white", "yellow", "orange", "blue", "green"))
grid(dim(dados)[2],dim(dados)[1])
par(op)
}
|
#' @title Create Rocky Mountain plots from taxa relative abundance correlations
#' @name cor_rocky_mtn
#' @description Calculate the correlation between the relative abundance of each taxa within a specified table and a continuous variable of interest. Correlation is calculated by \code{\link[stats]{cor}}. By default, Kendall's correlation is used to account for the prevalence of ties that often occur (lots of 0s)
#' @param micro_set A tidy_micro data set
#' @param table OTU table of interest
#' @param x Continuous variable of interest
#' @param y The taxa information. The centered log ratio (clr) is recommended.
#' @param method Correlation type; must be supported by \code{\link[stats]{cor}}. By default it is "spearman" to use with clr. If you'd like to use taxa ra, it is recommend you switch to Kendall's correlation to account for the large number of ties common in taxa ra (lots of 0s)
#' @param main Plot title
#' @param xlab Lable for x-axis
#' @param ylab Label for y-axis
#' @param subtitle Plot subtitle
#' @param cut_lines Add lines for p-value cutoffs
#' @param line_text Label p-value cut-offs
#' @param sig_text Label taxa with correlations greater than \code{cor_label} in magnitude
#' @param lwd line width for cut_lines
#' @param cor_label Cutoff for correlations to be labeled
#' @param breaks Where to place cut_lines along y-axis
#'
#' @author Charlie Carpenter, Dan Frank
#' @return A ggplot you can add geoms to if you'd like
#' @examples
#' data(bpd_phy); data(bpd_cla); data(bpd_ord); data(bpd_fam); data(bpd_clin)
#'
#' otu_tabs <- list(Phylum = bpd_phy, Class = bpd_cla,
#' Order = bpd_ord, Family = bpd_fam)
#' set <- tidy_micro(otu_tabs = otu_tabs, clinical = bpd_clin) %>%
#' filter(day == 7) ## Only including the first week
#'
#' set %>% cor_rocky_mtn(table = "Family", weight, cor_label = 0.3)
#' @export
cor_rocky_mtn <- function(micro_set, table, x, y = clr,
method = c("pearson", "kendall", "spearman"),
main = NULL, xlab = NULL, ylab = NULL, subtitle = NULL,
cut_lines = TRUE, line_text = TRUE,
sig_text = TRUE, lwd = 1, cor_label = 0.5,
breaks = c(-0.6, -0.5, -0.3, 0.3, 0.5, 0.6)){
if(table %nin% unique(micro_set$Table)) stop("Specified table is not in supplied micro_set")
if(missing(method)) method <- "spearman"
if(method %nin% c("pearson", "kendall", "spearman")){
stop("'method' must be one of: pearson, kendall, spearman")
}
if(is.null(ylab)) ylab = paste(method, "correlations") %>% stringr::str_to_title()
cor_set <- micro_set %>%
dplyr::filter(.data$Table == table) %>%
dplyr::group_by(.data$Taxa) %>%
dplyr::summarise(CC = stats::cor(!!rlang::enquo(x), !!rlang::enquo(y),
method = method, use = "pairwise.complete.obs"))
if(table != "Phylum"){
cor_set %<>%
dplyr::filter(!is.na(.data$CC)) %>%
dplyr::mutate(phyl = sapply(.data$Taxa, phy_fun)) %>%
phyl_ord() %>% ## Make a Phylum variable for color
dplyr::arrange(.data$phyl) %>%
dplyr::mutate(ys = 0) %>% ## start of y segment
tibble::rowid_to_column(var = "pos") ## Position along x-axis
gg <- cor_set %>%
ggplot2::ggplot(ggplot2::aes(x = .data$pos, y = .data$CC)) +
ggplot2::geom_segment(ggplot2::aes(x = .data$pos, xend =.data$pos,
y = .data$ys, yend = .data$CC,
colour = .data$phyl),
data = cor_set)
}else{
cor_set %<>%
dplyr::filter(!is.na(.data$CC)) %>%
Taxa_ord() %>%
dplyr::arrange(.data$Taxa) %>%
dplyr::mutate(ys = 0 ) %>% ## start of y segment
tibble::rowid_to_column(var = "pos") ## Position along x-axis
gg <- cor_set %>%
ggplot2::ggplot(ggplot2::aes(x = .data$pos, y = .data$CC)) +
ggplot2::geom_segment(ggplot2::aes(x = .data$pos, xend = .data$pos,
y = .data$ys, yend = .data$CC,
colour = .data$Taxa),
data = cor_set)
}
if(cut_lines){
gg <- gg + ggplot2::geom_hline(yintercept = breaks, linetype = "dotdash")
}
if(line_text){
gg <- gg +
ggplot2::scale_y_continuous(breaks = breaks,
labels = as.character(breaks))
}else{
gg <- gg +
ggplot2::theme(axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank())
}
if(sig_text){
gg <- gg + ggrepel::geom_label_repel(data = cor_set %>%
dplyr::filter(abs(.data$CC) >= cor_label),
ggplot2::aes(label = .data$Taxa),
show.legend = F)
}
gg +
ggplot2::theme_bw() +
ggplot2::labs(colour = "Phylum", y = ylab, x = xlab, title = main, subtitle = subtitle) +
ggplot2::theme(axis.title.x=ggplot2::element_blank(),
axis.text.x=ggplot2::element_blank(),
axis.ticks.x=ggplot2::element_blank()) +
ggplot2::geom_hline(yintercept = 0)
}
|
/R/cor_rocky_mtn.R
|
no_license
|
CharlieCarpenter/tidyMicro
|
R
| false
| false
| 5,224
|
r
|
#' @title Create Rocky Mountain plots from taxa relative abundance correlations
#' @name cor_rocky_mtn
#' @description Calculate the correlation between the relative abundance of each taxa within a specified table and a continuous variable of interest. Correlation is calculated by \code{\link[stats]{cor}}. By default, Kendall's correlation is used to account for the prevalence of ties that often occur (lots of 0s)
#' @param micro_set A tidy_micro data set
#' @param table OTU table of interest
#' @param x Continuous variable of interest
#' @param y The taxa information. The centered log ratio (clr) is recommended.
#' @param method Correlation type; must be supported by \code{\link[stats]{cor}}. By default it is "spearman" to use with clr. If you'd like to use taxa ra, it is recommend you switch to Kendall's correlation to account for the large number of ties common in taxa ra (lots of 0s)
#' @param main Plot title
#' @param xlab Lable for x-axis
#' @param ylab Label for y-axis
#' @param subtitle Plot subtitle
#' @param cut_lines Add lines for p-value cutoffs
#' @param line_text Label p-value cut-offs
#' @param sig_text Label taxa with correlations greater than \code{cor_label} in magnitude
#' @param lwd line width for cut_lines
#' @param cor_label Cutoff for correlations to be labeled
#' @param breaks Where to place cut_lines along y-axis
#'
#' @author Charlie Carpenter, Dan Frank
#' @return A ggplot you can add geoms to if you'd like
#' @examples
#' data(bpd_phy); data(bpd_cla); data(bpd_ord); data(bpd_fam); data(bpd_clin)
#'
#' otu_tabs <- list(Phylum = bpd_phy, Class = bpd_cla,
#' Order = bpd_ord, Family = bpd_fam)
#' set <- tidy_micro(otu_tabs = otu_tabs, clinical = bpd_clin) %>%
#' filter(day == 7) ## Only including the first week
#'
#' set %>% cor_rocky_mtn(table = "Family", weight, cor_label = 0.3)
#' @export
cor_rocky_mtn <- function(micro_set, table, x, y = clr,
method = c("pearson", "kendall", "spearman"),
main = NULL, xlab = NULL, ylab = NULL, subtitle = NULL,
cut_lines = TRUE, line_text = TRUE,
sig_text = TRUE, lwd = 1, cor_label = 0.5,
breaks = c(-0.6, -0.5, -0.3, 0.3, 0.5, 0.6)){
if(table %nin% unique(micro_set$Table)) stop("Specified table is not in supplied micro_set")
if(missing(method)) method <- "spearman"
if(method %nin% c("pearson", "kendall", "spearman")){
stop("'method' must be one of: pearson, kendall, spearman")
}
if(is.null(ylab)) ylab = paste(method, "correlations") %>% stringr::str_to_title()
cor_set <- micro_set %>%
dplyr::filter(.data$Table == table) %>%
dplyr::group_by(.data$Taxa) %>%
dplyr::summarise(CC = stats::cor(!!rlang::enquo(x), !!rlang::enquo(y),
method = method, use = "pairwise.complete.obs"))
if(table != "Phylum"){
cor_set %<>%
dplyr::filter(!is.na(.data$CC)) %>%
dplyr::mutate(phyl = sapply(.data$Taxa, phy_fun)) %>%
phyl_ord() %>% ## Make a Phylum variable for color
dplyr::arrange(.data$phyl) %>%
dplyr::mutate(ys = 0) %>% ## start of y segment
tibble::rowid_to_column(var = "pos") ## Position along x-axis
gg <- cor_set %>%
ggplot2::ggplot(ggplot2::aes(x = .data$pos, y = .data$CC)) +
ggplot2::geom_segment(ggplot2::aes(x = .data$pos, xend =.data$pos,
y = .data$ys, yend = .data$CC,
colour = .data$phyl),
data = cor_set)
}else{
cor_set %<>%
dplyr::filter(!is.na(.data$CC)) %>%
Taxa_ord() %>%
dplyr::arrange(.data$Taxa) %>%
dplyr::mutate(ys = 0 ) %>% ## start of y segment
tibble::rowid_to_column(var = "pos") ## Position along x-axis
gg <- cor_set %>%
ggplot2::ggplot(ggplot2::aes(x = .data$pos, y = .data$CC)) +
ggplot2::geom_segment(ggplot2::aes(x = .data$pos, xend = .data$pos,
y = .data$ys, yend = .data$CC,
colour = .data$Taxa),
data = cor_set)
}
if(cut_lines){
gg <- gg + ggplot2::geom_hline(yintercept = breaks, linetype = "dotdash")
}
if(line_text){
gg <- gg +
ggplot2::scale_y_continuous(breaks = breaks,
labels = as.character(breaks))
}else{
gg <- gg +
ggplot2::theme(axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank())
}
if(sig_text){
gg <- gg + ggrepel::geom_label_repel(data = cor_set %>%
dplyr::filter(abs(.data$CC) >= cor_label),
ggplot2::aes(label = .data$Taxa),
show.legend = F)
}
gg +
ggplot2::theme_bw() +
ggplot2::labs(colour = "Phylum", y = ylab, x = xlab, title = main, subtitle = subtitle) +
ggplot2::theme(axis.title.x=ggplot2::element_blank(),
axis.text.x=ggplot2::element_blank(),
axis.ticks.x=ggplot2::element_blank()) +
ggplot2::geom_hline(yintercept = 0)
}
|
defineModule(sim, list(
name = "fixPreLCC05Classes",
description = c("This module has been develop to address a concern regarding the lack of ",
"knowledge on which classes were harvested pre-2005 as the bird",
"model used for the hindcasting project takes into account the 2005 layer.",
"This means that we needed to: 1) correct the density estimates for all ",
"harvested places pre-2005, and 2) help to deal with the underestimated ",
"densities coming from Solymos et al., that we use as covariate in our ",
"model, potentially without the need for an intercept.",
"For this, we developed in the current model the necessary steps for this as:",
"1) Identify the proportion of each cover type harvested between 2006-2011 using ",
"White and Wulder 2017 to identify which pixels were harvested, and LCC05 to get ",
"the land cover types of these pixels;",
"2) As we have bird density estimates for all combinations of BCR-Province-LCC, ",
"we identify the pixels from 1984 to 2005 that were harvested from the WW layers, ",
"and replace its values in the original density layers with the (i) maximum and the ",
"(ii) minimum bird density values of the BCR-Province-LCC classes that were most ",
"harvested between 2006-2011 (which will highly likely be something like 2 or 3). ",
"This would give us 2 'expected/undisturbed density' maps for each bird: a maximum, ",
"and a minimum -- this is where the uncertainty I said comes in;",
"3) Once this is done, I can refit the models -- without the intercept, as now we ",
"have an 'unbiased expected/undisturbed density' for the places that were harvested ",
"pre-2005 (this will be the 2 layers, portraying the harvested pixels with a maximum",
" and minimum values for densities as mentioned in point 2);",
"4) We then predict from the posteriors from these models using the time-series of",
" disturbances, and we should have an interval of the possible real effect of ",
"forestry (i.e. both the ",
"(i) direct conversion of mature forest habitat to a ",
"regenerating clear cut;",
"(ii) the 'shadow' as if were these openings cast onto their unharvested surroundings)",
" on the boreal birds."),
keywords = c("helper hindcasting project", "correct LCC05 classes pre-2005"),
authors = structure(list(list(given = "Tati", family = "Micheletti", role = c("aut", "cre"),
email = "tati.micheletti@gmail.com", comment = NULL)), class = "person"),
childModules = character(0),
version = list(SpaDES.core = "1.0.1", fixPreLCC05Classes = "0.0.0.9000"),
timeframe = as.POSIXlt(c(NA, NA)),
timeunit = "year",
citation = list("citation.bib"),
documentation = deparse(list("README.txt", "fixPreLCC05Classes.Rmd")),
reqdPkgs = list(),
parameters = rbind(
defineParameter(".plotInitialTime", "numeric", NA, NA, NA,
"Describes the simulation time at which the first plot event should occur."),
defineParameter(".plotInterval", "numeric", NA, NA, NA,
"Describes the simulation time interval between plot events."),
defineParameter(".saveInitialTime", "numeric", NA, NA, NA,
"Describes the simulation time at which the first save event should occur."),
defineParameter(".saveInterval", "numeric", NA, NA, NA,
"This describes the simulation time interval between save events."),
defineParameter(".useCache", "logical", FALSE, NA, NA,
paste("Should this entire module be run with caching activated?",
"This is generally intended for data-type modules, where stochasticity",
"and time are not relevant"))
),
inputObjects = bind_rows(
#expectsInput("objectName", "objectClass", "input object description", sourceURL, ...),
expectsInput(objectName = NA, objectClass = NA, desc = NA, sourceURL = NA)
),
outputObjects = bind_rows(
#createsOutput("objectName", "objectClass", "output object description", ...),
createsOutput(objectName = NA, objectClass = NA, desc = NA)
)
))
## event types
# - type `init` is required for initialization
doEvent.fixPreLCC05Classes = function(sim, eventTime, eventType) {
switch(
eventType,
init = {
### check for more detailed object dependencies:
### (use `checkObject` or similar)
# do stuff for this event
sim <- Init(sim)
# schedule future event(s)
sim <- scheduleEvent(sim, P(sim)$.plotInitialTime, "fixPreLCC05Classes", "plot")
sim <- scheduleEvent(sim, P(sim)$.saveInitialTime, "fixPreLCC05Classes", "save")
},
plot = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
#plotFun(sim) # uncomment this, replace with object to plot
# schedule future event(s)
# e.g.,
#sim <- scheduleEvent(sim, time(sim) + P(sim)$.plotInterval, "fixPreLCC05Classes", "plot")
# ! ----- STOP EDITING ----- ! #
},
save = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + P(sim)$.saveInterval, "fixPreLCC05Classes", "save")
# ! ----- STOP EDITING ----- ! #
},
event1 = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + increment, "fixPreLCC05Classes", "templateEvent")
# ! ----- STOP EDITING ----- ! #
},
event2 = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + increment, "fixPreLCC05Classes", "templateEvent")
# ! ----- STOP EDITING ----- ! #
},
warning(paste("Undefined event type: \'", current(sim)[1, "eventType", with = FALSE],
"\' in module \'", current(sim)[1, "moduleName", with = FALSE], "\'", sep = ""))
)
return(invisible(sim))
}
## event functions
# - keep event functions short and clean, modularize by calling subroutines from section below.
### template initialization
Init <- function(sim) {
# # ! ----- EDIT BELOW ----- ! #
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for save events
Save <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
sim <- saveFiles(sim)
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for plot events
plotFun <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
#Plot(sim$object)
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for your event1
Event1 <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# THE NEXT TWO LINES ARE FOR DUMMY UNIT TESTS; CHANGE OR DELETE THEM.
# sim$event1Test1 <- " this is test for event 1. " # for dummy unit test
# sim$event1Test2 <- 999 # for dummy unit test
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for your event2
Event2 <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# THE NEXT TWO LINES ARE FOR DUMMY UNIT TESTS; CHANGE OR DELETE THEM.
# sim$event2Test1 <- " this is test for event 2. " # for dummy unit test
# sim$event2Test2 <- 777 # for dummy unit test
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
.inputObjects <- function(sim) {
# Any code written here will be run during the simInit for the purpose of creating
# any objects required by this module and identified in the inputObjects element of defineModule.
# This is useful if there is something required before simulation to produce the module
# object dependencies, including such things as downloading default datasets, e.g.,
# downloadData("LCC2005", modulePath(sim)).
# Nothing should be created here that does not create a named object in inputObjects.
# Any other initiation procedures should be put in "init" eventType of the doEvent function.
# Note: the module developer can check if an object is 'suppliedElsewhere' to
# selectively skip unnecessary steps because the user has provided those inputObjects in the
# simInit call, or another module will supply or has supplied it. e.g.,
# if (!suppliedElsewhere('defaultColor', sim)) {
# sim$map <- Cache(prepInputs, extractURL('map')) # download, extract, load file from url in sourceURL
# }
#cacheTags <- c(currentModule(sim), "function:.inputObjects") ## uncomment this if Cache is being used
dPath <- asPath(getOption("reproducible.destinationPath", dataPath(sim)), 1)
message(currentModule(sim), ": using dataPath '", dPath, "'.")
# ! ----- EDIT BELOW ----- ! #
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### add additional events as needed by copy/pasting from above
|
/modules/fixPreLCC05Classes/fixPreLCC05Classes.R
|
no_license
|
tati-micheletti/borealBirdsAndForestry
|
R
| false
| false
| 9,687
|
r
|
defineModule(sim, list(
name = "fixPreLCC05Classes",
description = c("This module has been develop to address a concern regarding the lack of ",
"knowledge on which classes were harvested pre-2005 as the bird",
"model used for the hindcasting project takes into account the 2005 layer.",
"This means that we needed to: 1) correct the density estimates for all ",
"harvested places pre-2005, and 2) help to deal with the underestimated ",
"densities coming from Solymos et al., that we use as covariate in our ",
"model, potentially without the need for an intercept.",
"For this, we developed in the current model the necessary steps for this as:",
"1) Identify the proportion of each cover type harvested between 2006-2011 using ",
"White and Wulder 2017 to identify which pixels were harvested, and LCC05 to get ",
"the land cover types of these pixels;",
"2) As we have bird density estimates for all combinations of BCR-Province-LCC, ",
"we identify the pixels from 1984 to 2005 that were harvested from the WW layers, ",
"and replace its values in the original density layers with the (i) maximum and the ",
"(ii) minimum bird density values of the BCR-Province-LCC classes that were most ",
"harvested between 2006-2011 (which will highly likely be something like 2 or 3). ",
"This would give us 2 'expected/undisturbed density' maps for each bird: a maximum, ",
"and a minimum -- this is where the uncertainty I said comes in;",
"3) Once this is done, I can refit the models -- without the intercept, as now we ",
"have an 'unbiased expected/undisturbed density' for the places that were harvested ",
"pre-2005 (this will be the 2 layers, portraying the harvested pixels with a maximum",
" and minimum values for densities as mentioned in point 2);",
"4) We then predict from the posteriors from these models using the time-series of",
" disturbances, and we should have an interval of the possible real effect of ",
"forestry (i.e. both the ",
"(i) direct conversion of mature forest habitat to a ",
"regenerating clear cut;",
"(ii) the 'shadow' as if were these openings cast onto their unharvested surroundings)",
" on the boreal birds."),
keywords = c("helper hindcasting project", "correct LCC05 classes pre-2005"),
authors = structure(list(list(given = "Tati", family = "Micheletti", role = c("aut", "cre"),
email = "tati.micheletti@gmail.com", comment = NULL)), class = "person"),
childModules = character(0),
version = list(SpaDES.core = "1.0.1", fixPreLCC05Classes = "0.0.0.9000"),
timeframe = as.POSIXlt(c(NA, NA)),
timeunit = "year",
citation = list("citation.bib"),
documentation = deparse(list("README.txt", "fixPreLCC05Classes.Rmd")),
reqdPkgs = list(),
parameters = rbind(
defineParameter(".plotInitialTime", "numeric", NA, NA, NA,
"Describes the simulation time at which the first plot event should occur."),
defineParameter(".plotInterval", "numeric", NA, NA, NA,
"Describes the simulation time interval between plot events."),
defineParameter(".saveInitialTime", "numeric", NA, NA, NA,
"Describes the simulation time at which the first save event should occur."),
defineParameter(".saveInterval", "numeric", NA, NA, NA,
"This describes the simulation time interval between save events."),
defineParameter(".useCache", "logical", FALSE, NA, NA,
paste("Should this entire module be run with caching activated?",
"This is generally intended for data-type modules, where stochasticity",
"and time are not relevant"))
),
inputObjects = bind_rows(
#expectsInput("objectName", "objectClass", "input object description", sourceURL, ...),
expectsInput(objectName = NA, objectClass = NA, desc = NA, sourceURL = NA)
),
outputObjects = bind_rows(
#createsOutput("objectName", "objectClass", "output object description", ...),
createsOutput(objectName = NA, objectClass = NA, desc = NA)
)
))
## event types
# - type `init` is required for initialization
doEvent.fixPreLCC05Classes = function(sim, eventTime, eventType) {
switch(
eventType,
init = {
### check for more detailed object dependencies:
### (use `checkObject` or similar)
# do stuff for this event
sim <- Init(sim)
# schedule future event(s)
sim <- scheduleEvent(sim, P(sim)$.plotInitialTime, "fixPreLCC05Classes", "plot")
sim <- scheduleEvent(sim, P(sim)$.saveInitialTime, "fixPreLCC05Classes", "save")
},
plot = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
#plotFun(sim) # uncomment this, replace with object to plot
# schedule future event(s)
# e.g.,
#sim <- scheduleEvent(sim, time(sim) + P(sim)$.plotInterval, "fixPreLCC05Classes", "plot")
# ! ----- STOP EDITING ----- ! #
},
save = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + P(sim)$.saveInterval, "fixPreLCC05Classes", "save")
# ! ----- STOP EDITING ----- ! #
},
event1 = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + increment, "fixPreLCC05Classes", "templateEvent")
# ! ----- STOP EDITING ----- ! #
},
event2 = {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
# e.g., call your custom functions/methods here
# you can define your own methods below this `doEvent` function
# schedule future event(s)
# e.g.,
# sim <- scheduleEvent(sim, time(sim) + increment, "fixPreLCC05Classes", "templateEvent")
# ! ----- STOP EDITING ----- ! #
},
warning(paste("Undefined event type: \'", current(sim)[1, "eventType", with = FALSE],
"\' in module \'", current(sim)[1, "moduleName", with = FALSE], "\'", sep = ""))
)
return(invisible(sim))
}
## event functions
# - keep event functions short and clean, modularize by calling subroutines from section below.
### template initialization
Init <- function(sim) {
# # ! ----- EDIT BELOW ----- ! #
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for save events
Save <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
sim <- saveFiles(sim)
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for plot events
plotFun <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# do stuff for this event
#Plot(sim$object)
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for your event1
Event1 <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# THE NEXT TWO LINES ARE FOR DUMMY UNIT TESTS; CHANGE OR DELETE THEM.
# sim$event1Test1 <- " this is test for event 1. " # for dummy unit test
# sim$event1Test2 <- 999 # for dummy unit test
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### template for your event2
Event2 <- function(sim) {
# ! ----- EDIT BELOW ----- ! #
# THE NEXT TWO LINES ARE FOR DUMMY UNIT TESTS; CHANGE OR DELETE THEM.
# sim$event2Test1 <- " this is test for event 2. " # for dummy unit test
# sim$event2Test2 <- 777 # for dummy unit test
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
.inputObjects <- function(sim) {
# Any code written here will be run during the simInit for the purpose of creating
# any objects required by this module and identified in the inputObjects element of defineModule.
# This is useful if there is something required before simulation to produce the module
# object dependencies, including such things as downloading default datasets, e.g.,
# downloadData("LCC2005", modulePath(sim)).
# Nothing should be created here that does not create a named object in inputObjects.
# Any other initiation procedures should be put in "init" eventType of the doEvent function.
# Note: the module developer can check if an object is 'suppliedElsewhere' to
# selectively skip unnecessary steps because the user has provided those inputObjects in the
# simInit call, or another module will supply or has supplied it. e.g.,
# if (!suppliedElsewhere('defaultColor', sim)) {
# sim$map <- Cache(prepInputs, extractURL('map')) # download, extract, load file from url in sourceURL
# }
#cacheTags <- c(currentModule(sim), "function:.inputObjects") ## uncomment this if Cache is being used
dPath <- asPath(getOption("reproducible.destinationPath", dataPath(sim)), 1)
message(currentModule(sim), ": using dataPath '", dPath, "'.")
# ! ----- EDIT BELOW ----- ! #
# ! ----- STOP EDITING ----- ! #
return(invisible(sim))
}
### add additional events as needed by copy/pasting from above
|
check_pkg <- function(name, reason) {
if (requireNamespace(name, quietly = TRUE))
return(invisible(TRUE))
glubort('The {name} package is required to {reason}.
Please install it with `install.packages("{name}")`'
)
}
# ngettext() does extra work, this function is a simpler version
ntext <- function(n, msg1, msg2) {
if (n == 1) msg1 else msg2
}
bad <- function(..., .envir = parent.frame()) {
glubort(NULL, ..., .envir = parent.frame())
}
bad_args <- function(args, ..., .envir = parent.frame()) {
glubort(fmt_args(args), ..., .envir = .envir)
}
bad_pos_args <- function(pos_args, ..., .envir = parent.frame()) {
glubort(fmt_pos_args(pos_args), ..., .envir = .envir)
}
bad_calls <- function(calls, ..., .envir = parent.frame()) {
glubort(fmt_calls(calls), ..., .envir = .envir)
}
bad_named_calls <- function(named_calls, ..., .envir = parent.frame()) {
glubort(fmt_named_calls(named_calls), ..., .envir = .envir)
}
bad_cols <- function(cols, ..., .envir = parent.frame()) {
glubort(fmt_cols(cols), ..., .envir = .envir)
}
bad_measures <- function(measures, ..., .envir = parent.frame()) {
glubort(fmt_measures(measures), ..., .envir = .envir)
}
glubort <- function(header, ..., .envir = parent.frame()) {
text <- glue(..., .envir = .envir)
if (!is_null(header)) text <- paste0(header, ": ", text)
abort(text)
}
fmt_args <- function(x) {
x <- parse_args(x)
fmt_obj(x)
}
fmt_pos_args <- function(x) {
args <- ntext(length(x), "Argument", "Arguments")
glue("{args} {fmt_comma(x)}")
}
fmt_calls <- function(...) {
x <- parse_named_call(...)
fmt_comma(x)
}
fmt_named_calls <- function(...) {
x <- parse_named_call(...)
fmt_named(x)
}
fmt_cols <- function(x) {
cols <- ntext(length(x), "Column", "Columns")
glue("{cols} {fmt_obj(x)}")
}
fmt_measures <- function(x) {
measures <- ntext(length(x), "Measure", "Measures")
glue("{measures} {fmt_obj(x)}")
}
fmt_named <- function(x) {
fmt_comma(paste0(fmt_obj1(names2(x)), " = ", x))
}
fmt_obj <- function(x) {
fmt_comma(fmt_obj1(x))
}
fmt_obj1 <- function(x) {
paste0("`", x, "`")
}
fmt_classes <- function(x) {
paste(class(x), collapse = "/")
}
fmt_dims <- function(x) {
paste0("[", paste0(x, collapse = " x "), "]")
}
fmt_comma <- function(...) {
MAX_ITEMS <- 6L
x <- paste0(...)
if (length(x) > MAX_ITEMS) {
length(x) <- MAX_ITEMS
x[[MAX_ITEMS]] <- "..."
}
commas(x)
}
parse_args <- function(x) {
# convert single formula to list of length 1
x <- unlist(list(x), recursive = FALSE)
is_fml <- map_lgl(x, is_formula)
x[is_fml] <- map_chr(map(x[is_fml], "[[", 2), as_string)
unlist(x)
}
parse_named_call <- function(x) {
# convert single formula to list of length 1
x <- unlist(list(x), recursive = FALSE)
x <- map_chr(map(x, f_rhs), deparse_trunc)
x
}
|
/R/error.R
|
permissive
|
skribled/dplyr
|
R
| false
| false
| 2,825
|
r
|
check_pkg <- function(name, reason) {
if (requireNamespace(name, quietly = TRUE))
return(invisible(TRUE))
glubort('The {name} package is required to {reason}.
Please install it with `install.packages("{name}")`'
)
}
# ngettext() does extra work, this function is a simpler version
ntext <- function(n, msg1, msg2) {
if (n == 1) msg1 else msg2
}
bad <- function(..., .envir = parent.frame()) {
glubort(NULL, ..., .envir = parent.frame())
}
bad_args <- function(args, ..., .envir = parent.frame()) {
glubort(fmt_args(args), ..., .envir = .envir)
}
bad_pos_args <- function(pos_args, ..., .envir = parent.frame()) {
glubort(fmt_pos_args(pos_args), ..., .envir = .envir)
}
bad_calls <- function(calls, ..., .envir = parent.frame()) {
glubort(fmt_calls(calls), ..., .envir = .envir)
}
bad_named_calls <- function(named_calls, ..., .envir = parent.frame()) {
glubort(fmt_named_calls(named_calls), ..., .envir = .envir)
}
bad_cols <- function(cols, ..., .envir = parent.frame()) {
glubort(fmt_cols(cols), ..., .envir = .envir)
}
bad_measures <- function(measures, ..., .envir = parent.frame()) {
glubort(fmt_measures(measures), ..., .envir = .envir)
}
glubort <- function(header, ..., .envir = parent.frame()) {
text <- glue(..., .envir = .envir)
if (!is_null(header)) text <- paste0(header, ": ", text)
abort(text)
}
fmt_args <- function(x) {
x <- parse_args(x)
fmt_obj(x)
}
fmt_pos_args <- function(x) {
args <- ntext(length(x), "Argument", "Arguments")
glue("{args} {fmt_comma(x)}")
}
fmt_calls <- function(...) {
x <- parse_named_call(...)
fmt_comma(x)
}
fmt_named_calls <- function(...) {
x <- parse_named_call(...)
fmt_named(x)
}
fmt_cols <- function(x) {
cols <- ntext(length(x), "Column", "Columns")
glue("{cols} {fmt_obj(x)}")
}
fmt_measures <- function(x) {
measures <- ntext(length(x), "Measure", "Measures")
glue("{measures} {fmt_obj(x)}")
}
fmt_named <- function(x) {
fmt_comma(paste0(fmt_obj1(names2(x)), " = ", x))
}
fmt_obj <- function(x) {
fmt_comma(fmt_obj1(x))
}
fmt_obj1 <- function(x) {
paste0("`", x, "`")
}
fmt_classes <- function(x) {
paste(class(x), collapse = "/")
}
fmt_dims <- function(x) {
paste0("[", paste0(x, collapse = " x "), "]")
}
fmt_comma <- function(...) {
MAX_ITEMS <- 6L
x <- paste0(...)
if (length(x) > MAX_ITEMS) {
length(x) <- MAX_ITEMS
x[[MAX_ITEMS]] <- "..."
}
commas(x)
}
parse_args <- function(x) {
# convert single formula to list of length 1
x <- unlist(list(x), recursive = FALSE)
is_fml <- map_lgl(x, is_formula)
x[is_fml] <- map_chr(map(x[is_fml], "[[", 2), as_string)
unlist(x)
}
parse_named_call <- function(x) {
# convert single formula to list of length 1
x <- unlist(list(x), recursive = FALSE)
x <- map_chr(map(x, f_rhs), deparse_trunc)
x
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NorNorCVTest.R
\name{NorNorCVTest}
\alias{NorNorCVTest}
\title{Test normality and homogeneity of variance of Input data}
\usage{
NorNorCVTest(data = data_wt, i = 4, method_cv = "leveneTest", ...)
}
\arguments{
\item{data}{a data.frame contain the input data}
\item{i}{col index wtich need to test}
\item{method_cv}{which method to test variance, included "leveneTest","bartlett.test".}
}
\value{
data frame
}
\description{
Test normality and homogeneity of variance of Input data
}
\examples{
# data(data_wt)
NorNorCVTest(data = data_wt, i= 4)
}
\references{
Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q
Root exudates drive the soil-borne legacy of aboveground pathogen infection
Microbiome 2018,DOI: \url{doi: 10.1186/s40168-018-0537-x}
}
\author{
Contact: Tao Wen \email{2018203048@njau.edu.cn} Jun Yuan \email{junyuan@njau.edu.cn}
}
|
/man/NorNorCVTest.Rd
|
no_license
|
JohnMeatbun/EasyStat
|
R
| false
| true
| 984
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NorNorCVTest.R
\name{NorNorCVTest}
\alias{NorNorCVTest}
\title{Test normality and homogeneity of variance of Input data}
\usage{
NorNorCVTest(data = data_wt, i = 4, method_cv = "leveneTest", ...)
}
\arguments{
\item{data}{a data.frame contain the input data}
\item{i}{col index wtich need to test}
\item{method_cv}{which method to test variance, included "leveneTest","bartlett.test".}
}
\value{
data frame
}
\description{
Test normality and homogeneity of variance of Input data
}
\examples{
# data(data_wt)
NorNorCVTest(data = data_wt, i= 4)
}
\references{
Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q
Root exudates drive the soil-borne legacy of aboveground pathogen infection
Microbiome 2018,DOI: \url{doi: 10.1186/s40168-018-0537-x}
}
\author{
Contact: Tao Wen \email{2018203048@njau.edu.cn} Jun Yuan \email{junyuan@njau.edu.cn}
}
|
###########################################################
# Title: Simulation 1 - Bayesian Profile Regression of NATA 2014 for NC
# Authors: Kara McCormack (kara.mccormack@duke.edu)
# Date created: 12/10/2019
# Input files:
# /Users/karamccormack/Box/SES-environment/Spatial LCM Paper/Data/NC_NATA_wide_z_transform.csv
#
# Output file:
# /Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/simulation-1/sweepsbyclusters.png
###########################################################
# Goals:
# 1. Run a simulation of bayesian profile regression using the
# PReMiuM package on the NATA data. Examine plot of number
# of clusters versus number of sweeps by different number of
# initial clusters to get an idea if the model converged.
# load libraries
library(PReMiuM)
library(stringr)
library(ggplot2)
# set up paths
data.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/Data/"
# output.dir is where temporary output (.txt) files will go from 'profRegr' command
output.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/scratch/scratch1/"
# outputPlots.dir is where final plot will go
outputPlots.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/simulation-1/"
# setwd to where the output (.txt) files should go
setwd(output.dir)
# load log-transformed NC NATA data
NC_df_log = read.csv(file.path(data.dir, "NC_NATA_wide_log_transform.csv"))[,-1]
# define covariate names
# do not include the first column since this is 'Tract'
covariate_names <- colnames(NC_df_log)[-1]
# define inputs for function
seed = 898 # set random seed
nBurn = 10000 # number of burn iterations
nSweeps = 20000 # number of sweeps of MCMC algorithm
data = NC_df_log # assign log transformed dataset
output = "output_scratch" # assign prefix for temporary output .txt files
covNames = covariate_names # assign covariate names from data frame
nClusInit = 35 # number of initial clusters
plotBySweep = 20 # in final plots, how often (in terms of sweeps) to plot a point
# note: a ratio of about 1/10 seems to get good resolution if plotting
# a single plot.
# create function that inputs a number of initial clusters
# and outputs a data frame with two columns:
# 1. Sweep number
# 2. Number of clusters during that sweep
myFunction = function(nClusInit){
runInfoObj = profRegr(yModel = "Normal",
xModel = "Normal",
nSweeps = nSweeps,
nBurn = nBurn,
data = data,
output = output,
covNames = covNames,
nClusInit = nClusInit,
whichLabelSwitch="123",
run = TRUE,
excludeY = TRUE,
seed = seed)
# plot the number of clusters by number of sweeps
nClustersSweep<-read.table(paste0(output, "_nClusters.txt"))
nClusters_dat = as.data.frame(cbind(sweeps = seq(1:nSweeps),
nClusters = nClustersSweep),
nClusInit = rep(nClusInit, nSweeps))
# subset every 20th sweep
nClusters_dat2 = nClusters_dat[seq(1,nSweeps, by = plotBySweep),]
return(nClusters_dat2)
}
# get results for different number of initial clusters
nClusInitVec = c(10, 35, 50, 70)
b = rbind(lapply(nClusInitVec, myFunction)) # note: this returns a list
# bring together each dataframe of results
c = list() # initialize empty list, need to add on nclusinit column for plotting
for(i in 1:length(nClusInitVec)){
c[[i]] <- cbind(b[[i]], nclusinit = rep(nClusInitVec[i], dim(b[[i]])[1]))
}
b_full = rbind(c[[1]],c[[2]], c[[3]], c[[4]])
# create overall plot of results
p2 <- ggplot(data = b_full,
aes(x = sweeps, y = V1)) +
facet_wrap(~nclusinit) +
geom_line(color = "#00AFBB", size = .5) +
stat_smooth(color = "#FC4E07",
fill = "#FC4E07",
method = "loess") +
labs(y="Number of Clusters", x = "Number of Sweeps (After Burn-In)") +
ggtitle("Number of Clusters by Number of Sweeps for \n Different Number of Initial Clusters")
# output plot
ggsave("sweepsbyclusters.png",
plot = last_plot(),
path = outputPlots.dir)
# End of Simulation 1
########################################
|
/code/simulations/simulation_nata_1.R
|
no_license
|
kara-mccormack/spatial-mpe-ses
|
R
| false
| false
| 4,440
|
r
|
###########################################################
# Title: Simulation 1 - Bayesian Profile Regression of NATA 2014 for NC
# Authors: Kara McCormack (kara.mccormack@duke.edu)
# Date created: 12/10/2019
# Input files:
# /Users/karamccormack/Box/SES-environment/Spatial LCM Paper/Data/NC_NATA_wide_z_transform.csv
#
# Output file:
# /Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/simulation-1/sweepsbyclusters.png
###########################################################
# Goals:
# 1. Run a simulation of bayesian profile regression using the
# PReMiuM package on the NATA data. Examine plot of number
# of clusters versus number of sweeps by different number of
# initial clusters to get an idea if the model converged.
# load libraries
library(PReMiuM)
library(stringr)
library(ggplot2)
# set up paths
data.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/Data/"
# output.dir is where temporary output (.txt) files will go from 'profRegr' command
output.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/scratch/scratch1/"
# outputPlots.dir is where final plot will go
outputPlots.dir = "/Users/karamccormack/Box/SES-environment/Spatial LCM Paper/v2/spatial-mpe-ses/output/simulation-1/"
# setwd to where the output (.txt) files should go
setwd(output.dir)
# load log-transformed NC NATA data
NC_df_log = read.csv(file.path(data.dir, "NC_NATA_wide_log_transform.csv"))[,-1]
# define covariate names
# do not include the first column since this is 'Tract'
covariate_names <- colnames(NC_df_log)[-1]
# define inputs for function
seed = 898 # set random seed
nBurn = 10000 # number of burn iterations
nSweeps = 20000 # number of sweeps of MCMC algorithm
data = NC_df_log # assign log transformed dataset
output = "output_scratch" # assign prefix for temporary output .txt files
covNames = covariate_names # assign covariate names from data frame
nClusInit = 35 # number of initial clusters
plotBySweep = 20 # in final plots, how often (in terms of sweeps) to plot a point
# note: a ratio of about 1/10 seems to get good resolution if plotting
# a single plot.
# create function that inputs a number of initial clusters
# and outputs a data frame with two columns:
# 1. Sweep number
# 2. Number of clusters during that sweep
myFunction = function(nClusInit){
runInfoObj = profRegr(yModel = "Normal",
xModel = "Normal",
nSweeps = nSweeps,
nBurn = nBurn,
data = data,
output = output,
covNames = covNames,
nClusInit = nClusInit,
whichLabelSwitch="123",
run = TRUE,
excludeY = TRUE,
seed = seed)
# plot the number of clusters by number of sweeps
nClustersSweep<-read.table(paste0(output, "_nClusters.txt"))
nClusters_dat = as.data.frame(cbind(sweeps = seq(1:nSweeps),
nClusters = nClustersSweep),
nClusInit = rep(nClusInit, nSweeps))
# subset every 20th sweep
nClusters_dat2 = nClusters_dat[seq(1,nSweeps, by = plotBySweep),]
return(nClusters_dat2)
}
# get results for different number of initial clusters
nClusInitVec = c(10, 35, 50, 70)
b = rbind(lapply(nClusInitVec, myFunction)) # note: this returns a list
# bring together each dataframe of results
c = list() # initialize empty list, need to add on nclusinit column for plotting
for(i in 1:length(nClusInitVec)){
c[[i]] <- cbind(b[[i]], nclusinit = rep(nClusInitVec[i], dim(b[[i]])[1]))
}
b_full = rbind(c[[1]],c[[2]], c[[3]], c[[4]])
# create overall plot of results
p2 <- ggplot(data = b_full,
aes(x = sweeps, y = V1)) +
facet_wrap(~nclusinit) +
geom_line(color = "#00AFBB", size = .5) +
stat_smooth(color = "#FC4E07",
fill = "#FC4E07",
method = "loess") +
labs(y="Number of Clusters", x = "Number of Sweeps (After Burn-In)") +
ggtitle("Number of Clusters by Number of Sweeps for \n Different Number of Initial Clusters")
# output plot
ggsave("sweepsbyclusters.png",
plot = last_plot(),
path = outputPlots.dir)
# End of Simulation 1
########################################
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linear-optimization-model-impl.R
\name{sum_over}
\alias{sum_over}
\alias{sum_expr}
\title{Sum over indexes}
\usage{
sum_over(.expr, ...)
sum_expr(.expr, ...)
}
\arguments{
\item{.expr}{an expression that can be expanded to a sum}
\item{...}{bind variables in expr using dots. See examples.}
}
\value{
the sum over all the indexes
}
\description{
This functions helps to create summations over indexes.
}
\examples{
if (FALSE) {
# create a sum from x_1 to x_10
sum_over(x[i], i = 1:10)
# create a sum from x_2 to x_10 with even indexes
sum_over(x[i], i = 1:10, i \%\% 2 == 0)
sum_over(x[i, j], i = 1:10, j = 1:i)
}
}
\seealso{
\code{\link{add_constraint}}
\code{\link{set_objective}}
Please note that \code{sum_expr} is deprecated when used together with
\code{MIPModel}.
}
|
/man/sum_over.Rd
|
permissive
|
dirkschumacher/ompr
|
R
| false
| true
| 865
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linear-optimization-model-impl.R
\name{sum_over}
\alias{sum_over}
\alias{sum_expr}
\title{Sum over indexes}
\usage{
sum_over(.expr, ...)
sum_expr(.expr, ...)
}
\arguments{
\item{.expr}{an expression that can be expanded to a sum}
\item{...}{bind variables in expr using dots. See examples.}
}
\value{
the sum over all the indexes
}
\description{
This functions helps to create summations over indexes.
}
\examples{
if (FALSE) {
# create a sum from x_1 to x_10
sum_over(x[i], i = 1:10)
# create a sum from x_2 to x_10 with even indexes
sum_over(x[i], i = 1:10, i \%\% 2 == 0)
sum_over(x[i, j], i = 1:10, j = 1:i)
}
}
\seealso{
\code{\link{add_constraint}}
\code{\link{set_objective}}
Please note that \code{sum_expr} is deprecated when used together with
\code{MIPModel}.
}
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/dataset.R
\name{Dataset.retrieve}
\alias{Dataset.retrieve}
\title{Dataset.retrieve}
\usage{
Dataset.retrieve(id)
}
\arguments{
\item{id}{String The ID or full name of a SolveBio dataset}
}
\description{
Retrieves the metadata about a specific dataset from SolveBio.
}
\examples{
\dontrun{
Dataset.retrieve("ClinVar/ClinVar")
}
}
\references{
\url{https://docs.solvebio.com/}
}
|
/man/Dataset.retrieve.Rd
|
permissive
|
h3biomed/solvebio-r
|
R
| false
| false
| 464
|
rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/dataset.R
\name{Dataset.retrieve}
\alias{Dataset.retrieve}
\title{Dataset.retrieve}
\usage{
Dataset.retrieve(id)
}
\arguments{
\item{id}{String The ID or full name of a SolveBio dataset}
}
\description{
Retrieves the metadata about a specific dataset from SolveBio.
}
\examples{
\dontrun{
Dataset.retrieve("ClinVar/ClinVar")
}
}
\references{
\url{https://docs.solvebio.com/}
}
|
##----------------------------------------------------------------
## Init by deleting all variables and functions
rm(list=ls())
## Set the working directory
setwd(".")
## Source scripts in the functions folder
sapply(dir("functions",full.names=TRUE), source)
## Load the data
Data <- readRDS("data_soenderborg.RDS")
## Make the k = 1 "design matrix"
X <- data.frame(t = Data$t, Ph = Data$Ph4, Ta = lag_vector(Data$Tanwp$k1,1), G = lag_vector(Data$Gnwp$k1,1), tday = Data$tday)
## The index of training and test set
itrain <- which(per("2010-09-01",X$t,"2010-12-01"))
itest <- which(per("2010-12-01",X$t,"2011-01-01"))
##----------------------------------------------------------------
##----------------------------------------------------------------
## We know that there are dynamics from Ta to Ph
## The low-pass filter function is defined in "functions/lp_vector.R"
## Apply a low-pass filter on the input
X$Ta_lp <- lp_vector(X$Ta, a1 = 0.99)
## and plot the test set
plot(X$t[itest], X$Ta[itest], type = "l")
lines(X$t[itest], X$Ta_lp[itest], col = 2)
## Use it to make a model
## Fit a linear model on the training set
fit <- lm(Ph ~ Ta_lp + G, X[itrain, ])
## Are the coefficients significant?
summary(fit)
## Predict and plot
X$Ph_hat_lp1 <- predict(fit, X)
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_lp1[itest], col = 2)
## The score
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
## Are we happy now?
##----------------------------------------------------------------
##----------------------------------------------------------------
## We have to tune the low-pass coefficient, do it
obj <- function(prm, frml, data) {
## Find the inputs to lowpass filter
## Just overwrite the column in data
for(nm in names(prm)){
data[ ,nm] <- lp_vector(data[ ,nm], a1 = prm[nm])
}
## Fit the model
fit <- lm(frml, data)
## Calculate the objective function
print(val <- rmse(fit$residuals))
return(val)
}
frml <- as.formula(Ph ~ Ta + G)
obj(c(Ta=0.98, G=0.98), frml, X[itrain, ])
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=X[itrain, ], method="L-BFGS-B")
result
##----------------------------------------------------------------
##----------------------------------------------------------------
## Now lets analyse the predictions
## Hmm, lets extend our objective function, such that it also can return the predictions
obj <- function(prm, frml, data, itrain, return_fit = FALSE) {
## Find the inputs to lowpass filter
## Just overwrite the column in data
for(nm in names(prm)){
data[ ,nm] <- lp_vector(data[ ,nm], a1 = prm[nm])
}
## Fit the model, ONLY on the training set
fit <- lm(frml, data[itrain, ])
## Calculate the objective function
print(val <- rmse(fit$residuals))
## Either return the fit and more
if(return_fit){
return(list(val = val,
fit = fit,
yhat = predict(fit, data)))
}else{
## Or just return the score
return(val)
}
}
## It can be used in optim
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml = frml, data = X[itrain, ], method = "L-BFGS-B", itrain = itrain)
## But now we can also get the fit and the predictions
L <- obj(result$par, frml, X, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_lpopt <- L$yhat
## Plot the predictions
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_lpopt[itest], col = 2)
## The score
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
## Did we improve?
##----------------------------------------------------------------
##----------------------------------------------------------------
## We miss something: a diurnal curve
## Use base splines
library(splines)
## We have the hour of the day
X$tday
## Hey thats simple now! Add base splines to the formula
frml <- as.formula(Ph ~ bs(tday,df=4) + Ta + G)
## Tune the low-pass coefficients
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=X[itrain, ], method="L-BFGS-B")
result
## Get the fit and the predictions
L <- obj(result$par, frml, X, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_diur <- L$yhat
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_diur[itest], col = 2)
## The score, compare it to the "simpler" models
rmse(X$Ph[itest] - X$Ph_hat_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
##----------------------------------------------------------------
##----------------------------------------------------------------
## (Out of scope) We could use Fourier series as base functions
## Fourier series for one day of hourly values
x <- (0:23 + 0.5) / 24
n_harmonics <- 4
L <- lapply(1:n_harmonics, function(i) {
val <- data.frame(sin(i * x * 2 * pi), cos(i * x * 2 * pi))
names(val) <- paste0(c("sin_", "cos_"), i)
return(val)
})
Xtmp <- do.call("cbind", L)
##
par(mfrow = c(2,1))
plot(Xtmp$sin_1, type = "b")
for(i in 2:ncol(Xtmp)) {
lines(Xtmp[ ,i], col = i, type = "b")
}
##
plot(Xtmp[ ,ncol(Xtmp)], type = "b")
## A linear combination can form any harmonic function
plot(apply(runif(n_harmonics * 2) * t(Xtmp), 2, sum), type = "b")
## How many harmonics make sense to maximum include when the period is in 24 steps?
##----------------------------------------------------------------
##----------------------------------------------------------------
## Fit a diurnal curve with Fourier series as base functions
tmp <- fs(X$tday/24, n_harmonics=3)
tmp <- do.call("cbind", tmp)
Xfit <- cbind(X,tmp)
frml <- as.formula(Ph ~ sin_1 + cos_1 + sin_2 + cos_2 + sin_3 + cos_3 + Ta + G)
## Tune the low-pass coefficients
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=Xfit[itrain, ], method="L-BFGS-B")
result
## But now we can also get the fit and the predictions
L <- obj(result$par, frml, Xfit, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_fs_diur<- L$yhat
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_fs_diur[itest], col = 2)
## The score, compare it to the "simpler" models
rmse(X$Ph[itest] - X$Ph_hat_fs_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
##----------------------------------------------------------------
|
/Ex_4/q3_load_forecast.R
|
no_license
|
marcohv/exercises_ts_dtu
|
R
| false
| false
| 6,516
|
r
|
##----------------------------------------------------------------
## Init by deleting all variables and functions
rm(list=ls())
## Set the working directory
setwd(".")
## Source scripts in the functions folder
sapply(dir("functions",full.names=TRUE), source)
## Load the data
Data <- readRDS("data_soenderborg.RDS")
## Make the k = 1 "design matrix"
X <- data.frame(t = Data$t, Ph = Data$Ph4, Ta = lag_vector(Data$Tanwp$k1,1), G = lag_vector(Data$Gnwp$k1,1), tday = Data$tday)
## The index of training and test set
itrain <- which(per("2010-09-01",X$t,"2010-12-01"))
itest <- which(per("2010-12-01",X$t,"2011-01-01"))
##----------------------------------------------------------------
##----------------------------------------------------------------
## We know that there are dynamics from Ta to Ph
## The low-pass filter function is defined in "functions/lp_vector.R"
## Apply a low-pass filter on the input
X$Ta_lp <- lp_vector(X$Ta, a1 = 0.99)
## and plot the test set
plot(X$t[itest], X$Ta[itest], type = "l")
lines(X$t[itest], X$Ta_lp[itest], col = 2)
## Use it to make a model
## Fit a linear model on the training set
fit <- lm(Ph ~ Ta_lp + G, X[itrain, ])
## Are the coefficients significant?
summary(fit)
## Predict and plot
X$Ph_hat_lp1 <- predict(fit, X)
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_lp1[itest], col = 2)
## The score
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
## Are we happy now?
##----------------------------------------------------------------
##----------------------------------------------------------------
## We have to tune the low-pass coefficient, do it
obj <- function(prm, frml, data) {
## Find the inputs to lowpass filter
## Just overwrite the column in data
for(nm in names(prm)){
data[ ,nm] <- lp_vector(data[ ,nm], a1 = prm[nm])
}
## Fit the model
fit <- lm(frml, data)
## Calculate the objective function
print(val <- rmse(fit$residuals))
return(val)
}
frml <- as.formula(Ph ~ Ta + G)
obj(c(Ta=0.98, G=0.98), frml, X[itrain, ])
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=X[itrain, ], method="L-BFGS-B")
result
##----------------------------------------------------------------
##----------------------------------------------------------------
## Now lets analyse the predictions
## Hmm, lets extend our objective function, such that it also can return the predictions
obj <- function(prm, frml, data, itrain, return_fit = FALSE) {
## Find the inputs to lowpass filter
## Just overwrite the column in data
for(nm in names(prm)){
data[ ,nm] <- lp_vector(data[ ,nm], a1 = prm[nm])
}
## Fit the model, ONLY on the training set
fit <- lm(frml, data[itrain, ])
## Calculate the objective function
print(val <- rmse(fit$residuals))
## Either return the fit and more
if(return_fit){
return(list(val = val,
fit = fit,
yhat = predict(fit, data)))
}else{
## Or just return the score
return(val)
}
}
## It can be used in optim
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml = frml, data = X[itrain, ], method = "L-BFGS-B", itrain = itrain)
## But now we can also get the fit and the predictions
L <- obj(result$par, frml, X, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_lpopt <- L$yhat
## Plot the predictions
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_lpopt[itest], col = 2)
## The score
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
## Did we improve?
##----------------------------------------------------------------
##----------------------------------------------------------------
## We miss something: a diurnal curve
## Use base splines
library(splines)
## We have the hour of the day
X$tday
## Hey thats simple now! Add base splines to the formula
frml <- as.formula(Ph ~ bs(tday,df=4) + Ta + G)
## Tune the low-pass coefficients
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=X[itrain, ], method="L-BFGS-B")
result
## Get the fit and the predictions
L <- obj(result$par, frml, X, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_diur <- L$yhat
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_diur[itest], col = 2)
## The score, compare it to the "simpler" models
rmse(X$Ph[itest] - X$Ph_hat_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
##----------------------------------------------------------------
##----------------------------------------------------------------
## (Out of scope) We could use Fourier series as base functions
## Fourier series for one day of hourly values
x <- (0:23 + 0.5) / 24
n_harmonics <- 4
L <- lapply(1:n_harmonics, function(i) {
val <- data.frame(sin(i * x * 2 * pi), cos(i * x * 2 * pi))
names(val) <- paste0(c("sin_", "cos_"), i)
return(val)
})
Xtmp <- do.call("cbind", L)
##
par(mfrow = c(2,1))
plot(Xtmp$sin_1, type = "b")
for(i in 2:ncol(Xtmp)) {
lines(Xtmp[ ,i], col = i, type = "b")
}
##
plot(Xtmp[ ,ncol(Xtmp)], type = "b")
## A linear combination can form any harmonic function
plot(apply(runif(n_harmonics * 2) * t(Xtmp), 2, sum), type = "b")
## How many harmonics make sense to maximum include when the period is in 24 steps?
##----------------------------------------------------------------
##----------------------------------------------------------------
## Fit a diurnal curve with Fourier series as base functions
tmp <- fs(X$tday/24, n_harmonics=3)
tmp <- do.call("cbind", tmp)
Xfit <- cbind(X,tmp)
frml <- as.formula(Ph ~ sin_1 + cos_1 + sin_2 + cos_2 + sin_3 + cos_3 + Ta + G)
## Tune the low-pass coefficients
result <- optim(c(Ta=0.98,G=0.98), obj, lower = c(0.3,0.1), upper = c(0.999,0.999), frml=frml, data=Xfit[itrain, ], method="L-BFGS-B")
result
## But now we can also get the fit and the predictions
L <- obj(result$par, frml, Xfit, itrain, return_fit = TRUE)
summary(L$fit)
X$Ph_hat_fs_diur<- L$yhat
##
plot(X$t[itest], X$Ph[itest], type = "l")
lines(X$t[itest], X$Ph_hat_fs_diur[itest], col = 2)
## The score, compare it to the "simpler" models
rmse(X$Ph[itest] - X$Ph_hat_fs_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_diur[itest])
rmse(X$Ph[itest] - X$Ph_hat_lpopt[itest])
rmse(X$Ph[itest] - X$Ph_hat_lp1[itest])
##----------------------------------------------------------------
|
#' Perform ANOVA and run multiple comparisons
#'
#' @param data a data.frame contain the input data
#' @param i col index wtich need to test
#' @param method_Mc which specify the method of multiple comparisons.included:"Tukey","LSD","SNK","Duncan","scheffe","scheffe".
#' @examples
#' # data(data_wt)
#' result= aovMcomper (data = data_wt, i= 5,method_Mc = "Tukey")
#' # utput result for multiple comparison results
#' result[[1]]
#' @return data frame
#' @author Contact: Tao Wen \email{2018203048@@njau.edu.cn} Jun Yuan \email{junyuan@@njau.edu.cn}
#' @references
#'
#' Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q
#' Root exudates drive the soil-borne legacy of aboveground pathogen infection
#' Microbiome 2018,DOI: \url{doi: 10.1186/s40168-018-0537-x}
#' @export
aovMcomper = function( data = data_wt, i= 3,method_Mc = "Tukey"){
ss <- data %>%
dplyr::select("group",count = i)
# variance analysis
model<-aov(count ~ group, data= ss)
wtx1 = summary(model)
wtx2 = wtx1[[1]]
wtx3 = wtx2[5]#
# Tukey
if (method_Mc == "Tukey") {
litter.mc <- multcomp::glht(model, linfct = multcomp::mcp(group = 'Tukey'))
insx = multcomp::cld(litter.mc)
aa <- insx$mcletters$monospacedLetters
aa = as.data.frame(aa)
colnames(aa) = c("groups")
head(aa)
aa$group = row.names(aa)
}
# LSD
if (method_Mc == "LSD") {
out <- agricolae::LSD.test(model,"group", p.adj="none")
aa = out$group#
aa$group = row.names(aa)
aa = aa[2:3]
aa
}
#SNK method (Student-Newman-Keuls)The results are similar to LSD.test.
if (method_Mc == "SNK") {
out <- agricolae::SNK.test(model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa = aa[2:3]
}
#Duncan
if (method_Mc == "Duncan") {
out <-agricolae::duncan.test (model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa
aa = aa[2:3]
}
#Scheffe features：The number of samples in each group is equal or unequal, but it is more used if the number of samples in each group is not equal;
if (method_Mc == "scheffe") {
out <-agricolae::scheffe.test (model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa = aa[2:3]
}
# aa =ord_sig(data = aa,ID = "groups")
return(list(Muicomper = aa,model))
}
|
/R/aovMcomper.R
|
no_license
|
zhao-hx/EasyStat
|
R
| false
| false
| 2,456
|
r
|
#' Perform ANOVA and run multiple comparisons
#'
#' @param data a data.frame contain the input data
#' @param i col index wtich need to test
#' @param method_Mc which specify the method of multiple comparisons.included:"Tukey","LSD","SNK","Duncan","scheffe","scheffe".
#' @examples
#' # data(data_wt)
#' result= aovMcomper (data = data_wt, i= 5,method_Mc = "Tukey")
#' # utput result for multiple comparison results
#' result[[1]]
#' @return data frame
#' @author Contact: Tao Wen \email{2018203048@@njau.edu.cn} Jun Yuan \email{junyuan@@njau.edu.cn}
#' @references
#'
#' Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q
#' Root exudates drive the soil-borne legacy of aboveground pathogen infection
#' Microbiome 2018,DOI: \url{doi: 10.1186/s40168-018-0537-x}
#' @export
aovMcomper = function( data = data_wt, i= 3,method_Mc = "Tukey"){
ss <- data %>%
dplyr::select("group",count = i)
# variance analysis
model<-aov(count ~ group, data= ss)
wtx1 = summary(model)
wtx2 = wtx1[[1]]
wtx3 = wtx2[5]#
# Tukey
if (method_Mc == "Tukey") {
litter.mc <- multcomp::glht(model, linfct = multcomp::mcp(group = 'Tukey'))
insx = multcomp::cld(litter.mc)
aa <- insx$mcletters$monospacedLetters
aa = as.data.frame(aa)
colnames(aa) = c("groups")
head(aa)
aa$group = row.names(aa)
}
# LSD
if (method_Mc == "LSD") {
out <- agricolae::LSD.test(model,"group", p.adj="none")
aa = out$group#
aa$group = row.names(aa)
aa = aa[2:3]
aa
}
#SNK method (Student-Newman-Keuls)The results are similar to LSD.test.
if (method_Mc == "SNK") {
out <- agricolae::SNK.test(model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa = aa[2:3]
}
#Duncan
if (method_Mc == "Duncan") {
out <-agricolae::duncan.test (model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa
aa = aa[2:3]
}
#Scheffe features：The number of samples in each group is equal or unequal, but it is more used if the number of samples in each group is not equal;
if (method_Mc == "scheffe") {
out <-agricolae::scheffe.test (model,"group")
aa = out$groups# View label of each group
aa$group = row.names(aa)
stat = aa
aa = aa[2:3]
}
# aa =ord_sig(data = aa,ID = "groups")
return(list(Muicomper = aa,model))
}
|
library(dplyr)
library(tidyr)
library(ggplot2)
library(sf)
source(file.path("explore","src","explore_summaries.R"))
# Fluorescence:
fl_signals_all <- fl_signals()
glri <- readRDS(file.path("process","out","glri_fl_MRL_adjusted.rds"))
mmsd <- readRDS(file.path("process","out","mmsd_fl_MRL_adjusted.rds"))
glpf <- readRDS(file.path("process","out","glpf_fl_MRL_adjusted.rds"))
rmks_fl <- combine_remarks(glri, mmsd, glpf)
df_fl <- combine_values(glri, mmsd, glpf)
rm(glpf, glri, mmsd)
fl_MRLs <- readRDS(file.path("process","out","fl_MRLs.rds"))
fl_MRL_clean <- split_exem(fl_MRLs, wavelength = "Wavelength") %>%
select(x, y, MRL)
fl_remark_summary <- split_exem(rmks_fl) %>%
left_join(fl_MRL_clean, by=c("x","y"))
peaks <- em_peaks(fl_signals_all, fl_remark_summary)
lines <- em_long(fl_signals_all, fl_remark_summary)
boxes <- eems_boxes(fl_signals_all, fl_remark_summary)
ggplot() +
geom_point(data = peaks, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = lines, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = boxes, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_text(data = peaks,
aes(x=x,y=y,label=Peak), size=3) +
facet_grid(. ~ study) +
theme_bw() +
scale_color_gradient(
trans = "log",
low = "blue", high = "white")
all_signals <- bind_rows(peaks, lines, boxes)
summary_signals <- all_signals %>%
group_by(Peak, study ) %>%
summarize(mean_freq = mean(freq, na.rm = TRUE),
total = mean(total, na.rm = TRUE),
mean_cen = mean(censored, na.rm = TRUE),
mean_MRL = mean(MRL, na.rm = TRUE))
fl_sum_values <- split_exem(df_fl) %>%
tidyr::gather("stat","value", -x, -y, -study)
peaks_vals <- em_peaks(fl_signals_all, fl_sum_values)
lines_vals <- em_long(fl_signals_all, fl_sum_values)
boxes_vals <- eems_boxes(fl_signals_all, fl_sum_values)
ggplot() +
geom_point(data = peaks_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = lines_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = boxes_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_text(data = peaks_vals,
aes(x=x,y=y,label=Peak), size=3) +
facet_grid(stat ~ study, scales = "free") +
theme_bw() +
scale_color_gradient(trans = "log",
low = "red", high = "white")
all_signals_stats <- bind_rows(peaks_vals,
lines_vals,
boxes_vals)
summary_signals_stats <- all_signals_stats %>%
filter(stat == "mean") %>%
group_by(Peak, study) %>%
summarize(mean = mean(value, na.rm = TRUE))
summary_fl <- summary_signals_stats %>%
left_join(summary_signals, by=c("Peak","study"))
data.table::fwrite(summary_fl, file = file.path("explore","out","summary_fl.csv"))
######################################
# Absorbance
abs_signals_all <- abs_signals()
abs_MRLs <- readRDS(file.path("process","out","abs_MRLs.rds"))
# Abs Data:
glri_abs <- readRDS(file.path("process","out","glri_abs_MRL_adjusted.rds"))
mmsd_abs <- readRDS(file.path("process","out","mmsd_abs_MRL_adjusted.rds"))
glpf_abs <- readRDS(file.path("process","out","glpf_abs_MRL_adjusted.rds"))
rmks_abs <- combine_remarks(glri_abs, mmsd_abs, glpf_abs, wavelength = "Wavelength")
df_abs <- combine_values(glri_abs, mmsd_abs, glpf_abs, wavelength = "Wavelength")
rm(glpf_abs, glri_abs, mmsd_abs)
rmks_abs <- rmks_abs %>%
left_join(select(abs_MRLs, Wavelength, MRL), by="Wavelength")
peaks_censor <- abs_peaks(abs_signals_all, rmks_abs)
ggplot() +
geom_line(data = peaks_censor,
aes(x = Wavelength, y = freq)) +
facet_grid(. ~ study) +
theme_bw()
summary_abs_det <- peaks_censor %>%
group_by(Wavelength, study) %>%
summarize(mean_freq = mean(freq, na.rm = TRUE),
mean_censored = mean(censored, na.rm = TRUE),
total = mean(total, na.rm = TRUE),
mean_MRL = mean(MRL, na.rm = TRUE))
peaks_values <- abs_peaks(abs_signals_all, df_abs)
peaks_long <- peaks_values %>%
tidyr::gather("stat","value",-Wavelength, -study)
ggplot() +
geom_line(data = peaks_long,
aes(x = Wavelength, y = value)) +
geom_point(data = peaks_long,
aes(x = Wavelength, y = value)) +
facet_grid(stat ~ study, scales = "free") +
theme_bw()
summary_abs_stats <- peaks_long %>%
filter(stat == "mean") %>%
group_by(Wavelength, study) %>%
summarize(mean = mean(value, na.rm = TRUE))
summary_abs <- summary_abs_stats %>%
left_join(summary_abs_det, by=c("Wavelength","study"))
data.table::fwrite(summary_abs, file = file.path("explore","out","summary_abs.csv"))
|
/explore/summarize_detections.R
|
no_license
|
ldecicco-USGS/GLPF_manuscript
|
R
| false
| false
| 4,831
|
r
|
library(dplyr)
library(tidyr)
library(ggplot2)
library(sf)
source(file.path("explore","src","explore_summaries.R"))
# Fluorescence:
fl_signals_all <- fl_signals()
glri <- readRDS(file.path("process","out","glri_fl_MRL_adjusted.rds"))
mmsd <- readRDS(file.path("process","out","mmsd_fl_MRL_adjusted.rds"))
glpf <- readRDS(file.path("process","out","glpf_fl_MRL_adjusted.rds"))
rmks_fl <- combine_remarks(glri, mmsd, glpf)
df_fl <- combine_values(glri, mmsd, glpf)
rm(glpf, glri, mmsd)
fl_MRLs <- readRDS(file.path("process","out","fl_MRLs.rds"))
fl_MRL_clean <- split_exem(fl_MRLs, wavelength = "Wavelength") %>%
select(x, y, MRL)
fl_remark_summary <- split_exem(rmks_fl) %>%
left_join(fl_MRL_clean, by=c("x","y"))
peaks <- em_peaks(fl_signals_all, fl_remark_summary)
lines <- em_long(fl_signals_all, fl_remark_summary)
boxes <- eems_boxes(fl_signals_all, fl_remark_summary)
ggplot() +
geom_point(data = peaks, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = lines, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = boxes, aes(x=x, y=y, color = freq),
size = 5, shape=16, alpha = 0.3) +
geom_text(data = peaks,
aes(x=x,y=y,label=Peak), size=3) +
facet_grid(. ~ study) +
theme_bw() +
scale_color_gradient(
trans = "log",
low = "blue", high = "white")
all_signals <- bind_rows(peaks, lines, boxes)
summary_signals <- all_signals %>%
group_by(Peak, study ) %>%
summarize(mean_freq = mean(freq, na.rm = TRUE),
total = mean(total, na.rm = TRUE),
mean_cen = mean(censored, na.rm = TRUE),
mean_MRL = mean(MRL, na.rm = TRUE))
fl_sum_values <- split_exem(df_fl) %>%
tidyr::gather("stat","value", -x, -y, -study)
peaks_vals <- em_peaks(fl_signals_all, fl_sum_values)
lines_vals <- em_long(fl_signals_all, fl_sum_values)
boxes_vals <- eems_boxes(fl_signals_all, fl_sum_values)
ggplot() +
geom_point(data = peaks_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = lines_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_point(data = boxes_vals, aes(x=x, y=y, color = value),
size = 5, shape=16, alpha = 0.3) +
geom_text(data = peaks_vals,
aes(x=x,y=y,label=Peak), size=3) +
facet_grid(stat ~ study, scales = "free") +
theme_bw() +
scale_color_gradient(trans = "log",
low = "red", high = "white")
all_signals_stats <- bind_rows(peaks_vals,
lines_vals,
boxes_vals)
summary_signals_stats <- all_signals_stats %>%
filter(stat == "mean") %>%
group_by(Peak, study) %>%
summarize(mean = mean(value, na.rm = TRUE))
summary_fl <- summary_signals_stats %>%
left_join(summary_signals, by=c("Peak","study"))
data.table::fwrite(summary_fl, file = file.path("explore","out","summary_fl.csv"))
######################################
# Absorbance
abs_signals_all <- abs_signals()
abs_MRLs <- readRDS(file.path("process","out","abs_MRLs.rds"))
# Abs Data:
glri_abs <- readRDS(file.path("process","out","glri_abs_MRL_adjusted.rds"))
mmsd_abs <- readRDS(file.path("process","out","mmsd_abs_MRL_adjusted.rds"))
glpf_abs <- readRDS(file.path("process","out","glpf_abs_MRL_adjusted.rds"))
rmks_abs <- combine_remarks(glri_abs, mmsd_abs, glpf_abs, wavelength = "Wavelength")
df_abs <- combine_values(glri_abs, mmsd_abs, glpf_abs, wavelength = "Wavelength")
rm(glpf_abs, glri_abs, mmsd_abs)
rmks_abs <- rmks_abs %>%
left_join(select(abs_MRLs, Wavelength, MRL), by="Wavelength")
peaks_censor <- abs_peaks(abs_signals_all, rmks_abs)
ggplot() +
geom_line(data = peaks_censor,
aes(x = Wavelength, y = freq)) +
facet_grid(. ~ study) +
theme_bw()
summary_abs_det <- peaks_censor %>%
group_by(Wavelength, study) %>%
summarize(mean_freq = mean(freq, na.rm = TRUE),
mean_censored = mean(censored, na.rm = TRUE),
total = mean(total, na.rm = TRUE),
mean_MRL = mean(MRL, na.rm = TRUE))
peaks_values <- abs_peaks(abs_signals_all, df_abs)
peaks_long <- peaks_values %>%
tidyr::gather("stat","value",-Wavelength, -study)
ggplot() +
geom_line(data = peaks_long,
aes(x = Wavelength, y = value)) +
geom_point(data = peaks_long,
aes(x = Wavelength, y = value)) +
facet_grid(stat ~ study, scales = "free") +
theme_bw()
summary_abs_stats <- peaks_long %>%
filter(stat == "mean") %>%
group_by(Wavelength, study) %>%
summarize(mean = mean(value, na.rm = TRUE))
summary_abs <- summary_abs_stats %>%
left_join(summary_abs_det, by=c("Wavelength","study"))
data.table::fwrite(summary_abs, file = file.path("explore","out","summary_abs.csv"))
|
\name{DFA}
\alias{DFA}
\title{Discriminant function analysis}
\description{Produces SPSS- and SAS-like output for linear discriminant function analysis.}
\usage{DFA(data, groups, variables, plot, predictive, priorprob, covmat_type, CV, verbose)}
\arguments{
\item{data}{
\code{}A dataframe where the rows are cases & the columns are the variables.}
\item{groups}{
\code{}The name of the groups variable in the dataframe, \cr e.g., groups = 'Group'.}
\item{variables}{
\code{}The names of the continuous variables in the dataframe that will be used in the DFA,
e.g., variables = c('varA', 'varB', 'varC').}
\item{plot}{
\code{}Should a plot of the mean standardized discriminant function scores
\cr for the groups be produced? The options are: TRUE (default) or FALSE.}
\item{predictive}{
\code{}Should a predictive DFA be conducted? \cr The options are: TRUE (default) or FALSE.}
\item{priorprob}{
\code{}If predictive = TRUE, how should the prior probabilities of the group sizes be computed?
The options are:\cr
'EQUAL' for equal group sizes; or\cr
'SIZES' (default) for the group sizes to be based on the sizes of the groups in the dataframe.}
\item{covmat_type}{
\code{}The kind of covariance to be used for a predictive DFA. The options are:
\cr 'within' (for the pooled within-groups covariance matrix, which is the default) or
\cr 'separate' (for separate-groups covariance matrices).}
\item{CV}{
\code{}If predictive = TRUE, should cross-validation (leave-one-out cross-validation) analyses also be conducted?
The options are: TRUE (default) or FALSE.}
\item{verbose}{
\code{}Should detailed results be displayed in console? \cr The options are: TRUE (default) or FALSE.}
}
\details{The predictive DFA option using separate-groups covariance matrices (which is often called 'quadratic DFA')
is conducted following the procedures described by Rencher (2002). The covariance matrices
in this case are based on the scores on the continuous variables. In contrast, the 'separate-groups'
option in SPSS involves use of the group scores on the discriminant functions (not the original continuous variables),
which can produce different classifications.
When data has many cases (e.g., > 1000), the leave-one-out cross-validation analyses can
be time-consuming to run. Set CV = FALSE to bypass the predictive DFA cross-validation analyses.
See the documentation below for the GROUP.DIFFS function for information on the interpretation of the
Bayes factors and effect sizes that are produced for the group comparisons.
}
\value{If verbose = TRUE, the displayed output includes descriptive statistics for the groups,
tests of univariate and multivariate normality,
the results of tests of the homogeneity of the group variance-covariance matrices,
eigenvalues & canonical correlations, Wilks' lambda & peel-down statistics,
raw and standardized discriminant function coefficients,
structure coefficients, functions at group centroids,
one-way ANOVA tests of group differences in scores on each discriminant function,
one-way ANOVA tests of group differences in scores on each original DV,
significance tests for group differences on the original DVs according to Bird et al. (2014),
a plot of the group means on the standardized discriminant functions,
and extensive output from predictive discriminant function analyses (if requested).
The returned output is a list with elements
\item{evals}{eigenvalues and canonical correlations}
\item{mv_Wilks}{The Wilks' lambda multivariate test}
\item{mv_Pillai}{The Pillai-Bartlett multivariate test}
\item{mv_Hotelling}{The Lawley-Hotelling multivariate test}
\item{mv_Roy}{Roy's greatest characteristic root multivariate test}
\item{coefs_raw}{canonical discriminant function coefficients}
\item{coefs_structure}{structure coefficients}
\item{coefs_standardized}{standardized coefficients}
\item{coefs_standardizedSPSS}{standardized coefficients from SPSS}
\item{centroids}{unstandardized canonical discriminant functions evaluated at the group means}
\item{centroidSDs}{group standard deviations on the unstandardized functions}
\item{centroidsZ}{standardized canonical discriminant functions evaluated at the group means}
\item{centroidSDsZ}{group standard deviations on the standardized functions}
\item{dfa_scores}{scores on the discriminant functions}
\item{anovaDFoutput}{One-way ANOVAs using the scores on a discriminant function as the DV}
\item{anovaDVoutput}{One-way ANOVAs on the original DVs}
\item{MFWER1.sigtest}{Significance tests when controlling the MFWER by (only) carrying out multiple t tests}
\item{MFWER2.sigtest}{Significance tests for the two-stage approach to controling the MFWER}
\item{classes_PRED}{The predicted group classifications}
\item{classes_CV}{The classifications from leave-one-out cross-validations, if requested}
\item{posteriors}{The posterior probabilities for the predicted group classifications}
\item{grp_post_stats}{Group mean posterior classification probabilities}
\item{classes_CV}{Classifications from leave-one-out cross-validations}
\item{freqs_ORIG_PRED}{Cross-tabulation of the original and predicted group memberships}
\item{chi_square_ORIG_PRED}{Chi-square test of independence}
\item{PressQ_ORIG_PRED}{Press's Q significance test of classifiation accuracy for original vs. predicted group memberships}
\item{kappas_ORIG_PRED}{Agreement (kappas) between the predicted and original group memberships}
\item{PropOrigCorrect}{Proportion of original grouped cases correctly classified}
\item{freqs_ORIG_CV}{Cross-Tabulation of the cross-validated and predicted group memberships}
\item{chi_square_ORIG_CV}{Chi-square test of indepedence}
\item{PressQ_ORIG_CV}{Press's Q significance test of classifiation accuracy for cross-validated vs. predicted group memberships}
\item{kappas_ORIG_CV}{Agreement (kappas) between the cross-validated and original group memberships}
\item{PropCrossValCorrect}{Proportion of cross-validated grouped cases correctly classified}
}
\references{
{Bird, K. D., & Hadzi-Pavlovic, D. (2013). Controlling the maximum familywise Type I error
rate in analyses of multivariate experiments. \emph{Psychological Methods, 19(2),} p. 265-280.}
\cr\cr {Manly, B. F. J., & Alberto, J. A. (2017). \emph{Multivariate statistical methods:
A primer (4th Edition).} Chapman & Hall/CRC, Boca Raton, FL.}
\cr\cr {Rencher, A. C. (2002). \emph{Methods of Multivariate Analysis} (2nd ed.). New York, NY: John Wiley & Sons.}
\cr\cr {Sherry, A. (2006). Discriminant analysis in counseling research. \emph{Counseling Psychologist, 34,} 661-683.}
\cr\cr {Tabachnik, B. G., & Fidell, L. S. (2019). \emph{Using multivariate statistics (7th ed.).} New York, NY: Pearson.}
}
\author{Brian P. O'Connor}
\examples{
# data from Field et al. (2012, Chapter 16 MANOVA)
DFA_Field=DFA(data = data_DFA$Field_2012,
groups = 'Group',
variables = c('Actions','Thoughts'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within', # altho better to use 'separate' for these data
verbose = TRUE)
\donttest{
# plots of posterior probabilities by group
# hoping to see correct separations between cases from different groups
# first, display the posterior probabilities
print(cbind(round(DFA_Field$posteriors[1:3],3), DFA_Field$posteriors[4]))
# group NT vs CBT
plot(DFA_Field$posteriors$posterior_NT, DFA_Field$posteriors$posterior_CBT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Original Group Memberships',
xlab='Posterior Probability of Being in Group NT',
ylab='Posterior Probability of Being in Group CBT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# group NT vs BT
plot(DFA_Field$posteriors$posterior_NT, DFA_Field$posteriors$posterior_BT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group NT',
ylab='Posterior Probability of Being in Group BT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2,col=c('red', 'blue', 'green'), pch=16, bty='n')
# group CBT vs BT
plot(DFA_Field$posteriors$posterior_CBT, DFA_Field$posteriors$posterior_BT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group CBT',
ylab='Posterior Probability of Being in Group BT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# data from Green & Salkind (2008, Lesson 35)
DFA(data = data_DFA$Green_2008,
groups = 'job_cat',
variables = c('friendly','gpa','job_hist','job_test'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Ho (2014, Chapter 15)
# with group_1 as numeric
DFA(data = data_DFA$Ho_2014,
groups = 'group_1_num',
variables = c("fast_ris", "disresp", "sen_seek", "danger"),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Ho (2014, Chapter 15)
# with group_1 as a factor
DFA(data = data_DFA$Ho_2014,
groups = 'group_1_fac',
variables = c("fast_ris", "disresp", "sen_seek", "danger"),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Huberty (2006, p 45)
DFA_Huberty=DFA(data = data_DFA$Huberty_2019_p45,
groups = 'treatmnt_S',
variables = c('Y1','Y2'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate', # altho better to used 'separate' for these data
verbose = TRUE)
# data from Huberty (2006, p 285)
DFA_Huberty=DFA(data = data_DFA$Huberty_2019_p285,
groups = 'Grade',
variables = c('counsum','gainsum','learnsum','qelib','qefac','qestacq',
'qeamt','qewrite','qesci'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within',
verbose = TRUE)
# data from Norusis (2012, Chaper 15)
DFA_Norusis=DFA(data = data_DFA$Norusis_2012,
groups = 'internet',
variables = c('age','gender','income','kids','suburban','work','yearsed'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within',
verbose = TRUE)
# data from Rencher (2002, p 170) - rootstock
DFA(data = data_DFA$Rencher_2002_root,
groups = 'rootstock',
variables = c('girth4','ext4','girth15','weight15'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# data from Rencher (2002, p 280) - football
DFA(data = data_DFA$Rencher_2002_football,
groups = 'grp',
variables = c('WDIM','CIRCUM','FBEYE','EYEHD','EARHD','JAW'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# Sherry (2006) - with Group as numeric
DFA_Sherry <- DFA(data = data_DFA$Sherry_2006,
groups = 'Group_num',
variables = c('Neuroticism','Extroversion','Openness',
'Agreeableness','Conscientiousness'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# Sherry (2006) - with Group as a factor
DFA_Sherry <- DFA(data = data_DFA$Sherry_2006,
groups = 'Group_fac',
variables = c('Neuroticism','Extroversion','Openness',
'Agreeableness','Conscientiousness'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# plots of posterior probabilities by group
# hoping to see correct separations between cases from different groups
# first, display the posterior probabilities
print(cbind(round(DFA_Sherry$posteriors[1:3],3), DFA_Sherry$posteriors[4]))
# group 1 vs 2
plot(DFA_Sherry$posteriors$posterior_1, DFA_Sherry$posteriors$posterior_2,
pch = 16, cex = 1, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Original Group Memberships',
xlab='Posterior Probability of Being in Group 1',
ylab='Posterior Probability of Being in Group 2' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# group 1 vs 3
plot(DFA_Sherry$posteriors$posterior_1, DFA_Sherry$posteriors$posterior_3,
pch = 16, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group 1',
ylab='Posterior Probability of Being in Group 3' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2,col=c('red', 'blue', 'green'), pch=16, bty='n')
# group 2 vs 3
plot(DFA_Sherry$posteriors$posterior_2, DFA_Sherry$posteriors$posterior_3,
pch = 16, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group 2',
ylab='Posterior Probability of Being in Group 3' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# Tabachnik & Fiddel (2019, p 307, 311) - small - with group as numeric
DFA(data = data_DFA$TabFid_2019_small,
groups = 'group_num',
variables = c('perf','info','verbexp','age'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# Tabachnik & Fiddel (2019, p 307, 311) - small - with group as a factor
DFA(data = data_DFA$TabFid_2019_small,
groups = 'group_fac',
variables = c('perf','info','verbexp','age'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# Tabachnik & Fiddel (2019, p 324) - complete - with WORKSTAT as numeric
DFA(data = data_DFA$TabFid_2019_complete,
groups = 'WORKSTAT_num',
variables = c('CONTROL','ATTMAR','ATTROLE','ATTHOUSE'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# Tabachnik & Fiddel (2019, p 324) - complete - with WORKSTAT as a factor
DFA(data = data_DFA$TabFid_2019_complete,
groups = 'WORKSTAT_fac',
variables = c('CONTROL','ATTMAR','ATTROLE','ATTHOUSE'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
}
}
|
/man/DFA.Rd
|
no_license
|
cran/DFA.CANCOR
|
R
| false
| false
| 15,186
|
rd
|
\name{DFA}
\alias{DFA}
\title{Discriminant function analysis}
\description{Produces SPSS- and SAS-like output for linear discriminant function analysis.}
\usage{DFA(data, groups, variables, plot, predictive, priorprob, covmat_type, CV, verbose)}
\arguments{
\item{data}{
\code{}A dataframe where the rows are cases & the columns are the variables.}
\item{groups}{
\code{}The name of the groups variable in the dataframe, \cr e.g., groups = 'Group'.}
\item{variables}{
\code{}The names of the continuous variables in the dataframe that will be used in the DFA,
e.g., variables = c('varA', 'varB', 'varC').}
\item{plot}{
\code{}Should a plot of the mean standardized discriminant function scores
\cr for the groups be produced? The options are: TRUE (default) or FALSE.}
\item{predictive}{
\code{}Should a predictive DFA be conducted? \cr The options are: TRUE (default) or FALSE.}
\item{priorprob}{
\code{}If predictive = TRUE, how should the prior probabilities of the group sizes be computed?
The options are:\cr
'EQUAL' for equal group sizes; or\cr
'SIZES' (default) for the group sizes to be based on the sizes of the groups in the dataframe.}
\item{covmat_type}{
\code{}The kind of covariance to be used for a predictive DFA. The options are:
\cr 'within' (for the pooled within-groups covariance matrix, which is the default) or
\cr 'separate' (for separate-groups covariance matrices).}
\item{CV}{
\code{}If predictive = TRUE, should cross-validation (leave-one-out cross-validation) analyses also be conducted?
The options are: TRUE (default) or FALSE.}
\item{verbose}{
\code{}Should detailed results be displayed in console? \cr The options are: TRUE (default) or FALSE.}
}
\details{The predictive DFA option using separate-groups covariance matrices (which is often called 'quadratic DFA')
is conducted following the procedures described by Rencher (2002). The covariance matrices
in this case are based on the scores on the continuous variables. In contrast, the 'separate-groups'
option in SPSS involves use of the group scores on the discriminant functions (not the original continuous variables),
which can produce different classifications.
When data has many cases (e.g., > 1000), the leave-one-out cross-validation analyses can
be time-consuming to run. Set CV = FALSE to bypass the predictive DFA cross-validation analyses.
See the documentation below for the GROUP.DIFFS function for information on the interpretation of the
Bayes factors and effect sizes that are produced for the group comparisons.
}
\value{If verbose = TRUE, the displayed output includes descriptive statistics for the groups,
tests of univariate and multivariate normality,
the results of tests of the homogeneity of the group variance-covariance matrices,
eigenvalues & canonical correlations, Wilks' lambda & peel-down statistics,
raw and standardized discriminant function coefficients,
structure coefficients, functions at group centroids,
one-way ANOVA tests of group differences in scores on each discriminant function,
one-way ANOVA tests of group differences in scores on each original DV,
significance tests for group differences on the original DVs according to Bird et al. (2014),
a plot of the group means on the standardized discriminant functions,
and extensive output from predictive discriminant function analyses (if requested).
The returned output is a list with elements
\item{evals}{eigenvalues and canonical correlations}
\item{mv_Wilks}{The Wilks' lambda multivariate test}
\item{mv_Pillai}{The Pillai-Bartlett multivariate test}
\item{mv_Hotelling}{The Lawley-Hotelling multivariate test}
\item{mv_Roy}{Roy's greatest characteristic root multivariate test}
\item{coefs_raw}{canonical discriminant function coefficients}
\item{coefs_structure}{structure coefficients}
\item{coefs_standardized}{standardized coefficients}
\item{coefs_standardizedSPSS}{standardized coefficients from SPSS}
\item{centroids}{unstandardized canonical discriminant functions evaluated at the group means}
\item{centroidSDs}{group standard deviations on the unstandardized functions}
\item{centroidsZ}{standardized canonical discriminant functions evaluated at the group means}
\item{centroidSDsZ}{group standard deviations on the standardized functions}
\item{dfa_scores}{scores on the discriminant functions}
\item{anovaDFoutput}{One-way ANOVAs using the scores on a discriminant function as the DV}
\item{anovaDVoutput}{One-way ANOVAs on the original DVs}
\item{MFWER1.sigtest}{Significance tests when controlling the MFWER by (only) carrying out multiple t tests}
\item{MFWER2.sigtest}{Significance tests for the two-stage approach to controling the MFWER}
\item{classes_PRED}{The predicted group classifications}
\item{classes_CV}{The classifications from leave-one-out cross-validations, if requested}
\item{posteriors}{The posterior probabilities for the predicted group classifications}
\item{grp_post_stats}{Group mean posterior classification probabilities}
\item{classes_CV}{Classifications from leave-one-out cross-validations}
\item{freqs_ORIG_PRED}{Cross-tabulation of the original and predicted group memberships}
\item{chi_square_ORIG_PRED}{Chi-square test of independence}
\item{PressQ_ORIG_PRED}{Press's Q significance test of classifiation accuracy for original vs. predicted group memberships}
\item{kappas_ORIG_PRED}{Agreement (kappas) between the predicted and original group memberships}
\item{PropOrigCorrect}{Proportion of original grouped cases correctly classified}
\item{freqs_ORIG_CV}{Cross-Tabulation of the cross-validated and predicted group memberships}
\item{chi_square_ORIG_CV}{Chi-square test of indepedence}
\item{PressQ_ORIG_CV}{Press's Q significance test of classifiation accuracy for cross-validated vs. predicted group memberships}
\item{kappas_ORIG_CV}{Agreement (kappas) between the cross-validated and original group memberships}
\item{PropCrossValCorrect}{Proportion of cross-validated grouped cases correctly classified}
}
\references{
{Bird, K. D., & Hadzi-Pavlovic, D. (2013). Controlling the maximum familywise Type I error
rate in analyses of multivariate experiments. \emph{Psychological Methods, 19(2),} p. 265-280.}
\cr\cr {Manly, B. F. J., & Alberto, J. A. (2017). \emph{Multivariate statistical methods:
A primer (4th Edition).} Chapman & Hall/CRC, Boca Raton, FL.}
\cr\cr {Rencher, A. C. (2002). \emph{Methods of Multivariate Analysis} (2nd ed.). New York, NY: John Wiley & Sons.}
\cr\cr {Sherry, A. (2006). Discriminant analysis in counseling research. \emph{Counseling Psychologist, 34,} 661-683.}
\cr\cr {Tabachnik, B. G., & Fidell, L. S. (2019). \emph{Using multivariate statistics (7th ed.).} New York, NY: Pearson.}
}
\author{Brian P. O'Connor}
\examples{
# data from Field et al. (2012, Chapter 16 MANOVA)
DFA_Field=DFA(data = data_DFA$Field_2012,
groups = 'Group',
variables = c('Actions','Thoughts'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within', # altho better to use 'separate' for these data
verbose = TRUE)
\donttest{
# plots of posterior probabilities by group
# hoping to see correct separations between cases from different groups
# first, display the posterior probabilities
print(cbind(round(DFA_Field$posteriors[1:3],3), DFA_Field$posteriors[4]))
# group NT vs CBT
plot(DFA_Field$posteriors$posterior_NT, DFA_Field$posteriors$posterior_CBT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Original Group Memberships',
xlab='Posterior Probability of Being in Group NT',
ylab='Posterior Probability of Being in Group CBT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# group NT vs BT
plot(DFA_Field$posteriors$posterior_NT, DFA_Field$posteriors$posterior_BT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group NT',
ylab='Posterior Probability of Being in Group BT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2,col=c('red', 'blue', 'green'), pch=16, bty='n')
# group CBT vs BT
plot(DFA_Field$posteriors$posterior_CBT, DFA_Field$posteriors$posterior_BT,
pch = 16, col = c('red', 'blue', 'green')[DFA_Field$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group CBT',
ylab='Posterior Probability of Being in Group BT' )
legend(x=.8, y=.99, c('CBT','BT','NT'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# data from Green & Salkind (2008, Lesson 35)
DFA(data = data_DFA$Green_2008,
groups = 'job_cat',
variables = c('friendly','gpa','job_hist','job_test'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Ho (2014, Chapter 15)
# with group_1 as numeric
DFA(data = data_DFA$Ho_2014,
groups = 'group_1_num',
variables = c("fast_ris", "disresp", "sen_seek", "danger"),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Ho (2014, Chapter 15)
# with group_1 as a factor
DFA(data = data_DFA$Ho_2014,
groups = 'group_1_fac',
variables = c("fast_ris", "disresp", "sen_seek", "danger"),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# data from Huberty (2006, p 45)
DFA_Huberty=DFA(data = data_DFA$Huberty_2019_p45,
groups = 'treatmnt_S',
variables = c('Y1','Y2'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate', # altho better to used 'separate' for these data
verbose = TRUE)
# data from Huberty (2006, p 285)
DFA_Huberty=DFA(data = data_DFA$Huberty_2019_p285,
groups = 'Grade',
variables = c('counsum','gainsum','learnsum','qelib','qefac','qestacq',
'qeamt','qewrite','qesci'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within',
verbose = TRUE)
# data from Norusis (2012, Chaper 15)
DFA_Norusis=DFA(data = data_DFA$Norusis_2012,
groups = 'internet',
variables = c('age','gender','income','kids','suburban','work','yearsed'),
predictive = TRUE,
priorprob = 'EQUAL',
covmat_type='within',
verbose = TRUE)
# data from Rencher (2002, p 170) - rootstock
DFA(data = data_DFA$Rencher_2002_root,
groups = 'rootstock',
variables = c('girth4','ext4','girth15','weight15'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# data from Rencher (2002, p 280) - football
DFA(data = data_DFA$Rencher_2002_football,
groups = 'grp',
variables = c('WDIM','CIRCUM','FBEYE','EYEHD','EARHD','JAW'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# Sherry (2006) - with Group as numeric
DFA_Sherry <- DFA(data = data_DFA$Sherry_2006,
groups = 'Group_num',
variables = c('Neuroticism','Extroversion','Openness',
'Agreeableness','Conscientiousness'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# Sherry (2006) - with Group as a factor
DFA_Sherry <- DFA(data = data_DFA$Sherry_2006,
groups = 'Group_fac',
variables = c('Neuroticism','Extroversion','Openness',
'Agreeableness','Conscientiousness'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='separate',
verbose = TRUE)
# plots of posterior probabilities by group
# hoping to see correct separations between cases from different groups
# first, display the posterior probabilities
print(cbind(round(DFA_Sherry$posteriors[1:3],3), DFA_Sherry$posteriors[4]))
# group 1 vs 2
plot(DFA_Sherry$posteriors$posterior_1, DFA_Sherry$posteriors$posterior_2,
pch = 16, cex = 1, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Original Group Memberships',
xlab='Posterior Probability of Being in Group 1',
ylab='Posterior Probability of Being in Group 2' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# group 1 vs 3
plot(DFA_Sherry$posteriors$posterior_1, DFA_Sherry$posteriors$posterior_3,
pch = 16, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group 1',
ylab='Posterior Probability of Being in Group 3' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2,col=c('red', 'blue', 'green'), pch=16, bty='n')
# group 2 vs 3
plot(DFA_Sherry$posteriors$posterior_2, DFA_Sherry$posteriors$posterior_3,
pch = 16, col = c('red', 'blue', 'green')[DFA_Sherry$posteriors$Group],
xlim=c(0,1), ylim=c(0,1),
main = 'DFA Posterior Probabilities by Group Membership',
xlab='Posterior Probability of Being in Group 2',
ylab='Posterior Probability of Being in Group 3' )
legend(x=.8, y=.99, c('1','2','3'), cex=1.2, col=c('red', 'blue', 'green'), pch=16, bty='n')
# Tabachnik & Fiddel (2019, p 307, 311) - small - with group as numeric
DFA(data = data_DFA$TabFid_2019_small,
groups = 'group_num',
variables = c('perf','info','verbexp','age'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# Tabachnik & Fiddel (2019, p 307, 311) - small - with group as a factor
DFA(data = data_DFA$TabFid_2019_small,
groups = 'group_fac',
variables = c('perf','info','verbexp','age'),
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
verbose = TRUE)
# Tabachnik & Fiddel (2019, p 324) - complete - with WORKSTAT as numeric
DFA(data = data_DFA$TabFid_2019_complete,
groups = 'WORKSTAT_num',
variables = c('CONTROL','ATTMAR','ATTROLE','ATTHOUSE'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
# Tabachnik & Fiddel (2019, p 324) - complete - with WORKSTAT as a factor
DFA(data = data_DFA$TabFid_2019_complete,
groups = 'WORKSTAT_fac',
variables = c('CONTROL','ATTMAR','ATTROLE','ATTHOUSE'),
plot=TRUE,
predictive = TRUE,
priorprob = 'SIZES',
covmat_type='within',
CV=TRUE,
verbose=TRUE)
}
}
|
# Get the "Wine" dataset
#install.packages("rattle.data")
library(rattle.data)
head(wine)
table(wine$Type)
# Take only Type='1'
temp <-wine[wine$Type=='1',]
# Generate 1,000 row samples with replacement. Note if replace=False, it won't work.
temp2<-temp[sample(nrow(temp), 4000,replace=T), ]
dim(temp2)
# Put together
wine2<-rbind(wine[wine$Type=='2',],temp2)
# Shuffle the rows
wine2 <- wine2[sample(nrow(wine2)),]
dim(wine2)
# Change the target value to 1 and 0.
wine2$Type <-as.factor(ifelse(wine2$Type=='1','0','1'))
head(wine2)
#mean(as.numeric(wine2$Type))
table(wine2$Type)
group1<-wine2[wine2$Type=='1',]
group0 <-wine2[wine2$Type=='0',]
df2 <-group0[sample(nrow(group0), 800), ]
df<-rbind(df2,group1)
#mean(as.numeric(df$Type))
table(df$Type)
|
/Lecture notes with R code /Module 9_10 - Supervised - Wine down-sampling.r
|
no_license
|
FANYifei94/FraudDetection
|
R
| false
| false
| 755
|
r
|
# Get the "Wine" dataset
#install.packages("rattle.data")
library(rattle.data)
head(wine)
table(wine$Type)
# Take only Type='1'
temp <-wine[wine$Type=='1',]
# Generate 1,000 row samples with replacement. Note if replace=False, it won't work.
temp2<-temp[sample(nrow(temp), 4000,replace=T), ]
dim(temp2)
# Put together
wine2<-rbind(wine[wine$Type=='2',],temp2)
# Shuffle the rows
wine2 <- wine2[sample(nrow(wine2)),]
dim(wine2)
# Change the target value to 1 and 0.
wine2$Type <-as.factor(ifelse(wine2$Type=='1','0','1'))
head(wine2)
#mean(as.numeric(wine2$Type))
table(wine2$Type)
group1<-wine2[wine2$Type=='1',]
group0 <-wine2[wine2$Type=='0',]
df2 <-group0[sample(nrow(group0), 800), ]
df<-rbind(df2,group1)
#mean(as.numeric(df$Type))
table(df$Type)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/asc.R
\name{asc}
\alias{asc}
\alias{chr}
\title{Convert between characters and ASCII codes}
\usage{
asc(char, simplify = TRUE)
chr(ascii)
}
\arguments{
\item{char}{vector of character strings}
\item{simplify}{logical indicating whether to attempt to convert the result
into a vector or matrix object. See \code{\link[base]{sapply}} for details.}
\item{ascii}{vector or list of vectors containing integer ASCII codes}
}
\value{
\code{asc} returns the integer ASCII values for each character in
the elements of \code{char}. If \code{simplify=FALSE} the result will be a
list containing one vector per element of \code{char}. If
\code{simplify=TRUE}, the code will attempt to convert the result into a
vector or matrix.
\code{asc} returns the characters corresponding to the provided ASCII
values.
}
\description{
Convert between characters and ASCII codes
}
\section{Functions}{
\itemize{
\item \code{asc}: return the characters corresponding to the specified ASCII codes
\item \code{chr}: return the ASCII codes for the specified characters.
}}
\examples{
## ascii codes for lowercase letters
asc(letters)
## uppercase letters from ascii codes
chr(65:90)
## works on muti-character strings
(tmp <- asc("hello!"))
chr(tmp)
## Use 'simplify=FALSE' to return the result as a list
(tmp <- asc("hello!", simplify = FALSE))
chr(tmp)
## When simplify=FALSE the results can be...
asc(c("a", "e", "i", "o", "u", "y")) # a vector
asc(c("ae", "io", "uy")) # or a matrix
## When simplify=TRUE the results are always a list...
asc(c("a", "e", "i", "o", "u", "y"), simplify = FALSE)
asc(c("ae", "io", "uy"), simplify = FALSE)
}
\seealso{
\code{\link[base]{strtoi}}, \code{\link[base]{charToRaw}},
\code{\link[base]{rawToChar}}, \code{\link[base]{as.raw}}
}
\author{
Adapted by Gregory R. Warnes \email{greg@warnes.net} from code
posted on the 'Data Debrief' blog on 2011-03-09 at
\url{https://datadebrief.blogspot.com/2011/03/ascii-code-table-in-r.html}.
}
\keyword{character}
\keyword{programming}
|
/man/asc.Rd
|
no_license
|
JDixonCS/gtools
|
R
| false
| true
| 2,076
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/asc.R
\name{asc}
\alias{asc}
\alias{chr}
\title{Convert between characters and ASCII codes}
\usage{
asc(char, simplify = TRUE)
chr(ascii)
}
\arguments{
\item{char}{vector of character strings}
\item{simplify}{logical indicating whether to attempt to convert the result
into a vector or matrix object. See \code{\link[base]{sapply}} for details.}
\item{ascii}{vector or list of vectors containing integer ASCII codes}
}
\value{
\code{asc} returns the integer ASCII values for each character in
the elements of \code{char}. If \code{simplify=FALSE} the result will be a
list containing one vector per element of \code{char}. If
\code{simplify=TRUE}, the code will attempt to convert the result into a
vector or matrix.
\code{asc} returns the characters corresponding to the provided ASCII
values.
}
\description{
Convert between characters and ASCII codes
}
\section{Functions}{
\itemize{
\item \code{asc}: return the characters corresponding to the specified ASCII codes
\item \code{chr}: return the ASCII codes for the specified characters.
}}
\examples{
## ascii codes for lowercase letters
asc(letters)
## uppercase letters from ascii codes
chr(65:90)
## works on muti-character strings
(tmp <- asc("hello!"))
chr(tmp)
## Use 'simplify=FALSE' to return the result as a list
(tmp <- asc("hello!", simplify = FALSE))
chr(tmp)
## When simplify=FALSE the results can be...
asc(c("a", "e", "i", "o", "u", "y")) # a vector
asc(c("ae", "io", "uy")) # or a matrix
## When simplify=TRUE the results are always a list...
asc(c("a", "e", "i", "o", "u", "y"), simplify = FALSE)
asc(c("ae", "io", "uy"), simplify = FALSE)
}
\seealso{
\code{\link[base]{strtoi}}, \code{\link[base]{charToRaw}},
\code{\link[base]{rawToChar}}, \code{\link[base]{as.raw}}
}
\author{
Adapted by Gregory R. Warnes \email{greg@warnes.net} from code
posted on the 'Data Debrief' blog on 2011-03-09 at
\url{https://datadebrief.blogspot.com/2011/03/ascii-code-table-in-r.html}.
}
\keyword{character}
\keyword{programming}
|
#' Data containing nodes and weights of graph
#'
#' A dataset is containing the edges and weights of graph.
#'
#' @format A data frame with 18 rows and 3 variables:
#' \describe{
#' \item{v1}{edges of the graph-from}
#' \item{v2}{edges of the graph-to}
#' \item{w}{weight of the edge from v1 to v2}
#' ...
#' }
#' @source \url{https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)}
"wiki_graph"
|
/R/wiki_graph.R
|
no_license
|
shwva184/LiuLab3
|
R
| false
| false
| 408
|
r
|
#' Data containing nodes and weights of graph
#'
#' A dataset is containing the edges and weights of graph.
#'
#' @format A data frame with 18 rows and 3 variables:
#' \describe{
#' \item{v1}{edges of the graph-from}
#' \item{v2}{edges of the graph-to}
#' \item{w}{weight of the edge from v1 to v2}
#' ...
#' }
#' @source \url{https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)}
"wiki_graph"
|
#' Edit a single attribute
#'
#' This function edits the slots of a single attribute in an existing list of attributes for a data object.
#'
#' This function can only be used on attributes entirely defined within the 'attributes' slot of attributeList;
#' it cannot be used to edit the factors table of an enumeratedDomain.
#'
#' In cases with very large attribute lists, use [arcticdatautils::which_in_eml()] first to locate
#' the attribute index number in the attributeList.
#'
#' @param attribute (attribute) The attribute in the the attributeList of a data object.
#' @param attributeName (character) The new name to give to the attribute.
#' @param attributeLabel (character) The new label to give to the attribute.
#' @param attributeDefinition (character) The new attributeDefinition to give to the attribute.
#' @param domain (character) The new domain to give to the attribute.
#' @param measurementScale (character) The new measurementScale to give to the attribute.
#' @param unit (character) The new unit (for numericDomain) to give to the attribute.
#' @param numberType (character) The new numberType (for numericDomain) to give to the attribute.
#' @param definition (character) The new definition (for textDomain) to give to the attribute.
#' @param formatString (character) The new formatString (for dateTime) to give to the attribute.
#' @param missingValueCode (character) The new missing value code to give to the attribute.
#' @param missingValueCodeExplanation (character) The new missing value code explanation to give to the attribute.
#'
#' @return (attribute) The modified attribute.
#'
#' @import EML
#' @importFrom methods new
#'
#' @export
#'
#' @examples
#' \dontrun{
#' cn <- dataone::CNode('PROD')
#' mn <- dataone::getMNode(cn, 'urn:node:ARCTIC')
#' doc <- EML::read_eml(rawToChar(dataone::getObject(mn, "doi:10.18739/A23W02")))
#' new_attribute <- edit_attribute(doc$dataset$dataTable[[1]]$attributeList$attribute[[1]],
#' attributeName = "new name")
#' doc$dataset$dataTable[[1]]$attributeList$attribute[[1]] <- new_attribute
#'
#' # Change an attribute's measurementScale from ratio to nominal
#' # (requires updating domain to textDomain or enumeratedDomain, setting unit and numberType
#' # to NA and adding a setting definition
#' new_attribute <- edit_attribute(doc$dataset$dataTable[[1]]$attributeList$attribute[[1]],
#' domain = "textDomain", measurementScale = "nominal", unit = NA,
#' numberType = NA, definition = 'new definition')
#' doc$dataset$dataTable[[1]]$attributeList$attribute[[1]] <- new_attribute
#' EML::eml_validate(doc) # validating complex EML changes is usually a good idea
#'
#' # Add the same missing value codes to all attributes for a data object
#' new_attributes <- lapply(doc$dataset$dataTable[[1]]$attributeList$attribute, edit_attribute,
#' missingValueCode = "NA", missingValueCodeExplanation = "data unavailable")
#' doc$dataset$dataTable[[1]]$attributeList$attribute <- new_attributes
#' }
edit_attribute <- function(attribute, attributeName = NULL, attributeLabel = NULL, attributeDefinition = NULL, domain = NULL,
measurementScale = NULL, unit = NULL, numberType = NULL, definition = NULL, formatString = NULL,
missingValueCode = NULL, missingValueCodeExplanation = NULL) {
stopifnot(methods::is(attribute, "list"))
if (!is.null(attributeName)) stopifnot(is.character(attributeName) && nchar(attributeName) > 0)
if (!is.null(attributeLabel) && !is.na(attributeLabel)) stopifnot(is.character(attributeLabel) && nchar(attributeLabel) > 0)
if (!is.null(attributeDefinition)) stopifnot(is.character(attributeDefinition) && nchar(attributeDefinition) > 0)
if (!is.null(domain)) stopifnot(is.character(domain) && any(c("textDomain", "enumeratedDomain", "numericDomain", "dateTime") %in% domain))
if (!is.null(measurementScale)) stopifnot(is.character(measurementScale) && any(c("nominal", "ordinal", "dateTime", "ratio", "interval") %in% measurementScale))
if (!is.null(unit) && !is.na(unit)) stopifnot(is.character(unit) && nchar(unit) > 0)
if (!is.null(numberType) && !is.na(numberType)) stopifnot(is.character(numberType) && any(c("real", "natural", "whole", "integer") %in% numberType))
if (!is.null(definition) && !is.na(definition)) stopifnot(is.character(definition) && nchar(definition) > 0)
if (!is.null(formatString) && !is.na(formatString)) stopifnot(is.character(formatString) && nchar(formatString) > 0)
if (!is.null(missingValueCode) && !is.na(missingValueCode)) stopifnot(is.character(missingValueCode) && nchar(missingValueCode) > 0)
if (!is.null(missingValueCodeExplanation) && !is.na(missingValueCodeExplanation)) stopifnot(is.character(missingValueCodeExplanation) && nchar(missingValueCodeExplanation) > 0)
if (length(c(missingValueCode, missingValueCodeExplanation)) == 1) stop("Need both missingValueCode and missingValueCodeExplanation.")
# Assign attribute to attributeList in order to convert attribute slots to data.frame
attList <- list()
attList$attribute[[1]] <- attribute
data <- EML::get_attributes(attList)
# enumeratedDomain does not contain the following fields
if (!is.null(data$factors)) {
fields <-c(unit, numberType, definition, formatString)
if (!is.null(fields)) {
stop('enumeratedDomain attributes cannot contain "unit", "numberType", "definition", or "formatString"')
}
}
attribute_edits <- cbind(attributeName, attributeLabel, attributeDefinition, domain,
measurementScale, unit, numberType, definition, formatString,
missingValueCode, missingValueCodeExplanation)
for (i in colnames(attribute_edits)) {
data$attributes[ , i] <- attribute_edits[ , i]
}
# Set edits to attributeList in order to convert data.frame to new attribute
new_attribute <- EML::set_attributes(data$attributes, data$factors)$attribute[[1]]
new_attribute
}
|
/R/edit_attribute.R
|
permissive
|
NCEAS/datamgmt
|
R
| false
| false
| 6,128
|
r
|
#' Edit a single attribute
#'
#' This function edits the slots of a single attribute in an existing list of attributes for a data object.
#'
#' This function can only be used on attributes entirely defined within the 'attributes' slot of attributeList;
#' it cannot be used to edit the factors table of an enumeratedDomain.
#'
#' In cases with very large attribute lists, use [arcticdatautils::which_in_eml()] first to locate
#' the attribute index number in the attributeList.
#'
#' @param attribute (attribute) The attribute in the the attributeList of a data object.
#' @param attributeName (character) The new name to give to the attribute.
#' @param attributeLabel (character) The new label to give to the attribute.
#' @param attributeDefinition (character) The new attributeDefinition to give to the attribute.
#' @param domain (character) The new domain to give to the attribute.
#' @param measurementScale (character) The new measurementScale to give to the attribute.
#' @param unit (character) The new unit (for numericDomain) to give to the attribute.
#' @param numberType (character) The new numberType (for numericDomain) to give to the attribute.
#' @param definition (character) The new definition (for textDomain) to give to the attribute.
#' @param formatString (character) The new formatString (for dateTime) to give to the attribute.
#' @param missingValueCode (character) The new missing value code to give to the attribute.
#' @param missingValueCodeExplanation (character) The new missing value code explanation to give to the attribute.
#'
#' @return (attribute) The modified attribute.
#'
#' @import EML
#' @importFrom methods new
#'
#' @export
#'
#' @examples
#' \dontrun{
#' cn <- dataone::CNode('PROD')
#' mn <- dataone::getMNode(cn, 'urn:node:ARCTIC')
#' doc <- EML::read_eml(rawToChar(dataone::getObject(mn, "doi:10.18739/A23W02")))
#' new_attribute <- edit_attribute(doc$dataset$dataTable[[1]]$attributeList$attribute[[1]],
#' attributeName = "new name")
#' doc$dataset$dataTable[[1]]$attributeList$attribute[[1]] <- new_attribute
#'
#' # Change an attribute's measurementScale from ratio to nominal
#' # (requires updating domain to textDomain or enumeratedDomain, setting unit and numberType
#' # to NA and adding a setting definition
#' new_attribute <- edit_attribute(doc$dataset$dataTable[[1]]$attributeList$attribute[[1]],
#' domain = "textDomain", measurementScale = "nominal", unit = NA,
#' numberType = NA, definition = 'new definition')
#' doc$dataset$dataTable[[1]]$attributeList$attribute[[1]] <- new_attribute
#' EML::eml_validate(doc) # validating complex EML changes is usually a good idea
#'
#' # Add the same missing value codes to all attributes for a data object
#' new_attributes <- lapply(doc$dataset$dataTable[[1]]$attributeList$attribute, edit_attribute,
#' missingValueCode = "NA", missingValueCodeExplanation = "data unavailable")
#' doc$dataset$dataTable[[1]]$attributeList$attribute <- new_attributes
#' }
edit_attribute <- function(attribute, attributeName = NULL, attributeLabel = NULL, attributeDefinition = NULL, domain = NULL,
measurementScale = NULL, unit = NULL, numberType = NULL, definition = NULL, formatString = NULL,
missingValueCode = NULL, missingValueCodeExplanation = NULL) {
stopifnot(methods::is(attribute, "list"))
if (!is.null(attributeName)) stopifnot(is.character(attributeName) && nchar(attributeName) > 0)
if (!is.null(attributeLabel) && !is.na(attributeLabel)) stopifnot(is.character(attributeLabel) && nchar(attributeLabel) > 0)
if (!is.null(attributeDefinition)) stopifnot(is.character(attributeDefinition) && nchar(attributeDefinition) > 0)
if (!is.null(domain)) stopifnot(is.character(domain) && any(c("textDomain", "enumeratedDomain", "numericDomain", "dateTime") %in% domain))
if (!is.null(measurementScale)) stopifnot(is.character(measurementScale) && any(c("nominal", "ordinal", "dateTime", "ratio", "interval") %in% measurementScale))
if (!is.null(unit) && !is.na(unit)) stopifnot(is.character(unit) && nchar(unit) > 0)
if (!is.null(numberType) && !is.na(numberType)) stopifnot(is.character(numberType) && any(c("real", "natural", "whole", "integer") %in% numberType))
if (!is.null(definition) && !is.na(definition)) stopifnot(is.character(definition) && nchar(definition) > 0)
if (!is.null(formatString) && !is.na(formatString)) stopifnot(is.character(formatString) && nchar(formatString) > 0)
if (!is.null(missingValueCode) && !is.na(missingValueCode)) stopifnot(is.character(missingValueCode) && nchar(missingValueCode) > 0)
if (!is.null(missingValueCodeExplanation) && !is.na(missingValueCodeExplanation)) stopifnot(is.character(missingValueCodeExplanation) && nchar(missingValueCodeExplanation) > 0)
if (length(c(missingValueCode, missingValueCodeExplanation)) == 1) stop("Need both missingValueCode and missingValueCodeExplanation.")
# Assign attribute to attributeList in order to convert attribute slots to data.frame
attList <- list()
attList$attribute[[1]] <- attribute
data <- EML::get_attributes(attList)
# enumeratedDomain does not contain the following fields
if (!is.null(data$factors)) {
fields <-c(unit, numberType, definition, formatString)
if (!is.null(fields)) {
stop('enumeratedDomain attributes cannot contain "unit", "numberType", "definition", or "formatString"')
}
}
attribute_edits <- cbind(attributeName, attributeLabel, attributeDefinition, domain,
measurementScale, unit, numberType, definition, formatString,
missingValueCode, missingValueCodeExplanation)
for (i in colnames(attribute_edits)) {
data$attributes[ , i] <- attribute_edits[ , i]
}
# Set edits to attributeList in order to convert data.frame to new attribute
new_attribute <- EML::set_attributes(data$attributes, data$factors)$attribute[[1]]
new_attribute
}
|
# Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations under the License.
#' @title Calculate long-term summary statistics from daily mean flows
#'
#' @description Calculates the long-term mean, median, maximum, minimum, and percentiles of daily flow values for over all months and
#' all data (Long-term) from a daily streamflow data set. Calculates statistics from all values, unless specified.
#' Returns a tibble with statistics.
#'
#' @inheritParams calc_daily_stats
#' @param percentiles Numeric vector of percentiles to calculate. Set to \code{NA} if none required. Default \code{c(10,90)}.
#' @param include_longterm Logical value indicating whether to include long-term calculation of all data. Default \code{TRUE}.
#' @param custom_months Numeric vector of months to combine to summarize (ex. \code{6:8} for Jun-Aug). Adds results to the end of table.
#' If wanting months that overlap calendar years (ex. Oct-Mar), choose \code{water_year_start} that begins before the first
#' month listed. Leave blank for no custom month summary.
#' @param custom_months_label Character string to label custom months. For example, if \code{months = 7:9} you may choose
#' \code{"Summer"} or \code{"Jul-Sep"}. Default \code{"Custom-Months"}.
#'
#' @return A tibble data frame with the following columns:
#' \item{Month}{month of the year, included 'Long-term' for all months, and 'Custom-Months' if selected}
#' \item{Mean}{mean of all daily data for a given month and long-term over all years}
#' \item{Median}{median of all daily data for a given month and long-term over all years}
#' \item{Maximum}{maximum of all daily data for a given month and long-term over all years}
#' \item{Minimum}{minimum of all daily data for a given month and long-term over all years}
#' \item{P'n'}{each n-th percentile selected for a given month and long-term over all years}
#' Default percentile columns:
#' \item{P10}{annual 10th percentile selected for a given month and long-term over all years}
#' \item{P90}{annual 90th percentile selected for a given month and long-term over all years}
#' Transposing data creates a column of "Statistics" and subsequent columns for each year selected.
#'
#' @examples
#' # Run if HYDAT database has been downloaded (using tidyhydat::download_hydat())
#' if (file.exists(tidyhydat::hy_downloaded_db())) {
#'
#' # Calculate long-term statistics using data argument with defaults
#' flow_data <- tidyhydat::hy_daily_flows(station_number = "08NM116")
#' calc_longterm_daily_stats(data = flow_data,
#' start_year = 1980)
#'
#' # Calculate long-term statistics using station_number argument with defaults
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980)
#'
#' # Calculate long-term statistics regardless if there is missing data for a given year
#' calc_longterm_daily_stats(station_number = "08NM116",
#' ignore_missing = TRUE)
#'
#' # Calculate long-term statistics for water years starting in October
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980,
#' water_year_start = 10)
#'
#' # Calculate long-term statistics with custom years
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1981,
#' end_year = 2010,
#' exclude_years = c(1991,1993:1995))
#'
#' # Calculate long-term statistics for 7-day flows for July-September months only,
#' # with 25 and 75th percentiles
#' calc_longterm_daily_stats(station_number = "08NM116",
#' roll_days = 7,
#' months = 7:9,
#' percentiles = c(25,75),
#' ignore_missing = TRUE,
#' include_longterm = FALSE) # removes the Long-term numbers
#'
#' # Calculate long-term statistics and add custom stats for July-September
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980,
#' custom_months = 7:9,
#' custom_months_label = "Summer")
#'
#' }
#' @export
calc_longterm_daily_stats <- function(data,
dates = Date,
values = Value,
groups = STATION_NUMBER,
station_number,
percentiles = c(10,90),
roll_days = 1,
roll_align = "right",
water_year_start = 1,
start_year,
end_year,
exclude_years,
months = 1:12,
complete_years = FALSE,
include_longterm = TRUE,
custom_months,
custom_months_label,
transpose = FALSE,
ignore_missing = FALSE){
## ARGUMENT CHECKS
## ---------------
if (missing(data)) {
data <- NULL
}
if (missing(station_number)) {
station_number <- NULL
}
if (missing(start_year)) {
start_year <- 0
}
if (missing(end_year)) {
end_year <- 9999
}
if (missing(exclude_years)) {
exclude_years <- NULL
}
if (missing(custom_months)) {
custom_months <- NULL
}
if (missing(custom_months_label)) {
custom_months_label <- "Custom-Months"
}
rolling_days_checks(roll_days, roll_align)
percentiles_checks(percentiles)
water_year_checks(water_year_start)
years_checks(start_year, end_year, exclude_years)
months_checks(months = months)
transpose_checks(transpose)
ignore_missing_checks(ignore_missing)
complete_yrs_checks(complete_years)
custom_months_checks(custom_months, custom_months_label)
include_longterm_checks(include_longterm)
## FLOW DATA CHECKS AND FORMATTING
## -------------------------------
# Check if data is provided and import it
flow_data <- flowdata_import(data = data,
station_number = station_number)
# Save the original columns (to check for STATION_NUMBER col at end) and ungroup if necessary
orig_cols <- names(flow_data)
flow_data <- dplyr::ungroup(flow_data)
# Check and rename columns
flow_data <- format_all_cols(data = flow_data,
dates = as.character(substitute(dates)),
values = as.character(substitute(values)),
groups = as.character(substitute(groups)),
rm_other_cols = TRUE)
## PREPARE FLOW DATA
## -----------------
# Fill missing dates, add date variables, and add WaterYear
flow_data <- analysis_prep(data = flow_data,
water_year_start = water_year_start)
# Add rolling means to end of dataframe
flow_data <- add_rolling_means(data = flow_data, roll_days = roll_days, roll_align = roll_align)
colnames(flow_data)[ncol(flow_data)] <- "RollingValue"
# Filter for the selected years
flow_data <- dplyr::filter(flow_data, WaterYear >= start_year & WaterYear <= end_year)
flow_data <- dplyr::filter(flow_data, !(WaterYear %in% exclude_years))
flow_data <- dplyr::filter(flow_data, Month %in% months)
# Stop if all data is NA
no_values_error(flow_data$RollingValue)
# Remove incomplete years if selected
flow_data <- filter_complete_yrs(complete_years = complete_years,
flow_data)
# Stop if all data is NA
no_values_error(flow_data$RollingValue)
## CALCULATE STATISTICS
## --------------------
# Calculate the monthly and longterm stats
Q_months <- dplyr::summarize(dplyr::group_by(flow_data, STATION_NUMBER, MonthName),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = ifelse(!is.na(Mean), max(RollingValue, na.rm = ignore_missing), NA),
Minimum = ifelse(!is.na(Mean), min(RollingValue, na.rm = ignore_missing), NA))
Q_months <- dplyr::ungroup(Q_months)
if (include_longterm) {
longterm_stats <- dplyr::summarize(dplyr::group_by(flow_data, STATION_NUMBER),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = ifelse(!is.na(Mean), max(RollingValue, na.rm = ignore_missing), NA),
Minimum = ifelse(!is.na(Mean), min(RollingValue, na.rm = ignore_missing), NA))
longterm_stats <- dplyr::ungroup(longterm_stats)
longterm_stats <- dplyr::mutate(longterm_stats, MonthName = as.factor("Long-term"))
longterm_stats <- rbind(Q_months, longterm_stats) #dplyr::bindrows gives unnecessary warnings
} else {
longterm_stats <- Q_months
}
# Calculate the monthly and longterm percentiles
if(!all(is.na(percentiles))) {
for (ptile in unique(percentiles)) {
Q_months_ptile <- dplyr::summarise(dplyr::group_by(flow_data, STATION_NUMBER, MonthName),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
names(Q_months_ptile)[names(Q_months_ptile) == "Percentile"] <- paste0("P", ptile)
Q_months_ptile <- dplyr::ungroup(Q_months_ptile)
if (include_longterm) {
longterm_stats_ptile <- dplyr::summarise(dplyr::group_by(flow_data, STATION_NUMBER),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
longterm_stats_ptile <- dplyr::mutate(longterm_stats_ptile, MonthName = "Long-term")
names(longterm_stats_ptile)[names(longterm_stats_ptile) == "Percentile"] <- paste0("P", ptile)
longterm_stats_ptile <- dplyr::ungroup(longterm_stats_ptile)
longterm_stats_ptile <- rbind(dplyr::ungroup(Q_months_ptile), dplyr::ungroup(longterm_stats_ptile)) #dplyr::bindrows gives unnecessary warnings
} else {
longterm_stats_ptile <- Q_months_ptile
}
# Merge with longterm_stats
longterm_stats <- merge(longterm_stats,longterm_stats_ptile, by = c("STATION_NUMBER", "MonthName"))
}
}
# Calculate custom_months is selected, append data to end
if(is.numeric(custom_months) & all(custom_months %in% c(1:12))) {
# Filter months for those selected and calculate stats
flow_data_temp <- dplyr::filter(flow_data, Month %in% custom_months)
Q_months_custom <- dplyr::summarize(dplyr::group_by(flow_data_temp, STATION_NUMBER),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = max(RollingValue,na.rm = ignore_missing),
Minimum = min(RollingValue,na.rm = ignore_missing))
Q_months_custom <- dplyr::mutate(Q_months_custom, MonthName = paste0(custom_months_label))
# Calculate percentiles
if (!all(is.na(percentiles))){
for (ptile in unique(percentiles)) {
Q_ptile_custom <- dplyr::summarize(dplyr::group_by(flow_data_temp, STATION_NUMBER),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
Q_ptile_custom <- dplyr::mutate(Q_ptile_custom, MonthName = paste0(custom_months_label))
names(Q_ptile_custom)[names(Q_ptile_custom) == "Percentile"] <- paste0("P", ptile)
# Merge with custom stats
Q_months_custom <- merge(dplyr::ungroup(Q_months_custom), dplyr::ungroup(Q_ptile_custom), by = c("STATION_NUMBER", "MonthName"))
}
}
# Merge with longterm_stats
longterm_stats <- rbind(longterm_stats, Q_months_custom)
}
# Rename Month column and reorder to proper levels (set in add_date_vars)
longterm_stats <- dplyr::rename(longterm_stats, Month = MonthName)
longterm_stats <- with(longterm_stats, longterm_stats[order(STATION_NUMBER, Month),])
# row.names(longterm_stats) <- c(1:nrow(longterm_stats))
# If transpose if selected, switch columns and rows
if (transpose) {
# Get list of columns to order the Statistic column after transposing
stat_levels <- names(longterm_stats[-(1:2)])
# Transpose the columns for rows
longterm_stats <- tidyr::gather(longterm_stats, Statistic, Value, -STATION_NUMBER, -Month)
longterm_stats <- tidyr::spread(longterm_stats, Month, Value)
# Order the columns
longterm_stats$Statistic <- factor(longterm_stats$Statistic, levels = stat_levels)
longterm_stats <- dplyr::arrange(longterm_stats, STATION_NUMBER, Statistic)
}
# Give warning if any NA values
missing_values_warning(longterm_stats[, 3:ncol(longterm_stats)])
# Recheck if station_number was in original flow_data and rename or remove as necessary
if(as.character(substitute(groups)) %in% orig_cols) {
names(longterm_stats)[names(longterm_stats) == "STATION_NUMBER"] <- as.character(substitute(groups))
} else {
longterm_stats <- dplyr::select(longterm_stats, -STATION_NUMBER)
}
dplyr::as_tibble(longterm_stats)
}
|
/R/calc_longterm_daily_stats.R
|
permissive
|
Rain3498/fasstr
|
R
| false
| false
| 14,932
|
r
|
# Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations under the License.
#' @title Calculate long-term summary statistics from daily mean flows
#'
#' @description Calculates the long-term mean, median, maximum, minimum, and percentiles of daily flow values for over all months and
#' all data (Long-term) from a daily streamflow data set. Calculates statistics from all values, unless specified.
#' Returns a tibble with statistics.
#'
#' @inheritParams calc_daily_stats
#' @param percentiles Numeric vector of percentiles to calculate. Set to \code{NA} if none required. Default \code{c(10,90)}.
#' @param include_longterm Logical value indicating whether to include long-term calculation of all data. Default \code{TRUE}.
#' @param custom_months Numeric vector of months to combine to summarize (ex. \code{6:8} for Jun-Aug). Adds results to the end of table.
#' If wanting months that overlap calendar years (ex. Oct-Mar), choose \code{water_year_start} that begins before the first
#' month listed. Leave blank for no custom month summary.
#' @param custom_months_label Character string to label custom months. For example, if \code{months = 7:9} you may choose
#' \code{"Summer"} or \code{"Jul-Sep"}. Default \code{"Custom-Months"}.
#'
#' @return A tibble data frame with the following columns:
#' \item{Month}{month of the year, included 'Long-term' for all months, and 'Custom-Months' if selected}
#' \item{Mean}{mean of all daily data for a given month and long-term over all years}
#' \item{Median}{median of all daily data for a given month and long-term over all years}
#' \item{Maximum}{maximum of all daily data for a given month and long-term over all years}
#' \item{Minimum}{minimum of all daily data for a given month and long-term over all years}
#' \item{P'n'}{each n-th percentile selected for a given month and long-term over all years}
#' Default percentile columns:
#' \item{P10}{annual 10th percentile selected for a given month and long-term over all years}
#' \item{P90}{annual 90th percentile selected for a given month and long-term over all years}
#' Transposing data creates a column of "Statistics" and subsequent columns for each year selected.
#'
#' @examples
#' # Run if HYDAT database has been downloaded (using tidyhydat::download_hydat())
#' if (file.exists(tidyhydat::hy_downloaded_db())) {
#'
#' # Calculate long-term statistics using data argument with defaults
#' flow_data <- tidyhydat::hy_daily_flows(station_number = "08NM116")
#' calc_longterm_daily_stats(data = flow_data,
#' start_year = 1980)
#'
#' # Calculate long-term statistics using station_number argument with defaults
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980)
#'
#' # Calculate long-term statistics regardless if there is missing data for a given year
#' calc_longterm_daily_stats(station_number = "08NM116",
#' ignore_missing = TRUE)
#'
#' # Calculate long-term statistics for water years starting in October
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980,
#' water_year_start = 10)
#'
#' # Calculate long-term statistics with custom years
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1981,
#' end_year = 2010,
#' exclude_years = c(1991,1993:1995))
#'
#' # Calculate long-term statistics for 7-day flows for July-September months only,
#' # with 25 and 75th percentiles
#' calc_longterm_daily_stats(station_number = "08NM116",
#' roll_days = 7,
#' months = 7:9,
#' percentiles = c(25,75),
#' ignore_missing = TRUE,
#' include_longterm = FALSE) # removes the Long-term numbers
#'
#' # Calculate long-term statistics and add custom stats for July-September
#' calc_longterm_daily_stats(station_number = "08NM116",
#' start_year = 1980,
#' custom_months = 7:9,
#' custom_months_label = "Summer")
#'
#' }
#' @export
calc_longterm_daily_stats <- function(data,
dates = Date,
values = Value,
groups = STATION_NUMBER,
station_number,
percentiles = c(10,90),
roll_days = 1,
roll_align = "right",
water_year_start = 1,
start_year,
end_year,
exclude_years,
months = 1:12,
complete_years = FALSE,
include_longterm = TRUE,
custom_months,
custom_months_label,
transpose = FALSE,
ignore_missing = FALSE){
## ARGUMENT CHECKS
## ---------------
if (missing(data)) {
data <- NULL
}
if (missing(station_number)) {
station_number <- NULL
}
if (missing(start_year)) {
start_year <- 0
}
if (missing(end_year)) {
end_year <- 9999
}
if (missing(exclude_years)) {
exclude_years <- NULL
}
if (missing(custom_months)) {
custom_months <- NULL
}
if (missing(custom_months_label)) {
custom_months_label <- "Custom-Months"
}
rolling_days_checks(roll_days, roll_align)
percentiles_checks(percentiles)
water_year_checks(water_year_start)
years_checks(start_year, end_year, exclude_years)
months_checks(months = months)
transpose_checks(transpose)
ignore_missing_checks(ignore_missing)
complete_yrs_checks(complete_years)
custom_months_checks(custom_months, custom_months_label)
include_longterm_checks(include_longterm)
## FLOW DATA CHECKS AND FORMATTING
## -------------------------------
# Check if data is provided and import it
flow_data <- flowdata_import(data = data,
station_number = station_number)
# Save the original columns (to check for STATION_NUMBER col at end) and ungroup if necessary
orig_cols <- names(flow_data)
flow_data <- dplyr::ungroup(flow_data)
# Check and rename columns
flow_data <- format_all_cols(data = flow_data,
dates = as.character(substitute(dates)),
values = as.character(substitute(values)),
groups = as.character(substitute(groups)),
rm_other_cols = TRUE)
## PREPARE FLOW DATA
## -----------------
# Fill missing dates, add date variables, and add WaterYear
flow_data <- analysis_prep(data = flow_data,
water_year_start = water_year_start)
# Add rolling means to end of dataframe
flow_data <- add_rolling_means(data = flow_data, roll_days = roll_days, roll_align = roll_align)
colnames(flow_data)[ncol(flow_data)] <- "RollingValue"
# Filter for the selected years
flow_data <- dplyr::filter(flow_data, WaterYear >= start_year & WaterYear <= end_year)
flow_data <- dplyr::filter(flow_data, !(WaterYear %in% exclude_years))
flow_data <- dplyr::filter(flow_data, Month %in% months)
# Stop if all data is NA
no_values_error(flow_data$RollingValue)
# Remove incomplete years if selected
flow_data <- filter_complete_yrs(complete_years = complete_years,
flow_data)
# Stop if all data is NA
no_values_error(flow_data$RollingValue)
## CALCULATE STATISTICS
## --------------------
# Calculate the monthly and longterm stats
Q_months <- dplyr::summarize(dplyr::group_by(flow_data, STATION_NUMBER, MonthName),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = ifelse(!is.na(Mean), max(RollingValue, na.rm = ignore_missing), NA),
Minimum = ifelse(!is.na(Mean), min(RollingValue, na.rm = ignore_missing), NA))
Q_months <- dplyr::ungroup(Q_months)
if (include_longterm) {
longterm_stats <- dplyr::summarize(dplyr::group_by(flow_data, STATION_NUMBER),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = ifelse(!is.na(Mean), max(RollingValue, na.rm = ignore_missing), NA),
Minimum = ifelse(!is.na(Mean), min(RollingValue, na.rm = ignore_missing), NA))
longterm_stats <- dplyr::ungroup(longterm_stats)
longterm_stats <- dplyr::mutate(longterm_stats, MonthName = as.factor("Long-term"))
longterm_stats <- rbind(Q_months, longterm_stats) #dplyr::bindrows gives unnecessary warnings
} else {
longterm_stats <- Q_months
}
# Calculate the monthly and longterm percentiles
if(!all(is.na(percentiles))) {
for (ptile in unique(percentiles)) {
Q_months_ptile <- dplyr::summarise(dplyr::group_by(flow_data, STATION_NUMBER, MonthName),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
names(Q_months_ptile)[names(Q_months_ptile) == "Percentile"] <- paste0("P", ptile)
Q_months_ptile <- dplyr::ungroup(Q_months_ptile)
if (include_longterm) {
longterm_stats_ptile <- dplyr::summarise(dplyr::group_by(flow_data, STATION_NUMBER),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
longterm_stats_ptile <- dplyr::mutate(longterm_stats_ptile, MonthName = "Long-term")
names(longterm_stats_ptile)[names(longterm_stats_ptile) == "Percentile"] <- paste0("P", ptile)
longterm_stats_ptile <- dplyr::ungroup(longterm_stats_ptile)
longterm_stats_ptile <- rbind(dplyr::ungroup(Q_months_ptile), dplyr::ungroup(longterm_stats_ptile)) #dplyr::bindrows gives unnecessary warnings
} else {
longterm_stats_ptile <- Q_months_ptile
}
# Merge with longterm_stats
longterm_stats <- merge(longterm_stats,longterm_stats_ptile, by = c("STATION_NUMBER", "MonthName"))
}
}
# Calculate custom_months is selected, append data to end
if(is.numeric(custom_months) & all(custom_months %in% c(1:12))) {
# Filter months for those selected and calculate stats
flow_data_temp <- dplyr::filter(flow_data, Month %in% custom_months)
Q_months_custom <- dplyr::summarize(dplyr::group_by(flow_data_temp, STATION_NUMBER),
Mean = mean(RollingValue, na.rm = ignore_missing),
Median = stats::median(RollingValue, na.rm = ignore_missing),
Maximum = max(RollingValue,na.rm = ignore_missing),
Minimum = min(RollingValue,na.rm = ignore_missing))
Q_months_custom <- dplyr::mutate(Q_months_custom, MonthName = paste0(custom_months_label))
# Calculate percentiles
if (!all(is.na(percentiles))){
for (ptile in unique(percentiles)) {
Q_ptile_custom <- dplyr::summarize(dplyr::group_by(flow_data_temp, STATION_NUMBER),
Percentile = ifelse(!is.na(mean(RollingValue, na.rm = FALSE)) | ignore_missing,
stats::quantile(RollingValue, ptile / 100, na.rm = TRUE), NA))
Q_ptile_custom <- dplyr::mutate(Q_ptile_custom, MonthName = paste0(custom_months_label))
names(Q_ptile_custom)[names(Q_ptile_custom) == "Percentile"] <- paste0("P", ptile)
# Merge with custom stats
Q_months_custom <- merge(dplyr::ungroup(Q_months_custom), dplyr::ungroup(Q_ptile_custom), by = c("STATION_NUMBER", "MonthName"))
}
}
# Merge with longterm_stats
longterm_stats <- rbind(longterm_stats, Q_months_custom)
}
# Rename Month column and reorder to proper levels (set in add_date_vars)
longterm_stats <- dplyr::rename(longterm_stats, Month = MonthName)
longterm_stats <- with(longterm_stats, longterm_stats[order(STATION_NUMBER, Month),])
# row.names(longterm_stats) <- c(1:nrow(longterm_stats))
# If transpose if selected, switch columns and rows
if (transpose) {
# Get list of columns to order the Statistic column after transposing
stat_levels <- names(longterm_stats[-(1:2)])
# Transpose the columns for rows
longterm_stats <- tidyr::gather(longterm_stats, Statistic, Value, -STATION_NUMBER, -Month)
longterm_stats <- tidyr::spread(longterm_stats, Month, Value)
# Order the columns
longterm_stats$Statistic <- factor(longterm_stats$Statistic, levels = stat_levels)
longterm_stats <- dplyr::arrange(longterm_stats, STATION_NUMBER, Statistic)
}
# Give warning if any NA values
missing_values_warning(longterm_stats[, 3:ncol(longterm_stats)])
# Recheck if station_number was in original flow_data and rename or remove as necessary
if(as.character(substitute(groups)) %in% orig_cols) {
names(longterm_stats)[names(longterm_stats) == "STATION_NUMBER"] <- as.character(substitute(groups))
} else {
longterm_stats <- dplyr::select(longterm_stats, -STATION_NUMBER)
}
dplyr::as_tibble(longterm_stats)
}
|
/Model_Prod_6_01.R
|
no_license
|
marcelomedre/B2WLabs
|
R
| false
| false
| 5,362
|
r
| ||
#' Extraction of intensity singles, given any excitation and emission wavelenghts.
#'
#' @param x_emission Selected emission wavelenght in nm. Default is 350 for exampling purposes.
#' @param y_excitation Selected excitation wavelenght in nm. Default is 350 for exampling purposes.
#' @param data The default value of "data_interpol" is assuming that "LBE_fluo3D_processing" have just been used. Otherwise a list containing x=vector of emission wavelenghts, y=vector of excitation wavelenghts, z= a matrix containing the z values (intensity) where X must be the lines, Y must be the columns and Z must be the values of the table.
#' @keywords 3D fluorescence
#' @export
#' @examples
#' > setwd(XXXX) #XXX is your directory contaning the .sp files (copy and paste from the brownse and replace all the "\" by "/").
#' > LBE_fluo3D_processing() #include your options if you want.
#' > single_point(350,420) #returns an intensity value at emission = 350 and excitation = 420 nm.
LBE_fluo3D_single_point=function(x_emission=350,
y_excitation=350,
data=data_interpol ##assuming that "LBE_fluo3D_processing" have just been used
) {
result=interp.surface(data_interpol,loc=matrix(c(x_emission,y_excitation),nrow = 1))
print(result)
return("result_extrapolation_xy"<<-result)
} #end of the function
|
/R/LBE_fluo3D_single_point.R
|
no_license
|
guilayn/lbe3df
|
R
| false
| false
| 1,407
|
r
|
#' Extraction of intensity singles, given any excitation and emission wavelenghts.
#'
#' @param x_emission Selected emission wavelenght in nm. Default is 350 for exampling purposes.
#' @param y_excitation Selected excitation wavelenght in nm. Default is 350 for exampling purposes.
#' @param data The default value of "data_interpol" is assuming that "LBE_fluo3D_processing" have just been used. Otherwise a list containing x=vector of emission wavelenghts, y=vector of excitation wavelenghts, z= a matrix containing the z values (intensity) where X must be the lines, Y must be the columns and Z must be the values of the table.
#' @keywords 3D fluorescence
#' @export
#' @examples
#' > setwd(XXXX) #XXX is your directory contaning the .sp files (copy and paste from the brownse and replace all the "\" by "/").
#' > LBE_fluo3D_processing() #include your options if you want.
#' > single_point(350,420) #returns an intensity value at emission = 350 and excitation = 420 nm.
LBE_fluo3D_single_point=function(x_emission=350,
y_excitation=350,
data=data_interpol ##assuming that "LBE_fluo3D_processing" have just been used
) {
result=interp.surface(data_interpol,loc=matrix(c(x_emission,y_excitation),nrow = 1))
print(result)
return("result_extrapolation_xy"<<-result)
} #end of the function
|
#' Temporarily mock requests
#'
#' Mocking allows you to selectively and temporarily replace the response
#' you would typically receive from a request with your own code. It's
#' primarily used for testing.
#'
#' @param mock A single argument function called with a request object.
#' It should return either `NULL` (if it doesn't want to handle the request)
#' or a [response] (if it does).
#' @param code Code to execute in the temporary environment.
#' @param env Environment to use for scoping changes.
#' @returns `with_mock()` returns the result of evaluating `code`.
#' @export
#' @examples
#' # This function should perform a response against google.com:
#' google <- function() {
#' request("http://google.com") %>%
#' req_perform()
#' }
#'
#' # But I can use a mock to instead return my own made up response:
#' my_mock <- function(req) {
#' response(status_code = 403)
#' }
#' with_mock(my_mock, google())
with_mock <- function(mock, code) {
withr::with_options(list(httr2_mock = mock), code)
}
#' @export
#' @rdname with_mock
local_mock <- function(mock, env = caller_env()) {
withr::local_options(httr2_mock = mock, .local_envir = env)
}
|
/R/req-mock.R
|
permissive
|
adithirgis/httr2
|
R
| false
| false
| 1,169
|
r
|
#' Temporarily mock requests
#'
#' Mocking allows you to selectively and temporarily replace the response
#' you would typically receive from a request with your own code. It's
#' primarily used for testing.
#'
#' @param mock A single argument function called with a request object.
#' It should return either `NULL` (if it doesn't want to handle the request)
#' or a [response] (if it does).
#' @param code Code to execute in the temporary environment.
#' @param env Environment to use for scoping changes.
#' @returns `with_mock()` returns the result of evaluating `code`.
#' @export
#' @examples
#' # This function should perform a response against google.com:
#' google <- function() {
#' request("http://google.com") %>%
#' req_perform()
#' }
#'
#' # But I can use a mock to instead return my own made up response:
#' my_mock <- function(req) {
#' response(status_code = 403)
#' }
#' with_mock(my_mock, google())
with_mock <- function(mock, code) {
withr::with_options(list(httr2_mock = mock), code)
}
#' @export
#' @rdname with_mock
local_mock <- function(mock, env = caller_env()) {
withr::local_options(httr2_mock = mock, .local_envir = env)
}
|
# Read the data in correctly
mydata_utf8 = read.table("WDIData.csv",sep=",",fileEncoding="UTF-8-BOM",header=TRUE)
country_code = read.table("country_code.csv",sep=",",header=TRUE)
# drop off the final blank column
mydata = mydata_utf8[,1:63]
# find the right variables
my_var = c("EN.ATM.CO2E.PC","BX.KLT.DINV.CD.WD","BX.TRF.PWKR.DT.GD.ZS",
"DT.ODA.ODAT.CD","NE.EXP.GNFS.ZS","NV.AGR.EMPL.KD",
"NV.IND.EMPL.KD","NV.IND.MANF.CD","NV.MNF.TECH.ZS.UN","NV.SRV.EMPL.KD",
"NY.ADJ.SVNX.GN.ZS","SL.AGR.EMPL.ZS","SL.IND.EMPL.ZS",
"SL.ISV.IFRM.ZS","SL.SRV.EMPL.ZS")
# subset the data
country_data = subset(mydata,mydata$Country.Code %in% country_code$Country.Code)
main_data = subset(country_data,country_data$Indicator.Code %in% my_var)
# find the columns with the year we want
main_idx <- match(c("Country.Code","Indicator.Code","X2014"), names(mydata))
# peel off only the column we want from 2014
main_data <- main_data[,main_idx]
# Name the columns
names(main_data) <- c("Country","Indicator","Value")
#install.packages('reshape')
library('reshape')
# Reshaping
combined_melt = melt(main_data, id=c("Country","Indicator","Value"))
combined_cast = cast(combined_melt, value = "Value", Country ~ Indicator)
final_data <- combined_cast
final_data <- na.omit(final_data)
final_data$y <- log(final_data$EN.ATM.CO2E.PC)
final_data$x1 <- log(final_data$NV.AGR.EMPL.KD)
final_data$x2 <- log(final_data$NV.IND.EMPL.KD)
final_data$x3 <- log(final_data$NV.SRV.EMPL.KD)
final_data$x4 <- log(final_data$SL.AGR.EMPL.ZS)
final_data$x5 <- log(final_data$SL.IND.EMPL.ZS)
final_data$x6 <- log(final_data$SL.SRV.EMPL.ZS)
#install.packages("ggpubr")
library("ggpubr")
#install.packages('tidyverse')
library('tidyverse')
# Try plotting an indicator against CO2/capita:
ggscatter(final_data, x = "x2", y = "y",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "kendall", color = "sky blue",
cor.coef.coord = c(7,3), xlim=c(6.6,12.7),
xlab = "Productivity in Industry (log scale)", ylab="CO2/capita (log scale)",
main = " (Productivity in Industry) vs (CO2/capita)")
# Create a dataframe for the result of the analysis:
correlation_table=data.frame(matrix(ncol=3,nrow=15))
names(correlation_table) <- c("Indicator","p_value","Cor_coefficient")
correlation_table$Indicator <- my_var
# Run the correlation analysis:
i=1
for (indicator in my_var) {
result <- cor.test(final_data1 %>% pull(indicator), final_data1 %>% pull(EN.ATM.CO2E.PC),
use="pairwise.complete.obs",method="kendall")
correlation_table[i,2] <- result$p.value
correlation_table[i,3] <- result$estimate
i=i+1
}
#install.packages("writexl")
library("writexl")
write_xlsx(correlation_table,"correlation_table_kendall.xlsx")
write_xlsx(final_data1,"final_data_excel.xlsx")
# To plot Q-Q plot and Density plot
library(ggpubr)
ggqqplot(log(combined_cast$EN.ATM.CO2E.PC), main="Q-Q plot of log(CO2/capita)", color = "royal blue")
ggdensity(log(combined_cast$EN.ATM.CO2E.PC),
main = "Density plot of log(CO2/capita)",
xlab = "log(CO2/capita)")
# To test whether the CO2 data follows normal distribution:
shapiro.test(log(combined_cast$EN.ATM.CO2E.PC))
|
/Normality and Correlation Analysis.R
|
no_license
|
respectisearnednotgiven/CO2-Emission-Project
|
R
| false
| false
| 3,365
|
r
|
# Read the data in correctly
mydata_utf8 = read.table("WDIData.csv",sep=",",fileEncoding="UTF-8-BOM",header=TRUE)
country_code = read.table("country_code.csv",sep=",",header=TRUE)
# drop off the final blank column
mydata = mydata_utf8[,1:63]
# find the right variables
my_var = c("EN.ATM.CO2E.PC","BX.KLT.DINV.CD.WD","BX.TRF.PWKR.DT.GD.ZS",
"DT.ODA.ODAT.CD","NE.EXP.GNFS.ZS","NV.AGR.EMPL.KD",
"NV.IND.EMPL.KD","NV.IND.MANF.CD","NV.MNF.TECH.ZS.UN","NV.SRV.EMPL.KD",
"NY.ADJ.SVNX.GN.ZS","SL.AGR.EMPL.ZS","SL.IND.EMPL.ZS",
"SL.ISV.IFRM.ZS","SL.SRV.EMPL.ZS")
# subset the data
country_data = subset(mydata,mydata$Country.Code %in% country_code$Country.Code)
main_data = subset(country_data,country_data$Indicator.Code %in% my_var)
# find the columns with the year we want
main_idx <- match(c("Country.Code","Indicator.Code","X2014"), names(mydata))
# peel off only the column we want from 2014
main_data <- main_data[,main_idx]
# Name the columns
names(main_data) <- c("Country","Indicator","Value")
#install.packages('reshape')
library('reshape')
# Reshaping
combined_melt = melt(main_data, id=c("Country","Indicator","Value"))
combined_cast = cast(combined_melt, value = "Value", Country ~ Indicator)
final_data <- combined_cast
final_data <- na.omit(final_data)
final_data$y <- log(final_data$EN.ATM.CO2E.PC)
final_data$x1 <- log(final_data$NV.AGR.EMPL.KD)
final_data$x2 <- log(final_data$NV.IND.EMPL.KD)
final_data$x3 <- log(final_data$NV.SRV.EMPL.KD)
final_data$x4 <- log(final_data$SL.AGR.EMPL.ZS)
final_data$x5 <- log(final_data$SL.IND.EMPL.ZS)
final_data$x6 <- log(final_data$SL.SRV.EMPL.ZS)
#install.packages("ggpubr")
library("ggpubr")
#install.packages('tidyverse')
library('tidyverse')
# Try plotting an indicator against CO2/capita:
ggscatter(final_data, x = "x2", y = "y",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "kendall", color = "sky blue",
cor.coef.coord = c(7,3), xlim=c(6.6,12.7),
xlab = "Productivity in Industry (log scale)", ylab="CO2/capita (log scale)",
main = " (Productivity in Industry) vs (CO2/capita)")
# Create a dataframe for the result of the analysis:
correlation_table=data.frame(matrix(ncol=3,nrow=15))
names(correlation_table) <- c("Indicator","p_value","Cor_coefficient")
correlation_table$Indicator <- my_var
# Run the correlation analysis:
i=1
for (indicator in my_var) {
result <- cor.test(final_data1 %>% pull(indicator), final_data1 %>% pull(EN.ATM.CO2E.PC),
use="pairwise.complete.obs",method="kendall")
correlation_table[i,2] <- result$p.value
correlation_table[i,3] <- result$estimate
i=i+1
}
#install.packages("writexl")
library("writexl")
write_xlsx(correlation_table,"correlation_table_kendall.xlsx")
write_xlsx(final_data1,"final_data_excel.xlsx")
# To plot Q-Q plot and Density plot
library(ggpubr)
ggqqplot(log(combined_cast$EN.ATM.CO2E.PC), main="Q-Q plot of log(CO2/capita)", color = "royal blue")
ggdensity(log(combined_cast$EN.ATM.CO2E.PC),
main = "Density plot of log(CO2/capita)",
xlab = "log(CO2/capita)")
# To test whether the CO2 data follows normal distribution:
shapiro.test(log(combined_cast$EN.ATM.CO2E.PC))
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ds_get_latest.R
\name{ds_get_latest}
\alias{ds_get_latest}
\title{ds_get_latest}
\usage{
ds_get_latest(ds, ds_name)
}
\arguments{
\item{ds}{path to datastore}
\item{ds_name}{name of data-object to retrieve}
}
\value{
the requested data-object
}
\description{
retrieves the latest version of a given dataset from a datastore
}
\examples{
## not yet
}
|
/man/ds_get_latest.Rd
|
no_license
|
bernhard-da/datastore
|
R
| false
| true
| 429
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ds_get_latest.R
\name{ds_get_latest}
\alias{ds_get_latest}
\title{ds_get_latest}
\usage{
ds_get_latest(ds, ds_name)
}
\arguments{
\item{ds}{path to datastore}
\item{ds_name}{name of data-object to retrieve}
}
\value{
the requested data-object
}
\description{
retrieves the latest version of a given dataset from a datastore
}
\examples{
## not yet
}
|
testlist <- list(data = structure(c(-1.39067116739524e-308, 9.32399348468785e-256, 2.22809558051354e-313, 4.16249482798317e-258, 0, 0, 1.6189543082926e-319, 0, 0, 0), .Dim = c(10L, 1L)), q = 5.85317984824666e+170)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result)
|
/biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610554520-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false
| false
| 285
|
r
|
testlist <- list(data = structure(c(-1.39067116739524e-308, 9.32399348468785e-256, 2.22809558051354e-313, 4.16249482798317e-258, 0, 0, 1.6189543082926e-319, 0, 0, 0), .Dim = c(10L, 1L)), q = 5.85317984824666e+170)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result)
|
comment("-------------Expressions---------")
2+2
4*3
2/7
7-3
3^(-2)
2^(1/2)
pi
exp(1)
log(1)
?log
log(exp(1))
?loglin
a <- 10
"OR"
a = 10
?comment
comment("--------------TYPES--------------")
42.5
is.numeric(42.5)
is.double(42.5)
TRUE
is.logical(TRUE)
"Stat"
is.character("Stat")
comment("-------------Vectors are Homgenous---------")
c(1,2,3,4,5,6)
x <- c(1,2,3,4,5,6)
is.vector(x)
(y <- 1:100)
comment("-------------Combine Various Types, Results In Type Coeercesion---------")
c(42, "Boshika", TRUE)
c(42, TRUE)
comment("-------------Sequence Vectors and Repeating Vectors---------")
seq(from = 100, to = 200, by = 12.5)
rep("Boshika", times = 5)
comment("-------------Subsetting---------")
x[1]
x[1:3]
x[c(1,3,4)]
x[-2]
x[c(TRUE,FALSE,TRUE,FALSE,FALSE,FALSE)]
c(1:100, rep(42, times=10))[seq(2,20,by=2)]
x[x>=3]
comment("-------------Vectorization---------")
x=1:10
x + 1
log(x)
comment("-------------Programming Basics---------")
fib = c(1,2,3,4,5,6)
ifelse(fib*2==6, "M", "NM")
seq_num = c(11:20)
for (i in 1:5) {
seq_num[i] = seq_num[i] * 10
}
seq_num
comment("-------------Easier Way-Avoid For Loops---------")
x = 11:20
x = x * 10
x
comment("-------------Functions---------")
silly_func = function(arg1, arg2, arg3=10){
a = arg1 + arg2
b = a + arg3
c(a, b, a + b)
}
silly_func(20, 30)
silly_func(20, 30, 40)
comment("-------------Matrices---------")
x = 1:9
X = matrix(x, nrow = 3, ncol = 3)
X
X[1,2]
X[,2]
X[2,]
comment("-------------Alternate Ways to Create Matrices---------")
cbind(col1=1:10, col2=rev(1:10), col3=rev(10:1))
comment("-------------Matrix Operations---------")
Y = matrix(11:19, nrow = 3, ncol = 3)
X + Y
X - Y
X * Y
X / Y
Y %*% X
Z = matrix(c(9, 2, -3, 2, 4, -2, -3, -2, 16), 3)
Z
solve(Z)
?solve
diag(3)
|
/Basic.R
|
no_license
|
boshika/uiuc_stat_420
|
R
| false
| false
| 1,760
|
r
|
comment("-------------Expressions---------")
2+2
4*3
2/7
7-3
3^(-2)
2^(1/2)
pi
exp(1)
log(1)
?log
log(exp(1))
?loglin
a <- 10
"OR"
a = 10
?comment
comment("--------------TYPES--------------")
42.5
is.numeric(42.5)
is.double(42.5)
TRUE
is.logical(TRUE)
"Stat"
is.character("Stat")
comment("-------------Vectors are Homgenous---------")
c(1,2,3,4,5,6)
x <- c(1,2,3,4,5,6)
is.vector(x)
(y <- 1:100)
comment("-------------Combine Various Types, Results In Type Coeercesion---------")
c(42, "Boshika", TRUE)
c(42, TRUE)
comment("-------------Sequence Vectors and Repeating Vectors---------")
seq(from = 100, to = 200, by = 12.5)
rep("Boshika", times = 5)
comment("-------------Subsetting---------")
x[1]
x[1:3]
x[c(1,3,4)]
x[-2]
x[c(TRUE,FALSE,TRUE,FALSE,FALSE,FALSE)]
c(1:100, rep(42, times=10))[seq(2,20,by=2)]
x[x>=3]
comment("-------------Vectorization---------")
x=1:10
x + 1
log(x)
comment("-------------Programming Basics---------")
fib = c(1,2,3,4,5,6)
ifelse(fib*2==6, "M", "NM")
seq_num = c(11:20)
for (i in 1:5) {
seq_num[i] = seq_num[i] * 10
}
seq_num
comment("-------------Easier Way-Avoid For Loops---------")
x = 11:20
x = x * 10
x
comment("-------------Functions---------")
silly_func = function(arg1, arg2, arg3=10){
a = arg1 + arg2
b = a + arg3
c(a, b, a + b)
}
silly_func(20, 30)
silly_func(20, 30, 40)
comment("-------------Matrices---------")
x = 1:9
X = matrix(x, nrow = 3, ncol = 3)
X
X[1,2]
X[,2]
X[2,]
comment("-------------Alternate Ways to Create Matrices---------")
cbind(col1=1:10, col2=rev(1:10), col3=rev(10:1))
comment("-------------Matrix Operations---------")
Y = matrix(11:19, nrow = 3, ncol = 3)
X + Y
X - Y
X * Y
X / Y
Y %*% X
Z = matrix(c(9, 2, -3, 2, 4, -2, -3, -2, 16), 3)
Z
solve(Z)
?solve
diag(3)
|
rm(list = ls())
pkgs <- c("readr", "data.table", "dplyr", "tidyr", "DT", "reshape2", "tm", "stringr", "gsubfn", "lubridate",
"ggplot2", "gridExtra", "highcharter", "plotly", "ggrepel", "leaflet", "leaflet.extras", "ggmap",
"RColorBrewer", "viridisLite", "countrycode", "ggmap", "zipcode")
for (pkg in pkgs) {
if (! (pkg %in% rownames(installed.packages())))
{ install.packages(pkg) }
require(pkg, character.only = TRUE)
}
rm(pkgs, pkg)
# load data
getwd()
df <- fread("E:\\Study\\R Projects\\Common files\\severeinjury.csv",
na.strings = "",
stringsAsFactors = FALSE,
data.table = FALSE # false = data.frame
)
glimpse(df)
# 21,578 X 26
df %>%
head(10) %>%
datatable(style = "bootstrap",
class = "table-condensed",
extensions = "Responsive")
# column names: ' ' has been changed to '_'
vas <- names(df)
vas <- gsub(' ','_',vas)
colnames(df) <- vas
df %>%
select(c(EventDate,Employer,Zip,City,State, Longitude, Latitude,
NatureTitle, Part_of_Body_Title, Hospitalized, Amputation,
EventTitle, SourceTitle, Secondary_Source_Title, Final_Narrative)) %>%
mutate(EventDate = mdy(EventDate)) -> df
head(df,4)
#### Zipcode package: to load zipcode based lat,long, city, state.
## can be pulled from govn site as well
data("zipcode")
head(zipcode)
latlong <- zipcode %>%
rename(Zip = zip)
head(latlong)
library(stringr)
df_z <- df %>% select(Zip)
df_z$Zip <- str_pad(df_z$Zip,5,pad = "0") # making the format same
colnames(df_z) <- c("Zip")
head(df_z)
df_z <- merge(df_z,latlong, by = "Zip", all.x = TRUE)
head(df_z,10)
df$Longitude <- df_z$longitude
df$Latitude <- df_z$latitude
df$State <- df_z$state
df$City <- df_z$city
vas <- names(df)
df$Employer =gsub(".*usps|us postal|united states postal|u.s. postal|u.s postal|u. s postal|u. s. postal.*","US_Postal_Service",
ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*US_Postal_Service.*","USPS", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*united parcel|ups |ups,.*","United_Parcel_Service", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*United_Parcel_Service.*","UPS", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*american airl.*","American Airlines", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*AT &|AT&.*","AT_T", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*AT_T.*","AT&T Inc", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*walmart|wallmart|wal-mart.*","wal_mart", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*wal_mart.*","Walmart", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Publix.*","Publix_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Publix_.*","Publix", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Asplundh.*","Asplundh_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Asplundh_.*","Asplundh", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*sodexo.*","sodexo_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*sodexo_.*","Sodexo", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Waste Management.*","Waste_Management", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Waste_Management.*","Waste Management", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Tyson Foods.*","Tyson_Foods", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Tyson_Foods.*","Tyson Foods", ignore.case = TRUE, df$Employer)
# Loading palette
library(RColorBrewer)
display.brewer.all()
# count by employer
df %>% group_by(Employer) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> emp
ggplot(emp, aes(x = reorder(Employer,-cnt), y = cnt, fill = Employer)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 Employees (Danger)", y="Count", x = "Employer") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
# count by City
df %>% group_by(City) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> ct
ggplot(ct, aes(x = reorder(City,-cnt), y = cnt, fill = City)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 Cities (Danger)", y="Count", x = "City") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
# count by state
df %>% group_by(State) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> stat
ggplot(stat, aes(x = reorder(State,-cnt), y = cnt, fill = State)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 States (Danger)", y="Count", x = "State") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
## State and Employer
df %>% group_by(Employer,State) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(30) %>%
plot_ly(x = ~Employer, y = ~State, z = ~cnt, color = ~cnt) %>%
add_markers()
## By Source (hchart)
df %>% group_by(SourceTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(30) %>%
hchart("pie", innerSize = '40%', showInLegend = F,
hcaes(x = SourceTitle, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_title(text = "Top 30 Sources")
## Top injuries
df %>% group_by(NatureTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(10) %>%
hchart("bar", innerSize = '40%', showInLegend = F,
hcaes(x = NatureTitle, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_economist()) %>%
hc_title(text = "Top 10 injury type")
## Injury Body parts
df %>% group_by(Part_of_Body_Title) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(10) %>%
hchart("bar",
hcaes(x = Part_of_Body_Title, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_economist()) %>%
hc_title(text = "Top 10 injured body parts")
## by events
df %>% group_by(EventTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(20) %>%
hchart(type = "treemap",
hcaes(x = EventTitle, y= cnt, color = cnt)) %>%
hc_add_theme(hc_theme_538()) %>%
hc_title(text = "Top 20 Events")
|
/Dangerous Places to work in US.R
|
no_license
|
ashishtele/Kaggle-Work-EDA-Kernel
|
R
| false
| false
| 6,791
|
r
|
rm(list = ls())
pkgs <- c("readr", "data.table", "dplyr", "tidyr", "DT", "reshape2", "tm", "stringr", "gsubfn", "lubridate",
"ggplot2", "gridExtra", "highcharter", "plotly", "ggrepel", "leaflet", "leaflet.extras", "ggmap",
"RColorBrewer", "viridisLite", "countrycode", "ggmap", "zipcode")
for (pkg in pkgs) {
if (! (pkg %in% rownames(installed.packages())))
{ install.packages(pkg) }
require(pkg, character.only = TRUE)
}
rm(pkgs, pkg)
# load data
getwd()
df <- fread("E:\\Study\\R Projects\\Common files\\severeinjury.csv",
na.strings = "",
stringsAsFactors = FALSE,
data.table = FALSE # false = data.frame
)
glimpse(df)
# 21,578 X 26
df %>%
head(10) %>%
datatable(style = "bootstrap",
class = "table-condensed",
extensions = "Responsive")
# column names: ' ' has been changed to '_'
vas <- names(df)
vas <- gsub(' ','_',vas)
colnames(df) <- vas
df %>%
select(c(EventDate,Employer,Zip,City,State, Longitude, Latitude,
NatureTitle, Part_of_Body_Title, Hospitalized, Amputation,
EventTitle, SourceTitle, Secondary_Source_Title, Final_Narrative)) %>%
mutate(EventDate = mdy(EventDate)) -> df
head(df,4)
#### Zipcode package: to load zipcode based lat,long, city, state.
## can be pulled from govn site as well
data("zipcode")
head(zipcode)
latlong <- zipcode %>%
rename(Zip = zip)
head(latlong)
library(stringr)
df_z <- df %>% select(Zip)
df_z$Zip <- str_pad(df_z$Zip,5,pad = "0") # making the format same
colnames(df_z) <- c("Zip")
head(df_z)
df_z <- merge(df_z,latlong, by = "Zip", all.x = TRUE)
head(df_z,10)
df$Longitude <- df_z$longitude
df$Latitude <- df_z$latitude
df$State <- df_z$state
df$City <- df_z$city
vas <- names(df)
df$Employer =gsub(".*usps|us postal|united states postal|u.s. postal|u.s postal|u. s postal|u. s. postal.*","US_Postal_Service",
ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*US_Postal_Service.*","USPS", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*united parcel|ups |ups,.*","United_Parcel_Service", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*United_Parcel_Service.*","UPS", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*american airl.*","American Airlines", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*AT &|AT&.*","AT_T", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*AT_T.*","AT&T Inc", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*walmart|wallmart|wal-mart.*","wal_mart", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*wal_mart.*","Walmart", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Publix.*","Publix_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Publix_.*","Publix", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Asplundh.*","Asplundh_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Asplundh_.*","Asplundh", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*sodexo.*","sodexo_", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*sodexo_.*","Sodexo", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Waste Management.*","Waste_Management", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Waste_Management.*","Waste Management", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Tyson Foods.*","Tyson_Foods", ignore.case = TRUE, df$Employer)
df$Employer =gsub(".*Tyson_Foods.*","Tyson Foods", ignore.case = TRUE, df$Employer)
# Loading palette
library(RColorBrewer)
display.brewer.all()
# count by employer
df %>% group_by(Employer) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> emp
ggplot(emp, aes(x = reorder(Employer,-cnt), y = cnt, fill = Employer)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 Employees (Danger)", y="Count", x = "Employer") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
# count by City
df %>% group_by(City) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> ct
ggplot(ct, aes(x = reorder(City,-cnt), y = cnt, fill = City)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 Cities (Danger)", y="Count", x = "City") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
# count by state
df %>% group_by(State) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
top_n(10)-> stat
ggplot(stat, aes(x = reorder(State,-cnt), y = cnt, fill = State)) +
geom_bar(stat = "identity") +
geom_text(aes(label = cnt), vjust = -0.5) +
labs(title = "Top 10 States (Danger)", y="Count", x = "State") +
scale_fill_brewer(palette = 'YlOrRd') +
theme_minimal(base_size = 11)
## State and Employer
df %>% group_by(Employer,State) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(30) %>%
plot_ly(x = ~Employer, y = ~State, z = ~cnt, color = ~cnt) %>%
add_markers()
## By Source (hchart)
df %>% group_by(SourceTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(30) %>%
hchart("pie", innerSize = '40%', showInLegend = F,
hcaes(x = SourceTitle, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_title(text = "Top 30 Sources")
## Top injuries
df %>% group_by(NatureTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(10) %>%
hchart("bar", innerSize = '40%', showInLegend = F,
hcaes(x = NatureTitle, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_economist()) %>%
hc_title(text = "Top 10 injury type")
## Injury Body parts
df %>% group_by(Part_of_Body_Title) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(10) %>%
hchart("bar",
hcaes(x = Part_of_Body_Title, y= cnt, color = -cnt)) %>%
hc_add_theme(hc_theme_economist()) %>%
hc_title(text = "Top 10 injured body parts")
## by events
df %>% group_by(EventTitle) %>%
filter(Hospitalized != 0 || Amputation != 0) %>%
summarise(cnt = n()) %>%
arrange(-cnt) %>%
head(20) %>%
hchart(type = "treemap",
hcaes(x = EventTitle, y= cnt, color = cnt)) %>%
hc_add_theme(hc_theme_538()) %>%
hc_title(text = "Top 20 Events")
|
#' get_toggl_api_token
#' @description return the toggle api token
#' @param ask booleen do we have to ask if missing
#' @importFrom magrittr %>%
#' @importFrom keyring key_set_with_value
#' @import keyring
#' @export
get_toggl_api_token <- function(ask=TRUE){
token <-NULL
try(token<-key_get(service = "toggl_api_token"),silent=TRUE)
if ( is.null(token) & ask){
delete_toggl_api_token()
token <- ask_toggl_api_token()
token %>% key_set_with_value(service = "toggl_api_token",password = .)
}
token
}
#' set_toggl_api_token
#' @description set the toggle api token
#' @param token toggl api token
#' @importFrom magrittr %>%
#' @import assertthat
#' @export
set_toggl_api_token <- function(token){
if ( missing(token) ){
token <- ask_toggl_api_token()
}
if (is.null(token)){return(invisible(NULL))}
delete_toggl_api_token()
assert_that(is.character(token))
token %>% key_set_with_value(service = "toggl_api_token",password = .)
token
}
#' update_toggl_api_token
#' @description update the toggle api token
#' @importFrom magrittr %>%
#' @export
update_toggl_api_token <- function(){
delete_toggl_api_token()
ask_toggl_api_token() %>% key_set_with_value(service = "toggl_api_token",password = .)
}
#' @title delete_toggl_api_token
#' @description delete the toggle api token
#' @export
delete_toggl_api_token <- function(){
try(key_delete("toggl_api_token"),silent=TRUE)
}
#' ask_toggl_api_token
#' @param msg the message
#' @description ask for the toggle api token
#' @import getPass
#' @export
ask_toggl_api_token <- function (msg="toggl api token")
{
passwd <- tryCatch({
newpass <- getPass::getPass(msg)
}, interrupt = NULL)
if (!length(passwd) || !nchar(passwd)) {
return(NULL)
}
else {
return(as.character(passwd))
}
}
#' correct_date
#' @description tricks to obtain iso 8601
#'
#' @param time a POSIXt
correct_date <- function(time){
paste0(gsub(" ","T",as.character(time)),"+01:00")
}
|
/R/tools.R
|
no_license
|
ColinFay/togglr
|
R
| false
| false
| 2,015
|
r
|
#' get_toggl_api_token
#' @description return the toggle api token
#' @param ask booleen do we have to ask if missing
#' @importFrom magrittr %>%
#' @importFrom keyring key_set_with_value
#' @import keyring
#' @export
get_toggl_api_token <- function(ask=TRUE){
token <-NULL
try(token<-key_get(service = "toggl_api_token"),silent=TRUE)
if ( is.null(token) & ask){
delete_toggl_api_token()
token <- ask_toggl_api_token()
token %>% key_set_with_value(service = "toggl_api_token",password = .)
}
token
}
#' set_toggl_api_token
#' @description set the toggle api token
#' @param token toggl api token
#' @importFrom magrittr %>%
#' @import assertthat
#' @export
set_toggl_api_token <- function(token){
if ( missing(token) ){
token <- ask_toggl_api_token()
}
if (is.null(token)){return(invisible(NULL))}
delete_toggl_api_token()
assert_that(is.character(token))
token %>% key_set_with_value(service = "toggl_api_token",password = .)
token
}
#' update_toggl_api_token
#' @description update the toggle api token
#' @importFrom magrittr %>%
#' @export
update_toggl_api_token <- function(){
delete_toggl_api_token()
ask_toggl_api_token() %>% key_set_with_value(service = "toggl_api_token",password = .)
}
#' @title delete_toggl_api_token
#' @description delete the toggle api token
#' @export
delete_toggl_api_token <- function(){
try(key_delete("toggl_api_token"),silent=TRUE)
}
#' ask_toggl_api_token
#' @param msg the message
#' @description ask for the toggle api token
#' @import getPass
#' @export
ask_toggl_api_token <- function (msg="toggl api token")
{
passwd <- tryCatch({
newpass <- getPass::getPass(msg)
}, interrupt = NULL)
if (!length(passwd) || !nchar(passwd)) {
return(NULL)
}
else {
return(as.character(passwd))
}
}
#' correct_date
#' @description tricks to obtain iso 8601
#'
#' @param time a POSIXt
correct_date <- function(time){
paste0(gsub(" ","T",as.character(time)),"+01:00")
}
|
library(BSDA)
### Name: Statclas
### Title: Grades for two introductory statistics classes
### Aliases: Statclas
### Keywords: datasets
### ** Examples
str(Statclas)
boxplot(score ~ class, data = Statclas, col = "red")
t.test(score ~ class, data = Statclas)
|
/data/genthat_extracted_code/BSDA/examples/Statclas.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 267
|
r
|
library(BSDA)
### Name: Statclas
### Title: Grades for two introductory statistics classes
### Aliases: Statclas
### Keywords: datasets
### ** Examples
str(Statclas)
boxplot(score ~ class, data = Statclas, col = "red")
t.test(score ~ class, data = Statclas)
|
#' Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
#'
#' This function runs a Monte Carlo simulation study given a set of predefined simulation functions,
#' design conditions, and number of replications. Results can be saved as temporary files in case of interruptions
#' and may be restored by re-running \code{runSimulation}, provided that the respective temp
#' file can be found in the working directory. \code{runSimulation} supports parallel
#' and cluster computing, global and local debugging, error handling (including fail-safe
#' stopping when functions fail too often, even across nodes), provides bootstrap estimates of the
#' sampling variability (optional), and tracking of error and warning messages.
#' For convenience, all functions available in the R workspace are exported across all computational nodes
#' so that they are more easily accessible (however, other R objects are not, and therefore
#' must be passed to the \code{fixed_objects} input to become available across nodes).
#' For a didactic presentation of the package refer to Sigal and Chalmers
#' (2016; \doi{10.1080/10691898.2016.1246953}), and see the associated
#' wiki on Github (\url{https://github.com/philchalmers/SimDesign/wiki})
#' for other tutorial material, examples, and applications of \code{SimDesign} to real-world simulations.
#'
#' The strategy for organizing the Monte Carlo simulation work-flow is to
#'
#' \describe{
#' \item{1)}{Define a suitable \code{design} data.frame object containing fixed conditional
#' information about the Monte Carlo simulations. This is often expedited by using the
#' \code{\link{expand.grid}} function, and if necessary using the \code{\link{subset}}
#' function to remove redundant or non-applicable rows}
#' \item{2)}{Define the three step functions to generate the data (\code{\link{Generate}}; see also
#' \url{https://CRAN.R-project.org/view=Distributions} for a list of distributions in R),
#' analyse the generated data by computing the respective parameter estimates, detection rates,
#' etc (\code{\link{Analyse}}), and finally summarise the results across the total
#' number of replications (\code{\link{Summarise}}). Note that these functions can be
#' automatically generated by using the \code{\link{SimFunctions}} function.
#' }
#' \item{3)}{Pass the above objects to the \code{runSimulation} function, and declare the
#' number of replications to perform with the \code{replications} input. This function will accept
#' a \code{design} data.frame object and will return a suitable data.frame object with the
#' simulation results}
#' \item{4)}{Analyze the output from \code{runSimulation}, possibly using ANOVA techniques
#' (\code{\link{SimAnova}}) and generating suitable plots and tables}
#' }
#'
#' More succinctly, the functions to be called follow the following form with the exact inputs required
#' by the \code{SimDesign} package
#'
#' \describe{
#' \item{\code{Design <- expand.grid(...)}}{}
#' \item{\code{Generate <- function(condition, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{Analyse <- function(condition, dat, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{Summarise <- function(condition, results, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{results <- runSimulation(design=Design, replications, generate=Generate,
#' analyse=Analyse, summarise=Summarise)}}{}
#' }
#'
#' For a skeleton version of the work-flow, which is often useful when initially defining a simulation,
#' see \code{\link{SimFunctions}}. This function will write template simulation code
#' to one/two files so that modifying the required functions and objects can begin immediately
#' with minimal error. This means that you can focus on your Monte Carlo simulation immediately rather
#' than worrying about the administrative code-work required to organize the simulation work-flow.
#'
#' Additional information for each condition are also contained in the \code{data.frame} object returned by
#' \code{runSimulation}: \code{REPLICATIONS} to indicate the number of Monte Carlo replications,
#' \code{SIM_TIME} to indicate how long (in seconds) it took to complete
#' all the Monte Carlo replications for each respective design condition,
#' \code{COMPLETED} to indicate the date in which the given simulation condition completed,
#' \code{SEED} for the integer values in the \code{seed} argument,
#' columns containing the number of replications which had to be re-run due to errors (where the error messages
#' represent the names of the columns prefixed with a \code{ERROR:} string), and
#' columns containing the number of warnings prefixed with a \code{WARNING:} string. Finally,
#' if \code{bootSE = TRUE} was included then the final right-most columns will contain the labels
#' \code{BOOT_SE.} followed by the name of the associated meta-statistic defined in \code{summarise()}.
#'
#' Additional examples, presentation files, and tutorials can be found on the package wiki located at
#' \url{https://github.com/philchalmers/SimDesign/wiki}.
#'
#' @section Saving data, results, seeds, and the simulation state:
#'
#' To conserve RAM, temporary objects (such as data generated across conditions and replications)
#' are discarded; however, these can be saved to the hard-disk by passing the appropriate flags.
#' For longer simulations it is recommended to use \code{save = TRUE} to temporarily save the
#' simulation state, and to use the \code{save_results} flag to write the analysis results
#' the to hard-disc.
#'
#' The generated data can be saved by passing
#' \code{save_generate_data = TRUE}, however it is often more memory efficient to use the
#' \code{save_seeds} option instead to only save R's \code{.Random.seed} state instead (still
#' allowing for complete reproducibility); individual \code{.Random.seed} terms may also be read in with the
#' \code{load_seed} input to reproduce the exact simulation state at any given replication. Finally,
#' providing a vector of \code{seeds} is also possible to ensure
#' that each simulation condition is completely reproducible under the single/multi-core method selected.
#'
#' Finally, when the Monte Carlo simulation is complete
#' it is recommended to write the results to a hard-drive for safe keeping, particularly with the
#' \code{save} and \code{filename} arguments provided (for reasons that are more obvious in the parallel computation
#' descriptions below). Using the \code{filename} argument (along with \code{save = TRUE})
#' supplied is much safer than using something
#' like \code{\link{saveRDS}} directly because files will never accidentally be overwritten,
#' and instead a new file name will be created when a conflict arises; this type of safety
#' is prevalent in many aspects of the package and helps to avoid many unrecoverable (yet surprisingly common)
#' mistakes.
#'
#' @section Resuming temporary results:
#'
#' In the event of a computer crash, power outage, etc, if \code{save = TRUE} was used
#' then the original code used to execute \code{runSimulation()} need only be re-run to resume the simulation.
#' The saved temp file will be read into the function automatically, and the simulation will continue
#' one the condition where it left off before the simulation state was terminated.
#'
#' @section A note on parallel computing:
#'
#' When running simulations in parallel (either with \code{parallel = TRUE} or \code{MPI = TRUE})
#' R objects defined in the global environment will generally \emph{not} be visible across nodes.
#' Hence, you may see errors such as \code{Error: object 'something' not found} if you try to use an object
#' that is defined in the workspace but is not passed to \code{runSimulation}.
#' To avoid this type or error, simply pass additional objects to the
#' \code{fixed_objects} input (usually it's convenient to supply a named list of these objects).
#' Fortunately, however, \emph{custom functions defined in the global environment are exported across
#' nodes automatically}. This makes it convenient when writing code because custom functions will
#' always be available across nodes if they are visible in the R workspace. As well, note the
#' \code{packages} input to declare packages which must be loaded via \code{library()} in order to make
#' specific non-standard R functions available across nodes.
#'
#' @section Cluster computing:
#'
#' SimDesign code may be released to a computing system which supports parallel cluster computations using
#' the industry standard Message Passing Interface (MPI) form. This simply
#' requires that the computers be setup using the usual MPI requirements (typically, running some flavor
#' of Linux, have password-less open-SSH access, IP addresses have been added to the \code{/etc/hosts} file
#' or \code{~/.ssh/config}, etc).
#' More generally though, these resources are widely available through professional
#' organizations dedicated to super-computing.
#'
#' To setup the R code for an MPI cluster one need only add the argument \code{MPI = TRUE},
#' wrap the appropriate MPI directives around \code{runSimulation}, and submit the
#' files using the suitable BASH commands to execute the \code{mpirun} tool. For example,
#'
#' \describe{
#' \item{\code{library(doMPI)}}{}
#' \item{\code{cl <- startMPIcluster()}}{}
#' \item{\code{registerDoMPI(cl)}}{}
#' \item{\code{runSimulation(design=Design, replications=1000, save=TRUE, filename='mysimulation',
#' generate=Generate, analyse=Analyse, summarise=Summarise, MPI=TRUE)}}{}
#' \item{\code{closeCluster(cl)}}{}
#' \item{\code{mpi.quit()}}{}
#' }
#'
#' The necessary \code{SimDesign} files must be uploaded to the dedicated master node
#' so that a BASH call to \code{mpirun} can be used to distribute the work across slaves.
#' For instance, if the following BASH command is run on the master node then 16 processes
#' will be summoned (1 master, 15 slaves) across the computers named \code{localhost}, \code{slave1},
#' and \code{slave2} in the ssh \code{config} file.
#'
#' \code{mpirun -np 16 -H localhost,slave1,slave2 R --slave -f simulation.R}
#'
#' @section Network computing:
#'
#' If you access have to a set of computers which can be linked via secure-shell (ssh) on the same LAN network then
#' Network computing (a.k.a., a Beowulf cluster) may be a viable and useful option.
#' This approach is similar to MPI computing approach
#' except that it offers more localized control and requires more hands-on administrative access to the master
#' and slave nodes. The setup generally requires that the master node
#' has \code{SimDesign} installed and the slave/master nodes have all the required R packages pre-installed
#' (Unix utilities such as \code{dsh} are very useful for this purpose). Finally,
#' the master node must have ssh access to the slave nodes, each slave node must have ssh access
#' with the master node, and a cluster object (\code{cl}) from the \code{parallel} package must be defined on the
#' master node.
#'
#' Setup for network computing is generally more straightforward and controlled
#' than the setup for MPI jobs in that it only requires the specification of a) the respective
#' IP addresses within a defined R script, and b) the user name
#' (if different from the master node's user name. Otherwise, only a) is required).
#' However, on Linux I have found it is also important to include relevant information about the host names
#' and IP addresses in the \code{/etc/hosts} file on the master and slave nodes, and to ensure that
#' the selected port (passed to \code{\link{makeCluster}}) on the master node is not hindered by a firewall.
#'
#' As an example, using the following code the master node (primary) will spawn 7 slaves and 1 master,
#' while a separate computer on the network with the associated IP address will spawn an additional 6 slaves.
#' Information will be collected on the master node, which is also where the files
#' and objects will be saved using the \code{save} inputs (if requested).
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- '192.168.2.1'}}{}
#' \item{\code{IPs <- list(list(host=primary, user='myname', ncore=8), list(host='192.168.2.2', user='myname', ncore=6))}}{}
#' \item{\code{spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host, user=IP$user)), IP$ncore))}}{}
#' \item{\code{spec <- unlist(spec, recursive=FALSE)}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#' The object \code{cl} is passed to \code{runSimulation} on the master node
#' and the computations are distributed across the respective
#' IP addresses. Finally, it's usually good practice to use \code{stopCluster(cl)}
#' when all the simulations are said and done to release the communication between the computers,
#' which is what the above code shows.
#'
#' Alternatively, if you have provided suitable names for each respective slave node, as well as the master,
#' then you can define the \code{cl} object using these instead (rather than supplying the IP addresses in
#' your R script). This requires that the master node has itself and all the slave nodes defined in the
#' \code{/etc/hosts} and \code{~/.ssh/config} files, while the slave nodes require themselves and the
#' master node in the same files (only 2 IP addresses required on each slave).
#' Following this setup, and assuming the user name is the same across all nodes,
#' the \code{cl} object could instead be defined with
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- 'master'}}{}
#' \item{\code{IPs <- list(list(host=primary, ncore=8), list(host='slave', ncore=6))}}{}
#' \item{\code{spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host)), IP$ncore))}}{}
#' \item{\code{spec <- unlist(spec, recursive=FALSE)}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#' Or, even more succinctly if all communication elements required are identical to the master node,
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- 'master'}}{}
#' \item{\code{spec <- c(rep(primary, 8), rep('slave', 6))}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#'
#' @section Poor man's cluster computing for independent nodes:
#'
#' In the event that you do not have access to a Beowulf-type cluster (described in the section on
#' "Network Computing") but have multiple personal
#' computers then the simulation code can be manually distributed across each independent computer instead.
#' This simply requires passing a smaller value to the \code{replications} argument on each computer and later
#' aggregating the results using the \code{\link{aggregate_simulations}} function.
#'
#' For instance, if you have two computers available on different networks and wanted a total of 500 replications you
#' could pass \code{replications = 300} to one computer and \code{replications = 200} to the other along
#' with a \code{filename} argument (or simply saving the final objects as \code{.rds} files manually after
#' \code{runSimulation()} has finished). This will create two distinct \code{.rds} files which can be
#' combined later with the \code{\link{aggregate_simulations}} function. The benefit of this approach over
#' MPI or setting up a Beowulf cluster is that computers need not be linked on the same network,
#' and, should the need arise, the temporary
#' simulation results can be migrated to another computer in case of a complete hardware failure by moving the
#' saved temp files to another node, modifying
#' the suitable \code{compname} input to \code{save_details} (or, if the \code{filename} and \code{tmpfilename}
#' were modified, matching those files accordingly), and resuming the simulation as normal.
#'
#' Note that this is also a useful tactic if the MPI or Network computing options require you to
#' submit smaller jobs due to time and resource constraint-related reasons,
#' where fewer replications/nodes should be requested. After all the jobs are completed and saved to their
#' respective files, \code{\link{aggregate_simulations}}
#' can then collapse the files as if the simulations were run all at once. Hence, SimDesign makes submitting
#' smaller jobs to super-computing resources considerably less error prone than managing a number of smaller
#' jobs manually .
#'
#' @param design a \code{data.frame} object containing the Monte Carlo simulation conditions to
#' be studied, where each row represents a unique condition and each column a factor to be varied
#'
#' @param generate user-defined data and parameter generating function.
#' See \code{\link{Generate}} for details
#'
#' @param analyse user-defined computation function which acts on the data generated from
#' \code{\link{Generate}}. See \code{\link{Analyse}} for details
#'
#' @param summarise optional (but highly recommended) user-defined summary function to be used
#' after all the replications have completed within each \code{design} condition. Omitting this function
#' will return a list of matrices (or a single matrix, if only one row in \code{design} is supplied)
#' or, for more general objects (such as lists), a list containing the results returned form \code{\link{Analyse}}.
#' Ommiting this function is only recommended for didactic purposes because it leaves out a large amount of
#' information (e.g., try-errors, warning messages, etc), can witness memory related issues,
#' and generally is not as flexible internally. See
#' the \code{save_results} option for a more RAM friendly alternative to storing all the Generate-Analyse results
#' in the working enviroment
#'
#' @param replications number of replication to perform per condition (i.e., each row in \code{design}).
#' Must be greater than 0
#'
#' @param fixed_objects (optional) an object (usually a named \code{list})
#' containing additional user-defined objects
#' that should remain fixed across conditions. This is useful when including
#' long fixed vectors/matrices of population parameters, data
#' that should be used across all conditions and replications (e.g., including a fixed design matrix
#' for linear regression), or simply control constant global elements such as sample size
#'
#' @param parallel logical; use parallel processing from the \code{parallel} package over each
#' unique condition?
#'
#' @param cl cluster object defined by \code{\link{makeCluster}} used to run code in parallel.
#' If \code{NULL} and \code{parallel = TRUE}, a local cluster object will be defined which
#' selects the maximum number cores available
#' and will be stop the cluster when the simulation is complete. Note that supplying a \code{cl}
#' object will automatically set the \code{parallel} argument to \code{TRUE}
#'
#' @param packages a character vector of external packages to be used during the simulation (e.g.,
#' \code{c('MASS', 'extraDistr', 'simsem')} ). Use this input when \code{parallel = TRUE} or
#' \code{MPI = TRUE} to use non-standard functions from additional packages,
#' otherwise the functions must be made available by using explicit
#' \code{\link{library}} or \code{\link{require}} calls within the provided simulation functions.
#' Alternatively, functions can be called explicitly without attaching the package with the \code{::} operator
#' (e.g., \code{extraDistr::rgumbel()})
#'
#' @param warnings_as_errors logical; treat warning messages as errors during the simulation? Default is FALSE,
#' therefore warnings are only collected and not used to restart the data generation step
#'
#' @param as.factor logical; coerce the input \code{design} elements into \code{factor}s when the
#' simulation is complete? If the columns inputs are numeric then these will be treated
#' as \code{ordered}. Default is \code{TRUE}
#'
#' @param save_results logical; save the results returned from \code{\link{Analyse}} to external
#' \code{.rds} files located in the defined \code{save_results_dirname} directory/folder?
#' Use this if you would like to keep track of the individual parameters returned from the analyses.
#' Each saved object will contain a list of three elements containing the condition (row from \code{design}),
#' results (as a \code{list} or \code{matrix}), and try-errors. When \code{TRUE}, a temp file will be used to track the simulation
#' state (in case of power outages, crashes, etc). When \code{TRUE}, temporary files will also be saved
#' to the working directory (in the same way as when \code{save = TRUE}).
#' See \code{\link{SimResults}} for an example of how to read these \code{.rds} files back into R
#' after the simulation is complete. Default is \code{FALSE}.
#'
#' WARNING: saving results to your hard-drive can fill up space very quickly for larger simulations. Be sure to
#' test this option using a smaller number of replications before the full Monte Carlo simulation is performed.
#'
#' @param save_seeds logical; save the \code{.Random.seed} states prior to performing each replication into
#' plain text files located in the defined \code{save_seeds_dirname} directory/folder?
#' Use this if you would like to keep track of the simulation state within each replication and design
#' condition. Primarily, this is useful for completely replicating any cell in the simulation if need be,
#' especially when tracking down hard-to-find errors and bugs. As well, see the \code{load_seed} input
#' to load a given \code{.Random.seed} to exactly replicate the generated data and analysis state (handy
#' for debugging). When \code{TRUE}, temporary files will also be saved
#' to the working directory (in the same way as when \code{save = TRUE}).
#' Default is \code{FALSE}
#'
#' @param save_generate_data logical; save the data returned from \code{\link{Generate}} to external \code{.rds} files
#' located in the defined \code{save_generate_data_dirname} directory/folder?
#' When \code{TRUE}, temporary files will also be saved to the working directory
#' (in the same way as when \code{save = TRUE}). Default is \code{FALSE}
#'
#' WARNING: saving data to your hard-drive can fill up space very quickly for larger simulations. Be sure to
#' test this option using a smaller number of replications before the full Monte Carlo simulation is performed.
#' It is generally recommended to leave this argument as \code{FALSE} because saving datasets will often consume
#' a needless amount of disk space, and by-and-large saving data is not required for simulations.
#'
#' @param load_seed a character object indicating which file to load from when the \code{.Random.seed}s have
#' be saved (after a call with \code{save_seeds = TRUE}). E.g., \code{load_seed = 'design-row-2/seed-1'}
#' will load the first seed in the second row of the \code{design} input. Note that it is important NOT
#' to modify the \code{design} input object, otherwise the path may not point to the correct saved location.
#' Default is \code{NULL}
#'
#' @param filename (optional) the name of the \code{.rds} file to save the final simulation results to
#' when \code{save = TRUE}. If the same file name already exists in the working
#' directly at the time of saving then a new
#' file will be generated instead and a warning will be thrown. This helps to avoid accidentally overwriting
#' existing files. Default is \code{'SimDesign-results'}
#'
#' @param save_details a list pertaining to information regarding how and where files should be saved
#' when the \code{save}, \code{save_results}, or \code{save_generate_data} flags are triggered.
#'
#' \describe{
#'
#' \item{\code{safe}}{logical; trigger whether safe-saving should be performed. When \code{TRUE} files
#' will never be overwritten accidentally, and where appropriate the program will either stop or generate
#' new files with unique names. Default is \code{TRUE}}
#'
#' \item{\code{compname}}{name of the computer running the simulation. Normally this doesn't need
#' to be modified, but in the event that a manual node breaks down while running a simulation the
#' results from the temp files may be resumed on another computer by changing the name of the
#' node to match the broken computer. Default is the result of evaluating \code{unname(Sys.info()['nodename'])}}
#'
#' \item{\code{tmpfilename}}{the name of the temporary \code{.rds} file when any of the \code{save} flags are used.
#' This file will be read-in if it is in the working directory and the simulation will continue
#' at the last point this file was saved
#' (useful in case of power outages or broken nodes). Finally, this file will be deleted when the
#' simulation is complete. Default is the system name (\code{compname}) appended
#' to \code{'SIMDESIGN-TEMPFILE_'}}
#'
#' \item{\code{save_results_dirname}}{a string indicating the name of the folder to save
#' result objects to when \code{save_results = TRUE}. If a directory/folder does not exist
#' in the current working directory then a unique one will be created automatically. Default is
#' \code{'SimDesign-results_'} with the associated \code{compname} appended}
#'
#' \item{\code{save_seeds_dirname}}{a string indicating the name of the folder to save
#' \code{.Random.seed} objects to when \code{save_seeds = TRUE}. If a directory/folder does not exist
#' in the current working directory then one will be created automatically. Default is
#' \code{'SimDesign-seeds_'} with the associated \code{compname} appended}
#'
#' \item{\code{save_generate_data_dirname}}{a string indicating the name of the folder to save
#' data objects to when \code{save_generate_data = TRUE}. If a directory/folder does not exist
#' in the current working directory then one will be created automatically.
#' Within this folder nested directories will be created associated with each row in \code{design}.
#' Default is \code{'SimDesign-generate-data_'} with the \code{compname} appended}
#'
#' }
#'
#' @param max_errors the simulation will terminate when more than this number of consecutive errors are thrown in any
#' given condition. The purpose of this is to indicate that something fatally problematic is likely going
#' wrong in the generate-analyse phases and should be inspected. Default is 50
#'
#' @param ncores number of cores to be used in parallel execution. Default uses all available
#'
#' @param MPI logical; use the \code{foreach} package in a form usable by MPI to run simulation
#' in parallel on a cluster? Default is \code{FALSE}
#'
#' @param save logical; save the simulation state and final results to the hard-drive? This is useful
#' for simulations which require an extended amount of time. When \code{TRUE}, a temp file
#' will be created in the working directory which allows the simulation state to be saved
#' and recovered (in case of power outages, crashes, etc). To recover your simulation at the last known
#' location simply re-run the code you used to initially define the simulation and the external file
#' will automatically be detected and read-in. Upon completion, the final results will
#' be saved to the working directory, and the temp file will be removed. Default is \code{FALSE}
#'
#' @param edit a string indicating where to initiate a \code{browser()} call for editing and debugging.
#' General options are \code{'none'} (default) and \code{'all'}, which are used
#' to disable debugging and to debug all the user defined functions, respectively.
#' Specific options include: \code{'generate'}
#' to edit the data simulation function, \code{'analyse'} to edit the computational function, and
#' \code{'summarise'} to edit the aggregation function.
#'
#' Alternatively, users may place \code{\link{browser}} calls within the respective functions for
#' debugging at specific lines (note: parallel computation flags will automatically be disabled
#' when a \code{browser()} is detected)
#'
#' @param seed a vector of integers to be used for reproducibility.
#' The length of the vector must be equal the number of rows in \code{design}.
#' This argument calls \code{\link{set.seed}} or
#' \code{\link{clusterSetRNGStream}} for each condition, respectively,
#' but will not be run when \code{MPI = TRUE}.
#' Default randomly generates seeds within the range 1 to 2147483647 for each condition.
#'
#' @param progress logical; display a progress bar for each simulation condition?
#' This is useful when simulations conditions take a long time to run.
#' Uses the \code{pbapply} package to display the progress. Default is \code{FALSE}
#'
#' @param bootSE logical; perform a non-parametric bootstrap to compute bootstrap standard error
#' estimates for the respective meta-statistics computed by the \code{Summarise} function?
#' When \code{TRUE}, bootstrap samples for each row in \code{Design} will be obtained after
#' the generate-analyse steps have obtain the simulation results to be summarised so that
#' standard errors for each statistic can be computed. To compute large-sample confidence
#' intervals given the bootstrap SE estimates see \code{\link{SimBoot}}.
#'
#' This option is useful to approximate how accurate the resulting meta-statatistic estimates
#' were, particularly if the number of \code{replications} was relatively low (e.g., less than
#' 5000). If users prefer to obtain alternative bootstrap estimates then consider passing
#' \code{save_results = TRUE}, reading the generate-analyse data into R via
#' \code{\link{SimResults}}, and performing the bootstrap manually with function found in the
#' external \code{boot} package
#'
#' @param boot_draws number of non-parametric bootstrap draws to sample for the \code{summarise}
#' function after the generate-analyse replications are collected. Default is 1000
#'
#' @param store_results logical; store the complete tables of simulation results
#' in the returned object? This is \code{FALSE} by default to help avoid RAM
#' issues (see \code{save_results} as a more suitable alternative). To extract these results
#' pass the returned object to \code{\link{extract_results}}, which will return a named list
#' of all the simulation results for each condition
#'
#' @param verbose logical; print messages to the R console? Default is \code{TRUE}
#'
#' @return a \code{data.frame} (also of class \code{'SimDesign'})
#' with the original \code{design} conditions in the left-most columns,
#' simulation results and ERROR/WARNING's (if applicable) in the middle columns,
#' and additional information (such as REPLICATIONS, SIM_TIME, COMPLETED, and SEED) in the right-most
#' columns.
#'
#' @aliases runSimulation
#'
#' @seealso \code{\link{Generate}}, \code{\link{Analyse}}, \code{\link{Summarise}},
#' \code{\link{SimFunctions}}, \code{\link{SimClean}}, \code{\link{SimAnova}}, \code{\link{SimResults}},
#' \code{\link{SimBoot}}, \code{\link{aggregate_simulations}}, \code{\link{Attach}}, \code{\link{SimShiny}}
#'
#' @export runSimulation
#'
#' @author Phil Chalmers \email{rphilip.chalmers@@gmail.com}
#'
#' @references
#' Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
#' Carlo simulation. \code{Journal of Statistics Education, 24}(3), 136-156.
#' \doi{10.1080/10691898.2016.1246953}
#'
#' @examples
#'
#' #-------------------------------------------------------------------------------
#' # Example 1: Sampling distribution of mean
#'
#' # This example demonstrate some of the simpler uses of SimDesign,
#' # particularly for classroom settings. The only factor varied in this simulation
#' # is sample size.
#'
#' # skeleton functions to be saved and edited
#' SimFunctions()
#'
#' #### Step 1 --- Define your conditions under study and create design data.frame
#'
#' Design <- data.frame(N = c(10, 20, 30))
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 2 --- Define generate, analyse, and summarise functions
#'
#' # help(Generate)
#' Generate <- function(condition, fixed_objects = NULL){
#' dat <- with(condition, rnorm(N, 10, 5)) # distributed N(10, 5)
#' dat
#' }
#'
#' # help(Analyse)
#' Analyse <- function(condition, dat, fixed_objects = NULL){
#' ret <- mean(dat) # mean of the sample data vector
#' ret
#' }
#'
#' # help(Summarise)
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' ret <- c(mu=mean(results), SE=sd(results)) # mean and SD summary of the sample means
#' ret
#' }
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 3 --- Collect results by looping over the rows in design
#'
#' # run the simulation
#' Final <- runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' Final
#'
#' # reproduce exact simulation
#' Final_rep <- runSimulation(design=Design, replications=1000, seed=Final$SEED,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' Final_rep
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Extras
#' # compare SEs estimates to the true SEs from the formula sigma/sqrt(N)
#' 5 / sqrt(Design$N)
#'
#' # To store the results from the analyse function either
#' # a) omit a definition of of summarise(), or
#' # b) pass save_results = TRUE to runSimulation() and read the results in with SimResults()
#'
#' # e.g., the a) approach
#' results <- runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse)
#' str(results)
#' head(results[[1]])
#'
#' # or b) approach
#' Final <- runSimulation(design=Design, replications=1000, save_results=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' results <- SimResults(Final)
#' str(results)
#' head(results[[1]]$results)
#'
#' # remove the saved results from the hard-drive if you no longer want them
#' SimClean(results = TRUE)
#'
#'
#'
#'
#' #-------------------------------------------------------------------------------
#' # Example 2: t-test and Welch test when varying sample size, group sizes, and SDs
#'
#' # skeleton functions to be saved and edited
#' SimFunctions()
#'
#' \dontrun{
#' # in real-world simulations it's often better/easier to save
#' # these functions directly to your hard-drive with
#' SimFunctions('my-simulation')
#' }
#'
#' #### Step 1 --- Define your conditions under study and create design data.frame
#'
#' Design <- expand.grid(sample_size = c(30, 60, 90, 120),
#' group_size_ratio = c(1, 4, 8),
#' standard_deviation_ratio = c(.5, 1, 2))
#' dim(Design)
#' head(Design)
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 2 --- Define generate, analyse, and summarise functions
#'
#' Generate <- function(condition, fixed_objects = NULL){
#' N <- condition$sample_size # alternatively, could use Attach() to make objects available
#' grs <- condition$group_size_ratio
#' sd <- condition$standard_deviation_ratio
#' if(grs < 1){
#' N2 <- N / (1/grs + 1)
#' N1 <- N - N2
#' } else {
#' N1 <- N / (grs + 1)
#' N2 <- N - N1
#' }
#' group1 <- rnorm(N1)
#' group2 <- rnorm(N2, sd=sd)
#' dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)), DV = c(group1, group2))
#' dat
#' }
#'
#' Analyse <- function(condition, dat, fixed_objects = NULL){
#' welch <- t.test(DV ~ group, dat)
#' ind <- t.test(DV ~ group, dat, var.equal=TRUE)
#'
#' # In this function the p values for the t-tests are returned,
#' # and make sure to name each element, for future reference
#' ret <- c(welch = welch$p.value, independent = ind$p.value)
#' ret
#' }
#'
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' #find results of interest here (e.g., alpha < .1, .05, .01)
#' ret <- EDR(results, alpha = .05)
#' ret
#' }
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 3 --- Collect results by looping over the rows in design
#'
#' # first, test to see if it works
#' Final <- runSimulation(design=Design, replications=5, store_results=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' head(Final)
#'
#' \dontrun{
#' # complete run with 1000 replications per condition
#' Final <- runSimulation(design=Design, replications=1000, parallel=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' head(Final, digits = 3)
#' View(Final)
#'
#' ## save final results to a file upon completion (not run)
#' runSimulation(design=Design, replications=1000, parallel=TRUE, save=TRUE, filename = 'mysim',
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#'
#'
#'
#' ## Debug the generate function. See ?browser for help on debugging
#' ## Type help to see available commands (e.g., n, c, where, ...),
#' ## ls() to see what has been defined, and type Q to quit the debugger
#' runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise,
#' parallel=TRUE, edit='generate')
#'
#' ## Alternatively, place a browser() within the desired function line to
#' ## jump to a specific location
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' #find results of interest here (e.g., alpha < .1, .05, .01)
#' ret <- EDR(results[,nms], alpha = .05)
#' browser()
#' ret
#' }
#'
#' runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise,
#' parallel=TRUE)
#'
#'
#'
#'
#' ## EXTRA: To run the simulation on a MPI cluster, use the following setup on each node (not run)
#' # library(doMPI)
#' # cl <- startMPIcluster()
#' # registerDoMPI(cl)
#' # Final <- runSimulation(design=Design, replications=1000, MPI=TRUE, save=TRUE,
#' # generate=Generate, analyse=Analyse, summarise=Summarise)
#' # saveRDS(Final, 'mysim.rds')
#' # closeCluster(cl)
#' # mpi.quit()
#'
#'
#' ## Similarly, run simulation on a network linked via ssh
#' ## (two way ssh key-paired connection must be possible between master and slave nodes)
#' ##
#' ## define IP addresses, including primary IP
#' # primary <- '192.168.2.20'
#' # IPs <- list(
#' # list(host=primary, user='phil', ncore=8),
#' # list(host='192.168.2.17', user='phil', ncore=8)
#' # )
#' # spec <- lapply(IPs, function(IP)
#' # rep(list(list(host=IP$host, user=IP$user)), IP$ncore))
#' # spec <- unlist(spec, recursive=FALSE)
#' #
#' # cl <- parallel::makeCluster(type='PSOCK', master=primary, spec=spec)
#' # Final <- runSimulation(design=Design, replications=1000, parallel = TRUE, save=TRUE,
#' # generate=Generate, analyse=Analyse, summarise=Summarise, cl=cl)
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' ###### Post-analysis: Analyze the results via functions like lm() or SimAnova(), and create
#' ###### tables(dplyr) or plots (ggplot2) to help visualize the results.
#' ###### This is where you get to be a data analyst!
#'
#' library(dplyr)
#' Final2 <- tbl_df(Final)
#' Final2 %>% summarise(mean(welch), mean(independent))
#' Final2 %>% group_by(standard_deviation_ratio, group_size_ratio) %>%
#' summarise(mean(welch), mean(independent))
#'
#' # quick ANOVA analysis method with all two-way interactions
#' SimAnova( ~ (sample_size + group_size_ratio + standard_deviation_ratio)^2, Final)
#'
#' # or more specific anovas
#' SimAnova(independent ~ (group_size_ratio + standard_deviation_ratio)^2,
#' Final)
#'
#' # make some plots
#' library(ggplot2)
#' library(reshape2)
#' welch_ind <- Final[,c('group_size_ratio', "standard_deviation_ratio",
#' "welch", "independent")]
#' dd <- melt(welch_ind, id.vars = names(welch_ind)[1:2])
#'
#' ggplot(dd, aes(factor(group_size_ratio), value)) + geom_boxplot() +
#' geom_abline(intercept=0.05, slope=0, col = 'red') +
#' geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
#' geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
#' facet_wrap(~variable)
#'
#' ggplot(dd, aes(factor(group_size_ratio), value, fill = factor(standard_deviation_ratio))) +
#' geom_boxplot() + geom_abline(intercept=0.05, slope=0, col = 'red') +
#' geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
#' geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
#' facet_grid(variable~standard_deviation_ratio) +
#' theme(legend.position = 'none')
#'
#' }
#'
runSimulation <- function(design, replications, generate, analyse, summarise,
fixed_objects = NULL, packages = NULL, bootSE = FALSE,
boot_draws = 1000L, filename = 'SimDesign-results',
seed = rint(nrow(design), min=1L, max = 2147483647L),
save = FALSE, save_results = FALSE, store_results = FALSE,
warnings_as_errors = FALSE, save_seeds = FALSE, load_seed = NULL,
parallel = FALSE, ncores = parallel::detectCores(), cl = NULL, MPI = FALSE,
max_errors = 50L, as.factor = TRUE, save_generate_data = FALSE,
save_details = list(), edit = 'none', progress = FALSE, verbose = TRUE)
{
stopifnot(!missing(generate) || !missing(analyse))
if(!all(names(save_results) %in%
c('compname', 'tmpfilename', 'save_results_dirname', 'save_generate_data_dirname')))
stop('save_details contains elements that are not supported', call.=FALSE)
compname <- save_details$compname
safe <- save_details$safe
out_rootdir <- save_details$out_rootdir
tmpfilename <- save_details$tmpfilename
save_results_dirname <- save_details$save_results_dirname
save_generate_data_dirname <- save_details$save_generate_data_dirname
save_seeds_dirname <- save_details$save_seeds_dirname
if(is.null(compname)) compname <- Sys.info()['nodename']
if(is.null(safe)) safe <- TRUE
if(is.null(out_rootdir)) { out_rootdir <- '.' } else { dir.create(out_rootdir) }
if(is.null(tmpfilename)) tmpfilename <- paste0('SIMDESIGN-TEMPFILE_', compname, '.rds')
if(is.null(save_results_dirname)) save_results_dirname <- paste0('SimDesign-results_', compname)
if(is.null(save_generate_data_dirname)) save_generate_data_dirname <- paste0('SimDesign-generate-data_', compname)
if(is.null(save_seeds_dirname)) save_seeds_dirname <- paste0('SimDesign-seeds_', compname)
if(!is.null(filename)){
if(grepl('\\.rds', filename))
filename <- gsub('\\.rds', '', filename)
}
if(!is.null(cl)) parallel <- TRUE
if(!is.null(load_seed)) seed <- NULL
edit <- tolower(edit)
summarise_asis <- FALSE
if(missing(summarise)){
summarise <- function(condition, results, fixed_objects = NULL) results
summarise_asis <- TRUE
save_generate_data <- FALSE
stored_time <- 0
}
Functions <- list(generate=generate, analyse=analyse, summarise=summarise)
dummy_run <- FALSE
if(missing(design)){
design <- data.frame(dummy_run=NA)
dummy_run <- TRUE
}
if(nrow(design) == 1L) verbose <- FALSE
stopifnot(!missing(replications))
if(!is.null(seed))
stopifnot(nrow(design) == length(seed))
edit <- tolower(edit)
if(!save && any(save_results, save_generate_data, save_seeds)) filename <- NULL
for(i in names(Functions)){
fms <- names(formals(Functions[[i]]))
truefms <- switch(i,
generate = c('condition', 'fixed_objects'),
analyse = c('dat', 'condition', 'fixed_objects'),
summarise = c('results', 'condition', 'fixed_objects'))
if(!all(truefms %in% fms))
stop(paste0('Function arguments for ', i, ' are not correct.'), call. = FALSE)
}
start <- 1L; end <- nrow(design)
if(!is.null(load_seed)){
save <- save_seeds <- parallel <- MPI <- FALSE
replications <- 1L
load_seed2 <- gsub('design-row-', '', load_seed)
start <- end <- as.numeric(gsub('/.*', '', load_seed2))
load_seed <- paste0(save_seeds_dirname, '/', load_seed)
}
if(MPI){
parallel <- FALSE
verbose <- FALSE
}
packages <- c('SimDesign', packages)
char_functions <- deparse(substitute(Functions[[i]]))
if(any(grepl('browser\\(', char_functions))){
if(verbose && parallel)
message(paste0('A browser() call was detected. Parallel processing/object ',
'saving will be disabled while visible'))
save <- save_results <- save_generate_data <- save_seeds <- parallel <- MPI <- FALSE
}
if(any(grepl('attach\\(', char_functions)))
stop('Did you mean to use Attach() instead of attach()?', call.=FALSE)
if(!is.data.frame(design))
stop('design must be a data.frame object', call. = FALSE)
if(replications < 1L)
stop('number of replications must be greater than or equal to 1', call. = FALSE)
if(!(edit %in% c('none', 'analyse', 'generate', 'summarise', 'all')))
stop('edit location is not valid', call. = FALSE)
if(is.null(design$ID)){
design <- data.frame(ID=1L:nrow(design), design)
} else stopifnot(length(unique(design$ID)) == nrow(design))
if(edit != 'none'){
save <- save_results <- save_generate_data <- save_seeds <- FALSE
if(!(edit %in% 'summarise')) parallel <- MPI <- FALSE
if(edit == 'recover'){
old_recover <- getOption('error')
options(error = utils::recover)
on.exit(options(error = old_recover))
} else if(edit == 'all'){
debug(Functions[['generate']]); debug(Functions[['analyse']])
debug(Functions[['summarise']])
on.exit({undebug(Functions[['generate']]); undebug(Functions[['analyse']])
undebug(Functions[['summarise']])})
} else {
debug(Functions[[edit]])
on.exit(undebug(Functions[[edit]]))
}
}
export_funs <- parent_env_fun()
if(parallel){
if(is.null(cl)){
cl <- parallel::makeCluster(ncores)
on.exit(parallel::stopCluster(cl))
}
parallel::clusterExport(cl=cl, export_funs, envir = parent.frame(1L))
}
Result_list <- stored_Results_list <- vector('list', nrow(design))
names(Result_list) <- names(stored_Results_list) <- rownames(design)
time0 <- time1 <- proc.time()[3L]
files <- dir(out_rootdir)
if(!MPI && any(files == tmpfilename) && is.null(load_seed)){
if(verbose)
message(sprintf('Resuming simulation from %s file with %i replications.',
file.path(out_rootdir, tmpfilename), replications))
Result_list <- readRDS(file.path(out_rootdir, tmpfilename))
if(!is.null(Result_list[[1L]]$REPLICATIONS))
replications <- Result_list[[1L]]$REPLICATIONS
if(nrow(design) != length(Result_list))
stop(paste0("Number of rows in design input not equal to the length of temp .rds file.",
"Simulations conditions are not identical. Either fix design object or ",
"consider removing temp file and re-running"))
start <- min(which(sapply(Result_list, is.null)))
time0 <- time1 - Result_list[[start-1L]]$SIM_TIME
}
if(file.exists(tmpfilename)){
tmp <- attr(Result_list, 'SimDesign_names')
save_generate_data_dirname <- tmp['save_generate_data_dirname']
save_results_dirname <- tmp['save_results_dirname']
save_seeds_dirname <- tmp['save_seeds_dirname']
}
if(save_results){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_results_dirname
count <- 1L
while(dir.exists(file.path(out_rootdir, save_results_dirname))) {
save_results_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_results_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_results_dirname)))
}
dir.create(file.path(out_rootdir, save_results_dirname))
}
if(length(dir(file.path(out_rootdir, save_results_dirname))) != (start - 1L))
stop('save_results_dirname not starting from correct location according to tempfile',
call.=FALSE)
}
if(save_seeds){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_seeds_dirname
count <- 1L
while(dir.exists(file.path(out_rootdir, save_seeds_dirname))) {
save_seeds_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_seeds_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_seeds_dirname)))
}
dir.create(file.path(out_rootdir, save_seeds_dirname))
}
}
if(save_generate_data){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_generate_data_dirname
count <- 1L
while(dir.exists(save_generate_data_dirname)){
save_generate_data_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_generate_data_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_generate_data_dirname)))
}
dir.create(file.path(out_rootdir, save_generate_data_dirname))
}
}
if(safe && (parallel || MPI)){
# this is great because it also primes the pipes
tmp <- packages[packages != 'SimDesign']
if(!length(tmp)) tmp <- 'stats'
for(i in 1:length(tmp)){
packs <- if(parallel){
try(table(parallel::parSapply(cl, rep(tmp[i], each=length(cl)*2),
get_packages)))
} else {
p <- character()
try(table(foreach(p=rep(tmp[i], each=length(cl)*2L)) %dopar% get_packages(p)))
}
if(tmp[i] == 'stats') next
if(length(packs) > 1L)
message(sprintf('Warning message:\nVersions of %s differ across clusters: %s',
tmp[i], paste0(names(packs), collapse = ', ')))
}
}
if(is.null(attr(Result_list, 'SimDesign_names')))
attr(Result_list, 'SimDesign_names') <-
c(save_generate_data_dirname=file.path(out_rootdir, save_generate_data_dirname),
save_results_dirname=file.path(out_rootdir, save_results_dirname),
save_seeds_dirname=file.path(out_rootdir, save_seeds_dirname))
if(progress) verbose <- TRUE
for(i in start:end){
if(summarise_asis){
if(verbose)
print_progress(i, nrow(design), time1=time1, time0=time0,
stored_time=stored_time, progress=progress)
time0 <- proc.time()[3L]
Result_list[[i]] <- Analysis(Functions=Functions,
condition=design[i,],
replications=replications,
fixed_objects=fixed_objects,
cl=cl, MPI=MPI, seed=seed,
bootSE=bootSE, boot_draws=boot_draws,
save_results=save_results,
save_results_dirname=save_results_dirname,
save_generate_data=save_generate_data,
save_generate_data_dirname=save_generate_data_dirname,
save_seeds=save_seeds, summarise_asis=summarise_asis,
save_seeds_dirname=save_seeds_dirname,
max_errors=max_errors, packages=packages,
load_seed=load_seed, export_funs=export_funs,
warnings_as_errors=warnings_as_errors,
progress=progress, store_results=FALSE)
time1 <- proc.time()[3L]
stored_time <- stored_time + (time1 - time0)
} else {
stored_time <- do.call(c, lapply(Result_list, function(x) x$SIM_TIME))
if(verbose)
print_progress(i, nrow(design), time1=time1, time0=time0,
stored_time=stored_time, progress=progress)
time0 <- proc.time()[3L]
if(save_generate_data)
dir.create(file.path(out_rootdir, paste0(save_generate_data_dirname, '/design-row-', i)), showWarnings = FALSE)
if(save_seeds)
dir.create(file.path(out_rootdir, paste0(save_seeds_dirname, '/design-row-', i)), showWarnings = FALSE)
tmp <- Analysis(Functions=Functions,
condition=design[i,],
replications=replications,
fixed_objects=fixed_objects,
cl=cl, MPI=MPI, seed=seed,
bootSE=bootSE, boot_draws=boot_draws,
save_results=save_results,
save_results_out_rootdir = out_rootdir,
save_results_dirname=save_results_dirname,
save_generate_data=save_generate_data,
save_generate_data_dirname=save_generate_data_dirname,
save_seeds=save_seeds, summarise_asis=summarise_asis,
save_seeds_dirname=save_seeds_dirname,
max_errors=max_errors, packages=packages,
load_seed=load_seed, export_funs=export_funs,
warnings_as_errors=warnings_as_errors,
progress=progress, store_results=store_results)
if(store_results){
stored_Results_list[[i]] <- attr(tmp, 'full_results')
attr(tmp, 'full_results') <- NULL
}
Result_list[[i]] <- data.frame(design[i, ], as.list(tmp),
check.names=FALSE)
time1 <- proc.time()[3L]
Result_list[[i]]$SIM_TIME <- time1 - time0
Result_list[[i]]$COMPLETED <- date()
if(save || save_results || save_generate_data) saveRDS(Result_list, file.path(out_rootdir, tmpfilename))
}
}
attr(Result_list, 'SimDesign_names') <- NULL
if(summarise_asis){
design$ID <- NULL
nms <- colnames(design)
nms2 <- matrix(character(0L), nrow(design), ncol(design))
for(i in 1L:ncol(design))
nms2[,i] <- paste0(nms[i], '=', design[,i], if(i < ncol(design)) '; ')
nms2 <- apply(nms2, 1L, paste0, collapse='')
names(Result_list) <- nms2
if(is.list(Result_list[[1L]][[1L]]))
for(i in seq_len(length(Result_list)))
attr(Result_list[[i]][[1L]], 'try_errors') <- NULL
if(nrow(design) == 1L) Result_list <- Result_list[[1L]]
return(Result_list)
}
stored_time <- do.call(c, lapply(Result_list, function(x) x$SIM_TIME))
if(verbose)
message('\nSimulation complete. Total execution time: ', timeFormater(sum(stored_time)))
Final <- plyr::rbind.fill(Result_list)
SIM_TIME <- Final$SIM_TIME
COMPLETED <- Final$COMPLETED
REPLICATIONS <- Final$REPLICATIONS
Final$SIM_TIME <- Final$ID <- Final$REPLICATIONS <- Final$COMPLETED <- NULL
Final <- data.frame(Final, REPLICATIONS, SIM_TIME, COMPLETED, check.names=FALSE)
if(!is.null(seed)) Final$SEED <- seed
if(!is.null(filename) && safe){ #save file
files <- dir(out_rootdir)
filename0 <- filename
count <- 1L
# create a new file name if old one exists, and throw warning
while(TRUE){
filename <- paste0(filename, '.rds')
if(filename %in% files){
filename <- paste0(filename0, '-', count)
count <- count + 1L
} else break
}
if(count > 1L)
if(verbose && save)
message(paste0('\nWARNING:\n', filename0, ' existed in the working directory.
Using a unique file name instead.\n'))
}
dn <- colnames(design)
dn <- dn[dn != 'ID']
if(as.factor){
Final[dn] <- lapply(Final[dn], function(x){
if(is.list(x)) return(x)
if(is.numeric(x)) return(ordered(x))
else return(factor(x))
})
}
ten <- colnames(Final)[grepl('ERROR:', colnames(Final))]
wen <- colnames(Final)[grepl('WARNING:', colnames(Final))]
en <- c('REPLICATIONS', 'SIM_TIME', 'COMPLETED', 'SEED')
bsen <- colnames(Final)[grepl('BOOT_SE.', colnames(Final))]
if(length(bsen))
Final <- data.frame(Final[,!(colnames(Final) %in% bsen)], Final[,colnames(Final) %in% bsen])
sn <- colnames(Final)[!(colnames(Final) %in% c(dn, en, ten, wen, bsen))]
attr(Final, 'design_names') <-
list(design=dn, sim=sn, bootSE=bsen, extra=en, errors=ten, warnings=wen)
if(length(packages) > 1L){
pack <- packages[packages != 'SimDesign']
versions <- character(length(pack))
for(i in 1L:length(pack))
versions[i] <- as.character(packageVersion(pack[i]))
pack_vers <- data.frame(packages=pack, versions=versions)
} else pack_vers <- NULL
pick <- c(save_generate_data, save_results, save_seeds)
if(!is.null(filename)) pick <- c(save, pick)
attr(Final, 'extra_info') <- list(sessionInfo = sessionInfo(), packages=pack_vers,
save_info = c(filename=filename,
save_generate_data_dirname=save_generate_data_dirname,
save_results_dirname=save_results_dirname,
save_seeds_dirname=save_seeds_dirname)[pick],
ncores = if(parallel) length(cl) else if(MPI) NA else 1L,
number_of_conditions = nrow(design),
date_completed = date(), total_elapsed_time = sum(Final$SIM_TIME),
stored_results = if(store_results) stored_Results_list else NULL)
if(dummy_run) Final$dummy_run <- NULL
class(Final) <- c('SimDesign', 'data.frame')
if(!is.null(filename) && save){ #save file
if(verbose)
message(paste('\nSaving simulation results to file:', filename))
saveRDS(Final, file.path(out_rootdir, filename))
}
if(save || save_results || save_generate_data || save_seeds) file.remove(file.path(out_rootdir, tmpfilename))
return(Final)
}
#' @rdname runSimulation
#' @param x SimDesign object returned from \code{\link{runSimulation}}
#' @param drop.extras logical; don't print information about warnings, errors, simulation time, and replications?
#' Default is \code{FALSE}
#' @param drop.design logical; don't include information about the (potentially factorized) simulation design?
#' This may be useful if you wish to \code{cbind()} the original design \code{data.frame} to the simulation
#' results instead of using the auto-factorized version. Default is \code{FALSE}
#' @param format.time logical; format \code{SIM_TIME} into a day/hour/min/sec character vector? Default is
#' \code{TRUE}
#' @export
print.SimDesign <- function(x, drop.extras = FALSE, drop.design = FALSE, format.time = TRUE, ...){
att <- attr(x, 'design_names')
if(format.time)
if(!is.null(x$SIM_TIME))
x$SIM_TIME <- sapply(x$SIM_TIME, timeFormater, TRUE)
if(drop.extras) x <- x[ ,c(att$design, att$sim), drop=FALSE]
if(drop.design) x <- x[ ,!(names(x) %in% att$design), drop=FALSE]
class(x) <- 'data.frame'
ldots <- list(...)
if(is.null(ldots$print)) print(x, ...)
else return(x)
}
#' @rdname runSimulation
#' @export
head.SimDesign <- function(x, ...){
x <- print(x, print = FALSE, ...)
class(x) <- 'data.frame'
print(head(x, ...), ...)
}
#' @rdname runSimulation
#' @export
tail.SimDesign <- function(x, ...){
x <- print(x, print = FALSE, ...)
class(x) <- 'data.frame'
print(tail(x, ...), ...)
}
#' @rdname runSimulation
#' @param object SimDesign object returned from \code{\link{runSimulation}}
#' @param ... additional arguments
#' @export
summary.SimDesign <- function(object, ...){
ret <- attr(object, 'extra_info')
ret$total_elapsed_time <- timeFormater(ret$total_elapsed_time, TRUE)
ret$stored_results <- NULL
ret
}
#' @rdname runSimulation
#' @export
extract_results <- function(object){
stopifnot(is(object, "SimDesign"))
extra_info <- attr(object, 'extra_info')
if(is.null(extra_info$stored_results)) return(NULL)
design_names <- attr(object, "design_names")
pick <- design_names$design
design <- subset(as.data.frame(object), select=pick)
nms <- colnames(design)
nms2 <- matrix(character(0L), nrow(design), ncol(design))
for(i in 1L:ncol(design))
nms2[,i] <- paste0(nms[i], '=', design[,i], if(i < ncol(design)) '; ')
nms2 <- apply(nms2, 1L, paste0, collapse='')
ret <- extra_info$stored_results
names(ret) <- nms2
ret
}
#' @rdname runSimulation
#' @export
as.data.frame.SimDesign <- function(x, ...){
class(x) <- 'data.frame'
x
}
#' @export
"[<-.SimDesign" <- function(x, i, j, value){
x <- as.data.frame(x)
x[i, j] <- value
class(x) <- c('SimDesign', 'data.frame')
x
}
#' @export
"[[<-.SimDesign" <- function(x, i, j, value){
x <- as.data.frame(x)
x[[i,j]] <- value
class(x) <- c('SimDesign', 'data.frame')
x
}
|
/R/runSimulation.R
|
no_license
|
oguzhanogreden/SimDesign
|
R
| false
| false
| 63,289
|
r
|
#' Run a Monte Carlo simulation given a data.frame of conditions and simulation functions
#'
#' This function runs a Monte Carlo simulation study given a set of predefined simulation functions,
#' design conditions, and number of replications. Results can be saved as temporary files in case of interruptions
#' and may be restored by re-running \code{runSimulation}, provided that the respective temp
#' file can be found in the working directory. \code{runSimulation} supports parallel
#' and cluster computing, global and local debugging, error handling (including fail-safe
#' stopping when functions fail too often, even across nodes), provides bootstrap estimates of the
#' sampling variability (optional), and tracking of error and warning messages.
#' For convenience, all functions available in the R workspace are exported across all computational nodes
#' so that they are more easily accessible (however, other R objects are not, and therefore
#' must be passed to the \code{fixed_objects} input to become available across nodes).
#' For a didactic presentation of the package refer to Sigal and Chalmers
#' (2016; \doi{10.1080/10691898.2016.1246953}), and see the associated
#' wiki on Github (\url{https://github.com/philchalmers/SimDesign/wiki})
#' for other tutorial material, examples, and applications of \code{SimDesign} to real-world simulations.
#'
#' The strategy for organizing the Monte Carlo simulation work-flow is to
#'
#' \describe{
#' \item{1)}{Define a suitable \code{design} data.frame object containing fixed conditional
#' information about the Monte Carlo simulations. This is often expedited by using the
#' \code{\link{expand.grid}} function, and if necessary using the \code{\link{subset}}
#' function to remove redundant or non-applicable rows}
#' \item{2)}{Define the three step functions to generate the data (\code{\link{Generate}}; see also
#' \url{https://CRAN.R-project.org/view=Distributions} for a list of distributions in R),
#' analyse the generated data by computing the respective parameter estimates, detection rates,
#' etc (\code{\link{Analyse}}), and finally summarise the results across the total
#' number of replications (\code{\link{Summarise}}). Note that these functions can be
#' automatically generated by using the \code{\link{SimFunctions}} function.
#' }
#' \item{3)}{Pass the above objects to the \code{runSimulation} function, and declare the
#' number of replications to perform with the \code{replications} input. This function will accept
#' a \code{design} data.frame object and will return a suitable data.frame object with the
#' simulation results}
#' \item{4)}{Analyze the output from \code{runSimulation}, possibly using ANOVA techniques
#' (\code{\link{SimAnova}}) and generating suitable plots and tables}
#' }
#'
#' More succinctly, the functions to be called follow the following form with the exact inputs required
#' by the \code{SimDesign} package
#'
#' \describe{
#' \item{\code{Design <- expand.grid(...)}}{}
#' \item{\code{Generate <- function(condition, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{Analyse <- function(condition, dat, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{Summarise <- function(condition, results, fixed_objects = NULL) \{...\} }}{}
#' \item{\code{results <- runSimulation(design=Design, replications, generate=Generate,
#' analyse=Analyse, summarise=Summarise)}}{}
#' }
#'
#' For a skeleton version of the work-flow, which is often useful when initially defining a simulation,
#' see \code{\link{SimFunctions}}. This function will write template simulation code
#' to one/two files so that modifying the required functions and objects can begin immediately
#' with minimal error. This means that you can focus on your Monte Carlo simulation immediately rather
#' than worrying about the administrative code-work required to organize the simulation work-flow.
#'
#' Additional information for each condition are also contained in the \code{data.frame} object returned by
#' \code{runSimulation}: \code{REPLICATIONS} to indicate the number of Monte Carlo replications,
#' \code{SIM_TIME} to indicate how long (in seconds) it took to complete
#' all the Monte Carlo replications for each respective design condition,
#' \code{COMPLETED} to indicate the date in which the given simulation condition completed,
#' \code{SEED} for the integer values in the \code{seed} argument,
#' columns containing the number of replications which had to be re-run due to errors (where the error messages
#' represent the names of the columns prefixed with a \code{ERROR:} string), and
#' columns containing the number of warnings prefixed with a \code{WARNING:} string. Finally,
#' if \code{bootSE = TRUE} was included then the final right-most columns will contain the labels
#' \code{BOOT_SE.} followed by the name of the associated meta-statistic defined in \code{summarise()}.
#'
#' Additional examples, presentation files, and tutorials can be found on the package wiki located at
#' \url{https://github.com/philchalmers/SimDesign/wiki}.
#'
#' @section Saving data, results, seeds, and the simulation state:
#'
#' To conserve RAM, temporary objects (such as data generated across conditions and replications)
#' are discarded; however, these can be saved to the hard-disk by passing the appropriate flags.
#' For longer simulations it is recommended to use \code{save = TRUE} to temporarily save the
#' simulation state, and to use the \code{save_results} flag to write the analysis results
#' the to hard-disc.
#'
#' The generated data can be saved by passing
#' \code{save_generate_data = TRUE}, however it is often more memory efficient to use the
#' \code{save_seeds} option instead to only save R's \code{.Random.seed} state instead (still
#' allowing for complete reproducibility); individual \code{.Random.seed} terms may also be read in with the
#' \code{load_seed} input to reproduce the exact simulation state at any given replication. Finally,
#' providing a vector of \code{seeds} is also possible to ensure
#' that each simulation condition is completely reproducible under the single/multi-core method selected.
#'
#' Finally, when the Monte Carlo simulation is complete
#' it is recommended to write the results to a hard-drive for safe keeping, particularly with the
#' \code{save} and \code{filename} arguments provided (for reasons that are more obvious in the parallel computation
#' descriptions below). Using the \code{filename} argument (along with \code{save = TRUE})
#' supplied is much safer than using something
#' like \code{\link{saveRDS}} directly because files will never accidentally be overwritten,
#' and instead a new file name will be created when a conflict arises; this type of safety
#' is prevalent in many aspects of the package and helps to avoid many unrecoverable (yet surprisingly common)
#' mistakes.
#'
#' @section Resuming temporary results:
#'
#' In the event of a computer crash, power outage, etc, if \code{save = TRUE} was used
#' then the original code used to execute \code{runSimulation()} need only be re-run to resume the simulation.
#' The saved temp file will be read into the function automatically, and the simulation will continue
#' one the condition where it left off before the simulation state was terminated.
#'
#' @section A note on parallel computing:
#'
#' When running simulations in parallel (either with \code{parallel = TRUE} or \code{MPI = TRUE})
#' R objects defined in the global environment will generally \emph{not} be visible across nodes.
#' Hence, you may see errors such as \code{Error: object 'something' not found} if you try to use an object
#' that is defined in the workspace but is not passed to \code{runSimulation}.
#' To avoid this type or error, simply pass additional objects to the
#' \code{fixed_objects} input (usually it's convenient to supply a named list of these objects).
#' Fortunately, however, \emph{custom functions defined in the global environment are exported across
#' nodes automatically}. This makes it convenient when writing code because custom functions will
#' always be available across nodes if they are visible in the R workspace. As well, note the
#' \code{packages} input to declare packages which must be loaded via \code{library()} in order to make
#' specific non-standard R functions available across nodes.
#'
#' @section Cluster computing:
#'
#' SimDesign code may be released to a computing system which supports parallel cluster computations using
#' the industry standard Message Passing Interface (MPI) form. This simply
#' requires that the computers be setup using the usual MPI requirements (typically, running some flavor
#' of Linux, have password-less open-SSH access, IP addresses have been added to the \code{/etc/hosts} file
#' or \code{~/.ssh/config}, etc).
#' More generally though, these resources are widely available through professional
#' organizations dedicated to super-computing.
#'
#' To setup the R code for an MPI cluster one need only add the argument \code{MPI = TRUE},
#' wrap the appropriate MPI directives around \code{runSimulation}, and submit the
#' files using the suitable BASH commands to execute the \code{mpirun} tool. For example,
#'
#' \describe{
#' \item{\code{library(doMPI)}}{}
#' \item{\code{cl <- startMPIcluster()}}{}
#' \item{\code{registerDoMPI(cl)}}{}
#' \item{\code{runSimulation(design=Design, replications=1000, save=TRUE, filename='mysimulation',
#' generate=Generate, analyse=Analyse, summarise=Summarise, MPI=TRUE)}}{}
#' \item{\code{closeCluster(cl)}}{}
#' \item{\code{mpi.quit()}}{}
#' }
#'
#' The necessary \code{SimDesign} files must be uploaded to the dedicated master node
#' so that a BASH call to \code{mpirun} can be used to distribute the work across slaves.
#' For instance, if the following BASH command is run on the master node then 16 processes
#' will be summoned (1 master, 15 slaves) across the computers named \code{localhost}, \code{slave1},
#' and \code{slave2} in the ssh \code{config} file.
#'
#' \code{mpirun -np 16 -H localhost,slave1,slave2 R --slave -f simulation.R}
#'
#' @section Network computing:
#'
#' If you access have to a set of computers which can be linked via secure-shell (ssh) on the same LAN network then
#' Network computing (a.k.a., a Beowulf cluster) may be a viable and useful option.
#' This approach is similar to MPI computing approach
#' except that it offers more localized control and requires more hands-on administrative access to the master
#' and slave nodes. The setup generally requires that the master node
#' has \code{SimDesign} installed and the slave/master nodes have all the required R packages pre-installed
#' (Unix utilities such as \code{dsh} are very useful for this purpose). Finally,
#' the master node must have ssh access to the slave nodes, each slave node must have ssh access
#' with the master node, and a cluster object (\code{cl}) from the \code{parallel} package must be defined on the
#' master node.
#'
#' Setup for network computing is generally more straightforward and controlled
#' than the setup for MPI jobs in that it only requires the specification of a) the respective
#' IP addresses within a defined R script, and b) the user name
#' (if different from the master node's user name. Otherwise, only a) is required).
#' However, on Linux I have found it is also important to include relevant information about the host names
#' and IP addresses in the \code{/etc/hosts} file on the master and slave nodes, and to ensure that
#' the selected port (passed to \code{\link{makeCluster}}) on the master node is not hindered by a firewall.
#'
#' As an example, using the following code the master node (primary) will spawn 7 slaves and 1 master,
#' while a separate computer on the network with the associated IP address will spawn an additional 6 slaves.
#' Information will be collected on the master node, which is also where the files
#' and objects will be saved using the \code{save} inputs (if requested).
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- '192.168.2.1'}}{}
#' \item{\code{IPs <- list(list(host=primary, user='myname', ncore=8), list(host='192.168.2.2', user='myname', ncore=6))}}{}
#' \item{\code{spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host, user=IP$user)), IP$ncore))}}{}
#' \item{\code{spec <- unlist(spec, recursive=FALSE)}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#' The object \code{cl} is passed to \code{runSimulation} on the master node
#' and the computations are distributed across the respective
#' IP addresses. Finally, it's usually good practice to use \code{stopCluster(cl)}
#' when all the simulations are said and done to release the communication between the computers,
#' which is what the above code shows.
#'
#' Alternatively, if you have provided suitable names for each respective slave node, as well as the master,
#' then you can define the \code{cl} object using these instead (rather than supplying the IP addresses in
#' your R script). This requires that the master node has itself and all the slave nodes defined in the
#' \code{/etc/hosts} and \code{~/.ssh/config} files, while the slave nodes require themselves and the
#' master node in the same files (only 2 IP addresses required on each slave).
#' Following this setup, and assuming the user name is the same across all nodes,
#' the \code{cl} object could instead be defined with
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- 'master'}}{}
#' \item{\code{IPs <- list(list(host=primary, ncore=8), list(host='slave', ncore=6))}}{}
#' \item{\code{spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host)), IP$ncore))}}{}
#' \item{\code{spec <- unlist(spec, recursive=FALSE)}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#' Or, even more succinctly if all communication elements required are identical to the master node,
#'
#' \describe{
#' \item{\code{library(parallel)}}{}
#' \item{\code{primary <- 'master'}}{}
#' \item{\code{spec <- c(rep(primary, 8), rep('slave', 6))}}{}
#' \item{\code{cl <- makeCluster(master=primary, spec=spec)}}{}
#' \item{\code{Final <- runSimulation(..., cl=cl)}}{}
#' \item{\code{stopCluster(cl)}}{}
#' }
#'
#'
#' @section Poor man's cluster computing for independent nodes:
#'
#' In the event that you do not have access to a Beowulf-type cluster (described in the section on
#' "Network Computing") but have multiple personal
#' computers then the simulation code can be manually distributed across each independent computer instead.
#' This simply requires passing a smaller value to the \code{replications} argument on each computer and later
#' aggregating the results using the \code{\link{aggregate_simulations}} function.
#'
#' For instance, if you have two computers available on different networks and wanted a total of 500 replications you
#' could pass \code{replications = 300} to one computer and \code{replications = 200} to the other along
#' with a \code{filename} argument (or simply saving the final objects as \code{.rds} files manually after
#' \code{runSimulation()} has finished). This will create two distinct \code{.rds} files which can be
#' combined later with the \code{\link{aggregate_simulations}} function. The benefit of this approach over
#' MPI or setting up a Beowulf cluster is that computers need not be linked on the same network,
#' and, should the need arise, the temporary
#' simulation results can be migrated to another computer in case of a complete hardware failure by moving the
#' saved temp files to another node, modifying
#' the suitable \code{compname} input to \code{save_details} (or, if the \code{filename} and \code{tmpfilename}
#' were modified, matching those files accordingly), and resuming the simulation as normal.
#'
#' Note that this is also a useful tactic if the MPI or Network computing options require you to
#' submit smaller jobs due to time and resource constraint-related reasons,
#' where fewer replications/nodes should be requested. After all the jobs are completed and saved to their
#' respective files, \code{\link{aggregate_simulations}}
#' can then collapse the files as if the simulations were run all at once. Hence, SimDesign makes submitting
#' smaller jobs to super-computing resources considerably less error prone than managing a number of smaller
#' jobs manually .
#'
#' @param design a \code{data.frame} object containing the Monte Carlo simulation conditions to
#' be studied, where each row represents a unique condition and each column a factor to be varied
#'
#' @param generate user-defined data and parameter generating function.
#' See \code{\link{Generate}} for details
#'
#' @param analyse user-defined computation function which acts on the data generated from
#' \code{\link{Generate}}. See \code{\link{Analyse}} for details
#'
#' @param summarise optional (but highly recommended) user-defined summary function to be used
#' after all the replications have completed within each \code{design} condition. Omitting this function
#' will return a list of matrices (or a single matrix, if only one row in \code{design} is supplied)
#' or, for more general objects (such as lists), a list containing the results returned form \code{\link{Analyse}}.
#' Ommiting this function is only recommended for didactic purposes because it leaves out a large amount of
#' information (e.g., try-errors, warning messages, etc), can witness memory related issues,
#' and generally is not as flexible internally. See
#' the \code{save_results} option for a more RAM friendly alternative to storing all the Generate-Analyse results
#' in the working enviroment
#'
#' @param replications number of replication to perform per condition (i.e., each row in \code{design}).
#' Must be greater than 0
#'
#' @param fixed_objects (optional) an object (usually a named \code{list})
#' containing additional user-defined objects
#' that should remain fixed across conditions. This is useful when including
#' long fixed vectors/matrices of population parameters, data
#' that should be used across all conditions and replications (e.g., including a fixed design matrix
#' for linear regression), or simply control constant global elements such as sample size
#'
#' @param parallel logical; use parallel processing from the \code{parallel} package over each
#' unique condition?
#'
#' @param cl cluster object defined by \code{\link{makeCluster}} used to run code in parallel.
#' If \code{NULL} and \code{parallel = TRUE}, a local cluster object will be defined which
#' selects the maximum number cores available
#' and will be stop the cluster when the simulation is complete. Note that supplying a \code{cl}
#' object will automatically set the \code{parallel} argument to \code{TRUE}
#'
#' @param packages a character vector of external packages to be used during the simulation (e.g.,
#' \code{c('MASS', 'extraDistr', 'simsem')} ). Use this input when \code{parallel = TRUE} or
#' \code{MPI = TRUE} to use non-standard functions from additional packages,
#' otherwise the functions must be made available by using explicit
#' \code{\link{library}} or \code{\link{require}} calls within the provided simulation functions.
#' Alternatively, functions can be called explicitly without attaching the package with the \code{::} operator
#' (e.g., \code{extraDistr::rgumbel()})
#'
#' @param warnings_as_errors logical; treat warning messages as errors during the simulation? Default is FALSE,
#' therefore warnings are only collected and not used to restart the data generation step
#'
#' @param as.factor logical; coerce the input \code{design} elements into \code{factor}s when the
#' simulation is complete? If the columns inputs are numeric then these will be treated
#' as \code{ordered}. Default is \code{TRUE}
#'
#' @param save_results logical; save the results returned from \code{\link{Analyse}} to external
#' \code{.rds} files located in the defined \code{save_results_dirname} directory/folder?
#' Use this if you would like to keep track of the individual parameters returned from the analyses.
#' Each saved object will contain a list of three elements containing the condition (row from \code{design}),
#' results (as a \code{list} or \code{matrix}), and try-errors. When \code{TRUE}, a temp file will be used to track the simulation
#' state (in case of power outages, crashes, etc). When \code{TRUE}, temporary files will also be saved
#' to the working directory (in the same way as when \code{save = TRUE}).
#' See \code{\link{SimResults}} for an example of how to read these \code{.rds} files back into R
#' after the simulation is complete. Default is \code{FALSE}.
#'
#' WARNING: saving results to your hard-drive can fill up space very quickly for larger simulations. Be sure to
#' test this option using a smaller number of replications before the full Monte Carlo simulation is performed.
#'
#' @param save_seeds logical; save the \code{.Random.seed} states prior to performing each replication into
#' plain text files located in the defined \code{save_seeds_dirname} directory/folder?
#' Use this if you would like to keep track of the simulation state within each replication and design
#' condition. Primarily, this is useful for completely replicating any cell in the simulation if need be,
#' especially when tracking down hard-to-find errors and bugs. As well, see the \code{load_seed} input
#' to load a given \code{.Random.seed} to exactly replicate the generated data and analysis state (handy
#' for debugging). When \code{TRUE}, temporary files will also be saved
#' to the working directory (in the same way as when \code{save = TRUE}).
#' Default is \code{FALSE}
#'
#' @param save_generate_data logical; save the data returned from \code{\link{Generate}} to external \code{.rds} files
#' located in the defined \code{save_generate_data_dirname} directory/folder?
#' When \code{TRUE}, temporary files will also be saved to the working directory
#' (in the same way as when \code{save = TRUE}). Default is \code{FALSE}
#'
#' WARNING: saving data to your hard-drive can fill up space very quickly for larger simulations. Be sure to
#' test this option using a smaller number of replications before the full Monte Carlo simulation is performed.
#' It is generally recommended to leave this argument as \code{FALSE} because saving datasets will often consume
#' a needless amount of disk space, and by-and-large saving data is not required for simulations.
#'
#' @param load_seed a character object indicating which file to load from when the \code{.Random.seed}s have
#' be saved (after a call with \code{save_seeds = TRUE}). E.g., \code{load_seed = 'design-row-2/seed-1'}
#' will load the first seed in the second row of the \code{design} input. Note that it is important NOT
#' to modify the \code{design} input object, otherwise the path may not point to the correct saved location.
#' Default is \code{NULL}
#'
#' @param filename (optional) the name of the \code{.rds} file to save the final simulation results to
#' when \code{save = TRUE}. If the same file name already exists in the working
#' directly at the time of saving then a new
#' file will be generated instead and a warning will be thrown. This helps to avoid accidentally overwriting
#' existing files. Default is \code{'SimDesign-results'}
#'
#' @param save_details a list pertaining to information regarding how and where files should be saved
#' when the \code{save}, \code{save_results}, or \code{save_generate_data} flags are triggered.
#'
#' \describe{
#'
#' \item{\code{safe}}{logical; trigger whether safe-saving should be performed. When \code{TRUE} files
#' will never be overwritten accidentally, and where appropriate the program will either stop or generate
#' new files with unique names. Default is \code{TRUE}}
#'
#' \item{\code{compname}}{name of the computer running the simulation. Normally this doesn't need
#' to be modified, but in the event that a manual node breaks down while running a simulation the
#' results from the temp files may be resumed on another computer by changing the name of the
#' node to match the broken computer. Default is the result of evaluating \code{unname(Sys.info()['nodename'])}}
#'
#' \item{\code{tmpfilename}}{the name of the temporary \code{.rds} file when any of the \code{save} flags are used.
#' This file will be read-in if it is in the working directory and the simulation will continue
#' at the last point this file was saved
#' (useful in case of power outages or broken nodes). Finally, this file will be deleted when the
#' simulation is complete. Default is the system name (\code{compname}) appended
#' to \code{'SIMDESIGN-TEMPFILE_'}}
#'
#' \item{\code{save_results_dirname}}{a string indicating the name of the folder to save
#' result objects to when \code{save_results = TRUE}. If a directory/folder does not exist
#' in the current working directory then a unique one will be created automatically. Default is
#' \code{'SimDesign-results_'} with the associated \code{compname} appended}
#'
#' \item{\code{save_seeds_dirname}}{a string indicating the name of the folder to save
#' \code{.Random.seed} objects to when \code{save_seeds = TRUE}. If a directory/folder does not exist
#' in the current working directory then one will be created automatically. Default is
#' \code{'SimDesign-seeds_'} with the associated \code{compname} appended}
#'
#' \item{\code{save_generate_data_dirname}}{a string indicating the name of the folder to save
#' data objects to when \code{save_generate_data = TRUE}. If a directory/folder does not exist
#' in the current working directory then one will be created automatically.
#' Within this folder nested directories will be created associated with each row in \code{design}.
#' Default is \code{'SimDesign-generate-data_'} with the \code{compname} appended}
#'
#' }
#'
#' @param max_errors the simulation will terminate when more than this number of consecutive errors are thrown in any
#' given condition. The purpose of this is to indicate that something fatally problematic is likely going
#' wrong in the generate-analyse phases and should be inspected. Default is 50
#'
#' @param ncores number of cores to be used in parallel execution. Default uses all available
#'
#' @param MPI logical; use the \code{foreach} package in a form usable by MPI to run simulation
#' in parallel on a cluster? Default is \code{FALSE}
#'
#' @param save logical; save the simulation state and final results to the hard-drive? This is useful
#' for simulations which require an extended amount of time. When \code{TRUE}, a temp file
#' will be created in the working directory which allows the simulation state to be saved
#' and recovered (in case of power outages, crashes, etc). To recover your simulation at the last known
#' location simply re-run the code you used to initially define the simulation and the external file
#' will automatically be detected and read-in. Upon completion, the final results will
#' be saved to the working directory, and the temp file will be removed. Default is \code{FALSE}
#'
#' @param edit a string indicating where to initiate a \code{browser()} call for editing and debugging.
#' General options are \code{'none'} (default) and \code{'all'}, which are used
#' to disable debugging and to debug all the user defined functions, respectively.
#' Specific options include: \code{'generate'}
#' to edit the data simulation function, \code{'analyse'} to edit the computational function, and
#' \code{'summarise'} to edit the aggregation function.
#'
#' Alternatively, users may place \code{\link{browser}} calls within the respective functions for
#' debugging at specific lines (note: parallel computation flags will automatically be disabled
#' when a \code{browser()} is detected)
#'
#' @param seed a vector of integers to be used for reproducibility.
#' The length of the vector must be equal the number of rows in \code{design}.
#' This argument calls \code{\link{set.seed}} or
#' \code{\link{clusterSetRNGStream}} for each condition, respectively,
#' but will not be run when \code{MPI = TRUE}.
#' Default randomly generates seeds within the range 1 to 2147483647 for each condition.
#'
#' @param progress logical; display a progress bar for each simulation condition?
#' This is useful when simulations conditions take a long time to run.
#' Uses the \code{pbapply} package to display the progress. Default is \code{FALSE}
#'
#' @param bootSE logical; perform a non-parametric bootstrap to compute bootstrap standard error
#' estimates for the respective meta-statistics computed by the \code{Summarise} function?
#' When \code{TRUE}, bootstrap samples for each row in \code{Design} will be obtained after
#' the generate-analyse steps have obtain the simulation results to be summarised so that
#' standard errors for each statistic can be computed. To compute large-sample confidence
#' intervals given the bootstrap SE estimates see \code{\link{SimBoot}}.
#'
#' This option is useful to approximate how accurate the resulting meta-statatistic estimates
#' were, particularly if the number of \code{replications} was relatively low (e.g., less than
#' 5000). If users prefer to obtain alternative bootstrap estimates then consider passing
#' \code{save_results = TRUE}, reading the generate-analyse data into R via
#' \code{\link{SimResults}}, and performing the bootstrap manually with function found in the
#' external \code{boot} package
#'
#' @param boot_draws number of non-parametric bootstrap draws to sample for the \code{summarise}
#' function after the generate-analyse replications are collected. Default is 1000
#'
#' @param store_results logical; store the complete tables of simulation results
#' in the returned object? This is \code{FALSE} by default to help avoid RAM
#' issues (see \code{save_results} as a more suitable alternative). To extract these results
#' pass the returned object to \code{\link{extract_results}}, which will return a named list
#' of all the simulation results for each condition
#'
#' @param verbose logical; print messages to the R console? Default is \code{TRUE}
#'
#' @return a \code{data.frame} (also of class \code{'SimDesign'})
#' with the original \code{design} conditions in the left-most columns,
#' simulation results and ERROR/WARNING's (if applicable) in the middle columns,
#' and additional information (such as REPLICATIONS, SIM_TIME, COMPLETED, and SEED) in the right-most
#' columns.
#'
#' @aliases runSimulation
#'
#' @seealso \code{\link{Generate}}, \code{\link{Analyse}}, \code{\link{Summarise}},
#' \code{\link{SimFunctions}}, \code{\link{SimClean}}, \code{\link{SimAnova}}, \code{\link{SimResults}},
#' \code{\link{SimBoot}}, \code{\link{aggregate_simulations}}, \code{\link{Attach}}, \code{\link{SimShiny}}
#'
#' @export runSimulation
#'
#' @author Phil Chalmers \email{rphilip.chalmers@@gmail.com}
#'
#' @references
#' Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
#' Carlo simulation. \code{Journal of Statistics Education, 24}(3), 136-156.
#' \doi{10.1080/10691898.2016.1246953}
#'
#' @examples
#'
#' #-------------------------------------------------------------------------------
#' # Example 1: Sampling distribution of mean
#'
#' # This example demonstrate some of the simpler uses of SimDesign,
#' # particularly for classroom settings. The only factor varied in this simulation
#' # is sample size.
#'
#' # skeleton functions to be saved and edited
#' SimFunctions()
#'
#' #### Step 1 --- Define your conditions under study and create design data.frame
#'
#' Design <- data.frame(N = c(10, 20, 30))
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 2 --- Define generate, analyse, and summarise functions
#'
#' # help(Generate)
#' Generate <- function(condition, fixed_objects = NULL){
#' dat <- with(condition, rnorm(N, 10, 5)) # distributed N(10, 5)
#' dat
#' }
#'
#' # help(Analyse)
#' Analyse <- function(condition, dat, fixed_objects = NULL){
#' ret <- mean(dat) # mean of the sample data vector
#' ret
#' }
#'
#' # help(Summarise)
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' ret <- c(mu=mean(results), SE=sd(results)) # mean and SD summary of the sample means
#' ret
#' }
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 3 --- Collect results by looping over the rows in design
#'
#' # run the simulation
#' Final <- runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' Final
#'
#' # reproduce exact simulation
#' Final_rep <- runSimulation(design=Design, replications=1000, seed=Final$SEED,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' Final_rep
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Extras
#' # compare SEs estimates to the true SEs from the formula sigma/sqrt(N)
#' 5 / sqrt(Design$N)
#'
#' # To store the results from the analyse function either
#' # a) omit a definition of of summarise(), or
#' # b) pass save_results = TRUE to runSimulation() and read the results in with SimResults()
#'
#' # e.g., the a) approach
#' results <- runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse)
#' str(results)
#' head(results[[1]])
#'
#' # or b) approach
#' Final <- runSimulation(design=Design, replications=1000, save_results=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' results <- SimResults(Final)
#' str(results)
#' head(results[[1]]$results)
#'
#' # remove the saved results from the hard-drive if you no longer want them
#' SimClean(results = TRUE)
#'
#'
#'
#'
#' #-------------------------------------------------------------------------------
#' # Example 2: t-test and Welch test when varying sample size, group sizes, and SDs
#'
#' # skeleton functions to be saved and edited
#' SimFunctions()
#'
#' \dontrun{
#' # in real-world simulations it's often better/easier to save
#' # these functions directly to your hard-drive with
#' SimFunctions('my-simulation')
#' }
#'
#' #### Step 1 --- Define your conditions under study and create design data.frame
#'
#' Design <- expand.grid(sample_size = c(30, 60, 90, 120),
#' group_size_ratio = c(1, 4, 8),
#' standard_deviation_ratio = c(.5, 1, 2))
#' dim(Design)
#' head(Design)
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 2 --- Define generate, analyse, and summarise functions
#'
#' Generate <- function(condition, fixed_objects = NULL){
#' N <- condition$sample_size # alternatively, could use Attach() to make objects available
#' grs <- condition$group_size_ratio
#' sd <- condition$standard_deviation_ratio
#' if(grs < 1){
#' N2 <- N / (1/grs + 1)
#' N1 <- N - N2
#' } else {
#' N1 <- N / (grs + 1)
#' N2 <- N - N1
#' }
#' group1 <- rnorm(N1)
#' group2 <- rnorm(N2, sd=sd)
#' dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)), DV = c(group1, group2))
#' dat
#' }
#'
#' Analyse <- function(condition, dat, fixed_objects = NULL){
#' welch <- t.test(DV ~ group, dat)
#' ind <- t.test(DV ~ group, dat, var.equal=TRUE)
#'
#' # In this function the p values for the t-tests are returned,
#' # and make sure to name each element, for future reference
#' ret <- c(welch = welch$p.value, independent = ind$p.value)
#' ret
#' }
#'
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' #find results of interest here (e.g., alpha < .1, .05, .01)
#' ret <- EDR(results, alpha = .05)
#' ret
#' }
#'
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' #### Step 3 --- Collect results by looping over the rows in design
#'
#' # first, test to see if it works
#' Final <- runSimulation(design=Design, replications=5, store_results=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' head(Final)
#'
#' \dontrun{
#' # complete run with 1000 replications per condition
#' Final <- runSimulation(design=Design, replications=1000, parallel=TRUE,
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#' head(Final, digits = 3)
#' View(Final)
#'
#' ## save final results to a file upon completion (not run)
#' runSimulation(design=Design, replications=1000, parallel=TRUE, save=TRUE, filename = 'mysim',
#' generate=Generate, analyse=Analyse, summarise=Summarise)
#'
#'
#'
#' ## Debug the generate function. See ?browser for help on debugging
#' ## Type help to see available commands (e.g., n, c, where, ...),
#' ## ls() to see what has been defined, and type Q to quit the debugger
#' runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise,
#' parallel=TRUE, edit='generate')
#'
#' ## Alternatively, place a browser() within the desired function line to
#' ## jump to a specific location
#' Summarise <- function(condition, results, fixed_objects = NULL){
#' #find results of interest here (e.g., alpha < .1, .05, .01)
#' ret <- EDR(results[,nms], alpha = .05)
#' browser()
#' ret
#' }
#'
#' runSimulation(design=Design, replications=1000,
#' generate=Generate, analyse=Analyse, summarise=Summarise,
#' parallel=TRUE)
#'
#'
#'
#'
#' ## EXTRA: To run the simulation on a MPI cluster, use the following setup on each node (not run)
#' # library(doMPI)
#' # cl <- startMPIcluster()
#' # registerDoMPI(cl)
#' # Final <- runSimulation(design=Design, replications=1000, MPI=TRUE, save=TRUE,
#' # generate=Generate, analyse=Analyse, summarise=Summarise)
#' # saveRDS(Final, 'mysim.rds')
#' # closeCluster(cl)
#' # mpi.quit()
#'
#'
#' ## Similarly, run simulation on a network linked via ssh
#' ## (two way ssh key-paired connection must be possible between master and slave nodes)
#' ##
#' ## define IP addresses, including primary IP
#' # primary <- '192.168.2.20'
#' # IPs <- list(
#' # list(host=primary, user='phil', ncore=8),
#' # list(host='192.168.2.17', user='phil', ncore=8)
#' # )
#' # spec <- lapply(IPs, function(IP)
#' # rep(list(list(host=IP$host, user=IP$user)), IP$ncore))
#' # spec <- unlist(spec, recursive=FALSE)
#' #
#' # cl <- parallel::makeCluster(type='PSOCK', master=primary, spec=spec)
#' # Final <- runSimulation(design=Design, replications=1000, parallel = TRUE, save=TRUE,
#' # generate=Generate, analyse=Analyse, summarise=Summarise, cl=cl)
#'
#' #~~~~~~~~~~~~~~~~~~~~~~~~
#' ###### Post-analysis: Analyze the results via functions like lm() or SimAnova(), and create
#' ###### tables(dplyr) or plots (ggplot2) to help visualize the results.
#' ###### This is where you get to be a data analyst!
#'
#' library(dplyr)
#' Final2 <- tbl_df(Final)
#' Final2 %>% summarise(mean(welch), mean(independent))
#' Final2 %>% group_by(standard_deviation_ratio, group_size_ratio) %>%
#' summarise(mean(welch), mean(independent))
#'
#' # quick ANOVA analysis method with all two-way interactions
#' SimAnova( ~ (sample_size + group_size_ratio + standard_deviation_ratio)^2, Final)
#'
#' # or more specific anovas
#' SimAnova(independent ~ (group_size_ratio + standard_deviation_ratio)^2,
#' Final)
#'
#' # make some plots
#' library(ggplot2)
#' library(reshape2)
#' welch_ind <- Final[,c('group_size_ratio', "standard_deviation_ratio",
#' "welch", "independent")]
#' dd <- melt(welch_ind, id.vars = names(welch_ind)[1:2])
#'
#' ggplot(dd, aes(factor(group_size_ratio), value)) + geom_boxplot() +
#' geom_abline(intercept=0.05, slope=0, col = 'red') +
#' geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
#' geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
#' facet_wrap(~variable)
#'
#' ggplot(dd, aes(factor(group_size_ratio), value, fill = factor(standard_deviation_ratio))) +
#' geom_boxplot() + geom_abline(intercept=0.05, slope=0, col = 'red') +
#' geom_abline(intercept=0.075, slope=0, col = 'red', linetype='dotted') +
#' geom_abline(intercept=0.025, slope=0, col = 'red', linetype='dotted') +
#' facet_grid(variable~standard_deviation_ratio) +
#' theme(legend.position = 'none')
#'
#' }
#'
runSimulation <- function(design, replications, generate, analyse, summarise,
fixed_objects = NULL, packages = NULL, bootSE = FALSE,
boot_draws = 1000L, filename = 'SimDesign-results',
seed = rint(nrow(design), min=1L, max = 2147483647L),
save = FALSE, save_results = FALSE, store_results = FALSE,
warnings_as_errors = FALSE, save_seeds = FALSE, load_seed = NULL,
parallel = FALSE, ncores = parallel::detectCores(), cl = NULL, MPI = FALSE,
max_errors = 50L, as.factor = TRUE, save_generate_data = FALSE,
save_details = list(), edit = 'none', progress = FALSE, verbose = TRUE)
{
stopifnot(!missing(generate) || !missing(analyse))
if(!all(names(save_results) %in%
c('compname', 'tmpfilename', 'save_results_dirname', 'save_generate_data_dirname')))
stop('save_details contains elements that are not supported', call.=FALSE)
compname <- save_details$compname
safe <- save_details$safe
out_rootdir <- save_details$out_rootdir
tmpfilename <- save_details$tmpfilename
save_results_dirname <- save_details$save_results_dirname
save_generate_data_dirname <- save_details$save_generate_data_dirname
save_seeds_dirname <- save_details$save_seeds_dirname
if(is.null(compname)) compname <- Sys.info()['nodename']
if(is.null(safe)) safe <- TRUE
if(is.null(out_rootdir)) { out_rootdir <- '.' } else { dir.create(out_rootdir) }
if(is.null(tmpfilename)) tmpfilename <- paste0('SIMDESIGN-TEMPFILE_', compname, '.rds')
if(is.null(save_results_dirname)) save_results_dirname <- paste0('SimDesign-results_', compname)
if(is.null(save_generate_data_dirname)) save_generate_data_dirname <- paste0('SimDesign-generate-data_', compname)
if(is.null(save_seeds_dirname)) save_seeds_dirname <- paste0('SimDesign-seeds_', compname)
if(!is.null(filename)){
if(grepl('\\.rds', filename))
filename <- gsub('\\.rds', '', filename)
}
if(!is.null(cl)) parallel <- TRUE
if(!is.null(load_seed)) seed <- NULL
edit <- tolower(edit)
summarise_asis <- FALSE
if(missing(summarise)){
summarise <- function(condition, results, fixed_objects = NULL) results
summarise_asis <- TRUE
save_generate_data <- FALSE
stored_time <- 0
}
Functions <- list(generate=generate, analyse=analyse, summarise=summarise)
dummy_run <- FALSE
if(missing(design)){
design <- data.frame(dummy_run=NA)
dummy_run <- TRUE
}
if(nrow(design) == 1L) verbose <- FALSE
stopifnot(!missing(replications))
if(!is.null(seed))
stopifnot(nrow(design) == length(seed))
edit <- tolower(edit)
if(!save && any(save_results, save_generate_data, save_seeds)) filename <- NULL
for(i in names(Functions)){
fms <- names(formals(Functions[[i]]))
truefms <- switch(i,
generate = c('condition', 'fixed_objects'),
analyse = c('dat', 'condition', 'fixed_objects'),
summarise = c('results', 'condition', 'fixed_objects'))
if(!all(truefms %in% fms))
stop(paste0('Function arguments for ', i, ' are not correct.'), call. = FALSE)
}
start <- 1L; end <- nrow(design)
if(!is.null(load_seed)){
save <- save_seeds <- parallel <- MPI <- FALSE
replications <- 1L
load_seed2 <- gsub('design-row-', '', load_seed)
start <- end <- as.numeric(gsub('/.*', '', load_seed2))
load_seed <- paste0(save_seeds_dirname, '/', load_seed)
}
if(MPI){
parallel <- FALSE
verbose <- FALSE
}
packages <- c('SimDesign', packages)
char_functions <- deparse(substitute(Functions[[i]]))
if(any(grepl('browser\\(', char_functions))){
if(verbose && parallel)
message(paste0('A browser() call was detected. Parallel processing/object ',
'saving will be disabled while visible'))
save <- save_results <- save_generate_data <- save_seeds <- parallel <- MPI <- FALSE
}
if(any(grepl('attach\\(', char_functions)))
stop('Did you mean to use Attach() instead of attach()?', call.=FALSE)
if(!is.data.frame(design))
stop('design must be a data.frame object', call. = FALSE)
if(replications < 1L)
stop('number of replications must be greater than or equal to 1', call. = FALSE)
if(!(edit %in% c('none', 'analyse', 'generate', 'summarise', 'all')))
stop('edit location is not valid', call. = FALSE)
if(is.null(design$ID)){
design <- data.frame(ID=1L:nrow(design), design)
} else stopifnot(length(unique(design$ID)) == nrow(design))
if(edit != 'none'){
save <- save_results <- save_generate_data <- save_seeds <- FALSE
if(!(edit %in% 'summarise')) parallel <- MPI <- FALSE
if(edit == 'recover'){
old_recover <- getOption('error')
options(error = utils::recover)
on.exit(options(error = old_recover))
} else if(edit == 'all'){
debug(Functions[['generate']]); debug(Functions[['analyse']])
debug(Functions[['summarise']])
on.exit({undebug(Functions[['generate']]); undebug(Functions[['analyse']])
undebug(Functions[['summarise']])})
} else {
debug(Functions[[edit]])
on.exit(undebug(Functions[[edit]]))
}
}
export_funs <- parent_env_fun()
if(parallel){
if(is.null(cl)){
cl <- parallel::makeCluster(ncores)
on.exit(parallel::stopCluster(cl))
}
parallel::clusterExport(cl=cl, export_funs, envir = parent.frame(1L))
}
Result_list <- stored_Results_list <- vector('list', nrow(design))
names(Result_list) <- names(stored_Results_list) <- rownames(design)
time0 <- time1 <- proc.time()[3L]
files <- dir(out_rootdir)
if(!MPI && any(files == tmpfilename) && is.null(load_seed)){
if(verbose)
message(sprintf('Resuming simulation from %s file with %i replications.',
file.path(out_rootdir, tmpfilename), replications))
Result_list <- readRDS(file.path(out_rootdir, tmpfilename))
if(!is.null(Result_list[[1L]]$REPLICATIONS))
replications <- Result_list[[1L]]$REPLICATIONS
if(nrow(design) != length(Result_list))
stop(paste0("Number of rows in design input not equal to the length of temp .rds file.",
"Simulations conditions are not identical. Either fix design object or ",
"consider removing temp file and re-running"))
start <- min(which(sapply(Result_list, is.null)))
time0 <- time1 - Result_list[[start-1L]]$SIM_TIME
}
if(file.exists(tmpfilename)){
tmp <- attr(Result_list, 'SimDesign_names')
save_generate_data_dirname <- tmp['save_generate_data_dirname']
save_results_dirname <- tmp['save_results_dirname']
save_seeds_dirname <- tmp['save_seeds_dirname']
}
if(save_results){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_results_dirname
count <- 1L
while(dir.exists(file.path(out_rootdir, save_results_dirname))) {
save_results_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_results_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_results_dirname)))
}
dir.create(file.path(out_rootdir, save_results_dirname))
}
if(length(dir(file.path(out_rootdir, save_results_dirname))) != (start - 1L))
stop('save_results_dirname not starting from correct location according to tempfile',
call.=FALSE)
}
if(save_seeds){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_seeds_dirname
count <- 1L
while(dir.exists(file.path(out_rootdir, save_seeds_dirname))) {
save_seeds_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_seeds_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_seeds_dirname)))
}
dir.create(file.path(out_rootdir, save_seeds_dirname))
}
}
if(save_generate_data){
save <- TRUE
if(!file.exists(file.path(out_rootdir, tmpfilename))) {
if(safe){
tmp <- save_generate_data_dirname
count <- 1L
while(dir.exists(save_generate_data_dirname)){
save_generate_data_dirname <- paste0(tmp, '_', count)
count <- count + 1L
}
if(tmp != save_generate_data_dirname && verbose)
message(sprintf('%s already exists; using %s directory instead',
file.path(out_rootdir, tmp), file.path(out_rootdir, save_generate_data_dirname)))
}
dir.create(file.path(out_rootdir, save_generate_data_dirname))
}
}
if(safe && (parallel || MPI)){
# this is great because it also primes the pipes
tmp <- packages[packages != 'SimDesign']
if(!length(tmp)) tmp <- 'stats'
for(i in 1:length(tmp)){
packs <- if(parallel){
try(table(parallel::parSapply(cl, rep(tmp[i], each=length(cl)*2),
get_packages)))
} else {
p <- character()
try(table(foreach(p=rep(tmp[i], each=length(cl)*2L)) %dopar% get_packages(p)))
}
if(tmp[i] == 'stats') next
if(length(packs) > 1L)
message(sprintf('Warning message:\nVersions of %s differ across clusters: %s',
tmp[i], paste0(names(packs), collapse = ', ')))
}
}
if(is.null(attr(Result_list, 'SimDesign_names')))
attr(Result_list, 'SimDesign_names') <-
c(save_generate_data_dirname=file.path(out_rootdir, save_generate_data_dirname),
save_results_dirname=file.path(out_rootdir, save_results_dirname),
save_seeds_dirname=file.path(out_rootdir, save_seeds_dirname))
if(progress) verbose <- TRUE
for(i in start:end){
if(summarise_asis){
if(verbose)
print_progress(i, nrow(design), time1=time1, time0=time0,
stored_time=stored_time, progress=progress)
time0 <- proc.time()[3L]
Result_list[[i]] <- Analysis(Functions=Functions,
condition=design[i,],
replications=replications,
fixed_objects=fixed_objects,
cl=cl, MPI=MPI, seed=seed,
bootSE=bootSE, boot_draws=boot_draws,
save_results=save_results,
save_results_dirname=save_results_dirname,
save_generate_data=save_generate_data,
save_generate_data_dirname=save_generate_data_dirname,
save_seeds=save_seeds, summarise_asis=summarise_asis,
save_seeds_dirname=save_seeds_dirname,
max_errors=max_errors, packages=packages,
load_seed=load_seed, export_funs=export_funs,
warnings_as_errors=warnings_as_errors,
progress=progress, store_results=FALSE)
time1 <- proc.time()[3L]
stored_time <- stored_time + (time1 - time0)
} else {
stored_time <- do.call(c, lapply(Result_list, function(x) x$SIM_TIME))
if(verbose)
print_progress(i, nrow(design), time1=time1, time0=time0,
stored_time=stored_time, progress=progress)
time0 <- proc.time()[3L]
if(save_generate_data)
dir.create(file.path(out_rootdir, paste0(save_generate_data_dirname, '/design-row-', i)), showWarnings = FALSE)
if(save_seeds)
dir.create(file.path(out_rootdir, paste0(save_seeds_dirname, '/design-row-', i)), showWarnings = FALSE)
tmp <- Analysis(Functions=Functions,
condition=design[i,],
replications=replications,
fixed_objects=fixed_objects,
cl=cl, MPI=MPI, seed=seed,
bootSE=bootSE, boot_draws=boot_draws,
save_results=save_results,
save_results_out_rootdir = out_rootdir,
save_results_dirname=save_results_dirname,
save_generate_data=save_generate_data,
save_generate_data_dirname=save_generate_data_dirname,
save_seeds=save_seeds, summarise_asis=summarise_asis,
save_seeds_dirname=save_seeds_dirname,
max_errors=max_errors, packages=packages,
load_seed=load_seed, export_funs=export_funs,
warnings_as_errors=warnings_as_errors,
progress=progress, store_results=store_results)
if(store_results){
stored_Results_list[[i]] <- attr(tmp, 'full_results')
attr(tmp, 'full_results') <- NULL
}
Result_list[[i]] <- data.frame(design[i, ], as.list(tmp),
check.names=FALSE)
time1 <- proc.time()[3L]
Result_list[[i]]$SIM_TIME <- time1 - time0
Result_list[[i]]$COMPLETED <- date()
if(save || save_results || save_generate_data) saveRDS(Result_list, file.path(out_rootdir, tmpfilename))
}
}
attr(Result_list, 'SimDesign_names') <- NULL
if(summarise_asis){
design$ID <- NULL
nms <- colnames(design)
nms2 <- matrix(character(0L), nrow(design), ncol(design))
for(i in 1L:ncol(design))
nms2[,i] <- paste0(nms[i], '=', design[,i], if(i < ncol(design)) '; ')
nms2 <- apply(nms2, 1L, paste0, collapse='')
names(Result_list) <- nms2
if(is.list(Result_list[[1L]][[1L]]))
for(i in seq_len(length(Result_list)))
attr(Result_list[[i]][[1L]], 'try_errors') <- NULL
if(nrow(design) == 1L) Result_list <- Result_list[[1L]]
return(Result_list)
}
stored_time <- do.call(c, lapply(Result_list, function(x) x$SIM_TIME))
if(verbose)
message('\nSimulation complete. Total execution time: ', timeFormater(sum(stored_time)))
Final <- plyr::rbind.fill(Result_list)
SIM_TIME <- Final$SIM_TIME
COMPLETED <- Final$COMPLETED
REPLICATIONS <- Final$REPLICATIONS
Final$SIM_TIME <- Final$ID <- Final$REPLICATIONS <- Final$COMPLETED <- NULL
Final <- data.frame(Final, REPLICATIONS, SIM_TIME, COMPLETED, check.names=FALSE)
if(!is.null(seed)) Final$SEED <- seed
if(!is.null(filename) && safe){ #save file
files <- dir(out_rootdir)
filename0 <- filename
count <- 1L
# create a new file name if old one exists, and throw warning
while(TRUE){
filename <- paste0(filename, '.rds')
if(filename %in% files){
filename <- paste0(filename0, '-', count)
count <- count + 1L
} else break
}
if(count > 1L)
if(verbose && save)
message(paste0('\nWARNING:\n', filename0, ' existed in the working directory.
Using a unique file name instead.\n'))
}
dn <- colnames(design)
dn <- dn[dn != 'ID']
if(as.factor){
Final[dn] <- lapply(Final[dn], function(x){
if(is.list(x)) return(x)
if(is.numeric(x)) return(ordered(x))
else return(factor(x))
})
}
ten <- colnames(Final)[grepl('ERROR:', colnames(Final))]
wen <- colnames(Final)[grepl('WARNING:', colnames(Final))]
en <- c('REPLICATIONS', 'SIM_TIME', 'COMPLETED', 'SEED')
bsen <- colnames(Final)[grepl('BOOT_SE.', colnames(Final))]
if(length(bsen))
Final <- data.frame(Final[,!(colnames(Final) %in% bsen)], Final[,colnames(Final) %in% bsen])
sn <- colnames(Final)[!(colnames(Final) %in% c(dn, en, ten, wen, bsen))]
attr(Final, 'design_names') <-
list(design=dn, sim=sn, bootSE=bsen, extra=en, errors=ten, warnings=wen)
if(length(packages) > 1L){
pack <- packages[packages != 'SimDesign']
versions <- character(length(pack))
for(i in 1L:length(pack))
versions[i] <- as.character(packageVersion(pack[i]))
pack_vers <- data.frame(packages=pack, versions=versions)
} else pack_vers <- NULL
pick <- c(save_generate_data, save_results, save_seeds)
if(!is.null(filename)) pick <- c(save, pick)
attr(Final, 'extra_info') <- list(sessionInfo = sessionInfo(), packages=pack_vers,
save_info = c(filename=filename,
save_generate_data_dirname=save_generate_data_dirname,
save_results_dirname=save_results_dirname,
save_seeds_dirname=save_seeds_dirname)[pick],
ncores = if(parallel) length(cl) else if(MPI) NA else 1L,
number_of_conditions = nrow(design),
date_completed = date(), total_elapsed_time = sum(Final$SIM_TIME),
stored_results = if(store_results) stored_Results_list else NULL)
if(dummy_run) Final$dummy_run <- NULL
class(Final) <- c('SimDesign', 'data.frame')
if(!is.null(filename) && save){ #save file
if(verbose)
message(paste('\nSaving simulation results to file:', filename))
saveRDS(Final, file.path(out_rootdir, filename))
}
if(save || save_results || save_generate_data || save_seeds) file.remove(file.path(out_rootdir, tmpfilename))
return(Final)
}
#' @rdname runSimulation
#' @param x SimDesign object returned from \code{\link{runSimulation}}
#' @param drop.extras logical; don't print information about warnings, errors, simulation time, and replications?
#' Default is \code{FALSE}
#' @param drop.design logical; don't include information about the (potentially factorized) simulation design?
#' This may be useful if you wish to \code{cbind()} the original design \code{data.frame} to the simulation
#' results instead of using the auto-factorized version. Default is \code{FALSE}
#' @param format.time logical; format \code{SIM_TIME} into a day/hour/min/sec character vector? Default is
#' \code{TRUE}
#' @export
print.SimDesign <- function(x, drop.extras = FALSE, drop.design = FALSE, format.time = TRUE, ...){
att <- attr(x, 'design_names')
if(format.time)
if(!is.null(x$SIM_TIME))
x$SIM_TIME <- sapply(x$SIM_TIME, timeFormater, TRUE)
if(drop.extras) x <- x[ ,c(att$design, att$sim), drop=FALSE]
if(drop.design) x <- x[ ,!(names(x) %in% att$design), drop=FALSE]
class(x) <- 'data.frame'
ldots <- list(...)
if(is.null(ldots$print)) print(x, ...)
else return(x)
}
#' @rdname runSimulation
#' @export
head.SimDesign <- function(x, ...){
x <- print(x, print = FALSE, ...)
class(x) <- 'data.frame'
print(head(x, ...), ...)
}
#' @rdname runSimulation
#' @export
tail.SimDesign <- function(x, ...){
x <- print(x, print = FALSE, ...)
class(x) <- 'data.frame'
print(tail(x, ...), ...)
}
#' @rdname runSimulation
#' @param object SimDesign object returned from \code{\link{runSimulation}}
#' @param ... additional arguments
#' @export
summary.SimDesign <- function(object, ...){
ret <- attr(object, 'extra_info')
ret$total_elapsed_time <- timeFormater(ret$total_elapsed_time, TRUE)
ret$stored_results <- NULL
ret
}
#' @rdname runSimulation
#' @export
extract_results <- function(object){
stopifnot(is(object, "SimDesign"))
extra_info <- attr(object, 'extra_info')
if(is.null(extra_info$stored_results)) return(NULL)
design_names <- attr(object, "design_names")
pick <- design_names$design
design <- subset(as.data.frame(object), select=pick)
nms <- colnames(design)
nms2 <- matrix(character(0L), nrow(design), ncol(design))
for(i in 1L:ncol(design))
nms2[,i] <- paste0(nms[i], '=', design[,i], if(i < ncol(design)) '; ')
nms2 <- apply(nms2, 1L, paste0, collapse='')
ret <- extra_info$stored_results
names(ret) <- nms2
ret
}
#' @rdname runSimulation
#' @export
as.data.frame.SimDesign <- function(x, ...){
class(x) <- 'data.frame'
x
}
#' @export
"[<-.SimDesign" <- function(x, i, j, value){
x <- as.data.frame(x)
x[i, j] <- value
class(x) <- c('SimDesign', 'data.frame')
x
}
#' @export
"[[<-.SimDesign" <- function(x, i, j, value){
x <- as.data.frame(x)
x[[i,j]] <- value
class(x) <- c('SimDesign', 'data.frame')
x
}
|
#' Returns games played by a CFL team in one season
#'
#' These functions return games played in the CFL at a high level
#' @param team Abbreviated team name
#' @param season The season of play
#' @return data.frame
#' @import jsonlite
#' @keywords canadian football, CFL, cflstats
#' @export
#' @examples \dontrun{
#' # Games played by Saskatchewan in 2014
#' get_team_season_games('SSK', 2014)
#' }
get_team_season_games <- function(team, season) {
validTeams <- c('BC', "SSK", "CGY", "EDM", "WPG", "HAM", 'TOR', 'MTL', 'ORB')
validSeasons <- 2009:2015
if (is.element(team, validTeams)==FALSE) {
stop('Incorrect team name')
}
if (is.element(season, validSeasons)==FALSE) {
stop('Season data not available')
}
url <- paste('http://cflstats.ca/team', '/', team, '/', season, '.json', sep='')
url_return <- tryCatch({
fromJSON(url)$Games
}, error = function(err) {
print(paste('CFLSTATS ERROR: ', err))
return(NULL)
})
return(url_return)
}
#' Returns games played by a CFL team in one season
#'
#' @param team Abbreviated team name
#' @param season The season of play
#' @return data.frame
#' @import jsonlite
#' @keywords canadian football, CFL, cflstats
#' @export
#' @examples \dontrun{
#' # All games played in 2014
#' get_all_season_games(2014)
#' }
get_all_season_games <- function(season) {
# Check that the seasons are valid
validSeasons <- 2009:2015
if (is.element(season, validSeasons)==FALSE) {
stop('Invalid season')
}
validTeams <- c('BC', "SSK", "CGY", "EDM", "WPG", "HAM", 'TOR', 'MTL')
validTeams <- if (season < 2014) {
validTeams
} else {
c(validTeams, 'ORB')
}
output <- tryCatch({
unique(
do.call(
'rbind',
lapply(
validTeams,
function(x) get_team_season_games(x, season)
)
)
)
}, error = function(err) {
print('CFLSTATS ERROR: ', err)
return(NULL)
})
return(output[order(output$GameId),])
}
|
/R/games.R
|
no_license
|
andrewjdyck/CanadianFootball
|
R
| false
| false
| 2,007
|
r
|
#' Returns games played by a CFL team in one season
#'
#' These functions return games played in the CFL at a high level
#' @param team Abbreviated team name
#' @param season The season of play
#' @return data.frame
#' @import jsonlite
#' @keywords canadian football, CFL, cflstats
#' @export
#' @examples \dontrun{
#' # Games played by Saskatchewan in 2014
#' get_team_season_games('SSK', 2014)
#' }
get_team_season_games <- function(team, season) {
validTeams <- c('BC', "SSK", "CGY", "EDM", "WPG", "HAM", 'TOR', 'MTL', 'ORB')
validSeasons <- 2009:2015
if (is.element(team, validTeams)==FALSE) {
stop('Incorrect team name')
}
if (is.element(season, validSeasons)==FALSE) {
stop('Season data not available')
}
url <- paste('http://cflstats.ca/team', '/', team, '/', season, '.json', sep='')
url_return <- tryCatch({
fromJSON(url)$Games
}, error = function(err) {
print(paste('CFLSTATS ERROR: ', err))
return(NULL)
})
return(url_return)
}
#' Returns games played by a CFL team in one season
#'
#' @param team Abbreviated team name
#' @param season The season of play
#' @return data.frame
#' @import jsonlite
#' @keywords canadian football, CFL, cflstats
#' @export
#' @examples \dontrun{
#' # All games played in 2014
#' get_all_season_games(2014)
#' }
get_all_season_games <- function(season) {
# Check that the seasons are valid
validSeasons <- 2009:2015
if (is.element(season, validSeasons)==FALSE) {
stop('Invalid season')
}
validTeams <- c('BC', "SSK", "CGY", "EDM", "WPG", "HAM", 'TOR', 'MTL')
validTeams <- if (season < 2014) {
validTeams
} else {
c(validTeams, 'ORB')
}
output <- tryCatch({
unique(
do.call(
'rbind',
lapply(
validTeams,
function(x) get_team_season_games(x, season)
)
)
)
}, error = function(err) {
print('CFLSTATS ERROR: ', err)
return(NULL)
})
return(output[order(output$GameId),])
}
|
# Install and load packages
package_names <- c("survey","dplyr","foreign","devtools")
lapply(package_names, function(x) if(!x %in% installed.packages()) install.packages(x))
lapply(package_names, require, character.only=T)
install_github("e-mitchell/meps_r_pkg/MEPS")
library(MEPS)
options(survey.lonely.psu="adjust")
# Load FYC file
FYC <- read.xport('C:/MEPS/.FYC..ssp');
year <- .year.
if(year <= 2001) FYC <- FYC %>% mutate(VARPSU = VARPSU.yy., VARSTR=VARSTR.yy.)
if(year <= 1998) FYC <- FYC %>% rename(PERWT.yy.F = WTDPER.yy.)
if(year == 1996) FYC <- FYC %>% mutate(AGE42X = AGE2X, AGE31X = AGE1X)
FYC <- FYC %>%
mutate_at(vars(starts_with("AGE")),funs(replace(., .< 0, NA))) %>%
mutate(AGELAST = coalesce(AGE.yy.X, AGE42X, AGE31X))
FYC$ind = 1
# Perceived mental health
if(year == 1996)
FYC <- FYC %>% mutate(MNHLTH53 = MNTHLTH2, MNHLTH42 = MNTHLTH2, MNHLTH31 = MNTHLTH1)
FYC <- FYC %>%
mutate_at(vars(starts_with("MNHLTH")), funs(replace(., .< 0, NA))) %>%
mutate(mnhlth = coalesce(MNHLTH53, MNHLTH42, MNHLTH31)) %>%
mutate(mnhlth = recode_factor(mnhlth, .default = "Missing", .missing = "Missing",
"1" = "Excellent",
"2" = "Very good",
"3" = "Good",
"4" = "Fair",
"5" = "Poor"))
# Keep only needed variables from FYC
FYCsub <- FYC %>% select(mnhlth,ind, DUPERSID, PERWT.yy.F, VARSTR, VARPSU)
# Load event files
RX <- read.xport('C:/MEPS/.RX..ssp') %>% rename(EVNTIDX = LINKIDX)
IPT <- read.xport('C:/MEPS/.IP..ssp')
ERT <- read.xport('C:/MEPS/.ER..ssp')
OPT <- read.xport('C:/MEPS/.OP..ssp')
OBV <- read.xport('C:/MEPS/.OB..ssp')
HHT <- read.xport('C:/MEPS/.HH..ssp')
# Stack events (condition data not collected for dental visits and other medical expenses)
stacked_events <- stack_events(RX, IPT, ERT, OPT, OBV, HHT)
stacked_events <- stacked_events %>%
mutate(event = data,
PR.yy.X = PV.yy.X + TR.yy.X,
OZ.yy.X = OF.yy.X + SL.yy.X + OT.yy.X + OR.yy.X + OU.yy.X + WC.yy.X + VA.yy.X) %>%
select(DUPERSID, event, EVNTIDX,
XP.yy.X, SF.yy.X, MR.yy.X, MD.yy.X, PR.yy.X, OZ.yy.X)
# Read in event-condition linking file
clink1 = read.xport('C:/MEPS/.CLNK..ssp') %>%
select(DUPERSID,CONDIDX,EVNTIDX)
# Read in conditions file and merge with condition_codes, link file
cond <- read.xport('C:/MEPS/.Conditions..ssp') %>%
select(DUPERSID, CONDIDX, CCCODEX) %>%
mutate(CCS_Codes = as.numeric(as.character(CCCODEX))) %>%
left_join(condition_codes, by = "CCS_Codes") %>%
full_join(clink1, by = c("DUPERSID", "CONDIDX")) %>%
distinct(DUPERSID, EVNTIDX, Condition, .keep_all=T)
# Merge events with conditions-link file and FYCsub
all_events <- full_join(stacked_events, cond, by=c("DUPERSID","EVNTIDX")) %>%
filter(!is.na(Condition),XP.yy.X >= 0) %>%
mutate(count = 1) %>%
full_join(FYCsub, by = "DUPERSID")
# Sum by person, condition, across event
all_pers <- all_events %>%
group_by(mnhlth,ind, DUPERSID, VARSTR, VARPSU, PERWT.yy.F, Condition, count) %>%
summarize_at(vars(SF.yy.X, PR.yy.X, MR.yy.X, MD.yy.X, OZ.yy.X, XP.yy.X),sum) %>% ungroup
PERSdsgn <- svydesign(
id = ~VARPSU,
strata = ~VARSTR,
weights = ~PERWT.yy.F,
data = all_pers,
nest = TRUE)
results <- svyby(~XP.yy.X, by = ~Condition + mnhlth, FUN = svymean, design = PERSdsgn)
print(results)
|
/mepstrends/hc_cond/json/code/r/meanEXP__Condition__mnhlth__.r
|
permissive
|
RandomCriticalAnalysis/MEPS-summary-tables
|
R
| false
| false
| 3,376
|
r
|
# Install and load packages
package_names <- c("survey","dplyr","foreign","devtools")
lapply(package_names, function(x) if(!x %in% installed.packages()) install.packages(x))
lapply(package_names, require, character.only=T)
install_github("e-mitchell/meps_r_pkg/MEPS")
library(MEPS)
options(survey.lonely.psu="adjust")
# Load FYC file
FYC <- read.xport('C:/MEPS/.FYC..ssp');
year <- .year.
if(year <= 2001) FYC <- FYC %>% mutate(VARPSU = VARPSU.yy., VARSTR=VARSTR.yy.)
if(year <= 1998) FYC <- FYC %>% rename(PERWT.yy.F = WTDPER.yy.)
if(year == 1996) FYC <- FYC %>% mutate(AGE42X = AGE2X, AGE31X = AGE1X)
FYC <- FYC %>%
mutate_at(vars(starts_with("AGE")),funs(replace(., .< 0, NA))) %>%
mutate(AGELAST = coalesce(AGE.yy.X, AGE42X, AGE31X))
FYC$ind = 1
# Perceived mental health
if(year == 1996)
FYC <- FYC %>% mutate(MNHLTH53 = MNTHLTH2, MNHLTH42 = MNTHLTH2, MNHLTH31 = MNTHLTH1)
FYC <- FYC %>%
mutate_at(vars(starts_with("MNHLTH")), funs(replace(., .< 0, NA))) %>%
mutate(mnhlth = coalesce(MNHLTH53, MNHLTH42, MNHLTH31)) %>%
mutate(mnhlth = recode_factor(mnhlth, .default = "Missing", .missing = "Missing",
"1" = "Excellent",
"2" = "Very good",
"3" = "Good",
"4" = "Fair",
"5" = "Poor"))
# Keep only needed variables from FYC
FYCsub <- FYC %>% select(mnhlth,ind, DUPERSID, PERWT.yy.F, VARSTR, VARPSU)
# Load event files
RX <- read.xport('C:/MEPS/.RX..ssp') %>% rename(EVNTIDX = LINKIDX)
IPT <- read.xport('C:/MEPS/.IP..ssp')
ERT <- read.xport('C:/MEPS/.ER..ssp')
OPT <- read.xport('C:/MEPS/.OP..ssp')
OBV <- read.xport('C:/MEPS/.OB..ssp')
HHT <- read.xport('C:/MEPS/.HH..ssp')
# Stack events (condition data not collected for dental visits and other medical expenses)
stacked_events <- stack_events(RX, IPT, ERT, OPT, OBV, HHT)
stacked_events <- stacked_events %>%
mutate(event = data,
PR.yy.X = PV.yy.X + TR.yy.X,
OZ.yy.X = OF.yy.X + SL.yy.X + OT.yy.X + OR.yy.X + OU.yy.X + WC.yy.X + VA.yy.X) %>%
select(DUPERSID, event, EVNTIDX,
XP.yy.X, SF.yy.X, MR.yy.X, MD.yy.X, PR.yy.X, OZ.yy.X)
# Read in event-condition linking file
clink1 = read.xport('C:/MEPS/.CLNK..ssp') %>%
select(DUPERSID,CONDIDX,EVNTIDX)
# Read in conditions file and merge with condition_codes, link file
cond <- read.xport('C:/MEPS/.Conditions..ssp') %>%
select(DUPERSID, CONDIDX, CCCODEX) %>%
mutate(CCS_Codes = as.numeric(as.character(CCCODEX))) %>%
left_join(condition_codes, by = "CCS_Codes") %>%
full_join(clink1, by = c("DUPERSID", "CONDIDX")) %>%
distinct(DUPERSID, EVNTIDX, Condition, .keep_all=T)
# Merge events with conditions-link file and FYCsub
all_events <- full_join(stacked_events, cond, by=c("DUPERSID","EVNTIDX")) %>%
filter(!is.na(Condition),XP.yy.X >= 0) %>%
mutate(count = 1) %>%
full_join(FYCsub, by = "DUPERSID")
# Sum by person, condition, across event
all_pers <- all_events %>%
group_by(mnhlth,ind, DUPERSID, VARSTR, VARPSU, PERWT.yy.F, Condition, count) %>%
summarize_at(vars(SF.yy.X, PR.yy.X, MR.yy.X, MD.yy.X, OZ.yy.X, XP.yy.X),sum) %>% ungroup
PERSdsgn <- svydesign(
id = ~VARPSU,
strata = ~VARSTR,
weights = ~PERWT.yy.F,
data = all_pers,
nest = TRUE)
results <- svyby(~XP.yy.X, by = ~Condition + mnhlth, FUN = svymean, design = PERSdsgn)
print(results)
|
library(qtlc)
### Name: spot2D
### Title: Locate spots manually.
### Aliases: spot2D
### ** Examples
## Not run:
##D print(object)
##D object <- spot2D(object)
## End(Not run)
|
/data/genthat_extracted_code/qtlc/examples/spot2D.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 185
|
r
|
library(qtlc)
### Name: spot2D
### Title: Locate spots manually.
### Aliases: spot2D
### ** Examples
## Not run:
##D print(object)
##D object <- spot2D(object)
## End(Not run)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/common-connections.R
\name{selectData}
\alias{selectData}
\title{Pull data into R via an ODBC connection}
\usage{
selectData(MSSQLConnectionString = NULL, query, SQLiteFileName = NULL,
randomize = FALSE)
}
\arguments{
\item{MSSQLConnectionString}{A string specifying the driver, server,
database, and whether Windows Authentication will be used.}
\item{query}{The SQL query (in ticks or quotes)}
\item{SQLiteFileName}{A string. If dbtype is SQLite, here one specifies the
database file to query from}
\item{randomize}{Boolean that dictates whether returned rows are randomized}
}
\value{
df A data frame containing the selected rows
}
\description{
Select data from an ODBC database and return the results as
a data frame.
}
\examples{
\donttest{
# This example is specific to SQL Server
# To instead pull data from Oracle see here
# https://cran.r-project.org/web/packages/ROracle/ROracle.pdf
# To pull data from MySQL see here
# https://cran.r-project.org/web/packages/RMySQL/RMySQL.pdf
# To pull data from Postgres see here
# https://cran.r-project.org/web/packages/RPostgreSQL/RPostgreSQL.pdf
connectionString <- '
driver={SQL Server};
server=localhost;
database=SAM;
trustedConnection=true
'
query <- '
SELECT
A1CNBR
FROM SAM.dbo.HCRDiabetesClinical
'
df <- selectData(connectionString, query)
head(df)
}
}
\references{
\url{http://healthcare.ai}
}
\seealso{
\code{\link{healthcareai}}
}
|
/man/selectData.Rd
|
permissive
|
niilante/healthcareai-r
|
R
| false
| true
| 1,506
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/common-connections.R
\name{selectData}
\alias{selectData}
\title{Pull data into R via an ODBC connection}
\usage{
selectData(MSSQLConnectionString = NULL, query, SQLiteFileName = NULL,
randomize = FALSE)
}
\arguments{
\item{MSSQLConnectionString}{A string specifying the driver, server,
database, and whether Windows Authentication will be used.}
\item{query}{The SQL query (in ticks or quotes)}
\item{SQLiteFileName}{A string. If dbtype is SQLite, here one specifies the
database file to query from}
\item{randomize}{Boolean that dictates whether returned rows are randomized}
}
\value{
df A data frame containing the selected rows
}
\description{
Select data from an ODBC database and return the results as
a data frame.
}
\examples{
\donttest{
# This example is specific to SQL Server
# To instead pull data from Oracle see here
# https://cran.r-project.org/web/packages/ROracle/ROracle.pdf
# To pull data from MySQL see here
# https://cran.r-project.org/web/packages/RMySQL/RMySQL.pdf
# To pull data from Postgres see here
# https://cran.r-project.org/web/packages/RPostgreSQL/RPostgreSQL.pdf
connectionString <- '
driver={SQL Server};
server=localhost;
database=SAM;
trustedConnection=true
'
query <- '
SELECT
A1CNBR
FROM SAM.dbo.HCRDiabetesClinical
'
df <- selectData(connectionString, query)
head(df)
}
}
\references{
\url{http://healthcare.ai}
}
\seealso{
\code{\link{healthcareai}}
}
|
## ----ex1-----------------------------------------------------------------
library("wrapr")
'a' := 5
c('a' := 5, 'b' := 6)
c('a', 'b') := c(5, 6)
## ----key1----------------------------------------------------------------
`:=` <- wrapr::`:=` # in case data.tables "catch calls" definition is active
key = 'keycode'
key := 'value'
## ----print, eval=FALSE---------------------------------------------------
# help(`:=`, package = 'wrapr')
|
/packrat/lib/x86_64-pc-linux-gnu/3.4.4/wrapr/doc/named_map_builder.R
|
no_license
|
h-dychko/zno_analysis
|
R
| false
| false
| 447
|
r
|
## ----ex1-----------------------------------------------------------------
library("wrapr")
'a' := 5
c('a' := 5, 'b' := 6)
c('a', 'b') := c(5, 6)
## ----key1----------------------------------------------------------------
`:=` <- wrapr::`:=` # in case data.tables "catch calls" definition is active
key = 'keycode'
key := 'value'
## ----print, eval=FALSE---------------------------------------------------
# help(`:=`, package = 'wrapr')
|
/MacOSX10.3.9.sdk/System/Library/Frameworks/Carbon.framework/Versions/A/Frameworks/OpenScripting.framework/Versions/A/Headers/FinderRegistry.r
|
no_license
|
alexey-lysiuk/macos-sdk
|
R
| false
| false
| 20,299
|
r
| ||
testlist <- list(b = c(-1667457875L, -1667457892L, 420285709L, 420285709L, -1835887972L))
result <- do.call(mcga:::ByteVectorToDoubles,testlist)
str(result)
|
/mcga/inst/testfiles/ByteVectorToDoubles/AFL_ByteVectorToDoubles/ByteVectorToDoubles_valgrind_files/1613106656-test.R
|
no_license
|
akhikolla/updatedatatype-list3
|
R
| false
| false
| 157
|
r
|
testlist <- list(b = c(-1667457875L, -1667457892L, 420285709L, 420285709L, -1835887972L))
result <- do.call(mcga:::ByteVectorToDoubles,testlist)
str(result)
|
# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
init_context <- function(threads = 1L) {
.Call('_clustermq_init_context', PACKAGE = 'clustermq', threads)
}
init_socket <- function(context, socket_type) {
.Call('_clustermq_init_socket', PACKAGE = 'clustermq', context, socket_type)
}
bind_socket <- function(socket, address) {
invisible(.Call('_clustermq_bind_socket', PACKAGE = 'clustermq', socket, address))
}
connect_socket <- function(socket, address) {
invisible(.Call('_clustermq_connect_socket', PACKAGE = 'clustermq', socket, address))
}
disconnect_socket <- function(socket, address) {
invisible(.Call('_clustermq_disconnect_socket', PACKAGE = 'clustermq', socket, address))
}
poll_socket <- function(sockets, timeout = -1L) {
.Call('_clustermq_poll_socket', PACKAGE = 'clustermq', sockets, timeout)
}
receive_socket <- function(socket, dont_wait = FALSE, unserialize = TRUE) {
.Call('_clustermq_receive_socket', PACKAGE = 'clustermq', socket, dont_wait, unserialize)
}
receive_multipart <- function(socket, dont_wait = FALSE, unserialize = TRUE) {
.Call('_clustermq_receive_multipart', PACKAGE = 'clustermq', socket, dont_wait, unserialize)
}
send_socket <- function(socket, data, dont_wait = FALSE, send_more = FALSE) {
invisible(.Call('_clustermq_send_socket', PACKAGE = 'clustermq', socket, data, dont_wait, send_more))
}
|
/clustermq/R/RcppExports.R
|
permissive
|
akhikolla/TestedPackages-NoIssues
|
R
| false
| false
| 1,458
|
r
|
# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
init_context <- function(threads = 1L) {
.Call('_clustermq_init_context', PACKAGE = 'clustermq', threads)
}
init_socket <- function(context, socket_type) {
.Call('_clustermq_init_socket', PACKAGE = 'clustermq', context, socket_type)
}
bind_socket <- function(socket, address) {
invisible(.Call('_clustermq_bind_socket', PACKAGE = 'clustermq', socket, address))
}
connect_socket <- function(socket, address) {
invisible(.Call('_clustermq_connect_socket', PACKAGE = 'clustermq', socket, address))
}
disconnect_socket <- function(socket, address) {
invisible(.Call('_clustermq_disconnect_socket', PACKAGE = 'clustermq', socket, address))
}
poll_socket <- function(sockets, timeout = -1L) {
.Call('_clustermq_poll_socket', PACKAGE = 'clustermq', sockets, timeout)
}
receive_socket <- function(socket, dont_wait = FALSE, unserialize = TRUE) {
.Call('_clustermq_receive_socket', PACKAGE = 'clustermq', socket, dont_wait, unserialize)
}
receive_multipart <- function(socket, dont_wait = FALSE, unserialize = TRUE) {
.Call('_clustermq_receive_multipart', PACKAGE = 'clustermq', socket, dont_wait, unserialize)
}
send_socket <- function(socket, data, dont_wait = FALSE, send_more = FALSE) {
invisible(.Call('_clustermq_send_socket', PACKAGE = 'clustermq', socket, data, dont_wait, send_more))
}
|
#THIS IS A TEST!!!
rnorm(100)
#again
|
/Test rnorm.R
|
no_license
|
ZacharyThompsonMoffitt/NewTestProject
|
R
| false
| false
| 38
|
r
|
#THIS IS A TEST!!!
rnorm(100)
#again
|
\name{bart_predict_for_test_data}
\alias{bart_predict_for_test_data}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Predict for Test Data with Known Outcomes
}
\description{
Utility wrapper function for computing out-of-sample metrics for a BART model when the test set outcomes are known.
}
\usage{
bart_predict_for_test_data(bart_machine, Xtest, ytest, prob_rule_class = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{bart_machine}{
An object of class ``bartMachine''.
}
\item{Xtest}{
Data frame for test data containing rows at which predictions are to be made. Colnames should match that of the training data.
}
\item{ytest}{
Actual outcomes for test data.
}
\item{prob_rule_class}{
Threshold for classification.
}
}
\value{
For regression models, a list with the following components is returned:
\item{y_hat}{Predictions (as posterior means) for the test observations.}
\item{L1_err}{L1 error for predictions.}
\item{L2_err}{L2 error for predictions.}
\item{rmse}{RMSE for predictions.}
For classification models, a list with the following components is returned:
\item{y_hat}{Class predictions for the test observations.}
\item{p_hat}{Probability estimates for the test observations.}
\item{confusion_matrix}{A confusion matrix for the test observations.}
%% ...
}
\author{
Adam Kapelner and Justin Bleich
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{predict}}
}
\examples{
\dontrun{
#generate Friedman data
set.seed(11)
n = 250
p = 5
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)
##split into train and test
train_X = X[1 : 200, ]
test_X = X[201 : 250, ]
train_y = y[1 : 200]
test_y = y[201 : 250]
##build BART regression model
bart_machine = bartMachine(train_X, train_y)
#explore performance on test data
oos_perf = bart_predict_for_test_data(bart_machine, test_X, test_y)
print(oos_perf$rmse)
}
}
|
/bartMachine/man/bart_predict_for_test_data.Rd
|
permissive
|
kapelner/bartMachine
|
R
| false
| false
| 2,043
|
rd
|
\name{bart_predict_for_test_data}
\alias{bart_predict_for_test_data}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Predict for Test Data with Known Outcomes
}
\description{
Utility wrapper function for computing out-of-sample metrics for a BART model when the test set outcomes are known.
}
\usage{
bart_predict_for_test_data(bart_machine, Xtest, ytest, prob_rule_class = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{bart_machine}{
An object of class ``bartMachine''.
}
\item{Xtest}{
Data frame for test data containing rows at which predictions are to be made. Colnames should match that of the training data.
}
\item{ytest}{
Actual outcomes for test data.
}
\item{prob_rule_class}{
Threshold for classification.
}
}
\value{
For regression models, a list with the following components is returned:
\item{y_hat}{Predictions (as posterior means) for the test observations.}
\item{L1_err}{L1 error for predictions.}
\item{L2_err}{L2 error for predictions.}
\item{rmse}{RMSE for predictions.}
For classification models, a list with the following components is returned:
\item{y_hat}{Class predictions for the test observations.}
\item{p_hat}{Probability estimates for the test observations.}
\item{confusion_matrix}{A confusion matrix for the test observations.}
%% ...
}
\author{
Adam Kapelner and Justin Bleich
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{predict}}
}
\examples{
\dontrun{
#generate Friedman data
set.seed(11)
n = 250
p = 5
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)
##split into train and test
train_X = X[1 : 200, ]
test_X = X[201 : 250, ]
train_y = y[1 : 200]
test_y = y[201 : 250]
##build BART regression model
bart_machine = bartMachine(train_X, train_y)
#explore performance on test data
oos_perf = bart_predict_for_test_data(bart_machine, test_X, test_y)
print(oos_perf$rmse)
}
}
|
#
# common-con-distrib.R, 30 Dec 15
#
# Example from:
# Empirical Software Engineering using R
# Derek M. Jones
source("ESEUR_config.r")
plot_layout(2, 2)
brew_col=rainbow(3)
plot(c(0, 1, 1, 4, 4, 5), c(0, 0, 0.25, 0.25, 0, 0), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlab="x", ylab="")
x_exp=seq(0, 2, by=0.1)
plot(x_exp, dexp(x_exp, rate=1), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_exp), ylim=c(0, 4),
xlab="x", ylab="")
lines(x_exp, dexp(x_exp, rate=2), col=brew_col[2])
lines(x_exp, dexp(x_exp, rate=4), col=brew_col[3])
legend(x="topright", legend=c("rate=1", "rate=2", "rate=4"), bty="n", fill=brew_col, cex=1.3)
sigma_str=function(num) as.expression(substitute(sigma == num))
x_norm=seq(-8, 8, by=0.1)
plot(x_norm, dnorm(x_norm, sd=1), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_norm), ylim=c(0, 0.4),
xlab="x", ylab="")
lines(x_norm, dnorm(x_norm, sd=2), col=brew_col[2])
lines(x_norm, dnorm(x_norm, sd=4), col=brew_col[3])
legend(x="topright", legend=c(sigma_str(1), sigma_str(2), sigma_str(4)),
bty="n", fill=brew_col, cex=1.3)
x_beta=seq(0, 1, by=0.01)
plot(x_beta, dbeta(x_beta, shape1=0.5, shape2=0.5), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_beta), ylim=c(0, 2.5),
xlab="x", ylab="")
lines(x_beta, dbeta(x_beta, shape1=2, shape2=2), col=brew_col[2])
lines(x_beta, dbeta(x_beta, shape1=2, shape2=5), col=brew_col[3])
legend(x="topright", legend=c("shape1=0.5\nshape2=0.5\n", "shape1=2\nshape2=2\n", "shape1=2\nshape2=5"), bty="n", fill=brew_col, cex=1.3)
|
/statistics/common-con-distrib.R
|
no_license
|
vinodrajendran001/ESEUR-code-data
|
R
| false
| false
| 1,556
|
r
|
#
# common-con-distrib.R, 30 Dec 15
#
# Example from:
# Empirical Software Engineering using R
# Derek M. Jones
source("ESEUR_config.r")
plot_layout(2, 2)
brew_col=rainbow(3)
plot(c(0, 1, 1, 4, 4, 5), c(0, 0, 0.25, 0.25, 0, 0), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlab="x", ylab="")
x_exp=seq(0, 2, by=0.1)
plot(x_exp, dexp(x_exp, rate=1), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_exp), ylim=c(0, 4),
xlab="x", ylab="")
lines(x_exp, dexp(x_exp, rate=2), col=brew_col[2])
lines(x_exp, dexp(x_exp, rate=4), col=brew_col[3])
legend(x="topright", legend=c("rate=1", "rate=2", "rate=4"), bty="n", fill=brew_col, cex=1.3)
sigma_str=function(num) as.expression(substitute(sigma == num))
x_norm=seq(-8, 8, by=0.1)
plot(x_norm, dnorm(x_norm, sd=1), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_norm), ylim=c(0, 0.4),
xlab="x", ylab="")
lines(x_norm, dnorm(x_norm, sd=2), col=brew_col[2])
lines(x_norm, dnorm(x_norm, sd=4), col=brew_col[3])
legend(x="topright", legend=c(sigma_str(1), sigma_str(2), sigma_str(4)),
bty="n", fill=brew_col, cex=1.3)
x_beta=seq(0, 1, by=0.01)
plot(x_beta, dbeta(x_beta, shape1=0.5, shape2=0.5), type="l",
bty="n", yaxt="n",
col=brew_col[1],
xlim=range(x_beta), ylim=c(0, 2.5),
xlab="x", ylab="")
lines(x_beta, dbeta(x_beta, shape1=2, shape2=2), col=brew_col[2])
lines(x_beta, dbeta(x_beta, shape1=2, shape2=5), col=brew_col[3])
legend(x="topright", legend=c("shape1=0.5\nshape2=0.5\n", "shape1=2\nshape2=2\n", "shape1=2\nshape2=5"), bty="n", fill=brew_col, cex=1.3)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/StudyInvariant.R
\name{email}
\alias{email}
\title{Email results}
\usage{
email(from, to, subject, dataDescription, file)
}
\arguments{
\item{from}{Return email address}
\item{to}{(Optional) Delivery email address (must be a gmail.com acccount)}
\item{subject}{(Optional) Subject line of email}
\item{dataDescription}{A short description of the database}
\item{file}{(Optional) Name of local file with results; makee sure to use forward slashes (/)}
}
\value{
A list of files that were emailed.
}
\details{
This function emails the result CSV files to the study coordinator.
}
|
/phenotypeDataExtraction/man/email.Rd
|
no_license
|
amrapalijz/StudyProtocolSandbox
|
R
| false
| true
| 660
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/StudyInvariant.R
\name{email}
\alias{email}
\title{Email results}
\usage{
email(from, to, subject, dataDescription, file)
}
\arguments{
\item{from}{Return email address}
\item{to}{(Optional) Delivery email address (must be a gmail.com acccount)}
\item{subject}{(Optional) Subject line of email}
\item{dataDescription}{A short description of the database}
\item{file}{(Optional) Name of local file with results; makee sure to use forward slashes (/)}
}
\value{
A list of files that were emailed.
}
\details{
This function emails the result CSV files to the study coordinator.
}
|
\name{get_default_mapping}
\alias{get_default_mapping}
\title{Get default mapping}
\usage{
get_default_mapping(engine = NA, debug = FALSE)
}
\arguments{
\item{engine}{Data engine. See function
\code{\link{create_schema}} for details.}
\item{debug}{Print additional information useful for
debugging.}
}
\description{
This function returns data frame with default mapping of
data types to schema properties for given data engine.
}
\details{
If successful, dataset with the following columns is
returned: \tabular{ll}{ Column \tab Description\cr
\code{class} \tab Class of data type. Raw data types are
aggregated into high-level groups. For example in
PostgreSQL, data types \code{smallint, integer, bigint}
have class \code{integer}. This grouping can be used when
using argument \code{aggregator} in function
\code{\link{create_schema}}.\cr \code{type} \tab Data type
as defined by data engine.\cr \code{dimension} \tab If
\code{TRUE} than columns with given data type will be used
to create dimensions.\cr \code{aggregator} \tab Defines
default aggregators for given data type. Character string
consisting of exactly six digits. If \code{1} than
aggregator is enabled. If \code{0} then aggregator is
disabled. Aggregators are defined in the following order:
\code{average, count, distinct-count, maximum, minimum,
sum}.\cr } Examples for column \code{aggregator}: \itemize{
\item \code{111111} - All aggregators enabled \item
\code{100001} - Only average and sumenabled \item
\code{010000} - Only count enabled \item \code{000000} -
All aggregators disabled - for columns with given data type
there will be no measure. }
}
\examples{
get_default_mapping('PostgreSQL')
get_default_mapping('R')
get_default_mapping('R', debug=TRUE)
}
|
/man/get_default_mapping.Rd
|
no_license
|
tomasgreif/mondrianr
|
R
| false
| false
| 1,744
|
rd
|
\name{get_default_mapping}
\alias{get_default_mapping}
\title{Get default mapping}
\usage{
get_default_mapping(engine = NA, debug = FALSE)
}
\arguments{
\item{engine}{Data engine. See function
\code{\link{create_schema}} for details.}
\item{debug}{Print additional information useful for
debugging.}
}
\description{
This function returns data frame with default mapping of
data types to schema properties for given data engine.
}
\details{
If successful, dataset with the following columns is
returned: \tabular{ll}{ Column \tab Description\cr
\code{class} \tab Class of data type. Raw data types are
aggregated into high-level groups. For example in
PostgreSQL, data types \code{smallint, integer, bigint}
have class \code{integer}. This grouping can be used when
using argument \code{aggregator} in function
\code{\link{create_schema}}.\cr \code{type} \tab Data type
as defined by data engine.\cr \code{dimension} \tab If
\code{TRUE} than columns with given data type will be used
to create dimensions.\cr \code{aggregator} \tab Defines
default aggregators for given data type. Character string
consisting of exactly six digits. If \code{1} than
aggregator is enabled. If \code{0} then aggregator is
disabled. Aggregators are defined in the following order:
\code{average, count, distinct-count, maximum, minimum,
sum}.\cr } Examples for column \code{aggregator}: \itemize{
\item \code{111111} - All aggregators enabled \item
\code{100001} - Only average and sumenabled \item
\code{010000} - Only count enabled \item \code{000000} -
All aggregators disabled - for columns with given data type
there will be no measure. }
}
\examples{
get_default_mapping('PostgreSQL')
get_default_mapping('R')
get_default_mapping('R', debug=TRUE)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/attachmentBlock.R
\name{attachmentBlock}
\alias{attachmentBlock}
\title{AdminLTE2 attachment container}
\usage{
attachmentBlock(..., src = NULL, title = NULL, title_url = NULL)
}
\arguments{
\item{...}{any element.}
\item{src}{url or path to the image.}
\item{title}{attachment title.}
\item{title_url}{external link.}
}
\description{
Create an attachment container, nice to wrap articles...
}
\examples{
if (interactive()) {
library(shiny)
library(shinydashboard)
shinyApp(
ui = dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
box(
title = "Attachment example",
attachmentBlock(
src = "http://kiev.carpediem.cd/data/afisha/o/2d/c7/2dc7670333.jpg",
title = "Test",
title_url = "http://google.com",
"This is the content"
)
)
),
title = "Description Blocks"
),
server = function(input, output) { }
)
}
}
\author{
David Granjon, \email{dgranjon@ymail.com}
}
|
/man/attachmentBlock.Rd
|
no_license
|
pvictor/shinydashboardPlus
|
R
| false
| true
| 1,044
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/attachmentBlock.R
\name{attachmentBlock}
\alias{attachmentBlock}
\title{AdminLTE2 attachment container}
\usage{
attachmentBlock(..., src = NULL, title = NULL, title_url = NULL)
}
\arguments{
\item{...}{any element.}
\item{src}{url or path to the image.}
\item{title}{attachment title.}
\item{title_url}{external link.}
}
\description{
Create an attachment container, nice to wrap articles...
}
\examples{
if (interactive()) {
library(shiny)
library(shinydashboard)
shinyApp(
ui = dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
box(
title = "Attachment example",
attachmentBlock(
src = "http://kiev.carpediem.cd/data/afisha/o/2d/c7/2dc7670333.jpg",
title = "Test",
title_url = "http://google.com",
"This is the content"
)
)
),
title = "Description Blocks"
),
server = function(input, output) { }
)
}
}
\author{
David Granjon, \email{dgranjon@ymail.com}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gLV_simulations.R
\name{convert_series_clr}
\alias{convert_series_clr}
\title{CLR-convert a set of species abundance series, calculate covariance across species, and return
the upper triangular portion of this covariance matrix}
\usage{
convert_series_clr(TS)
}
\arguments{
\item{TS}{multi-species time series returned by generate_series}
}
\value{
atomic vector
}
\description{
CLR-convert a set of species abundance series, calculate covariance across species, and return
the upper triangular portion of this covariance matrix
}
|
/man/convert_series_clr.Rd
|
no_license
|
kimberlyroche/rulesoflife
|
R
| false
| true
| 609
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gLV_simulations.R
\name{convert_series_clr}
\alias{convert_series_clr}
\title{CLR-convert a set of species abundance series, calculate covariance across species, and return
the upper triangular portion of this covariance matrix}
\usage{
convert_series_clr(TS)
}
\arguments{
\item{TS}{multi-species time series returned by generate_series}
}
\value{
atomic vector
}
\description{
CLR-convert a set of species abundance series, calculate covariance across species, and return
the upper triangular portion of this covariance matrix
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/matrix_functions.R
\name{set_rownames}
\alias{set_rownames}
\title{Set rownames of object}
\usage{
set_rownames(.m, row_names = NULL)
}
\arguments{
\item{.m}{A 2d matrix}
\item{row_names}{Numeric or character vector or NULL}
}
\value{
Copy of matrix with rownames
}
\description{
Set rownames of object
}
\examples{
set_rownames(matrix(1:6, nrow = 2), c("a", "b"))
set_rownames(matrix(1:6, nrow = 3), c("a", "b", "c"))
set_rownames(matrix(1:6, nrow = 3, byrow = TRUE, dimnames = list(c("a", "b", "c"))), NULL)
}
|
/man/set_rownames.Rd
|
permissive
|
user01/uvadsi
|
R
| false
| true
| 592
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/matrix_functions.R
\name{set_rownames}
\alias{set_rownames}
\title{Set rownames of object}
\usage{
set_rownames(.m, row_names = NULL)
}
\arguments{
\item{.m}{A 2d matrix}
\item{row_names}{Numeric or character vector or NULL}
}
\value{
Copy of matrix with rownames
}
\description{
Set rownames of object
}
\examples{
set_rownames(matrix(1:6, nrow = 2), c("a", "b"))
set_rownames(matrix(1:6, nrow = 3), c("a", "b", "c"))
set_rownames(matrix(1:6, nrow = 3, byrow = TRUE, dimnames = list(c("a", "b", "c"))), NULL)
}
|
rd <- read.csv2("../data/redmine.csv")[c(1, 7, 8, 12, 19, 21, 20)]
names(rd) <- c("id", "job", "short.name", "ddate", "start.hour", "start.quater", "duration")
rd$ddate <- as.Date(rd$ddate, format = "%d.%m.%Y")
if (FALSE) {
rd$start.hour <- sample(0:23, nrow(rd), replace = TRUE)
rd$start.quater <- sample(c(0, 15, 30, 45), nrow(rd), replace = TRUE)
rd$duration <- rgamma(nrow(rd), 4, 2)
}
day.0 <- as.Date(paste(DATA.year, DATA.month, "01", sep = "-")) # First day of the month
day.1 <- lastDayInMonth(day.0) # Last day of the month
rd <- rd[(rd$ddate >= day.0 & rd$ddate <= day.1), ]
rd$time0 <- as.POSIXct(paste(rd$ddate, paste(rd$start.hour, rd$start.quater, sep = ":")), format = "%Y-%m-%d %H:%M")
rd$time1 <- rd$time0 + as.difftime(rd$duration, units = "hours")
rd <- merge(rd, emp[c("emph", "empl", "short.name")], by = "short.name")[ ,c("emph", "empl", "time0", "time1")]
rd$src <- "crm"
|
/code/rdread.R
|
no_license
|
anthony01/inout
|
R
| false
| false
| 1,019
|
r
|
rd <- read.csv2("../data/redmine.csv")[c(1, 7, 8, 12, 19, 21, 20)]
names(rd) <- c("id", "job", "short.name", "ddate", "start.hour", "start.quater", "duration")
rd$ddate <- as.Date(rd$ddate, format = "%d.%m.%Y")
if (FALSE) {
rd$start.hour <- sample(0:23, nrow(rd), replace = TRUE)
rd$start.quater <- sample(c(0, 15, 30, 45), nrow(rd), replace = TRUE)
rd$duration <- rgamma(nrow(rd), 4, 2)
}
day.0 <- as.Date(paste(DATA.year, DATA.month, "01", sep = "-")) # First day of the month
day.1 <- lastDayInMonth(day.0) # Last day of the month
rd <- rd[(rd$ddate >= day.0 & rd$ddate <= day.1), ]
rd$time0 <- as.POSIXct(paste(rd$ddate, paste(rd$start.hour, rd$start.quater, sep = ":")), format = "%Y-%m-%d %H:%M")
rd$time1 <- rd$time0 + as.difftime(rd$duration, units = "hours")
rd <- merge(rd, emp[c("emph", "empl", "short.name")], by = "short.name")[ ,c("emph", "empl", "time0", "time1")]
rd$src <- "crm"
|
testthat::context("Download NetLogo")
testthat::test_that("Download NetLogo", {
# Run these tests only on TRAVIS:
testthat::skip_if(!identical(Sys.getenv("TRAVIS"), "true"))
## Version 6.0.0
nlversion <- "5.3.1"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"netlogo-5.3.1-64",
"app",
paste0("NetLogo.jar"))))
## Version 6.0.0
nlversion <- "6.0"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0",
"app",
paste0("netlogo-",
nlversion,
".0.jar"))))
## Version 6.0.1
nlversion <- "6.0.1"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.1",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.2
nlversion <- "6.0.2"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.2",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.3
nlversion <- "6.0.3"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.3",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.4
nlversion <- "6.0.4"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.4",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
})
|
/data/genthat_extracted_code/nlrx/tests/test_download_netlogo.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 3,740
|
r
|
testthat::context("Download NetLogo")
testthat::test_that("Download NetLogo", {
# Run these tests only on TRAVIS:
testthat::skip_if(!identical(Sys.getenv("TRAVIS"), "true"))
## Version 6.0.0
nlversion <- "5.3.1"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"netlogo-5.3.1-64",
"app",
paste0("NetLogo.jar"))))
## Version 6.0.0
nlversion <- "6.0"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0",
"app",
paste0("netlogo-",
nlversion,
".0.jar"))))
## Version 6.0.1
nlversion <- "6.0.1"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.1",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.2
nlversion <- "6.0.2"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.2",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.3
nlversion <- "6.0.3"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.3",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
## Version 6.0.4
nlversion <- "6.0.4"
nlpath <- tempdir()
download_netlogo(to = nlpath,
version = nlversion,
extract = TRUE)
### Check that download worked and netlogo jar file exists:
testthat::expect_true(file.exists(file.path(nlpath,
"NetLogo 6.0.4",
"app",
paste0("netlogo-",
nlversion,
".jar"))))
})
|
#' Feature Quality score
#'
#' Calculate the feature quality score based on the presence of non failed gaussian fits.
#'
#'
#' @param XCMSobject An xcmsSet object
#' @param indices.to.keep Vector with indices to keep. Either numeric indices or a vector with TRUE/FALSE (same length as nr. of groups in XCMSobject)
#'
#'
#' @return
#' A vector with QC scores (between 0 and 1)
#'
#' @author Charlie Beirnaert, \email{charlie.beirnaert@@uantwerpen.be}
#'
#'
#' @export
FeatureQualityScore = function(XCMSobject, indices.to.keep = NULL){
if(! "xcmsSet" %in% class(XCMSobject)){
stop("XCMSobject is not an xcmsSet")
}
if(length(XCMSobject@groupidx) == 0){
stop("No groups in XCMSobject. Perform grouping step first")
}
if(is.null(indices.to.keep)){
indices.to.keep = seq_along(XCMSobject@groupidx)
} else if( "logical" %in% class(indices.to.keep)){
indices.to.keep = which(indices.to.keep)
}
if(max(indices.to.keep) > length(XCMSobject@groupidx)){
stop("The maximal index to keep is larger than the available amount of features.")
}
group.data = data.frame(XCMSobject@groups)
Quality.score = vector("double",length(XCMSobject@groupidx))
for(k in seq_along(indices.to.keep)){
Quality.score = NA
}
}
|
/R/FeatureQualityScore.R
|
permissive
|
Beirnaert/MetaboMeeseeks
|
R
| false
| false
| 1,351
|
r
|
#' Feature Quality score
#'
#' Calculate the feature quality score based on the presence of non failed gaussian fits.
#'
#'
#' @param XCMSobject An xcmsSet object
#' @param indices.to.keep Vector with indices to keep. Either numeric indices or a vector with TRUE/FALSE (same length as nr. of groups in XCMSobject)
#'
#'
#' @return
#' A vector with QC scores (between 0 and 1)
#'
#' @author Charlie Beirnaert, \email{charlie.beirnaert@@uantwerpen.be}
#'
#'
#' @export
FeatureQualityScore = function(XCMSobject, indices.to.keep = NULL){
if(! "xcmsSet" %in% class(XCMSobject)){
stop("XCMSobject is not an xcmsSet")
}
if(length(XCMSobject@groupidx) == 0){
stop("No groups in XCMSobject. Perform grouping step first")
}
if(is.null(indices.to.keep)){
indices.to.keep = seq_along(XCMSobject@groupidx)
} else if( "logical" %in% class(indices.to.keep)){
indices.to.keep = which(indices.to.keep)
}
if(max(indices.to.keep) > length(XCMSobject@groupidx)){
stop("The maximal index to keep is larger than the available amount of features.")
}
group.data = data.frame(XCMSobject@groups)
Quality.score = vector("double",length(XCMSobject@groupidx))
for(k in seq_along(indices.to.keep)){
Quality.score = NA
}
}
|
swap <- function(rota, i, j) {
# Mantem o caminho de 1 -> (i - 1) e (j + 1) -> length(rota) intacto
novaRota <- rota
# Altera apenas o caminho de i -> j
for (k in i:j) {
novaRota[k] <- rota[j + i - k]
}
return(novaRota)
}
|
/swap.R
|
no_license
|
tiagoyukio12/PCS3438-EP01
|
R
| false
| false
| 254
|
r
|
swap <- function(rota, i, j) {
# Mantem o caminho de 1 -> (i - 1) e (j + 1) -> length(rota) intacto
novaRota <- rota
# Altera apenas o caminho de i -> j
for (k in i:j) {
novaRota[k] <- rota[j + i - k]
}
return(novaRota)
}
|
#-------------------------------------------------------------------------------#
# Last updated date: 7/10/2020
# Author: Duzhe Wang (dwang282@wisc.edu)
#--------------------------------------------------------------------------------#
rm(list=ls())
setwd("/Users/peterwang/Desktop/HHLM/model3")
source("/Users/peterwang/Desktop/HHLM/funcs/model.R")
source("/Users/peterwang/Desktop/HHLM/funcs/lasso.R")
set.seed(2020)
#---------------Step 1: Set parameters--------------------------------#
Nvec=c(90, 120, 150, 180, 210, 240, 270, 300, 330, 360) # sample size
n=300 # ambient dimension
nsim=200 # number of simulation times
rho=0.5
lambdaseqconstant=seq(from=0.700, to=0.900, 0.05)
L1=0.05
L2=5
# true beta
s=6
Supp=c(1,2,3, 4, 5, 6)
truebeta=rep(0, n)
truebeta[1:s]=c(3, 4, 3, 1.5, 2, 1.5)
# generate covariance matrix
Sigmax=Sigmax(rho, n, s)
maxeigen=max(eigen(Sigmax)$values) # maxeigen is around 3.
#-------------------------------------------------------------------#
#-------------Step 2: SIMULATION-------------------------#
# each row records N, lambda, simu iter, l2 estimation error, l1 estimation error, sparsity of estimaed beta and MSE
lassoestiresults=matrix(NA, nrow=length(Nvec)*nsim, ncol=7*length(lambdaseqconstant))
# each row records estimated beta in each iteration
lassocoefmat=matrix(NA, nrow=length(Nvec)*nsim, ncol=n*length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
for (i in 1:length(Nvec)){
lambda=lambdaseqconstant[k]*sqrt(maxeigen*L2*log(n)/Nvec[i])
for (iter in 1:nsim){
print(paste("lambda constant:",lambdaseqconstant[k],"N:", Nvec[i], "iteration:", iter))
trainingdata=HHR3(Nvec[i], n, Sigmax, truebeta, L1, L2)
lassosimu=lassoest(trainingdata, lambda=lambda, truebeta)
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+1]=Nvec[i]
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+2]=lambda
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+3]=iter
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+4]=lassosimu$l2error
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+5]=lassosimu$l1error
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+6]=lassosimu$Shatlength
lassoestiresults[(i-1)*nsim+iter,k*7]=lassosimu$MSE
lassocoefmat[(i-1)*nsim+iter, ((k-1)*n+1):(k*n)]=lassosimu$lassocoef
}
}
}
#--------------------------------------------------------------#
#---------------Step 3: Analysis of results--------------------#
## Estimation
avgl2=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgl1=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgMSE=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdl2=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdl1=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdMSE=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
lassoestiresults.df=as.data.frame(lassoestiresults[,((k-1)*7+1):(k*7)])
colnames(lassoestiresults.df)=c("N", "lambda", "iter", "l2error", "l1error", "betahatsparsity", "MSE")
for (i in 1:length(Nvec)){
avgl2[i,k]=mean(lassoestiresults.df$l2error[lassoestiresults.df$N==Nvec[i]])
avgl1[i,k]=mean(lassoestiresults.df$l1error[lassoestiresults.df$N==Nvec[i]])
avgMSE[i,k]=mean(lassoestiresults.df$MSE[lassoestiresults.df$N==Nvec[i]])
sdl2[i,k]=sd(lassoestiresults.df$l2error[lassoestiresults.df$N==Nvec[i]])
sdl1[i,k]=sd(lassoestiresults.df$l1error[lassoestiresults.df$N==Nvec[i]])
sdMSE[i,k]=sd(lassoestiresults.df$MSE[lassoestiresults.df$N==Nvec[i]])
}
}
## Variable selection
Supphat=matrix(NA, nrow=length(Nvec)*nsim, ncol=n*length(lambdaseqconstant)) # support set: 1 represents nonzero
shat=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant)) # size of support set
successindex=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
recall=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
precision=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
FDR=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
successprob=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgrecall=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgprecision=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgFDR=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
for(i in 1:(length(Nvec)*nsim)){
Supphat[i, ((k-1)*n+1):(k*n)]=as.integer(lassocoefmat[i,((k-1)*n+1):(k*n)]!=0)
shat[i,k]=sum(Supphat[i, ((k-1)*n+1):(k*n)])
supportset=which(lassocoefmat[i,((k-1)*n+1):(k*n)]!=0)
recall[i,k]=length(intersect(supportset, Supp))/s
precision[i,k]=length(intersect(supportset, Supp))/shat[i,k]
FDR[i,k]=1-precision[i,k]
if (isTRUE(shat[i,k]!=s)){
successindex[i,k]=0 # variable selection fail
}else if (all(supportset%in%Supp)=="FALSE"){
successindex[i,k]=0 # variable selection fail
} else{
successindex[i,k]=1
}
}
}
for (k in 1:length(lambdaseqconstant)){
for(i in 1:length(Nvec)){
successprob[i, k]=sum(successindex[((i-1)*nsim+1):(i*nsim), k])/nsim
avgrecall[i, k]=mean(recall[((i-1)*nsim+1):(i*nsim), k])
avgprecision[i, k]=mean(precision[((i-1)*nsim+1):(i*nsim), k])
avgFDR[i, k]=mean(FDR[((i-1)*nsim+1):(i*nsim), k])
}
}
#-------------------------------------------------------------#
#-------------Print and save results--------------------------#
finalresults=list(avgl2=avgl2, avgl1=avgl1, avgMSE=avgMSE,
sdl2=sdl2, sdl1=sdl1, sdMSE=sdMSE,
successprob=successprob, avgrecall=avgrecall,
avgprecision=avgprecision, avgFDR=avgFDR)
#-------------------------------------------------------------#
|
/model3/simu2.R
|
no_license
|
duzhewang/HHLM
|
R
| false
| false
| 6,135
|
r
|
#-------------------------------------------------------------------------------#
# Last updated date: 7/10/2020
# Author: Duzhe Wang (dwang282@wisc.edu)
#--------------------------------------------------------------------------------#
rm(list=ls())
setwd("/Users/peterwang/Desktop/HHLM/model3")
source("/Users/peterwang/Desktop/HHLM/funcs/model.R")
source("/Users/peterwang/Desktop/HHLM/funcs/lasso.R")
set.seed(2020)
#---------------Step 1: Set parameters--------------------------------#
Nvec=c(90, 120, 150, 180, 210, 240, 270, 300, 330, 360) # sample size
n=300 # ambient dimension
nsim=200 # number of simulation times
rho=0.5
lambdaseqconstant=seq(from=0.700, to=0.900, 0.05)
L1=0.05
L2=5
# true beta
s=6
Supp=c(1,2,3, 4, 5, 6)
truebeta=rep(0, n)
truebeta[1:s]=c(3, 4, 3, 1.5, 2, 1.5)
# generate covariance matrix
Sigmax=Sigmax(rho, n, s)
maxeigen=max(eigen(Sigmax)$values) # maxeigen is around 3.
#-------------------------------------------------------------------#
#-------------Step 2: SIMULATION-------------------------#
# each row records N, lambda, simu iter, l2 estimation error, l1 estimation error, sparsity of estimaed beta and MSE
lassoestiresults=matrix(NA, nrow=length(Nvec)*nsim, ncol=7*length(lambdaseqconstant))
# each row records estimated beta in each iteration
lassocoefmat=matrix(NA, nrow=length(Nvec)*nsim, ncol=n*length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
for (i in 1:length(Nvec)){
lambda=lambdaseqconstant[k]*sqrt(maxeigen*L2*log(n)/Nvec[i])
for (iter in 1:nsim){
print(paste("lambda constant:",lambdaseqconstant[k],"N:", Nvec[i], "iteration:", iter))
trainingdata=HHR3(Nvec[i], n, Sigmax, truebeta, L1, L2)
lassosimu=lassoest(trainingdata, lambda=lambda, truebeta)
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+1]=Nvec[i]
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+2]=lambda
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+3]=iter
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+4]=lassosimu$l2error
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+5]=lassosimu$l1error
lassoestiresults[(i-1)*nsim+iter,(k-1)*7+6]=lassosimu$Shatlength
lassoestiresults[(i-1)*nsim+iter,k*7]=lassosimu$MSE
lassocoefmat[(i-1)*nsim+iter, ((k-1)*n+1):(k*n)]=lassosimu$lassocoef
}
}
}
#--------------------------------------------------------------#
#---------------Step 3: Analysis of results--------------------#
## Estimation
avgl2=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgl1=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgMSE=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdl2=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdl1=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
sdMSE=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
lassoestiresults.df=as.data.frame(lassoestiresults[,((k-1)*7+1):(k*7)])
colnames(lassoestiresults.df)=c("N", "lambda", "iter", "l2error", "l1error", "betahatsparsity", "MSE")
for (i in 1:length(Nvec)){
avgl2[i,k]=mean(lassoestiresults.df$l2error[lassoestiresults.df$N==Nvec[i]])
avgl1[i,k]=mean(lassoestiresults.df$l1error[lassoestiresults.df$N==Nvec[i]])
avgMSE[i,k]=mean(lassoestiresults.df$MSE[lassoestiresults.df$N==Nvec[i]])
sdl2[i,k]=sd(lassoestiresults.df$l2error[lassoestiresults.df$N==Nvec[i]])
sdl1[i,k]=sd(lassoestiresults.df$l1error[lassoestiresults.df$N==Nvec[i]])
sdMSE[i,k]=sd(lassoestiresults.df$MSE[lassoestiresults.df$N==Nvec[i]])
}
}
## Variable selection
Supphat=matrix(NA, nrow=length(Nvec)*nsim, ncol=n*length(lambdaseqconstant)) # support set: 1 represents nonzero
shat=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant)) # size of support set
successindex=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
recall=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
precision=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
FDR=matrix(NA, nrow=length(Nvec)*nsim, ncol=length(lambdaseqconstant))
successprob=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgrecall=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgprecision=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
avgFDR=matrix(NA, nrow=length(Nvec), ncol=length(lambdaseqconstant))
for (k in 1:length(lambdaseqconstant)){
for(i in 1:(length(Nvec)*nsim)){
Supphat[i, ((k-1)*n+1):(k*n)]=as.integer(lassocoefmat[i,((k-1)*n+1):(k*n)]!=0)
shat[i,k]=sum(Supphat[i, ((k-1)*n+1):(k*n)])
supportset=which(lassocoefmat[i,((k-1)*n+1):(k*n)]!=0)
recall[i,k]=length(intersect(supportset, Supp))/s
precision[i,k]=length(intersect(supportset, Supp))/shat[i,k]
FDR[i,k]=1-precision[i,k]
if (isTRUE(shat[i,k]!=s)){
successindex[i,k]=0 # variable selection fail
}else if (all(supportset%in%Supp)=="FALSE"){
successindex[i,k]=0 # variable selection fail
} else{
successindex[i,k]=1
}
}
}
for (k in 1:length(lambdaseqconstant)){
for(i in 1:length(Nvec)){
successprob[i, k]=sum(successindex[((i-1)*nsim+1):(i*nsim), k])/nsim
avgrecall[i, k]=mean(recall[((i-1)*nsim+1):(i*nsim), k])
avgprecision[i, k]=mean(precision[((i-1)*nsim+1):(i*nsim), k])
avgFDR[i, k]=mean(FDR[((i-1)*nsim+1):(i*nsim), k])
}
}
#-------------------------------------------------------------#
#-------------Print and save results--------------------------#
finalresults=list(avgl2=avgl2, avgl1=avgl1, avgMSE=avgMSE,
sdl2=sdl2, sdl1=sdl1, sdMSE=sdMSE,
successprob=successprob, avgrecall=avgrecall,
avgprecision=avgprecision, avgFDR=avgFDR)
#-------------------------------------------------------------#
|
\name{selectSample}
\Rdversion{1.3}
\alias{selectSample}
\title{
Selection of a stratified sample from the frame with srswor method
}
\description{
Once optimal stratification has been obtained,
and a new frame has been
built by assigning to the units of the old one the new strata labels, it is possible to select
a stratified sample from the frame
with the simple random sampling without replacement (srswor) method.
The result of the execution of "selectSample" function is a dataframe containing the selected
units, with their weights (inverse of the probabilities of inclusion).
It is possible to output this dataframe in a .csv file.
One more .csv file is produced ("sampling_check"), containing coeherence checks between
(a) population in frame strata
(b) population in optimised strata
(c) planned units to be selected in optimised strata
(d) actually selected units
(e) sum of weights in each stratum
}
\usage{
selectSample(frame, outstrata, writeFiles = FALSE,verbatim=TRUE)
}
\arguments{
\item{frame}{
This is the (mandatory) dataframe containing the sampling frame, as it has been modified
by the execution of the "updateFrame" function.
}
\item{outstrata}{
This is the (mandatory) dataframe containing the information related to resulting stratification obtained by the execution of "optimizeStrata" function. It should coincide with 'solution$aggr_strata'.
}
\item{writeFiles}{
Indicates if at the end of the processing the resulting strata will be outputted in a delimited file.
Default is "FALSE".
}
\item{verbatim}{
Indicates if information on the drawn sample must be printed or not.
Default is "TRUE".
}
}
\value{
A dataframe containing the sample
}
\author{
Giulio Barcaroli with contribution from Diego Zardetto
}
\examples{
\dontrun{
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata (
errors = swisserrors,
strata = swissstrata
)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution)
# updating sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,newstrata=newstrata)
# selection of sample
sample <- selectSample(frame=framenew,outstrata=solution$aggr_strata)
head(sample)
}
}
\keyword{ survey }
|
/man/selectSample.Rd
|
no_license
|
cran/SamplingStrata
|
R
| false
| false
| 2,405
|
rd
|
\name{selectSample}
\Rdversion{1.3}
\alias{selectSample}
\title{
Selection of a stratified sample from the frame with srswor method
}
\description{
Once optimal stratification has been obtained,
and a new frame has been
built by assigning to the units of the old one the new strata labels, it is possible to select
a stratified sample from the frame
with the simple random sampling without replacement (srswor) method.
The result of the execution of "selectSample" function is a dataframe containing the selected
units, with their weights (inverse of the probabilities of inclusion).
It is possible to output this dataframe in a .csv file.
One more .csv file is produced ("sampling_check"), containing coeherence checks between
(a) population in frame strata
(b) population in optimised strata
(c) planned units to be selected in optimised strata
(d) actually selected units
(e) sum of weights in each stratum
}
\usage{
selectSample(frame, outstrata, writeFiles = FALSE,verbatim=TRUE)
}
\arguments{
\item{frame}{
This is the (mandatory) dataframe containing the sampling frame, as it has been modified
by the execution of the "updateFrame" function.
}
\item{outstrata}{
This is the (mandatory) dataframe containing the information related to resulting stratification obtained by the execution of "optimizeStrata" function. It should coincide with 'solution$aggr_strata'.
}
\item{writeFiles}{
Indicates if at the end of the processing the resulting strata will be outputted in a delimited file.
Default is "FALSE".
}
\item{verbatim}{
Indicates if information on the drawn sample must be printed or not.
Default is "TRUE".
}
}
\value{
A dataframe containing the sample
}
\author{
Giulio Barcaroli with contribution from Diego Zardetto
}
\examples{
\dontrun{
library(SamplingStrata)
data(swisserrors)
data(swissstrata)
# optimisation of sampling strata
solution <- optimizeStrata (
errors = swisserrors,
strata = swissstrata
)
# updating sampling strata with new strata labels
newstrata <- updateStrata(swissstrata, solution)
# updating sampling frame with new strata labels
data(swissframe)
framenew <- updateFrame(frame=swissframe,newstrata=newstrata)
# selection of sample
sample <- selectSample(frame=framenew,outstrata=solution$aggr_strata)
head(sample)
}
}
\keyword{ survey }
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 537906
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 537006
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 537006
c
c Input Parameter (command line, file):
c input filename QBFLIB/Kronegger-Pfandler-Pichler/bomb/p20-20.pddl_planlen=6.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 3640
c no.of clauses 537906
c no.of taut cls 2500
c
c Output Parameters:
c remaining no.of clauses 537006
c
c QBFLIB/Kronegger-Pfandler-Pichler/bomb/p20-20.pddl_planlen=6.qdimacs 3640 537906 E1 [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 45 46 47 48 49 50 51 52 54 55 56 59 60 61 62 63 64 65 66 67 68 69 71 73 74 75 76 77 78 79 80 82 83 84 85 86 88 89 91 92 93 94 95 96 97 98 99 100 102 103 104 105 106 107 110 111 112 113 114 116 117 118 119 122 123 124 125 126 127 128 129 130 131 132 133 134 135 137 138 139 140 141 143 144 145 147 148 149 150 151 152 153 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640] 2500 20 2740 537006 RED
|
/code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Kronegger-Pfandler-Pichler/bomb/p20-20.pddl_planlen=6/p20-20.pddl_planlen=6.R
|
no_license
|
arey0pushpa/dcnf-autarky
|
R
| false
| false
| 4,794
|
r
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 537906
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 537006
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 537006
c
c Input Parameter (command line, file):
c input filename QBFLIB/Kronegger-Pfandler-Pichler/bomb/p20-20.pddl_planlen=6.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 3640
c no.of clauses 537906
c no.of taut cls 2500
c
c Output Parameters:
c remaining no.of clauses 537006
c
c QBFLIB/Kronegger-Pfandler-Pichler/bomb/p20-20.pddl_planlen=6.qdimacs 3640 537906 E1 [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 45 46 47 48 49 50 51 52 54 55 56 59 60 61 62 63 64 65 66 67 68 69 71 73 74 75 76 77 78 79 80 82 83 84 85 86 88 89 91 92 93 94 95 96 97 98 99 100 102 103 104 105 106 107 110 111 112 113 114 116 117 118 119 122 123 124 125 126 127 128 129 130 131 132 133 134 135 137 138 139 140 141 143 144 145 147 148 149 150 151 152 153 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640] 2500 20 2740 537006 RED
|
library(ctools)
e.files <- list.files("../data/classify/estimates", pattern = "-1.*.Rda", full.names = T)
c.lapply(e.files, function(f) {
print(f)
load(f)
sub.est <- lapply(sub.est, function(sub) {
sub <- lapply(sub, function (inst) {
if (!is.na(inst$EUT[1])) {
inst$EUT$pfunc <- "EUT"
}
if (!is.na(inst$POW[1])) {
inst$POW$pfunc <- "POW"
}
if (!is.na(inst$INVS[1])) {
inst$INVS$pfunc <- "INVS"
}
if (!is.na(inst$PRE[1])) {
inst$PRE$pfunc <- "PRELEC"
}
inst
})
sub
})
save(sub.est, real, file = f)
})
|
/welcalc/addpf.R
|
no_license
|
bamonroe/code-ch4
|
R
| false
| false
| 558
|
r
|
library(ctools)
e.files <- list.files("../data/classify/estimates", pattern = "-1.*.Rda", full.names = T)
c.lapply(e.files, function(f) {
print(f)
load(f)
sub.est <- lapply(sub.est, function(sub) {
sub <- lapply(sub, function (inst) {
if (!is.na(inst$EUT[1])) {
inst$EUT$pfunc <- "EUT"
}
if (!is.na(inst$POW[1])) {
inst$POW$pfunc <- "POW"
}
if (!is.na(inst$INVS[1])) {
inst$INVS$pfunc <- "INVS"
}
if (!is.na(inst$PRE[1])) {
inst$PRE$pfunc <- "PRELEC"
}
inst
})
sub
})
save(sub.est, real, file = f)
})
|
# Author: Gino
# Live Assignment 06
# Part 3
#required Cleanup_02.R
source("~/Github/DataScienceLiveSessionAssignment06/Analysis/Data/Cleanup_02.R",echo=FALSE)
#Cleanng up manhattan file and setting the data for analysis "Cleanup_03.R"
#plot gross sqft to price
plot(mn.sale$gross.sqft, mn.sale$sale.price.n)
plot(log10(mn.sale$gross.sqft), log10(mn.sale$sale.price.n))
#Analysis on the data
hist(log10(mn.sale$sale.price.n))
qqplot(log10(mn.sale$gross.sqft), log10(mn.sale$sale.price.n))
#Creating a new data set, limiting to family homes from the list.
mn.homes <- mn.sale[which(grepl("FAMILY",mn.sale$building.class.category)),]
mn.homes$Price.per.gross.sqft <- mn.homes$sale.price.n/mn.homes$gross.sqft
mn.homes$Price.per.land.sqft <- mn.homes$sale.price.n/mn.homes$land.sqft
mn.homes$Price.per.gross.sqft <- replace(mn.homes$Price.per.gross.sqft, which(is.na(mn.homes$Price.per.gross.sqft)),0)
mn.homes$Price.per.land.sqft <- replace(mn.homes$Price.per.land.sqft, which(is.na(mn.homes$Price.per.land.sqft)),0)
dim(mn.homes)
#reviewed the data set and it contained outliers,
plot(log10(mn.homes$gross.sqft), log10(mn.homes$sale.price.n))
hist(log10(mn.homes$sale.price.n))
summary(mn.homes[which(mn.homes$sale.price.n<100000),])
#removed outliers that are lesser than and equal to 5.
mn.homes$outliers <- (log10(mn.homes$sale.price.n) <= 5) + 0
mn.homes <- mn.homes[which(mn.homes$outliers == 0),]
#reviweing data prior to analysis
plot(mn.homes$gross.sqft, mn.homes$sale.price.n)
#writing out to clean file
#writing out intermediate cleanedup file with family home
write.csv(mn.homes,file="rollingsales_manhattan_familyhome.csv")
|
/Analysis/Data/Cleanup_03.R
|
no_license
|
smusimham/ManhattanAssignment
|
R
| false
| false
| 1,654
|
r
|
# Author: Gino
# Live Assignment 06
# Part 3
#required Cleanup_02.R
source("~/Github/DataScienceLiveSessionAssignment06/Analysis/Data/Cleanup_02.R",echo=FALSE)
#Cleanng up manhattan file and setting the data for analysis "Cleanup_03.R"
#plot gross sqft to price
plot(mn.sale$gross.sqft, mn.sale$sale.price.n)
plot(log10(mn.sale$gross.sqft), log10(mn.sale$sale.price.n))
#Analysis on the data
hist(log10(mn.sale$sale.price.n))
qqplot(log10(mn.sale$gross.sqft), log10(mn.sale$sale.price.n))
#Creating a new data set, limiting to family homes from the list.
mn.homes <- mn.sale[which(grepl("FAMILY",mn.sale$building.class.category)),]
mn.homes$Price.per.gross.sqft <- mn.homes$sale.price.n/mn.homes$gross.sqft
mn.homes$Price.per.land.sqft <- mn.homes$sale.price.n/mn.homes$land.sqft
mn.homes$Price.per.gross.sqft <- replace(mn.homes$Price.per.gross.sqft, which(is.na(mn.homes$Price.per.gross.sqft)),0)
mn.homes$Price.per.land.sqft <- replace(mn.homes$Price.per.land.sqft, which(is.na(mn.homes$Price.per.land.sqft)),0)
dim(mn.homes)
#reviewed the data set and it contained outliers,
plot(log10(mn.homes$gross.sqft), log10(mn.homes$sale.price.n))
hist(log10(mn.homes$sale.price.n))
summary(mn.homes[which(mn.homes$sale.price.n<100000),])
#removed outliers that are lesser than and equal to 5.
mn.homes$outliers <- (log10(mn.homes$sale.price.n) <= 5) + 0
mn.homes <- mn.homes[which(mn.homes$outliers == 0),]
#reviweing data prior to analysis
plot(mn.homes$gross.sqft, mn.homes$sale.price.n)
#writing out to clean file
#writing out intermediate cleanedup file with family home
write.csv(mn.homes,file="rollingsales_manhattan_familyhome.csv")
|
#VCS, 29.10.2019, extremely important example to understand the begining of the data analysis,
#also works to get the number of Hu genes that only have intronic enhancers,
#which means calculating intronic enhancers, enhancers with another annotations, a subset, and then a setdiff of both.
library(data.table)
HomerTabHufile="/Users/vitorcalistosilva/Documents/Zebrafish-i3S/Masters_Zebrafish/Docs/HomerAnnotationsHumanEnhs_08.10.2019"
HomerTabHu=fread(file = HomerTabHufile,sep = "\t",header = T,stringsAsFactors = F,data.table = F, blank.lines.skip = TRUE)
#what and how many are the genes associated with 14753 putative whole pancreas Hu enhs
UniqGenes=unique(HomerTabHu$`Gene Name`)
NrUniqGs=length(UniqGenes)
#line/row indices / positions of annotations to intronic enhancers:
Idx=grep(pattern = "intron",x = HomerTabHu$Annotation,ignore.case = T)
#genes of these indices from intronic enhancers; here we look at the 'gene name' column:
GenesIntEnh=unique(HomerTabHu$`Gene Name`[Idx])
length(GenesIntEnh)
#get lines of the table where these unique 2265 genes appear: which gives these positions/indices:
IdxGs=which(HomerTabHu$`Gene Name`%in%GenesIntEnh)
#get the annotation for them (see the different size/length):
AnnotGsInt=HomerTabHu$Annotation[IdxGs]
GenesGsInt=HomerTabHu$`Gene Name`[IdxGs]
#So, we already know, what are the genes with intronic enhs -> GenesIntEnh
# IdxV1=grep(pattern = "intron",x = AnnotGsInt,ignore.case = T)
# GsV1=unique(HomerTabHu$`Gene Name`[IdxGs[IdxV1]])
#Now, to know which of them only have intronic enhancers, we do an inverse grep for intron
#(gives lines that dont contain intron) on their annotation, and then get the associated genes
#which is a subter of 2265
#heree, check to descomplicar......
IdxV2=grep(pattern = "intron",x = AnnotGsInt,ignore.case = T,invert = T)
GsV2=unique(GenesGsInt[IdxV2])
#GsV2=unique(HomerTabHu$`Gene Name`[IdxGs[IdxV2]])
#OnlyIntr=setdiff(GsV1,GsV2)
#how many of 2265 genes with intronic enhancers don't have any other annotation
OnlyIntr=setdiff(GenesIntEnh,GsV2)
#setdiff(GsV2,GenesIntEnh)=0
|
/R/HowManyHuGenesOnlyHaveIntronicEnhancers.R
|
no_license
|
vitorcalistosilva/VDR
|
R
| false
| false
| 2,081
|
r
|
#VCS, 29.10.2019, extremely important example to understand the begining of the data analysis,
#also works to get the number of Hu genes that only have intronic enhancers,
#which means calculating intronic enhancers, enhancers with another annotations, a subset, and then a setdiff of both.
library(data.table)
HomerTabHufile="/Users/vitorcalistosilva/Documents/Zebrafish-i3S/Masters_Zebrafish/Docs/HomerAnnotationsHumanEnhs_08.10.2019"
HomerTabHu=fread(file = HomerTabHufile,sep = "\t",header = T,stringsAsFactors = F,data.table = F, blank.lines.skip = TRUE)
#what and how many are the genes associated with 14753 putative whole pancreas Hu enhs
UniqGenes=unique(HomerTabHu$`Gene Name`)
NrUniqGs=length(UniqGenes)
#line/row indices / positions of annotations to intronic enhancers:
Idx=grep(pattern = "intron",x = HomerTabHu$Annotation,ignore.case = T)
#genes of these indices from intronic enhancers; here we look at the 'gene name' column:
GenesIntEnh=unique(HomerTabHu$`Gene Name`[Idx])
length(GenesIntEnh)
#get lines of the table where these unique 2265 genes appear: which gives these positions/indices:
IdxGs=which(HomerTabHu$`Gene Name`%in%GenesIntEnh)
#get the annotation for them (see the different size/length):
AnnotGsInt=HomerTabHu$Annotation[IdxGs]
GenesGsInt=HomerTabHu$`Gene Name`[IdxGs]
#So, we already know, what are the genes with intronic enhs -> GenesIntEnh
# IdxV1=grep(pattern = "intron",x = AnnotGsInt,ignore.case = T)
# GsV1=unique(HomerTabHu$`Gene Name`[IdxGs[IdxV1]])
#Now, to know which of them only have intronic enhancers, we do an inverse grep for intron
#(gives lines that dont contain intron) on their annotation, and then get the associated genes
#which is a subter of 2265
#heree, check to descomplicar......
IdxV2=grep(pattern = "intron",x = AnnotGsInt,ignore.case = T,invert = T)
GsV2=unique(GenesGsInt[IdxV2])
#GsV2=unique(HomerTabHu$`Gene Name`[IdxGs[IdxV2]])
#OnlyIntr=setdiff(GsV1,GsV2)
#how many of 2265 genes with intronic enhancers don't have any other annotation
OnlyIntr=setdiff(GenesIntEnh,GsV2)
#setdiff(GsV2,GenesIntEnh)=0
|
library(seqinr)
#This expression can be a boolean and if true add to the list of desired sequences
all_pax6_fasta[grep("p63", ignore.case = TRUE, value = TRUE, names(all_pax6_fasta))]
names(all_pax6_fasta)
try <- names(all_pax6_fasta)[which(ortholog_names==names(all_pax6_fasta))]
|
/R_folder/14:06:18 copy.R
|
no_license
|
martinastoycheva/Project_Lovell
|
R
| false
| false
| 284
|
r
|
library(seqinr)
#This expression can be a boolean and if true add to the list of desired sequences
all_pax6_fasta[grep("p63", ignore.case = TRUE, value = TRUE, names(all_pax6_fasta))]
names(all_pax6_fasta)
try <- names(all_pax6_fasta)[which(ortholog_names==names(all_pax6_fasta))]
|
GetKnownCorrectors2 = function(addNewCompounds=TRUE){
# the integers are pubchem ids
correctors = c(
'VALPROIC ACID' = 3121,
'CHLORZOXAZONE' = 2733,
'NEOSTIGMINE BROMIDE' = 8246,
'MIDODRINE' = 4195,
'TRICHOSTATIN A' = 444732,
'MS-275' = 4261,
'GLAFENINE' = 3474,
'IBUPROFEN' = 3672,
'GLYCEROL' = 753,
'PYRIDOSTIGMINE' = 4991,
'VARDENAFIL' = 110634,
'TADALAFIL' = 110635,
'KM11060' = 1241327,
'RDR01752' = 9566157,
'MIGLUSTAT' = 51634,
'CHLORAMPHENICOL' = 5959,
'OUABAIN' = 439501,
'DIGOXIN' = 2724385,
'PIZOTIFEN' = 27400,
'BIPERIDEN' = 2381,
'MG-132' = 462382,
'VORINOSTAT' = 5311,
'SCRIPTAID' = 5186,
'CURCUMIN' = 969516)
if(addNewCompounds){
correctors = c(correctors,
'15-DELTA PROSTAGLANDIN J2' = 5311211,
'AZACITIDINE' = 23760137,
'BRD-K94991378' = 6731789,
'CD1530' = 24868309,
'LDN-193189' = 25195294,
'MD-II-008-P' = 60194076,
'STROPHANTHIDIN' = 6185,
'WITHAFERIN-A' = 73707417)
}
return(correctors)
}
load('/Users/rhodos/Desktop/Research/LINCS/submission/data/metadata/tensor_annot.RData')
load(DataDir('expr/drug/tensor_features_for_drug_property_prediction_10cells_knn.RData'))
L = L[setdiff(names(L), c('allcell','pca200','pca978'))]
cf2 = GetKnownCorrectors2(addNewCompounds=TRUE)
idx = which(annot$pubchemIds %in% as.character(cf2)) %>%
union(which(toupper(annot$pertName) %in% names(cf2)))
cfDrugs = annot$pertId[idx]
L_perts = rownames(L$mean$obs)
Y = data.frame(cf_drugs=ifelse(L_perts %in% cfDrugs, 1, 0))
rownames(Y) = L_perts
save(Y, file='~/Desktop/Box/CFDR/output/ML_drug_pred/cf_drug_labels.RData')
|
/R/scripts/ATC_and_target_prediction/process_cf_drug_labels_into_matrix.R
|
no_license
|
RaymondSHANG/dgc_predict
|
R
| false
| false
| 1,799
|
r
|
GetKnownCorrectors2 = function(addNewCompounds=TRUE){
# the integers are pubchem ids
correctors = c(
'VALPROIC ACID' = 3121,
'CHLORZOXAZONE' = 2733,
'NEOSTIGMINE BROMIDE' = 8246,
'MIDODRINE' = 4195,
'TRICHOSTATIN A' = 444732,
'MS-275' = 4261,
'GLAFENINE' = 3474,
'IBUPROFEN' = 3672,
'GLYCEROL' = 753,
'PYRIDOSTIGMINE' = 4991,
'VARDENAFIL' = 110634,
'TADALAFIL' = 110635,
'KM11060' = 1241327,
'RDR01752' = 9566157,
'MIGLUSTAT' = 51634,
'CHLORAMPHENICOL' = 5959,
'OUABAIN' = 439501,
'DIGOXIN' = 2724385,
'PIZOTIFEN' = 27400,
'BIPERIDEN' = 2381,
'MG-132' = 462382,
'VORINOSTAT' = 5311,
'SCRIPTAID' = 5186,
'CURCUMIN' = 969516)
if(addNewCompounds){
correctors = c(correctors,
'15-DELTA PROSTAGLANDIN J2' = 5311211,
'AZACITIDINE' = 23760137,
'BRD-K94991378' = 6731789,
'CD1530' = 24868309,
'LDN-193189' = 25195294,
'MD-II-008-P' = 60194076,
'STROPHANTHIDIN' = 6185,
'WITHAFERIN-A' = 73707417)
}
return(correctors)
}
load('/Users/rhodos/Desktop/Research/LINCS/submission/data/metadata/tensor_annot.RData')
load(DataDir('expr/drug/tensor_features_for_drug_property_prediction_10cells_knn.RData'))
L = L[setdiff(names(L), c('allcell','pca200','pca978'))]
cf2 = GetKnownCorrectors2(addNewCompounds=TRUE)
idx = which(annot$pubchemIds %in% as.character(cf2)) %>%
union(which(toupper(annot$pertName) %in% names(cf2)))
cfDrugs = annot$pertId[idx]
L_perts = rownames(L$mean$obs)
Y = data.frame(cf_drugs=ifelse(L_perts %in% cfDrugs, 1, 0))
rownames(Y) = L_perts
save(Y, file='~/Desktop/Box/CFDR/output/ML_drug_pred/cf_drug_labels.RData')
|
# 그래픽 기초
setwd('d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part4')
# plot() 함수
# plot( y축 데이터, 옵션 )
# plot( x축 데이터, y축 데이터, 옵션 )
var1 <- 1:5
plot(var1)
var2 <- c(2, 2, 2)
plot(var2)
x <- 1:3
y <- 4:2
plot(x, y)
plot(x, y, xlim = c(0, 4), ylim = c(0, 5))
plot(x, y, xlim = c(0, 4), ylim = c(0, 5),
xlab = 'x축 값', ylab = 'y축 값', main = 'Plot Test')
v1 <- c(100, 130, 120, 160, 150)
plot(v1, type = 'o', col = 'red', ylim = c(0, 200), axes = F, ann = F) # ann = F : x, y축 제목을 지정하지 않음
# axes = F : x, y축을 표시하지 않음
axis(1, at = 1:5, lab = c('MON', 'TUE', 'WED', 'THU', 'FRI')) # axis : x, y축을 사용자 지정 값으로 표시
axis(2, ylim = c(0, 200)) # 1 : x축 / 2: y축
title(main = 'FRUIT', col.main = 'red', font.main = 4)
title(xlab = 'DAY', col.lab = 'black')
title(ylab = 'PRICE', col.lab = 'blue')
par(mfrow = c(1, 3)) # mfrow() : 그래프의 배치 조정
plot(v1, type = 'o')
plot(v1, type = 's')
plot(v1, type = 'l')
par(mfrow = c(1, 3))
pie(v1)
plot(v1, type = 'b')
barplot(v1)
par(mfrow = c(1, 1))
a <- c(1, 2, 3)
plot(a, xlab = 'aaa')
par(mgp = c(0, 1, 0)) # mgp = c(제목 위치, 지표값 위치, 지표선 위치)
plot(a, xlab = 'aaa')
par(mgp = c(3, 1, 0)); plot(a, xlab = 'aaa')
par(mgp = c(3, 2, 0)); plot(a, xlab = 'aaa')
par(mgp = c(3, 2, 1)); plot(a, xlab = 'aaa')
par(oma = c(2, 1, 0, 0)); plot(a, xlab = 'aaa') # oma = c(bottom , left, top, right) : 그래프 전체의 여백 조정
par(oma = c(0, 2, 0, 0)); plot(a, xlab = 'aaa')
?par() # par 옵션 도움말
# 여러 개의 그래프 중첩해서 그리기
v1 <- c(1, 2, 3, 4, 5)
v2 <- c(5, 4, 3, 2, 1)
v3 <- c(3, 4, 5, 6, 7)
plot(v1, type = 's', col = 'red', ylim = c(1, 7))
par(new = T) # 이 부분이 중복 허용 부분입니다
plot(v2, type = 'o', col = 'blue', ylim = c(1, 7))
par(new = T)
plot(v3, type = 'l', col = 'green', ylim = c(1, 7))
# lines() 를 사용하여 보다 쉽게 그리기
plot(v1, type = 's', col = 'red', ylim = c(1, 10))
lines(v2, type = 'o', col = 'blue', ylim = c(1, 5))
lines(v3, type = 'l', col = 'green', ylim = c(1, 15))
# 그래프에 범례 추가하기 : legend()
# legend( x축 위치, y축 위치, 내용, cex = 글자크기, col = 색상, pch = 점의 모양, lty = 선 모양 )
plot(v1, type = 's', col = 'red', ylim = c(1, 10))
lines(v2, type = 'o', col = 'blue', ylim = c(1, 5))
lines(v3, type = 'l', col = 'green', ylim = c(1, 15))
legend(4, 9, c('v1', 'v2', 'v3'), cex = 1.2, col = c('red', 'blue', 'green'), lty = 1)
# barplot() : 막대 그래프 그리기
x <- c(1, 2, 3, 4, 5);
barplot(x)
barplot(x, horiz = T) # 그래프를 가로로 출력
# beside = T : 그룹으로 묶어서 출력
x <- matrix(c(5, 4, 3, 2), 2, 2)
x <- matrix(c(5, 4, 3, 2), nrow = 2); x
barplot(x, names = c(5, 3), col = c('green', 'yellow'))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'), ylim = c(0, 12))
par(oma = c(1, 0.5, 1, 0.5))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'), horiz = T)
barplot(x, names = c(5, 3), col = c('green', 'yellow'), xlim = c(0, 12), horiz = T)
# 여러 막대 그래프를 그룹으로 묶어서 한번에 출력하기
par(oma = c(0, 0, 0, 0))
v1 <- c(100, 120, 140, 160, 180)
v2 <- c(120, 130, 150, 140, 170)
v3 <- c(140, 170, 120, 110, 160)
qty <- data.frame(BANANA = v1, CHERRY = v2, ORANGE = v3); qty
barplot(as.matrix(qty), main = 'Fruit\'s Sales qty', beside = T, col = rainbow(nrow(qty)), ylim = c(0, 400))
legend(14, 400, c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.8, fill = rainbow(nrow(qty)))
# 하나의 막대 그래프에 여러 가지 내용을 한꺼번에 출력하기
qty
t(qty) # t : transform : 행과 열을 바꾸어줌
barplot(t(qty), main = 'Fruits Sales qty', ylim = c(0, 900), col = rainbow(length(qty)),
space = 0.1, cex.axis = 0.8, las = 1, names.arg = c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.8)
legend(0.2, 800, names(qty), cex = 0.7, fill = rainbow(length(qty)))
# 조건을 주고 그래프 그리기
peach <- c(180, 200, 250, 198, 170)
colors <- c()
for (i in 1:length(peach)) {
if (peach[i] >= 200) {
colors <- c(colors, 'red')
} else if (peach[i] >= 180) {
colors <- c(colors, 'yellow')
} else {
colors <- c(colors, 'green')
}
}
barplot(peach, main = 'Peach Sales QTY', names.arg = c('MON', 'TUE', 'WED', 'THU', 'FRI'), col = colors)
# hist() : 히스토그램 그래프 그리기
height <- c(182, 175, 167, 172, 163, 178, 181, 166, 159, 155)
hist(height, main = 'Histogram of height')
par(mfrow = c(1, 2), oma = c(2, 2, 0.1, 0.1))
barplot(height, main = 'Barplot of height', ylim = c(0, 200))
hist(height, main = 'Histogram of height')
# pie() : 파이(pie) 차트 그리기
par(mfrow = c(1, 1), oma = c(0.5, 0.5, 0.1, 0.1))
p1 <- c(10, 20, 30, 40)
pie(p1, radius = 1) # radius : 반지름(크기) / defalt 값 : 0.8
pie(p1, init.angle = 90)
pie(p1, radius = 0.8, init.angle = 90) # init.angle = 각도 : 시작 각도 지정
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)))
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = c('Week 1', 'Week 2', 'Week 3', 'Week 4'))
# 수치 값을 함께 출력하기
pct <- round(p1/sum(p1)*100, 1); pct
lab <- paste(pct, '%'); lab
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = lab, cex = 2)
legend(1, 1, c('Week 1', 'Week 2', 'Week 3', 'Week 4'), cex = 0.7, fill = rainbow(length(p1)))
# 범례를 생략하고 그래프에 바로 출력하기
lab1 <- c('Week 1', 'Week 2', 'Week 3', 'Week 4')
lab2 <- paste(lab1, '\n', pct, '%'); lab2
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = lab2, cex = 2)
# pie3D() 함수
install.packages('plotrix')
library(plotrix)
p1 <- seq(10, 50, 10)
f_day <- round(p1/sum(p1)*100, 1)
f_label <- paste(f_day, '%')
pie3D(p1, main = '3D Pie Chart', col = rainbow(length(p1)), cex = 0.5, labels = f_label, explode = 0.05)
legend(0.5, 1, c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.6, fill = rainbow(length(p1)))
# boxplot() : 상자 차트
v1 <- c(10, 12, 15, 11, 20)
v2 <- c(5, 7, 15, 8, 9)
v3 <- c(11, 20, 15, 18, 13)
boxplot(v1, v2, v3)
boxplot(v1, v2, v3, col = c('blue', 'yellow', 'pink'), names = c('Blue', 'Yellow', 'Pink'), horizontal = T)
# igraph() : 관계도 그리기
install.packages('igraph')
library(igraph)
g1 <- graph(c(1,2, 2,3, 2,4, 1,4, 5,5, 3,6)); g1
plot(g1)
str(g1) # str(data)(structure) : 데이터의 구조 파악
name <- c('서진수 대표이사', '일지매 부장', '김유신 과장', '손흥민 대리', '노정호 대리', '이순신 부장',
'유관순 과장', '신사임당 대리', '강감찬 부장', '광개토 과장', '정몽주 대리')
pemp <- c('서진수 대표이사', '서진수 대표이사', '일지매 부장', '김유신 과장', '김유신 과장', '서진수 대표이사',
'이순신 부장', '유관순 과장', '서진수 대표이사', '강감찬 부장', '광개토 과장')
emp <- data.frame(이름 = name, 상사이름 = pemp); emp
g <- graph.data.frame(emp, directed = T); g
png('graph/network_1.png', 600, 600) # 그림으로 그래프 저장
plot(g, layout = layout.fruchterman.reingold, vertax.size = 8, edge.arrow.size = 0.5)
dev.off()
savePlot('graph/network_1.png', type = 'png') # 실행 안됨
png('graph/network_2.png', width = 600, height = 600)
plot(g, layout = layout.fruchterman.reingold, vertax.size = 8, edge.arrow.size = 0.5)
dev.off()
g3 <- graph.data.frame(emp, directed = F); g3
plot(g3, layout = layout.fruchterman.reingold, vertex.size = 8, edge.arrow.size = 0.5, vertex.label = NA)
plot(g3, layout = layout.kamada.kawai, vertex.size = 8, edge.arrow.size = 0.5, vertex.label = NA)
# d3Network() 패키지를 이용하여 다른 관계도 그래프 그리기
install.packages('devtools')
install.packages('d3Network')
install.packages('RCurl')
library(devtools)
library(d3Network)
name <- c('Angela Bassett','Jessica Lange','Winona Ryder','Michelle Pfeiffer',
'Whoopi Goldberg','Emma Thompson','Julia Roberts','Sharon Stone','Meryl Streep',
'Susan Sarandon','Nicole Kidman')
pemp <- c('Angela Bassett','Angela Bassett','Jessica Lange','Winona Ryder','Winona Ryder',
'Angela Bassett','Emma Thompson', 'Julia Roberts','Angela Bassettlibrary(RCurl)',
'Meryl Streep','Susan Sarandon')
emp <- data.frame(이름=name,상사이름=pemp)
d3SimpleNetwork(emp,width=600,height=600,file="c:\\temp\\d3.html")
# 군집 분석
g <- read.csv("d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part3/data/군집분석.csv", head=T)
str(g)
graph <- data.frame(학생=g$학생, 교수=g$교수); head(graph, 10)
g <- graph.data.frame(graph, directed=T)
plot(g, layout=layout.fruchterman.reingold, vertex.size=2, edge.arrow.size=0.5,
vertex.color="green", vertex.label=NA)
# 모양 바꾸어서 출력하기
plot(g, layout=layout.kamada.kawai, vertex.size=2, edge.arrow.size=0.5, vertex.label=NA)
# 학생과 교수의 색상과 크기를 구분해서 출력하기
library(stringr)
V(g)$name
gubun1 <- V(g)$name; gubun1
gubun <- str_sub(gubun1, start=1, end=1); gubun
colors <- c()
sizes <- c()
for (i in 1:length(gubun)) {
if (gubun[i] == 'S') {
colors <- c(colors, 'red')
sizes <- c(sizes, 2)
} else {
colors <- c(colors, 'green')
sizes <- c(sizes, 6)
}
}
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0.5,
vertex.color=colors)
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0.5,
vertex.color=colors, vertex.label=NA)
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA)
plot(g, layout=layout.kamada.kawai, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA)
shapes <- c()
for (i in 1:length(gubun)) {
if (gubun[i] == 'S') {
shapes <- c(shapes, 'circle')
} else {
shapes <- c(shapes, 'square')
}
}
plot(g, layout=layout.kamada.kawai, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA, vertex.shape=shapes)
# d3Network() 패키지로 메르스 환자 표시
library(devtools)
library(RCurl)
library(d3Network)
virus1 <- read.csv('d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part3/data/메르스전염현황.csv', header=T)
d3SimpleNetwork(virus1, width = 600, height = 600, file = 'c:/temp/mers.html')
|
/Part4/sec1_graphic.R
|
no_license
|
Blockhead4/R-Lecture
|
R
| false
| false
| 10,697
|
r
|
# 그래픽 기초
setwd('d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part4')
# plot() 함수
# plot( y축 데이터, 옵션 )
# plot( x축 데이터, y축 데이터, 옵션 )
var1 <- 1:5
plot(var1)
var2 <- c(2, 2, 2)
plot(var2)
x <- 1:3
y <- 4:2
plot(x, y)
plot(x, y, xlim = c(0, 4), ylim = c(0, 5))
plot(x, y, xlim = c(0, 4), ylim = c(0, 5),
xlab = 'x축 값', ylab = 'y축 값', main = 'Plot Test')
v1 <- c(100, 130, 120, 160, 150)
plot(v1, type = 'o', col = 'red', ylim = c(0, 200), axes = F, ann = F) # ann = F : x, y축 제목을 지정하지 않음
# axes = F : x, y축을 표시하지 않음
axis(1, at = 1:5, lab = c('MON', 'TUE', 'WED', 'THU', 'FRI')) # axis : x, y축을 사용자 지정 값으로 표시
axis(2, ylim = c(0, 200)) # 1 : x축 / 2: y축
title(main = 'FRUIT', col.main = 'red', font.main = 4)
title(xlab = 'DAY', col.lab = 'black')
title(ylab = 'PRICE', col.lab = 'blue')
par(mfrow = c(1, 3)) # mfrow() : 그래프의 배치 조정
plot(v1, type = 'o')
plot(v1, type = 's')
plot(v1, type = 'l')
par(mfrow = c(1, 3))
pie(v1)
plot(v1, type = 'b')
barplot(v1)
par(mfrow = c(1, 1))
a <- c(1, 2, 3)
plot(a, xlab = 'aaa')
par(mgp = c(0, 1, 0)) # mgp = c(제목 위치, 지표값 위치, 지표선 위치)
plot(a, xlab = 'aaa')
par(mgp = c(3, 1, 0)); plot(a, xlab = 'aaa')
par(mgp = c(3, 2, 0)); plot(a, xlab = 'aaa')
par(mgp = c(3, 2, 1)); plot(a, xlab = 'aaa')
par(oma = c(2, 1, 0, 0)); plot(a, xlab = 'aaa') # oma = c(bottom , left, top, right) : 그래프 전체의 여백 조정
par(oma = c(0, 2, 0, 0)); plot(a, xlab = 'aaa')
?par() # par 옵션 도움말
# 여러 개의 그래프 중첩해서 그리기
v1 <- c(1, 2, 3, 4, 5)
v2 <- c(5, 4, 3, 2, 1)
v3 <- c(3, 4, 5, 6, 7)
plot(v1, type = 's', col = 'red', ylim = c(1, 7))
par(new = T) # 이 부분이 중복 허용 부분입니다
plot(v2, type = 'o', col = 'blue', ylim = c(1, 7))
par(new = T)
plot(v3, type = 'l', col = 'green', ylim = c(1, 7))
# lines() 를 사용하여 보다 쉽게 그리기
plot(v1, type = 's', col = 'red', ylim = c(1, 10))
lines(v2, type = 'o', col = 'blue', ylim = c(1, 5))
lines(v3, type = 'l', col = 'green', ylim = c(1, 15))
# 그래프에 범례 추가하기 : legend()
# legend( x축 위치, y축 위치, 내용, cex = 글자크기, col = 색상, pch = 점의 모양, lty = 선 모양 )
plot(v1, type = 's', col = 'red', ylim = c(1, 10))
lines(v2, type = 'o', col = 'blue', ylim = c(1, 5))
lines(v3, type = 'l', col = 'green', ylim = c(1, 15))
legend(4, 9, c('v1', 'v2', 'v3'), cex = 1.2, col = c('red', 'blue', 'green'), lty = 1)
# barplot() : 막대 그래프 그리기
x <- c(1, 2, 3, 4, 5);
barplot(x)
barplot(x, horiz = T) # 그래프를 가로로 출력
# beside = T : 그룹으로 묶어서 출력
x <- matrix(c(5, 4, 3, 2), 2, 2)
x <- matrix(c(5, 4, 3, 2), nrow = 2); x
barplot(x, names = c(5, 3), col = c('green', 'yellow'))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'), ylim = c(0, 12))
par(oma = c(1, 0.5, 1, 0.5))
barplot(x, beside = T, names = c(5, 3), col = c('green', 'yellow'), horiz = T)
barplot(x, names = c(5, 3), col = c('green', 'yellow'), xlim = c(0, 12), horiz = T)
# 여러 막대 그래프를 그룹으로 묶어서 한번에 출력하기
par(oma = c(0, 0, 0, 0))
v1 <- c(100, 120, 140, 160, 180)
v2 <- c(120, 130, 150, 140, 170)
v3 <- c(140, 170, 120, 110, 160)
qty <- data.frame(BANANA = v1, CHERRY = v2, ORANGE = v3); qty
barplot(as.matrix(qty), main = 'Fruit\'s Sales qty', beside = T, col = rainbow(nrow(qty)), ylim = c(0, 400))
legend(14, 400, c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.8, fill = rainbow(nrow(qty)))
# 하나의 막대 그래프에 여러 가지 내용을 한꺼번에 출력하기
qty
t(qty) # t : transform : 행과 열을 바꾸어줌
barplot(t(qty), main = 'Fruits Sales qty', ylim = c(0, 900), col = rainbow(length(qty)),
space = 0.1, cex.axis = 0.8, las = 1, names.arg = c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.8)
legend(0.2, 800, names(qty), cex = 0.7, fill = rainbow(length(qty)))
# 조건을 주고 그래프 그리기
peach <- c(180, 200, 250, 198, 170)
colors <- c()
for (i in 1:length(peach)) {
if (peach[i] >= 200) {
colors <- c(colors, 'red')
} else if (peach[i] >= 180) {
colors <- c(colors, 'yellow')
} else {
colors <- c(colors, 'green')
}
}
barplot(peach, main = 'Peach Sales QTY', names.arg = c('MON', 'TUE', 'WED', 'THU', 'FRI'), col = colors)
# hist() : 히스토그램 그래프 그리기
height <- c(182, 175, 167, 172, 163, 178, 181, 166, 159, 155)
hist(height, main = 'Histogram of height')
par(mfrow = c(1, 2), oma = c(2, 2, 0.1, 0.1))
barplot(height, main = 'Barplot of height', ylim = c(0, 200))
hist(height, main = 'Histogram of height')
# pie() : 파이(pie) 차트 그리기
par(mfrow = c(1, 1), oma = c(0.5, 0.5, 0.1, 0.1))
p1 <- c(10, 20, 30, 40)
pie(p1, radius = 1) # radius : 반지름(크기) / defalt 값 : 0.8
pie(p1, init.angle = 90)
pie(p1, radius = 0.8, init.angle = 90) # init.angle = 각도 : 시작 각도 지정
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)))
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = c('Week 1', 'Week 2', 'Week 3', 'Week 4'))
# 수치 값을 함께 출력하기
pct <- round(p1/sum(p1)*100, 1); pct
lab <- paste(pct, '%'); lab
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = lab, cex = 2)
legend(1, 1, c('Week 1', 'Week 2', 'Week 3', 'Week 4'), cex = 0.7, fill = rainbow(length(p1)))
# 범례를 생략하고 그래프에 바로 출력하기
lab1 <- c('Week 1', 'Week 2', 'Week 3', 'Week 4')
lab2 <- paste(lab1, '\n', pct, '%'); lab2
pie(p1, radius = 1, init.angle = 90, col = rainbow(length(p1)), label = lab2, cex = 2)
# pie3D() 함수
install.packages('plotrix')
library(plotrix)
p1 <- seq(10, 50, 10)
f_day <- round(p1/sum(p1)*100, 1)
f_label <- paste(f_day, '%')
pie3D(p1, main = '3D Pie Chart', col = rainbow(length(p1)), cex = 0.5, labels = f_label, explode = 0.05)
legend(0.5, 1, c('MON', 'TUE', 'WED', 'THU', 'FRI'), cex = 0.6, fill = rainbow(length(p1)))
# boxplot() : 상자 차트
v1 <- c(10, 12, 15, 11, 20)
v2 <- c(5, 7, 15, 8, 9)
v3 <- c(11, 20, 15, 18, 13)
boxplot(v1, v2, v3)
boxplot(v1, v2, v3, col = c('blue', 'yellow', 'pink'), names = c('Blue', 'Yellow', 'Pink'), horizontal = T)
# igraph() : 관계도 그리기
install.packages('igraph')
library(igraph)
g1 <- graph(c(1,2, 2,3, 2,4, 1,4, 5,5, 3,6)); g1
plot(g1)
str(g1) # str(data)(structure) : 데이터의 구조 파악
name <- c('서진수 대표이사', '일지매 부장', '김유신 과장', '손흥민 대리', '노정호 대리', '이순신 부장',
'유관순 과장', '신사임당 대리', '강감찬 부장', '광개토 과장', '정몽주 대리')
pemp <- c('서진수 대표이사', '서진수 대표이사', '일지매 부장', '김유신 과장', '김유신 과장', '서진수 대표이사',
'이순신 부장', '유관순 과장', '서진수 대표이사', '강감찬 부장', '광개토 과장')
emp <- data.frame(이름 = name, 상사이름 = pemp); emp
g <- graph.data.frame(emp, directed = T); g
png('graph/network_1.png', 600, 600) # 그림으로 그래프 저장
plot(g, layout = layout.fruchterman.reingold, vertax.size = 8, edge.arrow.size = 0.5)
dev.off()
savePlot('graph/network_1.png', type = 'png') # 실행 안됨
png('graph/network_2.png', width = 600, height = 600)
plot(g, layout = layout.fruchterman.reingold, vertax.size = 8, edge.arrow.size = 0.5)
dev.off()
g3 <- graph.data.frame(emp, directed = F); g3
plot(g3, layout = layout.fruchterman.reingold, vertex.size = 8, edge.arrow.size = 0.5, vertex.label = NA)
plot(g3, layout = layout.kamada.kawai, vertex.size = 8, edge.arrow.size = 0.5, vertex.label = NA)
# d3Network() 패키지를 이용하여 다른 관계도 그래프 그리기
install.packages('devtools')
install.packages('d3Network')
install.packages('RCurl')
library(devtools)
library(d3Network)
name <- c('Angela Bassett','Jessica Lange','Winona Ryder','Michelle Pfeiffer',
'Whoopi Goldberg','Emma Thompson','Julia Roberts','Sharon Stone','Meryl Streep',
'Susan Sarandon','Nicole Kidman')
pemp <- c('Angela Bassett','Angela Bassett','Jessica Lange','Winona Ryder','Winona Ryder',
'Angela Bassett','Emma Thompson', 'Julia Roberts','Angela Bassettlibrary(RCurl)',
'Meryl Streep','Susan Sarandon')
emp <- data.frame(이름=name,상사이름=pemp)
d3SimpleNetwork(emp,width=600,height=600,file="c:\\temp\\d3.html")
# 군집 분석
g <- read.csv("d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part3/data/군집분석.csv", head=T)
str(g)
graph <- data.frame(학생=g$학생, 교수=g$교수); head(graph, 10)
g <- graph.data.frame(graph, directed=T)
plot(g, layout=layout.fruchterman.reingold, vertex.size=2, edge.arrow.size=0.5,
vertex.color="green", vertex.label=NA)
# 모양 바꾸어서 출력하기
plot(g, layout=layout.kamada.kawai, vertex.size=2, edge.arrow.size=0.5, vertex.label=NA)
# 학생과 교수의 색상과 크기를 구분해서 출력하기
library(stringr)
V(g)$name
gubun1 <- V(g)$name; gubun1
gubun <- str_sub(gubun1, start=1, end=1); gubun
colors <- c()
sizes <- c()
for (i in 1:length(gubun)) {
if (gubun[i] == 'S') {
colors <- c(colors, 'red')
sizes <- c(sizes, 2)
} else {
colors <- c(colors, 'green')
sizes <- c(sizes, 6)
}
}
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0.5,
vertex.color=colors)
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0.5,
vertex.color=colors, vertex.label=NA)
plot(g, layout=layout.fruchterman.reingold, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA)
plot(g, layout=layout.kamada.kawai, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA)
shapes <- c()
for (i in 1:length(gubun)) {
if (gubun[i] == 'S') {
shapes <- c(shapes, 'circle')
} else {
shapes <- c(shapes, 'square')
}
}
plot(g, layout=layout.kamada.kawai, vertex.size=sizes, edge.arrow.size=0,
vertex.color=colors, vertex.label=NA, vertex.shape=shapes)
# d3Network() 패키지로 메르스 환자 표시
library(devtools)
library(RCurl)
library(d3Network)
virus1 <- read.csv('d:/Workspace-JWP/R_Data_Analysis/R-Lecture/Part3/data/메르스전염현황.csv', header=T)
d3SimpleNetwork(virus1, width = 600, height = 600, file = 'c:/temp/mers.html')
|
elibrary(quickpsy)
library(tidyverse)
library(circular)
library(cowplot)
library(PropCIs)
library(R.utils)
sourceDirectory('R')
source('graphical_parameters.R')
################################################################################
### phase ######################################################################
################################################################################
datpha <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al',
'bj','he','lk', 'ad'),
task = 'phase', session = as.character(1:4))
datpha$participant <- datpha$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
datpha <- datpha %>% select(participant, freq, same, response) %>%
mutate(response = ifelse(
(same == 'True' & response == 'right') |
(same == 'False' & response == 'left'),
1,0))
avpha <- datpha %>%
group_by(participant, freq) %>%
summarise(n = n(), k = sum(response)) %>%
group_by(participant, freq) %>%
do({
m <- .$k / .$n
ci <- exactci(.$k, .$n, .05)$conf.int
data.frame(m = m, inf = ci[1], sup = ci[2])
})
################################################################################
### tracking ###################################################################
################################################################################
dattrack <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al',
'bj','he','lk', 'ad'),
task = c('tracking'), session = as.character(1:4))
dattrack$participant <- dattrack$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
dattrack <- dattrack %>% select(participant, freq, same, response) %>%
mutate(response = ifelse(
(same == 'True' & response == 'right') |
(same == 'False' & response == 'left'),
1,0))
fit_psycho <- function(d, prob) {
quickpsy(d, freq, response, grouping = .(participant),
fun = psychom, parini = list(c(.5, 4), c(.05, 5), c(0,.5)),
prob = prob,
bootstrap = 'none')
}
fit70 <- fit_psycho(dattrack, .7)
fit80 <- fit_psycho(dattrack, .8)
fit90 <- fit_psycho(dattrack, .9)
thres <- fit70$thresholds %>%
bind_rows(fit80$thresholds) %>%
bind_rows(fit90$thresholds) %>%
spread(key = prob, value = thre, sep = '_')
### binomial tests
trackbinom <- fit70$averages %>%
group_by(participant, freq) %>%
mutate(pvalue = binom.test(response, n)$p.value) %>%
mutate(chance = ifelse(pvalue > 0.05, TRUE, FALSE))
summarise(rayp = rayleigh.test(radFull)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### figure
ptrack <- ggplot() + facet_wrap(~participant) +
geom_ribbon(data = fit70$curves %>% merge(fitthre) %>%
filter(x > thre90 & x< thre70),
aes(x = x, ymin = .4, ymax = y), alpha =.1) +
geom_point(data = fit70$averages, size = .8,
aes(x =freq, y = prob, color = 'Tracking')) +
geom_line(data = fit70$curves, size = sizeLine1,
aes(x = x, y = y, color = 'Tracking')) +
geom_point(data =avpha, size = .8,
aes(x = freq, y = m, ymin = inf, ymax = sup,
color = 'Alignment')) +
geom_line(data =avpha, size = sizeLine1,
aes(x = freq, y = m, ymin = inf, ymax = sup,
color = 'Alignment')) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = .5) +
scale_color_brewer(palette = 'Set1') +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
scale_y_continuous(breaks = seq(0.5,1,.25)) +
labs(x = 'Frequency (rps)', y = 'Probability of correct responses') #+
# theme(legend.key = element_blank(),
# legend.title = element_blank(),
# legend.position = c(.8,.16)),
# axis.title.x = element_text(hjust = 0.01))
ptrack
save_plot('analysis/figures/trackalig.pdf', ptrack,
base_width = twoColumnWidth)
################################################################################
### sync #######################################################################
################################################################################
dat <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al','bj','he','lk','ad'),
task = c('press'),
session = as.character(1:4))
dat$participant <- dat$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
dat <- dat %>%
select(participant, direction, angleLandmark, freq, response) %>%
filter(response != 9999) %>% # the participant didn't respond
mutate(totalerror = direction * (response - angleLandmark),
degSemi = totalerror %>% totalerrorToDegSemi(),
degFull = degSemi %>% degSemiToDegFull(),
radFull = degFull %>% degFullToRadFull()) %>%
group_by(participant, freq)
minfreq <- dat %>% group_by(participant) %>% summarise(freq = min(freq))
allfreqs <- dat %>% group_by(participant) %>%
do({tibble(freqsim = unique(.$freq))})
### Null hypothesis testing: uniformity
uniformity <- dat %>%
summarise(rayp = rayleigh.test(radFull)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, '>.05',
ifelse(rayp > .01, '<.05',
ifelse(rayp > .001, '<.01','<.001'))))
datuniformity <- dat %>% left_join(uniformity)
### means
circmeans <- datuniformity %>%
group_by(participant, freq, uniform) %>%
summarise(mrad = mean.circular(radFull),
mdegSemi = mrad %>% radFullToDegFull() %>% degFullToDegSemi()) %>%
filter(!uniform)
pmean <- ggplot(circmeans) +
facet_wrap(~participant) +
geom_point(data = datuniformity, alpha = .4,
aes(x = freq, y = degSemi, color = uniform)) +
geom_line(aes(x = freq, y = mdegSemi))
pmean
save_plot('analysis/figures/mean.pdf', pmean, base_width = twoColumnWidth)
### circular representation
anglestoplot <- tibble(n = 1:4,
x = rep(3.6, 4),
y = c(0, 45, 90, -45),
angle = c('0', '45', ' 90\n-90', '-45'),
participant = 'Participant 5')
zeroline <- tibble(participant = datuniformity$participant %>% unique(),
x = 0,
xend = c(3.8, 3.8, 3.8, 3.2, 3.2, 3.2, 3.2, 3.2),
y = 0,
yend = 0)
### figure raw
praw <- ggplot() + facet_wrap(~participant, ncol = 3) +
geom_rect(data=fitthre, fill = 'black', alpha =.2,
aes(xmin = thre70, xmax = thre90, ymin = -90, ymax = 90)) +
geom_point(data= datuniformity, aes(x = freq, y = degSemi, color = uniform),
size = .35, alpha = 0.5, shape = 16) +
#size=1) +
geom_text(data = anglestoplot, aes(x = x, y = y, label= angle, group = n),
size=sizeText) +
geom_segment(data = zeroline, aes(y = y, yend = yend, x = x, xend = xend),
size = sizeLine1)+
scale_color_manual(name = 'p',
values=c('#08519c','#3182bd','#6baed6', '#b30000')) +
scale_x_continuous(breaks = seq(0, 3, 1),labels = as.character(0:3)) +
scale_y_continuous(breaks = seq(-45,90,45), limits = c(-90, 90),
labels =c('-45','0','45','90')) +
xlab('Frequency (rps)') +
ylab('Error (deg)') +
coord_polar(theta='y', start = pi/2, direction = -1)+
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
axis.line.y = element_line(size=sizeLine1),
panel.spacing = unit(-.32,'lines'),
legend.key = element_blank(),
legend.key.height = unit(.7,'lines'),
legend.position = c(.85,.16)) +
guides(colour = guide_legend(override.aes = list(size=2)))
praw
save_plot('analysis/figures/raw.pdf', praw, base_width = oneColumnWidth)
### correlations
nonuniformspeed <- uniformity %>%
filter(!uniform) %>%
summarise(freq = max(freq)) %>%
rename(frequni=freq)
nontrackspeed <- trackbinom %>%
group_by(participant) %>%
filter(!chance) %>%
summarise(freq = max(freq)) %>%
rename(freqtrack=freq)
freqs <- nonuniformspeed %>%
left_join(nontrackspeed) %>%
left_join(fitthre80)
ggplot(freqs, aes(frequni, thre90)) +
geom_point() +
geom_abline() +
coord_equal(xlim = c(0, 2.5), ylim = c(0, 2.5))
cor.test(freqs$frequni, freqs$thre90)
### t-test
t.test(freqs$frequni,freqs$thre90, paired = TRUE)
### bootstrap ##################################################################
datboot <- expand.grid(n = 1:1000) %>%
group_by(n) %>%
do({
dat %>%
group_by(participant, freq) %>%
sample_frac(1, replace = T)
}) %>%
group_by(participant, freq, n)
### simulated data #############################################################
datsim <- data_frame(freqsim = unique(dat$freq)) %>%
group_by(freqsim) %>%
do({dat}) %>%
filter(freqsim >= freq) %>%
mutate(degFullSim = freqsim / freq * degFull,
radFullSim = degFullSim %>% degFullToRadFull()) %>%
semi_join(allfreqs) %>%
group_by(participant, freq, freqsim)
### uniformity sim
uniformitysim <- datsim %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### simulated data track #######################################################
trackingprob <- fit80$averages %>% select(participant, freq, prob)
trackingprobminfreq <- trackingprob %>% semi_join(minfreq) %>% ungroup() %>%
rename(probfreqmin = prob) %>% select(-freq)
trackingprob <- trackingprob %>% left_join(trackingprobminfreq) %>%
mutate(probnotrack = 2*(probfreqmin - prob)) %>%
mutate(probnotrack = ifelse(probnotrack > 1, 1, probnotrack),
probnotrack = ifelse(probnotrack < 0, 0, probnotrack)) %>%
rename(freqsim=freq)
datsimtrack <- datsim %>%
select(participant, freqsim, freq, radFullSim) %>%
left_join(trackingprob) %>%
do({
n <-length(.$radFullSim)
probnotrack <- unique(.$probnotrack)
nnotrack <- round(n * probnotrack)
track <- sample_n(.,n - nnotrack)
notrack <- sample_n(.,nnotrack)
notrack$radFullSim <- runif(nnotrack,0, 2*pi)
rbind(track, notrack)
})
### uniformity sim
uniformitysimtrack <- datsimtrack %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### bootstrap simulated data ###################################################
datsimboot <- expand.grid(n = 1:1000) %>%
group_by(n) %>%
do({
datsim %>%
group_by(participant, freq, freqsim) %>%
sample_frac(1, replace = T)
})%>%
group_by(participant, freq, freqsim, n)
### rho #######################################################################
dispersion <- dat %>% summarise(rho = rho.circular(radFull))
dispersionboot <- datboot %>%
summarise(rho = rho.circular(radFull %>% circular()))
dispersionbootci <- dispersionboot %>%
summarise(inf = quantile(rho, .025),
sup = quantile(rho, .975))
dispersionuniformity <- dispersion %>% left_join(dispersionbootci) %>%
left_join(uniformity)
### rho sim ###################################################################
dispersionsim <- datsim %>% summarise(rho = rho.circular(radFullSim))
dispersionsimboot <- datsimboot %>%
summarise(rho = rho.circular(radFullSim %>% circular()))
dispersionsimbootci <- dispersionsimboot %>%
summarise(inf = quantile(rho, .025),
sup = quantile(rho, .975))
dispersionsimuniformity <- dispersionsim %>%
left_join(dispersionsimbootci) %>%
left_join(uniformitysim)
### rho sim for min ferq
dispersionsimuniformitylowfreq <- dispersionsimuniformity %>%
semi_join(minfreq) %>% semi_join(allfreqs)
### rho sim track #############################################################
dispersionsimtrack <- datsimtrack %>% summarise(rho = rho.circular(radFullSim))
dispersionsimtrackuniformity <- dispersionsimtrack %>%
left_join(uniformitysimtrack)
### rho sim for min ferq
dispersionsimtrackuniformitylowfreq <- dispersionsimtrackuniformity %>%
semi_join(minfreq)
### figure dispersion ##########################################################
prho <- ggplot() + facet_wrap(~participant, scales = 'free_x') +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionsimuniformitylowfreq, show.legend = FALSE,
size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_line(data = dispersionsimtrackuniformity %>% semi_join(minfreq),
show.legend = FALSE,
size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_line(data = dispersionsimtrackuniformity %>% semi_join(minfreq),
show.legend = FALSE,
size = sizeLine1, lty = 3,
aes(x = freqsim, y = rho)) +
geom_point(data = dispersionuniformity, size = .6, shape = 2,
aes(x = freq, y = rho, color = uniform)) +
# geom_linerange(data = dispersionuniformity, alpha =.5, size = sizeLine1,
# aes(x = freq, ymin = inf, ymax = sup, color = uniform))+
labs(x = 'Frequency (rps)', y = 'Radial vector length') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.title = element_blank(),
legend.position = c(.8,.13),
axis.title.x = element_text(hjust = 0.25))
prho
save_plot('analysis/figures/prho.pdf', prho,
base_width = oneColumnWidth)
geom_line(data = dispersionsimuniformityplot, size = sizeLine1,
aes(x = freqsim2, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = 0) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho
dispersionsimuniformityplot <- dispersionsimuniformity %>%
mutate(freq2 = freq, freqsim2 = freqsim)
### figure dispersion
prho <- ggplot() + facet_grid(participant~freq2, scales = 'free') +
geom_line(data = dispersionsimuniformityplot, size = sizeLine1,
aes(x = freqsim2, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = 0) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho
save_plot('analysis/figures/rho.pdf', prho,
base_width = 2*twoColumnWidth,
base_height = twoColumnWidth)
### two lower speeds
oneminfreqsim <- dispersion %>% group_by(participant) %>% do(head(.,1))
twominfreqsim <- dispersion %>% group_by(participant) %>% do(head(.,2))
datsim2 <- datsim %>% semi_join(twominfreqsim) %>%
group_by(participant, freqsim)
uniformitysim2 <- datsim2 %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
dispersionsim2 <- datsim2 %>%
summarise(rho = rho.circular(radFullSim)) %>%
anti_join(oneminfreqsim) %>%
left_join(uniformitysim2)
prho2 <- ggplot() + facet_wrap(~participant, scales = 'free') +
geom_line(data = dispersionsim2, size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho2
### uniformity analysis
dispersionbootciunifotest <- datsim %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE)) %>%
rename(freq2 = freq, freqsim2 = freqsim)
prhouni <- ggplot() + facet_wrap(~freq) +
# geom_rect(data = fitthre, fill = 'black', alpha =.1,
# aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionbootciunifotest %>% filter(freq < 2.5),
aes(x = freqsim, y = m)) +
geom_line(data = dispersionuniformityplot,size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform))+
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,.5)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))+
geom_vline(xintercept = 2)+geom_hline(yintercept = .05)
prhouni
dispersionsimbootciunifotest <- datsimboot %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE)) %>%
group_by(participant, freq, freqsim) %>%
summarise(m = mean(uniform))
dispersionuniformityplot <- dispersionuniformity %>%
rename(freqsim=freq)
prhounisim <- ggplot() + facet_wrap(~freq) +
# geom_rect(data = fitthre, fill = 'black', alpha =.1,
# aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionsimbootciunifotest %>% filter(freq < 2.5),
aes(x = freqsim, y = m)) +
geom_line(data = dispersionuniformityplot,size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform))+
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,.5)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))+
geom_vline(xintercept = 2)+geom_hline(yintercept = .05)
prhounisim
save_plot('analysis/figures/rhounif.pdf', prhouni,
base_width = oneColumnWidth)
### dispersion wrapped normal
funWrap <- function(d){
sta <- mle.wrappednormal(d$radFull)$sd
stams <- sta / (4*pi*first(d$freq))*1000 # it is not 2*pi (it is half lap)
data.frame(sta, stams)
}
dispersionwrap <- dat %>%
group_by(participant, freq) %>%
do(funWrap(.))
dispersiowrapnuniformity <- dispersionwrap %>% merge(uniformity)
dispersionwrapboot <- datboot %>%
group_by(participant, freq, n) %>%
do(funWrap(.))
dispersionwrapbootci <- dispersionwrapboot %>%
group_by(participant, freq) %>%
summarise(inf = quantile(stams, .025),
sup = quantile(stams, .975))
dispersionwrapbootciuniformity <- dispersionwrapbootci %>% merge(uniformity)
dispersionwrapsim <- datsim %>%
group_by(participant, freq, freqsim) %>%
do(funWrap(.))
dispersionwrapsimboot <- datsimboot %>%
filter(freq == .75) %>%
group_by(participant, freq, freqsim, n) %>%
do(funWrap(.))
dispersionwrapsimbootci <- dispersionwrapsimboot %>%
group_by(participant, freq, freqsim) %>%
summarise(inf = quantile(stams, .025),
sup = quantile(stams, .975))
pwra <- ggplot() + facet_wrap(~participant, scales = 'free_x') +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 160)) +
geom_line(data = dispersionwrapsimbootci, size = sizeLine1,
aes(x = freqsim, y = inf), color='grey') +
geom_line(data = dispersionwrapsimbootci, size = sizeLine1,
aes(x = freqsim, y = sup), color='grey') +
geom_line(data = dispersiowrapnuniformity,size = sizeLine1,
aes(x = freq, y = stams, color = uniform))+
geom_point(data = dispersiowrapnuniformity, size = .8,
aes(x = freq, y = stams, color = uniform))+
geom_ribbon(data = dispersionwrapbootciuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Standard deviation (ms)') +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
pwra
save_plot('analysis/figures/wrap.pdf', pwra,
base_width = oneColumnWidth)
dif <- dispersionsim %>% filter(freq == .75) %>%
ungroup() %>%
select(-freq) %>%
rename(freq= freqsim, rhosim = rho) %>%
merge(dispersion) %>%
group_by(participant, freq) %>%
summarise(dif = rhosim - rho)
ggplot()+
facet_wrap(~participant) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dif,
aes(x = freq, y = dif))
geom_line(data = dispersionsim %>% filter(freq == .75),
aes(x =freqsim, rho)) +
geom_line(data = dispersion,
aes(x =freq, rho))
|
/analysis/tracking3.R
|
no_license
|
danilinares/tracking
|
R
| false
| false
| 24,632
|
r
|
elibrary(quickpsy)
library(tidyverse)
library(circular)
library(cowplot)
library(PropCIs)
library(R.utils)
sourceDirectory('R')
source('graphical_parameters.R')
################################################################################
### phase ######################################################################
################################################################################
datpha <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al',
'bj','he','lk', 'ad'),
task = 'phase', session = as.character(1:4))
datpha$participant <- datpha$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
datpha <- datpha %>% select(participant, freq, same, response) %>%
mutate(response = ifelse(
(same == 'True' & response == 'right') |
(same == 'False' & response == 'left'),
1,0))
avpha <- datpha %>%
group_by(participant, freq) %>%
summarise(n = n(), k = sum(response)) %>%
group_by(participant, freq) %>%
do({
m <- .$k / .$n
ci <- exactci(.$k, .$n, .05)$conf.int
data.frame(m = m, inf = ci[1], sup = ci[2])
})
################################################################################
### tracking ###################################################################
################################################################################
dattrack <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al',
'bj','he','lk', 'ad'),
task = c('tracking'), session = as.character(1:4))
dattrack$participant <- dattrack$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
dattrack <- dattrack %>% select(participant, freq, same, response) %>%
mutate(response = ifelse(
(same == 'True' & response == 'right') |
(same == 'False' & response == 'left'),
1,0))
fit_psycho <- function(d, prob) {
quickpsy(d, freq, response, grouping = .(participant),
fun = psychom, parini = list(c(.5, 4), c(.05, 5), c(0,.5)),
prob = prob,
bootstrap = 'none')
}
fit70 <- fit_psycho(dattrack, .7)
fit80 <- fit_psycho(dattrack, .8)
fit90 <- fit_psycho(dattrack, .9)
thres <- fit70$thresholds %>%
bind_rows(fit80$thresholds) %>%
bind_rows(fit90$thresholds) %>%
spread(key = prob, value = thre, sep = '_')
### binomial tests
trackbinom <- fit70$averages %>%
group_by(participant, freq) %>%
mutate(pvalue = binom.test(response, n)$p.value) %>%
mutate(chance = ifelse(pvalue > 0.05, TRUE, FALSE))
summarise(rayp = rayleigh.test(radFull)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### figure
ptrack <- ggplot() + facet_wrap(~participant) +
geom_ribbon(data = fit70$curves %>% merge(fitthre) %>%
filter(x > thre90 & x< thre70),
aes(x = x, ymin = .4, ymax = y), alpha =.1) +
geom_point(data = fit70$averages, size = .8,
aes(x =freq, y = prob, color = 'Tracking')) +
geom_line(data = fit70$curves, size = sizeLine1,
aes(x = x, y = y, color = 'Tracking')) +
geom_point(data =avpha, size = .8,
aes(x = freq, y = m, ymin = inf, ymax = sup,
color = 'Alignment')) +
geom_line(data =avpha, size = sizeLine1,
aes(x = freq, y = m, ymin = inf, ymax = sup,
color = 'Alignment')) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = .5) +
scale_color_brewer(palette = 'Set1') +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
scale_y_continuous(breaks = seq(0.5,1,.25)) +
labs(x = 'Frequency (rps)', y = 'Probability of correct responses') #+
# theme(legend.key = element_blank(),
# legend.title = element_blank(),
# legend.position = c(.8,.16)),
# axis.title.x = element_text(hjust = 0.01))
ptrack
save_plot('analysis/figures/trackalig.pdf', ptrack,
base_width = twoColumnWidth)
################################################################################
### sync #######################################################################
################################################################################
dat <- quickreadfiles(path = 'analysis/data',
participant = c('pa','cc','da','al','bj','he','lk','ad'),
task = c('press'),
session = as.character(1:4))
dat$participant <- dat$participant %>%
recode(pa = 'Participant 1',
cc = 'Participant 2',
da = 'Participant 3',
al = 'Participant 4',
bj = 'Participant 5',
he = 'Participant 6',
lk = 'Participant 7',
ad = 'Participant 8')
dat <- dat %>%
select(participant, direction, angleLandmark, freq, response) %>%
filter(response != 9999) %>% # the participant didn't respond
mutate(totalerror = direction * (response - angleLandmark),
degSemi = totalerror %>% totalerrorToDegSemi(),
degFull = degSemi %>% degSemiToDegFull(),
radFull = degFull %>% degFullToRadFull()) %>%
group_by(participant, freq)
minfreq <- dat %>% group_by(participant) %>% summarise(freq = min(freq))
allfreqs <- dat %>% group_by(participant) %>%
do({tibble(freqsim = unique(.$freq))})
### Null hypothesis testing: uniformity
uniformity <- dat %>%
summarise(rayp = rayleigh.test(radFull)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, '>.05',
ifelse(rayp > .01, '<.05',
ifelse(rayp > .001, '<.01','<.001'))))
datuniformity <- dat %>% left_join(uniformity)
### means
circmeans <- datuniformity %>%
group_by(participant, freq, uniform) %>%
summarise(mrad = mean.circular(radFull),
mdegSemi = mrad %>% radFullToDegFull() %>% degFullToDegSemi()) %>%
filter(!uniform)
pmean <- ggplot(circmeans) +
facet_wrap(~participant) +
geom_point(data = datuniformity, alpha = .4,
aes(x = freq, y = degSemi, color = uniform)) +
geom_line(aes(x = freq, y = mdegSemi))
pmean
save_plot('analysis/figures/mean.pdf', pmean, base_width = twoColumnWidth)
### circular representation
anglestoplot <- tibble(n = 1:4,
x = rep(3.6, 4),
y = c(0, 45, 90, -45),
angle = c('0', '45', ' 90\n-90', '-45'),
participant = 'Participant 5')
zeroline <- tibble(participant = datuniformity$participant %>% unique(),
x = 0,
xend = c(3.8, 3.8, 3.8, 3.2, 3.2, 3.2, 3.2, 3.2),
y = 0,
yend = 0)
### figure raw
praw <- ggplot() + facet_wrap(~participant, ncol = 3) +
geom_rect(data=fitthre, fill = 'black', alpha =.2,
aes(xmin = thre70, xmax = thre90, ymin = -90, ymax = 90)) +
geom_point(data= datuniformity, aes(x = freq, y = degSemi, color = uniform),
size = .35, alpha = 0.5, shape = 16) +
#size=1) +
geom_text(data = anglestoplot, aes(x = x, y = y, label= angle, group = n),
size=sizeText) +
geom_segment(data = zeroline, aes(y = y, yend = yend, x = x, xend = xend),
size = sizeLine1)+
scale_color_manual(name = 'p',
values=c('#08519c','#3182bd','#6baed6', '#b30000')) +
scale_x_continuous(breaks = seq(0, 3, 1),labels = as.character(0:3)) +
scale_y_continuous(breaks = seq(-45,90,45), limits = c(-90, 90),
labels =c('-45','0','45','90')) +
xlab('Frequency (rps)') +
ylab('Error (deg)') +
coord_polar(theta='y', start = pi/2, direction = -1)+
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
axis.line.y = element_line(size=sizeLine1),
panel.spacing = unit(-.32,'lines'),
legend.key = element_blank(),
legend.key.height = unit(.7,'lines'),
legend.position = c(.85,.16)) +
guides(colour = guide_legend(override.aes = list(size=2)))
praw
save_plot('analysis/figures/raw.pdf', praw, base_width = oneColumnWidth)
### correlations
nonuniformspeed <- uniformity %>%
filter(!uniform) %>%
summarise(freq = max(freq)) %>%
rename(frequni=freq)
nontrackspeed <- trackbinom %>%
group_by(participant) %>%
filter(!chance) %>%
summarise(freq = max(freq)) %>%
rename(freqtrack=freq)
freqs <- nonuniformspeed %>%
left_join(nontrackspeed) %>%
left_join(fitthre80)
ggplot(freqs, aes(frequni, thre90)) +
geom_point() +
geom_abline() +
coord_equal(xlim = c(0, 2.5), ylim = c(0, 2.5))
cor.test(freqs$frequni, freqs$thre90)
### t-test
t.test(freqs$frequni,freqs$thre90, paired = TRUE)
### bootstrap ##################################################################
datboot <- expand.grid(n = 1:1000) %>%
group_by(n) %>%
do({
dat %>%
group_by(participant, freq) %>%
sample_frac(1, replace = T)
}) %>%
group_by(participant, freq, n)
### simulated data #############################################################
datsim <- data_frame(freqsim = unique(dat$freq)) %>%
group_by(freqsim) %>%
do({dat}) %>%
filter(freqsim >= freq) %>%
mutate(degFullSim = freqsim / freq * degFull,
radFullSim = degFullSim %>% degFullToRadFull()) %>%
semi_join(allfreqs) %>%
group_by(participant, freq, freqsim)
### uniformity sim
uniformitysim <- datsim %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### simulated data track #######################################################
trackingprob <- fit80$averages %>% select(participant, freq, prob)
trackingprobminfreq <- trackingprob %>% semi_join(minfreq) %>% ungroup() %>%
rename(probfreqmin = prob) %>% select(-freq)
trackingprob <- trackingprob %>% left_join(trackingprobminfreq) %>%
mutate(probnotrack = 2*(probfreqmin - prob)) %>%
mutate(probnotrack = ifelse(probnotrack > 1, 1, probnotrack),
probnotrack = ifelse(probnotrack < 0, 0, probnotrack)) %>%
rename(freqsim=freq)
datsimtrack <- datsim %>%
select(participant, freqsim, freq, radFullSim) %>%
left_join(trackingprob) %>%
do({
n <-length(.$radFullSim)
probnotrack <- unique(.$probnotrack)
nnotrack <- round(n * probnotrack)
track <- sample_n(.,n - nnotrack)
notrack <- sample_n(.,nnotrack)
notrack$radFullSim <- runif(nnotrack,0, 2*pi)
rbind(track, notrack)
})
### uniformity sim
uniformitysimtrack <- datsimtrack %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
### bootstrap simulated data ###################################################
datsimboot <- expand.grid(n = 1:1000) %>%
group_by(n) %>%
do({
datsim %>%
group_by(participant, freq, freqsim) %>%
sample_frac(1, replace = T)
})%>%
group_by(participant, freq, freqsim, n)
### rho #######################################################################
dispersion <- dat %>% summarise(rho = rho.circular(radFull))
dispersionboot <- datboot %>%
summarise(rho = rho.circular(radFull %>% circular()))
dispersionbootci <- dispersionboot %>%
summarise(inf = quantile(rho, .025),
sup = quantile(rho, .975))
dispersionuniformity <- dispersion %>% left_join(dispersionbootci) %>%
left_join(uniformity)
### rho sim ###################################################################
dispersionsim <- datsim %>% summarise(rho = rho.circular(radFullSim))
dispersionsimboot <- datsimboot %>%
summarise(rho = rho.circular(radFullSim %>% circular()))
dispersionsimbootci <- dispersionsimboot %>%
summarise(inf = quantile(rho, .025),
sup = quantile(rho, .975))
dispersionsimuniformity <- dispersionsim %>%
left_join(dispersionsimbootci) %>%
left_join(uniformitysim)
### rho sim for min ferq
dispersionsimuniformitylowfreq <- dispersionsimuniformity %>%
semi_join(minfreq) %>% semi_join(allfreqs)
### rho sim track #############################################################
dispersionsimtrack <- datsimtrack %>% summarise(rho = rho.circular(radFullSim))
dispersionsimtrackuniformity <- dispersionsimtrack %>%
left_join(uniformitysimtrack)
### rho sim for min ferq
dispersionsimtrackuniformitylowfreq <- dispersionsimtrackuniformity %>%
semi_join(minfreq)
### figure dispersion ##########################################################
prho <- ggplot() + facet_wrap(~participant, scales = 'free_x') +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionsimuniformitylowfreq, show.legend = FALSE,
size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_line(data = dispersionsimtrackuniformity %>% semi_join(minfreq),
show.legend = FALSE,
size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_line(data = dispersionsimtrackuniformity %>% semi_join(minfreq),
show.legend = FALSE,
size = sizeLine1, lty = 3,
aes(x = freqsim, y = rho)) +
geom_point(data = dispersionuniformity, size = .6, shape = 2,
aes(x = freq, y = rho, color = uniform)) +
# geom_linerange(data = dispersionuniformity, alpha =.5, size = sizeLine1,
# aes(x = freq, ymin = inf, ymax = sup, color = uniform))+
labs(x = 'Frequency (rps)', y = 'Radial vector length') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.title = element_blank(),
legend.position = c(.8,.13),
axis.title.x = element_text(hjust = 0.25))
prho
save_plot('analysis/figures/prho.pdf', prho,
base_width = oneColumnWidth)
geom_line(data = dispersionsimuniformityplot, size = sizeLine1,
aes(x = freqsim2, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = 0) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho
dispersionsimuniformityplot <- dispersionsimuniformity %>%
mutate(freq2 = freq, freqsim2 = freqsim)
### figure dispersion
prho <- ggplot() + facet_grid(participant~freq2, scales = 'free') +
geom_line(data = dispersionsimuniformityplot, size = sizeLine1,
aes(x = freqsim2, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
geom_text(data = trackbinom %>% mutate(ast = if_else(chance, '','*')),
aes(x = freq, label =ast), y = 0) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho
save_plot('analysis/figures/rho.pdf', prho,
base_width = 2*twoColumnWidth,
base_height = twoColumnWidth)
### two lower speeds
oneminfreqsim <- dispersion %>% group_by(participant) %>% do(head(.,1))
twominfreqsim <- dispersion %>% group_by(participant) %>% do(head(.,2))
datsim2 <- datsim %>% semi_join(twominfreqsim) %>%
group_by(participant, freqsim)
uniformitysim2 <- datsim2 %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE))
dispersionsim2 <- datsim2 %>%
summarise(rho = rho.circular(radFullSim)) %>%
anti_join(oneminfreqsim) %>%
left_join(uniformitysim2)
prho2 <- ggplot() + facet_wrap(~participant, scales = 'free') +
geom_line(data = dispersionsim2, size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform)) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_point(data = dispersionuniformity, size = .8,
aes(x = freq, y = rho, color = uniform))+
geom_ribbon(data = dispersionuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5), limits = c(0,1)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
prho2
### uniformity analysis
dispersionbootciunifotest <- datsim %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE)) %>%
rename(freq2 = freq, freqsim2 = freqsim)
prhouni <- ggplot() + facet_wrap(~freq) +
# geom_rect(data = fitthre, fill = 'black', alpha =.1,
# aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionbootciunifotest %>% filter(freq < 2.5),
aes(x = freqsim, y = m)) +
geom_line(data = dispersionuniformityplot,size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform))+
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,.5)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))+
geom_vline(xintercept = 2)+geom_hline(yintercept = .05)
prhouni
dispersionsimbootciunifotest <- datsimboot %>%
summarise(rayp = rayleigh.test(radFullSim)$p) %>%
mutate(uniform = ifelse(rayp > 0.05, TRUE, FALSE)) %>%
group_by(participant, freq, freqsim) %>%
summarise(m = mean(uniform))
dispersionuniformityplot <- dispersionuniformity %>%
rename(freqsim=freq)
prhounisim <- ggplot() + facet_wrap(~freq) +
# geom_rect(data = fitthre, fill = 'black', alpha =.1,
# aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dispersionsimbootciunifotest %>% filter(freq < 2.5),
aes(x = freqsim, y = m)) +
geom_line(data = dispersionuniformityplot,size = sizeLine1,
aes(x = freqsim, y = rho, color = uniform))+
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Rho') +
scale_y_continuous(breaks = seq(0,1,.5)) +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,.5)) +
theme(legend.key = element_blank(),
# legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))+
geom_vline(xintercept = 2)+geom_hline(yintercept = .05)
prhounisim
save_plot('analysis/figures/rhounif.pdf', prhouni,
base_width = oneColumnWidth)
### dispersion wrapped normal
funWrap <- function(d){
sta <- mle.wrappednormal(d$radFull)$sd
stams <- sta / (4*pi*first(d$freq))*1000 # it is not 2*pi (it is half lap)
data.frame(sta, stams)
}
dispersionwrap <- dat %>%
group_by(participant, freq) %>%
do(funWrap(.))
dispersiowrapnuniformity <- dispersionwrap %>% merge(uniformity)
dispersionwrapboot <- datboot %>%
group_by(participant, freq, n) %>%
do(funWrap(.))
dispersionwrapbootci <- dispersionwrapboot %>%
group_by(participant, freq) %>%
summarise(inf = quantile(stams, .025),
sup = quantile(stams, .975))
dispersionwrapbootciuniformity <- dispersionwrapbootci %>% merge(uniformity)
dispersionwrapsim <- datsim %>%
group_by(participant, freq, freqsim) %>%
do(funWrap(.))
dispersionwrapsimboot <- datsimboot %>%
filter(freq == .75) %>%
group_by(participant, freq, freqsim, n) %>%
do(funWrap(.))
dispersionwrapsimbootci <- dispersionwrapsimboot %>%
group_by(participant, freq, freqsim) %>%
summarise(inf = quantile(stams, .025),
sup = quantile(stams, .975))
pwra <- ggplot() + facet_wrap(~participant, scales = 'free_x') +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 160)) +
geom_line(data = dispersionwrapsimbootci, size = sizeLine1,
aes(x = freqsim, y = inf), color='grey') +
geom_line(data = dispersionwrapsimbootci, size = sizeLine1,
aes(x = freqsim, y = sup), color='grey') +
geom_line(data = dispersiowrapnuniformity,size = sizeLine1,
aes(x = freq, y = stams, color = uniform))+
geom_point(data = dispersiowrapnuniformity, size = .8,
aes(x = freq, y = stams, color = uniform))+
geom_ribbon(data = dispersionwrapbootciuniformity, alpha =.2,
aes(x = freq, ymin = inf, ymax = sup,fill = uniform)) +
scale_color_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
scale_fill_discrete(guide=guide_legend(title = NULL),
breaks = c(T, F),
labels = c('Uniform', 'Non-uniform')) +
labs(x = 'Frequency (rps)', y = 'Standard deviation (ms)') +
scale_x_continuous(limits = c(0,3.5), breaks = seq(0,3.5,1)) +
theme(legend.key = element_blank(),
legend.position = c(.7,.16),
axis.title.x = element_text(hjust = 0.01))
pwra
save_plot('analysis/figures/wrap.pdf', pwra,
base_width = oneColumnWidth)
dif <- dispersionsim %>% filter(freq == .75) %>%
ungroup() %>%
select(-freq) %>%
rename(freq= freqsim, rhosim = rho) %>%
merge(dispersion) %>%
group_by(participant, freq) %>%
summarise(dif = rhosim - rho)
ggplot()+
facet_wrap(~participant) +
geom_rect(data = fitthre, fill = 'black', alpha =.1,
aes(xmin = thre70, xmax = thre90, ymin = 0, ymax = 1)) +
geom_line(data = dif,
aes(x = freq, y = dif))
geom_line(data = dispersionsim %>% filter(freq == .75),
aes(x =freqsim, rho)) +
geom_line(data = dispersion,
aes(x =freq, rho))
|
#' Calculates the normalized inverse fft of an array
#'
#' @description Convenience function to calculates the normalized inverse fft of an array (like in MatLab)
#' @param x An array as used by fft()
#' @return The normalized inverse fft of x
#' @references \url{http://stackoverflow.com/questions/8162562/is-there-a-package-in-r-that-gives-normalized-inverse-fft}
ifft <- function(x) { fft(x, inverse=TRUE ) / length(x) }
|
/R/ifft.R
|
permissive
|
AdamWilsonLab/rasterAutocorr
|
R
| false
| false
| 426
|
r
|
#' Calculates the normalized inverse fft of an array
#'
#' @description Convenience function to calculates the normalized inverse fft of an array (like in MatLab)
#' @param x An array as used by fft()
#' @return The normalized inverse fft of x
#' @references \url{http://stackoverflow.com/questions/8162562/is-there-a-package-in-r-that-gives-normalized-inverse-fft}
ifft <- function(x) { fft(x, inverse=TRUE ) / length(x) }
|
# This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinverse <- function(inverse) m <<- inverse
getinverse <- function() m
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
# This function computes the inverse of the special "matrix" returned by
# makeCacheMatrix above. If the inverse has already been calculated and the
# matrix has not been changed, then the cacheSolve should retrieve the inverse
# from the cache.
cacheSolve <- function(x, ...) {
m <- x$getinverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinverse(m)
m
}
|
/cachematrix.R
|
no_license
|
Mhanik7951/ProgrammingAssignment2
|
R
| false
| false
| 971
|
r
|
# This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinverse <- function(inverse) m <<- inverse
getinverse <- function() m
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
# This function computes the inverse of the special "matrix" returned by
# makeCacheMatrix above. If the inverse has already been calculated and the
# matrix has not been changed, then the cacheSolve should retrieve the inverse
# from the cache.
cacheSolve <- function(x, ...) {
m <- x$getinverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setinverse(m)
m
}
|
# Graphics and Tables for section 5.3:
# "Results" (5) > "Prediction Bands to Predict True Tropical Cyclones" (5.3)
library(tidyverse)
library(reshape2)
library(xtable)
library(forcats)
library(progress)
library(latex2exp)
library(gridExtra)
library(TCpredictionbands)
# theme -----------------
tc_theme <- theme_minimal() +
theme(strip.background = element_rect(fill = "grey90", color = NA),
plot.title = element_text(hjust = 0.5, size = 18),
strip.text.x = element_text(size = 13),
strip.text.y = element_text(size = 13),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12),
plot.caption = element_text(size = 10))
# Load Data ------------------
data_loc <- "main/data/"
image_path <- "report/images/"
table_path <- "report/tables/"
latest_full_output_pipeline <- 'output_pipeline_alphalevel0.1_all.Rdata'
a <- load(paste0(data_loc, latest_full_output_pipeline))
eval(parse(text = paste0("output_list_pipeline <- ",a)))
###########################
# Accuracy vs Size of PBs #
###########################
# Process Accuracy and Size information ----------------
acc_size_df <- data.frame(prop_acc = 0, area = 0, tc = "CMU",
sim_type = "sim", cb_type = "circles",
smart_size = "check") %>%
mutate(tc = as.character(tc),
sim_type = as.character(sim_type),
cb_type = as.character(cb_type),
smart_size = as.character(smart_size))
if (is.null(names(output_list_pipeline))) {
warning(paste("output_list_pipeline list missing names.",
"Renaming output_list_pipeline's entries with test env's names"))
b <- load(paste0(data_loc,"Test_Sims_350.Rdata"))
names(output_list_pipeline) <- names(test_env)
}
for (tc in names(output_list_pipeline)) {
specific_tc_info <- output_list_pipeline[[tc]]
for (type in names(specific_tc_info)) {
area_vec <- c(specific_tc_info[[type]][["kde"]][["area"]],
specific_tc_info[[type]][["bubble_ci"]][["area"]],
specific_tc_info[[type]][["delta_ball"]][["area"]],
specific_tc_info[[type]][["convex_hull"]][["area"]]
)
convex_hull_vec <- specific_tc_info[[type]][["convex_hull"]][["in_vec"]]
if (type %in% c("Auto_DeathRegs", "Auto_NoDeathRegs")) {
convex_hull_vec[1:3] <- 1
} else {
convex_hull_vec[1:2] <- 1
}
prop_vec <- c(mean(specific_tc_info[[type]][["kde"]][["in_vec"]]),
mean(specific_tc_info[[type]][["bubble_ci"]][["in_vec"]]),
mean(specific_tc_info[[type]][["delta_ball"]][["in_vec"]]),
mean(convex_hull_vec)
)
cb_vec <- c("kde", "bubble_ci", "delta_ball", "convex_hull")
smart_size_vec <- c("small","small","large","large")
inner_df <- data.frame(prop_acc = prop_vec, area = area_vec,
tc = tc, sim_type = type, cb_type = cb_vec,
smart_size = smart_size_vec)
acc_size_df <- rbind(acc_size_df, inner_df)
}
}
acc_size_df <- acc_size_df[-1,]
acc_size_df <- acc_size_df %>%
mutate(prop_acc2 = ifelse(prop_acc == 1, runif(nrow(acc_size_df),1,1.1),
prop_acc))
cb_type_table_levels <- c("Kernel Density Estimate" = "kde",
"Spherical" = "bubble_ci",
"Delta Ball" = "delta_ball",
"Convex Hull" = "convex_hull")
cb_type_table_labels <- names(cb_type_table_levels)
cb_type_graphic_levels <- c("Kernel Density Estimate" = "kde",
"Spherical" = "bubble_ci",
"Delta Ball" = "delta_ball",
"Convex Hull" = "convex_hull")
cb_type_graphic_labels <- names(cb_type_graphic_levels)
sim_type_table_levels <- c("AR \\& Logistic" = "Auto_DeathRegs",
"AR \\& Kernel" = "Auto_NoDeathRegs",
"Non-AR \\& Logistic" = "NoAuto_DeathRegs",
"Non-AR \\& Kernel" = "NoAuto_NoDeathRegs")
sim_type_table_labels <- names(sim_type_table_levels)
sim_type_graphic_levels <- c("Autoregression & Logistic Death" = "Auto_DeathRegs",
"Autoregression & Kernel Death" = "Auto_NoDeathRegs",
"Non-Autoregression & Logistic Death" = "NoAuto_DeathRegs",
"Non-Autoregression & Kernel Death" = "NoAuto_NoDeathRegs")
sim_type_graphic_labels <- names(sim_type_graphic_levels)
data_run <- acc_size_df %>% mutate(
cb_type_full = factor(cb_type, levels = cb_type_graphic_levels,
labels = cb_type_graphic_labels),
sim_type_full = factor(sim_type, levels = sim_type_graphic_levels,
labels = sim_type_graphic_labels),
cb_type_full_table = factor(cb_type, levels = cb_type_table_levels,
labels = cb_type_table_labels),
sim_type_full_table = factor(sim_type, levels = sim_type_table_levels,
labels = sim_type_table_labels)) #%>%
# Visualization Function ------------------
#' add or create a set of boxplots but discretizing continuous variable
#'
#' @param data data frame
#' @param x_string string of column name for x variable (continuous)
#' @param y_string string of column name of y variable (continuous)
#' @param facet_string string of column name for faceting (discrete)
#' @param breaks vector of values for breaks
#' @param base_gg_obj base ggplot to add the graphic onto
#' @param frac fraction of bar width across
#' @param alpha opacity for bar plots fill color
#' @param fill fill color for boxplot
#' @param color line color for boxplot
#'
#' @return ggplot object
#' @export
#'
#' @examples
box_plus_scatter <- function(data, x_string, y_string, facet_string, breaks,
base_gg_obj = ggplot(), frac = .9, alpha = .3,
fill = "orange", color = "orange"){
data_new <- data
data_new[,"x_string_inner_break_ben"] <- cut(x = data_new[,x_string],
breaks = breaks)
#^just a new column
shift <- diff(breaks)*(1 - frac)/2
lower_breaks <- breaks[-length(breaks)] + shift
upper_breaks <- breaks[-1] - shift
middle_of_breaks <- (lower_breaks + upper_breaks)/2
x_break_df <- data.frame(
levels = levels(data_new[,"x_string_inner_break_ben"]),
xlower = lower_breaks,
xupper = upper_breaks,
xmiddle = middle_of_breaks)
data_new <- data_new %>% left_join(x_break_df,
by = c("x_string_inner_break_ben" = "levels"))
data_new[,"my_y"] <- data_new[,y_string]
group_by_columns <- c("x_string_inner_break_ben", facet_string)
data_sum <- data_new %>%
group_by_at(vars(one_of(group_by_columns))) %>%
summarize(ymax = max(my_y),
q75 = quantile(my_y, probs = .75),
median = median(my_y),
q25 = quantile(my_y, probs = .25),
ymin = min(my_y),
xlower = max(xlower),
xupper = max(xupper),
xmiddle = max(xmiddle)) %>%
mutate(iqr = q75 - q25)
# mutate wouldn't work correctly with min/ max functions
a <- ifelse(data_sum$ymax > data_sum$q75 + 1.5*data_sum$iqr,
data_sum$q75 + 1.5*data_sum$iqr, data_sum$ymax)
b <- ifelse(data_sum$ymin < data_sum$q25 - 1.5*data_sum$iqr,
data_sum$q25 - 1.5*data_sum$iqr, data_sum$ymin)
data_sum$upper <- a
data_sum$lower <- b
ggout <- base_gg_obj +
geom_rect(data = data_sum,
aes(xmin = xlower, xmax = xupper,
ymin = q25, ymax = q75),
alpha = alpha, color = color, fill = fill) +
geom_segment(data = data_sum, # median
aes(x = xlower, xend = xupper, y = median, yend = median),
color = color, size = 1) +
geom_segment(data = data_sum, # upper
aes(x = xmiddle, xend = xmiddle, y = q75, yend = upper),
color = color) +
geom_segment(data = data_sum, # lower
aes(x = xmiddle, xend = xmiddle, y = q25, yend = lower),
color = color)
return(ggout)
}
# Actual Graphics -------------
large_acc_vs_area <- data_run %>%
filter(smart_size == "large", area < 3250) %>%
ggplot() +
geom_point(aes(x = area, y = prop_acc2),alpha = .15) +
facet_grid(~cb_type_full, scales = "free_x" ) +
geom_hline(yintercept = 1) + tc_theme +
scale_x_continuous(breaks = seq(0, 3250, by = 250)) +
theme(axis.text.x = element_text(angle = 90)) +
labs(y = "Proportion Captured", x = "Area")
large_acc_vs_area2 <- box_plus_scatter(data = data_run %>%
filter(smart_size == "large", area < 3250),
x_string = "area", y_string = "prop_acc",
facet_string = "cb_type_full",
breaks = seq(0,3309,by = 250),
base_gg_obj = large_acc_vs_area,
frac = .7, fill = rgb(1, 0.3, 0.3, .7),
color = rgb(1, 0.3, 0.3, .7),
alpha = .1)
large_acc_vs_area_final <- large_acc_vs_area2 +
geom_smooth(aes(x = area, y = prop_acc))
small_acc_vs_area <- data_run %>%
filter(smart_size == "small",area < 2400) %>%
ggplot() +
geom_point(aes(x = area, y = prop_acc2),alpha = .15) +
facet_grid(~cb_type_full, scales = "free_x" ) +
geom_hline(yintercept = 1) +
tc_theme +
scale_x_continuous(breaks = seq(0, 2400, by = 125), lim = c(0, 2400)) +
theme(axis.text.x = element_text(angle = 90)) +
labs(y = "Proportion Captured", x = "Area")
small_acc_vs_area2 <- box_plus_scatter(
data = data_run %>% filter(smart_size == "small",area < 2400),
x_string = "area", y_string = "prop_acc",
facet_string = "cb_type_full",
breaks = seq(0,2400,by = 125), base_gg_obj = small_acc_vs_area,
frac = .7, fill = rgb(1,0.3,0.3,.7), color = rgb(1,0.3,0.3,.7),
alpha = .1)
small_acc_vs_area_final <- small_acc_vs_area2 +
geom_smooth(aes(x = area, y = prop_acc))
arrangement <- arrangeGrob(large_acc_vs_area_final +
labs(title = "Larger Prediction Bands",
x = "Area in Square Nautical Miles (maximum = 3250)"),
small_acc_vs_area_final +
labs(title = "Smaller Prediction Bands",
x = "Area in Square Nautical Miles (maximum = 2400)"), ncol = 1)
ggsave(plot = arrangement,
filename = paste0(image_path,"tc_results_area_vs_prop.png"),
device = "png", width = 10, height = 6.5, units = "in")
# Tables ------------
# ~ bolding solution from https://stackoverflow.com/questions/33218469/boldify-the-contents-of-bottom-row-in-xtable
bold_somerows <- function(x) gsub('BOLD(.*)',paste0('\\\\textbf{\\1','}'),x)
# ~ for multistacked headers: https://cran.r-project.org/web/packages/xtable/vignettes/xtableGallery.pdf
# page: 27
# in Latex file you'll need need:
# \newcolumntype{L}[1]{>{\raggedright\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{C}[1]{>{\centering\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{R}[1]{>{\raggedleft\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{P}[1]{>{\raggedright\tabularxbackslash}p{#1}}
#
# average proportion captured
table <- data_run %>%
group_by(sim_type_full_table, cb_type_full_table) %>%
dplyr::summarize(
full_acc = sprintf("%.2f / %.2f" ,
round(mean(prop_acc),2),
round(median(prop_acc),2)) ) %>%
dcast(sim_type_full_table ~ cb_type_full_table) %>%
rename("Simulation Curve Type" = "sim_type_full_table")
xtable1 <- table %>% xtable(align = c("r|R{1.2in}||L{.95in}L{.8in}|L{.75in}L{.75in}|"),
digits = 2,
caption = paste("Mean and median proportion",
"of points of a true TC captured by the PB.",
"PBs based on 350 simulated curves; created",
"with either Autoregressive (AR) or",
"Non-Autoregressive (AR) models for changes",
"in bearing and speed and with either a",
"Kernel-based lysis model (Kernel) or",
"Logistic-based lysis models (Logistic)."),
label = "tab:average_captured")
print(xtable1,
table.placement = "ht!",
include.rownames = FALSE,
sanitize.text.function = bold_somerows,
#^for some reason we need this - even though not used
file = paste0(table_path,"tc_average_proportion_update.tex"))
# proportion with capture above .3, .9, =1
# ~ adding math in caption from https://cran.r-project.org/web/packages/xtable/vignettes/xtableGallery.pdf
# and https://www.sharelatex.com/learn/Mathematical_expressions
table2 <- data_run %>%
group_by(sim_type_full_table, cb_type_full_table) %>%
dplyr::summarize(
both = paste0(sprintf("%.2f",round(mean(prop_acc >= .30),2)),
" / ",
sprintf("%.2f",round(mean(prop_acc >= .90),2)),
" BOLD(",
sprintf("%.2f",round(mean(prop_acc >= 1), 2)),
")")) %>%
dcast(sim_type_full_table ~ cb_type_full_table) %>%
rename("Simulation Curve Type" = "sim_type_full_table")
xtable2 <- xtable(table2, align = c("r|R{1.2in}||L{.95in}L{.95in}|L{.95in}L{.95in}|"),
caption = paste("Proportion of points of TCs with proportion",
"of point captured \\((\\geq .3) /",
"(\\geq .9)\\) \\(\\textbf{(= 1)}\\).",
"PBs based on 350 simulated curves; created",
"with either Autoregressive (AR) or",
"Non-Autoregressive (AR) models for changes",
"in bearing and speed and with either a",
"Kernel-based lysis model (Kernel) or",
"Logistic-based lysis models (Logistic)."),
label = "tab:prop_captured")
print(xtable2,
table.placement = "ht!",
include.rownames = FALSE,
sanitize.text.function = bold_somerows,
file = paste0(table_path,"tc_prop_above_p3p9p10.tex"))
|
/main/R2-acc_vs_area_result.R
|
permissive
|
RobinMDunn/Hurricanes_701
|
R
| false
| false
| 14,745
|
r
|
# Graphics and Tables for section 5.3:
# "Results" (5) > "Prediction Bands to Predict True Tropical Cyclones" (5.3)
library(tidyverse)
library(reshape2)
library(xtable)
library(forcats)
library(progress)
library(latex2exp)
library(gridExtra)
library(TCpredictionbands)
# theme -----------------
tc_theme <- theme_minimal() +
theme(strip.background = element_rect(fill = "grey90", color = NA),
plot.title = element_text(hjust = 0.5, size = 18),
strip.text.x = element_text(size = 13),
strip.text.y = element_text(size = 13),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12),
plot.caption = element_text(size = 10))
# Load Data ------------------
data_loc <- "main/data/"
image_path <- "report/images/"
table_path <- "report/tables/"
latest_full_output_pipeline <- 'output_pipeline_alphalevel0.1_all.Rdata'
a <- load(paste0(data_loc, latest_full_output_pipeline))
eval(parse(text = paste0("output_list_pipeline <- ",a)))
###########################
# Accuracy vs Size of PBs #
###########################
# Process Accuracy and Size information ----------------
acc_size_df <- data.frame(prop_acc = 0, area = 0, tc = "CMU",
sim_type = "sim", cb_type = "circles",
smart_size = "check") %>%
mutate(tc = as.character(tc),
sim_type = as.character(sim_type),
cb_type = as.character(cb_type),
smart_size = as.character(smart_size))
if (is.null(names(output_list_pipeline))) {
warning(paste("output_list_pipeline list missing names.",
"Renaming output_list_pipeline's entries with test env's names"))
b <- load(paste0(data_loc,"Test_Sims_350.Rdata"))
names(output_list_pipeline) <- names(test_env)
}
for (tc in names(output_list_pipeline)) {
specific_tc_info <- output_list_pipeline[[tc]]
for (type in names(specific_tc_info)) {
area_vec <- c(specific_tc_info[[type]][["kde"]][["area"]],
specific_tc_info[[type]][["bubble_ci"]][["area"]],
specific_tc_info[[type]][["delta_ball"]][["area"]],
specific_tc_info[[type]][["convex_hull"]][["area"]]
)
convex_hull_vec <- specific_tc_info[[type]][["convex_hull"]][["in_vec"]]
if (type %in% c("Auto_DeathRegs", "Auto_NoDeathRegs")) {
convex_hull_vec[1:3] <- 1
} else {
convex_hull_vec[1:2] <- 1
}
prop_vec <- c(mean(specific_tc_info[[type]][["kde"]][["in_vec"]]),
mean(specific_tc_info[[type]][["bubble_ci"]][["in_vec"]]),
mean(specific_tc_info[[type]][["delta_ball"]][["in_vec"]]),
mean(convex_hull_vec)
)
cb_vec <- c("kde", "bubble_ci", "delta_ball", "convex_hull")
smart_size_vec <- c("small","small","large","large")
inner_df <- data.frame(prop_acc = prop_vec, area = area_vec,
tc = tc, sim_type = type, cb_type = cb_vec,
smart_size = smart_size_vec)
acc_size_df <- rbind(acc_size_df, inner_df)
}
}
acc_size_df <- acc_size_df[-1,]
acc_size_df <- acc_size_df %>%
mutate(prop_acc2 = ifelse(prop_acc == 1, runif(nrow(acc_size_df),1,1.1),
prop_acc))
cb_type_table_levels <- c("Kernel Density Estimate" = "kde",
"Spherical" = "bubble_ci",
"Delta Ball" = "delta_ball",
"Convex Hull" = "convex_hull")
cb_type_table_labels <- names(cb_type_table_levels)
cb_type_graphic_levels <- c("Kernel Density Estimate" = "kde",
"Spherical" = "bubble_ci",
"Delta Ball" = "delta_ball",
"Convex Hull" = "convex_hull")
cb_type_graphic_labels <- names(cb_type_graphic_levels)
sim_type_table_levels <- c("AR \\& Logistic" = "Auto_DeathRegs",
"AR \\& Kernel" = "Auto_NoDeathRegs",
"Non-AR \\& Logistic" = "NoAuto_DeathRegs",
"Non-AR \\& Kernel" = "NoAuto_NoDeathRegs")
sim_type_table_labels <- names(sim_type_table_levels)
sim_type_graphic_levels <- c("Autoregression & Logistic Death" = "Auto_DeathRegs",
"Autoregression & Kernel Death" = "Auto_NoDeathRegs",
"Non-Autoregression & Logistic Death" = "NoAuto_DeathRegs",
"Non-Autoregression & Kernel Death" = "NoAuto_NoDeathRegs")
sim_type_graphic_labels <- names(sim_type_graphic_levels)
data_run <- acc_size_df %>% mutate(
cb_type_full = factor(cb_type, levels = cb_type_graphic_levels,
labels = cb_type_graphic_labels),
sim_type_full = factor(sim_type, levels = sim_type_graphic_levels,
labels = sim_type_graphic_labels),
cb_type_full_table = factor(cb_type, levels = cb_type_table_levels,
labels = cb_type_table_labels),
sim_type_full_table = factor(sim_type, levels = sim_type_table_levels,
labels = sim_type_table_labels)) #%>%
# Visualization Function ------------------
#' add or create a set of boxplots but discretizing continuous variable
#'
#' @param data data frame
#' @param x_string string of column name for x variable (continuous)
#' @param y_string string of column name of y variable (continuous)
#' @param facet_string string of column name for faceting (discrete)
#' @param breaks vector of values for breaks
#' @param base_gg_obj base ggplot to add the graphic onto
#' @param frac fraction of bar width across
#' @param alpha opacity for bar plots fill color
#' @param fill fill color for boxplot
#' @param color line color for boxplot
#'
#' @return ggplot object
#' @export
#'
#' @examples
box_plus_scatter <- function(data, x_string, y_string, facet_string, breaks,
base_gg_obj = ggplot(), frac = .9, alpha = .3,
fill = "orange", color = "orange"){
data_new <- data
data_new[,"x_string_inner_break_ben"] <- cut(x = data_new[,x_string],
breaks = breaks)
#^just a new column
shift <- diff(breaks)*(1 - frac)/2
lower_breaks <- breaks[-length(breaks)] + shift
upper_breaks <- breaks[-1] - shift
middle_of_breaks <- (lower_breaks + upper_breaks)/2
x_break_df <- data.frame(
levels = levels(data_new[,"x_string_inner_break_ben"]),
xlower = lower_breaks,
xupper = upper_breaks,
xmiddle = middle_of_breaks)
data_new <- data_new %>% left_join(x_break_df,
by = c("x_string_inner_break_ben" = "levels"))
data_new[,"my_y"] <- data_new[,y_string]
group_by_columns <- c("x_string_inner_break_ben", facet_string)
data_sum <- data_new %>%
group_by_at(vars(one_of(group_by_columns))) %>%
summarize(ymax = max(my_y),
q75 = quantile(my_y, probs = .75),
median = median(my_y),
q25 = quantile(my_y, probs = .25),
ymin = min(my_y),
xlower = max(xlower),
xupper = max(xupper),
xmiddle = max(xmiddle)) %>%
mutate(iqr = q75 - q25)
# mutate wouldn't work correctly with min/ max functions
a <- ifelse(data_sum$ymax > data_sum$q75 + 1.5*data_sum$iqr,
data_sum$q75 + 1.5*data_sum$iqr, data_sum$ymax)
b <- ifelse(data_sum$ymin < data_sum$q25 - 1.5*data_sum$iqr,
data_sum$q25 - 1.5*data_sum$iqr, data_sum$ymin)
data_sum$upper <- a
data_sum$lower <- b
ggout <- base_gg_obj +
geom_rect(data = data_sum,
aes(xmin = xlower, xmax = xupper,
ymin = q25, ymax = q75),
alpha = alpha, color = color, fill = fill) +
geom_segment(data = data_sum, # median
aes(x = xlower, xend = xupper, y = median, yend = median),
color = color, size = 1) +
geom_segment(data = data_sum, # upper
aes(x = xmiddle, xend = xmiddle, y = q75, yend = upper),
color = color) +
geom_segment(data = data_sum, # lower
aes(x = xmiddle, xend = xmiddle, y = q25, yend = lower),
color = color)
return(ggout)
}
# Actual Graphics -------------
large_acc_vs_area <- data_run %>%
filter(smart_size == "large", area < 3250) %>%
ggplot() +
geom_point(aes(x = area, y = prop_acc2),alpha = .15) +
facet_grid(~cb_type_full, scales = "free_x" ) +
geom_hline(yintercept = 1) + tc_theme +
scale_x_continuous(breaks = seq(0, 3250, by = 250)) +
theme(axis.text.x = element_text(angle = 90)) +
labs(y = "Proportion Captured", x = "Area")
large_acc_vs_area2 <- box_plus_scatter(data = data_run %>%
filter(smart_size == "large", area < 3250),
x_string = "area", y_string = "prop_acc",
facet_string = "cb_type_full",
breaks = seq(0,3309,by = 250),
base_gg_obj = large_acc_vs_area,
frac = .7, fill = rgb(1, 0.3, 0.3, .7),
color = rgb(1, 0.3, 0.3, .7),
alpha = .1)
large_acc_vs_area_final <- large_acc_vs_area2 +
geom_smooth(aes(x = area, y = prop_acc))
small_acc_vs_area <- data_run %>%
filter(smart_size == "small",area < 2400) %>%
ggplot() +
geom_point(aes(x = area, y = prop_acc2),alpha = .15) +
facet_grid(~cb_type_full, scales = "free_x" ) +
geom_hline(yintercept = 1) +
tc_theme +
scale_x_continuous(breaks = seq(0, 2400, by = 125), lim = c(0, 2400)) +
theme(axis.text.x = element_text(angle = 90)) +
labs(y = "Proportion Captured", x = "Area")
small_acc_vs_area2 <- box_plus_scatter(
data = data_run %>% filter(smart_size == "small",area < 2400),
x_string = "area", y_string = "prop_acc",
facet_string = "cb_type_full",
breaks = seq(0,2400,by = 125), base_gg_obj = small_acc_vs_area,
frac = .7, fill = rgb(1,0.3,0.3,.7), color = rgb(1,0.3,0.3,.7),
alpha = .1)
small_acc_vs_area_final <- small_acc_vs_area2 +
geom_smooth(aes(x = area, y = prop_acc))
arrangement <- arrangeGrob(large_acc_vs_area_final +
labs(title = "Larger Prediction Bands",
x = "Area in Square Nautical Miles (maximum = 3250)"),
small_acc_vs_area_final +
labs(title = "Smaller Prediction Bands",
x = "Area in Square Nautical Miles (maximum = 2400)"), ncol = 1)
ggsave(plot = arrangement,
filename = paste0(image_path,"tc_results_area_vs_prop.png"),
device = "png", width = 10, height = 6.5, units = "in")
# Tables ------------
# ~ bolding solution from https://stackoverflow.com/questions/33218469/boldify-the-contents-of-bottom-row-in-xtable
bold_somerows <- function(x) gsub('BOLD(.*)',paste0('\\\\textbf{\\1','}'),x)
# ~ for multistacked headers: https://cran.r-project.org/web/packages/xtable/vignettes/xtableGallery.pdf
# page: 27
# in Latex file you'll need need:
# \newcolumntype{L}[1]{>{\raggedright\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{C}[1]{>{\centering\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{R}[1]{>{\raggedleft\let\newline\\
# \arraybackslash\hspace{0pt}}b{#1}}
# \newcolumntype{P}[1]{>{\raggedright\tabularxbackslash}p{#1}}
#
# average proportion captured
table <- data_run %>%
group_by(sim_type_full_table, cb_type_full_table) %>%
dplyr::summarize(
full_acc = sprintf("%.2f / %.2f" ,
round(mean(prop_acc),2),
round(median(prop_acc),2)) ) %>%
dcast(sim_type_full_table ~ cb_type_full_table) %>%
rename("Simulation Curve Type" = "sim_type_full_table")
xtable1 <- table %>% xtable(align = c("r|R{1.2in}||L{.95in}L{.8in}|L{.75in}L{.75in}|"),
digits = 2,
caption = paste("Mean and median proportion",
"of points of a true TC captured by the PB.",
"PBs based on 350 simulated curves; created",
"with either Autoregressive (AR) or",
"Non-Autoregressive (AR) models for changes",
"in bearing and speed and with either a",
"Kernel-based lysis model (Kernel) or",
"Logistic-based lysis models (Logistic)."),
label = "tab:average_captured")
print(xtable1,
table.placement = "ht!",
include.rownames = FALSE,
sanitize.text.function = bold_somerows,
#^for some reason we need this - even though not used
file = paste0(table_path,"tc_average_proportion_update.tex"))
# proportion with capture above .3, .9, =1
# ~ adding math in caption from https://cran.r-project.org/web/packages/xtable/vignettes/xtableGallery.pdf
# and https://www.sharelatex.com/learn/Mathematical_expressions
table2 <- data_run %>%
group_by(sim_type_full_table, cb_type_full_table) %>%
dplyr::summarize(
both = paste0(sprintf("%.2f",round(mean(prop_acc >= .30),2)),
" / ",
sprintf("%.2f",round(mean(prop_acc >= .90),2)),
" BOLD(",
sprintf("%.2f",round(mean(prop_acc >= 1), 2)),
")")) %>%
dcast(sim_type_full_table ~ cb_type_full_table) %>%
rename("Simulation Curve Type" = "sim_type_full_table")
xtable2 <- xtable(table2, align = c("r|R{1.2in}||L{.95in}L{.95in}|L{.95in}L{.95in}|"),
caption = paste("Proportion of points of TCs with proportion",
"of point captured \\((\\geq .3) /",
"(\\geq .9)\\) \\(\\textbf{(= 1)}\\).",
"PBs based on 350 simulated curves; created",
"with either Autoregressive (AR) or",
"Non-Autoregressive (AR) models for changes",
"in bearing and speed and with either a",
"Kernel-based lysis model (Kernel) or",
"Logistic-based lysis models (Logistic)."),
label = "tab:prop_captured")
print(xtable2,
table.placement = "ht!",
include.rownames = FALSE,
sanitize.text.function = bold_somerows,
file = paste0(table_path,"tc_prop_above_p3p9p10.tex"))
|
library(BioMark)
### Name: selection
### Title: Accessor function to the selected variables of a BioMark object
### Aliases: selection
### Keywords: models multivariate
### ** Examples
## stability-based selection
set.seed(17)
data(spikedApples)
apple.stab <- get.biom(X = spikedApples$dataMatrix,
Y = factor(rep(1:2, each = 10)),
ncomp = 2:3, type = "stab")
selected.variables <- selection(apple.stab)
|
/data/genthat_extracted_code/BioMark/examples/selection.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 456
|
r
|
library(BioMark)
### Name: selection
### Title: Accessor function to the selected variables of a BioMark object
### Aliases: selection
### Keywords: models multivariate
### ** Examples
## stability-based selection
set.seed(17)
data(spikedApples)
apple.stab <- get.biom(X = spikedApples$dataMatrix,
Y = factor(rep(1:2, each = 10)),
ncomp = 2:3, type = "stab")
selected.variables <- selection(apple.stab)
|
GetTotalMarks <- function(quiz.marks, viva.marks, assignment.marks = 5L,...) {
total.marks <- quiz.marks + viva.marks + assignment.marks + sum(...)
#extra.arguments <- list(...)
#print(extra.arguments)
total.marks
}
GetTotalMarks(quiz.marks = c(70L, 75L, 80L, 85L), viva.marks = c(7L, 5L, 8L, 6L),
assignment.marks = c(2L, 1L, 3L, 4L), creativity.marks = 2)
GetTotalMarks(quiz.marks = c(70L, 75L, 80L, 85L), viva.marks = c(7L, 5L, 8L, 6L),
assignment.marks = c(2L, 1L, 3L, 4L), creativity.marks = 2,
attendence.marks = 3)
|
/Pluralsight/Function/03AdditionalArgumentsUsingEllipsis.R
|
no_license
|
cyrsis/RSandBox
|
R
| false
| false
| 578
|
r
|
GetTotalMarks <- function(quiz.marks, viva.marks, assignment.marks = 5L,...) {
total.marks <- quiz.marks + viva.marks + assignment.marks + sum(...)
#extra.arguments <- list(...)
#print(extra.arguments)
total.marks
}
GetTotalMarks(quiz.marks = c(70L, 75L, 80L, 85L), viva.marks = c(7L, 5L, 8L, 6L),
assignment.marks = c(2L, 1L, 3L, 4L), creativity.marks = 2)
GetTotalMarks(quiz.marks = c(70L, 75L, 80L, 85L), viva.marks = c(7L, 5L, 8L, 6L),
assignment.marks = c(2L, 1L, 3L, 4L), creativity.marks = 2,
attendence.marks = 3)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graph_quebec_cas_par_age.R
\name{heatmap_cas}
\alias{heatmap_cas}
\title{heatmap_cas}
\usage{
heatmap_cas(prepped_data, variable, variable_title, type = "maximum")
}
\value{
}
\description{
heatmap_cas
}
|
/man/heatmap_cas.Rd
|
permissive
|
SimonCoulombe/covidtwitterbot
|
R
| false
| true
| 283
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graph_quebec_cas_par_age.R
\name{heatmap_cas}
\alias{heatmap_cas}
\title{heatmap_cas}
\usage{
heatmap_cas(prepped_data, variable, variable_title, type = "maximum")
}
\value{
}
\description{
heatmap_cas
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/psy.phd.R
\name{ci95_to_se}
\alias{ci95_to_se}
\title{Convert 95% confidence interval to standard error}
\usage{
ci95_to_se(u, l)
}
\arguments{
\item{u}{upper interval}
\item{l}{lower interval}
}
\value{
numeric
}
\description{
Convert 95% confidence interval to standard error
}
\details{
https://training.cochrane.org/handbook/current/chapter-06#section-6-3-1
}
|
/man/ci95_to_se.Rd
|
permissive
|
paulsharpeY/psy.phd
|
R
| false
| true
| 443
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/psy.phd.R
\name{ci95_to_se}
\alias{ci95_to_se}
\title{Convert 95% confidence interval to standard error}
\usage{
ci95_to_se(u, l)
}
\arguments{
\item{u}{upper interval}
\item{l}{lower interval}
}
\value{
numeric
}
\description{
Convert 95% confidence interval to standard error
}
\details{
https://training.cochrane.org/handbook/current/chapter-06#section-6-3-1
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/measures_binaryclass.R
\name{Brier}
\alias{Brier}
\title{Brier score}
\usage{
Brier(probabilities, truth, negative, positive)
}
\arguments{
\item{probabilities}{[numeric] vector of predicted probabilities}
\item{truth}{vector of true values}
\item{negative}{negative class}
\item{positive}{positive class}
}
\description{
The Brier score is defined as the quadratic difference between the probability and the value (1,0) for the class.
That means we use the numeric representation 1 and 0 for our target classes. It is similiar to the mean squared error in regression.
multiclass.brier is the sum over all one vs. all comparisons and for a binary classifcation 2 * brier.
}
\examples{
n = 20
set.seed(125)
truth = as.factor(sample(c(1,0), n, replace = TRUE))
probabilities = runif(n)
response = as.factor(as.numeric(probabilities > 0.5))
positive = 1
negative = 0
Brier(probabilities, truth, negative, positive)
}
|
/man/Brier.Rd
|
no_license
|
PhilippPro/measures
|
R
| false
| true
| 995
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/measures_binaryclass.R
\name{Brier}
\alias{Brier}
\title{Brier score}
\usage{
Brier(probabilities, truth, negative, positive)
}
\arguments{
\item{probabilities}{[numeric] vector of predicted probabilities}
\item{truth}{vector of true values}
\item{negative}{negative class}
\item{positive}{positive class}
}
\description{
The Brier score is defined as the quadratic difference between the probability and the value (1,0) for the class.
That means we use the numeric representation 1 and 0 for our target classes. It is similiar to the mean squared error in regression.
multiclass.brier is the sum over all one vs. all comparisons and for a binary classifcation 2 * brier.
}
\examples{
n = 20
set.seed(125)
truth = as.factor(sample(c(1,0), n, replace = TRUE))
probabilities = runif(n)
response = as.factor(as.numeric(probabilities > 0.5))
positive = 1
negative = 0
Brier(probabilities, truth, negative, positive)
}
|
# Usage example:
# Rscript L.saxatilis-Mate-choice/scripts/mods_comp.R --modelone models/gaus_skew/SKEW/gaus_skew.rds \
# --modeltwo models/gaus_skew/BCDG_shore/gaus_skew_hier_BCDG_shore.rds --modelthree models/gaus_skew/BCDG_eco/gaus_skew_hier_BCDG_eco.rds \
# --modelfour models/gaus_skew/BCDG/gaus_skew_hier_BCDG.rds -d data/CZ_all_mating_clean.csv -o tables/mods_comp/comp_
rm(list = ls())
.packages = c("ggplot2", "dplyr", "rstan", "tibble", "boot", "bayesplot", "Rmisc", "pander",
"bbmle", "loo", "ggpubr", "cowplot", "purrr", "reshape2", "gridExtra", "grid", "arm", "parallel",
"rstantools", "optparse", "pROC")
# Install CRAN packages (if not already installed)
.inst <- .packages %in% installed.packages()
if(length(.packages[!.inst]) > 0) install.packages(.packages[!.inst])
# Load packages into session
lapply(.packages, require, character.only=TRUE)
option_list = list(
make_option("--modelone", type="character", default=NULL,
help="first model for comparison", metavar="character"),
make_option("--modeltwo", type="character", default=NULL,
help="second model for comparison", metavar="character"),
make_option("--modelthree", type="character", default=NULL,
help="third model for comparison", metavar="character"),
make_option("--modelfour", type="character", default=NULL,
help="fourth model for comparison", metavar="character"),
make_option("--nnhier", type="character", default=NULL,
help="non-hierarchical model for comparison", metavar="character"),
make_option(c("-o", "--out"), type="character", default="mods_comp_out.csv",
help="output file name [default = %default]", metavar="character"),
make_option(c("-d", "--data"), type="character", default=NULL,
help="input data", metavar="character"))
opt_parser = OptionParser(option_list=option_list)
opt = parse_args(opt_parser)
if (is.null(opt$modelone) | is.null(opt$modeltwo) | is.null(opt$data)) {
print_help(opt_parser)
stop("At least three arguments must be supplied (two models and one dataset).\n", call.=FALSE)
}
########################################################
###### approximate leave-one-out cross-validation ######
# http://mc-stan.org/loo/articles/loo2-with-rstan.html #
########################################################
CZ_data = read.csv(opt$data, sep = ";")
# mod1 = readRDS("models/gaus_skew/gaus_skew.rds")
mod1 = readRDS(opt$modelone)
# mod2 = readRDS("models/gaus_skew/gaus_skew_hier_BCDG_shore.rds")
mod2 = readRDS(opt$modeltwo)
mod3 = readRDS(opt$modelthree)
mod4 = readRDS(opt$modelfour)
nnhier = readRDS(opt$nnhier)
out_comp_str = lapply(c(opt$modelone,opt$modeltwo,opt$modelthree,opt$modelfour,opt$nnhier), function(x) {
modsplit = strsplit(strsplit(basename(x), "[.]")[[1]][1], split = "_")[[1]]
return(modsplit[length(modsplit)])
})
# Extract pointwise log-likelihood and compute LOO
log_lik_1 <- extract_log_lik(mod1, merge_chains = FALSE)
log_lik_2 <- extract_log_lik(mod2, merge_chains = FALSE)
log_lik_3 <- extract_log_lik(mod3, merge_chains = FALSE)
log_lik_4 <- extract_log_lik(mod4, merge_chains = FALSE)
log_lik_nnhier <- extract_log_lik(nnhier, merge_chains = FALSE)
# provide relative effective sample sizes
r_eff <- relative_eff(exp(log_lik_1))
loo_1 <- loo(log_lik_1, r_eff = r_eff, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelone)))
print(loo_1)
# split_path <- function(x) if (dirname(x)==x) x else c(basename(x),split_path(dirname(x)))
# out_comp = "tables/mods_comp/comp_skew_shore.csv"
# inp_mod1 = "models/gaus_skew/gaus_skew.rds"
out_mod1 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelone), "[.]")[[1]][1], ".csv")
write.table(round(loo_1$estimates, 2), out_mod1, sep = ",", row.names = TRUE, col.names = TRUE)
# plot(loo_1)
r_eff_2 <- relative_eff(exp(log_lik_2))
loo_2 <- loo(log_lik_2, r_eff = r_eff_2, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modeltwo)))
print(loo_2)
out_mod2 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modeltwo), "[.]")[[1]][1], ".csv")
write.table(round(loo_2$estimates, 2), out_mod2, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_3 <- relative_eff(exp(log_lik_3))
loo_3 <- loo(log_lik_3, r_eff = r_eff_3, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelthree)))
print(loo_3)
out_mod3 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelthree), "[.]")[[1]][1], ".csv")
write.table(round(loo_3$estimates, 2), out_mod3, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_4 <- relative_eff(exp(log_lik_4))
loo_4 <- loo(log_lik_4, r_eff = r_eff_4, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelfour)))
print(loo_4)
out_mod4 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelfour), "[.]")[[1]][1], ".csv")
write.table(round(loo_4$estimates, 2), out_mod4, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_nnhier <- relative_eff(exp(log_lik_nnhier))
loo_nnhier <- loo(log_lik_nnhier, r_eff = r_eff_nnhier, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$nnhier)))
print(loo_nnhier)
out_modnnhier = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$nnhier), "[.]")[[1]][1], ".csv")
write.table(round(loo_nnhier$estimates, 2), out_modnnhier, sep = ",", row.names = TRUE, col.names = TRUE)
print(paste0("Parameters in model ", basename(opt$modelone)))
mod1@model_pars
print(paste0("Parameters in model ", basename(opt$modeltwo)))
mod2@model_pars
print(paste0("Parameters in model ", basename(opt$modelthree)))
mod3@model_pars
print(paste0("Parameters in model ", basename(opt$modelfour)))
mod4@model_pars
print(paste0("Parameters in non-hierarchical model ", basename(opt$nnhier)))
nnhier@model_pars
# plot(loo_2, label_points = TRUE)
comp1_2 <- compare(loo_1, loo_2)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modeltwo), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_2)
elpd_diff = comp1_2[1]
se_elpd_diff = comp1_2[2]
write.table(data.frame(elpd_diff, se_elpd_diff), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[2]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp1_3 <- compare(loo_1, loo_3)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modelthree), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_3)
elpd_diff13 = comp1_3[1]
se_elpd_diff13 = comp1_3[2]
write.table(data.frame(elpd_diff13, se_elpd_diff13), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[3]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp1_4 <- compare(loo_1, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_4)
elpd_diff14 = comp1_4[1]
se_elpd_diff14 = comp1_4[2]
write.table(data.frame(elpd_diff14, se_elpd_diff14), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp2_3 <- compare(loo_2, loo_3)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modeltwo), " and ", basename(opt$modelthree), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp2_3)
elpd_diff23 = comp2_3[1]
se_elpd_diff23 = comp2_3[2]
write.table(data.frame(elpd_diff23, se_elpd_diff23), file = paste0("tables/mods_comp/comp_", out_comp_str[[2]], "_", out_comp_str[[3]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp2_4 <- compare(loo_2, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modeltwo), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp2_4)
elpd_diff24 = comp2_4[1]
se_elpd_diff24 = comp2_4[2]
write.table(data.frame(elpd_diff24, se_elpd_diff24), file = paste0("tables/mods_comp/comp_", out_comp_str[[2]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp3_4 <- compare(loo_3, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelthree), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp3_4)
elpd_diff34 = comp3_4[1]
se_elpd_diff34 = comp3_4[2]
write.table(data.frame(elpd_diff34, se_elpd_diff34), file = paste0("tables/mods_comp/comp_", out_comp_str[[3]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
# y_rep_mod1 = summary(mod1, pars = c("y_rep"))$summary[,'mean']
# roc_obj_mod1 = roc(CZ_data$mountYNcontact, y_rep_mod1, ci = TRUE)
# cat(paste0("Area under the curve and 95% CI for ", basename(opt$modelone), ".\n"))
# auc(roc_obj_mod1)
# ci.auc(roc_obj_mod1)
#
# y_rep_mod2 = summary(mod2, pars = c("y_rep"))$summary[,'mean']
# roc_obj_mod2 = roc(CZ_data$mountYNcontact, y_rep_mod2, ci = TRUE)
# cat(paste0("Area under the curve and 95% CI for ", basename(opt$modeltwo), ".\n"))
# auc(roc_obj_mod2)
# ci.auc(roc_obj_mod2)
#
# cat(paste0("Bootstrap test for ROC comparison between ", basename(opt$modelone), " and ", basename(opt$modeltwo), ".\n"))
# roc.test(roc_obj_mod1, roc_obj_mod2, method = "bootstrap")
# Marginal posterior predictive checks
# y_rep1 = rstan::extract(mod1, pars = 'y_rep', permuted = TRUE)$y_rep
# ppc_loo_pit_overlay(
# y = CZ_data$mountYN,
# yrep = y_rep1,
# lw = weights(loo_1$psis_object)
# )
# The excessive number of values close to 1 indicates that the model is under-dispersed compared to the data,
# and we should consider a model that allows for greater dispersion.
# y_rep2 = rstan::extract(mod2, pars = 'y_rep', permuted = TRUE)$y_rep
# ppc_loo_pit_overlay(
# y = CZ_data$mountYN,
# yrep = y_rep2,
# lw = weights(loo_2$psis_object)
# )
# loo_1 <- loo(log_lik_1, k_threshold=0.7, cores = 2)
# loo_2 <- loo(log_lik_2, k_threshold=0.7, cores = 2)
# loo_list <- list(loo_1, loo_2)
# loo_model_weights(loo_list)
# loo_model_weights(loo_list, method = "pseudobma")
# loo_model_weights(loo_list, method = "pseudobma", BB = FALSE)
|
/scripts/mods_comp.R
|
no_license
|
sam0per/L.saxatilis-Mate-choice
|
R
| false
| false
| 11,331
|
r
|
# Usage example:
# Rscript L.saxatilis-Mate-choice/scripts/mods_comp.R --modelone models/gaus_skew/SKEW/gaus_skew.rds \
# --modeltwo models/gaus_skew/BCDG_shore/gaus_skew_hier_BCDG_shore.rds --modelthree models/gaus_skew/BCDG_eco/gaus_skew_hier_BCDG_eco.rds \
# --modelfour models/gaus_skew/BCDG/gaus_skew_hier_BCDG.rds -d data/CZ_all_mating_clean.csv -o tables/mods_comp/comp_
rm(list = ls())
.packages = c("ggplot2", "dplyr", "rstan", "tibble", "boot", "bayesplot", "Rmisc", "pander",
"bbmle", "loo", "ggpubr", "cowplot", "purrr", "reshape2", "gridExtra", "grid", "arm", "parallel",
"rstantools", "optparse", "pROC")
# Install CRAN packages (if not already installed)
.inst <- .packages %in% installed.packages()
if(length(.packages[!.inst]) > 0) install.packages(.packages[!.inst])
# Load packages into session
lapply(.packages, require, character.only=TRUE)
option_list = list(
make_option("--modelone", type="character", default=NULL,
help="first model for comparison", metavar="character"),
make_option("--modeltwo", type="character", default=NULL,
help="second model for comparison", metavar="character"),
make_option("--modelthree", type="character", default=NULL,
help="third model for comparison", metavar="character"),
make_option("--modelfour", type="character", default=NULL,
help="fourth model for comparison", metavar="character"),
make_option("--nnhier", type="character", default=NULL,
help="non-hierarchical model for comparison", metavar="character"),
make_option(c("-o", "--out"), type="character", default="mods_comp_out.csv",
help="output file name [default = %default]", metavar="character"),
make_option(c("-d", "--data"), type="character", default=NULL,
help="input data", metavar="character"))
opt_parser = OptionParser(option_list=option_list)
opt = parse_args(opt_parser)
if (is.null(opt$modelone) | is.null(opt$modeltwo) | is.null(opt$data)) {
print_help(opt_parser)
stop("At least three arguments must be supplied (two models and one dataset).\n", call.=FALSE)
}
########################################################
###### approximate leave-one-out cross-validation ######
# http://mc-stan.org/loo/articles/loo2-with-rstan.html #
########################################################
CZ_data = read.csv(opt$data, sep = ";")
# mod1 = readRDS("models/gaus_skew/gaus_skew.rds")
mod1 = readRDS(opt$modelone)
# mod2 = readRDS("models/gaus_skew/gaus_skew_hier_BCDG_shore.rds")
mod2 = readRDS(opt$modeltwo)
mod3 = readRDS(opt$modelthree)
mod4 = readRDS(opt$modelfour)
nnhier = readRDS(opt$nnhier)
out_comp_str = lapply(c(opt$modelone,opt$modeltwo,opt$modelthree,opt$modelfour,opt$nnhier), function(x) {
modsplit = strsplit(strsplit(basename(x), "[.]")[[1]][1], split = "_")[[1]]
return(modsplit[length(modsplit)])
})
# Extract pointwise log-likelihood and compute LOO
log_lik_1 <- extract_log_lik(mod1, merge_chains = FALSE)
log_lik_2 <- extract_log_lik(mod2, merge_chains = FALSE)
log_lik_3 <- extract_log_lik(mod3, merge_chains = FALSE)
log_lik_4 <- extract_log_lik(mod4, merge_chains = FALSE)
log_lik_nnhier <- extract_log_lik(nnhier, merge_chains = FALSE)
# provide relative effective sample sizes
r_eff <- relative_eff(exp(log_lik_1))
loo_1 <- loo(log_lik_1, r_eff = r_eff, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelone)))
print(loo_1)
# split_path <- function(x) if (dirname(x)==x) x else c(basename(x),split_path(dirname(x)))
# out_comp = "tables/mods_comp/comp_skew_shore.csv"
# inp_mod1 = "models/gaus_skew/gaus_skew.rds"
out_mod1 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelone), "[.]")[[1]][1], ".csv")
write.table(round(loo_1$estimates, 2), out_mod1, sep = ",", row.names = TRUE, col.names = TRUE)
# plot(loo_1)
r_eff_2 <- relative_eff(exp(log_lik_2))
loo_2 <- loo(log_lik_2, r_eff = r_eff_2, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modeltwo)))
print(loo_2)
out_mod2 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modeltwo), "[.]")[[1]][1], ".csv")
write.table(round(loo_2$estimates, 2), out_mod2, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_3 <- relative_eff(exp(log_lik_3))
loo_3 <- loo(log_lik_3, r_eff = r_eff_3, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelthree)))
print(loo_3)
out_mod3 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelthree), "[.]")[[1]][1], ".csv")
write.table(round(loo_3$estimates, 2), out_mod3, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_4 <- relative_eff(exp(log_lik_4))
loo_4 <- loo(log_lik_4, r_eff = r_eff_4, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$modelfour)))
print(loo_4)
out_mod4 = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$modelfour), "[.]")[[1]][1], ".csv")
write.table(round(loo_4$estimates, 2), out_mod4, sep = ",", row.names = TRUE, col.names = TRUE)
r_eff_nnhier <- relative_eff(exp(log_lik_nnhier))
loo_nnhier <- loo(log_lik_nnhier, r_eff = r_eff_nnhier, cores = 4, save_psis = TRUE)
print(paste0("LOO results for model ", basename(opt$nnhier)))
print(loo_nnhier)
out_modnnhier = paste0(dirname(opt$out), "/loo_", strsplit(basename(opt$nnhier), "[.]")[[1]][1], ".csv")
write.table(round(loo_nnhier$estimates, 2), out_modnnhier, sep = ",", row.names = TRUE, col.names = TRUE)
print(paste0("Parameters in model ", basename(opt$modelone)))
mod1@model_pars
print(paste0("Parameters in model ", basename(opt$modeltwo)))
mod2@model_pars
print(paste0("Parameters in model ", basename(opt$modelthree)))
mod3@model_pars
print(paste0("Parameters in model ", basename(opt$modelfour)))
mod4@model_pars
print(paste0("Parameters in non-hierarchical model ", basename(opt$nnhier)))
nnhier@model_pars
# plot(loo_2, label_points = TRUE)
comp1_2 <- compare(loo_1, loo_2)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modeltwo), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_2)
elpd_diff = comp1_2[1]
se_elpd_diff = comp1_2[2]
write.table(data.frame(elpd_diff, se_elpd_diff), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[2]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp1_3 <- compare(loo_1, loo_3)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modelthree), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_3)
elpd_diff13 = comp1_3[1]
se_elpd_diff13 = comp1_3[2]
write.table(data.frame(elpd_diff13, se_elpd_diff13), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[3]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp1_4 <- compare(loo_1, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelone), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp1_4)
elpd_diff14 = comp1_4[1]
se_elpd_diff14 = comp1_4[2]
write.table(data.frame(elpd_diff14, se_elpd_diff14), file = paste0("tables/mods_comp/comp_", out_comp_str[[1]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp2_3 <- compare(loo_2, loo_3)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modeltwo), " and ", basename(opt$modelthree), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp2_3)
elpd_diff23 = comp2_3[1]
se_elpd_diff23 = comp2_3[2]
write.table(data.frame(elpd_diff23, se_elpd_diff23), file = paste0("tables/mods_comp/comp_", out_comp_str[[2]], "_", out_comp_str[[3]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp2_4 <- compare(loo_2, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modeltwo), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp2_4)
elpd_diff24 = comp2_4[1]
se_elpd_diff24 = comp2_4[2]
write.table(data.frame(elpd_diff24, se_elpd_diff24), file = paste0("tables/mods_comp/comp_", out_comp_str[[2]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
comp3_4 <- compare(loo_3, loo_4)
cat(paste0("estimated difference of expected leave-one-out prediction errors\nbetween ",
basename(opt$modelthree), " and ", basename(opt$modelfour), " along with the standard error.\n",
" Positive difference in elpd (and its scale relative to the standard error)\nindicates a preference for the second model.\n"))
print(comp3_4)
elpd_diff34 = comp3_4[1]
se_elpd_diff34 = comp3_4[2]
write.table(data.frame(elpd_diff34, se_elpd_diff34), file = paste0("tables/mods_comp/comp_", out_comp_str[[3]], "_", out_comp_str[[4]], ".csv"),
row.names = FALSE, col.names = TRUE, sep = ",")
# y_rep_mod1 = summary(mod1, pars = c("y_rep"))$summary[,'mean']
# roc_obj_mod1 = roc(CZ_data$mountYNcontact, y_rep_mod1, ci = TRUE)
# cat(paste0("Area under the curve and 95% CI for ", basename(opt$modelone), ".\n"))
# auc(roc_obj_mod1)
# ci.auc(roc_obj_mod1)
#
# y_rep_mod2 = summary(mod2, pars = c("y_rep"))$summary[,'mean']
# roc_obj_mod2 = roc(CZ_data$mountYNcontact, y_rep_mod2, ci = TRUE)
# cat(paste0("Area under the curve and 95% CI for ", basename(opt$modeltwo), ".\n"))
# auc(roc_obj_mod2)
# ci.auc(roc_obj_mod2)
#
# cat(paste0("Bootstrap test for ROC comparison between ", basename(opt$modelone), " and ", basename(opt$modeltwo), ".\n"))
# roc.test(roc_obj_mod1, roc_obj_mod2, method = "bootstrap")
# Marginal posterior predictive checks
# y_rep1 = rstan::extract(mod1, pars = 'y_rep', permuted = TRUE)$y_rep
# ppc_loo_pit_overlay(
# y = CZ_data$mountYN,
# yrep = y_rep1,
# lw = weights(loo_1$psis_object)
# )
# The excessive number of values close to 1 indicates that the model is under-dispersed compared to the data,
# and we should consider a model that allows for greater dispersion.
# y_rep2 = rstan::extract(mod2, pars = 'y_rep', permuted = TRUE)$y_rep
# ppc_loo_pit_overlay(
# y = CZ_data$mountYN,
# yrep = y_rep2,
# lw = weights(loo_2$psis_object)
# )
# loo_1 <- loo(log_lik_1, k_threshold=0.7, cores = 2)
# loo_2 <- loo(log_lik_2, k_threshold=0.7, cores = 2)
# loo_list <- list(loo_1, loo_2)
# loo_model_weights(loo_list)
# loo_model_weights(loo_list, method = "pseudobma")
# loo_model_weights(loo_list, method = "pseudobma", BB = FALSE)
|
##get ContribUrl of the BrainConductor repository,
##the package should be available via the Url,
##or the function will check it for older R version.
.getContribUrl <-
function(BCoVersion)
{
.contribUrl <-
function(repos, replace_oldRepos = FALSE)
{
contribUrl <- contrib.url(repos)
if(replace_oldRepos) {
version <- getRversion()
currentVersion <- sprintf("%d.%d", version$major, version$minor)
lowerVersion <- .lowerRVersionString(version)
oldcontribUrl <- sub(currentVersion, lowerVersion, contribUrl)
if (oldcontribUrl == contribUrl)
.stop("'%s' while trying %s", conditionMessage(err),
repos, call.=FALSE)
contribUrl <- oldcontribUrl
}
pkgs <- available.packages(contribUrl)
if (nrow(pkgs) == 0L)
.stop("no packages in repository (no internet connection?)",
call.=FALSE)
else if (!"BrainCoSetup" %in% rownames(pkgs))
.stop("'BrainCoSetup' package not in repository",
call.=FALSE)
contribUrl
}
##remember to modify this line
repos <- .BCoInstallRepos(braincoversion=BCoVersion)["BrainCo_soft"]
suppressWarnings(tryCatch({
.contribUrl(repos)
}, error=function(err) {
## version <- getRversion()
## currentVersion <- sprintf("%d.%d", version$major, version$minor)
## lowerVersion <- .lowerRVersionString(version)
## oldRepos <- sub(currentVersion, lowerVersion, repos)
## if (oldRepos == repos)
## .stop("'%s' while trying %s", conditionMessage(err),
## repos, call.=FALSE)
.msg("'%s' while trying %s, trying older R version package",
conditionMessage(err), repos)
.contribUrl(repos, replace_oldRepos = TRUE)
}))
}
##Check whether the repository of the current BrainConductor version
##has a new version of BrainCoSetup package
BCoPkgIsCurrent <-
function()
{
installedSentinel <- availableSentinel <- package_version("0.0.0")
installedVersion <-
tryCatch(packageVersion("BrainCoSetup"),
error = function(err) installedSentinel)
contribUrl <- .getContribUrl(BrainCoVersion())
ap <- available.packages(contribUrl)
availableVersion <-
if ("BrainCoSetup" %in% rownames(ap))
package_version(ap["BrainCoSetup", "Version"])
else
availableSentinel
if ((installedVersion == availableVersion) &&
(installedVersion == installedSentinel))
.stop("'BrainCoSetup' package not installed, and not available")
availableVersion <= installedVersion
}
updateBCoPkg <-
function(pkgs, ask, suppressUpdates, ...)
{
.msg("before update, version is %s", packageVersion("BrainCoSetup"))
SetupPackageUpdate <-
function()
{
if ("package:BrainCoSetup" %in% search())
detach("package:BrainCoSetup", unload=TRUE, force=TRUE)
## contribUrl will be in SetupPackageUpdate's environment, i.e. BCoSetupEnv
suppressWarnings(tryCatch({
update.packages(contriburl=contribUrl, ask=FALSE,
checkBuilt=TRUE, oldPkgs="BrainCoSetup")
}, error=function(err) {
assign("failed", TRUE, "BCoSetupEnv")
NULL
}))
library(BrainCoSetup)
BrainCoSetup:::.updateBCoPkgFinish()
}
BCoSetupEnv <- new.env()
BCoSetupEnv[["pkgs"]] <- pkgs[pkgs != "BrainCoSetup"]
BCoSetupEnv[["ask"]] <- ask
BCoSetupEnv[["suppressUpdates"]] <- suppressUpdates
BCoSetupEnv[["contribUrl"]] <- .getContribUrl(BrainCoVersion())
BCoSetupEnv[["dotArgs"]] <- list(...)
.stepAside(BCoSetupEnv, SetupPackageUpdate)
}
.updateBCoPkgFinish <-
function()
{
args <- c(list(pkgs=get("pkgs", "BCoSetupEnv"),
ask=get("ask", "BCoSetupEnv"),
suppressUpdates=get("suppressUpdates", "BCoSetupEnv")),
get("dotArgs", "BCoSetupEnv"))
failed <- exists("failed", "BCoSetupEnv")
detach("BCoSetupEnv")
.msg("after update, version is %s", packageVersion("BrainCoSetup"))
vers <- packageVersion("BrainCoSetup")
if (!failed)
.msg("'BrainCoSetup' updated to version %s", vers)
else
.warning("'BrainCoSetup' update failed, using version %s",
vers, call.=FALSE)
if ("BCoUpgrade" %in% args$pkgs) {
.BCoUpgrade()
} else {
do.call(.BCoInstall, args)
}
}
|
/BrainCoSetup/R/updateBCoPkg.R
|
no_license
|
haixiao990/BrainConductor
|
R
| false
| false
| 4,547
|
r
|
##get ContribUrl of the BrainConductor repository,
##the package should be available via the Url,
##or the function will check it for older R version.
.getContribUrl <-
function(BCoVersion)
{
.contribUrl <-
function(repos, replace_oldRepos = FALSE)
{
contribUrl <- contrib.url(repos)
if(replace_oldRepos) {
version <- getRversion()
currentVersion <- sprintf("%d.%d", version$major, version$minor)
lowerVersion <- .lowerRVersionString(version)
oldcontribUrl <- sub(currentVersion, lowerVersion, contribUrl)
if (oldcontribUrl == contribUrl)
.stop("'%s' while trying %s", conditionMessage(err),
repos, call.=FALSE)
contribUrl <- oldcontribUrl
}
pkgs <- available.packages(contribUrl)
if (nrow(pkgs) == 0L)
.stop("no packages in repository (no internet connection?)",
call.=FALSE)
else if (!"BrainCoSetup" %in% rownames(pkgs))
.stop("'BrainCoSetup' package not in repository",
call.=FALSE)
contribUrl
}
##remember to modify this line
repos <- .BCoInstallRepos(braincoversion=BCoVersion)["BrainCo_soft"]
suppressWarnings(tryCatch({
.contribUrl(repos)
}, error=function(err) {
## version <- getRversion()
## currentVersion <- sprintf("%d.%d", version$major, version$minor)
## lowerVersion <- .lowerRVersionString(version)
## oldRepos <- sub(currentVersion, lowerVersion, repos)
## if (oldRepos == repos)
## .stop("'%s' while trying %s", conditionMessage(err),
## repos, call.=FALSE)
.msg("'%s' while trying %s, trying older R version package",
conditionMessage(err), repos)
.contribUrl(repos, replace_oldRepos = TRUE)
}))
}
##Check whether the repository of the current BrainConductor version
##has a new version of BrainCoSetup package
BCoPkgIsCurrent <-
function()
{
installedSentinel <- availableSentinel <- package_version("0.0.0")
installedVersion <-
tryCatch(packageVersion("BrainCoSetup"),
error = function(err) installedSentinel)
contribUrl <- .getContribUrl(BrainCoVersion())
ap <- available.packages(contribUrl)
availableVersion <-
if ("BrainCoSetup" %in% rownames(ap))
package_version(ap["BrainCoSetup", "Version"])
else
availableSentinel
if ((installedVersion == availableVersion) &&
(installedVersion == installedSentinel))
.stop("'BrainCoSetup' package not installed, and not available")
availableVersion <= installedVersion
}
updateBCoPkg <-
function(pkgs, ask, suppressUpdates, ...)
{
.msg("before update, version is %s", packageVersion("BrainCoSetup"))
SetupPackageUpdate <-
function()
{
if ("package:BrainCoSetup" %in% search())
detach("package:BrainCoSetup", unload=TRUE, force=TRUE)
## contribUrl will be in SetupPackageUpdate's environment, i.e. BCoSetupEnv
suppressWarnings(tryCatch({
update.packages(contriburl=contribUrl, ask=FALSE,
checkBuilt=TRUE, oldPkgs="BrainCoSetup")
}, error=function(err) {
assign("failed", TRUE, "BCoSetupEnv")
NULL
}))
library(BrainCoSetup)
BrainCoSetup:::.updateBCoPkgFinish()
}
BCoSetupEnv <- new.env()
BCoSetupEnv[["pkgs"]] <- pkgs[pkgs != "BrainCoSetup"]
BCoSetupEnv[["ask"]] <- ask
BCoSetupEnv[["suppressUpdates"]] <- suppressUpdates
BCoSetupEnv[["contribUrl"]] <- .getContribUrl(BrainCoVersion())
BCoSetupEnv[["dotArgs"]] <- list(...)
.stepAside(BCoSetupEnv, SetupPackageUpdate)
}
.updateBCoPkgFinish <-
function()
{
args <- c(list(pkgs=get("pkgs", "BCoSetupEnv"),
ask=get("ask", "BCoSetupEnv"),
suppressUpdates=get("suppressUpdates", "BCoSetupEnv")),
get("dotArgs", "BCoSetupEnv"))
failed <- exists("failed", "BCoSetupEnv")
detach("BCoSetupEnv")
.msg("after update, version is %s", packageVersion("BrainCoSetup"))
vers <- packageVersion("BrainCoSetup")
if (!failed)
.msg("'BrainCoSetup' updated to version %s", vers)
else
.warning("'BrainCoSetup' update failed, using version %s",
vers, call.=FALSE)
if ("BCoUpgrade" %in% args$pkgs) {
.BCoUpgrade()
} else {
do.call(.BCoInstall, args)
}
}
|
library(poker)
### Name: assignToPlayers
### Title: assignToPlayers
### Aliases: assignToPlayers
### ** Examples
assignToPlayers(9,9,1:23)
assignToPlayers(9,1,1:23)
assignToPlayers(9,1,c(1:17,24,48:52))
|
/data/genthat_extracted_code/poker/examples/assignToPlayers.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 210
|
r
|
library(poker)
### Name: assignToPlayers
### Title: assignToPlayers
### Aliases: assignToPlayers
### ** Examples
assignToPlayers(9,9,1:23)
assignToPlayers(9,1,1:23)
assignToPlayers(9,1,c(1:17,24,48:52))
|
#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Investment Property"),
sidebarPanel(
h4('Property'),
textInput('text', 'Address', value=''),
numericInput('price', 'Price ($)', 550000),
numericInput('weeklyRent', 'Weekly Rent ($)', 550),
h5('Costs'),
numericInput('weeklyRepayments', 'Weekly Repayments ($ per week)', 503),
numericInput('waterPerQuarter', 'Water ($ per quarter)', 180),
numericInput('managementFees', 'Management Fees ($ per week)', 38)
),
mainPanel(
h4('Rental Yield (%)'),
textOutput("rentalYield"),
h4('Cashflow Per Week ($)'),
textOutput("cashflowPerWeek"),
h4('Cashflow Per Year ($)'),
textOutput("cashflowPerYear"),
br(),
h4('Instructions'),
helpText("This application is for designed to allow investors to calculate the potential rental yield of a property and estimate the cashflow of owning the property."),
code("Rental Yield"),
helpText("Enter the property's price and expected weekly rent to calculate the rental yield."),
code("Cashflow"),
helpText("Enter the property's costs to estimate the cashflow of owning the property.")
)
))
|
/ui.R
|
no_license
|
Qaiserijaz/developingdataproduct
|
R
| false
| false
| 1,714
|
r
|
#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Investment Property"),
sidebarPanel(
h4('Property'),
textInput('text', 'Address', value=''),
numericInput('price', 'Price ($)', 550000),
numericInput('weeklyRent', 'Weekly Rent ($)', 550),
h5('Costs'),
numericInput('weeklyRepayments', 'Weekly Repayments ($ per week)', 503),
numericInput('waterPerQuarter', 'Water ($ per quarter)', 180),
numericInput('managementFees', 'Management Fees ($ per week)', 38)
),
mainPanel(
h4('Rental Yield (%)'),
textOutput("rentalYield"),
h4('Cashflow Per Week ($)'),
textOutput("cashflowPerWeek"),
h4('Cashflow Per Year ($)'),
textOutput("cashflowPerYear"),
br(),
h4('Instructions'),
helpText("This application is for designed to allow investors to calculate the potential rental yield of a property and estimate the cashflow of owning the property."),
code("Rental Yield"),
helpText("Enter the property's price and expected weekly rent to calculate the rental yield."),
code("Cashflow"),
helpText("Enter the property's costs to estimate the cashflow of owning the property.")
)
))
|
# Installing older version of statnet.common package (this may take a while)
devtools::install_version("statnet.common", version = "4.1.2", repos = "http://cran.us.r-project.org")
# Installing CRAN packages (this may take a while)
packages <- c("devtools", "tm","quanteda", "tuber","qdapRegex","rlang","purrr","ggplot2", "syuzhet", "lexicon") # create list of required packages
for (package in packages) { # check if those packages are already installed
if (!require(package, character.only=T)) { # if not, install & load them
install.packages(package)
library(package, character.only=T)
}
else {library(package, character.only=T)} # if they are already installed, only load them
}
rm(packages, package)
# GitHub packages (this may take a while)
install_github("hadley/emo") # install from GitHub
library(emo) # load the emo package
# Installing the emoGG package from github (not on CRAN yet): Displays emojis in ggplot objects
devtools::install_github("dill/emoGG") # install from GitHub
library(emoGG) # load the emo package
|
/binder/install.R
|
no_license
|
gesiscss/YouTubeComments
|
R
| false
| false
| 1,044
|
r
|
# Installing older version of statnet.common package (this may take a while)
devtools::install_version("statnet.common", version = "4.1.2", repos = "http://cran.us.r-project.org")
# Installing CRAN packages (this may take a while)
packages <- c("devtools", "tm","quanteda", "tuber","qdapRegex","rlang","purrr","ggplot2", "syuzhet", "lexicon") # create list of required packages
for (package in packages) { # check if those packages are already installed
if (!require(package, character.only=T)) { # if not, install & load them
install.packages(package)
library(package, character.only=T)
}
else {library(package, character.only=T)} # if they are already installed, only load them
}
rm(packages, package)
# GitHub packages (this may take a while)
install_github("hadley/emo") # install from GitHub
library(emo) # load the emo package
# Installing the emoGG package from github (not on CRAN yet): Displays emojis in ggplot objects
devtools::install_github("dill/emoGG") # install from GitHub
library(emoGG) # load the emo package
|
if(!require("pacman")) install.packages("pacman")
p_load("tidyverse", "xlsx", "sandwich", "lmtest")
library("lpirfs")
GW_dataset <- read.xlsx("data/Returns_short_interest_data.xlsx",
sheetName = "GW variables")
GW_varset <- as.data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197212) %>%
select(yyyymm, Index, D12, E12, b.m, ntis, tbl, lty, ltr, BAA,
AAA, corpr, infl) %>%
rename(SP = Index, D12 = D12, E12 = E12, BM = b.m, NTIS = ntis,
TBL = tbl, LTY = lty, LTR = ltr, BAA = BAA, AAA = AAA,
CORPR = corpr, INFL_lag = infl) %>%
mutate(log_DP = log(D12/SP), log_EP = log(E12/SP),
log_DE = log(D12/E12), log_DY = log(D12/lag(SP)),
TMS = LTY - TBL, DFY = BAA - AAA, DFR = CORPR - LTR,
INFL_lag = lag(INFL_lag)) %>%
select(log_DP, log_DY, log_EP, log_DE, BM, NTIS, TBL, LTY,
LTR, TMS, DFY, DFR, INFL_lag) %>%
na.omit()
# Stock excess return volatility (annualized) ----------------------------------
RVOL <- as.data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197201) %>%
select(yyyymm, CRSP_SPvw, Rfree) %>%
mutate(R_F_lag = lag(Rfree)) %>%
rename(SP_R = CRSP_SPvw) %>%
select(yyyymm, SP_R, R_F_lag) %>%
na.omit()
RVOL_mat <- matrix(NaN, nrow = nrow(RVOL) - 11, ncol = 1)
for(i in seq_along(RVOL_mat)){
RVOL_mat[i,] <- mean(abs(RVOL$SP_R[i:(i+11)] - RVOL$R_F_lag[i:(i+11)]))
}
RVOL_mat <- sqrt(pi/2)*sqrt(12)*RVOL_mat
# merge variables --------------------------------------------------------------
GW_predictor <- cbind(GW_varset, RVOL_mat) %>%
rename(RVOL = RVOL_mat) %>%
select(log_DP, log_DY, log_EP, log_DE, RVOL, BM, NTIS, TBL, LTY,
LTR, TMS, DFY, DFR, INFL_lag)
# Load equity risk premium data, 1973:01-2014:12 -------------------------------
equity_risk <- data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197212) %>%
select(yyyymm, Rfree, CRSP_SPvw) %>%
rename(R_SP500 = CRSP_SPvw) %>%
mutate(ER = R_SP500 - lag(Rfree), Rfree_lag = lag(Rfree),
r = log(1 + R_SP500) - log(1 + Rfree_lag)) %>%
na.omit()
# short-interest rate ----------------------------------------------------------
short_interest <- read.xlsx("data/Returns_short_interest_data.xlsx",
sheetName = "Short interest") %>%
select(Date, EWSI) %>%
mutate(log_EWSI = log(EWSI)) %>%
select(Date, log_EWSI)
# Compute log(EWSI) deviation from linear trend --------------------------------
X_linear <- apply(as.matrix(rownames(short_interest)), 2, as.numeric)
result_linear <- lm(short_interest$log_EWSI ~ X_linear)
SII <- scale(result_linear$residual)
# Compute cumulative returns ---------------------------------------------------
h <- c(1, 3, 6, 12)
r_h <- matrix(NaN, nrow = nrow(equity_risk), ncol = NROW(h))
for(i in seq_along(h)){
for(j in 1:(nrow(equity_risk) - (h[i] - 1))){
r_h[j,i] <- mean(equity_risk$r[j:(j+h[i]-1)])
}
}
# scale predictors -------------------------------------------------------------
GW_predictor_z <- apply(GW_predictor, 2, scale)
beta_hat <- array(NaN, c(NCOL(GW_predictor), 4, length(h)))
for(i in seq_along(h)){
for(j in 1:(ncol(GW_predictor))){
if(j <= ncol(GW_predictor)){
X_i_j <- as.matrix(GW_predictor_z[1:(NROW(GW_predictor_z)-h[i]), j])
Y_i_j <- 100*r_h[2:(NROW(r_h)-(h[i]-1)), i]
# results_i_j <- newey_west(Y_i_j , X_i_j, h[i])
results_i_j_lm <- lm(100*r_h[2:(NROW(r_h)-(h[i]-1)), i] ~ X_i_j)
results_i_j_lm_coef <- coeftest(results_i_j_lm, vcov.=NeweyWest(results_i_j_lm, lag=h[i], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat[j,,i] <- cbind(results_i_j_lm_coef[2,1],
results_i_j_lm_coef[2,3], # coef(summary(results_i_j))[, "t value"][2]
NaN,
summary(results_i_j_lm)$r.squared*100)
}
}
}
round(beta_hat, 2)
# IVX Wald and qLL -------------------------------------------------------------
IVX_Wald <- matrix(NaN, nrow = 2, ncol = NROW(h))
qLL_hat <- matrix(NaN, nrow = length(h), 1)
beta_hat_SII <- array(NaN, c(1, 4, length(h)))
source("Compute_IVX_Wald.R")
source("Compute_qLL_hat.R")
for(i in seq_along(h)){
X_i_j <- as.matrix(-SII[1:(NROW(SII)-h[i])])
Y_i_j <- 100*r_h[2:(NROW(r_h)-(h[i]-1)), i]
results_i_j_lm <- lm(100*r_h[2:(NROW(r_h)-(h[i]-1)), i] ~ X_i_j)
results_i_j_lm_coef <- coeftest(results_i_j_lm, vcov.=NeweyWest(results_i_j_lm, lag=h[i], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat_SII[,,i] <- cbind(results_i_j_lm_coef[2,1],
results_i_j_lm_coef[2,3],
NaN,
summary(results_i_j_lm)$r.squared*100)
IVX_results <- Compute_IVX_Wald(equity_risk$r, -SII, h[i], 0, 0.99)
IVX_Wald[1,i] <- IVX_results[[2]]
IVX_Wald[2,i] <- IVX_results[[3]]
y_i_j <- r_h[2:(NROW(r_h)-(h[i]-1)), i]
x_i_j <- as.matrix(-SII[1:(NROW(SII)-h[i])])
z_i_j <- matrix(1, nrow = (NROW(r_h) - h[i]), ncol = 1)
qLL_results <- Compute_qLL_hat(y_i_j, x_i_j, z_i_j, h[i])
qLL_hat[i] <- qLL_results[[1]]
}
round(IVX_Wald, 2)
round(qLL_hat, 2)
# Compute fixed-regressor wild bootstrap p-values ------------------------------
X_sink <- data.frame(cbind(GW_predictor_z, -SII)) %>%
select(-log_DE, -TMS)
results_sink <- lm(r_h[2:NROW(r_h),1] ~ as.matrix(X_sink[1:(NROW(X_sink)-1),]))
epsilon_hat <- as.matrix(results_sink$residuals)
B <- 1000
beta_hat_tstat_star <- array(NaN, c(B, NCOL(GW_predictor) + 1, length(h)))
saved.seed <- sample(1e9,1)
set.seed(saved.seed)
for(b in 1:B){
print(b)
u_star_b <- as.numeric(rnorm(NROW(r_h)-1, 0,1))
r_star_b <- rbind(equity_risk$r[1], mean(equity_risk$r) + epsilon_hat*u_star_b)
r_h_star_b <- matrix(NaN, NROW(equity_risk$r), length(h))
for(j in 1:length(h)){
for(t in 1:(length(equity_risk$r) - (h[j] - 1))){
r_h_star_b[t,j] <- mean(r_star_b[t:(t+(h[j]-1))])
}
}
for(j in 1:length(h)){
for(i in 1:(NCOL(GW_predictor))){
if(i <= NCOL(GW_predictor)){
X_i_j <- as.matrix(GW_predictor_z[1:(NROW(GW_predictor_z)-1-(h[j]-1)), i])
Y_i_j <- as.vector(100*r_h_star_b[2:(NROW(r_h_star_b)-(h[j]-1)), j])
results_i_j_star_b_lm <- lm(Y_i_j ~ X_i_j)
results_i_j_star_b <- coeftest(results_i_j_star_b_lm, vcov.=NeweyWest(results_i_j_star_b_lm, lag = h[j], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat_tstat_star[b,i,j] <- results_i_j_star_b[2,3] # results_i_j_star_b[[1]][2]/(sqrt(results_i_j_star_b[[2]][2,2])) or coef(summary(results_i_j))[, "t value"][2]
}
}
}
}
for(j in 1:length(h)){
for(i in 1:(NCOL(GW_predictor))){
beta_hat[i, 3, j] <- sum(beta_hat_tstat_star[,i,j] > beta_hat[i, 2, j])/B
}
}
colnames(beta_hat) <- c("Beta", "t_stat", "p_value", "R2")
rownames(beta_hat) <- c("DP","DY","EP","DE","RVOL","BM","NTIS",
"TBL","LTY","LTR","TMS","DFY","DFR","INFL")
round(beta_hat, 2)
# Take care of out-of-sample preliminaries -------------------------------------
T <- length(equity_risk$r)
in_sample_end <- 1989
R <- (in_sample_end - 1972)*12 # in-sample period
P <- T - R
FC_PM <- matrix(NaN, nrow = P, ncol = 1)
FC_PR <- array(NaN, c(P, NCOL(GW_predictor), NROW(h)))
# Compute out-of-sample forecasts ----------------------------------------------
for(p in 1:P){
print(p)
FC_PM[p] <- mean(equity_risk$r[1:(R+(p-1))])
for(j in 1:NROW(h)){
for(i in 1:NCOL(GW_predictor)){
X_i_j_p <- GW_predictor[1:(R+(p-1)-h[j]),i]
results_i_j_p <- lm(r_h[2:(R+p-h[j]),j] ~ X_i_j_p)
FC_PR[p,i,j] <- cbind(1, GW_predictor[R+(p-1),i])%*%results_i_j_p$coeff
}
}
}
# evaluate forecasts -----------------------------------------------------------
R2OS_PR <- array(NaN, c(NCOL(GW_predictor),2,NROW(h)))
for(j in 1:length(h)){
actual_j <- r_h[(R+1):(NROW(r_h)-(h[j]-1)),j]
u_PM_j <- actual_j - FC_PM[1:(NROW(FC_PM)-(h[j]-1))]
u_PR_j <- kronecker(matrix(1, nrow = 1, ncol = NCOL(FC_PR)), actual_j) - FC_PR[1:(NROW(FC_PR)-(h[j]-1)),,j]
MSFE_PM_j <- mean(u_PM_j^2)
MSFE_PR_j <- apply(u_PR_j^2, 2, mean)
R2OS_PR_j <- 100*(1-MSFE_PR_j/MSFE_PM_j)
R2OS_PR[,1,j] = t(R2OS_PR_j)
for(i in 1:NCOL(GW_predictor)){
f_CW_i_j <- u_PM_j^2 - u_PR_j[,i]^2 + (FC_PM[1:(NROW(FC_PM)-(h[j]-1))] - FC_PR[1:(NROW(FC_PR)-(h[j]-1)),i,j])^2
result_CW_i_j <- lm(f_CW_i_j ~ matrix(1, nrow = NROW(f_CW_i_j)))
R2OS_PR[i,2,j] <- coeftest(result_CW_i_j, vcov.=NeweyWest(result_CW_i_j, lag=h[j], adjust=FALSE, verbose = TRUE, prewhite = FALSE))[3]
}
}
round(R2OS_PR, 2)
|
/replication.R
|
no_license
|
GabboCg/IVX-Wald-and-qLLL-hat
|
R
| false
| false
| 9,156
|
r
|
if(!require("pacman")) install.packages("pacman")
p_load("tidyverse", "xlsx", "sandwich", "lmtest")
library("lpirfs")
GW_dataset <- read.xlsx("data/Returns_short_interest_data.xlsx",
sheetName = "GW variables")
GW_varset <- as.data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197212) %>%
select(yyyymm, Index, D12, E12, b.m, ntis, tbl, lty, ltr, BAA,
AAA, corpr, infl) %>%
rename(SP = Index, D12 = D12, E12 = E12, BM = b.m, NTIS = ntis,
TBL = tbl, LTY = lty, LTR = ltr, BAA = BAA, AAA = AAA,
CORPR = corpr, INFL_lag = infl) %>%
mutate(log_DP = log(D12/SP), log_EP = log(E12/SP),
log_DE = log(D12/E12), log_DY = log(D12/lag(SP)),
TMS = LTY - TBL, DFY = BAA - AAA, DFR = CORPR - LTR,
INFL_lag = lag(INFL_lag)) %>%
select(log_DP, log_DY, log_EP, log_DE, BM, NTIS, TBL, LTY,
LTR, TMS, DFY, DFR, INFL_lag) %>%
na.omit()
# Stock excess return volatility (annualized) ----------------------------------
RVOL <- as.data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197201) %>%
select(yyyymm, CRSP_SPvw, Rfree) %>%
mutate(R_F_lag = lag(Rfree)) %>%
rename(SP_R = CRSP_SPvw) %>%
select(yyyymm, SP_R, R_F_lag) %>%
na.omit()
RVOL_mat <- matrix(NaN, nrow = nrow(RVOL) - 11, ncol = 1)
for(i in seq_along(RVOL_mat)){
RVOL_mat[i,] <- mean(abs(RVOL$SP_R[i:(i+11)] - RVOL$R_F_lag[i:(i+11)]))
}
RVOL_mat <- sqrt(pi/2)*sqrt(12)*RVOL_mat
# merge variables --------------------------------------------------------------
GW_predictor <- cbind(GW_varset, RVOL_mat) %>%
rename(RVOL = RVOL_mat) %>%
select(log_DP, log_DY, log_EP, log_DE, RVOL, BM, NTIS, TBL, LTY,
LTR, TMS, DFY, DFR, INFL_lag)
# Load equity risk premium data, 1973:01-2014:12 -------------------------------
equity_risk <- data.frame(apply(GW_dataset, 2, as.numeric)) %>%
filter(yyyymm >= 197212) %>%
select(yyyymm, Rfree, CRSP_SPvw) %>%
rename(R_SP500 = CRSP_SPvw) %>%
mutate(ER = R_SP500 - lag(Rfree), Rfree_lag = lag(Rfree),
r = log(1 + R_SP500) - log(1 + Rfree_lag)) %>%
na.omit()
# short-interest rate ----------------------------------------------------------
short_interest <- read.xlsx("data/Returns_short_interest_data.xlsx",
sheetName = "Short interest") %>%
select(Date, EWSI) %>%
mutate(log_EWSI = log(EWSI)) %>%
select(Date, log_EWSI)
# Compute log(EWSI) deviation from linear trend --------------------------------
X_linear <- apply(as.matrix(rownames(short_interest)), 2, as.numeric)
result_linear <- lm(short_interest$log_EWSI ~ X_linear)
SII <- scale(result_linear$residual)
# Compute cumulative returns ---------------------------------------------------
h <- c(1, 3, 6, 12)
r_h <- matrix(NaN, nrow = nrow(equity_risk), ncol = NROW(h))
for(i in seq_along(h)){
for(j in 1:(nrow(equity_risk) - (h[i] - 1))){
r_h[j,i] <- mean(equity_risk$r[j:(j+h[i]-1)])
}
}
# scale predictors -------------------------------------------------------------
GW_predictor_z <- apply(GW_predictor, 2, scale)
beta_hat <- array(NaN, c(NCOL(GW_predictor), 4, length(h)))
for(i in seq_along(h)){
for(j in 1:(ncol(GW_predictor))){
if(j <= ncol(GW_predictor)){
X_i_j <- as.matrix(GW_predictor_z[1:(NROW(GW_predictor_z)-h[i]), j])
Y_i_j <- 100*r_h[2:(NROW(r_h)-(h[i]-1)), i]
# results_i_j <- newey_west(Y_i_j , X_i_j, h[i])
results_i_j_lm <- lm(100*r_h[2:(NROW(r_h)-(h[i]-1)), i] ~ X_i_j)
results_i_j_lm_coef <- coeftest(results_i_j_lm, vcov.=NeweyWest(results_i_j_lm, lag=h[i], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat[j,,i] <- cbind(results_i_j_lm_coef[2,1],
results_i_j_lm_coef[2,3], # coef(summary(results_i_j))[, "t value"][2]
NaN,
summary(results_i_j_lm)$r.squared*100)
}
}
}
round(beta_hat, 2)
# IVX Wald and qLL -------------------------------------------------------------
IVX_Wald <- matrix(NaN, nrow = 2, ncol = NROW(h))
qLL_hat <- matrix(NaN, nrow = length(h), 1)
beta_hat_SII <- array(NaN, c(1, 4, length(h)))
source("Compute_IVX_Wald.R")
source("Compute_qLL_hat.R")
for(i in seq_along(h)){
X_i_j <- as.matrix(-SII[1:(NROW(SII)-h[i])])
Y_i_j <- 100*r_h[2:(NROW(r_h)-(h[i]-1)), i]
results_i_j_lm <- lm(100*r_h[2:(NROW(r_h)-(h[i]-1)), i] ~ X_i_j)
results_i_j_lm_coef <- coeftest(results_i_j_lm, vcov.=NeweyWest(results_i_j_lm, lag=h[i], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat_SII[,,i] <- cbind(results_i_j_lm_coef[2,1],
results_i_j_lm_coef[2,3],
NaN,
summary(results_i_j_lm)$r.squared*100)
IVX_results <- Compute_IVX_Wald(equity_risk$r, -SII, h[i], 0, 0.99)
IVX_Wald[1,i] <- IVX_results[[2]]
IVX_Wald[2,i] <- IVX_results[[3]]
y_i_j <- r_h[2:(NROW(r_h)-(h[i]-1)), i]
x_i_j <- as.matrix(-SII[1:(NROW(SII)-h[i])])
z_i_j <- matrix(1, nrow = (NROW(r_h) - h[i]), ncol = 1)
qLL_results <- Compute_qLL_hat(y_i_j, x_i_j, z_i_j, h[i])
qLL_hat[i] <- qLL_results[[1]]
}
round(IVX_Wald, 2)
round(qLL_hat, 2)
# Compute fixed-regressor wild bootstrap p-values ------------------------------
X_sink <- data.frame(cbind(GW_predictor_z, -SII)) %>%
select(-log_DE, -TMS)
results_sink <- lm(r_h[2:NROW(r_h),1] ~ as.matrix(X_sink[1:(NROW(X_sink)-1),]))
epsilon_hat <- as.matrix(results_sink$residuals)
B <- 1000
beta_hat_tstat_star <- array(NaN, c(B, NCOL(GW_predictor) + 1, length(h)))
saved.seed <- sample(1e9,1)
set.seed(saved.seed)
for(b in 1:B){
print(b)
u_star_b <- as.numeric(rnorm(NROW(r_h)-1, 0,1))
r_star_b <- rbind(equity_risk$r[1], mean(equity_risk$r) + epsilon_hat*u_star_b)
r_h_star_b <- matrix(NaN, NROW(equity_risk$r), length(h))
for(j in 1:length(h)){
for(t in 1:(length(equity_risk$r) - (h[j] - 1))){
r_h_star_b[t,j] <- mean(r_star_b[t:(t+(h[j]-1))])
}
}
for(j in 1:length(h)){
for(i in 1:(NCOL(GW_predictor))){
if(i <= NCOL(GW_predictor)){
X_i_j <- as.matrix(GW_predictor_z[1:(NROW(GW_predictor_z)-1-(h[j]-1)), i])
Y_i_j <- as.vector(100*r_h_star_b[2:(NROW(r_h_star_b)-(h[j]-1)), j])
results_i_j_star_b_lm <- lm(Y_i_j ~ X_i_j)
results_i_j_star_b <- coeftest(results_i_j_star_b_lm, vcov.=NeweyWest(results_i_j_star_b_lm, lag = h[j], adjust=FALSE, verbose = TRUE, prewhite = FALSE))
beta_hat_tstat_star[b,i,j] <- results_i_j_star_b[2,3] # results_i_j_star_b[[1]][2]/(sqrt(results_i_j_star_b[[2]][2,2])) or coef(summary(results_i_j))[, "t value"][2]
}
}
}
}
for(j in 1:length(h)){
for(i in 1:(NCOL(GW_predictor))){
beta_hat[i, 3, j] <- sum(beta_hat_tstat_star[,i,j] > beta_hat[i, 2, j])/B
}
}
colnames(beta_hat) <- c("Beta", "t_stat", "p_value", "R2")
rownames(beta_hat) <- c("DP","DY","EP","DE","RVOL","BM","NTIS",
"TBL","LTY","LTR","TMS","DFY","DFR","INFL")
round(beta_hat, 2)
# Take care of out-of-sample preliminaries -------------------------------------
T <- length(equity_risk$r)
in_sample_end <- 1989
R <- (in_sample_end - 1972)*12 # in-sample period
P <- T - R
FC_PM <- matrix(NaN, nrow = P, ncol = 1)
FC_PR <- array(NaN, c(P, NCOL(GW_predictor), NROW(h)))
# Compute out-of-sample forecasts ----------------------------------------------
for(p in 1:P){
print(p)
FC_PM[p] <- mean(equity_risk$r[1:(R+(p-1))])
for(j in 1:NROW(h)){
for(i in 1:NCOL(GW_predictor)){
X_i_j_p <- GW_predictor[1:(R+(p-1)-h[j]),i]
results_i_j_p <- lm(r_h[2:(R+p-h[j]),j] ~ X_i_j_p)
FC_PR[p,i,j] <- cbind(1, GW_predictor[R+(p-1),i])%*%results_i_j_p$coeff
}
}
}
# evaluate forecasts -----------------------------------------------------------
R2OS_PR <- array(NaN, c(NCOL(GW_predictor),2,NROW(h)))
for(j in 1:length(h)){
actual_j <- r_h[(R+1):(NROW(r_h)-(h[j]-1)),j]
u_PM_j <- actual_j - FC_PM[1:(NROW(FC_PM)-(h[j]-1))]
u_PR_j <- kronecker(matrix(1, nrow = 1, ncol = NCOL(FC_PR)), actual_j) - FC_PR[1:(NROW(FC_PR)-(h[j]-1)),,j]
MSFE_PM_j <- mean(u_PM_j^2)
MSFE_PR_j <- apply(u_PR_j^2, 2, mean)
R2OS_PR_j <- 100*(1-MSFE_PR_j/MSFE_PM_j)
R2OS_PR[,1,j] = t(R2OS_PR_j)
for(i in 1:NCOL(GW_predictor)){
f_CW_i_j <- u_PM_j^2 - u_PR_j[,i]^2 + (FC_PM[1:(NROW(FC_PM)-(h[j]-1))] - FC_PR[1:(NROW(FC_PR)-(h[j]-1)),i,j])^2
result_CW_i_j <- lm(f_CW_i_j ~ matrix(1, nrow = NROW(f_CW_i_j)))
R2OS_PR[i,2,j] <- coeftest(result_CW_i_j, vcov.=NeweyWest(result_CW_i_j, lag=h[j], adjust=FALSE, verbose = TRUE, prewhite = FALSE))[3]
}
}
round(R2OS_PR, 2)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deploy.R
\name{deploy}
\alias{deploy}
\alias{deploy_current}
\title{Deploy a bundle}
\usage{
deploy(
connect,
bundle,
name = create_random_name(),
title = name,
guid = NULL,
...,
.pre_deploy = {
}
)
deploy_current(content)
}
\arguments{
\item{connect}{A Connect object}
\item{bundle}{A Bundle object}
\item{name}{The unique name for the content on the server}
\item{title}{optional The title to be used for the content on the server}
\item{guid}{optional The GUID if the content already exists on the server}
\item{...}{Additional arguments passed along to the content creation}
\item{.pre_deploy}{An expression to execute before deploying the new bundle. The variables \code{content} and \code{bundle_id} are supplied}
\item{content}{A Content object}
}
\value{
Task A task object
}
\description{
Deploys a bundle (tarball) to an Posit Connect server. If not provided,
\code{name} (a unique identifier) will be an auto-generated alphabetic string. If
deploying to an existing endpoint, you can set \code{name} or \code{guid} to the
desired content.
}
\details{
This function accepts the same arguments as \code{connectapi::content_update()}.
\code{deploy_current()} is a helper to easily redeploy the currently active bundle
for an existing content item.
}
\examples{
\dontrun{
client <- connect()
# beware bundling big directories, like `renv/`, `data/`, etc.
bnd <- bundle_dir(".")
deploy(client, bnd)
}
\dontshow{if (identical(Sys.getenv("IN_PKGDOWN"), "true")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
client <- connect(prefix = "TEST_1")
bnd <- bundle_path(system.file("tests/testthat/examples/static.tar.gz", package = "connectapi"))
deploy(client, bnd)
\dontshow{\}) # examplesIf}
}
\seealso{
connectapi::content_update
Other deployment functions:
\code{\link{bundle_dir}()},
\code{\link{bundle_path}()},
\code{\link{bundle_static}()},
\code{\link{download_bundle}()},
\code{\link{poll_task}()}
}
\concept{deployment functions}
|
/man/deploy.Rd
|
permissive
|
rstudio/connectapi
|
R
| false
| true
| 2,082
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deploy.R
\name{deploy}
\alias{deploy}
\alias{deploy_current}
\title{Deploy a bundle}
\usage{
deploy(
connect,
bundle,
name = create_random_name(),
title = name,
guid = NULL,
...,
.pre_deploy = {
}
)
deploy_current(content)
}
\arguments{
\item{connect}{A Connect object}
\item{bundle}{A Bundle object}
\item{name}{The unique name for the content on the server}
\item{title}{optional The title to be used for the content on the server}
\item{guid}{optional The GUID if the content already exists on the server}
\item{...}{Additional arguments passed along to the content creation}
\item{.pre_deploy}{An expression to execute before deploying the new bundle. The variables \code{content} and \code{bundle_id} are supplied}
\item{content}{A Content object}
}
\value{
Task A task object
}
\description{
Deploys a bundle (tarball) to an Posit Connect server. If not provided,
\code{name} (a unique identifier) will be an auto-generated alphabetic string. If
deploying to an existing endpoint, you can set \code{name} or \code{guid} to the
desired content.
}
\details{
This function accepts the same arguments as \code{connectapi::content_update()}.
\code{deploy_current()} is a helper to easily redeploy the currently active bundle
for an existing content item.
}
\examples{
\dontrun{
client <- connect()
# beware bundling big directories, like `renv/`, `data/`, etc.
bnd <- bundle_dir(".")
deploy(client, bnd)
}
\dontshow{if (identical(Sys.getenv("IN_PKGDOWN"), "true")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
client <- connect(prefix = "TEST_1")
bnd <- bundle_path(system.file("tests/testthat/examples/static.tar.gz", package = "connectapi"))
deploy(client, bnd)
\dontshow{\}) # examplesIf}
}
\seealso{
connectapi::content_update
Other deployment functions:
\code{\link{bundle_dir}()},
\code{\link{bundle_path}()},
\code{\link{bundle_static}()},
\code{\link{download_bundle}()},
\code{\link{poll_task}()}
}
\concept{deployment functions}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/foo.R
\name{plotpairs2}
\alias{plotpairs2}
\title{plot pair-wise correlations with p value. in: a dataframe. out: a figure.}
\usage{
plotpairs2(data, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = panel.diag, lwd = 2, col = "grey", labels = "", cex.labels = 4)
}
\arguments{
\item{data}{a dataframe}
\item{lower.panel}{can be panel.lm or panel.smooth}
\item{upper.panel}{panel.cor}
\item{diag.panel}{panel.diag}
\item{lwd}{numeric}
\item{col}{colors}
\item{labels}{character}
\item{cex.labels}{character}
}
\value{
a pair plot
}
\description{
plot pair-wise correlations with p value. in: a dataframe. out: a figure.
}
\examples{
df <- data.frame(a = 1:10, b = 1:10 + rnorm(10), c = 1:10 + rnorm(10))
plotpairs2(df)
}
|
/man/plotpairs2.Rd
|
no_license
|
JanusChoi/beginr
|
R
| false
| true
| 829
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/foo.R
\name{plotpairs2}
\alias{plotpairs2}
\title{plot pair-wise correlations with p value. in: a dataframe. out: a figure.}
\usage{
plotpairs2(data, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = panel.diag, lwd = 2, col = "grey", labels = "", cex.labels = 4)
}
\arguments{
\item{data}{a dataframe}
\item{lower.panel}{can be panel.lm or panel.smooth}
\item{upper.panel}{panel.cor}
\item{diag.panel}{panel.diag}
\item{lwd}{numeric}
\item{col}{colors}
\item{labels}{character}
\item{cex.labels}{character}
}
\value{
a pair plot
}
\description{
plot pair-wise correlations with p value. in: a dataframe. out: a figure.
}
\examples{
df <- data.frame(a = 1:10, b = 1:10 + rnorm(10), c = 1:10 + rnorm(10))
plotpairs2(df)
}
|
#' Plot simple features simply
#'
#' Overrides `sf::plot.sf` and wraps the call to `plot(st_geometry(x))`. When
#' working with spatial data it's often useful to create maps where we
#' overlay spatially coincident data in a plot.
#'
#' This is used so that plots aren't always facetted by all attributes, coloured
#' by them, or leaving the plot unusable for subsequent additional drawing. The
#' key feature is that we don't have to change our behaviour and good habits
#' depending on the format in use.
#'
#' @param x sf object (the data.frame one)
#' @param ... arguments passed to [sf::plot_sfc]
#'
#' @return used for its side effects of creating a plot
#' @export
#'
#' @examples
#' x <- read_sf(system.file("shape/nc.shp", package="sf"))
#' ## all we've changed is the plot command
#' plot(x)
#' ## we can overplot without format-specific acrobatics
#' plot(x[sample(1:nrow(x), 10), ], col = rainbow(10), add = TRUE)
#' abline(v = 34); axis(2)
#' @importFrom graphics plot
#' @importFrom sf st_geometry
plot.sf <- function(x, ...) {
plot(sf::st_geometry(x), ...)
}
# just for read_sf
#' @importFrom tibble tibble
dummy_internal <- function() {
tibble::tibble()
}
|
/R/plot.R
|
no_license
|
cran/basf
|
R
| false
| false
| 1,177
|
r
|
#' Plot simple features simply
#'
#' Overrides `sf::plot.sf` and wraps the call to `plot(st_geometry(x))`. When
#' working with spatial data it's often useful to create maps where we
#' overlay spatially coincident data in a plot.
#'
#' This is used so that plots aren't always facetted by all attributes, coloured
#' by them, or leaving the plot unusable for subsequent additional drawing. The
#' key feature is that we don't have to change our behaviour and good habits
#' depending on the format in use.
#'
#' @param x sf object (the data.frame one)
#' @param ... arguments passed to [sf::plot_sfc]
#'
#' @return used for its side effects of creating a plot
#' @export
#'
#' @examples
#' x <- read_sf(system.file("shape/nc.shp", package="sf"))
#' ## all we've changed is the plot command
#' plot(x)
#' ## we can overplot without format-specific acrobatics
#' plot(x[sample(1:nrow(x), 10), ], col = rainbow(10), add = TRUE)
#' abline(v = 34); axis(2)
#' @importFrom graphics plot
#' @importFrom sf st_geometry
plot.sf <- function(x, ...) {
plot(sf::st_geometry(x), ...)
}
# just for read_sf
#' @importFrom tibble tibble
dummy_internal <- function() {
tibble::tibble()
}
|
# data.table package - faster version of data.frame. Written in C
# new syntax
DF=data.frame(x=rnorm(9), y=rep(c("a","b","c"),each=3),z=rnorm(9))
head(DF)
DF
library(data.table)
DF=data.table(x=rnorm(9), y=rep(c("a","b","c"),each=3),z=rnorm(9))
DF
tables() # shows all tables in memory
DF[2,] subsets
DF[DF$y=="a",] # , not needed
DF[c(2,3),] # subsets 2nd and 3rd ROWS
DF[,c(2,3)] # !!!!DOES NOT PULL COLUMNS. UNIQUE FOR data.tables.
# do not understand why columns do not work or what to do about it #
DF[,list(mean(x),sum(z))] # so it will do work on the coumns instead of subset????
DF2<-DF ## THIS DOES NOT MAKE A COPY!!! MERELY LINKS THEM. CHANGES TO DF WILL BE MADE TO DF2
DF[,table(y)] # counts occurrences of each
# := adds a new variable to data table
DF[,w:=z^2]
DF
# Pretty cool
# add a new variable based on math or logic in one step (rather than calc it then cbind)
DF[,a:=x>0]
DF
# Awesome again
# plyr like operations
# calc mean of x+w but grouped by a (TRUE or FALSE)
DF[,b:=mean(x+w),by=a]
DF
# Special variables
# .N counts occurrences of a variable
DT<-data.table(x=sample(letters[1:3],1E5,TRUE)) # Creates huge set of random letters a to c
head(DT)
DT[1:25]
DT[,.N,by=x] # Counts each occurrence within X. Seems like should be ( after .N
# Setting a key makes everything much faster.
DT<-data.table(x=rep(c("a","b","c"),each=100),y=rnorm(300))
setkey(DT,x)
DT["a"]
# Joins
DT1<-data.table(x=c("a","a","b","dt1"),y=1:4)
DT1
DT2<-data.table(x=c("a","b","dt2"),z=5:7)
DT2
setkey(DT1,x);setkey(DT2,x)
DT3<-merge(DT1,DT2)
DT3
# useful for fast reading
|
/Week 1 - Data Table Package.R
|
no_license
|
MHCoursera/GettingCleaningDataLectures
|
R
| false
| false
| 1,603
|
r
|
# data.table package - faster version of data.frame. Written in C
# new syntax
DF=data.frame(x=rnorm(9), y=rep(c("a","b","c"),each=3),z=rnorm(9))
head(DF)
DF
library(data.table)
DF=data.table(x=rnorm(9), y=rep(c("a","b","c"),each=3),z=rnorm(9))
DF
tables() # shows all tables in memory
DF[2,] subsets
DF[DF$y=="a",] # , not needed
DF[c(2,3),] # subsets 2nd and 3rd ROWS
DF[,c(2,3)] # !!!!DOES NOT PULL COLUMNS. UNIQUE FOR data.tables.
# do not understand why columns do not work or what to do about it #
DF[,list(mean(x),sum(z))] # so it will do work on the coumns instead of subset????
DF2<-DF ## THIS DOES NOT MAKE A COPY!!! MERELY LINKS THEM. CHANGES TO DF WILL BE MADE TO DF2
DF[,table(y)] # counts occurrences of each
# := adds a new variable to data table
DF[,w:=z^2]
DF
# Pretty cool
# add a new variable based on math or logic in one step (rather than calc it then cbind)
DF[,a:=x>0]
DF
# Awesome again
# plyr like operations
# calc mean of x+w but grouped by a (TRUE or FALSE)
DF[,b:=mean(x+w),by=a]
DF
# Special variables
# .N counts occurrences of a variable
DT<-data.table(x=sample(letters[1:3],1E5,TRUE)) # Creates huge set of random letters a to c
head(DT)
DT[1:25]
DT[,.N,by=x] # Counts each occurrence within X. Seems like should be ( after .N
# Setting a key makes everything much faster.
DT<-data.table(x=rep(c("a","b","c"),each=100),y=rnorm(300))
setkey(DT,x)
DT["a"]
# Joins
DT1<-data.table(x=c("a","a","b","dt1"),y=1:4)
DT1
DT2<-data.table(x=c("a","b","dt2"),z=5:7)
DT2
setkey(DT1,x);setkey(DT2,x)
DT3<-merge(DT1,DT2)
DT3
# useful for fast reading
|
library(Interpol.T)
date<-date.time(day.begin="01/04/2020", day.end="31/07/2020", date.format= "d/m/y") # generate Date hour
tempd <- read.table("result.txt", sep="\t", header=FALSE) #from python program CIBSE Algorithm
x <- data.frame(date$year ,date$month, date$day, date$hour, tempd$V2)
y <- data.frame(x$year,x$month, x$day)
y <- unique(y)
write.table(x, file="2020_Rabat_1May-31Jul.txt",sep="\t")
pdf(file="2020_Rabat_1May-31Jul.pdf",height=5, width=40)
plot(tempd$V2, type="l", xaxt = "n", ylab="Temp °C", xlab="Time")
axis(1, at=c(1,720,1464,2208,2928), labels=c("01-04-2020", "01-05-2020", "01-06-2020","01-07-2020", "31-07-2020"))
dev.off()
|
/Split_day2hour.R
|
no_license
|
kashiff007/netCDF-Processing-Pipeline
|
R
| false
| false
| 658
|
r
|
library(Interpol.T)
date<-date.time(day.begin="01/04/2020", day.end="31/07/2020", date.format= "d/m/y") # generate Date hour
tempd <- read.table("result.txt", sep="\t", header=FALSE) #from python program CIBSE Algorithm
x <- data.frame(date$year ,date$month, date$day, date$hour, tempd$V2)
y <- data.frame(x$year,x$month, x$day)
y <- unique(y)
write.table(x, file="2020_Rabat_1May-31Jul.txt",sep="\t")
pdf(file="2020_Rabat_1May-31Jul.pdf",height=5, width=40)
plot(tempd$V2, type="l", xaxt = "n", ylab="Temp °C", xlab="Time")
axis(1, at=c(1,720,1464,2208,2928), labels=c("01-04-2020", "01-05-2020", "01-06-2020","01-07-2020", "31-07-2020"))
dev.off()
|
# run_analysis.R - script that does the following:
# 1. Merges the training and the test sets to create one data set.
# 2. Extracts only the measurements on the mean and standard deviation for each measurement.
# 3. Uses descriptive activity names to name the activities in the data set
# 4. Appropriately labels the data set with descriptive variable names.
# 5. From the data set in step 4, creates a second, independent tidy data set with the
# average of each variable for each activity and each subject.
if(!file.exists("wearable-dataset.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",
destfile = "wearable-dataset.zip", method="curl")
unzip("wearable-dataset.zip")
# creates directory: /UCI HAR Dataset/ containing "raw" data files
}
update.data = T
if(update.data) {
rm(list = ls())
# read in general metadata
activity.labels = read.table("UCI HAR Dataset/activity_labels.txt") # activity names
features = read.table("UCI HAR Dataset/features.txt") # activity data row names
# read in test data
subject.test = read.table("UCI HAR Dataset/test/subject_test.txt") # person
x.test = read.table("UCI HAR Dataset/test/X_test.txt") # activity data
y.test = read.table("UCI HAR Dataset/test/y_test.txt") # activity labels
# read in training data
subject.train = read.table("UCI HAR Dataset/train/subject_train.txt") # person
x.train = read.table("UCI HAR Dataset/train/X_train.txt") # activity data
y.train = read.table("UCI HAR Dataset/train/y_train.txt") # activity labels
# name columns
names(activity.labels) = c("activity.id", "activity.label")
names(subject.test) = "subject.id"
names(subject.train) = "subject.id"
names(y.test) = "activity.id"
names(y.train) = "activity.id"
names(x.test) = features[, 2]
names(x.train) = features[, 2]
# List only the mean() and standard deviation, std(), variables for each of
# the 33 measurements listed in features-info.txt. I am assuming that the *Mean
# vectors within the angle() measures and the meanFreq() measures are out of scope
vars.to.keep = as.character(features[c(grep("mean[[:punct:]]|std[[:punct:]]",
x = features[, 2])),
2])
# select just the mean() and std() columns from the x.* datasets
# Reduces ncol from 561 to 66 for each (which would be expected for 2 x 33 measures)
x.test.lim = x.test[, vars.to.keep]
x.train.lim = x.train[, vars.to.keep]
# first combine the 3 test-related datasets and the 3 train-related datasets
test.data = cbind(subject.test, y.test, x.test.lim)
test.data$group = "test"
train.data = cbind(subject.train, y.train, x.train.lim)
train.data$group = "train"
# then combine test and train into a single dataset
if(all(as.character(names(test.data)) != as.character(names(train.data)))){
stop("it would be unwise to rbind datasets with differently named columns")
} else {
comb.data = rbind(test.data, train.data)
}
# then add on the appropriate activity label for each activity id
comb.data = merge(x = comb.data, y = activity.labels, by = "activity.id")
# finally, create a second, independent tidy data set with the
# average of each variable for each activity and each subject.
library(reshape2)
comb.melt = melt(comb.data, id = c("subject.id", "activity.id", "activity.label"), measure.vars = names(comb.data)[3:68])
tidy.data = dcast(comb.melt, subject.id + activity.id + activity.label ~ variable, mean)
write.table(x = tidy.data, file = "output-tidy-dataset.txt", row.names = F)
}
|
/run_analysis.R
|
no_license
|
baderstine/gcd-course-project
|
R
| false
| false
| 3,727
|
r
|
# run_analysis.R - script that does the following:
# 1. Merges the training and the test sets to create one data set.
# 2. Extracts only the measurements on the mean and standard deviation for each measurement.
# 3. Uses descriptive activity names to name the activities in the data set
# 4. Appropriately labels the data set with descriptive variable names.
# 5. From the data set in step 4, creates a second, independent tidy data set with the
# average of each variable for each activity and each subject.
if(!file.exists("wearable-dataset.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",
destfile = "wearable-dataset.zip", method="curl")
unzip("wearable-dataset.zip")
# creates directory: /UCI HAR Dataset/ containing "raw" data files
}
update.data = T
if(update.data) {
rm(list = ls())
# read in general metadata
activity.labels = read.table("UCI HAR Dataset/activity_labels.txt") # activity names
features = read.table("UCI HAR Dataset/features.txt") # activity data row names
# read in test data
subject.test = read.table("UCI HAR Dataset/test/subject_test.txt") # person
x.test = read.table("UCI HAR Dataset/test/X_test.txt") # activity data
y.test = read.table("UCI HAR Dataset/test/y_test.txt") # activity labels
# read in training data
subject.train = read.table("UCI HAR Dataset/train/subject_train.txt") # person
x.train = read.table("UCI HAR Dataset/train/X_train.txt") # activity data
y.train = read.table("UCI HAR Dataset/train/y_train.txt") # activity labels
# name columns
names(activity.labels) = c("activity.id", "activity.label")
names(subject.test) = "subject.id"
names(subject.train) = "subject.id"
names(y.test) = "activity.id"
names(y.train) = "activity.id"
names(x.test) = features[, 2]
names(x.train) = features[, 2]
# List only the mean() and standard deviation, std(), variables for each of
# the 33 measurements listed in features-info.txt. I am assuming that the *Mean
# vectors within the angle() measures and the meanFreq() measures are out of scope
vars.to.keep = as.character(features[c(grep("mean[[:punct:]]|std[[:punct:]]",
x = features[, 2])),
2])
# select just the mean() and std() columns from the x.* datasets
# Reduces ncol from 561 to 66 for each (which would be expected for 2 x 33 measures)
x.test.lim = x.test[, vars.to.keep]
x.train.lim = x.train[, vars.to.keep]
# first combine the 3 test-related datasets and the 3 train-related datasets
test.data = cbind(subject.test, y.test, x.test.lim)
test.data$group = "test"
train.data = cbind(subject.train, y.train, x.train.lim)
train.data$group = "train"
# then combine test and train into a single dataset
if(all(as.character(names(test.data)) != as.character(names(train.data)))){
stop("it would be unwise to rbind datasets with differently named columns")
} else {
comb.data = rbind(test.data, train.data)
}
# then add on the appropriate activity label for each activity id
comb.data = merge(x = comb.data, y = activity.labels, by = "activity.id")
# finally, create a second, independent tidy data set with the
# average of each variable for each activity and each subject.
library(reshape2)
comb.melt = melt(comb.data, id = c("subject.id", "activity.id", "activity.label"), measure.vars = names(comb.data)[3:68])
tidy.data = dcast(comb.melt, subject.id + activity.id + activity.label ~ variable, mean)
write.table(x = tidy.data, file = "output-tidy-dataset.txt", row.names = F)
}
|
##Transcript abundance to Gene counts (ensembl)
# The following code takes transcript count data outputed from kallisto and summarises the abundance to the gene level for further use in coexpression networks
## Install Packages
#tximport
# try http:// if https:// URLs are not supported
# source("https://bioconductor.org/biocLite.R")
# biocLite("tximport")
#rdhf5
# bioccLite("rhdf5")
# Load libraries
library(rhdf5)
library(tximport)
###############WORKING TEST###############
##Set base directories
work_dir <- "~/doorknobdave/alecS/intermediary_data/kallisto_out"
meta_dir <- "~/doorknobdave/alecS/meta"
##get transcript to gene id table from ensembl for anotations between genes and transcripts
#http://www.ensembl.org/biomart/martview/af949c4506992d3c4c752b186d6cba44
#Read in tx2 gene file for conversion between transcripts and gene ids
tx2gene <- read.csv(file.path(metadir, "tx2gene94.txt"))
head(tx2gene)
#Directory where sequence files meta data etc are located
#dir <- "~/doorknobdave/alecS/intermediary_data/kallisto_out"
#Read all meta data in table consisting of everything known about samples as an object s2c - sample to covariate matrix
samples <- read.csv(file.path(meta_dir, "metadata_final_30.csv"), header = TRUE)
#Specify file paths for each of the samples h5 abundance estimate files using the sample title names (the same as the output file names created in kallisto)
files <- file.path(work_dir, samples$sample, "abundance.h5") #abundance.h5
#specify names of each sample
names(files) <- samples$sample
#Check all files exist
all(file.exists(files))
#Create txi object for the conversion
txi.kallisto <- tximport(files, type = "kallisto", tx2gene = tx2gene, ignoreTxVersion = TRUE)
#txi.kallisto <- tximport(files, type = "kallisto", txOut = TRUE)
tail(txi.kallisto$counts)
#See number of genes transcripts are summarised to
dim(txi.kallisto$counts)
#Write gene level abundance to file
out_dir <- "~/doorknobdave/alecS/intermediary_data/Kallisto_all"
setwd(out_dir)
write.table(txi.kallisto$counts, "genelevelabundance_30.csv", quote = FALSE, sep = ",")
|
/functions/convert_transcript_to_gene_tximport.R
|
no_license
|
alecstansell/code-masters
|
R
| false
| false
| 2,108
|
r
|
##Transcript abundance to Gene counts (ensembl)
# The following code takes transcript count data outputed from kallisto and summarises the abundance to the gene level for further use in coexpression networks
## Install Packages
#tximport
# try http:// if https:// URLs are not supported
# source("https://bioconductor.org/biocLite.R")
# biocLite("tximport")
#rdhf5
# bioccLite("rhdf5")
# Load libraries
library(rhdf5)
library(tximport)
###############WORKING TEST###############
##Set base directories
work_dir <- "~/doorknobdave/alecS/intermediary_data/kallisto_out"
meta_dir <- "~/doorknobdave/alecS/meta"
##get transcript to gene id table from ensembl for anotations between genes and transcripts
#http://www.ensembl.org/biomart/martview/af949c4506992d3c4c752b186d6cba44
#Read in tx2 gene file for conversion between transcripts and gene ids
tx2gene <- read.csv(file.path(metadir, "tx2gene94.txt"))
head(tx2gene)
#Directory where sequence files meta data etc are located
#dir <- "~/doorknobdave/alecS/intermediary_data/kallisto_out"
#Read all meta data in table consisting of everything known about samples as an object s2c - sample to covariate matrix
samples <- read.csv(file.path(meta_dir, "metadata_final_30.csv"), header = TRUE)
#Specify file paths for each of the samples h5 abundance estimate files using the sample title names (the same as the output file names created in kallisto)
files <- file.path(work_dir, samples$sample, "abundance.h5") #abundance.h5
#specify names of each sample
names(files) <- samples$sample
#Check all files exist
all(file.exists(files))
#Create txi object for the conversion
txi.kallisto <- tximport(files, type = "kallisto", tx2gene = tx2gene, ignoreTxVersion = TRUE)
#txi.kallisto <- tximport(files, type = "kallisto", txOut = TRUE)
tail(txi.kallisto$counts)
#See number of genes transcripts are summarised to
dim(txi.kallisto$counts)
#Write gene level abundance to file
out_dir <- "~/doorknobdave/alecS/intermediary_data/Kallisto_all"
setwd(out_dir)
write.table(txi.kallisto$counts, "genelevelabundance_30.csv", quote = FALSE, sep = ",")
|
#############################################################################################
#
# Mirror, mirror ... best batsman of them all
# Designed and developed by: Tinniam V Ganesh
# Date: 24 March 2015
# More details - https://gigadom.wordpress.com
#
##############################################################################################
#Install package plotrix for pie3D
library(ggplot2)
library(e1071)
setwd("C:\\software\\R\\batsman")
source("battingperf.R")
# Create an output directory if it does not exist
if(!file.exists("output")) {
dir.create("output")
}
# Create a directory to store plots
if(!file.exists("plots")) {
dir.create("plots")
}
#################################################################
# The following section compares 3 Indian icons of cricket
# 1. Sachin Tendulkar
# 2. Rahul Dravid
# 3. Sunil Gavaskar
#################################################################
# Sachin Tendulkar's batting performance
tendulkar = read.csv("tendulkar.csv")
name <- 'Sachin Tendulkar'
boxplotHist(tendulkar,name)
perfplot(tendulkar,name)
meanStrikeRate(tendulkar,name)
# Sunil Gavaskar's batting performance
gavaskar = read.csv("gavaskar.csv")
name <- 'Sunil Gavaskar'
boxplotHist(gavaskar,name)
perfplot(gavaskar,name)
meanStrikeRate(gavaskar,name)
# Rahul Dravid's batting performance
dravid = read.csv("dravid.csv")
name <- 'Rahul Dravid'
boxplotHist(dravid,name)
perfplot(dravid,name)
meanStrikeRate(dravid,name)
#Compute and plot the relative performance
name <-"ABC"
setwd("./plots")
relPerf <- "indian-batsman-relPerf.png"
png(relPerf)
relativePerf(tendulkar,name,"red",flag=TRUE,18)
relativePerf(gavaskar,name,"blue",19)
relativePerf(dravid,name,"green",20)
legend(x="topright",c("Tendulkar","Gavaskar","Dravid"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5),col=c("red", "blue","green"),bty="n")
dev.off()
setwd("..")
#Compute and plot the relative mean strike rate of the batsman
name <-"ABC"
setwd("./plots")
relSR <- "indian-batsman-relSR.png"
png(relSR)
relativeSR(tendulkar,name,"red",flag=TRUE,18)
relativeSR(gavaskar,name,"blue",19)
relativeSR(dravid,name,"green",20)
legend(x="bottomright",c("Tendulkar","Gavaskar","Dravid"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5),col=c("red", "blue","green"),bty="n")
dev.off()
setwd("..")
#################################################################
# The following section compares 4 international icons of cricket
# 1. Sachin Tendulkar
# 2. A B De Villiers
# 3. Ricky Ponting
# 4. Brian Lara
#################################################################
# A B DeVilliers batting performance
villiers = read.csv("villiers.csv")
name <- 'AB De Villiers'
boxplotHist(villiers,name)
perfplot(villiers,name)
meanStrikeRate(villiers,name)
# Brian Lara's batting performance
lara = read.csv("lara.csv")
name <- 'Brian Lara'
boxplotHist(lara,name)
perfplot(lara,name)
meanStrikeRate(lara,name)
# Ricky Ponting's batting performance
ponting = read.csv("ponting.csv")
name <- 'Ricky Ponting'
boxplotHist(ponting,name)
perfplot(ponting,name)
meanStrikeRate(ponting,name)
#Compute and plot the relative performance
name <-"ABC"
setwd("./plots")
relPerf <- "intl-batsman-relPerf.png"
png(relPerf)
relativePerf(tendulkar,name,"red",flag=TRUE,18)
relativePerf(ponting,name,"blue",)
relativePerf(lara,name,"orange")
relativePerf(villiers,name,"green")
legend(x="topright",c("Tendulkar","Ponting","Lara", "De Villiers"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5,2.5),col=c("red", "blue","orange", "green"),bty="n")
dev.off()
setwd("..")
#Compute and plot the relative mean strike rate of the batsman
name <-"ABC"
setwd("./plots")
relSR <- "intl-batsman-relSR.png"
png(relSR)
relativeSR(tendulkar,name,"red",flag=TRUE,18)
relativeSR(ponting,name,"blue",)
relativeSR(lara,name,"orange")
relativeSR(villiers,name,"green")
legend(x="bottomright",c("Tendulkar","Ponting","Lara", "De Villiers"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5,2.5),col=c("red", "blue","orange", "green"), bty="n")
dev.off()
setwd("..")
|
/src/batsman.R
|
no_license
|
tvganesh/bestBatsman
|
R
| false
| false
| 4,030
|
r
|
#############################################################################################
#
# Mirror, mirror ... best batsman of them all
# Designed and developed by: Tinniam V Ganesh
# Date: 24 March 2015
# More details - https://gigadom.wordpress.com
#
##############################################################################################
#Install package plotrix for pie3D
library(ggplot2)
library(e1071)
setwd("C:\\software\\R\\batsman")
source("battingperf.R")
# Create an output directory if it does not exist
if(!file.exists("output")) {
dir.create("output")
}
# Create a directory to store plots
if(!file.exists("plots")) {
dir.create("plots")
}
#################################################################
# The following section compares 3 Indian icons of cricket
# 1. Sachin Tendulkar
# 2. Rahul Dravid
# 3. Sunil Gavaskar
#################################################################
# Sachin Tendulkar's batting performance
tendulkar = read.csv("tendulkar.csv")
name <- 'Sachin Tendulkar'
boxplotHist(tendulkar,name)
perfplot(tendulkar,name)
meanStrikeRate(tendulkar,name)
# Sunil Gavaskar's batting performance
gavaskar = read.csv("gavaskar.csv")
name <- 'Sunil Gavaskar'
boxplotHist(gavaskar,name)
perfplot(gavaskar,name)
meanStrikeRate(gavaskar,name)
# Rahul Dravid's batting performance
dravid = read.csv("dravid.csv")
name <- 'Rahul Dravid'
boxplotHist(dravid,name)
perfplot(dravid,name)
meanStrikeRate(dravid,name)
#Compute and plot the relative performance
name <-"ABC"
setwd("./plots")
relPerf <- "indian-batsman-relPerf.png"
png(relPerf)
relativePerf(tendulkar,name,"red",flag=TRUE,18)
relativePerf(gavaskar,name,"blue",19)
relativePerf(dravid,name,"green",20)
legend(x="topright",c("Tendulkar","Gavaskar","Dravid"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5),col=c("red", "blue","green"),bty="n")
dev.off()
setwd("..")
#Compute and plot the relative mean strike rate of the batsman
name <-"ABC"
setwd("./plots")
relSR <- "indian-batsman-relSR.png"
png(relSR)
relativeSR(tendulkar,name,"red",flag=TRUE,18)
relativeSR(gavaskar,name,"blue",19)
relativeSR(dravid,name,"green",20)
legend(x="bottomright",c("Tendulkar","Gavaskar","Dravid"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5),col=c("red", "blue","green"),bty="n")
dev.off()
setwd("..")
#################################################################
# The following section compares 4 international icons of cricket
# 1. Sachin Tendulkar
# 2. A B De Villiers
# 3. Ricky Ponting
# 4. Brian Lara
#################################################################
# A B DeVilliers batting performance
villiers = read.csv("villiers.csv")
name <- 'AB De Villiers'
boxplotHist(villiers,name)
perfplot(villiers,name)
meanStrikeRate(villiers,name)
# Brian Lara's batting performance
lara = read.csv("lara.csv")
name <- 'Brian Lara'
boxplotHist(lara,name)
perfplot(lara,name)
meanStrikeRate(lara,name)
# Ricky Ponting's batting performance
ponting = read.csv("ponting.csv")
name <- 'Ricky Ponting'
boxplotHist(ponting,name)
perfplot(ponting,name)
meanStrikeRate(ponting,name)
#Compute and plot the relative performance
name <-"ABC"
setwd("./plots")
relPerf <- "intl-batsman-relPerf.png"
png(relPerf)
relativePerf(tendulkar,name,"red",flag=TRUE,18)
relativePerf(ponting,name,"blue",)
relativePerf(lara,name,"orange")
relativePerf(villiers,name,"green")
legend(x="topright",c("Tendulkar","Ponting","Lara", "De Villiers"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5,2.5),col=c("red", "blue","orange", "green"),bty="n")
dev.off()
setwd("..")
#Compute and plot the relative mean strike rate of the batsman
name <-"ABC"
setwd("./plots")
relSR <- "intl-batsman-relSR.png"
png(relSR)
relativeSR(tendulkar,name,"red",flag=TRUE,18)
relativeSR(ponting,name,"blue",)
relativeSR(lara,name,"orange")
relativeSR(villiers,name,"green")
legend(x="bottomright",c("Tendulkar","Ponting","Lara", "De Villiers"), lty=c(1,1,1),
lwd=c(2.5,2.5,2.5,2.5),col=c("red", "blue","orange", "green"), bty="n")
dev.off()
setwd("..")
|
# Samsara API
#
# # Introduction Samsara provides API endpoints for interacting with Samsara Cloud, so that you can build powerful applications and custom solutions with sensor data. Samsara has endpoints available to track and analyze sensors, vehicles, and entire fleets. The Samsara Cloud API is a [RESTful API](https://en.wikipedia.org/wiki/Representational_state_transfer) accessed by an [HTTP](https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol) client such as wget or curl, or HTTP libraries of most modern programming languages including python, ruby, java. We use built-in HTTP features, like HTTP authentication and HTTP verbs, which are understood by off-the-shelf HTTP clients. We allow you to interact securely with our API from a client-side web application (though you should never expose your secret API key). [JSON](http://www.json.org/) is returned by all API responses, including errors. If you’re familiar with what you can build with a REST API, the following API reference guide will be your go-to resource. API access to the Samsara cloud is available to all Samsara administrators. To start developing with Samsara APIs you will need to [obtain your API keys](#section/Authentication) to authenticate your API requests. If you have any questions you can reach out to us on [support@samsara.com](mailto:support@samsara.com) # Endpoints All our APIs can be accessed through HTTP requests to URLs like: ```curl https://api.samsara.com/<version>/<endpoint> ``` All our APIs are [versioned](#section/Versioning). If we intend to make breaking changes to an API which either changes the response format or request parameter, we will increment the version. # Authentication To authenticate your API request you will need to include your secret token. You can manage your API tokens in the [Dashboard](https://cloud.samsara.com). They are visible under `Settings->Organization->API Tokens`. Your API tokens carry many privileges, so be sure to keep them secure. Do not share your secret API tokens in publicly accessible areas such as GitHub, client-side code, and so on. Authentication to the API is performed via [HTTP Basic Auth](https://en.wikipedia.org/wiki/Basic_access_authentication). Provide your API token as the basic access_token value in the URL. You do not need to provide a password. ```curl https://api.samsara.com/<version>/<endpoint>?access_token={access_token} ``` All API requests must be made over [HTTPS](https://en.wikipedia.org/wiki/HTTPS). Calls made over plain HTTP or without authentication will fail. # Request Methods Our API endpoints use [HTTP request methods](https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol#Request_methods) to specify the desired operation to be performed. The documentation below specified request method supported by each endpoint and the resulting action. ## GET GET requests are typically used for fetching data (like data for a particular driver). ## POST POST requests are typically used for creating or updating a record (like adding new tags to the system). With that being said, a few of our POST requests can be used for fetching data (like current location data of your fleet). ## PUT PUT requests are typically used for updating an existing record (like updating all devices associated with a particular tag). ## PATCH PATCH requests are typically used for modifying an existing record (like modifying a few devices associated with a particular tag). ## DELETE DELETE requests are used for deleting a record (like deleting a tag from the system). # Response Codes All API requests will respond with appropriate [HTTP status code](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes). Your API client should handle each response class differently. ## 2XX These are successful responses and indicate that the API request returned the expected response. ## 4XX These indicate that there was a problem with the request like a missing parameter or invalid values. Check the response for specific [error details](#section/Error-Responses). Requests that respond with a 4XX status code, should be modified before retrying. ## 5XX These indicate server errors when the server is unreachable or is misconfigured. In this case, you should retry the API request after some delay. # Error Responses In case of a 4XX status code, the body of the response will contain information to briefly explain the error reported. To help debugging the error, you can refer to the following table for understanding the error message. | Status Code | Message | Description | |-------------|----------------|-------------------------------------------------------------------| | 401 | Invalid token | The API token is invalid and could not be authenticated. Please refer to the [authentication section](#section/Authentication). | | 404 | Page not found | The API endpoint being accessed is invalid. | | 400 | Bad request | Default response for an invalid request. Please check the request to make sure it follows the format specified in the documentation. | # Versioning All our APIs are versioned. Our current API version is `v1` and we are continuously working on improving it further and provide additional endpoints. If we intend to make breaking changes to an API which either changes the response format or request parameter, we will increment the version. Thus, you can use our current API version worry free. # FAQs Check out our [responses to FAQs here](https://kb.samsara.com/hc/en-us/sections/360000538054-APIs). Don’t see an answer to your question? Reach out to us on [support@samsara.com](mailto:support@samsara.com).
#
# OpenAPI spec version: 1.0.0
#
# Generated by: https://openapi-generator.tech
#' TripResponseTrips Class
#'
#' @field endOdometer
#' @field distanceMeters
#' @field endMs
#' @field startMs
#' @field fuelConsumedMl
#' @field startAddress
#' @field startCoordinates
#' @field endCoordinates
#' @field startOdometer
#' @field driverId
#' @field startLocation
#' @field tollMeters
#' @field endAddress
#' @field endLocation
#'
#' @importFrom R6 R6Class
#' @importFrom jsonlite fromJSON toJSON
#' @export
TripResponseTrips <- R6::R6Class(
'TripResponseTrips',
public = list(
`endOdometer` = NULL,
`distanceMeters` = NULL,
`endMs` = NULL,
`startMs` = NULL,
`fuelConsumedMl` = NULL,
`startAddress` = NULL,
`startCoordinates` = NULL,
`endCoordinates` = NULL,
`startOdometer` = NULL,
`driverId` = NULL,
`startLocation` = NULL,
`tollMeters` = NULL,
`endAddress` = NULL,
`endLocation` = NULL,
initialize = function(`endOdometer`=NULL, `distanceMeters`=NULL, `endMs`=NULL, `startMs`=NULL, `fuelConsumedMl`=NULL, `startAddress`=NULL, `startCoordinates`=NULL, `endCoordinates`=NULL, `startOdometer`=NULL, `driverId`=NULL, `startLocation`=NULL, `tollMeters`=NULL, `endAddress`=NULL, `endLocation`=NULL, ...){
local.optional.var <- list(...)
if (!is.null(`endOdometer`)) {
stopifnot(is.numeric(`endOdometer`), length(`endOdometer`) == 1)
self$`endOdometer` <- `endOdometer`
}
if (!is.null(`distanceMeters`)) {
stopifnot(is.numeric(`distanceMeters`), length(`distanceMeters`) == 1)
self$`distanceMeters` <- `distanceMeters`
}
if (!is.null(`endMs`)) {
stopifnot(is.numeric(`endMs`), length(`endMs`) == 1)
self$`endMs` <- `endMs`
}
if (!is.null(`startMs`)) {
stopifnot(is.numeric(`startMs`), length(`startMs`) == 1)
self$`startMs` <- `startMs`
}
if (!is.null(`fuelConsumedMl`)) {
stopifnot(is.numeric(`fuelConsumedMl`), length(`fuelConsumedMl`) == 1)
self$`fuelConsumedMl` <- `fuelConsumedMl`
}
if (!is.null(`startAddress`)) {
stopifnot(is.character(`startAddress`), length(`startAddress`) == 1)
self$`startAddress` <- `startAddress`
}
if (!is.null(`startCoordinates`)) {
stopifnot(R6::is.R6(`startCoordinates`))
self$`startCoordinates` <- `startCoordinates`
}
if (!is.null(`endCoordinates`)) {
stopifnot(R6::is.R6(`endCoordinates`))
self$`endCoordinates` <- `endCoordinates`
}
if (!is.null(`startOdometer`)) {
stopifnot(is.numeric(`startOdometer`), length(`startOdometer`) == 1)
self$`startOdometer` <- `startOdometer`
}
if (!is.null(`driverId`)) {
stopifnot(is.numeric(`driverId`), length(`driverId`) == 1)
self$`driverId` <- `driverId`
}
if (!is.null(`startLocation`)) {
stopifnot(is.character(`startLocation`), length(`startLocation`) == 1)
self$`startLocation` <- `startLocation`
}
if (!is.null(`tollMeters`)) {
stopifnot(is.numeric(`tollMeters`), length(`tollMeters`) == 1)
self$`tollMeters` <- `tollMeters`
}
if (!is.null(`endAddress`)) {
stopifnot(is.character(`endAddress`), length(`endAddress`) == 1)
self$`endAddress` <- `endAddress`
}
if (!is.null(`endLocation`)) {
stopifnot(is.character(`endLocation`), length(`endLocation`) == 1)
self$`endLocation` <- `endLocation`
}
},
toJSON = function() {
TripResponseTripsObject <- list()
if (!is.null(self$`endOdometer`)) {
TripResponseTripsObject[['endOdometer']] <-
self$`endOdometer`
}
if (!is.null(self$`distanceMeters`)) {
TripResponseTripsObject[['distanceMeters']] <-
self$`distanceMeters`
}
if (!is.null(self$`endMs`)) {
TripResponseTripsObject[['endMs']] <-
self$`endMs`
}
if (!is.null(self$`startMs`)) {
TripResponseTripsObject[['startMs']] <-
self$`startMs`
}
if (!is.null(self$`fuelConsumedMl`)) {
TripResponseTripsObject[['fuelConsumedMl']] <-
self$`fuelConsumedMl`
}
if (!is.null(self$`startAddress`)) {
TripResponseTripsObject[['startAddress']] <-
self$`startAddress`
}
if (!is.null(self$`startCoordinates`)) {
TripResponseTripsObject[['startCoordinates']] <-
self$`startCoordinates`$toJSON()
}
if (!is.null(self$`endCoordinates`)) {
TripResponseTripsObject[['endCoordinates']] <-
self$`endCoordinates`$toJSON()
}
if (!is.null(self$`startOdometer`)) {
TripResponseTripsObject[['startOdometer']] <-
self$`startOdometer`
}
if (!is.null(self$`driverId`)) {
TripResponseTripsObject[['driverId']] <-
self$`driverId`
}
if (!is.null(self$`startLocation`)) {
TripResponseTripsObject[['startLocation']] <-
self$`startLocation`
}
if (!is.null(self$`tollMeters`)) {
TripResponseTripsObject[['tollMeters']] <-
self$`tollMeters`
}
if (!is.null(self$`endAddress`)) {
TripResponseTripsObject[['endAddress']] <-
self$`endAddress`
}
if (!is.null(self$`endLocation`)) {
TripResponseTripsObject[['endLocation']] <-
self$`endLocation`
}
TripResponseTripsObject
},
fromJSON = function(TripResponseTripsJson) {
TripResponseTripsObject <- jsonlite::fromJSON(TripResponseTripsJson)
if (!is.null(TripResponseTripsObject$`endOdometer`)) {
self$`endOdometer` <- TripResponseTripsObject$`endOdometer`
}
if (!is.null(TripResponseTripsObject$`distanceMeters`)) {
self$`distanceMeters` <- TripResponseTripsObject$`distanceMeters`
}
if (!is.null(TripResponseTripsObject$`endMs`)) {
self$`endMs` <- TripResponseTripsObject$`endMs`
}
if (!is.null(TripResponseTripsObject$`startMs`)) {
self$`startMs` <- TripResponseTripsObject$`startMs`
}
if (!is.null(TripResponseTripsObject$`fuelConsumedMl`)) {
self$`fuelConsumedMl` <- TripResponseTripsObject$`fuelConsumedMl`
}
if (!is.null(TripResponseTripsObject$`startAddress`)) {
self$`startAddress` <- TripResponseTripsObject$`startAddress`
}
if (!is.null(TripResponseTripsObject$`startCoordinates`)) {
startCoordinatesObject <- TripResponseStartCoordinates$new()
startCoordinatesObject$fromJSON(jsonlite::toJSON(TripResponseTripsObject$startCoordinates, auto_unbox = TRUE))
self$`startCoordinates` <- startCoordinatesObject
}
if (!is.null(TripResponseTripsObject$`endCoordinates`)) {
endCoordinatesObject <- TripResponseEndCoordinates$new()
endCoordinatesObject$fromJSON(jsonlite::toJSON(TripResponseTripsObject$endCoordinates, auto_unbox = TRUE))
self$`endCoordinates` <- endCoordinatesObject
}
if (!is.null(TripResponseTripsObject$`startOdometer`)) {
self$`startOdometer` <- TripResponseTripsObject$`startOdometer`
}
if (!is.null(TripResponseTripsObject$`driverId`)) {
self$`driverId` <- TripResponseTripsObject$`driverId`
}
if (!is.null(TripResponseTripsObject$`startLocation`)) {
self$`startLocation` <- TripResponseTripsObject$`startLocation`
}
if (!is.null(TripResponseTripsObject$`tollMeters`)) {
self$`tollMeters` <- TripResponseTripsObject$`tollMeters`
}
if (!is.null(TripResponseTripsObject$`endAddress`)) {
self$`endAddress` <- TripResponseTripsObject$`endAddress`
}
if (!is.null(TripResponseTripsObject$`endLocation`)) {
self$`endLocation` <- TripResponseTripsObject$`endLocation`
}
},
toJSONString = function() {
sprintf(
'{
"endOdometer":
%d,
"distanceMeters":
%d,
"endMs":
%d,
"startMs":
%d,
"fuelConsumedMl":
%d,
"startAddress":
"%s",
"startCoordinates":
%s,
"endCoordinates":
%s,
"startOdometer":
%d,
"driverId":
%d,
"startLocation":
"%s",
"tollMeters":
%d,
"endAddress":
"%s",
"endLocation":
"%s"
}',
self$`endOdometer`,
self$`distanceMeters`,
self$`endMs`,
self$`startMs`,
self$`fuelConsumedMl`,
self$`startAddress`,
jsonlite::toJSON(self$`startCoordinates`$toJSON(), auto_unbox=TRUE),
jsonlite::toJSON(self$`endCoordinates`$toJSON(), auto_unbox=TRUE),
self$`startOdometer`,
self$`driverId`,
self$`startLocation`,
self$`tollMeters`,
self$`endAddress`,
self$`endLocation`
)
},
fromJSONString = function(TripResponseTripsJson) {
TripResponseTripsObject <- jsonlite::fromJSON(TripResponseTripsJson)
self$`endOdometer` <- TripResponseTripsObject$`endOdometer`
self$`distanceMeters` <- TripResponseTripsObject$`distanceMeters`
self$`endMs` <- TripResponseTripsObject$`endMs`
self$`startMs` <- TripResponseTripsObject$`startMs`
self$`fuelConsumedMl` <- TripResponseTripsObject$`fuelConsumedMl`
self$`startAddress` <- TripResponseTripsObject$`startAddress`
self$`startCoordinates` <- TripResponseStartCoordinates$new()$fromJSON(jsonlite::toJSON(TripResponseTripsObject$startCoordinates, auto_unbox = TRUE))
self$`endCoordinates` <- TripResponseEndCoordinates$new()$fromJSON(jsonlite::toJSON(TripResponseTripsObject$endCoordinates, auto_unbox = TRUE))
self$`startOdometer` <- TripResponseTripsObject$`startOdometer`
self$`driverId` <- TripResponseTripsObject$`driverId`
self$`startLocation` <- TripResponseTripsObject$`startLocation`
self$`tollMeters` <- TripResponseTripsObject$`tollMeters`
self$`endAddress` <- TripResponseTripsObject$`endAddress`
self$`endLocation` <- TripResponseTripsObject$`endLocation`
self
}
)
)
|
/openapi-generator/r/R/trip_response_trips.R
|
no_license
|
silverspace/samsara-sdks
|
R
| false
| false
| 15,984
|
r
|
# Samsara API
#
# # Introduction Samsara provides API endpoints for interacting with Samsara Cloud, so that you can build powerful applications and custom solutions with sensor data. Samsara has endpoints available to track and analyze sensors, vehicles, and entire fleets. The Samsara Cloud API is a [RESTful API](https://en.wikipedia.org/wiki/Representational_state_transfer) accessed by an [HTTP](https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol) client such as wget or curl, or HTTP libraries of most modern programming languages including python, ruby, java. We use built-in HTTP features, like HTTP authentication and HTTP verbs, which are understood by off-the-shelf HTTP clients. We allow you to interact securely with our API from a client-side web application (though you should never expose your secret API key). [JSON](http://www.json.org/) is returned by all API responses, including errors. If you’re familiar with what you can build with a REST API, the following API reference guide will be your go-to resource. API access to the Samsara cloud is available to all Samsara administrators. To start developing with Samsara APIs you will need to [obtain your API keys](#section/Authentication) to authenticate your API requests. If you have any questions you can reach out to us on [support@samsara.com](mailto:support@samsara.com) # Endpoints All our APIs can be accessed through HTTP requests to URLs like: ```curl https://api.samsara.com/<version>/<endpoint> ``` All our APIs are [versioned](#section/Versioning). If we intend to make breaking changes to an API which either changes the response format or request parameter, we will increment the version. # Authentication To authenticate your API request you will need to include your secret token. You can manage your API tokens in the [Dashboard](https://cloud.samsara.com). They are visible under `Settings->Organization->API Tokens`. Your API tokens carry many privileges, so be sure to keep them secure. Do not share your secret API tokens in publicly accessible areas such as GitHub, client-side code, and so on. Authentication to the API is performed via [HTTP Basic Auth](https://en.wikipedia.org/wiki/Basic_access_authentication). Provide your API token as the basic access_token value in the URL. You do not need to provide a password. ```curl https://api.samsara.com/<version>/<endpoint>?access_token={access_token} ``` All API requests must be made over [HTTPS](https://en.wikipedia.org/wiki/HTTPS). Calls made over plain HTTP or without authentication will fail. # Request Methods Our API endpoints use [HTTP request methods](https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol#Request_methods) to specify the desired operation to be performed. The documentation below specified request method supported by each endpoint and the resulting action. ## GET GET requests are typically used for fetching data (like data for a particular driver). ## POST POST requests are typically used for creating or updating a record (like adding new tags to the system). With that being said, a few of our POST requests can be used for fetching data (like current location data of your fleet). ## PUT PUT requests are typically used for updating an existing record (like updating all devices associated with a particular tag). ## PATCH PATCH requests are typically used for modifying an existing record (like modifying a few devices associated with a particular tag). ## DELETE DELETE requests are used for deleting a record (like deleting a tag from the system). # Response Codes All API requests will respond with appropriate [HTTP status code](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes). Your API client should handle each response class differently. ## 2XX These are successful responses and indicate that the API request returned the expected response. ## 4XX These indicate that there was a problem with the request like a missing parameter or invalid values. Check the response for specific [error details](#section/Error-Responses). Requests that respond with a 4XX status code, should be modified before retrying. ## 5XX These indicate server errors when the server is unreachable or is misconfigured. In this case, you should retry the API request after some delay. # Error Responses In case of a 4XX status code, the body of the response will contain information to briefly explain the error reported. To help debugging the error, you can refer to the following table for understanding the error message. | Status Code | Message | Description | |-------------|----------------|-------------------------------------------------------------------| | 401 | Invalid token | The API token is invalid and could not be authenticated. Please refer to the [authentication section](#section/Authentication). | | 404 | Page not found | The API endpoint being accessed is invalid. | | 400 | Bad request | Default response for an invalid request. Please check the request to make sure it follows the format specified in the documentation. | # Versioning All our APIs are versioned. Our current API version is `v1` and we are continuously working on improving it further and provide additional endpoints. If we intend to make breaking changes to an API which either changes the response format or request parameter, we will increment the version. Thus, you can use our current API version worry free. # FAQs Check out our [responses to FAQs here](https://kb.samsara.com/hc/en-us/sections/360000538054-APIs). Don’t see an answer to your question? Reach out to us on [support@samsara.com](mailto:support@samsara.com).
#
# OpenAPI spec version: 1.0.0
#
# Generated by: https://openapi-generator.tech
#' TripResponseTrips Class
#'
#' @field endOdometer
#' @field distanceMeters
#' @field endMs
#' @field startMs
#' @field fuelConsumedMl
#' @field startAddress
#' @field startCoordinates
#' @field endCoordinates
#' @field startOdometer
#' @field driverId
#' @field startLocation
#' @field tollMeters
#' @field endAddress
#' @field endLocation
#'
#' @importFrom R6 R6Class
#' @importFrom jsonlite fromJSON toJSON
#' @export
TripResponseTrips <- R6::R6Class(
'TripResponseTrips',
public = list(
`endOdometer` = NULL,
`distanceMeters` = NULL,
`endMs` = NULL,
`startMs` = NULL,
`fuelConsumedMl` = NULL,
`startAddress` = NULL,
`startCoordinates` = NULL,
`endCoordinates` = NULL,
`startOdometer` = NULL,
`driverId` = NULL,
`startLocation` = NULL,
`tollMeters` = NULL,
`endAddress` = NULL,
`endLocation` = NULL,
initialize = function(`endOdometer`=NULL, `distanceMeters`=NULL, `endMs`=NULL, `startMs`=NULL, `fuelConsumedMl`=NULL, `startAddress`=NULL, `startCoordinates`=NULL, `endCoordinates`=NULL, `startOdometer`=NULL, `driverId`=NULL, `startLocation`=NULL, `tollMeters`=NULL, `endAddress`=NULL, `endLocation`=NULL, ...){
local.optional.var <- list(...)
if (!is.null(`endOdometer`)) {
stopifnot(is.numeric(`endOdometer`), length(`endOdometer`) == 1)
self$`endOdometer` <- `endOdometer`
}
if (!is.null(`distanceMeters`)) {
stopifnot(is.numeric(`distanceMeters`), length(`distanceMeters`) == 1)
self$`distanceMeters` <- `distanceMeters`
}
if (!is.null(`endMs`)) {
stopifnot(is.numeric(`endMs`), length(`endMs`) == 1)
self$`endMs` <- `endMs`
}
if (!is.null(`startMs`)) {
stopifnot(is.numeric(`startMs`), length(`startMs`) == 1)
self$`startMs` <- `startMs`
}
if (!is.null(`fuelConsumedMl`)) {
stopifnot(is.numeric(`fuelConsumedMl`), length(`fuelConsumedMl`) == 1)
self$`fuelConsumedMl` <- `fuelConsumedMl`
}
if (!is.null(`startAddress`)) {
stopifnot(is.character(`startAddress`), length(`startAddress`) == 1)
self$`startAddress` <- `startAddress`
}
if (!is.null(`startCoordinates`)) {
stopifnot(R6::is.R6(`startCoordinates`))
self$`startCoordinates` <- `startCoordinates`
}
if (!is.null(`endCoordinates`)) {
stopifnot(R6::is.R6(`endCoordinates`))
self$`endCoordinates` <- `endCoordinates`
}
if (!is.null(`startOdometer`)) {
stopifnot(is.numeric(`startOdometer`), length(`startOdometer`) == 1)
self$`startOdometer` <- `startOdometer`
}
if (!is.null(`driverId`)) {
stopifnot(is.numeric(`driverId`), length(`driverId`) == 1)
self$`driverId` <- `driverId`
}
if (!is.null(`startLocation`)) {
stopifnot(is.character(`startLocation`), length(`startLocation`) == 1)
self$`startLocation` <- `startLocation`
}
if (!is.null(`tollMeters`)) {
stopifnot(is.numeric(`tollMeters`), length(`tollMeters`) == 1)
self$`tollMeters` <- `tollMeters`
}
if (!is.null(`endAddress`)) {
stopifnot(is.character(`endAddress`), length(`endAddress`) == 1)
self$`endAddress` <- `endAddress`
}
if (!is.null(`endLocation`)) {
stopifnot(is.character(`endLocation`), length(`endLocation`) == 1)
self$`endLocation` <- `endLocation`
}
},
toJSON = function() {
TripResponseTripsObject <- list()
if (!is.null(self$`endOdometer`)) {
TripResponseTripsObject[['endOdometer']] <-
self$`endOdometer`
}
if (!is.null(self$`distanceMeters`)) {
TripResponseTripsObject[['distanceMeters']] <-
self$`distanceMeters`
}
if (!is.null(self$`endMs`)) {
TripResponseTripsObject[['endMs']] <-
self$`endMs`
}
if (!is.null(self$`startMs`)) {
TripResponseTripsObject[['startMs']] <-
self$`startMs`
}
if (!is.null(self$`fuelConsumedMl`)) {
TripResponseTripsObject[['fuelConsumedMl']] <-
self$`fuelConsumedMl`
}
if (!is.null(self$`startAddress`)) {
TripResponseTripsObject[['startAddress']] <-
self$`startAddress`
}
if (!is.null(self$`startCoordinates`)) {
TripResponseTripsObject[['startCoordinates']] <-
self$`startCoordinates`$toJSON()
}
if (!is.null(self$`endCoordinates`)) {
TripResponseTripsObject[['endCoordinates']] <-
self$`endCoordinates`$toJSON()
}
if (!is.null(self$`startOdometer`)) {
TripResponseTripsObject[['startOdometer']] <-
self$`startOdometer`
}
if (!is.null(self$`driverId`)) {
TripResponseTripsObject[['driverId']] <-
self$`driverId`
}
if (!is.null(self$`startLocation`)) {
TripResponseTripsObject[['startLocation']] <-
self$`startLocation`
}
if (!is.null(self$`tollMeters`)) {
TripResponseTripsObject[['tollMeters']] <-
self$`tollMeters`
}
if (!is.null(self$`endAddress`)) {
TripResponseTripsObject[['endAddress']] <-
self$`endAddress`
}
if (!is.null(self$`endLocation`)) {
TripResponseTripsObject[['endLocation']] <-
self$`endLocation`
}
TripResponseTripsObject
},
fromJSON = function(TripResponseTripsJson) {
TripResponseTripsObject <- jsonlite::fromJSON(TripResponseTripsJson)
if (!is.null(TripResponseTripsObject$`endOdometer`)) {
self$`endOdometer` <- TripResponseTripsObject$`endOdometer`
}
if (!is.null(TripResponseTripsObject$`distanceMeters`)) {
self$`distanceMeters` <- TripResponseTripsObject$`distanceMeters`
}
if (!is.null(TripResponseTripsObject$`endMs`)) {
self$`endMs` <- TripResponseTripsObject$`endMs`
}
if (!is.null(TripResponseTripsObject$`startMs`)) {
self$`startMs` <- TripResponseTripsObject$`startMs`
}
if (!is.null(TripResponseTripsObject$`fuelConsumedMl`)) {
self$`fuelConsumedMl` <- TripResponseTripsObject$`fuelConsumedMl`
}
if (!is.null(TripResponseTripsObject$`startAddress`)) {
self$`startAddress` <- TripResponseTripsObject$`startAddress`
}
if (!is.null(TripResponseTripsObject$`startCoordinates`)) {
startCoordinatesObject <- TripResponseStartCoordinates$new()
startCoordinatesObject$fromJSON(jsonlite::toJSON(TripResponseTripsObject$startCoordinates, auto_unbox = TRUE))
self$`startCoordinates` <- startCoordinatesObject
}
if (!is.null(TripResponseTripsObject$`endCoordinates`)) {
endCoordinatesObject <- TripResponseEndCoordinates$new()
endCoordinatesObject$fromJSON(jsonlite::toJSON(TripResponseTripsObject$endCoordinates, auto_unbox = TRUE))
self$`endCoordinates` <- endCoordinatesObject
}
if (!is.null(TripResponseTripsObject$`startOdometer`)) {
self$`startOdometer` <- TripResponseTripsObject$`startOdometer`
}
if (!is.null(TripResponseTripsObject$`driverId`)) {
self$`driverId` <- TripResponseTripsObject$`driverId`
}
if (!is.null(TripResponseTripsObject$`startLocation`)) {
self$`startLocation` <- TripResponseTripsObject$`startLocation`
}
if (!is.null(TripResponseTripsObject$`tollMeters`)) {
self$`tollMeters` <- TripResponseTripsObject$`tollMeters`
}
if (!is.null(TripResponseTripsObject$`endAddress`)) {
self$`endAddress` <- TripResponseTripsObject$`endAddress`
}
if (!is.null(TripResponseTripsObject$`endLocation`)) {
self$`endLocation` <- TripResponseTripsObject$`endLocation`
}
},
toJSONString = function() {
sprintf(
'{
"endOdometer":
%d,
"distanceMeters":
%d,
"endMs":
%d,
"startMs":
%d,
"fuelConsumedMl":
%d,
"startAddress":
"%s",
"startCoordinates":
%s,
"endCoordinates":
%s,
"startOdometer":
%d,
"driverId":
%d,
"startLocation":
"%s",
"tollMeters":
%d,
"endAddress":
"%s",
"endLocation":
"%s"
}',
self$`endOdometer`,
self$`distanceMeters`,
self$`endMs`,
self$`startMs`,
self$`fuelConsumedMl`,
self$`startAddress`,
jsonlite::toJSON(self$`startCoordinates`$toJSON(), auto_unbox=TRUE),
jsonlite::toJSON(self$`endCoordinates`$toJSON(), auto_unbox=TRUE),
self$`startOdometer`,
self$`driverId`,
self$`startLocation`,
self$`tollMeters`,
self$`endAddress`,
self$`endLocation`
)
},
fromJSONString = function(TripResponseTripsJson) {
TripResponseTripsObject <- jsonlite::fromJSON(TripResponseTripsJson)
self$`endOdometer` <- TripResponseTripsObject$`endOdometer`
self$`distanceMeters` <- TripResponseTripsObject$`distanceMeters`
self$`endMs` <- TripResponseTripsObject$`endMs`
self$`startMs` <- TripResponseTripsObject$`startMs`
self$`fuelConsumedMl` <- TripResponseTripsObject$`fuelConsumedMl`
self$`startAddress` <- TripResponseTripsObject$`startAddress`
self$`startCoordinates` <- TripResponseStartCoordinates$new()$fromJSON(jsonlite::toJSON(TripResponseTripsObject$startCoordinates, auto_unbox = TRUE))
self$`endCoordinates` <- TripResponseEndCoordinates$new()$fromJSON(jsonlite::toJSON(TripResponseTripsObject$endCoordinates, auto_unbox = TRUE))
self$`startOdometer` <- TripResponseTripsObject$`startOdometer`
self$`driverId` <- TripResponseTripsObject$`driverId`
self$`startLocation` <- TripResponseTripsObject$`startLocation`
self$`tollMeters` <- TripResponseTripsObject$`tollMeters`
self$`endAddress` <- TripResponseTripsObject$`endAddress`
self$`endLocation` <- TripResponseTripsObject$`endLocation`
self
}
)
)
|
install.packages('tidyverse')
#install.packages('sva')
#install.packages('patchwork')
#install.packages('RColorBrewer')
install.packages('gplots')
install.packages('devtools')
install.packages('broom')
#install.packages('Biobase')
#install.packages('limma')
#install.packages('edge')
#install.packages('genefilter')
#install.packages('GEOquery')
#install.packages('qvalue')
install.packages('jackstraw')
install.packages('corpcor')
library(corpcor)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("Biobase","limma","genefilter","edge","qvalue"))
install.packages('devtools')
library(devtools)
library(Biobase)
library(limma)
library(edge)
library(genefilter)
library(qvalue)
library(tidyverse)
library(data.table)
load(file='bottomly.Rdata')
ls()
edata <- as.matrix(exprs(bottomly.eset))
dim(edata)
edata <- edata[rowMeans(edata) > 10, ]
edata <- log2(as.matrix(edata) + 1)
library(RColorBrewer)
library(gplots)
my_palette <- colorRampPalette(c("blue", "white", "yellow"))(n = 299)
heatmap.2(edata,
main = "Heatmap", # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(12,9), # widens margins around plot
col=my_palette, # use on color palette defined earlier
dendrogram="none", # only draw a row dendrogram
scale = "row",
Colv=FALSE)
dev.off()
#HHHHHHHHHHHHHHHHHHHHHHHHHH
heatmap.2(edata,
main = "Bottomly et al. Clustered", # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(12,9), # widens margins around plot
col=my_palette, # use on color palette defined earlier
dendrogram="column", # only draw a row dendrogram
scale = "column")
#homework no 1!!!!!!!!
#new heatmap, use heatmap.2
#same function with different arguments
#HHHHHHHHHHHHHHHHHHHHHHHHHH
edata<-t(scale(t(edata), scale=FALSE,center=TRUE))
svd.out<-svd(edata)
names(svd.out)
print(paste("Dimension of left singular vectors:",dim(svd.out$u)))
print(paste("Length of singular values:",length(svd.out$d)))
print(paste("Dimension of right singular values:",length(svd.out$v)))
par(mfrow=c(1,1))
plot(svd.out$d,pch=20,ylab="Singular values")
plot(svd.out$d^2/sum(svd.out$d^2)*100,pch=20,ylab="% variance dimension")
plot(1:ncol(edata),svd.out$v[,1],pch=20)
PC<-data.table(svd.out$v,pData(bottomly.eset))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(strain)))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(lane.number)))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(experiment.number)))
#HHHHHHHHHHHHHHHHHHHHHHHHH
#V2 i V3
ggplot(PC)+geom_point(aes(x=V2,y=V3,col=as.factor(strain)))
#homework no 2 !!!!!!
#opisane gdzieś tam
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
plot(1:ncol(edata),svd.out$u[1,],pch=20, col='red')
#legend(1,1,legend=c('Column 1', 'Column 2'), col=c('red', 'blue'))
points(1:ncol(edata),svd.out$u[2,],pch=20, col='blue')
legend(0.9,0.9,c('Column 1', 'Column 2'), col=c('red', 'blue'))
#plot(1:ncol(edata),svd.out$u[2,],pch=20, col='blue')
#homework no 3 !!!!
#scatter plot for left singular vector
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
#ggplot(PC) + geom_violin(aes(x=as.factor(strain), y=V1),draw_quantiles = c(0.25, 0.5, 0.75)) + geom_jitter(aes(x=as.factor(strain), y=V1))
u_dt<-data.table(svd.out$u)
u_dt_melt<-melt(u_dt[,1:5])
ggplot(u_dt_melt)+geom_violin(aes(x=as.factor(variable),y=value),draw_quantiles=c(0.25,0.5,0.75))
#homework no 4 !!!!
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
k_means<-kmeans(edata,5)
clust<-k_means$cluster
library(irlba)
library(Rtsne)
# Set a seed for reproducible results
set.seed(1)
# complexity is a hyperparameter needed for this algorithm. 30 is a default
tsne_out <- Rtsne(edata,pca=TRUE,perplexity=30)
tsne_out_k = data.table(tsne_out$Y, clust)
ggplot(tsne_out_k) + geom_point(aes(x=V1, y=V2, col=as.factor(clust)))
#kmeans
#HHHHHHHHHHHHHHHHHHHHHHHHH
#role of Normalization
pc1<-prcomp(edata)
plot(pc1$rotation[,1],svd.out$v[,1])
edata.col<-scale(edata,scale=FALSE,center=TRUE)
svd.col<-svd(edata.col)
plot(pc1$rotation[,1],svd.col$v[,1],col=2)
abline(0,1)
library(irlba)
library(ggplot2)
tsvd.out<-irlba(edata,nv=4)
#nv is number of singular vectors
dim(tsvd.out$u)
plot(tsvd.out$v[,1],-1*svd.out$v[,1]); abline(0,1,col='red')
install.packages('Rtsne')
library(Rtsne)
set.seed(1)
tsne_out<-Rtsne(edata,pca=TRUE, perplexity=30)
tsne_out<-data.table(tsne_out$Y)
ggplot(tsne_out) + geom_point(aes(x=V1,y=V2))
#tSNE moze byc lepsze niz PCA
#w niektorych przypadkach
#homework 5 !!!
#use Kmeans, with k=5
|
/H2/otreba_homework2.R
|
no_license
|
jakubotreba/jakubotreba
|
R
| false
| false
| 5,055
|
r
|
install.packages('tidyverse')
#install.packages('sva')
#install.packages('patchwork')
#install.packages('RColorBrewer')
install.packages('gplots')
install.packages('devtools')
install.packages('broom')
#install.packages('Biobase')
#install.packages('limma')
#install.packages('edge')
#install.packages('genefilter')
#install.packages('GEOquery')
#install.packages('qvalue')
install.packages('jackstraw')
install.packages('corpcor')
library(corpcor)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("Biobase","limma","genefilter","edge","qvalue"))
install.packages('devtools')
library(devtools)
library(Biobase)
library(limma)
library(edge)
library(genefilter)
library(qvalue)
library(tidyverse)
library(data.table)
load(file='bottomly.Rdata')
ls()
edata <- as.matrix(exprs(bottomly.eset))
dim(edata)
edata <- edata[rowMeans(edata) > 10, ]
edata <- log2(as.matrix(edata) + 1)
library(RColorBrewer)
library(gplots)
my_palette <- colorRampPalette(c("blue", "white", "yellow"))(n = 299)
heatmap.2(edata,
main = "Heatmap", # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(12,9), # widens margins around plot
col=my_palette, # use on color palette defined earlier
dendrogram="none", # only draw a row dendrogram
scale = "row",
Colv=FALSE)
dev.off()
#HHHHHHHHHHHHHHHHHHHHHHHHHH
heatmap.2(edata,
main = "Bottomly et al. Clustered", # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(12,9), # widens margins around plot
col=my_palette, # use on color palette defined earlier
dendrogram="column", # only draw a row dendrogram
scale = "column")
#homework no 1!!!!!!!!
#new heatmap, use heatmap.2
#same function with different arguments
#HHHHHHHHHHHHHHHHHHHHHHHHHH
edata<-t(scale(t(edata), scale=FALSE,center=TRUE))
svd.out<-svd(edata)
names(svd.out)
print(paste("Dimension of left singular vectors:",dim(svd.out$u)))
print(paste("Length of singular values:",length(svd.out$d)))
print(paste("Dimension of right singular values:",length(svd.out$v)))
par(mfrow=c(1,1))
plot(svd.out$d,pch=20,ylab="Singular values")
plot(svd.out$d^2/sum(svd.out$d^2)*100,pch=20,ylab="% variance dimension")
plot(1:ncol(edata),svd.out$v[,1],pch=20)
PC<-data.table(svd.out$v,pData(bottomly.eset))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(strain)))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(lane.number)))
ggplot(PC)+geom_point(aes(x=V1,y=V2,col=as.factor(experiment.number)))
#HHHHHHHHHHHHHHHHHHHHHHHHH
#V2 i V3
ggplot(PC)+geom_point(aes(x=V2,y=V3,col=as.factor(strain)))
#homework no 2 !!!!!!
#opisane gdzieś tam
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
plot(1:ncol(edata),svd.out$u[1,],pch=20, col='red')
#legend(1,1,legend=c('Column 1', 'Column 2'), col=c('red', 'blue'))
points(1:ncol(edata),svd.out$u[2,],pch=20, col='blue')
legend(0.9,0.9,c('Column 1', 'Column 2'), col=c('red', 'blue'))
#plot(1:ncol(edata),svd.out$u[2,],pch=20, col='blue')
#homework no 3 !!!!
#scatter plot for left singular vector
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
#ggplot(PC) + geom_violin(aes(x=as.factor(strain), y=V1),draw_quantiles = c(0.25, 0.5, 0.75)) + geom_jitter(aes(x=as.factor(strain), y=V1))
u_dt<-data.table(svd.out$u)
u_dt_melt<-melt(u_dt[,1:5])
ggplot(u_dt_melt)+geom_violin(aes(x=as.factor(variable),y=value),draw_quantiles=c(0.25,0.5,0.75))
#homework no 4 !!!!
#HHHHHHHHHHHHHHHHHHHHHHHHH
#HHHHHHHHHHHHHHHHHHHHHHHHH
k_means<-kmeans(edata,5)
clust<-k_means$cluster
library(irlba)
library(Rtsne)
# Set a seed for reproducible results
set.seed(1)
# complexity is a hyperparameter needed for this algorithm. 30 is a default
tsne_out <- Rtsne(edata,pca=TRUE,perplexity=30)
tsne_out_k = data.table(tsne_out$Y, clust)
ggplot(tsne_out_k) + geom_point(aes(x=V1, y=V2, col=as.factor(clust)))
#kmeans
#HHHHHHHHHHHHHHHHHHHHHHHHH
#role of Normalization
pc1<-prcomp(edata)
plot(pc1$rotation[,1],svd.out$v[,1])
edata.col<-scale(edata,scale=FALSE,center=TRUE)
svd.col<-svd(edata.col)
plot(pc1$rotation[,1],svd.col$v[,1],col=2)
abline(0,1)
library(irlba)
library(ggplot2)
tsvd.out<-irlba(edata,nv=4)
#nv is number of singular vectors
dim(tsvd.out$u)
plot(tsvd.out$v[,1],-1*svd.out$v[,1]); abline(0,1,col='red')
install.packages('Rtsne')
library(Rtsne)
set.seed(1)
tsne_out<-Rtsne(edata,pca=TRUE, perplexity=30)
tsne_out<-data.table(tsne_out$Y)
ggplot(tsne_out) + geom_point(aes(x=V1,y=V2))
#tSNE moze byc lepsze niz PCA
#w niektorych przypadkach
#homework 5 !!!
#use Kmeans, with k=5
|
# G64164008
# Rya Meyvriska
# Praktikum 2
# 1. Mempersiapkan data
dt <- read.table("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data", sep = ',')
dt <- dt[,-1] #menghilangkan fitur v1, karena isinya hanya ID
# 2. Split data
dtClassB <- dt[which(dt$V2=='B'),]
dtClassM <- dt[which(dt$V2=='M'),]
# 3. Cek Range
summary(dt)
# 4. Normalisasi data
normalize <- function(x) {
return ((x-min(x)) / (max(x) - min(x)))
}
normalizedDtB <- as.data.frame(lapply(dtClassB[,2:31], normalize))
normalizedDtM <- as.data.frame(lapply(dtClassM[,2:31], normalize))
# 5. Ambil training data (70%) dan testing data (30%)
# Ambil 70% data dari kelas B
nClassB <- nrow(normalizedDtB)
boundClassB <- ceiling(nClassB * 0.7)
trainingDtClassB <- normalizedDtB[1:boundClassB, ]
testingDtClassB <- normalizedDtB[(boundClassB + 1):nClassB, ]
trainingLabelsClassB <- dtClassB[1:boundClassB, 1]
testingLabelsClassB <- dtClassB[(boundClassB + 1):nClassB, 1]
# Ambil 70% data dari kelas M
nClassM <- nrow(normalizedDtM)
boundClassM <- ceiling(nClassM * 0.7)
trainingDtClassM <- normalizedDtM[1:boundClassM, ]
testingDtClassM <- normalizedDtM[(boundClassM + 1):nClassM, ]
trainingLabelsClassM <- dtClassM[1:boundClassM, 1]
testingLabelsClassM <- dtClassM[(boundClassM + 1):nClassM, 1]
# Satukan dalam satu data
trainingDt <- rbind(trainingDtClassB[,], trainingDtClassM[,])
testingDt <- rbind(testingDtClassB[,], testingDtClassM[,])
trainingLabels <- c(as.character(trainingLabelsClassB[]), as.character(trainingLabelsClassM[]))
trainingLabels <- as.factor(trainingLabels)
testingLabels <- c(as.character(testingLabelsClassB[]), as.character(testingLabelsClassM[]))
testingLabels <- as.factor(testingLabels)
# Oversampling pada trainingDt
library(ROSE) #Random OverUnder Sampling Example
dtOver <- cbind(trainingLabels,trainingDt)
trainingDtOver <- ovun.sample(trainingLabels~., data = dtOver, method = "over", N = 500)$data
trainingDtOverLabel <- trainingDtOver$trainingLabels
trainingDtOver <- trainingDtOver[,-1]
# 6.Membuat prediksi dengan kNN
library(class)
startK <- ceiling(sqrt(nrow(trainingDt)))-3
predictionLabels <- knn(train = trainingDtOver, test = testingDt, cl = trainingDtOverLabel, k = startK)
# 7. Evaluasi
# Tabel Hasil
confusionMatrix <- table(predictionLabels,testingLabels)
accuracy <- (confusionMatrix[1,1] + confusionMatrix [2,2]) / (confusionMatrix[1,1] + confusionMatrix[1,2] + confusionMatrix[2,1] +confusionMatrix[2,2])
recall <- (confusionMatrix[1,1]) / (confusionMatrix[1,1] + confusionMatrix[1,2])
specificity <- (confusionMatrix [2,2]) / (confusionMatrix[2,1] +confusionMatrix[2,2])
precision <- (confusionMatrix[1,1]) / (confusionMatrix[1,1] + confusionMatrix[2,1])
# List Data Evaluasi
evaluasiList <- list(confusionMatrix = confusionMatrix, accuracy = accuracy , recall = recall, specificity = specificity, precision = precision)
evaluasiList
# 8.Beberapa Plot
library(lattice)
xyplot(V10 ~ V28, dt, groups = dt$V2, pch= 20)
xyplot(V7 ~ V26, dt, groups = dt$V2, pch= 20)
xyplot(V4 ~ V23, dt, groups = dt$V2, pch= 20)
xyplot(V10 ~ V29, dt, groups = dt$V2, pch= 20)
|
/code/kNNOverSampling.R
|
no_license
|
RyMey/Classification-Breast-Cancer-Wisconsin-Diagnostic-Data-Set-with-kNN
|
R
| false
| false
| 3,119
|
r
|
# G64164008
# Rya Meyvriska
# Praktikum 2
# 1. Mempersiapkan data
dt <- read.table("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data", sep = ',')
dt <- dt[,-1] #menghilangkan fitur v1, karena isinya hanya ID
# 2. Split data
dtClassB <- dt[which(dt$V2=='B'),]
dtClassM <- dt[which(dt$V2=='M'),]
# 3. Cek Range
summary(dt)
# 4. Normalisasi data
normalize <- function(x) {
return ((x-min(x)) / (max(x) - min(x)))
}
normalizedDtB <- as.data.frame(lapply(dtClassB[,2:31], normalize))
normalizedDtM <- as.data.frame(lapply(dtClassM[,2:31], normalize))
# 5. Ambil training data (70%) dan testing data (30%)
# Ambil 70% data dari kelas B
nClassB <- nrow(normalizedDtB)
boundClassB <- ceiling(nClassB * 0.7)
trainingDtClassB <- normalizedDtB[1:boundClassB, ]
testingDtClassB <- normalizedDtB[(boundClassB + 1):nClassB, ]
trainingLabelsClassB <- dtClassB[1:boundClassB, 1]
testingLabelsClassB <- dtClassB[(boundClassB + 1):nClassB, 1]
# Ambil 70% data dari kelas M
nClassM <- nrow(normalizedDtM)
boundClassM <- ceiling(nClassM * 0.7)
trainingDtClassM <- normalizedDtM[1:boundClassM, ]
testingDtClassM <- normalizedDtM[(boundClassM + 1):nClassM, ]
trainingLabelsClassM <- dtClassM[1:boundClassM, 1]
testingLabelsClassM <- dtClassM[(boundClassM + 1):nClassM, 1]
# Satukan dalam satu data
trainingDt <- rbind(trainingDtClassB[,], trainingDtClassM[,])
testingDt <- rbind(testingDtClassB[,], testingDtClassM[,])
trainingLabels <- c(as.character(trainingLabelsClassB[]), as.character(trainingLabelsClassM[]))
trainingLabels <- as.factor(trainingLabels)
testingLabels <- c(as.character(testingLabelsClassB[]), as.character(testingLabelsClassM[]))
testingLabels <- as.factor(testingLabels)
# Oversampling pada trainingDt
library(ROSE) #Random OverUnder Sampling Example
dtOver <- cbind(trainingLabels,trainingDt)
trainingDtOver <- ovun.sample(trainingLabels~., data = dtOver, method = "over", N = 500)$data
trainingDtOverLabel <- trainingDtOver$trainingLabels
trainingDtOver <- trainingDtOver[,-1]
# 6.Membuat prediksi dengan kNN
library(class)
startK <- ceiling(sqrt(nrow(trainingDt)))-3
predictionLabels <- knn(train = trainingDtOver, test = testingDt, cl = trainingDtOverLabel, k = startK)
# 7. Evaluasi
# Tabel Hasil
confusionMatrix <- table(predictionLabels,testingLabels)
accuracy <- (confusionMatrix[1,1] + confusionMatrix [2,2]) / (confusionMatrix[1,1] + confusionMatrix[1,2] + confusionMatrix[2,1] +confusionMatrix[2,2])
recall <- (confusionMatrix[1,1]) / (confusionMatrix[1,1] + confusionMatrix[1,2])
specificity <- (confusionMatrix [2,2]) / (confusionMatrix[2,1] +confusionMatrix[2,2])
precision <- (confusionMatrix[1,1]) / (confusionMatrix[1,1] + confusionMatrix[2,1])
# List Data Evaluasi
evaluasiList <- list(confusionMatrix = confusionMatrix, accuracy = accuracy , recall = recall, specificity = specificity, precision = precision)
evaluasiList
# 8.Beberapa Plot
library(lattice)
xyplot(V10 ~ V28, dt, groups = dt$V2, pch= 20)
xyplot(V7 ~ V26, dt, groups = dt$V2, pch= 20)
xyplot(V4 ~ V23, dt, groups = dt$V2, pch= 20)
xyplot(V10 ~ V29, dt, groups = dt$V2, pch= 20)
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# Gene set enrichment analysis
# Package: piano
# https://github.com/sysmedicine/phd2018/blob/master/Workshop/TranscriptomicsWorkshop/Part2/Rcode.R
GSE_input_Table <- as.data.frame(read_excel("./Dataset_liv_kid_hepa/GSE_Table/atorvastatin.xlsx"))
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("piano")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("snowfall")
library(piano)
library(snowfall)
##################### Save objects/ variable ######
# Save an object to a file
saveRDS(gsaRes, file = "./gsaRes_liver_fenofibrate.rds")
# Restore the object
readRDS(file = "./gsaRes_liver_fenofibrate.rds")
######################## Three tissues GSE table ################################
list_common <- list.files("./Dataset_liv_kid_hepa/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_liv_kid_hepa/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:10] # col number
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Liver
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_l <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_l) <- gsa_l[,1]
# Kidney
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
# Hepatocyte
pval= as.matrix(GSE_input_Table[ ,8]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,7]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_h <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_h) <- gsa_h[,1]
table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
#table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
##############################################################################################
######################## Two tissues GSE table (Liver and Kidney) ################################
list_common <- list.files("./Dataset_liv_kid/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_liv_kid/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:7]
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Liver
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_l <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_l) <- gsa_l[,1]
# Kidney
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
#table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
#table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_liv_kid/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
###############################################################################################################
######################## Two tissues GSE table (Kidney and hepatocyte) ################################
list_common <- list.files("./Dataset_kid_hepa/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_kid_hepa/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:7]
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Kidney
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
# Hepatocyte
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_h <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_h) <- gsa_h[,1]
#table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
#table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
############################# intersect Table ######################################
list_common <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
#GSE_input_Table <- as.data.frame(read_excel("./Dataset_liv_kid_hepa/GSE_Table/azathioprine.xlsx"))
GSE_intersect_table <- data.frame('Compound'= c('Compound') ,
'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),
'GO term'= c('GO term'),
'Direction'= c('Direction'))
for (c in 1:length(list_common)) {
c_GSEname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "")
c_GSEname <- paste(c_GSEname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_GSEname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:40]
GO_UP <- which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05 & GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05 & GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)
g_UP <- rownames(GSE_input_Table[GO_UP,])
GO_DOWN <- which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05 & GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05 & GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)
g_DOWN <- rownames(GSE_input_Table[GO_DOWN,])
GO_NON <- which(GSE_input_Table$`p adj (non-dir.)` < 0.05 & GSE_input_Table$`p adj (non-dir.)__1` < 0.05 & GSE_input_Table$`p adj (non-dir.)__2` < 0.05)
g_NON <- rownames(GSE_input_Table[GO_NON,])
if (length(g_UP) > 0) {
GSE_summary_table_up <- data.frame('Compound'= c(rep(list_common[c],length(GO_UP))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_UP,])), 'Direction'= c(rep('UP',length(GO_UP))))
}
if (length(g_DOWN) > 0) {
GSE_summary_table_dn <- data.frame('Compound'= c(rep(list_common[c],length(GO_DOWN))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_DOWN,])), 'Direction'= c(rep('DOWN',length(GO_DOWN))))
}
if (length(g_NON) > 0) {
GSE_summary_table_non <- data.frame('Compound'= c(rep(list_common[c],length(GO_NON))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_NON,])), 'Direction'= c(rep('DOWN',length(GO_NON))))
}
if (length(g_UP) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_up)
}
if (length(g_DOWN) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_dn)
}
if (length(g_NON) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_non)
}
}
write.xlsx(GSE_intersect_table, file = './Dataset_liv_kid_hepa/GSE_intersect_table_0.05_correct.xlsx')
############################# Summary Table (3 Tissues) ################################################
list_common <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
DE_summary_table <- data.frame('FactorValue [COMPOUND]'= c('FactorValue [COMPOUND]') ,
'Characteristics [organism part]'= c('Characteristics [organism part]'),
'Num. of GO up (P.adj<0.05)'= c('Num. of GO up (P.adj<0.05)'),
'Num. of GO down (P.adj<0.05)'= c('Num. of GO down (P.adj<0.05)'),
'Num. of GO non-dir (P.adj<0.05)'= c('Num. of GO non-dir (P.adj<0.05)'),
'List of GO up (P.adj<0.05)'= c('List of GO up (P.adj<0.05)'),
'List of GO down (P.adj<0.05)'= c('List of GO down (P.adj<0.05)'),
'List of GO non-dir (P.adj<0.05)'= c('List of GO non-dir (P.adj<0.05)'))
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:40]
# Liver
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('liver'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Hepatocyte
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__2` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__2` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('hepatocyte'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
###################################################################################################
############################# Summary Table (2 Tissues) ################################################
list_liv_kid_hepa <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_liv_kid_hepa <- gsub('.{5}$', '', list_liv_kid_hepa)
list_liv_kid <- list.files("./Dataset_liv_kid/GSE_Table/") # Rename !!!1
list_liv_kid <- gsub('.{5}$', '', list_liv_kid)
list_kid_hepa <- list.files("./Dataset_kid_hepa/GSE_Table/") # Rename !!!1
list_kid_hepa <- gsub('.{5}$', '', list_kid_hepa)
list_common <- list_liv_kid[! list_liv_kid %in% list_liv_kid_hepa]
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_liv_kid/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:27]
# Liver
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('liver'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
list_common <- list_kid_hepa[! list_kid_hepa %in% list_liv_kid_hepa]
list_common <- list_common[! list_common %in% list_liv_kid]
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:27]
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Hepatocyte
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('hepatocyte'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
write.xlsx(DE_summary_table, file = './Dataset_liv_kid_hepa/GSE_Summary_Table_new_correct.xlsx') # Rename !!!1
#
|
/scripts/DrugM_Affymetrics_GSEA_process.R
|
no_license
|
YouchengZHANG/ProjectDrugRepurposing
|
R
| false
| false
| 25,398
|
r
|
# Gene set enrichment analysis
# Package: piano
# https://github.com/sysmedicine/phd2018/blob/master/Workshop/TranscriptomicsWorkshop/Part2/Rcode.R
GSE_input_Table <- as.data.frame(read_excel("./Dataset_liv_kid_hepa/GSE_Table/atorvastatin.xlsx"))
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("piano")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("snowfall")
library(piano)
library(snowfall)
##################### Save objects/ variable ######
# Save an object to a file
saveRDS(gsaRes, file = "./gsaRes_liver_fenofibrate.rds")
# Restore the object
readRDS(file = "./gsaRes_liver_fenofibrate.rds")
######################## Three tissues GSE table ################################
list_common <- list.files("./Dataset_liv_kid_hepa/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_liv_kid_hepa/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:10] # col number
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Liver
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_l <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_l) <- gsa_l[,1]
# Kidney
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
# Hepatocyte
pval= as.matrix(GSE_input_Table[ ,8]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,7]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 4)
gsa_h <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_h) <- gsa_h[,1]
table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
#table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
##############################################################################################
######################## Two tissues GSE table (Liver and Kidney) ################################
list_common <- list.files("./Dataset_liv_kid/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_liv_kid/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:7]
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Liver
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_sets_bp_symbols.gmt')
#gset=loadGSC('./Rattus_norvegicus_GSEA_GO_symbols_highquality.gmt')
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_l <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_l) <- gsa_l[,1]
# Kidney
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
#table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
#table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_liv_kid/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
###############################################################################################################
######################## Two tissues GSE table (Kidney and hepatocyte) ################################
list_common <- list.files("./Dataset_kid_hepa/DE_Table_adjPvalue/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
for (c in 1:length(list_common)) {
tCDname <- paste("./Dataset_kid_hepa/DE_Table_adjPvalue/",list_common[c], sep = "") # Rename !
tCDname <- paste(tCDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(tCDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:7]
rownames(GSE_input_Table) <- toupper(rownames(GSE_input_Table))
# Kidney
pval= as.matrix(GSE_input_Table[ ,2]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,1]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_k <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_k) <- gsa_k[,1]
# Hepatocyte
pval= as.matrix(GSE_input_Table[ ,5]) #extract P as a matrix # Column !
fc= as.matrix(GSE_input_Table[ ,4]) #extract fold changes as a matrix # Column !
row.names(pval)=row.names(GSE_input_Table)
row.names(fc)=row.names(GSE_input_Table)
#gsaRes <- runGSA(pval,fc,gsc=gset, nPerm = 1000, adjMethod = "fdr", gsSizeLim = c(1,Inf))
gsaRes <- runGSA(geneLevelStats = pval, directions = fc, gsc = gset,nPerm = 4000, adjMethod = "fdr", ncpus = 2)
gsa_h <- GSAsummaryTable(gsaRes, save=FALSE)
rownames(gsa_h) <- gsa_h[,1]
#table_GSE_all <- cbind(gsa_l[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_l), rownames(gsa_k)),]) #Rename!
#table_GSE_all <- cbind(table_GSE_all,gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(table_GSE_all), rownames(gsa_h)),])
table_GSE_all <- cbind(gsa_k[c(2,4,5,7,8,10,11,12,14,15,16,18,19)],gsa_h[c(2,4,5,7,8,10,11,12,14,15,16,18,19)][match(rownames(gsa_k), rownames(gsa_h)),]) #Rename!
tGSEname <- paste("./Dataset_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !
tGSEname <- paste(tGSEname,'.xlsx', sep = "")
write.xlsx(table_GSE_all, file = tGSEname)
}
############################# intersect Table ######################################
list_common <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
#GSE_input_Table <- as.data.frame(read_excel("./Dataset_liv_kid_hepa/GSE_Table/azathioprine.xlsx"))
GSE_intersect_table <- data.frame('Compound'= c('Compound') ,
'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),
'GO term'= c('GO term'),
'Direction'= c('Direction'))
for (c in 1:length(list_common)) {
c_GSEname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "")
c_GSEname <- paste(c_GSEname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_GSEname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:40]
GO_UP <- which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05 & GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05 & GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)
g_UP <- rownames(GSE_input_Table[GO_UP,])
GO_DOWN <- which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05 & GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05 & GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)
g_DOWN <- rownames(GSE_input_Table[GO_DOWN,])
GO_NON <- which(GSE_input_Table$`p adj (non-dir.)` < 0.05 & GSE_input_Table$`p adj (non-dir.)__1` < 0.05 & GSE_input_Table$`p adj (non-dir.)__2` < 0.05)
g_NON <- rownames(GSE_input_Table[GO_NON,])
if (length(g_UP) > 0) {
GSE_summary_table_up <- data.frame('Compound'= c(rep(list_common[c],length(GO_UP))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_UP,])), 'Direction'= c(rep('UP',length(GO_UP))))
}
if (length(g_DOWN) > 0) {
GSE_summary_table_dn <- data.frame('Compound'= c(rep(list_common[c],length(GO_DOWN))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_DOWN,])), 'Direction'= c(rep('DOWN',length(GO_DOWN))))
}
if (length(g_NON) > 0) {
GSE_summary_table_non <- data.frame('Compound'= c(rep(list_common[c],length(GO_NON))) ,'Characteristics [organism part]'= c('liver|kidney|hepatocyte'),'GO term'= c(rownames(GSE_input_Table[GO_NON,])), 'Direction'= c(rep('DOWN',length(GO_NON))))
}
if (length(g_UP) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_up)
}
if (length(g_DOWN) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_dn)
}
if (length(g_NON) > 0) {GSE_intersect_table <- rbind(GSE_intersect_table,GSE_summary_table_non)
}
}
write.xlsx(GSE_intersect_table, file = './Dataset_liv_kid_hepa/GSE_intersect_table_0.05_correct.xlsx')
############################# Summary Table (3 Tissues) ################################################
list_common <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_common <- gsub('.{5}$', '', list_common)
DE_summary_table <- data.frame('FactorValue [COMPOUND]'= c('FactorValue [COMPOUND]') ,
'Characteristics [organism part]'= c('Characteristics [organism part]'),
'Num. of GO up (P.adj<0.05)'= c('Num. of GO up (P.adj<0.05)'),
'Num. of GO down (P.adj<0.05)'= c('Num. of GO down (P.adj<0.05)'),
'Num. of GO non-dir (P.adj<0.05)'= c('Num. of GO non-dir (P.adj<0.05)'),
'List of GO up (P.adj<0.05)'= c('List of GO up (P.adj<0.05)'),
'List of GO down (P.adj<0.05)'= c('List of GO down (P.adj<0.05)'),
'List of GO non-dir (P.adj<0.05)'= c('List of GO non-dir (P.adj<0.05)'))
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_liv_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:40]
# Liver
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('liver'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Hepatocyte
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__2` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__2` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__2` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__2` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('hepatocyte'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
###################################################################################################
############################# Summary Table (2 Tissues) ################################################
list_liv_kid_hepa <- list.files("./Dataset_liv_kid_hepa/GSE_Table/") # Rename !!!1
list_liv_kid_hepa <- gsub('.{5}$', '', list_liv_kid_hepa)
list_liv_kid <- list.files("./Dataset_liv_kid/GSE_Table/") # Rename !!!1
list_liv_kid <- gsub('.{5}$', '', list_liv_kid)
list_kid_hepa <- list.files("./Dataset_kid_hepa/GSE_Table/") # Rename !!!1
list_kid_hepa <- gsub('.{5}$', '', list_kid_hepa)
list_common <- list_liv_kid[! list_liv_kid %in% list_liv_kid_hepa]
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_liv_kid/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:27]
# Liver
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('liver'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
list_common <- list_kid_hepa[! list_kid_hepa %in% list_liv_kid_hepa]
list_common <- list_common[! list_common %in% list_liv_kid]
for (c in 1:length(list_common)) {
c_CDname <- paste("./Dataset_kid_hepa/GSE_Table/",list_common[c], sep = "") # Rename !!!1
c_CDname <- paste(c_CDname,'.xlsx', sep = "")
GSE_input_Table <- as.data.frame(read_excel(c_CDname))
rownames(GSE_input_Table) <- GSE_input_Table$X__1
GSE_input_Table <- GSE_input_Table[2:27]
# Kidney
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('kidney'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
# Hepatocyte
NoGU_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)))
NoGN_Padj <- as.character(length(which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)))
NoNon_Padj <- as.character(length(which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)))
LoGU_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.up)__1` < 0.05)], collapse=";")
LoGD_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (dist.dir.dn)__1` < 0.05)], collapse=";")
LoNon_Padj <- paste(rownames(GSE_input_Table)[which(GSE_input_Table$`p adj (non-dir.)__1` < 0.05)], collapse=";")
DE_summary_table_tmp <- data.frame('FactorValue [COMPOUND]'= list_common[c] ,
'Characteristics [organism part]'= c('hepatocyte'),
'Num. of GO up (P.adj<0.05)'= NoGU_Padj,
'Num. of GO down (P.adj<0.05)'= NoGN_Padj,
'Num. of GO non-dir (P.adj<0.05)'= NoNon_Padj,
'List of GO up (P.adj<0.05)'= LoGU_Padj,
'List of GO down (P.adj<0.05)'= LoGD_Padj,
'List of GO non-dir (P.adj<0.05)'= LoNon_Padj)
DE_summary_table <- rbind(DE_summary_table,DE_summary_table_tmp)
}
write.xlsx(DE_summary_table, file = './Dataset_liv_kid_hepa/GSE_Summary_Table_new_correct.xlsx') # Rename !!!1
#
|
cyan <- 3
magenta <- 5
yellow <- 7
# Assign a variable `p` as the probability of choosing a cyan ball from the box
p <- cyan / (cyan + magenta + yellow)
p # 0.2
# probability that the ball you draw from the box will NOT be cyan
1-p # 0.8
|
/1_Discrete_Probability.R
|
no_license
|
rishi10819/probability
|
R
| false
| false
| 243
|
r
|
cyan <- 3
magenta <- 5
yellow <- 7
# Assign a variable `p` as the probability of choosing a cyan ball from the box
p <- cyan / (cyan + magenta + yellow)
p # 0.2
# probability that the ball you draw from the box will NOT be cyan
1-p # 0.8
|
library(BioGeoBEARS)
### Name: getAIC_weight_for_model1
### Title: Calculate Akaike Weight
### Aliases: getAIC_weight_for_model1
### ** Examples
test=1
AICval_1 = 20
AICvals = c(20,30,40)
getAIC_weight_for_model1(AICval_1, AICvals)
|
/data/genthat_extracted_code/BioGeoBEARS/examples/getAIC_weight_for_model1.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 240
|
r
|
library(BioGeoBEARS)
### Name: getAIC_weight_for_model1
### Title: Calculate Akaike Weight
### Aliases: getAIC_weight_for_model1
### ** Examples
test=1
AICval_1 = 20
AICvals = c(20,30,40)
getAIC_weight_for_model1(AICval_1, AICvals)
|
\name{BestMatches2ProjectedPoints}
\alias{BestMatches2ProjectedPoints}
\title{
BestMatches2ProjectedPoints
}
\description{
Converts quadgrid coordinates (= BestMatchingUnit coordinates) to a set of cartesian (x,y) coordinates
}
\usage{
BestMatches2ProjectedPoints(Bestmatches, Lines)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{Bestmatches}{
see package Umatrix
}
\item{Lines}{
Optional: smaller Dimensions of overlying grid, default Lines= 50;
}
}
\value{
cartesian x,y coordinates
}
\author{
Michael Thrun
}
|
/DbtTools/Projections/man/BestMatches2ProjectedPoints.Rd
|
no_license
|
markus-flicke/KD_Projekt_1
|
R
| false
| false
| 574
|
rd
|
\name{BestMatches2ProjectedPoints}
\alias{BestMatches2ProjectedPoints}
\title{
BestMatches2ProjectedPoints
}
\description{
Converts quadgrid coordinates (= BestMatchingUnit coordinates) to a set of cartesian (x,y) coordinates
}
\usage{
BestMatches2ProjectedPoints(Bestmatches, Lines)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{Bestmatches}{
see package Umatrix
}
\item{Lines}{
Optional: smaller Dimensions of overlying grid, default Lines= 50;
}
}
\value{
cartesian x,y coordinates
}
\author{
Michael Thrun
}
|
library(shiny)
library(plotly)
library(statsfoRstudents)
library(shinyjs)
#library(tidyverse)
library(dplyr)
#library(checkmate)
shinyUI(fluidPage(title="STAT 585",useShinyjs(),
tabsetPanel(#widths=c(2,10),
tabPanel(title = h4("Type I Error vs Type II error"),
# Sidebar input
sidebarPanel(
helpText("Choose the following parameters:"),
numericInput("Errors_means", "Mean: ", value=0),
numericInput("Errors_sds", "Standard deviation:",
value = 1, min=0.0001),
numericInput("alpha_eryn", "Alpha Level", value=0.5, min=0.00000001, max=0.49),
selectInput("dir_eryn",
label="Direction of Alternative Hypothesis",
choices = list(intToUtf8("8800"), #8800 is HTML for "not equal to"
">",
"<"))
),
# Show a plot of the generated distribution
mainPanel(
h4("Area where there could be a Type I error"),
plotlyOutput("plotup"),
h4("Area where there could be a Type II error"),
plotlyOutput("plotlw")
)
),
tabPanel(h4("Bootstrapping Process"),
fluid = TRUE,
sidebarLayout(
sidebarPanel(
helpText("Choose sample"),
# select method type
radioButtons("whichmethod", "Method", choices = c("Upload file",
"Generate sample"),
selected = "Generate sample"),
# condition 1: file
conditionalPanel(
helpText("Upload a file"),
condition = "input.whichmethod == 'Upload file'",
# Input: Select a file
fileInput("file", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Horizontal line
tags$hr(),
# Input: Checkbox if file has header
checkboxInput("header", "Header", TRUE),
# Input: Select separator
radioButtons("sep", "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Input: Select quotes
radioButtons("quote", "Quote",
choices = c(None = "",
"Double Quote" = '"',
"Single Quote" = "'"),
selected = '"')
),
# condition 2: generate sample
conditionalPanel(
helpText("Sample from Normal Distribution"),
condition = "input.whichmethod == 'Generate sample'",
numericInput("mean", "Mean", value = 0),
numericInput("sd", "Standard deviation",
value = 1,
min = 0.0001,
step = 0.001)
),
# select TRUE/FALSE for animation
selectInput("anime", label = h3("Animated Plot"),
choices = list(TRUE, FALSE),
selected = TRUE)
),
# type of output
mainPanel(
plotlyOutput("plot")
)
)
),
tabPanel(h4("Summary of Bootstrap Sample"),
fluid = TRUE,
sidebarLayout(
sidebarPanel(
helpText("Choose sample"),
# select method type
radioButtons("whichmethod2", "Method", choices = c("Upload file",
"Generate sample"),
selected = "Generate sample"),
# condition 1: file
conditionalPanel(
helpText("Upload a file"),
condition = "input.whichmethod2 == 'Upload file'",
# Input: Select a file
fileInput("file1", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Horizontal line
tags$hr(),
# Input: Checkbox if file has header
checkboxInput("header1", "Header", TRUE),
# Input: Select separator
radioButtons("sep1", "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Input: Select quotes
radioButtons("quote1", "Quote",
choices = c(None = "",
"Double Quote" = '"',
"Single Quote" = "'"),
selected = '"')
),
# condition 2: generate sample
conditionalPanel(
helpText("Sample from Normal Distribution"),
condition = "input.whichmethod2 == 'Generate sample'",
numericInput("mean1", "Mean", value = 0),
numericInput("sd1", "Standard deviation", value = 1, min = 0.0001)
),
# how many bootsrtrap samples
numericInput("n", "Number of bootstrap samples: ",
value = 10,
min = 1)
),
# type of output
mainPanel(
plotOutput("hist")
)
)
),
tabPanel(title = h4("Histogram Description Testing"),
sidebarPanel(
actionButton("redo", "New Distribution")
),
mainPanel(
tabsetPanel(
tabPanel("Modality",
plotOutput("plotModal"),
uiOutput("aspectModal"),
textOutput("feedbackModal")
),
tabPanel("Shape",
plotOutput("plotShape"),
uiOutput("aspectShape"),
textOutput("feedbackShape")
),
tabPanel("Outlier",
plotOutput("plotOutlier"),
uiOutput("aspectOutlier"),
textOutput("feedbackOutlier")
)
)
)
),
tabPanel(h4("Normal Plot"),
sidebarLayout(
sidebarPanel(width=3,
selectInput("distributionType",
label=h4("Population Shape"),
choices = list("Normal",
"Another Option"),
selected="Normal"),
numericInput("normalPlot_mu",
label = h4("Population Mean"),
value=0),
numericInput("normalPlot_sigma",
label = h4("Population Standard Deviation"),
value=1),
numericInput("alpha",
label = h4("Significance Level"),
value=.05),
selectInput("dir",
label= h4("Direction of Alternative Hypothesis"),
choices = list(intToUtf8("8800"), #8800 is HTML for "not equal to"
">",
"<")),
numericInput("obsZ",
label= h4("Observed Test Statistic"),
value=0)),
# Show a plot of the generated distribution
mainPanel(
plotlyOutput(outputId = "distPlot",height="700px")
))),
tabPanel(h4("Hypothesis Test Challenge"),
tags$head(
tags$style(
HTML(
"
#inputs-table {
border-collapse: collapse;
}
#inputs-table td {
padding: 10px;
vertical-align: bottom;
}
"
) #/ HTML
) #/ style
),
tags$table(id = "inputs-table"
, style = "width: 100%; border-bottom: 2px solid black"
, tags$tr(
tags$td(style = "width: 20%; text-align: left; vertical-align: top; padding-top: 38px"
, div(
actionButton("newQuestion",label=h4("New Hypothesis Test Problem")
) #/ actionButton
) #/ div
) #/ td
, tags$td(style = "width: 80%; text-align: left; vertical-align: top"
, div(
h3("Prompt:"),
h4(textOutput("prompt"))
) #/ div
) #/ td
)#/ tr
) #/ table
,tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 13%; vertical-align: middle; text-align: left"
,div(
h4(paste0("1) Null Hypothesis: ",intToUtf8("0x03BC")," ="))
) #/ div
) #/ td
, tags$td(style = "width: 5%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
uiOutput("nullHypVal")
) #/ div
) #/ td
, tags$td(style = "width: 82%; vertical-align: middle; text-align: left"
,div(
h5(textOutput("nullHypFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 15%; vertical-align: middle; text-align: left;"
,div(
h4(paste0("2) Alternative Hypothesis: ",intToUtf8("0x03BC")))
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left"
,div(style = "width: 60px"
, uiOutput("altHypDir")
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 4px"
,div(uiOutput("altHypVal")
) #/ div
) #/ td
, tags$td(style = "width: 80%; vertical-align: middle; text-align: left"
,div(
h5(textOutput("altHypFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 11%; vertical-align: middle; text-align: left"
,div(
h4("3) Test Statistic: z = ")
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 13px"
, div(
h4(uiOutput("testStat"))
) #/ div
) #/ td
, tags$td(style = "width: 87%; vertical-align: middle; text-align: left"
, div(
h5(textOutput("testStatFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 27%; vertical-align: middle; text-align: left"
,div(
h4("4) Compare Test Statistic to Null Distribution: ")
) #/ div
) #/ td
, tags$td(style = "width: 3%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
h4(uiOutput("testSize"))
) #/ div
) #/ td
, tags$td(style = "width: 10%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
actionButton("plotNull",h4("Plot Null Distribution"))
) #/ div
) #/ td
, tags$td(style = "width: 60%; vertical-align: middle"
,div(
h5(textOutput("testSizeFeedback"))
) #/ div
)
) #/ tr
) #/ table
, tags$table(class = "col-md-11",
style = "width: 100%"
, tags$tr(
tags$td(style = "width: 100%",
plotlyOutput("nullDist",height="300px")
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 7%; vertical-align: middle; text-align: left"
,div(
h4("5) Decision: ")
) #/ div
) #/ td
, tags$td(style = "width: 3%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 10px"
, div(style = "width: 200px",
h4(uiOutput("decision"))
) #/ div
)
, tags$td(style = "width: 87%; vertical-align: middle"
, div(
h5(textOutput("decisionFeedback"))
) #/ div
)
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 10%; vertical-align: middle; text-align: left"
,div(
h4("Problem Source: ")
) #/ div
) #/ td
, tags$td(style = "width: 90%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
h4(uiOutput("source"))
) #/ div
) #/ td
) #/ tr
) #/ table
),
tabPanel(h4("Sampling Distribution of Sample Means"),
sidebarLayout(
sidebarPanel(width=3,selectInput("distributionType",
label=h4("Population Shape"),
choices = list("Normal",
"Another Option"),
selected="Normal"),
numericInput("sampleDist_mu",
label = h4("Population Mean"),
value=0),
numericInput("sampleDist_sigma",
label = h4("Population Standard Deviation"),
value=1),
textInput("name",
label = h4("Name of Variable"),
value = "Prices"),
numericInput("sampleSize",
label = h4("Sample Size"),
value=25),
numericInput("numSamples",
label = h4("Number of Samples"),
value=1),
actionButton("drawSample",label = h4("Draw Additional Samples"),width = "220px"),
br(),
actionButton("resetMeanSample",label=h4("Reset Samples"),width = "150px"),
br(),
downloadButton("downloadSampleData",h4("Download Sample Data"))
), #/ sidebarPanel
# Show a plot of the generated distribution
mainPanel(
fluidRow(
h4("Distribution of Sample Data:"),
column(7,plotlyOutput(outputId = "randomSampleDist",height = "300px")),
h4("Sample Summary Table:"),
column(5,tableOutput("randomSampleTable"))),
h4("Distribution of Sample Means:"),
plotlyOutput(outputId = "meanSamplingDist",height="300px"),
h4("Means Summary Table:"),
tableOutput("meansTable")
)),
actionButton("showQuestions",h4("Show/Hide Questions")),
hidden(div(id="text_div",
h4(htmlOutput("samplingDistributionQuestions",style="padding-right: 150px"))
)
)
)
)
)
)
####### Joe's old hypothesis test tab. Only madness lies past here.
# sidebarLayout(
# sidebarPanel(width=3,
# actionButton("newQuestion",label="New Hypothesis Test Problem")
# ),
# column(width=9,#withMathJax(),
# # br(),
# h3("Prompt:"),
# h4(textOutput("prompt")),
# fluidRow(column(width=9,h3("1) Null Hypothesis:"))),
# fluidRow(column(width=1,h4(paste0(intToUtf8("0x03BC")," ="),
# style="padding: 0px; width: 30px;")),
# column(width=2,uiOutput("nullHypVal"),
# style="padding: 0px;"),
# column(width=6,textOutput("nullHypFeedback"),
# style="padding: 0px;")),
# fluidRow(column(width=9,h3("2) Alternative Hypothesis:"))),
# fluidRow(column(width=1,h4(intToUtf8("0x03BC")),
# style="padding: 0px; width: 20px;"),
# column(width=1,h4(uiOutput("altHypDir")),
# style="padding: 0px;"),
# column(width=2,h4(uiOutput("altHypVal")),
# style="padding: 0px;"),
# column(width=5,textOutput("altHypFeedback"),
# style="padding:0px;")),
# fluidRow(column(width=9,h3("3) Test Statistic (within .1 of true value):"))),
# fluidRow(column(width=1,h4("z ="),
# style="padding: 0px;"),
# column(width=2,h4(uiOutput("testStat")),
# style="padding: 0px;"),
# column(width=6,textOutput("testStatFeedback"),
# style="padding: 0px;")),
# fluidRow(column(width=9,h3("4) Compare Test Statistic to Null Distribution:"))),
# fluidRow(column(width=2,h4(uiOutput("testSize"))),
# # div(style="display: inline-block;width: 75px;",h4(uiOutput("nullSD"))),
# column(width=3,actionButton("plotNull",h4("Plot Null Distribution")),
# style="padding:0px;"),
# column(width=4,textOutput("testSizeFeedback"),
# style="padding:0px;")),
# plotlyOutput("nullDist",height="300px"),
# fluidRow(column(width=9,h3("5) Decision:"))),
# fluidRow(column(width=3,h4(uiOutput("decision")),
# style="padding:0px;"),
# column(width=6,textOutput("decisionFeedback"),
# style="padding:0px;")),
# h3("Problem Source:"),
# h4(uiOutput("source"))#,
# # fluidRow(column(width=9,h3("6) Conclusion:"))),
# # fluidRow(column(width=2,h4("There is ")),
# # column(width=2,h4(uiOutput("conclusionSignif"))),
# # column(width=5,h4(" evidence to conclude that the true mean is ")))
# )
|
/inst/shiny/ui.R
|
permissive
|
kgulzina/statsfoRstudents
|
R
| false
| false
| 29,715
|
r
|
library(shiny)
library(plotly)
library(statsfoRstudents)
library(shinyjs)
#library(tidyverse)
library(dplyr)
#library(checkmate)
shinyUI(fluidPage(title="STAT 585",useShinyjs(),
tabsetPanel(#widths=c(2,10),
tabPanel(title = h4("Type I Error vs Type II error"),
# Sidebar input
sidebarPanel(
helpText("Choose the following parameters:"),
numericInput("Errors_means", "Mean: ", value=0),
numericInput("Errors_sds", "Standard deviation:",
value = 1, min=0.0001),
numericInput("alpha_eryn", "Alpha Level", value=0.5, min=0.00000001, max=0.49),
selectInput("dir_eryn",
label="Direction of Alternative Hypothesis",
choices = list(intToUtf8("8800"), #8800 is HTML for "not equal to"
">",
"<"))
),
# Show a plot of the generated distribution
mainPanel(
h4("Area where there could be a Type I error"),
plotlyOutput("plotup"),
h4("Area where there could be a Type II error"),
plotlyOutput("plotlw")
)
),
tabPanel(h4("Bootstrapping Process"),
fluid = TRUE,
sidebarLayout(
sidebarPanel(
helpText("Choose sample"),
# select method type
radioButtons("whichmethod", "Method", choices = c("Upload file",
"Generate sample"),
selected = "Generate sample"),
# condition 1: file
conditionalPanel(
helpText("Upload a file"),
condition = "input.whichmethod == 'Upload file'",
# Input: Select a file
fileInput("file", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Horizontal line
tags$hr(),
# Input: Checkbox if file has header
checkboxInput("header", "Header", TRUE),
# Input: Select separator
radioButtons("sep", "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Input: Select quotes
radioButtons("quote", "Quote",
choices = c(None = "",
"Double Quote" = '"',
"Single Quote" = "'"),
selected = '"')
),
# condition 2: generate sample
conditionalPanel(
helpText("Sample from Normal Distribution"),
condition = "input.whichmethod == 'Generate sample'",
numericInput("mean", "Mean", value = 0),
numericInput("sd", "Standard deviation",
value = 1,
min = 0.0001,
step = 0.001)
),
# select TRUE/FALSE for animation
selectInput("anime", label = h3("Animated Plot"),
choices = list(TRUE, FALSE),
selected = TRUE)
),
# type of output
mainPanel(
plotlyOutput("plot")
)
)
),
tabPanel(h4("Summary of Bootstrap Sample"),
fluid = TRUE,
sidebarLayout(
sidebarPanel(
helpText("Choose sample"),
# select method type
radioButtons("whichmethod2", "Method", choices = c("Upload file",
"Generate sample"),
selected = "Generate sample"),
# condition 1: file
conditionalPanel(
helpText("Upload a file"),
condition = "input.whichmethod2 == 'Upload file'",
# Input: Select a file
fileInput("file1", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
# Horizontal line
tags$hr(),
# Input: Checkbox if file has header
checkboxInput("header1", "Header", TRUE),
# Input: Select separator
radioButtons("sep1", "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","),
# Input: Select quotes
radioButtons("quote1", "Quote",
choices = c(None = "",
"Double Quote" = '"',
"Single Quote" = "'"),
selected = '"')
),
# condition 2: generate sample
conditionalPanel(
helpText("Sample from Normal Distribution"),
condition = "input.whichmethod2 == 'Generate sample'",
numericInput("mean1", "Mean", value = 0),
numericInput("sd1", "Standard deviation", value = 1, min = 0.0001)
),
# how many bootsrtrap samples
numericInput("n", "Number of bootstrap samples: ",
value = 10,
min = 1)
),
# type of output
mainPanel(
plotOutput("hist")
)
)
),
tabPanel(title = h4("Histogram Description Testing"),
sidebarPanel(
actionButton("redo", "New Distribution")
),
mainPanel(
tabsetPanel(
tabPanel("Modality",
plotOutput("plotModal"),
uiOutput("aspectModal"),
textOutput("feedbackModal")
),
tabPanel("Shape",
plotOutput("plotShape"),
uiOutput("aspectShape"),
textOutput("feedbackShape")
),
tabPanel("Outlier",
plotOutput("plotOutlier"),
uiOutput("aspectOutlier"),
textOutput("feedbackOutlier")
)
)
)
),
tabPanel(h4("Normal Plot"),
sidebarLayout(
sidebarPanel(width=3,
selectInput("distributionType",
label=h4("Population Shape"),
choices = list("Normal",
"Another Option"),
selected="Normal"),
numericInput("normalPlot_mu",
label = h4("Population Mean"),
value=0),
numericInput("normalPlot_sigma",
label = h4("Population Standard Deviation"),
value=1),
numericInput("alpha",
label = h4("Significance Level"),
value=.05),
selectInput("dir",
label= h4("Direction of Alternative Hypothesis"),
choices = list(intToUtf8("8800"), #8800 is HTML for "not equal to"
">",
"<")),
numericInput("obsZ",
label= h4("Observed Test Statistic"),
value=0)),
# Show a plot of the generated distribution
mainPanel(
plotlyOutput(outputId = "distPlot",height="700px")
))),
tabPanel(h4("Hypothesis Test Challenge"),
tags$head(
tags$style(
HTML(
"
#inputs-table {
border-collapse: collapse;
}
#inputs-table td {
padding: 10px;
vertical-align: bottom;
}
"
) #/ HTML
) #/ style
),
tags$table(id = "inputs-table"
, style = "width: 100%; border-bottom: 2px solid black"
, tags$tr(
tags$td(style = "width: 20%; text-align: left; vertical-align: top; padding-top: 38px"
, div(
actionButton("newQuestion",label=h4("New Hypothesis Test Problem")
) #/ actionButton
) #/ div
) #/ td
, tags$td(style = "width: 80%; text-align: left; vertical-align: top"
, div(
h3("Prompt:"),
h4(textOutput("prompt"))
) #/ div
) #/ td
)#/ tr
) #/ table
,tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 13%; vertical-align: middle; text-align: left"
,div(
h4(paste0("1) Null Hypothesis: ",intToUtf8("0x03BC")," ="))
) #/ div
) #/ td
, tags$td(style = "width: 5%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
uiOutput("nullHypVal")
) #/ div
) #/ td
, tags$td(style = "width: 82%; vertical-align: middle; text-align: left"
,div(
h5(textOutput("nullHypFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 15%; vertical-align: middle; text-align: left;"
,div(
h4(paste0("2) Alternative Hypothesis: ",intToUtf8("0x03BC")))
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left"
,div(style = "width: 60px"
, uiOutput("altHypDir")
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 4px"
,div(uiOutput("altHypVal")
) #/ div
) #/ td
, tags$td(style = "width: 80%; vertical-align: middle; text-align: left"
,div(
h5(textOutput("altHypFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 11%; vertical-align: middle; text-align: left"
,div(
h4("3) Test Statistic: z = ")
) #/ div
) #/ td
, tags$td(style = "width: 1%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 13px"
, div(
h4(uiOutput("testStat"))
) #/ div
) #/ td
, tags$td(style = "width: 87%; vertical-align: middle; text-align: left"
, div(
h5(textOutput("testStatFeedback"))
) #/ div
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 27%; vertical-align: middle; text-align: left"
,div(
h4("4) Compare Test Statistic to Null Distribution: ")
) #/ div
) #/ td
, tags$td(style = "width: 3%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
h4(uiOutput("testSize"))
) #/ div
) #/ td
, tags$td(style = "width: 10%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
actionButton("plotNull",h4("Plot Null Distribution"))
) #/ div
) #/ td
, tags$td(style = "width: 60%; vertical-align: middle"
,div(
h5(textOutput("testSizeFeedback"))
) #/ div
)
) #/ tr
) #/ table
, tags$table(class = "col-md-11",
style = "width: 100%"
, tags$tr(
tags$td(style = "width: 100%",
plotlyOutput("nullDist",height="300px")
) #/ td
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 7%; vertical-align: middle; text-align: left"
,div(
h4("5) Decision: ")
) #/ div
) #/ td
, tags$td(style = "width: 3%; vertical-align: middle; text-align: left; padding-right: 10px; padding-bottom: 10px"
, div(style = "width: 200px",
h4(uiOutput("decision"))
) #/ div
)
, tags$td(style = "width: 87%; vertical-align: middle"
, div(
h5(textOutput("decisionFeedback"))
) #/ div
)
) #/ tr
) #/ table
, tags$table(class = "col-md-11"
, style = "width: 100%"
, tags$tr(
tags$td(style = "width: 10%; vertical-align: middle; text-align: left"
,div(
h4("Problem Source: ")
) #/ div
) #/ td
, tags$td(style = "width: 90%; vertical-align: middle; text-align: left; padding-right: 10px"
,div(
h4(uiOutput("source"))
) #/ div
) #/ td
) #/ tr
) #/ table
),
tabPanel(h4("Sampling Distribution of Sample Means"),
sidebarLayout(
sidebarPanel(width=3,selectInput("distributionType",
label=h4("Population Shape"),
choices = list("Normal",
"Another Option"),
selected="Normal"),
numericInput("sampleDist_mu",
label = h4("Population Mean"),
value=0),
numericInput("sampleDist_sigma",
label = h4("Population Standard Deviation"),
value=1),
textInput("name",
label = h4("Name of Variable"),
value = "Prices"),
numericInput("sampleSize",
label = h4("Sample Size"),
value=25),
numericInput("numSamples",
label = h4("Number of Samples"),
value=1),
actionButton("drawSample",label = h4("Draw Additional Samples"),width = "220px"),
br(),
actionButton("resetMeanSample",label=h4("Reset Samples"),width = "150px"),
br(),
downloadButton("downloadSampleData",h4("Download Sample Data"))
), #/ sidebarPanel
# Show a plot of the generated distribution
mainPanel(
fluidRow(
h4("Distribution of Sample Data:"),
column(7,plotlyOutput(outputId = "randomSampleDist",height = "300px")),
h4("Sample Summary Table:"),
column(5,tableOutput("randomSampleTable"))),
h4("Distribution of Sample Means:"),
plotlyOutput(outputId = "meanSamplingDist",height="300px"),
h4("Means Summary Table:"),
tableOutput("meansTable")
)),
actionButton("showQuestions",h4("Show/Hide Questions")),
hidden(div(id="text_div",
h4(htmlOutput("samplingDistributionQuestions",style="padding-right: 150px"))
)
)
)
)
)
)
####### Joe's old hypothesis test tab. Only madness lies past here.
# sidebarLayout(
# sidebarPanel(width=3,
# actionButton("newQuestion",label="New Hypothesis Test Problem")
# ),
# column(width=9,#withMathJax(),
# # br(),
# h3("Prompt:"),
# h4(textOutput("prompt")),
# fluidRow(column(width=9,h3("1) Null Hypothesis:"))),
# fluidRow(column(width=1,h4(paste0(intToUtf8("0x03BC")," ="),
# style="padding: 0px; width: 30px;")),
# column(width=2,uiOutput("nullHypVal"),
# style="padding: 0px;"),
# column(width=6,textOutput("nullHypFeedback"),
# style="padding: 0px;")),
# fluidRow(column(width=9,h3("2) Alternative Hypothesis:"))),
# fluidRow(column(width=1,h4(intToUtf8("0x03BC")),
# style="padding: 0px; width: 20px;"),
# column(width=1,h4(uiOutput("altHypDir")),
# style="padding: 0px;"),
# column(width=2,h4(uiOutput("altHypVal")),
# style="padding: 0px;"),
# column(width=5,textOutput("altHypFeedback"),
# style="padding:0px;")),
# fluidRow(column(width=9,h3("3) Test Statistic (within .1 of true value):"))),
# fluidRow(column(width=1,h4("z ="),
# style="padding: 0px;"),
# column(width=2,h4(uiOutput("testStat")),
# style="padding: 0px;"),
# column(width=6,textOutput("testStatFeedback"),
# style="padding: 0px;")),
# fluidRow(column(width=9,h3("4) Compare Test Statistic to Null Distribution:"))),
# fluidRow(column(width=2,h4(uiOutput("testSize"))),
# # div(style="display: inline-block;width: 75px;",h4(uiOutput("nullSD"))),
# column(width=3,actionButton("plotNull",h4("Plot Null Distribution")),
# style="padding:0px;"),
# column(width=4,textOutput("testSizeFeedback"),
# style="padding:0px;")),
# plotlyOutput("nullDist",height="300px"),
# fluidRow(column(width=9,h3("5) Decision:"))),
# fluidRow(column(width=3,h4(uiOutput("decision")),
# style="padding:0px;"),
# column(width=6,textOutput("decisionFeedback"),
# style="padding:0px;")),
# h3("Problem Source:"),
# h4(uiOutput("source"))#,
# # fluidRow(column(width=9,h3("6) Conclusion:"))),
# # fluidRow(column(width=2,h4("There is ")),
# # column(width=2,h4(uiOutput("conclusionSignif"))),
# # column(width=5,h4(" evidence to conclude that the true mean is ")))
# )
|
## Export: inla.pc.ddof
##! \name{pc.ddof}
##! \alias{pc.ddof}
##! \alias{inla.pc.ddof}
##! \alias{pc.t}
##! \alias{pc.dof}
##! \alias{inla.pc.t}
##! \alias{inla.pc.dof}
##!
##! \title{PC-prior for dof in a standarized Student-t}
##!
##! \description{A function to evaluate the PC-prior for the degrees of freedom
##! in a standarized Student-t distribution}
##! \usage{
##! inla.pc.ddof(dof, lambda, u, alpha, log=FALSE)
##! }
##! \arguments{
##! \item{dof}{Degrees of freedom}
##! \item{log}{Logical. Return the density or the log-density}
##! \item{lambda}{The optional value of \code{lambda}, instead of defining it implicitely
##! through \code{u} and \code{alpha}}
##! \item{u}{The upper value of dof used to elicitate \code{lambda}, \code{Prob(dof < u) = alpha}}
##! \item{alpha}{The probability \code{alpha} used to elicitate \code{lambda}}
##!}
##! \details{
##! These functions implements the PC-prior for the dof in a standarized Student-t
##! distribution (ie. with unit variance and \code{dof} > 2).
##! Either \code{lambda}, or \code{u} AND \code{alpha} must be given.
##! Due the internal tabulation, \code{dof} must be larger than 2.0025.
##! }
##! \value{%%
##! \code{inla.pc.ddof} returns the prior density for given \code{dof}.
##! }
##! \author{Havard Rue \email{hrue@r-inla.org}}
##! \examples{
##! }
`inla.pc.ddof` = function(dof, lambda, u, alpha, log=FALSE)
{
func.name.in.env = "inla.pc.t.dof.func"
dof.lim = 9.0
dof.min = 2.0025
pc.dof.kld.approx = function (dof)
{
t1 = dof * dof
t4 = t1 * dof
t7 = t1 * t1
t10 = t7 * dof
t13 = t7 * t1
t16 = t7 * t4
t19 = t7 * t7
t43 = t19 * t19
t61 = 0.7500000000000000e0 / t1 + 0.1500000000000000e1 / t4 + 0.2125000000000000e1 / t7 + 0.2900000000000000e1 / t10
+ 0.5083333333333333e1 / t13 + 0.1035714285714286e2 / t16 + 0.1706250000000000e2 / t19
+ 0.1983333333333333e2 / t19 / dof + 0.4345000000000000e2 / t19 / t1 + 0.1873181818181818e3 / t19 / t4
+ 0.2570416666666667e3 / t19 / t7 - 0.1155192307692308e4 / t19 / t10 - 0.7801071428571429e3 / t19 / t13
+ 0.3207790000000000e5 / t19 / t16 + 0.3109703125000000e5 / t43 - 0.8438102058823529e6 / t43 / dof
- 0.7932909722222222e6 / t43 / t1 + 0.2921518928947368e8 / t43 / t4 + 0.2776753702500000e8 / t43 / t7
return (t61)
}
pc.dof.create.spline = function()
{
xx = c(
2.002478752000,
2.002480761000,
2.002482771000,
2.002484783000,
2.002486797000,
2.002488812000,
2.002490829000,
2.002492848000,
2.002494868000,
2.002496890000,
2.002498914000,
2.002500939000,
2.002502966000,
2.002504994000,
2.002507024000,
2.002509056000,
2.002511089000,
2.002513124000,
2.002515161000,
2.002517199000,
2.002519239000,
2.002521281000,
2.002523324000,
2.002525369000,
2.002527415000,
2.002529464000,
2.002531514000,
2.002533565000,
2.002535618000,
2.002537673000,
2.002539730000,
2.002541788000,
2.002543848000,
2.002545909000,
2.002547973000,
2.002550038000,
2.002552104000,
2.002554172000,
2.002556242000,
2.002558314000,
2.002560387000,
2.002562462000,
2.002564539000,
2.002566617000,
2.002568697000,
2.002570779000,
2.002572862000,
2.002574947000,
2.002577034000,
2.002579123000,
2.002581213000,
2.002583305000,
2.002585398000,
2.002587493000,
2.002589590000,
2.002591689000,
2.002593789000,
2.002595891000,
2.002597995000,
2.002600100000,
2.002602208000,
2.002604316000,
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0.009719456554,
0.009705523746,
0.009691609627,
0.009677714175,
0.009663837368,
0.009649979183,
0.009636139598,
0.009622318590,
0.009608516139,
0.009594732220,
0.009580966813,
0.009567219894,
0.009553491442,
0.009539781435,
0.009526089851,
0.009512416666,
0.009498761861,
0.009485125411,
0.009471507296,
0.009457907493,
0.009444325980,
0.009430762736,
0.009417217738,
0.009403690965,
0.009390182394,
0.009376692004,
0.009363219773,
0.009349765679,
0.009336329700,
0.009322911815,
0.009309512001,
0.009296130237,
0.009282766501,
0.009269420771,
0.009256093026,
0.009242783245,
0.009229491405,
0.009216217484,
0.009202961462,
0.009189723316,
0.009176503025,
0.009163300568,
0.009150115923,
0.009136949068,
0.009123799982,
0.009110668644,
0.009097555032,
0.009084459124,
0.009071380900,
0.009058320337,
0.009045277415,
0.009032252112,
0.009019244407,
0.009006254279,
0.008993281706,
0.008980326666,
0.008967389140,
0.008954469105,
0.008941566541,
0.008928681425,
0.008915813738)
sfun = splinefun(xx, yy, method = "monoH.FC")
assign(func.name.in.env, sfun, envir = INLA:::inla.get.inlaEnv())
return (invisible())
}
pc.dof.dist = function(dof, deriv = FALSE)
{
## if 'deriv', then evaluate all 'dof' using the same approximation. in this case, dof
## are known to be close.
n = length(dof)
kld = numeric(n)
if (!deriv) {
idx = which(dof >= dof.lim)
if (length(idx) > 0) {
kld[idx] = pc.dof.kld.approx(dof[idx])
}
idx = which(dof < dof.lim)
if (length(idx) > 0) {
kld[idx] = (do.call(func.name.in.env, args = list(x = dof[idx]),
envir = INLA:::inla.get.inlaEnv()))
}
} else {
if (all(dof < dof.lim)) {
kld = (do.call(func.name.in.env, args = list(x = dof),
envir = INLA:::inla.get.inlaEnv()))
} else {
kld = pc.dof.kld.approx(dof)
}
}
return (sqrt(2.0 * kld))
}
if (!exists(func.name.in.env, envir = INLA:::inla.get.inlaEnv())) {
pc.dof.create.spline()
}
if (missing(lambda)) {
stopifnot(!(missing(u) && missing(alpha)))
stopifnot(u > 2.0 && (alpha > 0 && alpha < 1))
lambda = -log(alpha) / pc.dof.dist(u)
} else {
stopifnot(!(missing(lambda)))
stopifnot(lambda > 0.0)
}
stopifnot(all(dof > dof.min))
d = pc.dof.dist(dof)
wf = c( 1.0 / 12.0, -2.0 / 3.0, 0.0, 2.0 / 3.0, -1.0 / 12.0 )
jac = c()
for(df in dof) {
step = sqrt(df) * 1e-3
if (df - 2.0 * step < dof.min) {
step = (df - dof.min) / 2.0
}
dfs = df + (-2):2 * step
dd = pc.dof.dist(dfs, deriv=TRUE)
val = sum(dd * wf) / step
jac = c(jac, abs(val))
}
val = dexp(d, rate = lambda, log = TRUE) + log(jac)
return (if (log) val else exp(val))
}
|
/R/pc-t.R
|
no_license
|
inbo/INLA
|
R
| false
| false
| 564,567
|
r
|
## Export: inla.pc.ddof
##! \name{pc.ddof}
##! \alias{pc.ddof}
##! \alias{inla.pc.ddof}
##! \alias{pc.t}
##! \alias{pc.dof}
##! \alias{inla.pc.t}
##! \alias{inla.pc.dof}
##!
##! \title{PC-prior for dof in a standarized Student-t}
##!
##! \description{A function to evaluate the PC-prior for the degrees of freedom
##! in a standarized Student-t distribution}
##! \usage{
##! inla.pc.ddof(dof, lambda, u, alpha, log=FALSE)
##! }
##! \arguments{
##! \item{dof}{Degrees of freedom}
##! \item{log}{Logical. Return the density or the log-density}
##! \item{lambda}{The optional value of \code{lambda}, instead of defining it implicitely
##! through \code{u} and \code{alpha}}
##! \item{u}{The upper value of dof used to elicitate \code{lambda}, \code{Prob(dof < u) = alpha}}
##! \item{alpha}{The probability \code{alpha} used to elicitate \code{lambda}}
##!}
##! \details{
##! These functions implements the PC-prior for the dof in a standarized Student-t
##! distribution (ie. with unit variance and \code{dof} > 2).
##! Either \code{lambda}, or \code{u} AND \code{alpha} must be given.
##! Due the internal tabulation, \code{dof} must be larger than 2.0025.
##! }
##! \value{%%
##! \code{inla.pc.ddof} returns the prior density for given \code{dof}.
##! }
##! \author{Havard Rue \email{hrue@r-inla.org}}
##! \examples{
##! }
`inla.pc.ddof` = function(dof, lambda, u, alpha, log=FALSE)
{
func.name.in.env = "inla.pc.t.dof.func"
dof.lim = 9.0
dof.min = 2.0025
pc.dof.kld.approx = function (dof)
{
t1 = dof * dof
t4 = t1 * dof
t7 = t1 * t1
t10 = t7 * dof
t13 = t7 * t1
t16 = t7 * t4
t19 = t7 * t7
t43 = t19 * t19
t61 = 0.7500000000000000e0 / t1 + 0.1500000000000000e1 / t4 + 0.2125000000000000e1 / t7 + 0.2900000000000000e1 / t10
+ 0.5083333333333333e1 / t13 + 0.1035714285714286e2 / t16 + 0.1706250000000000e2 / t19
+ 0.1983333333333333e2 / t19 / dof + 0.4345000000000000e2 / t19 / t1 + 0.1873181818181818e3 / t19 / t4
+ 0.2570416666666667e3 / t19 / t7 - 0.1155192307692308e4 / t19 / t10 - 0.7801071428571429e3 / t19 / t13
+ 0.3207790000000000e5 / t19 / t16 + 0.3109703125000000e5 / t43 - 0.8438102058823529e6 / t43 / dof
- 0.7932909722222222e6 / t43 / t1 + 0.2921518928947368e8 / t43 / t4 + 0.2776753702500000e8 / t43 / t7
return (t61)
}
pc.dof.create.spline = function()
{
xx = c(
2.002478752000,
2.002480761000,
2.002482771000,
2.002484783000,
2.002486797000,
2.002488812000,
2.002490829000,
2.002492848000,
2.002494868000,
2.002496890000,
2.002498914000,
2.002500939000,
2.002502966000,
2.002504994000,
2.002507024000,
2.002509056000,
2.002511089000,
2.002513124000,
2.002515161000,
2.002517199000,
2.002519239000,
2.002521281000,
2.002523324000,
2.002525369000,
2.002527415000,
2.002529464000,
2.002531514000,
2.002533565000,
2.002535618000,
2.002537673000,
2.002539730000,
2.002541788000,
2.002543848000,
2.002545909000,
2.002547973000,
2.002550038000,
2.002552104000,
2.002554172000,
2.002556242000,
2.002558314000,
2.002560387000,
2.002562462000,
2.002564539000,
2.002566617000,
2.002568697000,
2.002570779000,
2.002572862000,
2.002574947000,
2.002577034000,
2.002579123000,
2.002581213000,
2.002583305000,
2.002585398000,
2.002587493000,
2.002589590000,
2.002591689000,
2.002593789000,
2.002595891000,
2.002597995000,
2.002600100000,
2.002602208000,
2.002604316000,
2.002606427000,
2.002608539000,
2.002610653000,
2.002612769000,
2.002614886000,
2.002617005000,
2.002619126000,
2.002621249000,
2.002623373000,
2.002625499000,
2.002627627000,
2.002629756000,
2.002631888000,
2.002634020000,
2.002636155000,
2.002638291000,
2.002640430000,
2.002642569000,
2.002644711000,
2.002646854000,
2.002648999000,
2.002651146000,
2.002653295000,
2.002655445000,
2.002657597000,
2.002659751000,
2.002661906000,
2.002664063000,
2.002666222000,
2.002668383000,
2.002670545000,
2.002672710000,
2.002674876000,
2.002677043000,
2.002679213000,
2.002681384000,
2.002683557000,
2.002685732000,
2.002687908000,
2.002690087000,
2.002692267000,
2.002694449000,
2.002696632000,
2.002698818000,
2.002701005000,
2.002703194000,
2.002705384000,
2.002707577000,
2.002709771000,
2.002711967000,
2.002714165000,
2.002716365000,
2.002718566000,
2.002720769000,
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2.002725181000,
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2.002729600000,
2.002731812000,
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2.002736241000,
2.002738459000,
2.002740678000,
2.002742899000,
2.002745122000,
2.002747347000,
2.002749573000,
2.002751801000,
2.002754031000,
2.002756263000,
2.002758497000,
2.002760733000,
2.002762970000,
2.002765209000,
2.002767450000,
2.002769693000,
2.002771937000,
2.002774184000,
2.002776432000,
2.002778682000,
2.002780934000,
2.002783188000,
2.002785443000,
2.002787700000,
2.002789960000,
2.002792221000,
2.002794483000,
2.002796748000,
2.002799015000,
2.002801283000,
2.002803553000,
2.002805825000,
2.002808099000,
2.002810375000,
2.002812652000,
2.002814932000,
2.002817213000,
2.002819496000,
2.002821781000,
2.002824068000,
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2.002828647000,
2.002830939000,
2.002833234000,
2.002835530000,
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2.002842429000,
2.002844733000,
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2.002849345000,
2.002851654000,
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2.002858593000,
2.002860910000,
2.002863228000,
2.002865548000,
2.002867871000,
2.002870195000,
2.002872521000,
2.002874849000,
2.002877179000,
2.002879510000,
2.002881844000,
2.002884179000,
2.002886517000,
2.002888856000,
2.002891197000,
2.002893540000,
2.002895885000,
2.002898232000,
2.002900581000,
2.002902931000,
2.002905284000,
2.002907638000,
2.002909995000,
2.002912353000,
2.002914713000,
2.002917075000,
2.002919439000,
2.002921805000,
2.002924173000,
2.002926543000,
2.002928915000,
2.002931288000,
2.002933664000,
2.002936041000,
2.002938421000,
2.002940802000,
2.002943185000,
2.002945571000,
2.002947958000,
2.002950347000,
2.002952738000,
2.002955131000,
2.002957525000,
2.002959922000,
2.002962321000,
2.002964722000,
2.002967124000,
2.002969529000,
2.002971936000,
2.002974344000,
2.002976754000,
2.002979167000,
2.002981581000,
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2.002998536000,
2.003000966000,
2.003003398000,
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2.003010706000,
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2.003015588000,
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2.003030281000,
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2.003067327000,
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2.003089772000,
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2.003201889000,
2.003204484000,
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2.003212281000,
2.003214884000,
2.003217489000,
2.003220097000,
2.003222706000,
2.003225318000,
2.003227932000,
2.003230548000,
2.003233166000,
2.003235786000,
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2.003248919000,
2.003251552000,
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0.014872733420,
0.014852060630,
0.014831414250,
0.014810794240,
0.014790200570,
0.014769633220,
0.014749092150,
0.014728577340,
0.014708088750,
0.014687626370,
0.014667190150,
0.014646780080,
0.014626396120,
0.014606038240,
0.014585706420,
0.014565400620,
0.014545120830,
0.014524867000,
0.014504639110,
0.014484437130,
0.014464261040,
0.014444110810,
0.014423986400,
0.014403887780,
0.014383814940,
0.014363767840,
0.014343746450,
0.014323750740,
0.014303780690,
0.014283836270,
0.014263917450,
0.014244024200,
0.014224156490,
0.014204314290,
0.014184497580,
0.014164706330,
0.014144940510,
0.014125200090,
0.014105485040,
0.014085795340,
0.014066130950,
0.014046491850,
0.014026878020,
0.014007289410,
0.013987726020,
0.013968187790,
0.013948674720,
0.013929186770,
0.013909723910,
0.013890286110,
0.013870873350,
0.013851485600,
0.013832122840,
0.013812785020,
0.013793472130,
0.013774184140,
0.013754921020,
0.013735682740,
0.013716469280,
0.013697280600,
0.013678116680,
0.013658977500,
0.013639863020,
0.013620773210,
0.013601708050,
0.013582667520,
0.013563651580,
0.013544660200,
0.013525693360,
0.013506751040,
0.013487833200,
0.013468939810,
0.013450070850,
0.013431226300,
0.013412406120,
0.013393610280,
0.013374838770,
0.013356091550,
0.013337368590,
0.013318669870,
0.013299995360,
0.013281345030,
0.013262718860,
0.013244116820,
0.013225538890,
0.013206985020,
0.013188455210,
0.013169949410,
0.013151467610,
0.013133009770,
0.013114575880,
0.013096165890,
0.013077779790,
0.013059417560,
0.013041079150,
0.013022764550,
0.013004473720,
0.012986206650,
0.012967963300,
0.012949743650,
0.012931547660,
0.012913375330,
0.012895226610,
0.012877101480,
0.012858999910,
0.012840921880,
0.012822867370,
0.012804836330,
0.012786828760,
0.012768844610,
0.012750883870,
0.012732946500,
0.012715032490,
0.012697141800,
0.012679274410,
0.012661430290,
0.012643609420,
0.012625811770,
0.012608037300,
0.012590286010,
0.012572557850,
0.012554852810,
0.012537170860,
0.012519511960,
0.012501876100,
0.012484263250,
0.012466673380,
0.012449106470,
0.012431562490,
0.012414041410,
0.012396543210,
0.012379067860,
0.012361615330,
0.012344185610,
0.012326778660,
0.012309394450,
0.012292032970,
0.012274694180,
0.012257378070,
0.012240084590,
0.012222813740,
0.012205565470,
0.012188339780,
0.012171136620,
0.012153955970,
0.012136797820,
0.012119662130,
0.012102548870,
0.012085458030,
0.012068389570,
0.012051343480,
0.012034319710,
0.012017318260,
0.012000339090,
0.011983382180,
0.011966447500,
0.011949535020,
0.011932644730,
0.011915776590,
0.011898930580,
0.011882106680,
0.011865304850,
0.011848525080,
0.011831767340,
0.011815031600,
0.011798317840,
0.011781626030,
0.011764956150,
0.011748308170,
0.011731682070,
0.011715077810,
0.011698495550,
0.011681934920,
0.011665396070,
0.011648878970,
0.011632383590,
0.011615909920,
0.011599457920,
0.011583027570,
0.011566618840,
0.011550231720,
0.011533866170,
0.011517522170,
0.011501199690,
0.011484898720,
0.011468619220,
0.011452361180,
0.011436124560,
0.011419909330,
0.011403715490,
0.011387543000,
0.011371391830,
0.011355261970,
0.011339153380,
0.011323066040,
0.011306999940,
0.011290955030,
0.011274931310,
0.011258928730,
0.011242947290,
0.011226986950,
0.011211047690,
0.011195129490,
0.011179232320,
0.011163356160,
0.011147500970,
0.011131666750,
0.011115853450,
0.011100061070,
0.011084289570,
0.011068538930,
0.011052809130,
0.011037100140,
0.011021411930,
0.011005744490,
0.010990097790,
0.010974471800,
0.010958866500,
0.010943281870,
0.010927717880,
0.010912174500,
0.010896651720,
0.010881149510,
0.010865667850,
0.010850206710,
0.010834766060,
0.010819345890,
0.010803946170,
0.010788566870,
0.010773207970,
0.010757869450,
0.010742551290,
0.010727253450,
0.010711975920,
0.010696718680,
0.010681481690,
0.010666264930,
0.010651068390,
0.010635892030,
0.010620735840,
0.010605599780,
0.010590483850,
0.010575388000,
0.010560312220,
0.010545256490,
0.010530220780,
0.010515205060,
0.010500209320,
0.010485233530,
0.010470277670,
0.010455341710,
0.010440425640,
0.010425529420,
0.010410653030,
0.010395796450,
0.010380959660,
0.010366142630,
0.010351345340,
0.010336567770,
0.010321809890,
0.010307071680,
0.010292353120,
0.010277654180,
0.010262974840,
0.010248315080,
0.010233674870,
0.010219054190,
0.010204453020,
0.010189871340,
0.010175309110,
0.010160766330,
0.010146242960,
0.010131738980,
0.010117254370,
0.010102789110,
0.010088343170,
0.010073916530,
0.010059509170,
0.010045121060,
0.010030752190,
0.010016402520,
0.010002072040,
0.009987760726,
0.009973468550,
0.009959195492,
0.009944941528,
0.009930706637,
0.009916490795,
0.009902293981,
0.009888116170,
0.009873957341,
0.009859817471,
0.009845696538,
0.009831594518,
0.009817511389,
0.009803447129,
0.009789401716,
0.009775375126,
0.009761367337,
0.009747378327,
0.009733408073,
0.009719456554,
0.009705523746,
0.009691609627,
0.009677714175,
0.009663837368,
0.009649979183,
0.009636139598,
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0.009608516139,
0.009594732220,
0.009580966813,
0.009567219894,
0.009553491442,
0.009539781435,
0.009526089851,
0.009512416666,
0.009498761861,
0.009485125411,
0.009471507296,
0.009457907493,
0.009444325980,
0.009430762736,
0.009417217738,
0.009403690965,
0.009390182394,
0.009376692004,
0.009363219773,
0.009349765679,
0.009336329700,
0.009322911815,
0.009309512001,
0.009296130237,
0.009282766501,
0.009269420771,
0.009256093026,
0.009242783245,
0.009229491405,
0.009216217484,
0.009202961462,
0.009189723316,
0.009176503025,
0.009163300568,
0.009150115923,
0.009136949068,
0.009123799982,
0.009110668644,
0.009097555032,
0.009084459124,
0.009071380900,
0.009058320337,
0.009045277415,
0.009032252112,
0.009019244407,
0.009006254279,
0.008993281706,
0.008980326666,
0.008967389140,
0.008954469105,
0.008941566541,
0.008928681425,
0.008915813738)
sfun = splinefun(xx, yy, method = "monoH.FC")
assign(func.name.in.env, sfun, envir = INLA:::inla.get.inlaEnv())
return (invisible())
}
pc.dof.dist = function(dof, deriv = FALSE)
{
## if 'deriv', then evaluate all 'dof' using the same approximation. in this case, dof
## are known to be close.
n = length(dof)
kld = numeric(n)
if (!deriv) {
idx = which(dof >= dof.lim)
if (length(idx) > 0) {
kld[idx] = pc.dof.kld.approx(dof[idx])
}
idx = which(dof < dof.lim)
if (length(idx) > 0) {
kld[idx] = (do.call(func.name.in.env, args = list(x = dof[idx]),
envir = INLA:::inla.get.inlaEnv()))
}
} else {
if (all(dof < dof.lim)) {
kld = (do.call(func.name.in.env, args = list(x = dof),
envir = INLA:::inla.get.inlaEnv()))
} else {
kld = pc.dof.kld.approx(dof)
}
}
return (sqrt(2.0 * kld))
}
if (!exists(func.name.in.env, envir = INLA:::inla.get.inlaEnv())) {
pc.dof.create.spline()
}
if (missing(lambda)) {
stopifnot(!(missing(u) && missing(alpha)))
stopifnot(u > 2.0 && (alpha > 0 && alpha < 1))
lambda = -log(alpha) / pc.dof.dist(u)
} else {
stopifnot(!(missing(lambda)))
stopifnot(lambda > 0.0)
}
stopifnot(all(dof > dof.min))
d = pc.dof.dist(dof)
wf = c( 1.0 / 12.0, -2.0 / 3.0, 0.0, 2.0 / 3.0, -1.0 / 12.0 )
jac = c()
for(df in dof) {
step = sqrt(df) * 1e-3
if (df - 2.0 * step < dof.min) {
step = (df - dof.min) / 2.0
}
dfs = df + (-2):2 * step
dd = pc.dof.dist(dfs, deriv=TRUE)
val = sum(dd * wf) / step
jac = c(jac, abs(val))
}
val = dexp(d, rate = lambda, log = TRUE) + log(jac)
return (if (log) val else exp(val))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{bypass_prop_Q}
\alias{bypass_prop_Q}
\title{Proportion Flow at Yolo and Sutter Bypasses}
\format{A 3 dimensional array: 12 by 21 by 6 [months, years, bypasses]
\itemize{
\item [ , , 1] Sutter Bypass 1
\item [ , , 2] Sutter Bypass 2
\item [ , , 3] Sutter Bypass 3
\item [ , , 4] Sutter Bypass 4
\item [ , , 5] Yolo Bypass 1
\item [ , , 6] Yolo Bypass 2
}}
\usage{
bypass_prop_Q
}
\description{
The proportion of Lower Sacramento River flow at each bypass weir
}
\details{
For more details see:
\itemize{
\item use this link within R \code{\link[cvpiaFlow]{propQbypass}}
\item use this \href{https://flowwest.github.io/cvpiaFlow/reference/propQbypass.html}{link} if in a web browser
}
}
\keyword{datasets}
|
/man/bypass_prop_Q.Rd
|
no_license
|
elizabethshaw/cvpiaData
|
R
| false
| true
| 828
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{bypass_prop_Q}
\alias{bypass_prop_Q}
\title{Proportion Flow at Yolo and Sutter Bypasses}
\format{A 3 dimensional array: 12 by 21 by 6 [months, years, bypasses]
\itemize{
\item [ , , 1] Sutter Bypass 1
\item [ , , 2] Sutter Bypass 2
\item [ , , 3] Sutter Bypass 3
\item [ , , 4] Sutter Bypass 4
\item [ , , 5] Yolo Bypass 1
\item [ , , 6] Yolo Bypass 2
}}
\usage{
bypass_prop_Q
}
\description{
The proportion of Lower Sacramento River flow at each bypass weir
}
\details{
For more details see:
\itemize{
\item use this link within R \code{\link[cvpiaFlow]{propQbypass}}
\item use this \href{https://flowwest.github.io/cvpiaFlow/reference/propQbypass.html}{link} if in a web browser
}
}
\keyword{datasets}
|
test_that("get_SDA_hydric works", {
skip_if_offline()
skip_on_cran()
# by areasymbol
expect_equal(nrow(get_SDA_hydric(areasymbols = c("CA077", "CA630"))), 313)
# by mukey
expect_equal(nrow(get_SDA_hydric(mukeys = c(461994, 461995))), 2)
})
|
/tests/testthat/test-SDA_hydric.R
|
no_license
|
Emory-ENVS-SihiLab/soilDB
|
R
| false
| false
| 256
|
r
|
test_that("get_SDA_hydric works", {
skip_if_offline()
skip_on_cran()
# by areasymbol
expect_equal(nrow(get_SDA_hydric(areasymbols = c("CA077", "CA630"))), 313)
# by mukey
expect_equal(nrow(get_SDA_hydric(mukeys = c(461994, 461995))), 2)
})
|
#' Check if Triangle has a Right Angle
#'
#' @param a Length of 1st side, doesn't need to be the longest or shortest
#' @param b Length of 2nd side, doesn't need to be the longest or shortest
#' @param c Length of 3rd side, doesn't need to be the longest or shortest
#'
#' @return The function returns true if it is right angle triangle, else false.
#' @export
#'
#' @examples is_rightangle(6,8,10)
is_rightangle = function(a = 3, b = 4, c = 5){
output = FALSE
if (a^2 + b^2 == c^2){
output = TRUE
}
if (a^2 + c^2 == b^2){
output = TRUE
}
if (b^2 + c^2 == a^2){
output = TRUE
}
return(output)
}
|
/R/is_rightangle.R
|
no_license
|
hzwangjiren/jiren441final
|
R
| false
| false
| 646
|
r
|
#' Check if Triangle has a Right Angle
#'
#' @param a Length of 1st side, doesn't need to be the longest or shortest
#' @param b Length of 2nd side, doesn't need to be the longest or shortest
#' @param c Length of 3rd side, doesn't need to be the longest or shortest
#'
#' @return The function returns true if it is right angle triangle, else false.
#' @export
#'
#' @examples is_rightangle(6,8,10)
is_rightangle = function(a = 3, b = 4, c = 5){
output = FALSE
if (a^2 + b^2 == c^2){
output = TRUE
}
if (a^2 + c^2 == b^2){
output = TRUE
}
if (b^2 + c^2 == a^2){
output = TRUE
}
return(output)
}
|
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