pierreqi/r_code_50k · Datasets at Hugging Face

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/rscripts/enrichment_utils.r

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bjcbjc/mylib

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enrichment_utils.r

enrich.go <- function (testGene, backgroundGene, inputGeneId= 'ENSEMBL', database= 'org.Hs.eg.db', ontologyList = c('BP', 'MF', 'CC'), extractGeneInGO = FALSE, pvalCutoff = 0.01, customMapping = FALSE, customMappingFile = '/nethome/bjchen/DATA/GenomicInfo/ENSEMBL/ensembl_entrez_10302014.txt') { library(database, character.only= TRUE) library(GOstats) eval(parse(text=paste0('databaseObj=',database))) eval(parse(text=paste0('goGenes=mappedkeys(',sub('.db', 'GO', database, fixed= TRUE), ')'))) if ( inputGeneId == 'ENTREZID' ) { testGeneVector = testGene univGeneVector = backgroundGene } else { if (customMapping && inputGeneId == 'ENSEMBL') { source('/nethome/bjchen/BJLib/rscripts/convertENSEMBL2ENTREZ.r') genemap = ENSEMBL2ENTREZ_HUGO(testGene, customMappingFile, goGenes) univmap = ENSEMBL2ENTREZ_HUGO(backgroundGene, customMappingFile, goGenes) #remove empty genemap = genemap[ genemap[, 'ENTREZID'] != '', ] univmap = univmap[ univmap[, 'ENTREZID'] != '', ] testGeneVector = genemap[, 'ENTREZID'] univGeneVector = univmap[, 'ENTREZID'] } else { genemap = select(databaseObj, testGene, c('ENTREZID', 'SYMBOL'), inputGeneId) univmap = select(databaseObj, backgroundGene, c('ENTREZID','SYMBOL'), inputGeneId) testGeneVector = genemap[, 'ENTREZID'] univGeneVector = univmap[, 'ENTREZID'] } } #remove genes not in GO or duplicates testGeneVector = unique( testGeneVector[ testGeneVector %in% goGenes ] ) univGeneVector = unique( univGeneVector[ univGeneVector %in% goGenes ] ) enrichRes = list() for (ontIdx in 1:length(ontologyList)) { param = new('GOHyperGParams', geneIds=testGeneVector, universeGeneIds=univGeneVector, annotation=database, ontology=ontologyList[ontIdx], pvalueCutoff=pvalCutoff, conditional=FALSE, testDirection='over') hyp = hyperGTest(param) sum_hyp = summary(hyp) enrichRes[[ontologyList[ontIdx]]] = sum_hyp #write.table(sum_hyp, outputFileName, sep='\t', quote=F, row.names=F) if (extractGeneInGO) { #extract genes that are in each annotation gn2go = select(databaseObj, geneIds(hyp), c('GOALL') ) gn2go = gn2go[ gn2go[,'GOALL'] %in% sum_hyp[,1], ] rowName = unique(gn2go[, 'ENTREZID']) colName = unique(gn2go[, 'GOALL']) rowIdx = match(gn2go[, 'ENTREZID'], rowName) colIdx = match(gn2go[, 'GOALL'], colName) idx = cbind(rowid=as.vector(t(rowIdx)), colid=as.vector(t(colIdx))) binaryTable = matrix(0, nrow=length(rowName), ncol=length(colName), dimnames=list(rowName, colName)) binaryTable[idx] = 1 #convert GO ID to terms idx = match(colnames(binaryTable), sum_hyp[,1]) colnames(binaryTable) = sum_hyp[idx,'Term'] #add ENSEMBL/SYMBOL idx = match(rownames(binaryTable), genemap[, 'ENTREZID']) geneInfo = genemap[idx, c('ENSEMBL', 'SYMBOL')] rownames(geneInfo) = rownames(binaryTable) binaryTable = cbind(geneInfo, binaryTable) #write.table(binaryTable, paste0(outputTag,'.geneByGo_binaryTable_',ontologyList[ontIdx], '.txt'), sep='\t',quote=F,col.names=NA) enrichRes[[paste0(ontologyList[ontIdx], '_binaryTable')]] = binaryTable } } return(enrichRes) } enrich.go.parallel <- function (testGeneLists, backgroundGene, inputGeneId= 'ENSEMBL', database= 'org.Hs.eg.db', ontologyList = c('BP', 'MF', 'CC'), pvalCutoff = 0.01, customMapping = FALSE, customMappingFile = '/nethome/bjchen/DATA/GenomicInfo/ENSEMBL/ensembl_entrez_10302014.txt', cores= 10) { library(database, character.only= TRUE) library(doParallel) eval(parse(text=paste0('databaseObj=',database))) eval(parse(text=paste0('goGenes=mappedkeys(',sub('.db', 'GO', database, fixed= TRUE), ')'))) ## testGeneLists are lists of genes; eg/ testGeneLists[[1]] contains a list of genes for enrichment if ( inputGeneId != 'ENTREZID' ) { cat('convert to ENTREZID first!\n') return(NULL) } ## need to detach because these packages use sqlite, which cann't be used in parallel; ## therefore, load these packages within the loop #detach('package:GOstats', unload= TRUE) detach(paste0('package:', database), unload= TRUE, character.only= TRUE) registerDoParallel(cores= cores) #remove genes not in GO or duplicates univGeneVector = intersect(backgroundGene, goGenes) allEnrichRes = foreach (eachIdx = 1:length(testGeneLists)) %dopar% { if (is.null(testGeneLists[[eachIdx]])) { enrichRes = list() } else { library(database, character.only= TRUE) library(GOstats) testGeneVector = intersect(testGeneLists[[eachIdx]], goGenes) cat(sprintf('%d, %d\n', eachIdx, length(testGeneVector))) enrichRes = list() if (length(testGeneVector) > 0) { for (ontIdx in 1:length(ontologyList)) { enrichRes[[ontologyList[ontIdx]]] = tryCatch ({ param = new('GOHyperGParams', geneIds=testGeneVector, universeGeneIds=univGeneVector, annotation=database, ontology=ontologyList[ontIdx], pvalueCutoff=pvalCutoff, conditional=FALSE, testDirection='over') hyp = hyperGTest(param) sum_hyp = summary(hyp) sum_hyp }, error = function(err) { cat(sprintf('Caught error, listIdx=%d, %d genes, %s\n', eachIdx, length(testGeneVector), err)) return(NA) }) } } detach('package:GOstats', unload= TRUE) detach(paste0('package:', database), unload= TRUE, character.only= TRUE) } enrichRes } return(allEnrichRes) }

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/aer/chap01.R

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ecodiegoale/trading

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chap01.R

data("Journals", package = "AER") dim(Journals) names(Journals) plot(log(subs) ~ log(price/citations), data = Journals) j_lm <- lm(log(subs) ~ log(price/citations), data = Journals) abline(j_lm) summary(j_lm) # experiment plot(Journals$subs) plot(log(Journals$subs)) plot(Journals$price / Journals$citations) plot(log(Journals$price / Journals$citations)) # elasticity of the demand with respect to the price per citation is −0.5331, which # is signiﬁcantly diﬀerent from 0 at all conventional levels. j_lm$coefficients[2] ## data("CPS1985", package = "AER") cps <- CPS1985 library("quantreg") cps_lm <- lm(formula = log(wage) ~ experience + I(experience^2) + education, data = cps) # quantile regression cps_rq <- rq(formula = log(wage) ~ experience + I(experience^2) + education, data = cps, tau = seq(0.2, 0.8, by = 0.15)) cps2 <- data.frame(education = mean(cps$education), experience = min(cps$experience):max(cps$experience)) cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2, interval = "prediction")) cps2 <- cbind(cps2, predict(cps_rq, newdata = cps2, type = "")) plot(log(wage) ~ experience, data = cps) for(i in 6:10) lines(cps2[,i] ~ experience, data = cps2, col = "red") plot(summary(cps_rq))

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/man/transform_by_feature.Rd

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zhuchcn/Metabase

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transform_by_feature.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods-mSet.R \name{transform_by_feature} \alias{transform_by_feature} \title{Transform an mSet object by features} \usage{ transform_by_feature(object, fun, ...) } \arguments{ \item{object}{An \code{\link{mSet-class}} or derived class object.} \item{fun}{A function to apply.} \item{...}{Arguments to pass to the function/} } \value{ An \code{\link{mSet-class}} or derived class object } \description{ Transform the conc_table slot of an \code{\link{mSet-class}} object, one feature at a time. This is similar to MARGIN = 1 in the \code{\link{apply}} function. } \seealso{ \code{\link{mSet-class}}, \code{\link{transform_by_features}} }

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/demo/MCMCGuide11.R

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cran/R2MLwiN

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############################################################################ # MLwiN MCMC Manual # # 11 Poisson Response Modelling . . . . . . . . . . . . . . . . . . . . 153 # # Browne, W.J. (2009) MCMC Estimation in MLwiN, v2.13. Centre for # Multilevel Modelling, University of Bristol. ############################################################################ # R script to replicate all analyses using R2MLwiN # # Zhang, Z., Charlton, C., Parker, R, Leckie, G., and Browne, W.J. # Centre for Multilevel Modelling, 2012 # http://www.bristol.ac.uk/cmm/software/R2MLwiN/ ############################################################################ library(R2MLwiN) # MLwiN folder mlwin <- getOption("MLwiN_path") while (!file.access(mlwin, mode = 1) == 0) { cat("Please specify the root MLwiN folder or the full path to the MLwiN executable:\n") mlwin <- scan(what = character(0), sep = "\n") mlwin <- gsub("\\", "/", mlwin, fixed = TRUE) } options(MLwiN_path = mlwin) # User's input if necessary ## Read mmmec data data(mmmec, package = "R2MLwiN") # 11.1 Simple Poisson regression model . . . . . . . . . . . . . . . . . 155 (mymodel1 <- runMLwiN(log(obs) ~ 1 + uvbi + offset(log(exp)), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 50000)), data = mmmec)) summary(mymodel1@chains[, "FP_uvbi"]) sixway(mymodel1@chains[, "FP_uvbi", drop = FALSE], "beta_1") # 11.2 Adding in region level random effects . . . . . . . . . . . . . . 157 (mymodel2 <- runMLwiN(log(obs) ~ 1 + uvbi + offset(log(exp)) + (1 | region), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 50000, seed = 13)), data = mmmec)) summary(mymodel2@chains[, "FP_uvbi"]) sixway(mymodel2@chains[, "FP_uvbi", drop = FALSE], "beta_1") # 11.3 Including nation effects in the model . . . . . . . . . . . . . . 159 (mymodel3 <- runMLwiN(log(obs) ~ 1 + uvbi + offset(log(exp)) + (1 | nation) + (1 | region), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 50000, seed = 13)), data = mmmec)) (mymodel4 <- runMLwiN(log(obs) ~ 0 + uvbi + nation + offset(log(exp)) + (1 | region), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 50000)), data = mmmec)) # 11.4 Interaction with UV exposure . . . . . . . . . . . . . . . . . . .161 (mymodel5 <- runMLwiN(log(obs) ~ 0 + nation + nation:uvbi + offset(log(exp)) + (1 | region), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 50000)), data = mmmec)) sixway(mymodel5@chains[, "FP_nationBelgium", drop = FALSE], acf.maxlag = 5000, "beta_1") # 11.5 Problems with univariate updating Metropolis procedures . . . . . 163 (mymodel6 <- runMLwiN(log(obs) ~ 0 + nation + nation:uvbi + offset(log(exp)) + (1 | region), D = "Poisson", estoptions = list(EstM = 1, mcmcMeth = list(iterations = 500000, thinning = 10)), data = mmmec)) sixway(mymodel6@chains[, "FP_nationBelgium", drop = FALSE], "beta_1") # Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . .128 ############################################################################

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/R/dynrFuncAddress.R

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mhunter1/dynr

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dynrFuncAddress.R

#-------------------------------------------------- # .C2funcaddresses # Purpose: # takes in a C file or C scripts # returns a list of addresses of the compiled model functions and maybe R functions for debug purposes #------------------------------------------------ # Changed DLL name and directory to be user-specified and permanent .C2funcaddress<-function(verbose,isContinuousTime, infile, outfile,compileLib){ #-------Set some variables: This function may later be extended---------- language <- "C" #-------Get the full name of the library---------- if ( .Platform$OS.type == "windows" ) outfile <- gsub("\\\\", "/", outfile) libLFile <- paste(outfile, .Platform$dynlib.ext, sep="") if (compileLib|(!file.exists(libLFile))){#when the compileLib flag is TRUE or when the libLFile does not exist #-------Check the input arguments---------------------------- code <- readLines(infile) # ---- Write and compile the code ---- print("Get ready!!!!") #filename<- basename(tempfile()) CompileCode(code, language, verbose, libLFile) #---- SET A FINALIZER TO PERFORM CLEANUP: register an R function to be called upon garbage collection of object or at the end of an R session--- #cleanup <- function(env) { # if ( filename %in% names(getLoadedDLLs()) ) dyn.unload(libLFile) # unlink(libLFile) # } #reg.finalizer(environment(), cleanup, onexit=TRUE) } #-----dynamically load the library------- DLL <- dyn.load( libLFile ) if (isContinuousTime){ res <- list(f_measure=getNativeSymbolInfo("function_measurement", DLL)$address, f_dx_dt=getNativeSymbolInfo("function_dx_dt", DLL)$address, f_dF_dx=getNativeSymbolInfo("function_dF_dx", DLL)$address, f_dP_dt=getNativeSymbolInfo("function_dP_dt", DLL)$address, f_initial_condition=getNativeSymbolInfo("function_initial_condition", DLL)$address, f_regime_switch=getNativeSymbolInfo("function_regime_switch", DLL)$address, f_noise_cov=getNativeSymbolInfo("function_noise_cov", DLL)$address, f_transform=getNativeSymbolInfo("function_transform", DLL)$address) }else{ res <- list(f_measure=getNativeSymbolInfo("function_measurement", DLL)$address, f_dynamic=getNativeSymbolInfo("function_dynam", DLL)$address, f_jacob_dynamic=getNativeSymbolInfo("function_jacob_dynam", DLL)$address, f_initial_condition=getNativeSymbolInfo("function_initial_condition", DLL)$address, f_regime_switch=getNativeSymbolInfo("function_regime_switch", DLL)$address, f_noise_cov=getNativeSymbolInfo("function_noise_cov", DLL)$address, f_transform=getNativeSymbolInfo("function_transform", DLL)$address) } return(list(address=res, libname=libLFile)) } #-------------------------------------------------- # CompileCode: A function adapted from the compileCode function in the inline pacakge # Purpose: compiles a C file to create a shared library #------------------------------------------------ CompileCode <- function(code, language, verbose, libLFile) { wd <- getwd() on.exit(setwd(wd)) ## Prepare temp file names if ( .Platform$OS.type == "windows" ) { ## windows files #outfile <- gsub("\\\\", "/", outfile) ## windows gsl flags LIB_GSL <- Sys.getenv("LIB_GSL") LIB_GSL <- gsub("\\\\", "/", LIB_GSL) # replace "\" with "/" LIB_GSL <- gsub("\"", "", LIB_GSL) # remove " gsl_cflags <- sprintf( "-I\"%s/include\"", LIB_GSL) gsl_libs <- sprintf( "-L\"%s/lib/%s\" -lgsl -lgslcblas", LIB_GSL, .Platform$r_arch) }else { ## UNIX-alike build ## Unix gsl flags gsl_cflags <- system( "gsl-config --cflags" , intern = TRUE ) gsl_libs <- system( "gsl-config --libs" , intern = TRUE ) # Perhaps change above to use similar to configure.win syntax # GSL_CFLAGS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe -e "RcppGSL:::CFlags()"` # GSL_LIBS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe -e "RcppGSL:::LdFlags()"` } if (verbose) cat("Setting PKG_CPPFLAGS to", gsl_cflags, "\n") Sys.setenv(PKG_CPPFLAGS=gsl_cflags) if (verbose) cat("Setting PKG_LIBS to", gsl_libs, "\n") Sys.setenv(PKG_LIBS=gsl_libs) #libCFile <- paste(outfile, ".EXT", sep="") libCFile <-sub(.Platform$dynlib.ext,".EXT",libLFile) extension <- switch(language, "C++"=".cpp", C=".c", Fortran=".f", F95=".f95", ObjectiveC=".m", "ObjectiveC++"=".mm") libCFile <- sub(".EXT$", extension, libCFile) #libLFile <- paste(outfile, .Platform$dynlib.ext, sep="") ## Write the code to the temp file for compilation write(code, libCFile) ## Compile the code using the running version of R if several available #if ( file.exists(libLFile) ) {file.remove( libLFile )} setwd(dirname(libCFile)) errfile <- paste( basename(libCFile), ".err.txt", sep = "" ) cmd <- paste(R.home(component="bin"), "/R CMD SHLIB ", basename(libCFile), " ", gsl_libs, " ", " 2> ", errfile, sep="") if (verbose) cat("Compilation argument:\n", cmd, "\n") compiled <- system2(paste0(R.home(component="bin"), "/R"), args=c("CMD", "SHLIB", basename(libCFile)), stderr=errfile, stdout=verbose) errmsg <- readLines(errfile) unlink(errfile) #if(length(errmsg) > 0){cat("May I present to you your error messages?\n")} writeLines(errmsg) setwd(wd) #### Error Messages #cat("I got here!!!") errmsg = errmsg[!grep("warning",errmsg)] if ( !file.exists(libLFile) | length(errmsg) > 0 ) { cat("\nERROR(s) during compilation: source code errors or compiler configuration errors!\n") cat("\nProgram source:\n") codeWithLineNums <- paste(sprintf(fmt="%3d: ", 1:length(code)), code, sep="") writeLines(codeWithLineNums) stop("Compilation ERROR, function(s)/method(s) not created!") } #return( libLFile ) } #------------------------------------------------------------------------------ # Check configuration ##' Check that dynr in configured properly ##' ##' @param verbose logical. Whether to print messages during/after checks ##' ##' @details ##' The 'dynr' package requires additional set-up and configuration beyond ##' just installing the package. In particular, it requires compiling C code ##' along with GSL to run (cook) models. This function runs some basic checks ##' of the configuration. We check that (1) R is on the PATH variable, (2) ##' Rtools exists and is on the PATH variable for Windows, (3) a C compiler ##' is available, and (4) GSL is available and on the PATH. ##' ##' In general, see the 'Installation for Users' vignette for set-up and ##' configuration instructions. ##' ##' @return No return value. ##' ##' @examples ##' \dontrun{dynr.config()} dynr.config <- function(verbose=FALSE){ genmsg <- paste0( "\nPlease read the 'Installation for Users' vignette at\n", "https://cran.r-project.org/web/packages/dynr/", "vignettes/InstallationForUsers.pdf", "\nor\n", "vignette(package='dynr', 'InstallationForUsers')\n") # Check that R is on the path noRmsg <- "R did not appear to be on the 'PATH' environment variable." # Check that Rtools exists and is on the path noRtoolsmsg <- "No Rtools found in 'PATH' environment variable." # Check for a C compiler noCmsg <- "No C compiler found." # Check for GSL noGSLmsg <- "LIB_GSL variable not found." if ( .Platform$OS.type == "windows" ) { path <- Sys.getenv("PATH") path <- normalizePath(strsplit(path, split=';', fixed=TRUE)[[1]]) path <- gsub("\\\\", "/", path) path <- paste(path, sep='', collapse=';') rpath <- normalizePath(R.home(component="bin")) rpath <- gsub("\\\\", "/", rpath) findR <- grep(rpath, path) if(length(findR) < 1 || findR != 1){ # Only warning # R does NOT need to be on the path for windows unless you're a developer warning(paste0(noRmsg, '\nThis is only needed for developers.\nIf that is not you, then happily ignore this warning.\n')) } if(grep('rtools', path, ignore.case=TRUE) != 1){ stop(paste0(noRtoolsmsg, genmsg)) } if(!checkForCompiler() ){ stop(paste0(noCmsg, genmsg)) } ## windows gsl flags LIB_GSL <- Sys.getenv("LIB_GSL") LIB_GSL <- gsub("\\\\", "/", LIB_GSL) # replace "\" with "/" LIB_GSL <- gsub("\"", "", LIB_GSL) # remove " if(nchar(LIB_GSL) < 1){ stop(paste0(noGSLmsg, genmsg)) } }else { ## UNIX-alike build if(!checkForCompiler() ){ stop(paste0(noCmsg, genmsg)) } ## Unix gsl flags # Wrap system command in try # If try error, say can't find gsl gsl_cflags <- try(system( "gsl-config --cflags", intern=TRUE), silent=TRUE) if(inherits(gsl_cflags, 'try-error')){ stop("'gsl-config --cflags' failed. You probably need to install GSL and/or add it to your path.") } gsl_libs <- try(system( "gsl-config --libs", intern=TRUE), silent=TRUE) if(inherits(gsl_cflags, 'try-error')){ stop("'gsl-config --libs' failed. You probably need to install GSL and/or add it to your path.") } # Sys.setenv(PATH=paste0(Sys.getenv("PATH"),":","/opt/local/bin")) } if(verbose){ message("Configuration check complete. Ready to rock and roll.") } } # Taken from Rcpp checkForCompiler <- function(minVersion = package_version("4.6.0")){ binaries <- c("g++", Sys.getenv("CXX", unset = ""), Sys.getenv("CXX1X", unset = "")) binpaths <- lapply(binaries, function(b) { if (b == "") NULL else Sys.which(b) }) allgood <- FALSE rl <- lapply(binpaths, function(b) { if (is.null(b)) return(NULL) con <- pipe(paste(b, "-v 2>&1"), "r") lines <- readLines(con) close(con) lines <- lines[grepl("^g.. version", lines)] if (length(lines) == 0) return(NULL) ver <- strsplit(lines, " ")[[1]][3] package_version(ver) >= minVersion }) all(do.call(c, rl)) }

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Analysis.r

library(ggplot2) library(tidyverse) library(lme4) library(wesanderson) library(languageR) #importing data set data <- read_csv(file = '/Users/jesushermosillo/Desktop/Spring 2021/LINGUIST_245B/variable_telicity/data/experiment_01/variable_telicity_and_verbs_of_consumption-merged.csv') #labels for plots data$levels <- sprintf('level %d',data$state) #level 1, level 2, ..., etc data$QDO_maximality <- factor(ifelse(data$maximality == 'maximal', 'Maximal', 'Non-Maximal')) # exclude participants who fail to correctly answer control items exclude_by_control <- data[data$item_type =='control' & data$response =='TRUE' , ]$workerid data_ <- data[is.element(data$workerid,exclude_by_control) == FALSE,] # exclude participants whose response to baseline is FALSE exclude_by_baseline <- data_[data_$item_type =='baseline' & data_$response == 'FALSE', ]$workerid data_1 <- data_[is.element(data_$workerid,exclude_by_baseline) == FALSE,] #number of participants after exclusions length(unique(unique(data_1$workerid))) #Plotting proportion of responses by event progression levels ggplot(data_1,aes( x = levels,fill = response)) + geom_bar( position = "fill") + theme_bw() + scale_fill_brewer(palette = "Paired") + labs(x = "Event Progression Levels", fill = 'Response', y ='Proportion of Responses') #Plotting proportion of responses by object maximality ggplot(data_1,aes( x = QDO_maximality,fill = response)) + geom_bar( position = "fill") + theme_bw() + scale_fill_brewer(palette = "Paired") + labs(x = "Object Maximality", fill = 'Response', y ='Proportion of Responses') #Plotting proportion of responses by event progression levels for maximal and non-maximal objects ggplot(data_1, aes( x = levels,fill = response)) + geom_bar( position = "fill") + facet_grid(~QDO_maximality) + theme_bw() + scale_fill_brewer(palette = "Paired") + labs(x = "Event Progression Levels", fill = 'Response', y ='Proportion of Responses') # critical data points data_critical = data_1[data_1$item_type =='critical',] data_critical = data_critical %>% mutate(numeric_DO = as.numeric((data_critical$QDO_maximality))) %>% mutate (centered_QDOM = numeric_DO - mean(numeric_DO)) data_critical = data_critical %>% mutate(numeric_EP = as.numeric((data_critical$state))) %>% mutate (centered_state = numeric_EP - mean(numeric_EP)) # model does not converge # Fixed effects: state + object maximality (both centered), # Random effects: object maximality slopes and participant and item intercepts model_0 = glmer(response ~ centered_state * centered_QDOM + (1 + centered_QDOM |workerid) + (1 + centered_QDOM |item) , data=data_critical, family="binomial") summary(model_0) # model does not converge model_1 = glmer(response ~ centered_state * centered_QDOM + (1 |workerid) + (1 + centered_QDOM |item) , data=data_critical, family="binomial") summary(model_1) # model does not converge model_2 = glmer(response ~ centered_state * centered_QDOM + (1 + centered_QDOM |workerid) + (1 |item) , data=data_critical, family="binomial") summary(model_2) # model does not converge model_3= glmer(response ~ centered_state * centered_QDOM + (1 |workerid) + (1 |item) , data=data_critical, family="binomial") summary(model_3) # model CONVERGES!!!! model_4 = glmer(response ~ centered_state * centered_QDOM + (1 |workerid) , data=data_critical, family="binomial") summary(model_4) # model does not converge model_4 = glmer(response ~ centered_state * centered_QDOM + (1 + centered_QDOM |workerid ) , data=data_critical, family="binomial") summary(model_4) # model CONVERGES!!!! model_5 = glmer(response ~ centered_state * centered_QDOM + (1 |item) , data=data_critical, family="binomial") summary(model_5) # final model summary(model_4)

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/man/longVarioMultiple.Rd

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PratheepaJ/bootLong

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refs/heads/master

2021-03-22T01:15:07.640516

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longVarioMultiple.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/longVarioMultiple.R \name{longVarioMultiple} \alias{longVarioMultiple} \title{Plots the variogram for multiple taxa given the indices.} \usage{ longVarioMultiple(pstr, main_factor, time_var, subjectID_var, starttaxa = 1, endtaxa = 4, point = FALSE, taxlevel = "Species") } \arguments{ \item{pstr}{(Required). A \code{\link{phyloseq-class}}. The arcsinh transformation of the otu table.} \item{main_factor}{Character string. The name of the variable to map main factor in the plot (factor vector).} \item{time_var}{Character string. The name of the variable to map time in the plot (integer vector).} \item{subjectID_var}{Character string. The name of the variable to map Subject Ids in the plot (factor vector).} \item{starttaxa}{Numeric. The first index of taxa ordered by total reads} \item{endtaxa}{Numeric. The last index of taxa ordered by total reads} \item{point}{Logical. Whether variogram with observed values.} \item{taxlevel}{Character string. The taxonomy level for the plot title} } \value{ \code{ggplot} object of variogram for for multiple taxa. } \description{ Plots the variogram for multiple taxa given the indices. }

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/lectures/data-raw/aklweather.R

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earowang/stats220

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aklweather.R

## code to prepare `aklweather` dataset goes here library(tidyverse) library(lubridate) # Read stations data stations <- read_table("data/ghcnd/ghcnd-stations.txt", col_names = c("id", "lat", "lon")) library(ggmap) akl_box <- c(left = 174.69, bottom = -37.09, right = 174.94, top = -36.60) akl_map <- get_map(akl_box) ggmap(akl_map) + geom_point(aes(x = lon, y = lat), stations) akl_id <- stations %>% filter(akl_box["left"] < lon, lon < akl_box["right"], akl_box["bottom"] < lat, lat < akl_box["top"]) library(rnoaa) startdate <- make_date(2019) + days(251) * 0:15 enddate <- startdate - days(1) enddate <- c(enddate[-1], today()) akl_ncdc <- map2_dfr(startdate, enddate, function(x, y) { ncdcout <- ncdc(datasetid = "GHCND", stationid = paste0("GHCND:", akl_id$id), limit = 1000, startdate = x, enddate = y) ncdcout$data }) aklweather <- akl_ncdc %>% mutate(date = as_date(ymd_hms(date))) %>% rename_with(tolower) write_csv(aklweather, "data/ghcnd/ghcnd-akl.csv")

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guptatan/staff_projection

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refs/heads/master

2023-07-14T19:22:54.894761

2021-09-01T17:39:59

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ui.R

library(shiny) library(tidyverse) library(rhandsontable) library(highcharter) library(shinyjs) library(plotly) # Define UI for application that draws a histogram shinyUI( fluidPage( includeCSS("styles.css"), titlePanel("Project Your Staffing Needs"), # Start - Sidebar sidebarLayout( sidebarPanel( width = 4, # step1 - Census h4("Step 1: Input Projected Census"), actionButton( "prejected_cesus", label = "Input Projected Census", width = "100%", icon("database"), class = "main-button margin-bottom20" ), # step1 - Advanced Section useShinyjs(), shinyWidgets::materialSwitch(inputId = "advanced_census_input", label = strong("Advanced"), value = FALSE, status = "success"), helpText(id = "advanced_input_help", "Estimate staffing for COVID and non-COVID patients"), numericInput( "total_bed", "Total number of beds", 1000, min = 0, max = 1000, step = 10, width = "70%" ), shinyWidgets::setSliderColor("#404040", c(3)), sliderInput("icu_perc", label = "Proportion of all beds allocated to ICU", min = 0, max = 100, post = " %", value = 30), sliderInput("capacity_perc", label = "Bed Occupancy", min = 0, max = 100, post = " %", value = 91), hr(), # step2 - Staff ratio h4("Step 2: Edit Staffing Ratios"), actionButton( "update_gen", "Edit Staffing Ratios", icon("user-md"), width = "100%", class = "main-button margin-bottom10" ), actionButton( "update_capacity", "Enter Total Employees", icon("clipboard-list"), width = "100%", class = "main-button margin-bottom10" ), hr(), # step3 - Plots h4("Step 3: Generate Plots"), actionButton( "generateButton", label = "Generate Plot", width = "100%", icon("chart-line"), class = "main-button margin-bottom20" ), useShinyjs(), shinyWidgets::materialSwitch(inputId = "show_icu_non_icu_plots", label = strong("Show ICU and Non-ICU plots"), value = FALSE, status = "success"), hr(), # step4 - Download h4("Step 4: Download Tables"), downloadButton( "downloadData_combine_file", "Download Combined File", class = "main-button", style = "width: 100%;" ) ), # End sidebar # Start mainPanel -------- mainPanel( width = 8, tags$style(HTML(" .tabbable > .nav > li[class=active] > a[data-value='Normal'] {background-color: #9dc183; color:black} .tabbable > .nav > li[class=active] > a[data-value='Crisis'] {background-color: #8D021F; color:white} ")), tabsetPanel( id = "inTabset", type = "tabs", # tabPanel("test", tableOutput("test")), # plots ------ tabPanel( "Normal", width = 8, div(uiOutput("plot_norm"), class = "plot"), div(tableOutput("table_result_normal"), class = "font-size") ), tabPanel( value = "Crisis", title = "Stretch", div(uiOutput("plot_crisis"), class = "plot"), div(tableOutput("table_result_crisis"), class = "font-size") ), # projected census ------ tabPanel( value = "census", title = "Projected Census", h4( "Option 1: Upload your projected patient census (from ", a("CHIME", target = "_blank", href = "https://penn-chime.phl.io/"), "or using our ", a("template", href = "data/projected_census_template.csv", target = "_blank", download = "projected_census_template.csv"), ")" ), fileInput( "chime_up", "Click browse to select file (.csv)", multiple = FALSE, accept = c( "text/csv", "text/comma-separated-values,text/plain", ".csv" ) ), hr(), h4("Option 2: Input your projected patient census manually"), p(strong("Right click"), "in a cell to add and delete row;", "select cell and type the new value.", class = "font-size13" ), actionButton( "reset_census", "Clear Table", icon("table"), class = "main-button margin-bottom20" ), actionButton( "default_chime", "Reset to Default", icon("undo"), class = "main-button margin-bottom20" ), div(rHandsontableOutput("prejected_census"), class = "font-size margin-bottom10"), helpText( class = "text-margin margin-top10", strong("hospitalized:"), "Number of patients that are hospitalized in", em("Non-ICU"), "units; ", br(), strong("icu:"), "Number of patients that are in", em("ICU"), "units" ) ), # editable tables ------- tabPanel( value = "edit_ratio_table", title = "Patient-to-Staff Ratios", # h4("Option 1: Upload Staffing Ratios File (using our", # a("template", href='data/Staffing_role_and_ratio_template.xlsx',target="_blank", download = 'Staffing_role_and_ratio_template.xlsx'), # ")"), # # fileInput( # "team_in", # "Click browse to select file (.xlsx)", # multiple = FALSE, # accept = c(".xlsx") # ), # hr(), h4("Option 1: Edit Staffing Ratio Table Below"), helpText( class = "text-margin", strong("Important note:"), "These estimates are designed to give a sense of general staffing needs, but your needs may vary based on local conditions." ), sliderInput("reduction", label = "Expected staffing reduction (due to sickness, quarantine restrictions, or other)", min = 0, max = 100, post = " %", value = 30, width = "600px"), p( strong("Right click"), "in a cell to add and delete row;", "select cell and type the new value", class = "font-size13 margin-top20 margin-bottom10" ), actionButton( "reset", "Clear Table", icon("table"), class = "main-button margin-bottom10" ), actionButton( "reset_to_ori", "Reset to Default", icon("undo"), class = "main-button margin-bottom10" ), h4("ICU"), div(rHandsontableOutput("x1"), class = "font-size margin-bottom20"), h4("Non-ICU"), div(rHandsontableOutput("x2"), class = "font-size margin-bottom20"), downloadButton( "downloadData_all_ratio", "Download Staffing Ratios Tables", class = "main-button" ), helpText( class = "text-margin margin-top10", strong("Role: "), "List of possible staff roles", br(), strong("Ratio (Normal): "), "The patient:staff ratio (i.e. how many patients each staff member cares for)", br(), strong("Ratio (Stretch): "), "The patient:staff ratio during a ‘Stretch mode’ (i.e. the maximum number patients each staff member can care for)", br(), br(), strong("*"), em("Default patient-to-staff ratios are based on real staffing ratios at a collaborating academic medical center that has undertaken extensive emergency preparedness work for this pandemic.") ) ), # Capacity tab UI code here tabPanel( value = "capacity_table", title = "Total Employees", h4("Option 1: Edit Staff Capacity Table Below"), p( strong("Right click"), "in a cell to add and delete row;", "select cell and type the new value", class = "font-size13" ), actionButton("clear_capacity", "Clear Table", icon("table"), class = "main-button margin-bottom20" ), actionButton("reset_default_capacity", "Reset to Default", icon("undo"), class = "main-button margin-bottom20" ), div(rHandsontableOutput("x3"), class = "font-size"), ) ) ) ) ) )

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/Uni/Projects/code/P046.Israel_MAIAC/archive/cnnew/buildfiles/aq25_body.r

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zeltak/org

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2021-01-21T04:27:34.752197

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aq25_body.r

#-------------------> Year 2010 #if needed load res table #res<- readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/resALL.AQ.rds") ### import data m1.2010 <-readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod1.AQ.2010.rds") ################# clean BAD STN PM25 and check if improved model? raWDaf <- ddply(m1.2010, c( "stn"), function(x) { mod1 <- lm(PM25 ~ aod, data=x) data.frame(R2 = round(summary(mod1)$r.squared, 5), nsamps = length(summary(mod1)$resid)) }) raWDaf raWDaf<-as.data.table(raWDaf) bad<- raWDaf[R2< 0.01] bad[,badid := paste(stn,sep="-")] #################BAD STN m1.2010[,badid := paste(stn,sep="-")] ####Take out bad stations m1.2010 <- m1.2010[!(m1.2010$badid %in% bad$badid), ] #scale data m1.2010[,elev.s:= scale(elev)] m1.2010[,tden.s:= scale(tden)] m1.2010[,pden.s:= scale(pden)] m1.2010[,dist2A1.s:= scale(dist2A1)] m1.2010[,dist2water.s:= scale(dist2water)] m1.2010[,dist2rail.s:= scale(dist2rail)] m1.2010[,Dist2road.s:= scale(Dist2road)] m1.2010[,ndvi.s:= scale(ndvi)] m1.2010[,MeanPbl.s:= scale(MeanPbl)] m1.2010[,p_ind.s:= scale(p_ind)] m1.2010[,p_for.s:= scale(p_for)] m1.2010[,p_farm.s:= scale(p_farm)] m1.2010[,p_dos.s:= scale(p_dos)] m1.2010[,p_dev.s:= scale(p_dev)] m1.2010[,p_os.s:= scale(p_os)] m1.2010[,tempa.s:= scale(tempa)] m1.2010[,WDa.s:= scale(WDa)] m1.2010[,WSa.s:= scale(WSa)] m1.2010[,RHa.s:= scale(RHa)] m1.2010[,Raina.s:= scale(Raina)] m1.2010[,NO2a.s:= scale(NO2a)] m1.formula <- as.formula(PM25~ aod +tempa.s+WDa.s+WSa.s+MeanPbl.s #temporal +elev.s+tden.s+pden.s+Dist2road.s+ndvi.s #spatial +p_os.s +p_dev.s+p_dos.s+p_farm.s+p_for.s+p_ind.s #land use #+aod*Dust #interactions +(1+aod|day/reg_num)) #+(1|stn) !!! stn screws up mod3 #full fit m1.fit.2010 <- lmer(m1.formula,data=m1.2010,weights=normwt) m1.2010$pred.m1 <- predict(m1.fit.2010) res[res$year=="2010", 'm1.R2'] <- print(summary(lm(PM25~pred.m1,data=m1.2010))$r.squared) #RMSPE res[res$year=="2010", 'm1.PE'] <- print(rmse(residuals(m1.fit.2010))) #spatial ###to check spatial2010<-m1.2010 %>% group_by(stn) %>% summarise(barpm = mean(PM25, na.rm=TRUE), barpred = mean(pred.m1, na.rm=TRUE)) m1.fit.2010.spat<- lm(barpm ~ barpred, data=spatial2010) res[res$year=="2010", 'm1.R2.s'] <- print(summary(lm(barpm ~ barpred, data=spatial2010))$r.squared) res[res$year=="2010", 'm1.PE.s'] <- print(rmse(residuals(m1.fit.2010.spat))) #temporal tempo2010<-left_join(m1.2010,spatial2010) tempo2010$delpm <-tempo2010$PM25-tempo2010$barpm tempo2010$delpred <-tempo2010$pred.m1-tempo2010$barpred mod_temporal <- lm(delpm ~ delpred, data=tempo2010) res[res$year=="2010", 'm1.R2.t'] <- print(summary(lm(delpm ~ delpred, data=tempo2010))$r.squared) saveRDS(m1.2010,"/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod1.AQ.2010.pred.rds") #---------------->>>> CV #s1 splits_s1 <- splitdf(m1.2010) test_s1 <- splits_s1$testset train_s1 <- splits_s1$trainset out_train_s1 <- lmer(m1.formula,data = train_s1,weights=normwt) test_s1$pred.m1.cv <- predict(object=out_train_s1 ,newdata=test_s1,allow.new.levels=TRUE,re.form=NULL ) test_s1$iter<-"s1" #s2 splits_s2 <- splitdf(m1.2010) test_s2 <- splits_s2$testset train_s2 <- splits_s2$trainset out_train_s2 <- lmer(m1.formula,data = train_s2,weights=normwt) test_s2$pred.m1.cv <- predict(object=out_train_s2 ,newdata=test_s2,allow.new.levels=TRUE,re.form=NULL ) test_s2$iter<-"s2" #s3 splits_s3 <- splitdf(m1.2010) test_s3 <- splits_s3$testset train_s3 <- splits_s3$trainset out_train_s3 <- lmer(m1.formula,data = train_s3,weights=normwt) test_s3$pred.m1.cv <- predict(object=out_train_s3 ,newdata=test_s3,allow.new.levels=TRUE,re.form=NULL ) test_s3$iter<-"s3" #s4 splits_s4 <- splitdf(m1.2010) test_s4 <- splits_s4$testset train_s4 <- splits_s4$trainset out_train_s4 <- lmer(m1.formula,data = train_s4,weights=normwt) test_s4$pred.m1.cv <- predict(object=out_train_s4 ,newdata=test_s4,allow.new.levels=TRUE,re.form=NULL ) test_s4$iter<-"s4" #s5 splits_s5 <- splitdf(m1.2010) test_s5 <- splits_s5$testset train_s5 <- splits_s5$trainset out_train_s5 <- lmer(m1.formula,data = train_s5,weights=normwt) test_s5$pred.m1.cv <- predict(object=out_train_s5 ,newdata=test_s5,allow.new.levels=TRUE,re.form=NULL ) test_s5$iter<-"s5" #s6 splits_s6 <- splitdf(m1.2010) test_s6 <- splits_s6$testset train_s6 <- splits_s6$trainset out_train_s6 <- lmer(m1.formula,data = train_s6,weights=normwt) test_s6$pred.m1.cv <- predict(object=out_train_s6 ,newdata=test_s6,allow.new.levels=TRUE,re.form=NULL ) test_s6$iter<-"s6" #s7 splits_s7 <- splitdf(m1.2010) test_s7 <- splits_s7$testset train_s7 <- splits_s7$trainset out_train_s7 <- lmer(m1.formula,data = train_s7,weights=normwt) test_s7$pred.m1.cv <- predict(object=out_train_s7 ,newdata=test_s7,allow.new.levels=TRUE,re.form=NULL ) test_s7$iter<-"s7" #s8 splits_s8 <- splitdf(m1.2010) test_s8 <- splits_s8$testset train_s8 <- splits_s8$trainset out_train_s8 <- lmer(m1.formula,data = train_s8,weights=normwt) test_s8$pred.m1.cv <- predict(object=out_train_s8 ,newdata=test_s8,allow.new.levels=TRUE,re.form=NULL ) test_s8$iter<-"s8" #s9 splits_s9 <- splitdf(m1.2010) test_s9 <- splits_s9$testset train_s9 <- splits_s9$trainset out_train_s9 <- lmer(m1.formula,data = train_s9,weights=normwt) test_s9$pred.m1.cv <- predict(object=out_train_s9 ,newdata=test_s9,allow.new.levels=TRUE,re.form=NULL ) test_s9$iter<-"s9" #s10 splits_s10 <- splitdf(m1.2010) test_s10 <- splits_s10$testset train_s10 <- splits_s10$trainset out_train_s10 <- lmer(m1.formula,data = train_s10,weights=normwt) test_s10$pred.m1.cv <- predict(object=out_train_s10 ,newdata=test_s10,allow.new.levels=TRUE,re.form=NULL ) test_s10$iter<-"s10" #BIND 1 dataset m1.2010.cv<- data.table(rbind(test_s1,test_s2,test_s3,test_s4,test_s5,test_s6,test_s7,test_s8,test_s9, test_s10)) # cleanup (remove from WS) objects from CV rm(list = ls(pattern = "train_|test_")) #table updates m1.fit.2010.cv<-lm(PM25~pred.m1.cv,data=m1.2010.cv) res[res$year=="2010", 'm1cv.R2'] <- print(summary(lm(PM25~pred.m1.cv,data=m1.2010.cv))$r.squared) res[res$year=="2010", 'm1cv.I'] <-print(summary(lm(PM25~pred.m1.cv,data=m1.2010.cv))$coef[1,1]) res[res$year=="2010", 'm1cv.I.se'] <-print(summary(lm(PM25~pred.m1.cv,data=m1.2010.cv))$coef[1,2]) res[res$year=="2010", 'm1cv.S'] <-print(summary(lm(PM25~pred.m1.cv,data=m1.2010.cv))$coef[2,1]) res[res$year=="2010", 'm1cv.S.se'] <-print(summary(lm(PM25~pred.m1.cv,data=m1.2010.cv))$coef[2,2]) #RMSPE res[res$year=="2010", 'm1cv.PE'] <- print(rmse(residuals(m1.fit.2010.cv))) #spatial spatial2010.cv<-m1.2010.cv %>% group_by(stn) %>% summarise(barpm = mean(PM25, na.rm=TRUE), barpred = mean(pred.m1, na.rm=TRUE)) m1.fit.2010.cv.s <- lm(barpm ~ barpred, data=spatial2010.cv) res[res$year=="2010", 'm1cv.R2.s'] <- print(summary(lm(barpm ~ barpred, data=spatial2010.cv))$r.squared) res[res$year=="2010", 'm1cv.PE.s'] <- print(rmse(residuals(m1.fit.2010.cv.s))) #temporal tempo2010.cv<-left_join(m1.2010.cv,spatial2010.cv) tempo2010.cv$delpm <-tempo2010.cv$PM25-tempo2010.cv$barpm tempo2010.cv$delpred <-tempo2010.cv$pred.m1.cv-tempo2010.cv$barpred mod_temporal.cv <- lm(delpm ~ delpred, data=tempo2010.cv) res[res$year=="2010", 'm1cv.R2.t'] <- print(summary(lm(delpm ~ delpred, data=tempo2010.cv))$r.squared) #-------->>> loc stage luf<-fread("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN004_LU_full_dataset/local.csv") setnames(luf,"tden","loc.tden") setnames(luf,"elev50","loc.elev") #add 50m LU to CV data setkey(m1.2010.cv,stn) setkey(luf,stn) m1.2010.cv.loc <- merge(m1.2010.cv, luf, all.x = T) #m1.2010.cv.loc<-na.omit(m1.2010.cv.loc) #create residual mp3 variable m1.2010.cv.loc$res.m1<-m1.2010.cv.loc$PM25-m1.2010.cv.loc$pred.m1.cv #The GAM model gam.out<-gam(res.m1~s(loc.tden)+s(tden,MeanPbl)+s(loc.tden,WSa)+s(loc_p_os,fx=FALSE,k=4,bs='cr')+s(loc.elev,fx=FALSE,k=4,bs='cr')+s(dA1,fx=FALSE,k=4,bs='cr')+s(dsea,fx=FALSE,k=4,bs='cr'),data=m1.2010.cv.loc) #plot(bp.model.ps) #summary(bp.model.ps) ## reg m1.2010.cv.loc$pred.m1.loc <-predict(gam.out) m1.2010.cv.loc$pred.m1.both <- m1.2010.cv.loc$pred.m1.cv + m1.2010.cv.loc$pred.m1.loc res[res$year=="2010", 'm1cv.loc.R2'] <- print(summary(lm(PM25~pred.m1.both,data=m1.2010.cv.loc))$r.squared) res[res$year=="2010", 'm1cv.loc.I'] <-print(summary(lm(PM25~pred.m1.both,data=m1.2010.cv.loc))$coef[1,1]) res[res$year=="2010", 'm1cv.loc.I.se'] <-print(summary(lm(PM25~pred.m1.both,data=m1.2010.cv.loc))$coef[1,2]) res[res$year=="2010", 'm1cv.loc.S'] <-print(summary(lm(PM25~pred.m1.both,data=m1.2010.cv.loc))$coef[2,1]) res[res$year=="2010", 'm1cv.loc.S.se'] <-print(summary(lm(PM25~pred.m1.both,data=m1.2010.cv.loc))$coef[2,2]) #RMSPE res[res$year=="2010", 'm1cv.loc.PE'] <- print(rmse(residuals(m1.fit.2010.cv))) #spatial spatial2010.cv.loc<-m1.2010.cv.loc %>% group_by(stn) %>% summarise(barpm = mean(PM25, na.rm=TRUE), barpred = mean(pred.m1, na.rm=TRUE)) m1.fit.2010.cv.loc.s <- lm(barpm ~ barpred, data=spatial2010.cv.loc) res[res$year=="2010", 'm1cv.loc.R2.s'] <- print(summary(lm(barpm ~ barpred, data=spatial2010.cv.loc))$r.squared) res[res$year=="2010", 'm1cv.loc.PE.s'] <- print(rmse(residuals(m1.fit.2010.cv.loc.s))) #temporal tempo2010.loc.cv<-left_join(m1.2010.cv.loc,spatial2010.cv.loc) tempo2010.loc.cv$delpm <-tempo2010.loc.cv$PM25-tempo2010.loc.cv$barpm tempo2010.loc.cv$delpred <-tempo2010.loc.cv$pred.m1.both-tempo2010.loc.cv$barpred mod_temporal.loc.cv <- lm(delpm ~ delpred, data=tempo2010.loc.cv) res[res$year=="2010", 'm1cv.loc.R2.t'] <- print(summary(lm(delpm ~ delpred, data=tempo2010.loc.cv))$r.squared) #############save midpoint saveRDS(res, "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/res.AQ.2010.rds") saveRDS(res, "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/resALL.AQ.rds") saveRDS(m1.2010.cv.loc,"/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod1.AQ.2010.predCV.rds") ############### #MOD2 ############### m2.2010<-readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod2.AQ.2010.rds") m2.2010[,elev.s:= scale(elev)] m2.2010[,tden.s:= scale(tden)] m2.2010[,pden.s:= scale(pden)] m2.2010[,dist2A1.s:= scale(dist2A1)] m2.2010[,dist2water.s:= scale(dist2water)] m2.2010[,dist2rail.s:= scale(dist2rail)] m2.2010[,Dist2road.s:= scale(Dist2road)] m2.2010[,ndvi.s:= scale(ndvi)] m2.2010[,MeanPbl.s:= scale(MeanPbl)] m2.2010[,p_ind.s:= scale(p_ind)] m2.2010[,p_for.s:= scale(p_for)] m2.2010[,p_farm.s:= scale(p_farm)] m2.2010[,p_dos.s:= scale(p_dos)] m2.2010[,p_dev.s:= scale(p_dev)] m2.2010[,p_os.s:= scale(p_os)] m2.2010[,tempa.s:= scale(tempa)] m2.2010[,WDa.s:= scale(WDa)] m2.2010[,WSa.s:= scale(WSa)] m2.2010[,RHa.s:= scale(RHa)] m2.2010[,Raina.s:= scale(Raina)] m2.2010[,NO2a.s:= scale(NO2a)] #generate predictions m2.2010[, pred.m2 := predict(object=m1.fit.2010,newdata=m2.2010,allow.new.levels=TRUE,re.form=NULL)] describe(m2.2010$pred.m2) #delete implossible valuesOA[24~ m2.2010 <- m2.2010[pred.m2 > 0.00000000000001 , ] m2.2010 <- m2.2010[pred.m2 < 1500 , ] saveRDS(m2.2010,"/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod2.AQ.2010.pred2.rds") #-------------->prepare for mod3 m2.2010[, bimon := (m + 1) %/% 2] setkey(m2.2010,day, aodid) m2.2010<-m2.2010[!is.na(meanPM25)] #run the lmer part regressing stage 2 pred Vs mean pm #in israel check per month, also check 30km band and other methods for meanpm m2.smooth = lme(pred.m2 ~ meanPM25,random = list(aodid= ~1 + meanPM25),control=lmeControl(opt = "optim"), data= m2.2010 ) #xm2.smooth = lmer(pred.m2 ~ meanPM25+(1+ meanPM25|aodid), data= m2.2010 ) #correlate to see everything from mod2 and the mpm works m2.2010[, pred.t31 := predict(m2.smooth)] m2.2010[, resid := residuals(m2.smooth)] res[res$year=="2010", 'm3.t31'] <- print(summary(lm(pred.m2~pred.t31,data=m2.2010))$r.squared) #split the files to the separate bi monthly datsets T2010_bimon1 <- subset(m2.2010 ,m2.2010$bimon == "1") T2010_bimon2 <- subset(m2.2010 ,m2.2010$bimon == "2") T2010_bimon3 <- subset(m2.2010 ,m2.2010$bimon == "3") T2010_bimon4 <- subset(m2.2010 ,m2.2010$bimon == "4") T2010_bimon5 <- subset(m2.2010 ,m2.2010$bimon == "5") T2010_bimon6 <- subset(m2.2010 ,m2.2010$bimon == "6") #run the separate splines (smooth) for x and y for each bimon #whats the default band (distance) that the spline goes out and uses fit2_1 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon1 ) fit2_2 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon2 ) fit2_3 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon3 ) fit2_4 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon4 ) fit2_5 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon5 ) fit2_6 <- gam(resid ~ s(x_aod_ITM,y_aod_ITM), data= T2010_bimon6 ) #get the predicted-fitted Xpred_1 <- (T2010_bimon1$pred.t31 - fit2_1$fitted) Xpred_2 <- (T2010_bimon2$pred.t31 - fit2_2$fitted) Xpred_3 <- (T2010_bimon3$pred.t31 - fit2_3$fitted) Xpred_4 <- (T2010_bimon4$pred.t31 - fit2_4$fitted) Xpred_5 <- (T2010_bimon5$pred.t31 - fit2_5$fitted) Xpred_6 <- (T2010_bimon6$pred.t31 - fit2_6$fitted) #remerge to 1 file m2.2010$pred.t32 <- c( Xpred_1,Xpred_2, Xpred_3, Xpred_4, Xpred_5, Xpred_6) #this is important so that its sorted as in the first gamm setkey(m2.2010,day, aodid) #rerun the lme on the predictions including the spatial spline (smooth) Final_pred_2010 <- lme(pred.t32 ~ meanPM25 ,random = list(aodid= ~1 + meanPM25 ),control=lmeControl(opt = "optim"),data= m2.2010 ) m2.2010[, pred.t33 := predict(Final_pred_2010)] #check correlations res[res$year=="2010", 'm3.t33'] <- print(summary(lm(pred.m2 ~ pred.t33,data=m2.2010))$r.squared) #------------------------>>> #import mod3 data.m3 <- readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod3.AQ.2010.rds") #for PM25 data.m3 <- data.m3[,c(1,2,5,29:32,52,53),with=FALSE] data.m3[, bimon := (m + 1) %/% 2] setkey(data.m3,day, aodid) data.m3<-data.m3[!is.na(meanPM25)] #generate m.3 initial pred data.m3$pred.m3.mix <- predict(Final_pred_2010,data.m3) #create unique grid ugrid <-data.m3 %>% group_by(aodid) %>% summarise(lat_aod = mean(lat_aod, na.rm=TRUE), long_aod = mean(long_aod, na.rm=TRUE),x_aod_ITM = mean(x_aod_ITM, na.rm=TRUE), y_aod_ITM = mean(y_aod_ITM, na.rm=TRUE)) #### PREDICT Gam part #split back into bimons to include the gam prediction in final prediction data.m3_bimon1 <- data.m3[bimon == 1, ] data.m3_bimon2 <- data.m3[bimon == 2, ] data.m3_bimon3 <- data.m3[bimon == 3, ] data.m3_bimon4 <- data.m3[bimon == 4, ] data.m3_bimon5 <- data.m3[bimon == 5, ] data.m3_bimon6 <- data.m3[bimon == 6, ] #addin unique grid to each bimon uniq_gid_bimon1 <- ugrid uniq_gid_bimon2 <- ugrid uniq_gid_bimon3 <- ugrid uniq_gid_bimon4 <- ugrid uniq_gid_bimon5 <- ugrid uniq_gid_bimon6 <- ugrid #get predictions for Bimon residuals uniq_gid_bimon1$gpred <- predict.gam(fit2_1,uniq_gid_bimon1) uniq_gid_bimon2$gpred <- predict.gam(fit2_2,uniq_gid_bimon2) uniq_gid_bimon3$gpred <- predict.gam(fit2_3,uniq_gid_bimon3) uniq_gid_bimon4$gpred <- predict.gam(fit2_4,uniq_gid_bimon4) uniq_gid_bimon5$gpred <- predict.gam(fit2_5,uniq_gid_bimon5) uniq_gid_bimon6$gpred <- predict.gam(fit2_6,uniq_gid_bimon6) #merge things back togheter #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> merges setkey(uniq_gid_bimon1,aodid) setkey(data.m3_bimon1,aodid) data.m3_bimon1 <- merge(data.m3_bimon1, uniq_gid_bimon1[,list(aodid,gpred)], all.x = T) setkey(uniq_gid_bimon2,aodid) setkey(data.m3_bimon2,aodid) data.m3_bimon2 <- merge(data.m3_bimon2, uniq_gid_bimon2[,list(aodid,gpred)], all.x = T) setkey(uniq_gid_bimon3,aodid) setkey(data.m3_bimon3,aodid) data.m3_bimon3 <- merge(data.m3_bimon3, uniq_gid_bimon3[,list(aodid,gpred)], all.x = T) setkey(uniq_gid_bimon4,aodid) setkey(data.m3_bimon4,aodid) data.m3_bimon4 <- merge(data.m3_bimon4, uniq_gid_bimon4[,list(aodid,gpred)], all.x = T) setkey(uniq_gid_bimon5,aodid) setkey(data.m3_bimon5,aodid) data.m3_bimon5 <- merge(data.m3_bimon5, uniq_gid_bimon5[,list(aodid,gpred)], all.x = T) setkey(uniq_gid_bimon6,aodid) setkey(data.m3_bimon6,aodid) data.m3_bimon6 <- merge(data.m3_bimon6, uniq_gid_bimon6[,list(aodid,gpred)], all.x = T) #reattach all parts mod3 <- rbind(data.m3_bimon1,data.m3_bimon2,data.m3_bimon3,data.m3_bimon4,data.m3_bimon5,data.m3_bimon6) # create pred.m3 mod3$pred.m3 <-mod3$pred.m3.mix+mod3$gpred #hist(mod3$pred.m3) #describe(mod3$pred.m3) #recode negative into zero mod3 <- mod3[pred.m3 >= 0] saveRDS(mod3,"/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod3.AQ.2010.pred.rds") #clean keep(mod3,res,rmse, sure=TRUE) gc() ######################### #prepare for m3.R2 ######################### #load mod1 mod1<- readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod1.AQ.2010.pred.rds") mod1[,aodid:= paste(mod1$long_aod,mod1$lat_aod,sep="-")] mod1<-mod1[,c("aodid","day","PM25","pred.m1","stn","MeanPbl","WSa","WDa","tempa"),with=FALSE] #R2.m3 setkey(mod3,day,aodid) setkey(mod1,day,aodid) m1.2010 <- merge(mod1,mod3[, list(day,aodid,pred.m3)], all.x = T) m3.fit.2010<- summary(lm(PM25~pred.m3,data=m1.2010)) res[res$year=="2010", 'm3.R2'] <- print(summary(lm(PM25~pred.m3,data=m1.2010))$r.squared) #RMSPE res[res$year=="2010", 'm3.PE'] <- print(rmse(residuals(m3.fit.2010))) #spatial ###to check spatial2010<-m1.2010 %>% group_by(stn) %>% summarise(barpm = mean(PM25, na.rm=TRUE), barpred = mean(pred.m3, na.rm=TRUE)) m1.fit.2010.spat<- lm(barpm ~ barpred, data=spatial2010) res[res$year=="2010", 'm3.R2.s'] <- print(summary(lm(barpm ~ barpred, data=spatial2010))$r.squared) res[res$year=="2010", 'm3.PE.s'] <- print(rmse(residuals(m1.fit.2010.spat))) #temporal tempo2010<-left_join(m1.2010,spatial2010) tempo2010$delpm <-tempo2010$PM25-tempo2010$barpm tempo2010$delpred <-tempo2010$pred.m3-tempo2010$barpred mod_temporal <- lm(delpm ~ delpred, data=tempo2010) res[res$year=="2010", 'm3.R2.t'] <- print(summary(lm(delpm ~ delpred, data=tempo2010))$r.squared) #############save midpoint saveRDS(res, "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/res.AQ.2010.rds") saveRDS(res, "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/resALL.AQ.rds") ######################### #import mod2 mod2<- readRDS( "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod2.AQ.2010.pred2.rds") mod2<-mod2[,c("aodid","day","pred.m2"),with=FALSE] #----------------> store the best available mod3best <- mod3[, list(aodid, x_aod_ITM, y_aod_ITM, day, pred.m3)] setkey(mod3best, day, aodid) setkey(mod2, day, aodid) mod3best <- merge(mod3best, mod2[,list(aodid, day, pred.m2)], all.x = T) #reload mod1 mod1<- readRDS("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod1.AQ.2010.pred.rds") mod1$aodid<-paste(mod1$long_aod,mod1$lat_aod,sep="-") mod1<-mod1[,c("aodid","day","PM25","pred.m1"),with=FALSE] setkey(mod1,day,aodid) mod3best <- merge(mod3best, mod1, all.x = T) mod3best[,bestpred := pred.m3] mod3best[!is.na(pred.m2),bestpred := pred.m2] mod3best[!is.na(pred.m1),bestpred := pred.m1] #save saveRDS(mod3best,"/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/mod3.AQ.2010.FINAL.rds") #save for GIS write.csv(mod3best[, list(LTPM = mean(bestpred, na.rm = T), predvariance = var(bestpred, na.rm = T), predmin = min(bestpred, na.rm = T), predmax = max(bestpred, na.rm = T), npred = sum(!is.na(bestpred)), npred.m1 = sum(!is.na(pred.m1)), npred.m2 = sum(!is.na(pred.m2)), npred.m3 = sum(!is.na(pred.m3)), x_aod_ITM = x_aod_ITM[1], y_aod_ITM = y_aod_ITM[1]),by=aodid], "/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN000_RWORKDIR/LTPM3.AQ.2010.csv", row.names = F) #-------->>> loc stage luf<-fread("/media/NAS/Uni/Projects/P046_Israel_MAIAC/3.Work/2.Gather_data/FN004_LU_full_dataset/local.csv") setnames(luf,"tden","loc.tden") setnames(luf,"elev50","loc.elev") #rename dataset m3.2010<-m1.2010 m3.2010<-na.omit(m3.2010) #add 50m LU to CV data setkey(m3.2010,stn) setkey(luf,stn) m3.2010.loc <- merge(m3.2010, luf, all.x = T) #create residual mp3 variable m3.2010.loc$res.m3<-m3.2010.loc$PM25-m3.2010.loc$pred.m3 #The GAM model gam.out<-gam(res.m3~s(loc.tden)+s(loc.tden,MeanPbl)+s(loc.tden,WSa)+s(loc_p_os,fx=FALSE,k=4,bs='cr')+s(loc.elev,fx=FALSE,k=4,bs='cr')+s(dA1,fx=FALSE,k=4,bs='cr')+s(dsea,fx=FALSE,k=4,bs='cr'),data=m3.2010.loc) #plot(bp.model.ps) #summary(bp.model.ps) ## reg m3.2010.loc$pred.m3.loc <-predict(gam.out) m3.2010.loc$pred.m3.both <- m3.2010.loc$pred.m3 + m3.2010.loc$pred.m3.loc res[res$year=="2010", 'm3.loc.R2'] <- print(summary(lm(PM25~pred.m3.both,data=m3.2010.loc))$r.squared) res[res$year=="2010", 'm3.loc.I'] <-print(summary(lm(PM25~pred.m3.both,data=m3.2010.loc))$coef[1,1]) res[res$year=="2010", 'm3.loc.I.se'] <-print(summary(lm(PM25~pred.m3.both,data=m3.2010.loc))$coef[1,2]) res[res$year=="2010", 'm3.loc.S'] <-print(summary(lm(PM25~pred.m3.both,data=m3.2010.loc))$coef[2,1]) res[res$year=="2010", 'm3.loc.S.se'] <-print(summary(lm(PM25~pred.m3.both,data=m3.2010.loc))$coef[2,2]) #RMSPE res[res$year=="2010", 'm3.loc.PE'] <- print(rmse(residuals(m3.fit.2010))) #spatial spatial2010.loc<-m3.2010.loc %>% group_by(stn) %>% summarise(barpm = mean(PM25, na.rm=TRUE), barpred = mean(pred.m3, na.rm=TRUE)) m3.fit.2010.loc.s <- lm(barpm ~ barpred, data=spatial2010.loc) res[res$year=="2010", 'm3.loc.R2.s'] <- print(summary(lm(barpm ~ barpred, data=spatial2010.loc))$r.squared) res[res$year=="2010", 'm3.loc.PE.s'] <- print(rmse(residuals(m3.fit.2010.loc.s))) #temporal tempo2010.loc<-left_join(m3.2010.loc,spatial2010.loc) tempo2010.loc$delpm <-tempo2010.loc$PM25-tempo2010.loc$barpm tempo2010.loc$delpred <-tempo2010.loc$pred.m3.both-tempo2010.loc$barpred mod_temporal.loc <- lm(delpm ~ delpred, data=tempo2010.loc) res[res$year=="2010", 'm3.loc.R2.t'] <- print(summary(lm(delpm ~ delpred, data=tempo2010.loc))$r.squared) keep(res, sure=TRUE) gc()

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d0p34m1n3/PortfolioOptimisation

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PCAExample.R

rm(list=ls()) require(RCurl) sit = getURLContent('https://github.com/systematicinvestor/SIT/raw/master/sit.gz', binary=TRUE, followlocation = TRUE, ssl.verifypeer = FALSE) con = gzcon(rawConnection(sit, 'rb')) source(con) close(con) load.packages('quantmod') data <- new.env() tickers<-spl("VBMFX,VTSMX,VGTSX,VGSIX") getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T) for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T) bt.prep(data, align='remove.na', dates='1990::2013') prices<-data$prices ret<-na.omit(prices/mlag(prices) - 1) weight<-matrix(1/ncol(ret),nrow=1,ncol=ncol(ret)) p.ret<-(weight) %*% t(ret) demean = scale(coredata(ret), center=TRUE, scale=FALSE) covm<-cov(demean) pca<-prcomp(ret,cor=F) evec<-pca$rotation[] #eigen vectors eval <- pca$sdev^2 #eigen values print(diag(t(evec) %*% covm %*% evec)) print(eval) inv.evec<-solve(evec) #inverse of eigenvector pc.port<-inv.evec %*% t(ret)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataBench.R \name{methodList} \alias{methodList} \title{helper for creating a methodlist} \usage{ methodList(methods = c("hdf5", "bigm", "sqlite", "ff")) } \arguments{ \item{methods}{a character vector with tags for available round trip methods} } \description{ helper for creating a methodlist }

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ShakespeareWordHist.Rd

\name{ShakespeareWordHist} \alias{ShakespeareWordHist} \docType{data} \title{Shakespeare's word type frequencies} \description{The Shakespeare's word type frequencies data was from Efron, B., & Thisted, R. (1976).} \references{ Efron, B., & Thisted, R. (1976). Estimating the number of unseen species: How many words did Shakespeare know?. Biometrika, 63(3), 435-447. } \examples{ ##load library library(preseqR) ##load data data(ShakespeareWordHist) } \keyword{ data }

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gbohner/Cambridge_talk_20190306

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setwd("~") system("cd ~") if (!file.exists("Cambridge_talk_20190306")){ system("git clone https://github.com/gbohner/Cambridge_talk_20190306") } setwd("~/Cambridge_talk_20190306") system("git pull") setwd("~") system("cp ~/Cambridge_talk_20190306/Code/R/* ~/") # Set the libPaths to the already installed packages curLibPaths = .libPaths() curLibPaths[[2]] = paste0(curLibPaths[[2]],"/3.5") .libPaths(curLibPaths) # Therefore we do NOT need to reinstall packages on every user (takes very long) #source("install_packages.R") # Open the file that we want to edit file.edit("scrnaseq_dimred_notebook.Rmd")

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geom-segment2.R

#' Segment2 layer #' #' @inheritParams ggplot2::layer #' @inheritParams ggplot2::geom_segment #' @section Aesthetics: #' \code{geom_segment2()} understands the following aesthetics (required aesthetics are in bold): #' \itemize{ #' \item \strong{\code{x}} #' \item \strong{\code{y}} #' \item \strong{\code{xend}} #' \item \strong{\code{yend}} #' \item \code{alpha} #' \item \code{colour} #' \item \code{linetype} #' \item \code{width} #' } #' @importFrom ggplot2 layer ggproto geom_segment GeomSegment aes draw_key_path #' @rdname geom_segment2 #' @author Houyun Huang, Lei Zhou, Jian Chen, Taiyun Wei #' @export geom_segment2 <- function(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) { layer( data = data, mapping = mapping, stat = stat, geom = GeomSegment2, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = na.rm, ... ) ) } #' @rdname geom_segment2 #' @format NULL #' @usage NULL #' @export GeomSegment2 <- ggproto( "GeomSegment2", GeomSegment, default_aes = aes(colour = "grey35", size = 0.5, linetype = 1, alpha = NA), required_aes = c("x", "y", "xend", "yend"), draw_panel = function(data, panel_params, coord, arrow = NULL, arrow.fill = NULL, lineend = "butt", linejoin = "round", na.rm = FALSE) { if(empty(data)) { return(ggplot2::zeroGrob()) } coords <- coord$transform(data, panel_params) arrow.fill <- arrow.fill %||% coords$colour x <- y <- xend <- yend <- NULL ends <- dplyr::rename(data[setdiff(names(data), c("x", "y"))], x = xend, y = yend) ends <- coord$transform(ends, panel_params) ends <- dplyr::rename(ends, xend = x, yend = y) coords <- cbind(coords[setdiff(names(coords), c("xend", "yend"))], ends[c("xend", "yend")]) ggname("geom_segment2", grid::segmentsGrob(coords$x, coords$y, coords$xend, coords$yend, default.units = "native", gp = grid::gpar( col = scales::alpha(coords$colour, coords$alpha), fill = scales::alpha(arrow.fill, coords$alpha), lwd = coords$size * ggplot2::.pt, lty = coords$linetype, lineend = lineend, linejoin = linejoin ), arrow = arrow ) ) }, draw_key = draw_key_path )

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export_data <- function(data_object, file_name){ write.table(data_object, file=file_name, sep="\t", col.names = NA, row.names = TRUE, quote = FALSE, eol="\n") }

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stockviz/blog

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source("d:/stockviz/r/config.r") source("D:/StockViz/public/blog/common/plot.common.R") reportPath<-"." library('RODBC') library('quantmod') library('PerformanceAnalytics') library('lubridate') library('tidyverse') library('reshape2') library('ggthemes') options(stringsAsFactors = FALSE) options("scipen"=100) pdf(NULL) lcon <- odbcDriverConnect(sprintf("Driver={ODBC Driver 17 for SQL Server};Server=%s;Database=%s;Uid=%s;Pwd=%s;", ldbserver, "StockViz", ldbuser, ldbpassword), case = "nochange", believeNRows = TRUE) startDate <- as.Date("2010-01-01") endDate <- as.Date("2015-12-31") #indexName <- "NIFTY MIDCAP150 MOMENTUM 50 TR" indexName <- "NIFTY200 MOMENTUM 30 TR" nEod <- sqlQuery(lcon, sprintf("select px_close, time_stamp from bhav_index where index_name='%s' and time_stamp >= '%s' and time_stamp <= '%s'", indexName, startDate, endDate)) nXts <- xts(nEod[,1], nEod[,2]) nXts <- merge(nXts, dailyReturn(nXts)) #1, 2 nXts <- merge(nXts, rollapply(nXts[,2], 50, sd), #3 rollapply(nXts[,2], 100, sd), #4 rollapply(nXts[,2], 200, sd), #5 rollapply(nXts[,2], 5, Return.cumulative), #6 rollapply(nXts[,2], 20, Return.cumulative) #7 ) nXts <- merge(nXts, stats::lag(nXts[, 6], -5), stats::lag(nXts[, 7], -20)) names(nXts) <- c("INDEX", "RET", "SD50", "SD100", "SD200", "RET5", "RET20", "RET5_LAG5", "RET20_LAG20") aNames <- c("SD50", "SD100", "SD200") bNames <- c("RET5_LAG5", "RET20_LAG20") doScatter <- function(aName, bName){ toPlot <- data.frame(nXts[, c(aName, bName)]) ggplot(toPlot, aes_string(x=aName, y=bName)) + theme_economist() + geom_point() + labs(fill="", color="", size="", title=sprintf("%s Forward Returns vs. Historical Volatility", indexName), subtitle=sprintf("[%s:%s]", startDate, endDate)) + annotate("text", x=min(toPlot[,1]), y=min(toPlot[,2]), label = "@StockViz", hjust=0, vjust=0, col="white", cex=6, fontface = "bold", alpha = 0.8) ggsave(sprintf("%s/%s.%s.%s.png", reportPath, indexName, aName, bName), width=16, height=8, units="in") } plotOverlapping <- function(){ for(i in 1:length(aNames)){ for(j in 1:length(bNames)){ doScatter(aNames[i], bNames[j]) } } } plotOverlapping() ################################################################ # monthly rebalance based on static look-back SD and static threshold backtest <- function(sdLb){ # back test to calc max return & min drawdown SDs nXts <- xts(nEod[,1], nEod[,2]) nXts <- merge(nXts, dailyReturn(nXts), monthlyReturn(nXts)) #1, 2, 3 nXts <- merge(nXts, rollapply(nXts[,2], sdLb, sd)) #4 nXts <- na.omit(nXts) nXts <- merge(nXts, stats::lag(nXts[, 3], -1)) #5 names(nXts) <- c("INDEX", "RET", "RETM", "SD200", "RETM_LAG1") sdThreshs <- seq(0.01, 0.05, by=0.005) threshRets <- do.call(merge, lapply(sdThreshs, function(X) ifelse(nXts$SD200 > X, 0, nXts$RETM_LAG1))) names(threshRets) <- sapply(sdThreshs, function(X) paste0("RETM", X)) cumRets <- sort(apply(threshRets, 2, Return.cumulative), decreasing=T) print(cumRets) maxDDs <- sort(apply(threshRets, 2, maxDrawdown)) maxRetSd <- as.numeric(gsub("RETM", "", names(cumRets[1]))) minDDSd <- as.numeric(gsub("RETM", "", names(maxDDs[1]))) toPlot <- na.omit(merge(nXts$RETM_LAG1, threshRets)) Common.PlotCumReturns(toPlot, sprintf("%s/%d-day std. dev.", indexName, sdLb), "(EOM rebalance)", sprintf("%s/%s.%d.cumulative.png", reportPath, indexName, sdLb)) # forward test startDate <- as.Date("2016-01-01") endDate <- as.Date("2022-10-31") nEod <- sqlQuery(lcon, sprintf("select px_close, time_stamp from bhav_index where index_name='%s' and time_stamp >= '%s' and time_stamp <= '%s'", indexName, startDate-360, endDate)) nXts <- xts(nEod[,1], nEod[,2]) nXts <- merge(nXts, dailyReturn(nXts), monthlyReturn(nXts)) #1, 2, 3 nXts <- merge(nXts, rollapply(nXts[,2], sdLb, sd)) #4 nXts <- na.omit(nXts) nXts <- merge(nXts, stats::lag(nXts[, 3], -1)) #5 names(nXts) <- c("INDEX", "RET", "RETM", "SD200", "RETM_LAG1") nXts$MAX_RET <- ifelse(nXts$SD200 > maxRetSd, 0, nXts$RETM_LAG1) nXts$MIN_DD <- ifelse(nXts$SD200 > minDDSd, 0, nXts$RETM_LAG1) toPlot <- na.omit(nXts[paste0(startDate, "/"), c("RETM_LAG1", "MAX_RET", "MIN_DD")]) Common.PlotCumReturns(toPlot, sprintf("%s/%.3f %.3f %d-day std. dev.", indexName, maxRetSd, minDDSd, sdLb), "(EOM rebalance)", sprintf("%s/%s.max.%d.cumulative.png", reportPath, indexName, sdLb)) toPlot <- toPlot["2020-05-01/",] Common.PlotCumReturns(toPlot, sprintf("%s/%.3f %.3f %d-day std. dev.", indexName, maxRetSd, minDDSd, sdLb), "(EOM rebalance)", sprintf("%s/%s.max.%d.cumulative.2020.png", reportPath, indexName, sdLb)) } sdLbs <- c(10, 20, 50, 100, 200) #days lapply(sdLbs, function(X) backtest(X))

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khliland/plsVarSel

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2023-01-24T07:50:38.251503

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truncation.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trunc.R \name{truncation} \alias{truncation} \title{Trunction PLS} \usage{ truncation(..., Y.add, weights, method = "truncation") } \arguments{ \item{...}{arguments passed on to \code{mvrV}).} \item{Y.add}{optional additional response vector/matrix found in the input data.} \item{weights}{optional object weighting vector.} \item{method}{choice (default = \code{truncation}).} } \value{ Returns an object of class mvrV, simliar to to mvr object of the pls package. } \description{ Distribution based truncation for variable selection in subspace methods for multivariate regression. } \details{ Loading weights are truncated around their median based on confidence intervals for modelling without replicates (Lenth et al.). The arguments passed to \code{mvrV} include all possible arguments to \code{\link[pls:mvr]{cppls}} and the following truncation parameters (with defaults) trunc.pow=FALSE, truncation=NULL, trunc.width=NULL, trunc.weight=0, reorth=FALSE, symmetric=FALSE. The default way of performing truncation involves the following parameter values: truncation="Lenth", trunc.width=0.95, indicating Lenth's confidence intervals (assymmetric), with a confidence of 95%. trunc.weight can be set to a number between 0 and 1 to give a shrinkage instead of a hard threshold. An alternative truncation strategy can be used with: truncation="quantile", in which a quantile line is used for detecting outliers/inliers. } \examples{ data(yarn, package = "pls") tr <- truncation(density ~ NIR, ncomp=5, data=yarn, validation="CV", truncation="Lenth", trunc.width=0.95) # Default truncation summary(tr) } \references{ K.H. Liland, M. Høy, H. Martens, S. Sæbø: Distribution based truncation for variable selection in subspace methods for multivariate regression, Chemometrics and Intelligent Laboratory Systems 122 (2013) 103-111. } \seealso{ \code{\link{VIP}} (SR/sMC/LW/RC), \code{\link{filterPLSR}}, \code{\link{shaving}}, \code{\link{stpls}}, \code{\link{truncation}}, \code{\link{bve_pls}}, \code{\link{ga_pls}}, \code{\link{ipw_pls}}, \code{\link{mcuve_pls}}, \code{\link{rep_pls}}, \code{\link{spa_pls}}, \code{\link{lda_from_pls}}, \code{\link{lda_from_pls_cv}}, \code{\link{setDA}}. } \author{ Kristian Hovde Liland. }

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ellierk/honors-thesis

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logreg code.R

df1 <- squid %>% select(site, date, spacing_m, temp_avg, sal_avg, dist_from_bags, reclaimed, absent) df2 <- df1[1,] %>% mutate(consumed = "1") v = df1$reclaimed i = 1 l=1 while(i < length(v)) { l=1 while(l < v[i]) { ifelse(l < df1$absent[i], (df2 = df2 %>% add_row(site = df1$site[i], date = df1$date[i], spacing_m = df1$spacing_m[i], temp_avg= df1$temp_avg[i], sal_avg= df1$sal_avg[i], dist_from_bags = df1$dist_from_bags[i], reclaimed = df1$reclaimed[i], absent = df1$absent[i], consumed = "1")), (df2 = df2 %>% add_row(site = df1$site[i], date = df1$date[i], spacing_m = df1$spacing_m[i], temp_avg= df1$temp_avg[i], sal_avg= df1$sal_avg[i], dist_from_bags = df1$dist_from_bags[i], reclaimed = df1$reclaimed[i], absent = df1$absent[i], consumed = "0")) ) l = l+1 } i = i+1 } write.csv(df2, "logreg.csv")

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ceik/coursera

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03_04_R_colors.R

##### Plotting and Color in R ##### ######################################### ### Problem # Colors can make it much easier for the reader to understand your data/plot. # The default color schemes in R are often suboptimal. This is true both from a # design and a functional (colors are often supposed to illustrate data) POV. # # E.g. the default colors for the col argument are: # 1 = Open black # 2 = Red # 3 = Green # # Default image colors are heat colors (red = low, yellow = high) or topo colors # (blue = low, green = mid, yellow = high) # Some packages and functions can help here. ### Color Utilities in R # the grDevices package has two functions (colorRamp() and colorRampPalette()) # that take palettes of colors and interpolate between them. # The function colors() # lists the names of colors that can be used in any plotting function. # The colorRamp() takes a palette of colors as an input and returns a function # that takes values between 0 and 1, indicating the extremes of the given color # palette. (See also the grey() function) # The new function returns a matrix of RGB values. pal <- colorRamp(c("red", "blue")) pal(0) pal(1) pal(0.5) pal(seq(0,1, len=10)) # The colorRampPalette() function also takes a palette of colors as an input and # returns a function. However this new function takes integer arguments and # retuns a vector of colors interpolating the palette. (Like heat.colors() and # topo.colors()) # The new function returns a character vector of hexadecimals. In these values # the first to digits represent red, the second two represent green, and the # last three represent blue. F is the highest, 0 the lowest number. palp <- colorRampPalette(c("red", "yellow")) palp(2) # The following gives a total number of 10 colors between red and yellow. palp(10) ### RColorBrewer Package # This package contains useful color palettes for three types of data: # Sequential data: Ordered data, e.g. going from something low to high. # Diverging data: Showing the deviation from something (e.g. a mean or specific # value). # Qualitative data: Categorical or factor data that is not ordered. # Palette information from this package can be used in conjunction with the # colorRamp() and colorRampPalette() functions. # Lecture slides contain all color palettes in the package. install.packages("RColorBrewer") library("RColorBrewer") # This also lists all available palettes: ?brewer.pal cols <- brewer.pal(3, "BuGn") cols palb <- colorRampPalette(cols) palb library(datasets) image(volcano, col = palb(20)) ### The smoothScatter() Function # The smoothScatter() function is very useful for plotting many points at once # (that would overlap each other). The function creates a 2-D histogram of the # points in your plot and plots those histograms x <- rnorm(10000) y <- rnorm(10000) smoothScatter(x,y) ### Other Useful Functions # The rgb() function converts any rgb values into hexadecimal color values, # which can be passed to functions that require hexadecimal then. Transparency # can be added via the alpha argument of the rgb() function. a <- rnorm(2000) b <- rnorm(2000) plot(a, b, pch = 19) # Adding some transparancy to this will make it a bit histogram like. plot(a, b, col = rgb(0,0,0, alpha = 0.2), pch = 19) # The colorspace package can be used to gain different kinds of controls over # colors. Read documentation for more info.

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jiqis/sandbox

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test.R

#ÉtÉ@ÉCÉč data <- read.csv("./data.csv") data.temp<-data$average_temperature data.dlen<-data$day_length m1<-lm(data.temp~data.dlen) summary(m1)

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ipaq_scores.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ipaq_scores.R \name{ipaq_scores} \alias{ipaq_scores} \title{IPAQ scores} \usage{ ipaq_scores(data, truncate = F) } \arguments{ \item{data}{A data frame object containing 25 columns with the replies to the IPAQ long format (parts 1-4). Yes/no replies should be coded as yes-1, no-0. Time should be in minutes.} \item{truncate}{Logical vector. If TRUE all walking, moderate and vigorous time variables are truncated following the IPAQ short rule. Variables exceeding 180 minutes are truncated to be equal to 180 minutes. Default FALSE.} } \value{ A data frame object with the continuous (metabolic equivalent of task minutes (MET-min)/week) and categorical scores (low, moderate, high). Returns NA for cases with missing values. } \description{ Calculates the continuous and categorical scores for the 'International Physical Activity Questionnaire (IPAQ)' long form. } \references{ The IPAQ Group (2005). Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire. Retrieved from <https://sites.google.com/site/theipaq/home> }

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cran/robeth

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2023-08-31T11:44:10.694653

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wfshat.Rd

\name{wfshat} \alias{wfshat} \title{ Schweppe original weight proposal } \description{ See Marazzi A. (1993), p.137} \usage{ wfshat(xt, n = nrow(xt), sh) } \arguments{ \item{xt}{ See reference} \item{n}{ See reference} \item{sh}{ See reference} } \value{ See reference } \references{ Marazzi A. (1993) \emph{Algorithm, Routines, and S functions for Robust Statistics}. Wadsworth & Brooks/cole, Pacific Grove, California. p.137 } \keyword{robust}

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/R/print.summary.spsur.R

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rominsal/spsur

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print.summary.spsur.R

#' @name print.summary.spsur #' @rdname print.summary.spsur #' #' @title Print method for objects of class summary.spsur. #' #' @param x object of class \emph{summary.spsur}. #' @param digits number of digits to show in printed tables. #' Default: max(3L, getOption("digits") - 3L). #' @param ... further arguments passed to or from other methods. #' #' @author #' \tabular{ll}{ #' Fernando Lopez \tab \email{fernando.lopez@@upct.es} \cr #' Roman Minguez \tab \email{roman.minguez@@uclm.es} \cr #' Jesus Mur \tab \email{jmur@@unizar.es} \cr #' } #' #' @seealso #' \code{\link{summary.spsur}}. #' #' #' @examples #' # See examples for \code{\link{spsurml}} or #' # \code{\link{spsur3sls}} functions. #' @export print.summary.spsur <- function(x, digits = max(3L, getOption("digits") - 3L), ...) { G <- x$G cat("Call:\n") print(x$call) cat("\n","\n") cat("Spatial SUR model type: ",x$type,"\n\n") for (i in 1:G){ cat("Equation ",i,"\n") printCoefmat(x$coef_table[[i]], P.values = TRUE, has.Pvalue = TRUE) cat("R-squared: ", formatC(x$R2[i+1], digits = 4), " ", sep = "") cat("\n"," ") } cat("\n") if (x$Tm>1 | x$G>1){ cat("Variance-Covariance Matrix of inter-equation residuals:") prmatrix(x$Sigma,digits=4, rowlab=rep("", nrow(x$Sigma)), collab = rep("", ncol(x$Sigma))) } else { cat("Residual standard error:",formatC(sqrt(x$Sigma), digits = 4, width = 6)) } if (x$Tm>1 | x$G>1){ cat("Correlation Matrix of inter-equation residuals:") prmatrix(x$Sigma_corr,digits=3, rowlab=rep("",nrow(x$Sigma)), collab = rep("",ncol(x$Sigma))) } if (x$Tm>1 | x$G>1){ cat("\n R-sq. pooled: ", formatC(x$R2[1], digits = 4)," ", sep = "") if(!is.null(x$llsur)){ cat("\n Log-Likelihood: ",formatC(x$llsur, digits = 6, width = 6)) } } # Se ajusta a una Chi con G*(G-1)/2 gl if (x$Tm>1 | x$G>1){ # Only report the BP test for multiequations if(!is.null(x$BP)){ cat("\n Breusch-Pagan: ",formatC(x$BP, digits = 4), " p-value: (",formatC(pchisq(x$BP, df = G*(G - 1)/2, lower.tail = FALSE), digits = 3, width = 4),") ", sep = "") } } if(!is.null(x$LMM)){ # If Tm=G=1 the LMM test have df=1 if (x$Tm==1 & x$G==1){ df= 1} else {df= G*(G - 1)/2 } cat("\n LMM: ",formatC(x$LMM, digits = 5), " p-value: (",formatC(pchisq(x$LMM, df = df, lower.tail = FALSE), digits = 3, width = 4),")\n", sep = "") } invisible(x) }

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/scripts/Group_google.R

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Group_google.R

#----------------------------------------------------------------------------------------------------------------------------------# # Function by : Lucien Fitzpatrick # Project: Living Collections Phenology Forecasting # Purpose: A function that will download all of the google forms of interest # Inputs: 2018 to present phenology monitoring data from the googlesheet "Phenology_Observations_GoogleForm" in the "LivingCollections-Phenology/Data_Observations/" folder # The clean_google_form.r script which defines the clean.google function. Found in the Github repository "Phenology_ LivingCollections" # Outputs:A dataframe containing the information from all desired google forms. #Enter the genus of interest as a vector and the final year of observations you want as a variable #The function will crash if you have a genus included without end years we have a form for i.e("Ulmus" with range 2018:2019) #This only matters for end years. Start years are adjusted to match the first year the genus has a form. #Tends to tke about 30 seconds per form group.google <- function(x, ystart, yend){ dat.pheno <- data.frame() #Checking which genus we are working with an setting correct parameters for(p in x){ if (p == "Quercus") { yrange <- c(yend:ystart) #THIS MUST BE REVERSED AS A WORKAROUND TO AN ISSUE WITH 2018 QUERCUS. SEEMS ONLY PC ISSUE } else if (p == "Acer") { if(ystart < 2019){ ystart <- 2019 } yrange <- c(ystart:yend) } else if (p == "Ulmus") { if(ystart < 2020){ ystart <- 2020 } yrange <- c(ystart:yend) } collection <- p #Downloading the googleform from every year in the requested range for(yr in yrange){ temp <- clean.google(google.key = "1eEsiJ9FdDiNj_2QwjT5-Muv-t0e-b1UGu0AFBRyITSg", collection=collection, dat.yr=yr) temp$Year <- yr temp$Collection <- as.factor(collection) # names(temp) <- tolower(names(temp)) #Work around for clean.google not changing 2018 names. THIS ALSO MEANS RANGE MUST GO REVERSE FOR QUERCUS if(yr == "2018"){ colnames(temp) <- as.character(colnames(dat.pheno)) } dat.pheno <- rbind(dat.pheno, temp) } } return(dat.pheno) }

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examples.exp2.R

# load data data(exp2) exp2$K <- acss(exp2$string, 6)[,"K.6"] m_log <- glm(response ~ K, exp2, family = binomial) summary(m_log) # odds ratio of K: exp(coef(m_log)[2]) # calculate threshold of 0.5 (threshold <- -coef(m_log)[1]/coef(m_log)[2]) require(effects) require(lattice) plot(Effect("K", m_log), rescale.axis = FALSE, ylim = c(0, 1)) trellis.focus("panel", 1, 1) panel.lines(rep(threshold, 2), c(0, 0.5), col = "black", lwd = 2.5, lty = 3) panel.lines(c(33,threshold), c(0.5, 0.5), col = "black", lwd = 2.5, lty = 3) trellis.unfocus()

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markus-flicke/KD_Projekt_1

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PlotGabriel4BestMatches.Rd

\name{PlotGabriel4BestMatches} \alias{PlotGabriel4BestMatches} \title{Plot Gabriel4BestMatches} \description{ Zeichnen des Delaunay Graphen, Punkte ggf. nach Cls gefaerbt } \usage{ PlotGabriel4BestMatches(BestMatches,MatrixOrSize,Cls,IsTiled) } \arguments{ \item{BestMatches}{BestMatches, (1:d,1:3)} \item{MatrixOrSize}{Default 50,82; A vector of length 2, containing the number of lines and columns of the SOM corresponding to the BestMatches, (2)} \item{Cls}{Classes of bestmatches or []} \item{IsTiled}{==1 (default) => Randkorrigiertes Universum} } \details{ ... } \value{ %- description of return values \item{Gabriel}{Sparse Vector des Gabrielgraphen in squareform, (1:d,1:d)} } \references{ \url{www.uni-marburg.de/fb12/datenbionik} } \author{ Rabea Griese } \keyword{Gabriel}%- zero or more keywords (all storaged in /doc/KEYWORDS, for search and help)

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krshedd/CKMRsim

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print.ckmr.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ckmr-class.R \name{print.ckmr} \alias{print.ckmr} \title{print method for ckmr class} \usage{ \method{print}{ckmr}(C) } \arguments{ \item{C}{an object of class \code{\link{ckmr_class}}.} } \description{ Just wraps a call to the format.ckmr function }

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thepingryschool/r-examples-at

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day2examples.R

# This example script shows the properties of vectors # 2 ways to create a vector v1 = c(1L, 2L, 3) v1 v2 = seq(1, 10) v2 v3 = 1:10 v3 # coercion v4 = c(1L, 2L, 3, "4", "h") v4 print("-------------------") #vectors hold same type typeof(v1) class(v1) typeof(v2) class(v2) typeof(v4) print("-------------------") #length of a vector length(v2) print("-------------------") #a vector of length 0 is NULL a = c() a print("-------------------") #vectors can have attributes or names at = c('A', 'B', 'C') names(v1) = at v1 print("-------------------") v5 = c(C = 1, D = 2) v5 print("-------------------") v6 = c("A" = 2, 'B' = 3) v6 print("-------------------") names(v6) attributes(v5) #vectors do not have dimension ve = c(1, v2, c("a", "b")) ve

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plot2.R

plot2fn <- function() { ##load data data <- read.csv("household_power_consumption.txt", header = TRUE, sep = ";") ##subset data from the dates 2007-02-01 and 2007-02-02 data <- subset(data, Date == "2/2/2007" | Date == "1/2/2007") #Convert date and time datetime <- as.POSIXct(paste(data$Date, data$Time), format="%d/%m/%Y %H:%M:%S") #Plot 2 plot(datetime, GAP, type = "l", ylab = "Global Active Power (kilowatts)", xlab = " ") }

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/R/loadDefinitiveModel.R

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loadDefinitiveModel.R

#' @title Load Rdata files storing definitive fitted model #' @description Load information from Rdata generated with function allModelling based #' in an index to select the definitive fitted model/s, ideally, the index returned by #' function indextent should be used. #' #' @param data Object with the same structure as the object returned by function bindPresAbs. #' @param extents Named integer returned by function indextent (a named index #' corresponding to the definitive extents to be considered) #' @param slot Any character of the following: "allmod", auc", "kappa", "tss", "mod", "p" #' @param algorithm Any character of the following: "glm", "svm", "maxent", "mars", "randomForest", #' "cart.rpart" or "cart.tree" #' @param sourcedir Character of the path where Rdata objects are #' #' #' #' @return Depending on the specified slot: #' \item{allmod }{fitted model using all data for training} #' \item{auc }{AUC statistic in the cross validation} #' \item{kappa }{kappa statistic in the cross validation} #' \item{tss }{true skill statistic in the cross validation } #' \item{mod }{fitted model with partitioned data} #' \item{p }{cross model prediction} #' #' #' @details detail. #' #' #' #' @author M. Iturbide \email{maibide@@gmail.com} #' #' @examples #' \dontrun{ #' data(presaus) #' data(biostack) #' ##modeling #' modirs <-allModeling(data = presaus, varstack = biostack, k = 10, "mars") #' ##loading#' #' auc_mars <-loadTestValues(data = presaus, "auc", "mars") #' ind <- indextent(testmat = auc_mars, diagrams = TRUE) #' #' def <-loadDefinitiveModel(data = presaus, extents = ind, slot = "allmod", algorithm = "mars") #' } #' #' @references Iturbide, M., Bedia, J., Herrera, S., del Hierro, O., Pinto, M., Gutierrez, J.M., 2015. #' A framework for species distribution modelling with improved pseudo-absence generation. Ecological #' Modelling. DOI:10.1016/j.ecolmodel.2015.05.018. #' #' @export loadDefinitiveModel<-function(data, extents, slot=c("allmod", "auc", "kappa", "tss", "mod", "p"), algorithm = c("glm","svm","maxent","mars","randomForest","cart.rpart", "cart.tree"), sourcedir = getwd()){ if (class(data[[1]]) != "list"){ dat<-list(data) }else{dat<-data} modelslot<-list() for (i in 1:length(data)){ g<-names(data)[i] if (class(data[[1]]) != "list"){ load(paste(sourcedir,"/", algorithm, "_bg", names(extents)[i],".Rdata",sep="")) } else { load(paste(sourcedir,"/", algorithm, "_bg", names(extents)[i],"_hg",g,".Rdata",sep="")) } if (slot == "allmod"){modelslot[[i]]<-mod$allmod } else if (slot == "auc"){modelslot[[i]]<-mod$auc } else if (slot == "kappa"){modelslot[[i]]<-mod$kappa } else if (slot == "tss"){modelslot[[i]]<-mod$tss } else if (slot == "mod"){modelslot[[i]]<-mod$mod } else if (slot == "p"){modelslot[[i]]<-mod$p} } names(modelslot)<-names(data) return (modelslot) }

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ckp-koiken/R4DS

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diamonds.R

library(tidyverse) # 6章ワークフロー：プロジェクト -------------------------------------------------------- ggplot(diamonds, aes(carat, price))+ geom_hex() ggsave("diamonds.pdf") write_csv(diamonds, "diamonds.csv")

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/Data Analysis/YaoZhang_HW2.R

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ohana1128/HW

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YaoZhang_HW2.R

pain <- read.csv("HW2Data2.csv") dim(pain) ##1.Draw a boxplot for pain relief scores against different pain levels. library(ggplot2) ggplot(pain,aes(x=factor(PainLevel),y=Relief))+geom_boxplot() ##2.Perform a single factor ANOVA and compare the differences in “Relief Scores” of each “PainLevel” group. ##check normality library(reshape) pain.melt <- melt(pain) group1 <- pain.melt[pain.melt$variable=="Relief",2] qqnorm(group1) qqline(group1) ##check equal variance bartlett.test(value~variable,data=pain.melt) ##ANOVA my.aov <- aov(PainLevel~Relief,data=pain) summary(my.aov) ##3.Perform a two factor ANOVA with the following factors: Codeine and pain levels. my.aovtwo <- aov(Relief~PainLevel*Codeine,data=pain) summary(my.aovtwo) ##4.Report any significant effects from the two factor ANOVA. ##5.Use Scheffe’s method to conduct a multiple comparison for the different pairs of pain level. library(sp) library(agricolae) agricolae::scheffe.test(my.aovtwo,"PainLevel",group=TRUE,console = TRUE) ##6. Based on the result from Scheffe’s method and the boxplot, discuss how to regroup different painlevels.

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polinazhornikova/Thesis-2018

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b_ssa_analysis_1dssa.R

###Fragment initial fragmentStart("fragments_ch1/1dssa_init.tex") source('main.grouping.auto.R') library(lattice) library(Rssa) N <- 199 omega1 <- 0.1 omega2 <- 0.25 fragmentStop() ###end ###Fragment 1dssa_series fragmentStart("fragments_ch1/1dssa_series.tex") x <- exp(0.01 * (1:N)) + 2*cos(2 * pi * omega1 * (1:N)) + exp(0.009 * (1:N)) * cos(2 * pi * omega2 * (1:N)) + rnorm(n=N,sd=3) s <- ssa(x, L = 100) fragmentSkip(pdf("img_ch1/1dssa_vectors.pdf", paper = "special", width = 5, height = 2)) plot(s, type="vectors", layout = c(5, 2)) fragmentSkip(dev.off()) fragmentSkip(pdf("img_ch1/1dssa_vectors_pair.pdf", paper = "special", width = 5, height = 2)) plot(s, type="paired", layout = c(5, 2)) fragmentSkip(dev.off()) fragmentStop() ###end ###Fragment 1dssa_trend fragmentStart("fragments_ch1/1dssa_trend.tex") g_trend <- grouping.auto(s, grouping.method = 'pgram', freq.bins = list(0.01), threshold = 0.9) print(g_trend$F1) fragmentStop() ###end ###Fragment 1dssa_em_freq fragmentStart("fragments_ch1/1dssa_em_freq.tex") g_em_freq <- general.grouping.auto(s, grouping.method = "freq.1dssa", s_0 = 1, rho_0 = 0.9) print(g_em_freq) fragmentStop() ###end ###Fragment 1dssa_em_tau fragmentStart("fragments_ch1/1dssa_em_tau.tex") g_em_tau <- general.grouping.auto(s, grouping.method = "tau.1dssa", treshold = 0.01) print(g_em_tau$idx) fragmentStop() ###end ###Fragment 1dssa_recon fragmentStart("fragments_ch1/1dssa_rec.tex") r <- reconstruct(s, groups = list(T = g_trend, P = c(g_em_freq$I_1, g_em_freq$I_2))) fragmentSkip(pdf("img_ch1/1dssa_rec.pdf", paper = "special", width = 4, height = 3)) d <- data.frame(X=x, N=1:N, T=r$T, P=r$P) xyplot(T + P +X ~ N, data = d, type ='l', ylab = '', auto.key = list(points = FALSE, lines = TRUE)) fragmentSkip(dev.off()) d <- data.frame(X=x, N=1:N) fragmentSkip(pdf("img_ch1/1dssa_ser.pdf", paper = "special", width = 4, height = 3)) xyplot(X ~ N, data = d, type ='l') fragmentSkip(dev.off()) fragmentStop() ###end

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WikiMultipleMonths.R

allURLs <- NULL for (year in (2008:2014)) { for (month in (1:12)) { if ((year == 2014) && (month > 10)) { next } theURL <- "http://stats.grok.se/json/en/" theURL <- paste0(theURL,year) if (month < 10) { theURL <- paste0(theURL,"0") } theURL <- paste0(theURL,month) theURL <- paste0(theURL,"/Friday") allURLs <- c(allURLs, theURL) } }

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2021-09-14T12:20:29.791693

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14_QCmetrics.R

### Spot-check rm(list=ls()) library(GenomicRanges); library(rtracklayer); library(stringr); library(Biostrings); library(Rsamtools) library(SummarizedExperiment); library(recountNNLSdata); library(recountNNLS) url_table <- recount::recount_url unique_ids = unique(url_table$project) unique_ids = as.character(unique_ids[! unique_ids %in% c("TCGA", "SRP012682")]) # out = NULL overall_map = NULL for(unique_id in unique_ids){ message(which(unique_ids == unique_id)) out_dir = paste0("/dcl01/leek/data/ta_poc/recount_out/rse_new/", unique_id) out_file = paste0(out_dir, "/rse_tx.RData") load(out_file) pheno = colData(rse_tx) total_reads = pheno$reads_downloaded/(pheno$paired_end+1) mapped_reads = pheno$mapped_read_count/(pheno$paired_end+1) tx_reads = apply(assays(rse_tx)$fragments, 2, sum, na.rm=TRUE) time = pheno$biosample_submission_date info = cbind(pheno[,c(1:4, 6, 8:9)], time, pheno$rls, pheno$rls_group, total_reads, mapped_reads, tx_reads) overall_map = rbind(overall_map, info) } map_perc = overall_map[,12]/overall_map[,11] tx_perc = overall_map[,13]/overall_map[,12] num_fail_tx = by(tx_perc, overall_map$project, function(x) sum(x<0.5)) num_fail_map = by(map_perc, overall_map$project, function(x) sum(x<0.8)) num_sample = by(rep(1, length(map_perc)), overall_map$project, sum) cbind(num_fail_tx, num_fail_map, num_sample) quantile(tx_perc, seq(0, 1, by=0.05)) quantile(map_perc, seq(0, 1, by=0.05)) ### # SRP050272 - 83 samples, lncRNAs, some low map rates, high intergenic region counts # ERP001908 - 62 miRNAs of tumors # ERP001344 - 34 viral hepatitis agents # ERP004592 - 23 miRNAs Hungtingtons # SRP014675 - 43 miRNAs

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# path to the bandt-pompe functions bandt_pompe_path="../bandt_pompe" # path to the skinny-dip files skinnydip_path='../skinny-dip/code/skinny-dip' # path to datasets dataset_path='./data' # print the debug messages DEBUG=TRUE # to load the pre-computed features for a given dataset LOAD_PRECOMPUTED=TRUE #LOAD_PRECOMPUTED=FALSE # the seed to use # NOTE: it will be overwritten if passed as command line argument SEED=1 # the percentage of train dataset to split TRAIN_PCT=0.8 # if parallelism is enabled for parameter tunning DO_PARALLEL=TRUE # number of cores to use CORES_NUM=3 #CORES_NUM=-1 # to use: detectCores()-1

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Fanirisoa/dynamic_pricing

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Methode HN.R

rm(list=ls()) gc() library(compiler) enableJIT(1) enableJIT(3) ##################################################### ### Load both data.contract and data.ret ####### ##################################################### load("DataPrice2009.Rdata") ## load("Dataprice2010.Rdata") ##################################################### ### Source function to use ####### ##################################################### ##source("C:/Users/e0g411k03dt/Desktop/Estimation Paper 2 April 2016/Calibration HN/Calibration Price Linear/Calibration HN.r") source("C:/Users/e0g411k03dt/Desktop/Estimation Paper 2 April 2016/Calibration HN/Calibration Price Linear/Calibration HN Nogammastart.r") source("C:/Users/e0g411k03dt/Desktop/Estimation Paper 2 April 2016/Calibration HN/Calibration Price Linear/Matrixe RMSE HN.r") ##################################################### ### Parameters of the model ####### ##################################################### ### Initial parameter ## ## a0=para_h[1]; a1=para_h[2]; gama=para_h[3]; b1= para_h[4] ; lamda0= para_h[5] para_h<-c(1.180234e-12, 1.547729e-06, 4.550518e+02, 6.500111e-01,0.25764 ) para_h<-c(5.02e-6, 1.32e-6, 421.39, 0.589,0.5) para_h<-c(5.436223e-06, 1.334501e-06, 4.634877e+02, 6.302493e-01,0.42273) ### Solution para_h1<-c(6.593975e-06, 1.094715e-06, 4.634877e+02 , 6.302488e-01,0.812503) para_h1<-c(6.228100e-06, 1.069426e-06, 4.550518e+02, 6.499864e-01, 2.576463e-01) ##################################################### ### Volatility ####### ##################################################### ts.vol=h(para_h1,Data.ret) ts.plot(ts.vol, col = "steelblue", main = "IG Garch Model",xlab="2009",ylab="Volatility") grid() ############################################################ #### NLS estimation ## ############################################################ start.time <- Sys.time() NLSMSE=optim(par=para_h,fn=RMSE,Data.ret=Data.ret,Data.N=Data.N,method="Nelder-Mead",control = list(maxit = 900)) end.time <- Sys.time() time.taken <- end.time - start.time time.taken para_h1<- NLSMSE$par sigma(NLSMSE) sigma.hat(para_h1) ############################################################ #### RMSE ## ############################################################ start.time <- Sys.time() ValRMSE=RMSE(para_h1,Data.ret,Data.N) end.time <- Sys.time() time.taken <- end.time - start.time time.taken start.time <- Sys.time() MRMSE=MatriceRMSE(para_h1,Data.N) end.time <- Sys.time() time.taken <- end.time - start.time time.taken ############################################################ #### Hessian Matrix ## ############################################################ start.time <- Sys.time() hess = hessian(func=RMSE, x=para_h,Data.ret=Data.ret,Data.contract=Data.contract) end.time <- Sys.time() time.taken <- end.time - start.time time.taken hessc <- hessian(func=RMSE, x=para_h, "complex",Data.ret=Data.ret,Data.contract=Data.contract) all.equal(hess, hessc, tolerance = .Machine$double.eps) ############################################################ #### Vega ## ############################################################ start.time <- Sys.time() Vega1=Vega(Data.N, type="C") end.time <- Sys.time() time.taken <- end.time - start.time time.taken ##################################################### ### Volatility and Price ####### ##################################################### l=100 start.time <- Sys.time() P=Price_fft(para_h=para_h,Data.ret=Data.ret,Data.contract=Data.N[1:l,]) end.time <- Sys.time() time.taken <- end.time - start.time time.taken C=Data.N$C Ca=C[1:l] Pa=P[1:l] ts.plot(Pa,ts(Ca), col =c("steelblue","red"), main = "Valuation with IG Garch Model",xlab="2009",ylab="Prices") legend(175, 250, c("C(t)","C*(t)"), col =c("steelblue","red"), lty = c(1, 1)) ##################################################### ### Implicite volatility ####### ##################################################### Ip <- function(para_h,Data.ret,Data.contract) { T=Data.contract$T #### Time to maturity expressed in terms of years in terms of days S=Data.contract$S #### Prix du sous-jacent: Data.contract$S K=Data.contract$K #### Strike Prix d'exercice: data$strike r=Data.contract$r #### Interest rate Data.contract$r C=Data.contract$C #### Call price d=Data.contract$d #### Call dividende Ip <- rep(NA, length(C)) for (i in 1:length(C)){ Ip[i] = implied.vol(S[i], K[i], T[i], r[i], C[i],d[i], type="C") } return(Ip) } start.time <- Sys.time() Ipv=Ip(para_h1,Data.ret,Data.N) end.time <- Sys.time() time.taken <- end.time - start.time time.taken

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simulate_Exp.R

# Load packages library(Matrix) library(lbfgs) library(ggplot2) library(dplyr) library(RSpectra) library(aricode) library(kpodclustr) library(janitor) library(tidyr) library(purrr) library(grid) library(microbenchmark) library(foreach) library(doParallel) library(igraph) library(gridExtra) library(lemon) library(latex2exp) library(GreedySBTM) library(xtable) library(stargazer) ############################# # Results on simulated data # ############################# repSim_miss<-function(nb,K,nb_nodes,L,rho,a,b,r){ score<-matrix(NA,nrow=6,ncol=nb) rownames(score)<-c("sum_of_adj","sum_of_sq","k_pod","OLMF","imput","tot_obs_nodes") pi_L<-array(NA,c(K,K,L)) for(i in 1:nb){ # Generate a MLSBM (with balanced communities) alpha<-rep(1/K,K) Z = t(rmultinom(nb_nodes,1, alpha)) convZ<-convertZ(Z) for(j in 1:L){ pi_L[,,j]<-array(conMat(K,a,b,r)) } tensorAdj<-sampleMLSBM(nb_nodes,K,pi_L,convZ) # Generate missing nodes miss<-missMLSBM(tensorAdj,rho,with_na = TRUE) tensorAdj<-miss[[1]] tot_obs_nodes<-miss[[2]] obs_nodes_mat<-miss[[3]] score[6,i]<-sum(tot_obs_nodes) #Delete nodes that not have been observed at least one time tensorAdj_del<-delete_missing_nodes_tensor(tensorAdj,tot_obs_nodes) convZ_del<-delete_missing_nodes_Z(convZ,tot_obs_nodes) obs_nodes_mat_del<-obs_nodes_mat[tot_obs_nodes%>%as.logical(),] tensorAdj_del0<-tensorAdj_del%>%replace_na(0) # Apply different clustering methods and compute the score ## Sum of adjacency matrices sum_of_adjMat<-sum_of_adj(tensorAdj_del0) clust<-clust_on_mat_eig(sum_of_adjMat,K) score[1,i]<-NMI(clust,convZ_del) # Sum of squared adjacency matrices # sum_of_square<-sum_of_square_adj(tensorAdj_del0) # clust<-clust_on_mat_eig(sum_of_square,K) # score[2,i]<-NMI(clust,convZ_del) # kpod_clust tot_obs_nodes<-tot_obs_nodes%>%as.logical() final_ag<-final_aggr_miss(tensorAdj_del,K,obs_nodes_mat_del) clust<-kpod(final_ag,K)$cluster score[3,i]<-NMI(clust,convZ_del) # OLMF modified for missing nodes n_obs<-dim(tensorAdj_del0)[1] score[4,i]<-NMI(lmfo_miss(tensorAdj_del0,obs_nodes_mat_del,K),convZ_del) #Imputation method tensorAdj_in<-tensorAdj_del0 for(rep in 1:20){ Z_in<-convertClust(clust) tensorAdj_out<-impute_nodes(Z_in,tensorAdj_in,obs_nodes_mat_del,K) sum_of_adjn<-sum_of_adj(tensorAdj_out) clust<-clust_on_mat_eig(sum_of_adjn,K) tensorAdj_in<-tensorAdj_out } score[5,i]<-NMI(clust,convZ_del) } return(t(rowMeans(score))) } # test<-repSim_miss(1,3,600,2,0.8,0.18,0.19,0.5) ############################# # Variying number of layers ############################# seq_l<-seq(3,6,by=3) cl <- makeForkCluster(10) registerDoParallel(cl) result <- foreach(rho=1:10,.combine = rbind) %dopar% { lapply(seq_l,function(l){repSim_miss(1,3,1000,l,rho/10,0.18,0.19,0.7)%>%as.data.frame()%>%mutate(rho=rho/10,layer=l)}) } stopCluster(cl) registerDoSEQ() data<-array(NA,dim=c(0,7)) for(i in 1:length(result)){ data<-rbind(data,result[[i]]) } ############################### # Variying number of nodes ############################### # Experiment 2' : L=3, K= 3 fixed and n vary. The experiment is repeated for different values of rho. # a= 0.18 b=0.19 r=0.7 so there is no overlap seq_n<-seq(600,2600,by=200) cl <- makeForkCluster(10) registerDoParallel(cl) result2 <- foreach(rho=1:10,.combine = rbind) %dopar% { lapply(seq_n,function(n){repSim_miss(20,3,n,3,rho/10,0.18,0.19,0.7)%>%as.data.frame()%>%mutate(rho=rho/10,nodes=n)}) } stopCluster(cl) registerDoSEQ() data2<-array(NA,dim=c(0,8)) for(i in 1:length(result2)){ data2<-rbind(data2,result2[[i]]) } ################################# # Plots ################################# for(l in seq_l){ assign(paste("d", l, sep = ""),data%>%filter(layer==l)) pv<-ggplot(get(paste("d", l, sep = "")), aes(rho,label=tot_obs_nodes)) + geom_line(aes(y = sum_of_adj, colour = "sum_of_adj",linetype="sum_of_adj")) + geom_line(aes(y = k_pod, colour = "k_pod_clust",linetype = "k_pod_clust"))+ geom_line(aes(y = OLMF, colour = "OLMF",linetype="OLMF"))+ geom_line(aes(y = imput, colour = "Impute",linetype="Impute"))+ scale_color_manual("",values=c("deepskyblue2","black","orange","deepskyblue3"))+ scale_linetype_manual("",values=c("dotted","solid","longdash","solid"))+ labs(y="NMI",x=TeX("$\\rho$"),title=paste("L=",l,", K=3, n=1000",sep=""))+ theme(plot.margin = unit(c(1,1,1,1), "lines"),plot.title = element_text(hjust = 0.5, vjust=5)) + coord_cartesian(clip = "off") for(i in 1:10){ pv<-pv + annotation_custom( grob = textGrob(label = round(get(paste("d", l, sep = ""))$tot_obs_nodes[i]), hjust = 0, gp = gpar(cex = 0.6)), xmin = get(paste("d", l, sep = ""))$rho[i], # Vertical position of the textGrob xmax = get(paste("d", l, sep = ""))$rho[i], ymin = 1.05, # Note: The grobs are positioned outside the plot area ymax = 1.05) } assign(paste("p",l,sep=""),pv) } grid_arrange_shared_legend(p3,p6,p9,p12,nrow=2,ncol=2) ## Varying number of layer for a given rho seq_rho<-seq(0.1,1,by=0.1) for(r in seq_rho){ assign(paste("d", r, sep = ""),data%>%filter(rho==r)) pv<-ggplot(get(paste("d", r, sep = "")), aes(layer,label=tot_obs_nodes)) + geom_line(aes(y = sum_of_adj, colour = "sum_of_adj",linetype="sum_of_adj")) + geom_line(aes(y = k_pod, colour = "k_pod_clust",linetype="k_pod_clust"))+ geom_line(aes(y = OLMF, colour = "OLMF",linetype="OLMF"))+ geom_line(aes(y = imput, colour = "Impute",linetype="Impute"))+ scale_color_manual("",values=c("deepskyblue2","black","orange","deepskyblue3"))+ scale_linetype_manual("",values=c("dotted","solid","longdash","solid"))+ labs(y="NMI",x="L",title=TeX(sprintf("$\\rho = %1.1f$ K=3, n=1000",r)))+ theme(plot.margin = unit(c(1,1,1,1), "lines"),legend.title=element_blank(),plot.title = element_text(hjust = 0.5, vjust=5)) + coord_cartesian(ylim=c(0,1),clip = "off") for(i in 1:4){ pv<-pv + annotation_custom( grob = textGrob(label = round(get(paste("d", r, sep = ""))$tot_obs_nodes[i]), hjust = 0, gp = gpar(cex = 0.6)), xmin = get(paste("d", r, sep = ""))$layer[i], # Vertical position of the textGrob xmax = get(paste("d", r, sep = ""))$layer[i], ymin = 1.05, # Note: The grobs are positioned outside the plot area ymax = 1.05) } assign(paste("p",r,sep=""),pv) } grid_arrange_shared_legend(p0.4,p0.5,p0.6,p0.8,nrow=2,ncol=2) # Variying number of nodes for(r in seq_rho){ assign(paste("d2", r, sep = ""),data2%>%filter(rho==r)) pv<-ggplot(get(paste("d2", r, sep = "")), aes(nodes,label=tot_obs_nodes)) + geom_line(aes(y = sum_of_adj, colour = "sum_of_adj",linetype="sum_of_adj")) + geom_line(aes(y = k_pod, colour = "k_pod_clust",linetype = "k_pod_clust"))+ geom_line(aes(y = OLMF, colour = "OLMF",linetype="OLMF"))+ geom_line(aes(y = imput, colour = "Impute",linetype="Impute"))+ scale_color_manual("",values=c("deepskyblue2","black","orange","deepskyblue3"))+ scale_linetype_manual("",values=c("dotted","solid","longdash","solid"))+ labs(y="NMI",x="n",title=TeX(sprintf("$\\rho = %1.1f$ K=3, n=1000",r)))+ theme(plot.margin = unit(c(1,1,1,1), "lines"),legend.title=element_blank(),plot.title = element_text(hjust = 0.5, vjust=5)) + coord_cartesian(ylim=c(0,1),clip = "off") for(i in 1:10){ pv<-pv + annotation_custom( grob = textGrob(label = round(get(paste("d2", r, sep = ""))$tot_obs_nodes[i]), hjust = 0, gp = gpar(cex = 0.6)), xmin = get(paste("d2", r, sep = ""))$nodes[i], # Vertical position of the textGrob xmax =get(paste("d2", r, sep = ""))$nodes[i], ymin = 1.05, # Note: The grobs are positioned outside the plot area ymax = 1.05) } assign(paste("pp",r,sep=""),pv) } grid_arrange_shared_legend(pp0.4,pp0.5,pp0.6,pp0.8,nrow=2,ncol=2)

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GammaExample.R

#' GammaExample #' #' Using the \code{real estate} data, perform a regression using #' a box-cox transformation #' #' @return Nothing #' @export #' @importFrom tibble tibble #' @importFrom magrittr %>% #' @import stats #' #' @examples{ #' GammaExample() #' } GammaExample <- function() { real.estate.data <- AdvancedRegression::real_estate #rescaling variables and specifying reference categories price10K <- real.estate.data$price / 10000 sqftK <- real.estate.data$sqft / 1000 heating.rel <- relevel(real.estate.data$heating, ref = "none") AC.rel <- relevel(real.estate.data$AC, ref = "no") lotK <- real.estate.data$lot / 1000 #fitting gamma regression fitted.model <- glm(price10K ~ beds + baths + sqftK + heating.rel + AC.rel + lotK, data = real.estate.data, family = stats::Gamma(link = log)) summary(fitted.model) %>% print() #checking model fit intercept.only.model <- glm(price10K ~ 1, family = stats::Gamma(link = log)) deviance_pvalue(intercept.only.model, fitted.model, df = 7) #using fitted model for prediction predict_data <- tibble::tibble(beds = 4, baths = 2, sqftK = 1.68, heating.rel = "central", AC.rel = "no", lotK = 5) prediction <- 10000 * predict(fitted.model, type = "response", predict_data) print(predict_data) paste0('Prediction: ', prediction) %>% print() }

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message("Loading input data...") bcftools = "/frazer01/software/bcftools-1.9/bcftools" vcftools = "/frazer01/software/vcftools-0.1.14/bin/vcftools" rscript = "/frazer01/home/matteo/software/R-3.5.1/bin/Rscript" ipscore_vcf_input = "/frazer01/projects/reference_files/cellType_Invariant/IPSCORE_WGS.biallelic.b37.vcf.gz" gtex_vcf_input = "/frazer01/projects/GTEx_v7/decrypted/GenotypeFiles/phg000830.v1.GTEx_WGS.genotype-calls-vcf.c1/GTEx_Analysis_2016-01-15_v7_WholeGenomeSeq_635Ind_AllVar_QC_metrics.vcf.gz" # Constants qtl_distance = 1e6 # Distance threshold between each gene/peak and variants maf_threshold = 0.01 # MAF threshold for variants used for QTL analysis phenotype_min_value = 0.5 # Threshold to consider a gene/peak expressed phenotype_min_samples = 0.1 # Fraction of samples that have expression above phenotype_min_value # For QTL analysis, divided by assay: #vars0_rna = c("gt" , "population1", "age_sample", "sex", paste("PC", 1:10, sep = ""), "(1|wgs_id)", "(1|family_id)") # list of variants for LMM formula #vars1_rna = c("gt:population1", "gt:age_sample", "gt:population1:age_sample") # list of interaction terms to test vs LMM without interactions

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spreadsheet.R

# Came from RExcelML if(FALSE) { setClass("OOWorkbook", contains = "ZipArchiveEntry") setClass("OOWorksheet", representation(content = "XMLInternalDocument", name = "ZipArchiveEntry")) setClass("OOWorksheetFile", contains = "ZipArchiveEntry") setMethod("names", "OOWorkbook", function(x) { doc = xmlParse(x[["content.xml"]]) unlist(getNodeSet(doc, "//table:table/@table:name", "table")) }) #setMethod("[[", "Workbook" } read.ods = function(file, header = TRUE, simplify = TRUE, doc = xmlParse(zipArchive(file)[["content.xml"]]), stringsAsFactors = TRUE, tz='') { # defining package variable ROpenOfficeConversionTZ ROpenOfficeConversionTZ<<-tz tb = getNodeSet(doc, "//table:table", "table") ans = lapply(tb, read.odsSheet, header = header, stringsAsFactors = stringsAsFactors) n = sapply(ans, is.null) if(any(n)) { ans = ans[!n] tb = tb[!n] } if(simplify && length(ans) == 1) return(ans[[1]]) names(ans) = sapply(tb, xmlGetAttr, "name") ans } read.odsSheet = # # # We need to deal with blank rows and where cells are repeated. # In progress # # tb is the table:table node. # function(tb, header = TRUE, stringsAsFactors = TRUE) # really a header? { # Get num columns from the first row. numCols = xmlGetAttr(tb[[1]], "number-columns-repeated", 0, as.integer) # Get all rows which have a cell with a office:value entry. Otherwise # it is an empty row. rows = getNodeSet(tb, "./table:table-row[./table:table-cell[@office:value | @office:value-type]]", OpenOfficeNamespaces[c("office", "table")]) if(length(rows) == 0) return(NULL) rowNames = FALSE varNames = character() if(header) { varNames = xmlSApply(rows[[1]], getCellValue) # if(length(varNames) && is.na(varNames[1])) { # rowNames = TRUE # varNames = varNames[-1] # } rows = rows[-1] } # This gets the types by row and this might be ragged, i.e. not by column # Now changed to expand the missing columns so won't be ragged. types = t(sapply(rows, function(x) unlist(xmlApply(x, getCellType)))) # Now get all the cells, expanding the elements that are missing so all the # the result will be a matrix ans = t(sapply(rows, function(x) { unlist(xmlApply(x, getCellValue), recursive = FALSE) })) realCols = apply(ans, 2, function(x) any(!is.na(x))) if(!realCols[1]) { rowNames = FALSE } if(header) { if(is.na(varNames[1]) && realCols[1]) { rowNames = TRUE } } ans = ans[ , realCols ] types = types[, realCols] if(!is.matrix(ans)) { tp = getColType(types) return(if(is.function(tp)) tp(ans) else as(ans, tp)) } if(length(varNames)) varNames = varNames[realCols] # This seems to go along rows, not columns tmp = lapply(seq(length = ncol(ans)), function(i) { if(all(is.na(types[,i]))) return(NULL) tp = unique(unlist(types[,i])) if(is.null(tp)) return(NULL) colType = getColType(tp) if(is.function(colType)) colType(ans[,i]) else as(unlist(ans[,i]), colType) }) tmp = tmp[!sapply(tmp, is.null)] ans = as.data.frame(tmp, stringsAsFactors = stringsAsFactors) if(rowNames) { rownames(ans) = ans[,1] ans = ans[,-1] varNames = varNames[-1] } structure(ans, names = if(length(varNames)) as.character(varNames) else paste("V", seq(length = length(tmp)), sep = "")) } getCellType = function(node) { n = xmlGetAttr(node, "number-columns-repeated", 0) txt = xmlGetAttr(node, "value-type", NA) if(n > 0) rep(txt, n) else txt } getCellValue = # # Get the cell value or a collection of NAs if this is a number-columns-repeated cell. # function(node) { n = xmlGetAttr(node, "number-columns-repeated", 0) txt = xmlGetAttr(node, "value", NA) if(is.na(txt)) txt = xmlGetAttr(node, "date-value", NA) if(is.na(txt) ) txt = xmlGetAttr(node, "time-value", NA) if(is.na(txt)) txt= { if(xmlSize(node)) xmlValue(node) else as.character(NA) } if(n > 0) rep(txt, n) else txt } # Added converion methods for time and datetime # As there is no R time class time values will get # converted to POSIXct of 1970-01-01 (S.Holzheu) Rtypes = c("string" = "character", "float" = "numeric", "time" = function(x) { as.POSIXct(paste('1970-01-01',x), format="%Y-%m-%d PT%HH%MM%SS",tz=ROpenOfficeConversionTZ) }, "date" = function(x) { if(grepl('T',x[1])) as.POSIXct(x, format="%Y-%m-%dT%H:%M:%S",tz=ROpenOfficeConversionTZ) else as.Date(x,format="%Y-%m-%d") }, "percentage" = "numeric") getColType = function(types) { types = types[!is.na(types)] if(length(types) == 1) Rtypes[[types]] else { i = match(types, names(Rtypes)) Rtypes[[min(i)]] } }

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#' Install pip. #' @return Nothing #' @examples #' \dontrun{ #' install_pip() #' } #' @author Richèl J.C. Bilderbeek #' @export install_pip <- function() { script_filename <- tempfile() utils::download.file( url = "https://bootstrap.pypa.io/get-pip.py", destfile = script_filename, quiet = TRUE ) system2( reticulate::py_config()$python, args = c(script_filename, "--user"), stdout = FALSE ) }

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synth_diff.R

#' Run synthetic diff-diff #' #' @template param_mdd_dat #' @param add_weights return the weights? #' #' @examples #' if(require(synthdid)){ #' data('california_prop99') #' mdd_california_prop99 <- mdd_data_format(california_prop99, #' y_var = "PacksPerCapita",time.index = "Year", #' treat = "treated", unit.index = "State") #' mdd_synthdid(mdd_dat=mdd_california_prop99) #' } #' @export mdd_synthdid <- function(mdd_dat, add_weights=FALSE){ if(!requireNamespace("synthdid", quietly = TRUE)) stop("Please install `synthdid`") ## prep data mdd_dat_slot <- intrnl_mdd_get_mdd_slot(mdd_dat) mdd_vars <- mdd_dat_slot$var_names tr_quo <- rlang::sym(mdd_vars$treat) time_quo <- rlang::sym(mdd_vars$time.index) unit_quo <- rlang::sym(mdd_vars$unit.index) setup <- synthdid::panel.matrices(as.data.frame(mdd_dat) %>% mutate({{tr_quo}} := as.integer({{tr_quo}})), unit=mdd_vars$unit.index, time = mdd_vars$time.index, outcome = mdd_vars$y_var, treatment = mdd_vars$treat) ## estimate res <- synthdid::synthdid_estimate(setup$Y, setup$N0, setup$T0) ## re-estimate? if(add_weights){ W <- attributes(res)$weights W_time <- W$lambda W_time_full <- c(W_time, rep(1, length(mdd_dat_slot$treated_periods))) W_time_full_df <- tibble({{time_quo}} := mdd_dat_slot$periods, weight_time=W_time_full) W_units <- W$omega W_units_full <- c(W_units, rep(1, mdd_dat_slot$n_units-length(W_units))) W_units_full_df <- tibble({{unit_quo}} := rownames(setup$Y), weight_unit=W_units_full) ## add weights to data mdd_dat_full <- mdd_dat %>% left_join(W_time_full_df, by = mdd_vars$time.index) %>% left_join(W_units_full_df, by = mdd_vars$unit.index) %>% mutate(weights= .data$weight_unit * .data$weight_time) attr(mdd_dat_full, "mdd_dat_slot") <- mdd_dat_slot ## attributes are lost by mutate!! ## re-estimate # res <- mdd_DD_simple(mdd_dat_full, weights = mdd_dat_full$weights) attr(res, "mdd_data") <- mdd_dat_full } attr(res, "mdd_dat_slot") <- mdd_dat_slot # class(res) <- c(class(res), "mdd_synthdid") ## see issue https://github.com/synth-inference/synthdid/issues/100 res } #' @export coef.synthdid_estimate <- function(object, ...) as.double(object) ## tidy method, see https://www.tidymodels.org/learn/develop/broom/ #' @importFrom generics tidy #' @export generics::tidy #' Tidy a 'synthdid_estimate' object #' @param x output from \code{\link{mdd_synthdid}} or synthdid::synthdid_estimate #' @param conf.int,conf.level,... as standard #' @param method method used in synthdid::vcov.synthdid_estimate #' @export tidy.synthdid_estimate <- function(x, conf.int=FALSE, conf.level=0.95, method='jackknife', ...){ term <- attr(x, "mdd_dat_slot")$var_names$treat if(is.null(term)) term <- NA_character_ coef <- as.double(x) # se = sqrt(synthdid::vcov.synthdid_estimate(x, method=method)) se = sqrt(stats::vcov(x, method=method)) res <- data.frame(term = term, estimate = coef, std.error =se, statistic =coef/se, p.value =2 * stats::pnorm(abs(coef/se), lower.tail = FALSE)) if(conf.int) { left_Q <- (1-conf.level)/2 quants <- stats::qnorm(c(left_Q, 1-left_Q)) CI_LH <- rep(coef,2) + quants * se[[1]] res <- cbind(res, data.frame(conf.low = CI_LH[1], conf.high =CI_LH[2])) } res } if(FALSE){ if(require(synthdid)){ # data('california_prop99') n_distinct(california_prop99$State) # 39 states n_distinct(filter(california_prop99, treated==1)$State) # 1 treated state n_distinct(california_prop99$Year) # 31 years n_distinct(filter(california_prop99, treated==1)$Year) # 12 treated years, 19 untreated mdd_california_prop99 <- mdd_data_format(california_prop99, y_var = "PacksPerCapita", time.index = "Year", treat = "treated", unit.index = "State") mdd_california_prop99 res <- mdd_synthdid(mdd_dat=mdd_california_prop99) res coef(res) tidy(res) ## General dat_sim_N1 <- mdd_data_format(sim_dat_common(N = 1000, timing_treatment = 5:10, perc_treat=0.001)) dat_sim <- mdd_data_format(sim_dat_common(N = 1000, timing_treatment = 5:10, perc_treat=0.1)) dat_sim # coef(mdd_DD_simple(dat_sim)) res_gen <- mdd_synthdid(dat_sim) coef(res_gen) vcov(res_gen, method = "jackknife") tidy(res_gen) tidy(res_gen, conf.int = TRUE) } }

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account_account_timezone.R

# Copyright (c) 2018, Brandseye PTY (LTD) # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #' Get an account's timezone #' #' Returns a vector of timezones for the given accounts. #' An account's data is reported in a particular timezone, set for that account. #' All dates provided in filters are assumed to be given in that timezone. #' #' @param account An account object, or list of account objects. #' #' @return A character vector of timezone that account data is reported in. #' @export #' @author Constance Neeser #' #' @examples #' #' account("TEST01AA") %>% #' account_timezone() #' #' account("TEST01AA", "TEST02AA") %>% #' account_timezone() account_timezone <- function(account) { UseMethod("account_timezone") } #' @export account_timezone.brandseyer2.account <- function(account) { account$timezone } #' @export account_timezone.list <- function(account) { map_chr(account, account_timezone) }

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GaussianProbability.R

GaussianProbability <- function(x, mean, sd) { (1/(2*pi)) * exp(-1 * ((x - mean)^2)/(2 * sd^2)) }

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attribute_scrap.R

# scraping attributes from HTML elements #' Scraping attributes from HTML elements #' #' @description This function is used to scrape attributes from HTML elements #' #' @param link the link of the web page to scrape #' @param node the HTML element to consider #' @param attr the attribute to scrape #' @param askRobot logical. Should the function ask the robots.txt if we're allowed or not to scrape the web page ? Default is FALSE. #' @return a character vector. #' @examples #' \donttest{ #' # Extracting the web links within the World Bank research and publications page #' #' link <- "https://ropensci.org/" #' #' # scraping the class attributes' names from all the anchor #' #' attribute_scrap(link = link, node = "a", attr = "class") #' } #' #' @export #' @importFrom rvest html_nodes html_text html_attr %>% #' @importFrom xml2 read_html #' @importFrom crayon green #' @importFrom crayon bgRed #' @importFrom robotstxt paths_allowed #' @importFrom curl has_internet attribute_scrap <- function(link, node, attr, askRobot = FALSE) { if (any(missing(link), missing(node), missing(attr))) { stop("'link', 'node' and 'attr' are a mandatory argument") } if (any(!is.character(link), !is.character(node), !is.character(attr))) { stop("'link', 'node' and 'attr' must be provided as character vectors") } ################################### Ask Robot Part ################################################# if (askRobot) { if (paths_allowed(link) == TRUE) { message(green("the robot.txt doesn't prohibit scraping this web page")) } else { message(bgRed( "WARNING: the robot.txt doesn't allow scraping this web page" )) } } ######################################################################################################## tryCatch( expr = { links <- lapply( link, function(url) { url %>% read_html() %>% html_nodes(node) %>% html_attr(attr) } ) return(unlist(links)) }, error = function(cond) { if (!has_internet()) { message("Please check your internet connexion: ") message(cond) return(NA) } else if (grepl("current working directory", cond) || grepl("HTTP error 404", cond)) { message(paste0("The URL doesn't seem to be a valid one: ", link)) message(paste0("Here the original error message: ", cond)) return(NA) } else { message("Undefined Error: ", cond) return(NA) } } ) }

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desc.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/util.R \name{desc} \alias{desc} \title{Arrange specified column in descending order} \usage{ desc(var) } \arguments{ \item{var}{Variable to arrange in descending order} } \description{ Arrange specified column in descending order } \examples{ \donttest{ dat <- Multiplyr (x=1:100, cl=2) dat \%>\% arrange(desc(x)) dat \%>\% shutdown() } } \seealso{ \code{\link{arrange}} }

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plan_prep_trendchanges.R

TrendChanges_prep1 <- function(input_data, input_param){ print("Start brocken stick function") analysis_3a = BrokenStick(input_data, input_param) out3a = { datPath = file.path(createPaths(), 'RData', 'TrendChanges.RData') save(analysis_3a, file = datPath) } print("completed brocken stick function") return(analysis_3a) } TrendChanges_prep2 <- function(input_data, input_param){ print("start brockenstick null function") analysis_3b = BrokenStick_null(input_data, input_param) out_3b = { datPath = file.path(createPaths(), 'RData', 'BS_results_plusNull_complete.RData') save(analysis_3b, file = datPath) } print("completed brockenstick null function") return(analysis_3b) }

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class_fuzzycluster.R

#' Fuzzy Result #' @import methods #' @name fuzzycluster-class #' @rdname fuzzycluster-class #' @slot centroid centroid matrix #' @slot distance distance matrix #' @slot func.obj function objective #' @slot call.func called function #' @slot fuzzyfier fuzzyness parameter #' @slot method.fuzzy method of fuzzy clustering used #' @slot member membership matrix #' @slot hard.label hard.label #' @exportClass fuzzycluster #' @include class_membership.R setClass("fuzzycluster", representation= representation(centroid="matrix", distance="matrix", func.obj="numeric", call.func="character", fuzzyfier="numeric", method.fuzzy="character"), contains = "membership" )

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train.R

######################################################### ### Train a classification model with training images ### ######################################################### ### Author: Group 2 ### Project 3 ### ADS Fall 2016 train <- function(feature.adv, feature.baseline, label_train){ ### Train a Gradient Boosting Model (GBM) using processed features from training images ### Input: ### - processed features in Feature_eval.RData ### - class labels for training images ### Output: two model object ### load libraries library("gbm") library("e1071") ### change names X.a <- t(feature.adv) X.b <- t(feature.baseline) y <- label_train ###################### ### Baseline Model ### ### Train with gradient boosting model using feature.baseline ### Model selection with cross-validation source("./final version/cross_validation.R") source("./final version/train_gbm.R") source("./final version/test_gbm.R") # Choosing between different values of depth and nm for GBM depth_values <- seq(3, 9, 2) nm_values <- c(5, 10, 20) err.cv <- matrix(NA, length(depth_values), length(nm_values)) K <- 5 # number of CV folds #we took a day to tune here for(i in 1:length(depth_values)){ cat("i=", i, "\n") d <- depth_values[i] for (j in 1:length(nm_values)){ cat("j=", j, "\n") nm <- nm_values[j] par <- list(depth=d, n.minobsinnode=nm) err.cv[i, j] <- cv.function(X.b, y, par, K, train_gbm, test_gbm) } } p.min <- which(err.cv == min(err.cv), arr.ind = TRUE) par.b.best <- list(depth = depth_values[p.min[1]], n.minobsinnode = nm_values[p.min[2]]) tm_train_gbm <- system.time(gbm_train <- train_gbm(X.b, y, par.b.best)) ##################### ### Advance Model ### ### Train with support vector machine using feature.adv ### Model selection with cross-validation source("./final version/train_svm.R") source("./final version/test_svm.R") # Choosing between different values of cost C for Linear SVM C <- c(1, 10, 100, 500, 1000) err_cv_svm_c <- rep(NA, length(C)) K <- 5 # number of CV folds for(k in 1:length(C)){ cat("k=", k, "\n") par <- list(kernel = "linear", cost = C[k]) err_cv_svm_c[k] <- cv.function(X.a, y, par, K, train_svm, test_svm) } # Choose the best parameter value C_best <- C[which.min(err_cv_svm_c)] # Choosing between different values of gamma G for RBF SVM G <- c(0.01, 0.001, 0.0001, 0.00001) err_cv_svm_g <- rep(NA, length(G)) K <- 5 # number of CV folds for(k in 1:length(G)){ cat("k=", k, "\n") par <- list(kernel = "radial", cost = C_best, gamma = G[k]) err_cv_svm_g[k] <- cv.function(X.a, y, par, K, train_svm, test_svm) } # Choose the best parameter value G_best <- G[which.min(err_cv_svm_g)] # Two best parameters par.a.best <- list(kernel = "radial", cost = C_best, gamma = G_best) tm_train_svm <- system.time(svm_train <- train_svm(X.a, y, par.a.best)) ################################# #return two trained model objects return(ba.train = list(baseline = gbm_train, advance = svm_train)) }

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cachematrix.R

## This pair of functions cache the inverse of a matrix, so that if the ## inverse is needed in subsequent computations, it does not need to be ## recomputed ## This creates a special matrix, which is really a list which ## 1) sets the value of the matrix ## 2) gets the value of the matrix ## 3) sets the value of the inverse ## 4) gets the value of the inverse makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv<<- NULL } get <- function() x setinverse <- function(inverse) inv <<- inverse getinverse <- function() inv list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## This functions looks to see if the inverse is cached, if so it ## returns the cached value, otherwise it computes the inverse and ## caches it. cacheSolve <- function(x, ...) { inv <- x$getinverse() if(!is.null(inv)) { message("getting cached value for the inverse") return(inv) ##function terminates } matrix <- x$get() inverse <- solve(matrix) x$setinverse(inverse) inverse }

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/man/compare_title_paper.Rd

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jamielatham15/bibliographica

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refs/heads/master

2021-01-23T01:51:17.343584

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compare_title_paper.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compare_title_paper.R \name{compare_title_paper} \alias{compare_title_paper} \title{Compare Title Count and Paper Consumption} \usage{ compare_title_paper(x, field, selected = NULL, plot.mode = "text") } \arguments{ \item{x}{data frame} \item{field}{Field to analyze} \item{selected}{Limit the analysis on selected entries} \item{plot.mode}{"text" or "point"} } \value{ List: \itemize{ \item{plot}{ggplot object} \item{table}{summary table} } } \description{ Compare title count and paper consumption for selected field. } \examples{ \dontrun{compare_title_paper(df, "author")} } \author{ Leo Lahti \email{leo.lahti@iki.fi} } \references{ See citation("bibliographica") } \keyword{utilities}

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/TempModelFunctions.R

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DCBraun/Temperature-Modelling

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refs/heads/master

2020-04-09T18:37:11.255255

2015-02-06T23:14:16

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TempModelFunctions.R

############################ # TempModelFunctions.R # Project: TemperatureModelling # File for the functions that appear in the temperature modelling files # Not all of these functions are currently used in the TempModel.r code # Some were used and have since been removed, others were created but never used. # Created January 29, 2015 # A Putt ############################# ########## HarmFunc ########## # Create a function for getting the harmonics # Data=the data to be modelled # Period is 365 HarmFunc <- function(data) { cosine <- cos(2*pi*data$dateindex/365) sine <- sin(2*pi*data$dateindex/365) cosine2 <- cos(2*pi*data$dateindex*2/365) sine2 <- sin(2*pi*data$dateindex*2/365) cosine3 <- cos(2*pi*data$dateindex*3/365) sine3 <- sin(2*pi*data$dateindex*3/365) return(list(cosine=cosine,sine=sine,cosine2=cosine2,sine2=sine2,cosine3=cosine3,sine3=sine3)) } ########## LmPlotFunc ########## # Create a function for plotting the linear model results (only shows one fit, but easily modified) # data=the data used for the lm; yvals=the variable modelled against the index; lmdata=the linear model ylabT <- expression(paste("Temperature (",degree,"C)")) ylabD <- paste("Discharge m3/s") LmPlotFunc <- function(data,yvals,lmdata,yLab,title) { windows() par(mfrow=c(1,1)) par(oma = rep(2, 4)) par(mar = c(4, 5, 1, 1)) plot(data$dateyear, yvals, type = "l", xlab = "Time (Days)", ylab = yLab, cex.lab = 1.6, lwd = 2, col = "darkgrey") mtext(side=3, line=1.5, cex=1.6, sprintf("Step 1: Harmonic Model Fits: %s",title)) lines(data$dateyear, predict(lmdata,newdata=data.frame(dateindex=data$dateindex)), lwd = 3, lty = 5) } ########## Model Diagnostics ########## # Create a function to test model fit # The stationarity test would probably be best done with plots ModelDiagnostics <- function(data,lmmodel,title) { windows() Stationarity <- Box.test(residuals(lmmodel),lag=5) # low p value indicates stationarity par(mfrow=c(2,3)) par(oma = rep(2, 4)) Predicted <- predict(lmmodel) Residuals <- data$watertemp-Predicted PartialAutocorrelation <- acf(Residuals,type="partial",na.action=na.pass) # Most of my models have significant autocorrelation at 1 SerialAutocorrelation <- acf(Residuals,na.action=na.pass) # Most of my models have significant autocorrelation at 1 ResidPlot <- plot(Residuals,Predicted) NormalQQPlot <- qqnorm(Residuals) Hist <- hist(Residuals) mtext(side=3, title, outer=TRUE) FuncOut <- list(Stationarity=Stationarity,SerialAutocorrelation=SerialAutocorrelation) return(FuncOut) } ########## tslag ########## # Create a function that outputs a lagged variable tslag <- function(var, lagNum=1) { n <- length(var) c(rep(NA,lagNum),var)[1:n] } ########## NormalTest ########## # Create a function to test for normality in a variable NormalTest <- function(variable,title) { windows() #pval <- shapiro.test(variable)$p.value #print(sprintf("Shapiro test p-value: %s", pval)) #print("Reject null hypothesis of normality if p <= 0.1") par(mfrow=c(1,2)) par(oma = rep(2, 4)) hist(variable) qqnorm(variable) mtext(side=3,title,outer=TRUE) }

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/plot4.R

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MariaMontesdeOca/ExData_PA1

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refs/heads/master

2020-06-04T16:52:02.780131

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plot4.R

#Read the data into R data<-read.table("household_power_consumption.txt",sep=";",header=TRUE,colClasses=c(rep("character",2),rep("numeric",7)),na="?") #Convierte Date en formato fecha data$Date<-as.Date(data$Date,"%d/%m/%Y") #Select only the dates that we're gonna plot refineddata<-subset(data,Date=="2007-02-02"|Date=="2007-02-01") #Creating the plot4.png file ##Creating the file and setting the format of the graph png(file="plot4.png",width=480,height=480,units="px",bg="white") par(mfrow=c(2,2),mar=c(5,4,2,1)) ## Creating plot 4.1 (plot2 modifiying ylab) plot(refineddata$Global_active_power,type="l",ylab="Global Active Power",xaxt="n",xlab="") axis(1,at=c(0,1500,nrow(refineddata)),labels=c("Thu","Fri","Sat")) ## Creating plot 4.2 plot(refineddata$Voltage,type="l",ylab="Voltage",xlab="datetime",xaxt="n") axis(1,at=c(0,1500,nrow(refineddata)),labels=c("Thu","Fri","Sat")) ## Creating plot 4.3 (plot3 modifiying bty="n") plot(refineddata$Sub_metering_1,type="l",ylab="Energy sub metering",xaxt="n", xlab="") lines(refineddata$Sub_metering_2,col="red") lines(refineddata$Sub_metering_3,col="blue") axis(1,at=c(0,1500,nrow(refineddata)),labels=c("Thu","Fri","Sat")) legend("topright",bty="n",lty=1,lwd=1,col=c("black","red","blue"),legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) ##Creating plot 4.4 plot(refineddata$Global_reactive_power,ylab="Global_reactive_power",type="l",xlab="datetime",xaxt="n") axis(1,at=c(0,1500,nrow(refineddata)),labels=c("Thu","Fri","Sat")) ##Closing the .png file dev.off()

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/cryptoJNS/man/affineCipher.Rd

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JamesSolum/Codes-and-Encryption

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refs/heads/master

2020-05-29T21:04:36.541728

2017-02-21T00:09:00

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affineCipher.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/affineCipher.R \name{affineCipher} \alias{affineCipher} \title{Encrypt a string using a shift cipher} \usage{ affineCipher(plainText, stretch, shift) } \arguments{ \item{plainText}{A string of lowercase letters} \item{stretch}{An integer used to stretch the function.} \item{shift}{An integer (mod 26) used to shift.} } \value{ An encrypted (or decrypted) string, where each letter has been shifted by \code{shift} and stretched by \code{stretch} places. } \description{ Encrypt a string using a shift cipher } \examples{ plainText <- "dog" cipherText <- affineCipher(plainText,3,7) print(cipherText) You cannot decrypt a cipherText using the encryptAffineCipher function }

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/man/merge.Rd

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HaidYi/DASC

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refs/heads/master

2021-01-18T17:50:31.758972

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merge.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DASC.R \name{merge} \alias{merge} \title{Combine two trees into one} \usage{ merge(x, y, X) } \arguments{ \item{x}{the index of the node} \item{y}{the index of the node} \item{X}{the saved vector with the information of the parent of every node} } \value{ \code{X} A updated X vector with updates on father of every node } \description{ Combine two trees into one } \details{ During the traversal of the graph matrix, merge function joins two disjoint sets into a single subset. It is a union step of Disjoint-set algorithm by Bernard A. Galler and Michael J. Fischer. For further details, please refer to: \url{https://en.wikipedia.org/wiki/Disjoint-set_data_structure} } \author{ Haidong Yi, Ayush T. Raman }

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/database/data/2016/player-stats/wk1-12/16_playerJoin.R

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designer-citrusbits/Fantasy-Football-Database

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refs/heads/master

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16_playerJoin.R

# File: 16_playerJoin.R # Description: combines multiple player csv's into one # Date: 1 December 2016 # Author: Mark Eidsaune library("RCurl") library("plyr") library("dplyr") durl <- "https://raw.githubusercontent.com/edavis25/Fantasy-Football-Database/master/database/data/2016/player-stats/wk1-12/11-29-2016-defense.csv" defense <- read.csv(text = getURL(durl)) names(defense) <- c("Name", "Tm", "DefInt","DefIntYds", "DefIntTd", "DefIntLng", "DefSk", "DefTkl", "DefAst", "DefFR", "DefFRYrds", "DefFRTd", "DefFF", "URL") kurl <- "https://raw.githubusercontent.com/edavis25/Fantasy-Football-Database/master/database/data/2016/player-stats/wk1-12/11-29-2016-kicking.csv" kicks <- read.csv(text = getURL(kurl)) # Remove punting stats kicks <- kicks[, c(1,2,3,4,5,6,11)] names(kicks) <- c("Name", "Tm", "XPMade", "XPAtt", "FGMade", "FGAtt", "URL") ourl <- "https://raw.githubusercontent.com/edavis25/Fantasy-Football-Database/master/database/data/2016/player-stats/wk1-12/11-29-2016-offense.csv" offense <- read.csv(text = getURL(ourl)) names(offense) <- c("Name", "Tm", "PassCmp", "PassAtt", "PassYds", "PassTD", "Interceptions", "SkTaken", "SkYds", "PassLng", "QbRating", "RushAtt", "RushYds", "RushTd", "RushLng", "RecTgt", "Receptions", "RecYds", "RecTd", "RecLng", "Fmb", "FL", "URL") rurl <- "https://raw.githubusercontent.com/edavis25/Fantasy-Football-Database/master/database/data/2016/player-stats/wk1-12/11-29-2016-returns.csv" returns <- read.csv(text = getURL(rurl)) names(returns) <- c("Name", "Tm", "KickRet", "KickRetYds", "KickYdsRet", "KickRetTD", "KickRetLng", "PuntRet", "PuntRetYds","PuntYdsReturn", "PuntRetTd", "PuntRetLng", "URL") allStats <- rbind.fill(defense, kicks, offense, returns) allStats[is.na(allStats)] <- 0 allStats <- allStats %>% select(Name, URL, PassCmp, PassAtt, PassYds, PassTD, Interceptions, SkTaken, SkYds, PassLng, QbRating, RushAtt, RushYds, RushTd, RushLng, RecTgt, Receptions, RecYds, RecTd, RecLng, Fmb, FL, DefInt, DefInt, DefIntYds, DefIntTd, DefIntLng, DefSk, DefTkl, DefAst, DefFR, DefFRYrds, DefFRTd, DefFF, KickRet, KickRetYds, KickYdsRet, KickRetTD, KickRetLng, PuntRet, PuntRetYds, PuntYdsReturn, PuntRetTd, PuntRetLng, XPMade, XPAtt, FGMade, FGAtt)

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/inversion/deprecated/inversion_ps.multiPFT.US-WCr.multivariate.prior.R

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serbinsh/edr-da

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refs/heads/multi-pft

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inversion_ps.multiPFT.US-WCr.multivariate.prior.R

#--------------------------------------------------------------------------------------------------# # # # S. Serbin & A. Shiklomanov #--------------------------------------------------------------------------------------------------# #---------------- Close all devices and delete all variables. -------------------------------------# rm(list=ls(all=TRUE)) # clear workspace graphics.off() # close any open graphics closeAllConnections() # close any open connections to files dlm <- .Platform$file.sep # <--- What is the platform specific delimiter? ## Load functions source("common.R") library(mvtnorm) ## Load priors load(file = normalizePath('priors/stan_priors_sun.RData')) # for sun exposed leaves only priors <- priors_sun$means PEcAn.utils::logger.info(" *** Multi PFT *** Running with sun exposed priors only") edr.exe.name <- 'ed_2.1' #load(file = normalizePath('priors/prior_all.RData')) # based on leaves from sun/shaded positions #priors <- priors_all$means #PEcAn.utils::logger.info(" *** Single PFT *** Running with priors using all leaves (sun and shade)") #--------------------------------------------------------------------------------------------------# #--------------------------------------------------------------------------------------------------# ## Set user email address email_add <- "sserbin@bnl.gov" ## Setup tag dttag <- strftime(Sys.time(), "%Y%m%d_%H%M%S") ## Define PFT and canopy structure #pft <- c("temperate.Late_Hardwood","temperate.North_Mid_Hardwood") pft <- list("temperate.Early_Hardwood","temperate.North_Mid_Hardwood","temperate.Late_Hardwood") dens <- 0.015 dbh <- 20 # 20, 30 or 40 lai <- getvar("LAI_CO", dbh, pft) num.cohorts <- 3 multi.pft <- "EMLH" data_dir <- normalizePath(paste0('../run-ed/',num.cohorts,'cohort/dens',dens,'/dbh',dbh,'/',multi.pft)) paths <- list(ed2in = file.path(data_dir, 'ED2IN'), history = file.path(data_dir, 'outputs')) #--------------------------------------------------------------------------------------------------# ## Setup PROSPECT for psuedo data prospect_ver <- 5 pp <- list() pp[["temperate.Early_Hardwood"]] <- c("N" = 1.4, "Cab" = 30, "Car" = 8, "Cw" = 0.01, "Cm" = 0.01) pp[["temperate.Late_Hardwood"]] <- c("N" = 1.95, "Cab" = 65, "Car" = 8, "Cw" = 0.01, "Cm" = 0.01) pp[["temperate.North_Mid_Hardwood"]] <- c("N" = 1.8, "Cab" = 45, "Car" = 8, "Cw" = 0.01, "Cm" = 0.01) spectra_list <- list() spectra_list[["temperate.Early_Hardwood"]] <- prospect(pp$temperate.Early_Hardwood, prospect_ver, TRUE) spectra_list[["temperate.Late_Hardwood"]] <- prospect(pp$temperate.Late_Hardwood, prospect_ver, TRUE) spectra_list[["temperate.North_Mid_Hardwood"]] <- prospect(pp$temperate.North_Mid_Hardwood, prospect_ver, TRUE) ## Setup EDR wavelengths and run date par.wl = 400:2499 nir.wl = 2500 datetime <- ISOdate(2004, 07, 01, 16, 00, 00) #--------------------------------------------------------------------------------------------------# ## Setup output main_out <- paste("PDA", format(Sys.time(), format="%Y%m%d_%H%M%S"), sep = "_") if (! file.exists(main_out)) dir.create(main_out,recursive=TRUE) PEcAn.utils::logger.info(paste0("Running inversion in dir: ",main_out)) #--------------------------------------------------------------------------------------------------# ## Output directory function outdir_path <- function(runID) { #paste("inversion_prior", dttag, runID, sep = ".") paste0(main_out,"/inversion_prior.", dttag, ".",runID) } #--------------------------------------------------------------------------------------------------# #--------------------------------------------------------------------------------------------------# # Setup the output directories run_first <- function(inputs) { outdir <- outdir_path(inputs$runID) dir.create(outdir, showWarnings = FALSE) try_link <- link_ed(outdir) trait.values <- list() for (i in seq_along(pft)) { trait.values[[pft[[i]]]] <- list() } albedo <- EDR(paths = paths, spectra_list = spectra_list, par.wl = par.wl, nir.wl = nir.wl, datetime = datetime, trait.values = trait.values, edr.exe.name = edr.exe.name, output.path = outdir) return(albedo) } # Create initial output directory first_run <- run_first(list(runID = 0)) #--------------------------------------------------------------------------------------------------# print(head(first_run)) print(tail(first_run)) print(range(first_run)) extract_params <- function(params) { list(prospect.params = list('temperate.Early_Hardwood' = params[1:5], 'temperate.North_Mid_Hardwood' = params[6:10], 'temperate.Late_Hardwood' = params[11:15]), trait.values = list('temperate.Early_Hardwood' = list('orient_factor' = params[16], 'clumping_factor' = params[17]), 'temperate.North_Mid_Hardwood' = list('orient_factor' = params[18], 'clumping_factor' = params[19]), 'temperate.Late_Hardwood' = list('orient_factor' = params[20], 'clumping_factor' = params[21]) ) ) } invert_model <- function(param, runID = 0) { outdir <- outdir_path(runID) paths_run <- list(ed2in = NA, history = outdir) # Parse parameters pars_list <- extract_params(param) spectra_list <- lapply(pars_list$prospect.params, prospect, version = 5, include.wl = TRUE) trait.values <- pars_list$trait.values albedo <- EDR(spectra_list = spectra_list, trait.values = trait.values, paths = paths_run, par.wl = par.wl, nir.wl = nir.wl, datetime = datetime, edr.exe.name = "ed_2.1", output.path = outdir, change.history.time = FALSE) # Create quick figure #waves <- seq(400,2500,1) #png(paste(outdir,"/",'simulated_albedo.png',sep="/"),width=4900, height =2700,res=400) #par(mfrow=c(1,1), mar=c(4.3,4.5,1.0,1), oma=c(0.1,0.1,0.1,0.1)) # B L T R #plot(waves,unlist(albedo)*100,type="l",lwd=3,ylim=c(0,60),xlab="Wavelength (nm)",ylab="Reflectance (%)", #cex.axis=1.5, cex.lab=1.7,col="black") #rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = #"grey80") #lines(waves,unlist(albedo)*100,lwd=3, col="black") #dev.off() return(albedo) } # Simulate observations inits <- c(unlist(pp[c('temperate.Early_Hardwood', 'temperate.North_Mid_Hardwood', 'temperate.Late_Hardwood')]), 0.5, 0.5, # Early orient, clumping 0.5, 0.5, # Mid orient, clumping 0.5, 0.5) # Late orient, clumping obs <- invert_model(inits, runID = 0) + generate.noise() prior <- function(params) { do_prosp_prior <- function(params, pft) { dmvnorm(params, priors_sun$means$M[1:5, pft], priors_sun$means$Sigma[1:5, 1:5, pft], log = TRUE) } prospect_prior <- do_prosp_prior(params[1:5], 'temperate.Early_Hardwood') + do_prosp_prior(params[6:10], 'temperate.North_Mid_Hardwood') + do_prosp_prior(params[11:15], 'temperate.Late_Hardwood') traits_prior <- # Early Hardwood dunif(params[16], 0, 1, log = TRUE) + # Orient dunif(params[17], -1, 1, log = TRUE) + # Clumping # North Mid Hardwood dunif(params[18], 0, 1, log = TRUE) + # Orient dunif(params[19], -1, 1, log = TRUE) + # Clumping # Late Hardwood dunif(params[20], 0, 1, log = TRUE) + # Orient dunif(params[21], -1, 1, log = TRUE) # Clumping return(prospect_prior + traits_prior) } init_function <- function() { c(1.1, 10, 2, 0.01, 0.01, 1.1, 10, 2, 0.01, 0.01, 1.1, 10, 2, 0.01, 0.01, 0, 0, 0, 0, 0, 0) } # Test that prior function generates meaningful values prior(inits) prior(init_function()) param.mins <- c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) param.maxs <- c(Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, 1, 1, 1, 1, 1, 1) ## Setup PDA options invert.options <- list(model = invert_model, run_first = run_first, inits.function = init_function, prior.function = prior, param.mins = param.mins, param.maxs = param.maxs, nchains = 5, ngibbs.step = 2000, adapt = 1000, do.lsq = FALSE) samples <- invert.auto(observed = obs, invert.options = invert.options, parallel = TRUE, save.samples = file.path(main_out, 'test_samples_ongoing.rds')) saveRDS(samples, file = file.path(main_out, 'PDA_samples_output.rds') samples.bt <- PEcAn.assim.batch::autoburnin(samples$samples) samples.bt <- PEcAn.assim.batch::makeMCMCList(samples.bt) par(mfrow=c(1,1), mar=c(2,2,0.3,0.4), oma=c(0.1,0.1,0.1,0.1)) # B, L, T, R png(paste0(main_out,"/",paste("trace", runtag, "png", sep = ".")), width = 1500, height = 1600, res=150) plot(samples.bt) dev.off() rawsamps <- do.call(rbind, samples.bt) par(mfrow=c(1,1), mar=c(2,2,0.3,0.4), oma=c(0.1,0.1,0.1,0.1)) # B, L, T, R png(paste0(main_out,"/",paste("pairs", runtag, "png", sep = ".")), width = 1500, height = 1600, res=150) pairs(rawsamps) dev.off() par(mfrow=c(1,1), mar=c(2,2,0.3,0.4), oma=c(0.1,0.1,0.1,0.1)) # B, L, T, R png(paste0(main_out,"/",paste("deviance", runtag, "png", sep = ".")), width = 1500, height = 1600, res=150) plot(PEcAn.assim.batch::makeMCMCList(input.pda.data$deviance)) dev.off() par(mfrow=c(1,1), mar=c(2,2,0.3,0.4), oma=c(0.1,0.1,0.1,0.1)) # B, L, T, R png(paste0(main_out,"/",paste("n_eff", runtag, "png", sep = ".")), width = 1500, height = 1600, res=150) plot(PEcAn.assim.batch::makeMCMCList(input.pda.data$n_eff_list)) dev.off()

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/R 프로그래밍 기초/R Basic(3) - String Function.R

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JEONSUN/Jeon-S_R

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refs/heads/master

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R Basic(3) - String Function.R

# 문자열을 위한 함수 # 함수 nchar() : 문자열을 구성하고 있는 문자 개수 x <- c('park','lee','kwon') nchar(x) # 한글도 글자수를 셈 nchar('응용통계학과') ######################### # 함수 paste(): 문자열의 결합 #옵션 sep의 활용 paste('모든','사람에게는','통계적','사고능력이','필요하다') paste('모든','사람에게는','통계적','사고능력이','필요하다', sep = '-') paste('모든','사람에게는','통계적','사고능력이','필요하다', sep = '') #입력된 숫자는 문자로 전환되어 문자열과 결합 paste('원주율은', pi,'이다') #문자형 벡터가 입력되면 대응되는 요소끼리 결합 #벡터의 길이가 서로 다르면 순환법칙 적용 paste('stat',1:3,sep = '') # stat이 3번 반복 paste(c('stat','math'),1:3, sep = '-') # 길이가 짧은 벡터를 순환법칙으로 맞춤 ############################## # 빈칸 없이 문자열 결합 # 1. 함수 paste()에 옵션 sep = ''사용 # 2. 함수 paste0()사용 paste0('stat',1:3) # sep보다 paste0가 훨씬 편하다 # 문자형 벡터의 문자열을 하나로 결합 : 옵션 collapse paste0(letters, collapse = '') # 문자형 벡터를 하나로 합칠때 paste0(LETTERS, collapse = ",") # 구분자를 ,로 줌 ############################################# # substr(): 주어진 문자열의 일부분 선택 # substy(x, start, stop) # -start, stop : 정수형 스칼라 또는 벡터(대응되는 숫자끼리 시작점과 끝점 구성) substr('statistics',1, 4) #시작점 1, 끝점 4 x <- c('응용통계학과','정보통계학과','학생회장') substr(x,3,6) #시작점 3번째~6번글자 출력 substr(x,c(1,3),c(2,6)) # 시작점 1,끝점 2 # 시작점 3,끝점 2 다시 1,2 반복 # 예제 : 문자형 벡터 x에는 미국의 세 도시와 그 도시가 속한 주 이름이 입력 x <- c("New York,NY","Ann Arbor, MI","Chicago, IL") # 세 도시가 속한 주 이름만을 선택하여 출력 substr(x, nchar(x)-1,nchar(x)) ######################################3 # 함수 strsplit() : 문자열의 분리 # 옵션 split에 지정된 기준으로 분리. 결과는 리스트 # 세 도시의 이름과 주 이름 분리 y <- strsplit(x, split = ",") y # unlist를 사용해 리스트 y를 벡터로 변환 unlist(y) # 문자열을 구성하는 모든 문자의 분리 unlist(strsplit('PARK',split = "")) # 점(.)을 기준으로 문자열 분리하는 경우 # 옵션 split = "." 원하는 결과를 얻을 수 없음. unlist(strsplit("a.b.c",split = ".")) # 옵션 split = "[.]" 또는 split = "||." unlist(strsplit("a.b.c",split = "[.]")) # 옵션 split에는 정규표현식이 사용되며 # 정규표현식에서 점(.)은 다른의미가 있다 ################################################# # 함수 toupper(), tolower() : 대(소)문자로 수정 x <-c('park','lee','kwon') (y <- toupper(x)) tolower(y) # 벡터 x의 첫 글자만 대문자로 변환 substr(x,1,1) <- toupper(substr(x,1,1)) x ############################################# # 함수 sub(), gsub() : 문자열의 치환 # sub(old,new,문자열) : 문자열의 첫 번째 old만 new로 치환 # gsub(old,new,문자열) : 문자열의 모든 old가 new로 치환 x <- "Park hates stats. He hates math, too." sub("hat", "lov", x) # 첫번째 hat만 바뀜 gsub("hat", "lov", x) # 전부 바뀜 # 예제 # 문자열 "banana1","banana2","banana3" 생성 x <- c("banana1","banana2","banana3") # 반복이므로 x <- paste0('banana',1:3) # paste0로 사용하는게 더 간단함 x # 첫 번째 a를 A로 변경 sub('a','A',x) # 모든 a를 A로 변경 gsub('a','A',x) # 문자열의 일부 삭제는 new에 ""입력 z <- "Everybody cannot do it" sub("not","",z)

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/ISLR/random_forest_class.R

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creyesp/r-course

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random_forest_class.R

# https://cran.r-project.org/web/packages/randomForest/randomForest.pdf # # install.packages('randomForest') # install.packages("caret") # install.packages("e1071") # install.packages("doParallel") library(caret) library(e1071) library(doParallel) library(dplyr) library(randomForest) # library(doMC) # registerDoMC(cores=2) set.seed(1234) cl <- makeCluster(detectCores()) registerDoParallel(cl) data_train <- read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/train.csv") %>% na.omit() data_test <- read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/test.csv") %>% na.omit() data_train <- data_train %>% mutate(Survived = factor(Survived)) data_test <- data_test %>% mutate(Survived = factor(Survived)) glimpse(data_train) glimpse(data_test) ##### Caret # Default setting # K-fold cross validation is controlled by the trainControl() function # # trainControl(method = "cv", number = n, search ="grid") # arguments # - method = "cv": The method used to resample the dataset. # - number = n: Number of folders to create # - search = "grid": Use the search grid method. For randomized method, use "grid" # Note: You can refer to the vignette to see the other arguments of the function. # Define the control trControl <- trainControl(method = "cv", number = 10, search = "grid", allowParallel=TRUE ) ## Caret # train(formula, df, method = "rf", metric= "Accuracy", trControl = trainControl(), tuneGrid = NULL) # argument # - `formula`: Define the formula of the algorithm # - `method`: Define which model to train. Note, at the end of the tutorial, there is a list of all the models that can be trained # - `metric` = "Accuracy": Define how to select the optimal model # - `trControl = trainControl()`: Define the control parameters # - `tuneGrid = NULL`: Return a data frame with all the possible combination # Run the model # rf_default <- train(Survived~., # data = data_train, # method = "rf", # metric = "Accuracy", # trControl = trControl) # # Print the results # print(rf_default) ## # Search best mtry # You can test the model with values of mtry from 1 to 10 tuneGrid <- expand.grid(.mtry = c(1:5)) rf_mtry <- train(Survived~., data = data_train, method = "rf", metric = "Accuracy", tuneGrid = tuneGrid, trControl = trControl, importance = TRUE, nodesize = 14, ntree = 300) print(rf_mtry) prediction <-predict(fit_rf, data_test) confusionMatrix(prediction, data_test$survived) varImpPlot(fit_rf) stopCluster(cl) # names>(getModelInfo())

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/AleatoriosShiny.R

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erikonchis/AleatoriosShiny

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AleatoriosShiny.R

library (shiny) ui <- fluidPage( titlePanel("Genera y grafica números aleatorios"), actionButton(inputId = "clicks", label="Genera datos"), plotOutput("grafica") ) server <- function(input, output){ datos <- eventReactive(input$clicks, {rnorm(100)}) output$grafica <- renderPlot({plot(datos())}) } shinyApp(ui=ui, server=server)

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/R/CompressedDataFrameList-class.R

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Bioconductor/IRanges

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refs/heads/devel

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CompressedDataFrameList-class.R

### ========================================================================= ### CompressedDataFrameList objects ### ------------------------------------------------------------------------- setClass("CompressedDataFrameList", contains=c("DataFrameList", "CompressedList"), representation("VIRTUAL", unlistData="DataFrame"), prototype(unlistData=new("DFrame")) ) setClass("CompressedDFrameList", contains=c("DFrameList", "CompressedDataFrameList"), representation(unlistData="DFrame") ) setClass("CompressedSplitDataFrameList", contains=c("SplitDataFrameList", "CompressedDataFrameList"), representation("VIRTUAL") ) setClass("CompressedSplitDFrameList", contains=c("SplitDFrameList", "CompressedDFrameList", "CompressedSplitDataFrameList") ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Accessor methods. ### ### Deprecated. ### IMPORTANT NOTE: We won't be able to go thru the Defunct cycle because ### a lot of code around assumes that ncol() can be called on an arbitrary ### object! setMethod("ncol", "CompressedSplitDataFrameList", function(x) { msg <- c("The ncol() method for CompressedSplitDataFrameList ", "objects is deprecated. Please use ncols() on these ", "objects instead.") .Deprecated(msg=wmsg(msg)) if (length(x) == 0L) 0L else structure(rep.int(ncol(x@unlistData), length(x)), names = names(x)) }) setMethod("ncols", "CompressedSplitDataFrameList", function(x, use.names=TRUE) { if (!isTRUEorFALSE(use.names)) stop(wmsg("'use.names' must be TRUE or FALSE")) ans_names <- if (use.names) names(x) else NULL structure(rep.int(ncol(x@unlistData), length(x)), names=ans_names) } ) setMethod("colnames", "CompressedSplitDataFrameList", function(x, do.NULL = TRUE, prefix = "col") { if (length(x)) { nms <- colnames(x@unlistData, do.NULL = do.NULL, prefix = prefix) rep(CharacterList(nms), length(x)) } else NULL }) setReplaceMethod("rownames", "CompressedSplitDataFrameList", function(x, value) { if (is.null(value)) { rownames(x@unlistData) <- NULL } else if (is(value, "CharacterList")){ if (length(x) != length(value)) stop("replacement value must be the same length as x") rownames(x@unlistData) <- unlist(value, use.names=FALSE) } else { stop("replacement value must either be NULL or a CharacterList") } x }) setReplaceMethod("colnames", "CompressedSplitDataFrameList", function(x, value) { if (is.null(value)) { colnames(x@unlistData) <- NULL } else if (is.character(value)) { colnames(x@unlistData) <- value } else if (is(value, "CharacterList")){ if (length(x) != length(value)) stop("replacement value must be the same length as x") if (length(x) > 0) colnames(x@unlistData) <- unlist(value[[1L]]) } else { stop("replacement value must either be NULL or a CharacterList") } x }) setMethod("commonColnames", "CompressedSplitDataFrameList", function(x) colnames(unlist(x, use.names=FALSE))) setMethod("columnMetadata", "CompressedSplitDataFrameList", function(x) { mcols(x@unlistData, use.names=FALSE) }) setReplaceMethod("columnMetadata", "CompressedSplitDataFrameList", function(x, value) { mcols(x@unlistData) <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### setMethod("[", "CompressedSplitDataFrameList", function(x, i, j, ..., drop=TRUE) { if (!missing(j)) x@unlistData <- x@unlistData[, j, drop=FALSE] if (!missing(i)) x <- callNextMethod(x, i) if (((nargs() - !missing(drop)) > 2) && (ncol(x@unlistData) == 1) && (missing(drop) || drop)) { x <- relist(x@unlistData[[1L]], x) } x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion ### setAs("ANY", "CompressedDataFrameList", function(from) as(from, "CompressedDFrameList") ) setAs("ANY", "CompressedSplitDataFrameList", function(from) as(from, "CompressedSplitDFrameList") ) setListCoercions("DFrame") setAs("ANY", "CompressedSplitDFrameList", function(from) { coerceToCompressedList(from, "DFrame") }) setAs("ANY", "SplitDFrameList", function(from) as(from, "CompressedSplitDFrameList")) setAs("DataFrame", "SplitDFrameList", function(from) as(from, "CompressedSplitDFrameList")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Display ### setMethod("classNameForDisplay", "CompressedDFrameList", function(x) sub("^Compressed", "", sub("DFrame", "DataFrame", class(x))) )

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/R/desolve.R

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richfitz/diversitree

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lsoda.trim <- function(...) { ret <- t(lsoda(...)[-1,-1,drop=FALSE]) dimnames(ret) <- NULL ret } ## This sets things up the way that deSolve likes them derivs.for.deSolve <- function(f) function(...) list(f(...)) make.ode.deSolve <- function(info, control) { if ( !is.function(info$derivs) ) stop("info$derivs must be a function") derivs <- derivs.for.deSolve(info$derivs) rtol <- atol <- control$tol if ( isTRUE(info$time.varying) ) { tm <- info$tm function(vars, times, pars) { tm$set(pars) lsoda.trim(vars, times, derivs, pars, rtol=rtol, atol=atol) } } else { function(vars, times, pars) { lsoda.trim(vars, times, derivs, pars, rtol=rtol, atol=atol) } } }

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library(testthat) library(LBDCore) singularity_version <- "default" test_check("LBDCore")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/m_get.script.name.R \name{get.script.name} \alias{get.script.name} \title{A get.script.name Function} \usage{ get.script.name() } \arguments{ \item{x}{A numeric vector.} } \description{ This function allows you to multi paste vector. } \examples{ median_function(seq(1:10)) } \keyword{median}

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library(SixSigma) ### Name: ss.data.pb1 ### Title: Particle Boards Example - Individual Data ### Aliases: ss.data.pb1 ### Keywords: cc data ### ** Examples data(ss.data.pb1) summary(ss.data.pb1) library(qcc) pb.groups.one <- with(ss.data.pb1, qcc.groups(pb.humidity, pb.group)) pb.xbar.one <- qcc(pb.groups.one, type="xbar.one") summary(pb.xbar.one) plot(pb.xbar.one)

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plot_figures.r

library(tidyr) library(ggplot2) library(dplyr) library(cowplot) library(readr) ####Figure: Analytically derived rate and inferred rate when true model is MutSel and inference model is JC##### r_an <- read_csv("../analytical_rates/ten_sites_aa.csv") r_inf<- read_csv("../inferred_rates/processed_rates/rates_ten_sites_aa.csv") #normalize inferred rates r_inf %>% group_by(time,rep) %>% mutate(rate_norm = rate / mean(rate)) -> r_norm_inf #sites to plot sites_to_plot <- c(1,2,4,5) plot_lst <- list() for (i in sites_to_plot){ r_an_filtered <- filter(r_an,site==i+1) r_inf_filtered <- filter(r_norm_inf,site==i) p_rates <- ggplot(r_an_filtered,aes(x=time)) + background_grid("xy")+ geom_line(aes(y=r_tilde),color="black",size=1.2) + geom_line(aes(y=r_tilde_small_t), color="royalblue1",size=1.2) + geom_line(aes(y=r_tilde_large_t), color="green3",size=1.2) + stat_summary(data= r_inf_filtered, inherit.aes=FALSE, aes(x=time,y=rate_norm), color="orangered", fun.y = mean, fun.ymin = function(x) mean(x) - sd(x)/sqrt(length(x)), fun.ymax = function(x) mean(x) + sd(x)/sqrt(length(x)), geom = "pointrange", size=0.5)+ xlab("Time") + ylab("Relative rate") + coord_cartesian(ylim=c(0,2.1),xlim=c(0,0.82))+ scale_y_continuous(breaks=seq(0,2.5,0.5),expand = c(0.01, 0),label=c("0","0.5","1.0","1.5","2.0","2.5")) + scale_x_continuous(breaks=seq(0,0.8,0.2),expand = c(0.01, 0),label=c("0","0.2","0.4","0.6","0.8")) + geom_hline(yintercept=1)+ theme(axis.title = element_text(size = 22), axis.text = element_text(size = 20), legend.position="none") if (i==2 | i==4 | i==5) p_rates <- p_rates+theme(axis.title.y = element_blank()) plot_lst[[length(plot_lst)+1]] <- p_rates } prow <- plot_grid(plotlist=plot_lst, labels="AUTO", label_size = 20, align = 'vh', hjust = -1, ncol=4, nrow=1) save_plot("../plots/rates_true_MutSel_inf_JC.png", prow, ncol = 4, # we're saving a grid plot of 2 columns nrow = 1, # and 2 rows # each individual subplot should have an aspect ratio of 1.3 base_aspect_ratio = 1.3) ####Figure: Accuracy of the inferred rate##### r_inf <- read.csv( "../inferred_rates/processed_rates/rates_site_dupl.csv") r_an <- read_csv("../analytical_rates/all_sites_aa.csv") true_r <- r_an %>% filter(site==4,time==0.480002) r <- r_inf %>% group_by(site_dupl, rep) %>% mutate(rate_norm=rate/mean(rate)) %>% filter(site==3) p <- ggplot(r,aes(site_dupl,rate_norm)) + geom_hline(aes(yintercept=true_r$r_tilde),color="orangered",size=0.5)+ geom_point(size=0.9, alpha=0.8, position = position_jitter(width = 0.15, height = 0.01)) + ylab("Relative rate") + xlab("Site duplicates") + coord_cartesian(ylim=c(0.001,1000),xlim=c(8.5,110000))+ scale_y_log10(breaks=c(0.001,0.01,0.1,1,10,100,1000),label=c("0.001","0.01","0.1","1","10","100","1,000")) + scale_x_log10(breaks=c(10,100,1000,10000,100000),label=c("10","100","1,000","10,000","100,000"))+ theme(axis.title = element_text(size = 10), axis.text = element_text(size = 10)) save_plot("../plots/inf_rate_accuracy_v_site_dupl.png",plot=p) ####Figure: Analytically derived rate and the true rate when true model and inference model is JC##### r_an <- read_csv("../analytical_rates/ten_sites_aa_true_JC.csv") r_inf <- read_csv("../inferred_rates/processed_rates/rates_ten_sites_JC.csv") r_inf %>% group_by(time,rep) %>% mutate(rate_norm = rate / mean(rate)) -> r_norm_inf sites_to_plot <- c(1:6) plot_lst <- list() for (i in sites_to_plot){ r_an_filtered <- filter(r_an,site==i) r_inf_filtered <- filter(r_norm_inf,site==i+1) p_rates <- ggplot(r_an_filtered) + background_grid("xy")+ geom_line(aes(time,true_r),color="black",size=1.2) + stat_summary(data= r_inf_filtered, inherit.aes=FALSE, aes(x=time,y=rate_norm), color="orangered", fun.y = mean, fun.ymin = function(x) mean(x) - sd(x)/sqrt(length(x)), fun.ymax = function(x) mean(x) + sd(x)/sqrt(length(x)), geom = "pointrange", size=0.5)+ xlab("Time") + ylab("Relative rate") + coord_cartesian(ylim=c(0,2.5),xlim=c(0,1))+ scale_y_continuous(breaks=seq(0,2.5,0.5),label=c("0","0.5","1.0","1.5","2.0","2.5")) + scale_x_continuous(breaks=seq(0,1,0.2),expand = c(0.01, 0),label=c("0","0.2","0.4","0.6","0.8","1.0")) + geom_hline(yintercept=1)+ theme(axis.title = element_text(size = 18), axis.text = element_text(size = 16), legend.position="none") if (i==2 | i==3 | i==5 | i==6) p_rates <- p_rates+theme(axis.title.y = element_blank()) plot_lst[[length(plot_lst)+1]] <- p_rates } prow <- plot_grid(plotlist=plot_lst, labels="AUTO", align = 'vh', hjust = -1, ncol=3, nrow=2) save_plot("../plots/rates_true_JC_inf_JC.png", prow, ncol = 3, # we're saving a grid plot of 2 columns nrow = 2, # and 2 rows # each individual subplot should have an aspect ratio of 1.3 base_aspect_ratio = 1.3) ####Figure: a comparison of rates inferred with Jukes-Cantor-like matrix with observed frequencies and equilibrium frequencies ##### r_an <- read_csv("../analytical_rates/all_sites_aa.csv") r_inf <- read_csv("../inferred_rates/processed_rates/rates_all_sites.csv") #get the mean inferred rate at each time point r_an_temp <- r_an %>% group_by(site) %>% summarise(r_tilde_small_t=r_tilde_small_t[1]) r_an_temp$site <- 1:length(r_an_temp$site) d_label <- data.frame(time=c(0.0009,0.009,0.09,0.9), time_label=c('0.0009','0.009','0.09','0.9')) r <- r_inf %>% group_by(time,model,num_taxa) %>% mutate(inf_rate_mean=mean(rate),inf_rate_norm=rate/inf_rate_mean) %>% filter(num_taxa==512, model=="JC" | model=="JC_equalf") %>% group_by(site,model,time) %>% summarise(inf_rate_norm_mean=mean(inf_rate_norm)) %>% left_join(r_an_temp,by='site') %>% left_join(d_label) r_JC <- r %>% ungroup() %>% filter(model=="JC") %>% mutate(rate_JC=inf_rate_norm_mean) %>% select(rate_JC,site,time) r_JC_equalf <- r %>% ungroup() %>% filter(model=="JC_equalf") %>% mutate(rate_JC_equalf=inf_rate_norm_mean) %>% select(rate_JC_equalf,site,time,time_label) r_combined <- r_JC %>% left_join(r_JC_equalf,by=c("site","time")) p <- ggplot(r_combined,aes(rate_JC_equalf,rate_JC)) + geom_point(size=1.2,alpha=0.8) + geom_abline(color="red")+ xlab("Inferred rate (equal frequencies)") + ylab("Inferred rate (observed frequencies)") + facet_wrap( ~ time_label) + coord_cartesian(ylim=c(0,2.1), xlim=c(0,2.1))+ scale_y_continuous(breaks=seq(0,2.2,1)) + scale_x_continuous(breaks=seq(0,2.2,1)) + theme(axis.title = element_text(size = 12), axis.text = element_text(size = 12))+ panel_border() save_plot("../plots/rates_inf_diff_JC.png", p, ncol = 1, # we're saving a grid plot of 2 columns nrow = 1, # and 2 rows # each individual subplot should have an aspect ratio of 1.3 base_aspect_ratio = 1.3) ####Figure: a comparison of rates inferred with JC, JTT, WAG, LG##### r_an <- read_csv("../analytical_rates/all_sites_aa.csv") r_inf <- read_csv("../inferred_rates/processed_rates/rates_all_sites.csv") #get the mean inferred rate at each time point r_an_temp <- r_an %>% group_by(site) %>% summarise(r_tilde_small_t=r_tilde_small_t[1]) r_an_temp$site <- 1:length(r_an_temp$site) d_label <- data.frame(time=c(0.0009,0.009,0.09,0.9), time_label=c('0.0009','0.009','0.09','0.9')) model_label <- data.frame(model = c("JC_equalf", "LG", "JTT", "WAG"), model_label = c("JC", "LG", "JTT", "WAG")) r <- r_inf %>% group_by(time,model,num_taxa) %>% mutate(inf_rate_mean=mean(rate),inf_rate_norm=rate/inf_rate_mean) %>% filter(num_taxa==512, model!="JC") %>% group_by(site,model,time) %>% summarise(inf_rate_norm_mean=mean(inf_rate_norm)) %>% left_join(r_an_temp,by='site') %>% left_join(d_label) %>% left_join(model_label) p <- ggplot(r,aes(r_tilde_small_t,inf_rate_norm_mean)) + #background_grid("xy")+ geom_point(size=1.2,alpha=0.8) + geom_abline(color="red")+ ylab("Inferred rate") + xlab("Analytically derived rate") + #xlab(expression(paste("Analytically Derived Rates (", hat(r)^(k), "for small t)"))) + facet_grid(model_label ~ time_label) + coord_cartesian(ylim=c(0,3), xlim=c(0,3))+ scale_y_continuous(breaks=seq(0,3,1)) + scale_x_continuous(breaks=seq(0,3,1)) + theme(axis.title = element_text(size = 16), axis.text = element_text(size = 14))+ panel_border() save_plot("../plots/inf_v_an_rates_all_matrices.png", p, ncol = 1, # we're saving a grid plot of 2 columns nrow = 1, # and 2 rows # each individual subplot should have an aspect ratio of 1.3 base_height=7, base_width=7) ####Figure: Analytically derived rate and inferred rate when true model is MutSel and inference model is JC##### r_an <- read_csv("../analytical_rates/ten_sites_codon.csv") r_inf<- read_csv("../inferred_rates/processed_rates/rates_translated.csv") #normalize inferred rates r_inf %>% group_by(time,rep) %>% mutate(rate_norm = rate / mean(rate)) -> r_norm_inf #sites to plot sites_to_plot <- c(1,2,4,5,7,9) plot_lst <- list() for (i in sites_to_plot){ r_an_filtered <- filter(r_an,site==i+1) r_inf_filtered <- filter(r_norm_inf,site==i) p_rates <- ggplot(r_an_filtered,aes(x=time*0.77)) + background_grid("xy")+ geom_line(aes(y=r_tilde),color="black",size=1.2) + geom_line(aes(y=r_tilde_small_t), color="royalblue1",size=1.2) + geom_line(aes(y=r_tilde_large_t), color="green3",size=1.2) + stat_summary(data= r_inf_filtered, inherit.aes=FALSE, aes(x=time*0.77,y=rate_norm), color="orangered", fun.y = mean, fun.ymin = function(x) mean(x) - sd(x)/sqrt(length(x)), fun.ymax = function(x) mean(x) + sd(x)/sqrt(length(x)), geom = "pointrange", size=0.5)+ xlab("Time") + ylab("Relative rate") + coord_cartesian(ylim=c(0,2),xlim=c(0,1))+ scale_y_continuous(breaks=seq(0,2,0.5),label=c("0","0.5","1.0","1.5","2.0")) + scale_x_continuous(breaks=seq(0,1,0.2),expand = c(0.01, 0),label=c("0","0.2","0.4","0.6","0.8","1.0")) + geom_hline(yintercept=1)+ theme(axis.title = element_text(size = 18), axis.text = element_text(size = 16), legend.position="none") if (i==2 | i==4 | i==7 | i==9) p_rates <- p_rates+theme(axis.title.y = element_blank()) plot_lst[[length(plot_lst)+1]] <- p_rates } prow <- plot_grid(plotlist=plot_lst, labels="AUTO", align = 'vh', hjust = -1, ncol=3, nrow=2) save_plot("../plots/rates_true_codon_MutSel_inf_JC.png", prow, ncol = 3, # we're saving a grid plot of 2 columns nrow = 2, # and 2 rows # each individual subplot should have an aspect ratio of 1.3 base_aspect_ratio = 1.3)

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\name{print-methods} \docType{methods} \alias{print-methods} \alias{print,ppmlasso-method} \title{Methods for function \code{print}} \description{ Methods for function \code{\link{print}} } \section{Methods}{ \describe{ \item{\code{signature(x = "ppmlasso")}}{ Prints output for a \code{ppmlasso} object with details controlled by arguments of the \code{\link{print.ppmlasso}} function. } }} \keyword{methods}

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server.R

library(shiny) source("rvg.R") shinyServer(function(input, output) { ####################################### # Probability Integral Transform # # Exponential Example exp.all.out<-reactiveValues() observe({ if(input$go.exp==0){exp.all.out$history<-init.all()} else{ exp.all.out$history<-add.exp(isolate(exp.all.out$history),isolate(input$lambda),isolate(input$num.exp)) } }) output$PITexpPlot <- renderPlot({ input$go.exp input$clear.exp input$lambda par(mfrow=c(1,2),oma=c(0,0,0,0),mar=c(5.1,2.1,1,1.1)) isolate(plot.unif(exp.all.out$history)) isolate(plot.exp(input$lambda,exp.all.out$history)) }) observe({ input$pitEx input$lambda input$clear.exp exp.all.out$history<-init.all() }) output$totalcountExp<-renderUI({ input$go.exp last<-length(exp.all.out$history$X) if(last>0){ isolate(paste("Total number of replicates: ",last)) } }) output$summaryExp <- renderUI({ input$go.exp last<-length(exp.all.out$history$U) if(last>0){ strexp1<-paste("The most recent value of U is:", round(exp.all.out$history$U[last],3)) strexp2<-"This gives the following for x:" HTML(paste(strexp1,strexp2,sep=' ')) }}) output$invExp<-renderUI({ input$go.exp last<-length(exp.all.out$history$X) u<-exp.all.out$history$U[last] x<-exp.all.out$history$X[last] lambda<-input$lambda if(last>0){ withMathJax(sprintf("$$x= \\frac{-ln(1-u)}{\\lambda} = \\frac{-ln(1-%0.3f)}{%0.1f} = %0.3f$$", u,lambda,x)) } }) # Linear example linear.all.out<-reactiveValues() observe({ if(input$go.linear==0){linear.all.out$history<-init.all()} else{ linear.all.out$history<-add.linear(isolate(linear.all.out$history),isolate(input$num.linear)) } }) output$PITlinearPlot <- renderPlot({ input$go.linear input$clear.linear par(mfrow=c(1,2),oma=c(0,0,0,0),mar=c(5.1,2.1,1,1.1)) isolate(plot.unif(linear.all.out$history)) isolate(plot.linear(linear.all.out$history)) }) observe({ input$pitEx input$clear.linear linear.all.out$history<-init.all() }) output$totalcountLin<-renderUI({ input$go.linear last<-length(linear.all.out$history$X) if(last>0){ isolate(paste("Total number of replicates: ",last)) } }) output$summaryLin <- renderUI({ input$go.linear last<-length(linear.all.out$history$U) if(last>0){ strexp1<-paste("The most recent value of U is:", round(linear.all.out$history$U[last],3)) strexp2<-"This gives the following for x:" HTML(paste(strexp1,strexp2,sep=' ')) }}) output$invLin<-renderUI({ input$go.linear last<-length(linear.all.out$history$X) u<-linear.all.out$history$U[last] x<-linear.all.out$history$X[last] if(last>0){ withMathJax(sprintf("$$x= 4\\sqrt{u} = 4\\sqrt{%0.3f} = %0.3f$$", u,x)) } }) ########################################## # Accept-Reject # # Beta example beta.all.out<-reactiveValues() observe({ if(input$go==0){beta.all.out$history<-temp.start(isolate(input$alpha),isolate(input$beta))} else{ beta.all.out$history<-temp.start2(isolate(beta.all.out$history),isolate(input$alpha),isolate(input$beta),isolate(input$num)) } }) observe({ input$alpha input$beta input$clear beta.all.out$history<-temp.start(input$alpha,input$beta) }) output$densityPlot <- renderPlot({ input$go input$clear input$alpha input$beta par(mfrow=c(1,2),oma=c(0,0,0,0),mar=c(5.1,2.1,1,1.1)) isolate(plot.unif(beta.all.out$history)) isolate(plot.beta(input$alpha,input$beta,beta.all.out$history)) }) output$summary <- renderUI({ input$go last<-length(beta.all.out$history$Y) if(last>0){ str1<-paste("The most recent value of U is:", round(beta.all.out$history$U[last],3), "(enlarged and green)") str2<-paste("The most recent value of Y is:", round(beta.all.out$history$Y[last],3), "(enlarged, and green if accepted; red if rejected)") str3<-paste("The value of Y is", ifelse(beta.all.out$history$status[last]=="accept","accepted","rejected"),"because:") HTML(paste(str1,str2,str3,sep=' ')) }}) output$accrej<-renderUI({ input$go last<-length(beta.all.out$history$Y) if(last>0){ u<-beta.all.out$history$U[last] fy<-beta.all.out$history$fy[last] M<-beta.all.out$history$M[last] gy<-beta.all.out$history$gy[last] ratio<-fy/(M*gy) if(beta.all.out$history$status[last]=="accept"){ withMathJax(sprintf("$$%0.3f \\leq \\frac{f(y)}{Mg(y)} = \\frac{\\frac{\\Gamma(\\alpha + \\beta)}{\\Gamma(\\alpha)\\Gamma(\\beta)}y^{\\alpha-1}(1-y)^{\\beta-1}}{M \\cdot 1_{\\{0 \\leq y \\leq 1\\}}} = \\frac{%0.2f}{%0.3f \\cdot 1} = %0.2f$$", u,fy,M,ratio)) } else { withMathJax(sprintf("$$%0.3f > \\frac{f(y)}{Mg(y)} = \\frac{\\frac{\\Gamma(\\alpha + \\beta)}{\\Gamma(\\alpha)\\Gamma(\\beta)}y^{\\alpha-1}(1-y)^{\\beta-1}}{M \\cdot 1_{\\{0 \\leq y \\leq 1\\}}} = \\frac{%0.2f}{%0.2f \\cdot 1} = %0.2f$$", u,fy,M,ratio)) } } }) output$unifnote<-renderText({ input$alpha input$beta if(input$alpha==1 & input$beta==1){ "Note that for the Beta(1,1) distribution, every point will be accepted, as we would expect since it is equivalent to the Uniform[0,1] distribution." } }) output$M<- renderText({ input$alpha input$beta isolate(paste("For the current set of parameter values, M = ", round(optimize(dbeta,shape1=input$alpha,shape2=input$beta,interval=c(0,1),maximum=T)$objective,digits=3),".",sep="")) }) output$totalcount<-renderUI({ input$go last<-length(beta.all.out$history$Y) if(last>0){ isolate(paste("Total number of replicates: ",last)) } }) # Truncated normal example tnorm.all.out<-reactiveValues() observe({ if(input$tnormGo==0){tnorm.all.out$history<-start.tnorm()} else{ tnorm.all.out$history<-add.tnorm(isolate(tnorm.all.out$history),isolate(input$tnormNum)) } }) observe({ input$tnormClear tnorm.all.out$history<-start.tnorm() }) output$tnormDensityPlot <- renderPlot({ input$tnormGo input$tnormClear par(mfrow=c(1,2),oma=c(0,0,0,0),mar=c(5.1,2.1,1,1.1)) isolate(plot.unif(tnorm.all.out$history)) isolate(plot.tnorm(tnorm.all.out$history)) }) output$tnormsummary <- renderUI({ input$tnormGo last<-length(tnorm.all.out$history$Y) if(last>0){ str1<-paste("The most recent value of U is:", round(tnorm.all.out$history$U[last],3), "(enlarged and green)") str2<-paste("The most recent value of Y is:", round(tnorm.all.out$history$Y[last],3), "(enlarged, and green if accepted; red if rejected)") str3<-paste("The value of Y is", ifelse(tnorm.all.out$history$status[last]=="accept","accepted","rejected"),"because:") HTML(paste(str1,str2,str3,sep=' ')) }}) output$tnormaccrej<-renderUI({ input$tnormGo last<-length(tnorm.all.out$history$Y) if(last>0){ u<-tnorm.all.out$history$U[last] fy<-tnorm.all.out$history$fy[last] M<-tnorm.all.out$history$M[last] gy<-tnorm.all.out$history$gy[last] y<-tnorm.all.out$history$Y[last] ratio<-fy/(M*gy) if(tnorm.all.out$history$status[last]=="accept"){ withMathJax(sprintf("$$%0.3f \\leq \\frac{f(y)}{Mg(y)} = \\frac{\\frac{1}{\\sqrt{2 \\pi}}e^{-\\frac{1}{2}(%0.2f)^2} \\cdot \\left[\\frac{1}{1-\\Phi(2)}\\right] }{M \\cdot e^{2-%0.2f} } = \\frac{%0.2f}{%0.3f \\cdot %0.3f} = %0.2f$$", u,y,y,fy,M,gy,ratio)) } else { withMathJax(sprintf("$$%0.3f > \\frac{f(y)}{Mg(y)} = \\frac{\\frac{1}{\\sqrt{2 \\pi}}e^{-\\frac{1}{2}(%0.2f)^2} \\cdot \\left[\\frac{1}{1-\\Phi(2)}\\right] }{M \\cdot e^{2-%0.2f} } = \\frac{%0.2f}{%0.3f \\cdot %0.3f} = %0.2f$$", u,y,y,fy,M,gy,ratio)) } } }) output$tnormRatio<-renderUI({ input$tnormGo num<-length(tnorm.all.out$history$Y) if(num>0){ str4<-paste("The proportion of points that have been accepted is ", round(sum(tnorm.all.out$history$status=="accept")/num,3)," (out of ",num,")",sep="") HTML(str4) } }) }) # end of shinyServer

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pad-trim.r

#' Pad a string. #' #' Vectorised over \code{string}, \code{width} and \code{pad}. #' #' @param string A character vector. #' @param width Minimum width of padded strings. #' @param side Side on which padding character is added (left, right or both). #' @param pad Single padding character (default is a space). #' @return A character vector. #' @seealso \code{\link{str_trim}} to remove whitespace #' @export #' @examples #' rbind( #' str_pad("hadley", 30, "left"), #' str_pad("hadley", 30, "right"), #' str_pad("hadley", 30, "both") #' ) #' #' # All arguments are vectorised except side #' str_pad(c("a", "abc", "abcdef"), 10) #' str_pad("a", c(5, 10, 20)) #' str_pad("a", 10, pad = c("-", "_", " ")) #' #' # Longer strings are returned unchanged #' str_pad("hadley", 3) str_pad <- function(string, width, side = c("left", "right", "both"), pad = " ") { side <- match.arg(side) switch(side, left = stri_pad_left(string, width, pad = pad), right = stri_pad_right(string, width, pad = pad), both = stri_pad_both(string, width, pad = pad) ) } #' Trim whitespace from start and end of string. #' #' @param string A character vector. #' @param side Side on which to remove whitespace (left, right or both). #' @return A character vector. #' @export #' @seealso \code{\link{str_pad}} to add whitespace #' @examples #' str_trim(" String with trailing and leading white space\t") #' str_trim("\n\nString with trailing and leading white space\n\n") str_trim <- function(string, side = c("both", "left", "right")) { side <- match.arg(side) switch(side, left = stri_trim_left(string), right = stri_trim_right(string), both = stri_trim_both(string) ) }

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makedata_partitions.R

################################################ # Description: # Partition samples to training and validation ################################################ library(Biobase) df <- readRDS(file="data/eset-final.rds") pdata <- pData(df) fdata <- fData(df) # select endogeneous genes counts <- exprs(df)[grep("ENSG", rownames(df)), ] log2cpm.all <- t(log2(1+(10^6)*(t(counts)/pdata$molecules))) #macosko <- readRDS("data/cellcycle-genes-previous-studies/rds/macosko-2015.rds") counts <- counts[,order(pdata$theta)] log2cpm.all <- log2cpm.all[,order(pdata$theta)] pdata <- pdata[order(pdata$theta),] log2cpm.quant <- readRDS("output/npreg-trendfilter-quantile.Rmd/log2cpm.quant.rds") #######################----- validation sample of random cells source("peco/R/primes.R") source("peco/R/partitionSamples.R") # select external validation samples set.seed(99) nvalid <- round(ncol(log2cpm.quant)*.15) ii.valid <- sample(1:ncol(log2cpm.quant), nvalid, replace = F) ii.nonvalid <- setdiff(1:ncol(log2cpm.quant), ii.valid) log2cpm.quant.nonvalid <- log2cpm.quant[,ii.nonvalid] log2cpm.quant.valid <- log2cpm.quant[,ii.valid] folds <- partitionSamples(1:ncol(log2cpm.quant.nonvalid), runs=5, nsize.each = rep(151,5)) fold_indices <- folds$partitions saveRDS(fold_indices, file="data/results/fold_indices.rds") #######################----- validation sample of random indivdiual for (ind in unique(pdata$chip_id)) { set.seed(99) # nvalid <- round(ncol(log2cpm.quant)*.15) ii.valid <- c(1:nrow(pdata))[which(pdata$chip_id == ind)] ii.nonvalid <- c(1:nrow(pdata))[which(pdata$chip_id != ind)] pdata.nonvalid <- pdata[ii.nonvalid,] pdata.valid <- pdata[ii.valid,] # log2cpm.quant.nonvalid <- log2cpm.quant[,ii.nonvalid] # log2cpm.quant.valid <- log2cpm.quant[,ii.valid] # theta <- pdata$theta # names(theta) <- rownames(pdata) # log2cpm.nonvalid <- log2cpm.all[,ii.nonvalid] # log2cpm.valid <- log2cpm.all[,ii.valid] # # theta.nonvalid <- theta[ii.nonvalid] # theta.valid <- theta[ii.valid] # #sig.genes <- readRDS("output/npreg-trendfilter-quantile.Rmd/out.stats.ordered.sig.476.rds") # data_training <- list(theta.nonvalid=theta.nonvalid, # log2cpm.quant.nonvalid=log2cpm.quant.nonvalid, # log2cpm.nonvalid=log2cpm.nonvalid, # pdata.nonvalid=pdata.nonvalid, # fdata=fdata) # # data_withheld <- list(theta.valid=theta.valid, # log2cpm.quant.valid=log2cpm.quant.valid, # log2cpm.valid=log2cpm.valid, # pdata.valid=pdata.valid, # fdata=fdata) # # saveRDS(data_training, file=paste0("data/results/ind_",ind,"_data_training.rds")) # saveRDS(data_withheld, file=paste0("data/results/ind_",ind,"_data_withheld.rds")) ############# <- get training partitions # split by individaul # # get predicted times # # set training samples # source("peco/R/primes.R") # source("peco/R/partitionSamples.R") # folds <- partitionSamples(1:ncol(log2cpm.quant.nonvalid), runs=5, # nsize.each = c(rep(round(ncol(log2cpm.quant.nonvalid)/5),4), # ncol(log2cpm.quant.nonvalid)-sum(rep(round(ncol(log2cpm.quant.nonvalid)/5),4)))) fold_indices <- lapply(1:length(unique(pdata.nonvalid$chip_id)), function(i) { ind_test <- unique(pdata.nonvalid$chip_id)[i] test <- which(pdata.nonvalid$chip_id==ind_test) train <- which(pdata.nonvalid$chip_id!=ind_test) return(list(test=test, train=train)) }) # folds$partitions saveRDS(fold_indices, file=paste0("data/results/ind_",ind,"_fold_indices.rds")) }

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save_custom_raking_outputs.R

#' @title Save outputs from custom raking function #' @description This function saves outputs from \code{custom_rake} #' #' @param custom_rake_output Output list from custom raking function #' @param outdir Directory for files to be saved to #' @param indicator Name of indicator being modeled #' @param age_group Name of age group #' @param prefix Character string to be added to files to avoid overwriting non-custom files in folder #' @param reg Name of region that was modeled #' #' @return NULL #' @export #' #' @examples #' \dontrun{ #' save_custom_raking_outputs(custom_rake_output, #' outdir = sprintf("/share/geospatial/mbg/u5m/\%s_\%s/output/\%s",indicator, age_group, run_date), #' indicator, #' age_group, #' prefix = "custom_india", #' reg = "south_asia") #' } save_custom_raking_outputs <- function(custom_rake_output, outdir, indicator, prefix, reg) { ol <- custom_rake_output raked_cell_pred <- ol[["raked_cell_pred"]] save(raked_cell_pred, file = sprintf("%s/%s_%s_raked_cell_draws_eb_bin0_%s_0.RData", outdir, prefix, indicator, reg)) simple_raster <- ol[["simple_raster"]] writeRaster(simple_raster, file = sprintf("%s/%s_%s_simple_raster", outdir, prefix, indicator), format = "GTiff") raking_factors <- ol[["raking_factors"]] write.csv(raking_factors, file = sprintf("%s/%s_%s_%s_rf.csv", outdir, prefix, indicator, reg)) adm0_geo <- ol[["adm0_geo"]] write.csv(adm0_geo, file = sprintf("%s/%s_%s_adm0_geo.csv", outdir, prefix, indicator)) mean_raster <- ol[["mean_raked_raster"]] writeRaster(mean_raster, file = sprintf("%s/%s_%s_mean_raked_2000_2015", outdir, prefix, indicator), format = "GTiff") cirange_raster <- ol[["cirange_raked_raster"]] writeRaster(cirange_raster, file = sprintf("%s/%s_%s_cirange_raked_2000_2015", outdir, prefix, indicator), format = "GTiff") upper_raster <- ol[["upper_raked_raster"]] writeRaster(upper_raster, file = sprintf("%s/%s_%s_upper_raked_2000_2015", outdir, prefix, indicator), format = "GTiff") lower_raster <- ol[["lower_raked_raster"]] writeRaster(lower_raster, file = sprintf("%s/%s_%s_lower_raked_2000_2015", outdir, prefix, indicator), format = "GTiff") }

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# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Purpose: Combine km estimates from different outcomes # - The script must be accompanied by three arguments: # `brand` - the brand used # `subgroup` - the subgroup variable # `outcome` - the outcome variable # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Preliminaries ---- # import libraries library('tidyverse') library('here') library('glue') library('survival') # import custom user functions and paramters source(here("analysis", "design.R")) source(here("analysis", "functions", "utility.R")) # create output directory outdir <- here("output", "sequential", "combine") fs::dir_create(outdir) # define metaparams metaparams <- expand_grid( brand = model_brands, outcome = model_outcomes, subgroup = model_subgroups ) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # combine files ---- combine_files <- function(filename) { metaparams %>% mutate( data = pmap(list(brand, subgroup, outcome), function(brand, subgroup, outcome) { subgroup <- as.character(subgroup) dat <- read_rds(here("output", "sequential", brand, "model", subgroup, outcome, glue("{filename}.rds"))) dat %>% ungroup() %>% add_column( subgroup_level = as.character(.[[subgroup]]), .before=1 ) %>% select(-all_of(subgroup)) }) ) %>% unnest(data) %>% write_csv(file.path(outdir, glue("{filename}.csv"))) } combine_files("km_estimates_rounded") combine_files("km_estimates_unrounded") combine_files("contrasts_km_cuts_rounded") combine_files("contrasts_cox_cuts") # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## move km plots to single folder ---- move_plots <- function(filename) { metaparams %>% rowwise() %>% mutate( plotdir = here("output", "sequential", brand, "model", subgroup, outcome, glue("{filename}.png")), plotnewdir = file.path(outdir, glue("{filename}_{brand}_{subgroup}_{outcome}.png")), ) %>% {walk2(.$plotdir, .$plotnewdir, ~fs::file_copy(.x, .y, overwrite = TRUE))} } move_plots("km_plot_rounded") move_plots("km_plot_unrounded")

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library(DESeq2) args<-commandArgs(TRUE) input_counts <- args[1] input_colnames <- args[2] output_header <- args[3] # Generating filenames filename_count_table <- paste(output_header, ".normalized_counts.txt", sep="") filename_de_plots <- paste(output_header, ".DE_plots.pdf", sep="") filename_de_results <- paste(output_header, ".DE_results.txt", sep="") ## ------------------------------- ## ## Reading the input data ## ## ------------------------------- # Input -- a file containing count data and info about conditions countData <- read.table(input_counts, sep='\t', header=TRUE) colData <- read.table(input_colnames, sep='\t', header=TRUE) print ("Data was successfully loaded.") # Converting input data into a deseq object dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ condition) # Filtering to remove low counts dds <- dds[ rowSums(counts(dds)) > 1, ] # Tell deseq which samples to compaire dds$condition <- factor(dds$condition, levels=c("untreated","treated")) print ("DESeq2 object was created.") ## ------------------------------- ## ## Running DE analysis ## ## ------------------------------- # Running DE analysis dds <- DESeq(dds) normalized_counts <- counts(dds, normalized=T) write.table(as.data.frame(normalized_counts), file=filename_count_table, quote=FALSE, sep='\t') print ("Differential expression analysis was finished.") # Extracting the results matrix # The results are shown for treated vs untreated, fold change log2 (treated/untreated) res <- results(dds) # alpha is the FDR cutoff, we can change it res05 <- results(dds, alpha=0.05) ## ------------------------------- ## ## Extra plots ## ## ------------------------------- library("ggplot2") library("pheatmap") library("RColorBrewer") print ("Started to generate plots.") pdf(filename_de_plots) # plot MA plotMA(res, main="DESeq2", ylim=c(-2,2)) resMLE <- results(dds, addMLE=TRUE) plotMA(resMLE, MLE=TRUE, main="unshrunken LFC", ylim=c(-2,2)) # plot counts plotCounts(dds, gene=which.min(res$padj), intgroup="condition") # plot counts in ggplot d <- plotCounts(dds, gene=which.min(res$padj), intgroup="condition", returnData=TRUE) ggplot(d, aes(x=condition, y=count)) + geom_point(position=position_jitter(w=0.1,h=0)) + scale_y_log10(breaks=c(25,100,400)) # plot heatmap of counts rld <- rlog(dds) vsd <- varianceStabilizingTransformation(dds) select <- order(rowMeans(counts(dds,normalized=TRUE)),decreasing=TRUE)[1:20] nt <- normTransform(dds) # defaults to log2(x+1) log2.norm.counts <- assay(nt)[select,] df <- as.data.frame(colData(dds)[,c("condition","type")]) pheatmap(log2.norm.counts, cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=FALSE, annotation_col=df) pheatmap(assay(rld)[select,], cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=FALSE, annotation_col=df) pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=FALSE, annotation_col=df) # plot sample to sample distance sampleDists <- dist(t(assay(rld))) sampleDistMatrix <- as.matrix(sampleDists) rownames(sampleDistMatrix) <- paste(rld$condition, rld$type, sep="-") colnames(sampleDistMatrix) <- NULL colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) pheatmap(sampleDistMatrix, clustering_distance_rows=sampleDists, clustering_distance_cols=sampleDists, col=colors) # plot PCA plotPCA(rld, intgroup=c("condition", "type")) data <- plotPCA(rld, intgroup=c("condition", "type"), returnData=TRUE) percentVar <- round(100 * attr(data, "percentVar")) ggplot(data, aes(PC1, PC2, color=condition, shape=type)) + geom_point(size=3) + xlab(paste0("PC1: ",percentVar[1],"% variance")) + ylab(paste0("PC2: ",percentVar[2],"% variance")) dev.off() print ("Finished generating plots.") ## ------------------------------- ## ## Exporting results ## ## ------------------------------- print ("Preparing to print the list of differentially expressed regions.") # Sort results on adjusted pvalue resOrdered <- res[order(res$padj),] # Write output in a csv file write.table(as.data.frame(resOrdered), file=filename_de_results, quote=FALSE, sep='\t') print ("Finished printing DESeq2 output.")

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Chapter 4_6.R

##################################################################################### # # # TITLE: Building Health State Transition Models in R: Chapter 9 # # # # DESCRIPTION: We apply costs and utilities to our survival model # # # # AUTHOR: Ian Cromwell # # # # DATE: November, 2014 # # # ##################################################################################### ### SET WORKING DIRECTORY setwd("WHATEVER DIRECTORY YOU'VE SAVED THESE FILES IN") ### LOAD IN VALUES FROM PREVIOUS CHAPTERS source("Chapter 4_5.R") ### APPLY COSTS TO HEALTH STATES ### # Create blank arrays for costs cost_HSW <- cost_HSX <- array(0, dim=c(ncycle,1,n)) cost_HSY <- cost_HSZ <- array(0, dim=c(ncycle,2,n)) # Populate arrays for (j in 1:n){ for (i in 1:ncycle){ cost_HSW[i,,j] <- HS_W[i,,j]*C_W[j] cost_HSX[i,,j] <- HS_X[i,,j]*C_X[j] cost_HSY[i,1,j] <- HS_Y[i,1,j]*(C_Ytransition[j] + C_Y[j]) cost_HSY[i,2,j] <- HS_Y[i,2,j]*C_Y[j] cost_HSZ[i,1,j] <- HS_Z[i,1,j]*C_Ztransition[j] }} ### APPLY UTILITIES TO HEALTH STATES ### # Create blank arrays for utilities qol_HSW <- qol_HSX <- array(0, dim=c(ncycle,1,n)) qol_HSY <- array(0, dim=c(ncycle,2,n)) # Populate arrays for (j in 1:n){ for (i in 1:ncycle){ qol_HSW[i,,j] <- HS_W[i,,j]*U_W[j] qol_HSX[i,,j] <- HS_X[i,,j]*U_X[j] qol_HSY[i,,j] <- HS_Y[i,,j]*U_Y[j] }}

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diaryRepository.R

library(RPostgreSQL) library(DBI) library(stringr) pw<- { "test123" } get_connection <- function() { con <-dbConnect(dbDriver("PostgreSQL"), dbname="diary", host="host.docker.internal", port=5432, user="test_user", password=pw) return (con) } get_all_posts <- function() { conn <- get_connection() res <- dbGetQuery(conn,' select * from "post"') dbDisconnect(conn) return (res) } get_post_by_id <- function(id) { conn <- get_connection() res <- dbGetQuery(conn,str_interp(' select * from "post" where id = ${id}')) dbDisconnect(conn) return(res) } insert_post <- function(post) { conn <- get_connection() rs <- dbSendStatement( conn, str_interp("INSERT INTO post (text_post, date_posted, headline) VALUES ('${post$description}', now(), '${post$headline}')") ) dbHasCompleted(rs) dbGetRowsAffected(rs) dbClearResult(rs) dbDisconnect(conn) } get_last_post <- function() { conn <- get_connection() res <- dbGetQuery(conn,str_interp('select * from "post" order by id desc limit 1')) dbDisconnect(conn) return(res) }

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\name{bal.tab.matchit} \alias{bal.tab.matchit} \title{ Balance Statistics for Matchit Objects } \description{ Generates balance statistics for \code{matchit} objects from \pkg{MatchIt}. } \usage{ \method{bal.tab}{matchit}(m, int = FALSE, distance = NULL, addl = NULL, data = NULL, continuous = c("std", "raw"), binary = c("raw", "std"), s.d.denom = c("treated", "control", "pooled"), m.threshold = NULL, v.threshold = NULL, ks.threshold = NULL, imbalanced.only = FALSE, un = FALSE, disp.bal.tab = TRUE, disp.means = FALSE, disp.v.ratio = FALSE, disp.ks = FALSE, disp.subclass = FALSE, cluster = NULL, which.cluster = NULL, cluster.summary = TRUE, quick = FALSE, ...) } \arguments{ \item{m}{ a \code{matchit} object; the output of a call to \code{matchit()} from the \pkg{MatchIt} package. } \item{int}{ \code{logical} or \code{numeric}; whether or not to include powers and 2-way interactions of covariates included in \code{covs} and in \code{addl}. If \code{numeric} and equal to \code{1} or \code{2}, squares of each covariate will be displayed; greater numbers will display corresponding powers up to the provided input (e.g., \code{3} will display squares and cubes of each covariate). } \item{distance}{ Optional; either a vector or data.frame containing distance values (e.g., propensity scores) for each unit or a string containing the name of the distance variable in \code{data}. Note that the distance measure generated by \code{matchit()} is automatically included. } \item{addl}{ an optional data frame or the quoted names of additional covariates for which to present balance. These may be covariates included in the original dataset but not included in the call to \code{matchit()}. If variable names are specified, \code{bal.tab()} will look first in the argument to \code{data}, if specified, and next in the \code{matchit} object. } \item{data}{ an optional data frame containing variables that might be named in arguments to \code{distance}, \code{addl}, and \code{cluster}. } \item{continuous}{ whether mean differences for continuous variables should be standardized ("std") or raw ("raw"). Default "std". Abbreviations allowed. } \item{binary}{ whether mean differences for binary variables (i.e., difference in proportion) should be standardized ("std") or raw ("raw"). Default "raw". Abbreviations allowed. } \item{s.d.denom}{ whether the denominator for standardized differences (if any are calculated) should be the standard deviation of the treated group ("treated"), the standard deviation of the control group ("control"), or the pooled standard deviation ("pooled"), computed as the square root of the mean of the group variances. Abbreviations allowed. The default is "treated". } \item{m.threshold}{ a numeric value for the threshold for mean differences. .1 is recommended. } \item{v.threshold}{ a numeric value for the threshold for variance ratios. Will automatically convert to the inverse if less than 1. } \item{ks.threshold}{ a numeric value for the threshold for Kolmogorov-Smirnov statistics. Must be between 0 and 1. } \item{imbalanced.only}{ \code{logical}; whether to display only the covariates that failed to meet at least one of balance thresholds. } \item{un}{ \code{logical}; whether to print statistics for the unadjusted sample as well as for the adjusted sample. } \item{disp.bal.tab}{ \code{logical}; whether to display the table of balance statistics. } \item{disp.means}{ \code{logical}; whether to print the group means in balance output. } \item{disp.v.ratio}{ \code{logical}; whether to display variance ratios in balance output. } \item{disp.ks}{ \code{logical}; whether to display Kolmogorov-Smirnov statistics in balance output. } \item{disp.subclass}{ \code{logical}; whether to display balance information for individual subclasses if subclassification is used in conditioning. See \code{\link{bal.tab.subclass}} for details. } \item{cluster}{ either a vector containing cluster membserhip for each unit or a string containing the name of the cluster membership variable in \code{data} or the CBPS object. See \code{\link{bal.tab.cluster}} for details. } \item{which.cluster}{ which cluster(s) to display if \code{cluster} is specified. See \code{\link{bal.tab.cluster}} for details. } \item{cluster.summary}{ \code{logical}; whether to display the cluster summary table if \code{cluster} is specified. See \code{\link{bal.tab.cluster}} for details. } \item{quick}{ \code{logical}; if \code{TRUE}, will not compute any values that will not be displayed. Leave \code{FALSE} if computed values not displayed will be used later. } \item{...}{ further arguments passed to or from other methods. They are ignored in this function. } } \details{ \code{bal.tab.matchit()} generates a list of balance summaries for the \code{matchit} object given, and functions similarly to \code{summary.matchit()} in \pkg{MatchIt}. \code{bal.tab()} behaves differently depending on whether subclasses are used in conditioning or not. If they are used, \code{bal.tab()} creates balance statistics for each subclass and for the sample in aggregate; see \code{\link{bal.tab.subclass}} for more information. All balance statistics are calculated whether they are displayed by \code{print} or not, unless \code{quick = TRUE}. The threshold values (\code{m.threshold}, \code{v.threshold}, and \code{ks.threshold}) control whether extra columns should be inserted into the Balance table describing whether the balance statistics in question exceeded or were within the threshold. Including these thresholds also creates summary tables tallying the number of variables that exceeded and were within the threshold and displaying the variables with the greatest imbalance on that balance measure. When subclassification is used, the extra threshold columns are placed within the balance tables for each subclass as well as in the aggregate balance table, and the summary tables display balance for each subclass. } \value{ If subclassification is used, an object of class \code{"bal.tab.subclass"} containing balance summaries within and across subclasses. See \code{\link{bal.tab.subclass}} fo details. If matching is used and clusters are not specified, an object of class \code{"bal.tab"} containing balance summaries for the \code{matchit} object. See \code{\link{bal.tab}} for details. If clusters are specified, an object of class \code{"bal.tab.cluster"} containing balance summaries within each cluster and a summary of balance across clusters. See \code{\link{bal.tab.cluster}} for details. } \author{ Noah Greifer } \seealso{ \code{\link{bal.tab}} for details of calculations. } \examples{library(MatchIt); data("lalonde", package = "cobalt") ## Nearest Neighbor matching m.out1 <- matchit(treat ~ age + educ + race + married + nodegree + re74 + re75, data = lalonde, method = "nearest") bal.tab(m.out1, un = TRUE, m.threshold = .1, v.threshold = 2) ## Subclassification m.out2 <- matchit(treat ~ age + educ + race + married + nodegree + re74 + re75, data = lalonde, method = "subclass") bal.tab(m.out2, disp.subclass = TRUE) }

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outcome.R

outcome <- read.csv("outcome-of-care-measures.csv",colClasses = "character") head(outcome) ncol(outcome) nrow(outcome) names(outcome) outcome[,11] <- as.numeric(outcome[,11]) hist(outcome[,11],xlab = "Muertes",main = "Tasas De Mortalidad Hospitalarias De 30 Dias(Al Mes) Por Ataque Cardiaco" ,col = "lightblue")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fun_logic_assg.r \name{fun_logic_assg} \alias{fun_logic_assg} \title{Logical assignment, a MJP team defined function} \usage{ fun_logic_assg(x, reference, typo_col = "TYPO", replace_col = "REPLACEMENT") } \arguments{ \item{x}{A input variable/ column/ vector of strings to perform the logical assignment on} \item{reference}{A reference table (lookup table) having at least 2 column for matching the typo and returning the replacement} \item{typo_col}{The name ( as a "character") of the column in the reference table that allows the function to match with the input column "x"} \item{replace_col}{The name (as a "character) of the column in the reference table that contains the coresponding values to be returned.} } \value{ A vector (character) that contains the replacement values from the reference table } \description{ Logical assignment, a MJP team defined function } \examples{ ref_table <- data.frame(cars = rownames(mtcars), cyl = mtcars$cyl) fun_logic_assg(rownames(mtcars) , ref_table, typo_col = "cars", replace_col = "cyl") }

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chk_names_complete.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/chk-composite.R, R/err-composite.R \name{chk_names_complete} \alias{chk_names_complete} \alias{err_names_complete} \title{Check that an object has a complete set of names} \usage{ chk_names_complete(x, name) err_names_complete(x, name) } \arguments{ \item{x}{An object, typically a vector.} \item{name}{The name for \code{x} that will be used in error messages.} } \description{ Check that an object has a complete set of names } \examples{ x <- c(a = 1, b = 1.2) chk_names_complete(x = x, name = "x")#' } \seealso{ \code{\link{chk_names_dimnames_complete}}, \code{\link{chk_has_dimnames}}, \code{\link{chk_array_metadata_complete}} }

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cleaning.R

# This is module for a Shiny web application. # # Application : Word Prediction module # by : Simon Kong # date : 21 Apr 2016 suppressPackageStartupMessages(c( library(shiny), library(tm), library(stylo), library(stringr))) # Loading N-gram for bi, tri and four-gram table quadData <- readRDS(file="./data/quadData.RData") triData <- readRDS(file="./data/triData.RData") biData <- readRDS(file="./data/biData.RData") # Cleaning model for input text text pre-processing before calling predict module dataCleaner<-function(text){ cleanText <- tolower(text) cleanText <- removePunctuation(cleanText) cleanText <- removeNumbers(cleanText) cleanText <- str_replace_all(cleanText, "[^[:alnum:]]", " ") cleanText <- stripWhitespace(cleanText) return(cleanText) } # cleaning module for input text as English text cleanText <- function(text){ textInput <- dataCleaner(text) textInput <- txt.to.words.ext(textInput, language="English.all", preserve.case = TRUE) return(textInput) } # Prediction next word module pre-formating before processing nextWordPrediction <- function(wordCount,textInput){ # if sentences more or equal to 3 words, take the last 3 words for prediction if (wordCount>=3) { textInput <- textInput[(wordCount-2):wordCount] } else if(wordCount==2) { textInput <- c(NA,textInput) } else { textInput <- c(NA,NA,textInput) } # Word prediction main module call # If no word prediction found in Quad-gram table, then back-off call to tri-gram, follows by bi-gram wordPrediction <- as.character(quadData[quadData$unigram==textInput[1] & quadData$bigram==textInput[2] & quadData$trigram==textInput[3],][1,]$quadgram) if(is.na(wordPrediction)) { wordPrediction1 <- as.character(triData[triData$unigram==textInput[2] & triData$bigram==textInput[3],][1,]$trigram) if(is.na(wordPrediction)) { wordPrediction <- as.character(biData[biData$unigram==textInput[3],][1,]$bigram) } } print(wordPrediction) }

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getNHINoBasedOnRCFNo.R

#' Get NHINO based on RCFNO #' #' @import dplyr #' @param df data.frame include RCFNO #' @param RCFNoColName A colum for RCFNo of df #' @export get.NHINoViaRCFNo <- function(df, RCFNoColName = RCFNO){ colnames(df)[colnames(df)==deparse(substitute(RCFNoColName))] <- "RCFNO1" NHINoData <- df %>% select(RCFNO1) %>% as.data.table() NHINoData[, RCFNo := substr(RCFNO1, 1, 7)] NHINoData <- left_join(NHINoData, resCGDAx,by = "RCFNo") %>% select(RCFNO1, NHINO1) colnames(NHINoData)[colnames(NHINoData)== "RCFNO1"] <- deparse(substitute(RCFNoColName)) return(NHINoData) }

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## ARMA(2,1)モデルのシミュレーション n <- 1000 a <- c(0.8, -0.64) # AR の係数 b <- -0.5 # MA の係数 # for文で生成 epsilon <- rnorm(n) x0 <- rnorm(2) # 初期値を乱数で指定 x <- ts(double(n)) x[1:2] <- x0 for(i in 3:n) x[i] <- a %*% x[i - 1:2] + b*epsilon[i-1] + epsilon[i] plot(x) # arima.simで生成する方法(初期値の指定は出来ない) # 関数arma.simのノイズはデフォルトでは標準正規列 y <- arima.sim(list(ar=a, ma = b), n) lines(y,col="red")

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decloess.Rd.R

library(pastecs) ### Name: decloess ### Title: Time series decomposition by the LOESS method ### Aliases: decloess ### Keywords: ts smooth ### ** Examples data(releve) melo.regy <- regul(releve$Day, releve$Melosul, xmin=9, n=87, units="daystoyears", frequency=24, tol=2.2, methods="linear", datemin="21/03/1989", dateformat="d/m/Y") melo.ts <- tseries(melo.regy) melo.dec <- decloess(melo.ts, s.window="periodic") plot(melo.dec, col=1:3)

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NikhilSinghChandel/CodeTheGame-IITBombay-Final

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ui.r

library(shiny) shinyUI(pageWithSidebar( headerPanel("Code the Game"), sidebarPanel( fileInput('file1', 'Choose CSV File', accept=c('text/csv', 'text/comma-separated-values,text/plain', '.csv')), tags$hr(), fileInput('file2', 'Choose CSV File', accept=c('text/csv', 'text/comma-separated-values,text/plain', '.csv')), tags$hr(), downloadButton('downloadData', 'Download')), mainPanel( tableOutput('table') ) ))

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/R/tm1_get_dimension_elements.R

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catgopal/tm1r

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tm1_get_dimension_elements.R

tm1_get_dimension_elements <- function(tm1_connection, dimension) { tm1_adminhost <- tm1_connection$adminhost tm1_httpport <- tm1_connection$port tm1_auth_key <- tm1_connection$key tm1_ssl <- tm1_connection$ssl # added because some http does not know space dimension <- gsub(" ", "%20", dimension, fixed=TRUE) u1 <- ifelse(tm1_ssl==TRUE, "https://", "http://") #u1 <- "https://" u2 <- tm1_adminhost u3 <- ":" u4 <- tm1_httpport u5 <- "/api/v1/Dimensions('" u6 <- dimension u7 <- "')/Hierarchies('" u8 <- dimension u9 <- "')/Members?$select=Name&$expand=Element($select=Type)&$format=application/json;odata.metadata=none" # url development url <- paste0(u1, u2, u3, u4, u5, u6, u7, u8, u9) #url = "https://localhost:8881/api/v1/Dimensions('Account1')/Hierarchies('Account1')/Members? #$select=Name&$expand=Element($select=Type)&$format=application/json;odata.metadata=none" # post request tm1_process_return <- httr::GET(url, httr::add_headers("Authorization" = tm1_auth_key)) # check return if error if (is.null(jsonlite::fromJSON(httr::content(tm1_process_return, "text"))$error$message) == FALSE) { message(jsonlite::fromJSON(httr::content(tm1_process_return, "text"))$error$message) stop() } # make it proper tm1_dim_els <- jsonlite::fromJSON(httr::content(tm1_process_return, "text"))$value #change to data frame tm1_dim_els <- as.data.frame(tm1_dim_els) colnames(tm1_dim_els)[2] <- "Type" #return return(tm1_dim_els) }

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/Map-Making-USA/mini_project-2_1999.R

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divyanshu93/Implementation-of-Statistical-Methods

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mini_project-2_1999.R

usa.df<-map_data("state") str(usa.df) colnames(usa.df)[5]<-"state" usa.df$state<-as.factor(usa.df$state) str(usa.df) usa.dat <- read.table("/home/div/Documents/Frank_top_2012.csv", header = T, sep = ",") str(usa.dat) usa.df <- join(usa.df, usa.dat, by = "state", type = "inner") str(usa.df) usa.df = usa.df[usa.df$year==1999,] str(usa.df) range(usa.df$top1) brks<-c(0.184, 0.210, 0.220, 0.250, 0.270, 0.312, 0.376, 0.44, 0.5) p <- ggplot() + geom_polygon(data = usa.df, aes(x = long, y = lat, group = group, fill = top1),color = "black", size = 0.15) + scale_fill_distiller(palette = "Reds", breaks = brks, trans = "reverse") + theme_nothing(legend = TRUE) + labs(title = "Top 1% earners in USA in 1999 ", fill = "") #+ #geom_text(aes(x = lat_c, y = lon_c, group = group, label = state), # data = centers, # alpha = 1, # color = "black") ggsave(p, file = "/home/div/Documents/usa_top1_1999.pdf") #--------------------------------------------OR---------------------------------------- #q <- ggplot() + # geom_polygon(data = usa.df, aes(x = long, y = lat, group = group, fill = top1), # + color = "black", size = 0.15) + # scale_color_brewer(palette="Set1") + # theme_nothing(legend = TRUE) + # labs(title = "Top 1% earners in USA in 2012 ", fill = "") #ggsave(q, file = "usa_top1_2012C.pdf")

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/R/cubinfAM.R

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cran/robcbi

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2020-03-13T20:51:04.996148

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cubinfAM.R

cubinf.control <- function(tlo = 0.001, tua = 1.e-06, mxx = 30, mxt = 10, mxf = 10 , ntm = 0, gma = 1, iug = 1, ipo = 1, ilg = 2, icn = 1, icv = 1, ufact=0, cpar=1.5, null.dev = TRUE, ...) {list(tlo = tlo, tua = tua, mxx = mxx, mxt = mxt, mxf = mxf, ntm = ntm, gma = gma, iug=iug, ipo=ipo, ilg=ilg, icn = icn, icv = icv, ufact=ufact, cpar=cpar, null.dev=null.dev, ...)} cubinf <- function(x, y, weights = NULL, start=NULL, etastart=NULL, mustart=NULL, offset = NULL, family = binomial(), control = cubinf.control(...), intercept=FALSE, ...){ # x <- as.matrix(x) n <- nobs <- nrow(x) if (is.null(weights)) weights <- rep.int(1,n) if(any(weights != round(weights))) stop("Weights must be integer number") if(is.character(family)) family <- get(family,mode="function",envir=parent.frame()) if(is.function(family)) family <- family() nn <- family$family #["name"] if (nn == "gaussian") stop("Use lm(formula, ..., method=\"robust\") for the gaussian case") wi <- weights ics <- 0 if (nn == "binomial") { ics <- 2 if (is.matrix(y)) { if (dim(y)[2]>2) stop("Only binomial response matrices (2 columns)") ni <- as.vector(y %*% c(1,1)) y <- y[,1] ly <- length(y)} else { ics <- 1 if (is.factor(y)) y <- as.numeric(y != levels(y)[1]) else y <- as.vector(y) ly <- length(y) ni <- rep(1,ly) } } if (nn == "poisson") { if (any(y < 0)) stop("Negative values not allowed for the Poisson family") ics <- 3; ly <- length(y) ni <- rep(1,ly)} if (ics == 0) stop(paste(nn,": family not implemented for method='cubinf'", sep=" ")) eta <- ci <- ai <- rsdev <- y yy <- y; nni <- ni dn <- dimnames(x) xn <- dn[[2]] yn <- dn[[1]] # # Data preprocessing for weights >= 0 if (intercept & any(x[,1]!=1)) x <- cbind(1,x) p <- ncol(x) EMPTY <- p==0 ncov <- p*(p+1)/2 if (is.null(offset) || length(offset)==1) offset <- rep(0,ly) zero <- wi == 0 if (any(zero)) { pos <- !zero x0 <- x[zero, , drop=FALSE] y0 <- y[zero] x <- x[pos, , drop=FALSE] y <- y[pos] ni <- ni[pos] wi <- wi[pos] offset <- offset[pos] } nw <- length(wi) ind <- rep.int(1:nw,wi) x <- x[ind, , drop=FALSE] y <- y[ind] offset <- offset[ind] ni <- ni[ind] # # Initializations qrx <- qr(x,tol=1e-7,LAPACK=FALSE)[c("qr", "rank", "pivot", "qraux")] qrx$tol <- 1e-7 rank <- qrx$rank piv <- 1:rank control <- do.call("cubinf.control", control) tlo <- control$tlo tua <- control$tua mxx <- control$mxx mxt <- control$mxt mxf <- control$mxf ntm <- control$ntm gma <- control$gma iug <- control$iug ipo <- control$ipo ilg <- control$ilg icn <- control$icn icv <- control$icv null.dev <- control$null.dev tmp <- control$singular.ok if (!is.null(tmp)) singular.ok <- tmp else singular.ok <- FALSE tmp <- control$qr.out if (!is.null(tmp)) qr.out <- tmp else qr.out <- FALSE if (rank < p) { if (!singular.ok) stop(paste("x is singular, rank(x)=", rank)) else {piv <- qrx$pivot[1:rank] x <- x[, piv, drop=FALSE] if (any(zero)) x0 <- x0[,piv,drop=FALSE] xn <- dimnames(x)[[1]] } } # old <- comval() ufact <- control$ufact if (ufact==0) ufact <- 1.1 upar <- ufact*sqrt(rank) cpar <- control$cpar # dev <- control$dev # Deviance for the model reduced to the constant term. if(null.dev) {Null.dev <- cubinf.null(x, y, ni, offset, ics, family, control) if (nrow(x)==1 & intercept) return(list(deviance=Null.dev)) } else Null.dev <- NULL # # Initial theta, A (A0) and c (c0) zdir <- tempdir(); tmpcbi <- tempfile("Rcbi",zdir) sink(tmpcbi, type="output") #Redirection of message 460 in RYWALG if (ufact >= 20) cpar <- 20*cpar z <- gintac(x, y, ni, offset, icase=ics, tolt=10*tlo,tola=10*tlo, b=upar, c=cpar) theta0 <- z$theta[1:rank]; A0 <- z$a; c0 <- z$ci # # Initial cut off points a_i (wa) wa <- upar/pmax(1.e-3,z$dist) # # Initial covariance matrix of coefficient estimates vtheta <- as.vector(x%*%theta0) # z <- gfedca(vtheta, c0, wa, ni, offset, ics) # zc <- ktaskw(x, z$D, z$E, f=1/n, f1=1, iainv=0); covi <- zc$cov z <- gfedcaAM(vtheta, c0, wa, ni, offset, ics) zc <- covarAM(x, z$D, z$E,intercept=FALSE)$Cov covi <- zc[row(zc) <= col(zc)] iii <- cumsum(1:p) adiag <- round(covi[iii],4) jjj <- (iii)[adiag==0] if (length(jjj)>0) { covi[jjj] <- 1; cat("Info: initial cov re-defined\n",covi[1:ncov],"\n")} if (icn != 1) { zc <- mchl(covi, rank) zi <- minv(zc$a, rank) covi <- mtt1(zi$r, rank)$b} # # Final theta, A, c (ci) and a(wa) zf <- gymain(x, y, ni, covi, A0, theta0, offset, b=upar, gam=gma, tau=tua, icase=ics, iugl=iug, iopt=ipo, ialg=ilg, icnvt=icn, icnva=icv, maxit=mxx, maxtt=mxt, maxta=mxf, maxtc=mxt, nitmnt=ntm, nitmna=ntm, tol=tlo, tolt=10*tlo, tola=10*tlo, tolc=10*tlo) sink() unlink(tmpcbi,force=TRUE) nit <- zf$nit; converged <- TRUE if (mxx > 1 && nit == mxx) {cat("\nWarning: Maximum number of iterations [mxx=", mxx,"] reached.\n") converged <- FALSE} coefs <- zf$theta # # Deviance # zd <- glmdev(y, ni, zf$ci, zf$wa, zf$vtheta, offset, icase = ics) # # Final covariance matrix of coeff. estimates (modified march 2018) # sink("tmpzzz", type="output") #Redirection of message 450 in KTASKW # z <- gfedca(zf$vtheta, zf$ci, zf$wa, ni, offset, ics) # zc <- ktaskw(x, z$D, z$E, f=1/n, f1=1, iainv=0) z <- gfedcaAM(zf$vtheta, zf$ci, zf$wa, ni, offset, ics) zc <- covarAM(x, z$D, z$E,intercept=FALSE)$Cov # sink() A <- matrix(0, nrow=rank, ncol=rank) cov <- zc[1:rank,1:rank] i2 <- 0 for(i in 1:rank) { i1 <- i2 + 1 i2 <- i1 + i - 1 A[i, 1:i] <- zf$a[i1:i2] # cov[i,1:i] <- zc$cov[i1:i2] # cov[1:i,i] <- zc$cov[i1:i2] } xn <- dimnames(x)[[2]] xn <- xn[piv] attributes(coefs) <- NULL attributes(A) <- NULL attributes(cov) <- NULL attr(A,"dim") <- c(rank,rank) attr(cov,"dim") <- c(rank,rank) names(coefs) <- xn dimnames(A) <- list(xn,xn) dimnames(cov) <- list(xn,xn) asgn <- attr(x, "assign") ai <- zf$wa; ci <- zf$ci; rsdev <- zd$li-zd$sc # zl <- lrfctd(ics,y,ci,vtheta,offset,ai,ni,1,1,1) # Li <- zl$f0; li <- zl$f1; lip <- zl$f2 # rs <- zf$rs # # Compute eta, mu and residuals. dni <- c(ind[1],diff(ind)) lll <- dni!=0 iii <- cumsum(dni[lll]) lll <- as.vector(1*lll) jjj <- (1:n)*lll eta[iii] <- zf$vtheta[jjj] if(any(offset!=0)) offset[iii] <- offset[jjj] ci[iii] <- zf$ci[jjj] ai[iii] <- zf$wa[jjj] # Li[iii] <- Li[jjj] # li[iii] <- li[jjj] # lip[iii] <- lip[jjj] # rs[iii] <- rs[jjj] rsdev[iii] <- zd$li[jjj]-zd$sc[jjj] dni <- nni ni <- rep(1,length(eta)) ni[iii] <- dni[jjj] if (any(zero)) { eta[zero] <- as.vector(x0 %*% coefs) ci[zero] <- 0 ai[zero] <- 0 # Li[zero] <- 0 # li[zero] <- 0 # lip[zero] <- 0 # rs[zero] <- 0 offset[zero] <- 0 rsdev[zero] <- 0} mu <- family$linkinv(eta+offset) names(eta) <- yn if(rank < p) { coefs[piv[ - seq(rank)]] <- NA pasgn <- asgn newpos <- match(1:p, piv) names(newpos) <- xn for(j in names(asgn)) { aj <- asgn[[j]] aj <- aj[ok <- (nj <- newpos[aj]) <= rank] if(length(aj)) { asgn[[j]] <- aj pasgn[[j]] <- nj[ok] } else asgn[[j]] <- pasgn[[j]] <- NULL } cnames <- xn[piv] } new.dev <- zd$dev # new.dev <- sum(family$dev.resids(yy/ni, mu, w = rep(1.0, n))) resp <- yy/nni - mu if (any(zero)) {resp[zero] <- 0} names(ai) <- yn df.residual <- ly - rank - sum(weights==0) fit <- list(coefficients = coefs, fitted.values=mu, # effects = effects, weights=weights, ci=ci, rank = rank, assign = asgn, df.residual = df.residual, control=control) if(rank < p) { if(df.residual > 0) fit$assign.residual <- (rank + 1):n} if(qr.out) fit$qr <- qrx fit$A <- A fit$ai <- ai fit$cov <- cov fit$class <- "cubinf" fit$converged <- converged if (!all(weights==1)) fit$prior.weights <- weights if (!all(ni==1)) fit$ni <- ni rsdev <- sign(y-mu)*sqrt(2*abs(rsdev)*weights) attributes(zf$grad) <- NULL attributes(zf$hessnv) <- NULL residuals <- yy/nni - ci - mu attributes(residuals) <- NULL # Restore common values for ROBETH # dfcomn(ipsi = old$ipsi, c = old$c, d = old$d, beta = old$bta) c(fit, list(family = family$family, ics=ics, linear.predictors = eta, deviance = new.dev, null.deviance=Null.dev, iter = nit, qr=qrx, y = yy/nni, contrasts = attr(x,"contrasts"), rsdev = rsdev, gradient = zf$grad, inv.hessian = zf$hessnv, residuals = residuals)) } cubinf.null <- function(x, y, ni, offset, ics, family, control) { control <- do.call("cubinf.control", control) tlo <- control$tlo tua <- control$tua mxx <- control$mxx mxt <- control$mxt mxf <- control$mxf ntm <- control$ntm gma <- control$gma iug <- control$iug ipo <- control$ipo ilg <- control$ilg icn <- control$icn icv <- control$icv rank <- 1 ufact <- control$ufact if (ufact==0) ufact <- 1.1 upar <- ufact cpar <- control$cpar if (ufact >= 20) cpar <- 20*cpar ly <- length(y) w <- rep(1,ly) ai <- ci <- rep(0,ly) # intl <- attr(x, "term.labels") # int <- if(is.null(intl)) FALSE else as.logical(match(intl[1], c("(Int.)", # "(Intercept)"), FALSE)) linkinv <- family$linkinv dev.resids <- family$dev.resids # if(!int) {eta <- rep(0,ly); mu <- linkinv(eta+offset) # cval <- 0.5; if (ics==3) cval <- 1 # ci <- rep(cval,ly); ai <- rep(9999.,ly) } else { X <- matrix(rep(1,ly),ncol=1) if (ufact >= 20) cpar <- 20*cpar zdir <- tempdir(); tmpcbi <- tempfile("Rcbi",zdir) sink(tmpcbi, type="output") #Redirection of message 460 in RYWALG z <- gintac(X, y, ni, offset, icase = ics, tolt=10*tlo, tola=10*tlo, b = upar, c = cpar) t0 <- z$theta[1]; A0 <- z$a; c0 <- z$ci wa <- upar/pmax(1.e-3,z$dist) vtheta <- rep(t0,ly) # z <- gfedca(vtheta, c0, wa, ni, offset, ics) z <- gfedcaAM(vtheta, c0, wa, ni, offset, ics) zc <- ktaskw(X, z$D, z$E, f=1/ly, f1=1, iainv=0) covi <- zc$cov if (icn != 1) covi <- 1/covi zf <- gymain(X, y, ni, covi, A0, t0, offset, b=upar, gam=gma, tau=tua, icase=ics, iugl=iug, iopt=ipo, ialg=ilg, icnvt=icn, icnva=icv, maxit=mxx, maxtt=mxt, maxta=mxf, maxtc=mxt, nitmnt=ntm, nitmna=ntm, tol=tlo, tolt=10*tlo, tola=10*tlo, tolc=10*tlo) sink(); unlink(tmpcbi,force=TRUE) ai <- zf$wa ci <- zf$ci eta <- zf$vtheta # mu <- linkinv(eta+offset) # } zd <- glmdev(y,ni,ci,ai,eta,offset=offset,icase=ics) zd$dev } gfedcaAM <- function(vtheta, c0, wa, ni, offset,ics, precision=0) { psi <- function(x,c) { max(-c,min(x,c) ) } vtheta1 <- vtheta+offset n <- length(vtheta) E <- D <- rep(0,n) for ( i in 1:n) { sumE <- sumD <- 0; j <- 0; termE <- termD <- 100 if (ics==1 | ics==2) {probi <- exp(vtheta1[i])/(1+exp(vtheta1[i])) lambdai <- ni[i]*probi } if (ics==3) {lambdai <- exp(vtheta1[i])} while (max(termE,termD) > precision) { if (ics==1 | ics==2) {lpij <- dbinom(j,ni[i], probi, log = TRUE) } if (ics==3) {lpij <- dpois(j,lambdai, log = TRUE) } tmpsi <- psi( j-c0[i]-lambdai, c=wa[i]) termE <- log( tmpsi^2 ) + lpij termE <- exp(termE) sumE <- sumE + termE tmpsi <- tmpsi*(j-lambdai) if (tmpsi>0) { termD <- log(tmpsi) + lpij termD <- exp(termD) sumD <- sumD + termD } else { termD <- tmpsi*exp(lpij) sumD <- sumD + termD termD <- abs(termD) } j <- j+1 } E[i] <- sumE; D[i] <- sumD} list(D=D,E=E) } covarAM <- function(X,D,E,intercept=TRUE,tol=sqrt(.Machine$double.eps)) { XI = X if (intercept) XI = cbind(1,X) S1 <- t(XI)%*%(D*XI) S2 <- t(XI)%*%(E*XI) #SI = solve(S1) Xsvd <- svd(S1) Positive <- Xsvd$d > max(tol * Xsvd$d[1L], 0) if (all(Positive)) SI <- Xsvd$v %*% (1/Xsvd$d * t(Xsvd$u)) else if (!any(Positive)) SI <- array(0, dim(S1)[2L:1L]) else SI <- Xsvd$v[, Positive, drop = FALSE] %*% ((1/Xsvd$d[Positive]) * t(Xsvd$u[, Positive, drop = FALSE])) Cov <- SI%*%S2%*%SI list(Cov=Cov) }

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d191f64fc6fbe26d7927e1b1fc69d6015eb57e79

/ForecastContestExample.R

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XQ2013/Forcast-R-Example

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refs/heads/master

2020-07-01T21:15:45.191004

2019-10-01T18:07:56

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ForecastContestExample.R

# Load the raw training data and replace missing values with NA donations<- read.csv('donations.csv',header=T,na.strings=c("")) donations <- donations[,-c(1,18)] # Output the number of missing values for each column sapply(donations,function(x) sum(is.na(x))) # Quick check for how many different values for each feature sapply(donations, function(x) length(unique(x))) # R should automatically code Gifts, Gender as a factor(). A factor is R's way of dealing with # categorical variables is.factor(donations$Gender) # Returns TRUE is.factor(donations$Gifts) # Returns False donations$Gifts <- factor(x = donations$Gifts, levels = c(0,1), labels = c("No", "Yes")) is.factor(donations$Gifts) # Returns TRUE # Check categorical variables encoding for better understanding of the fitted model contrasts(donations$Gender) for (i in c(3, 15, 16)) { donations[[i]] <- gsub(pattern = "\\$", replacement = "", x = donations[[i]]) donations[[i]] <- as.numeric(donations[[i]]) } donations$Gifts <- ifelse(donations$Gifts == "Yes", 1,0) donations$Largest <- as.numeric(donations$Largest) donations$Last <- as.numeric(donations$Last) donations$St <- as.character(donations$St) donations$St <- ifelse(donations$St =="IL",1,0) donations$Type <- as.character(donations$Type) donations$Type <- ifelse(donations$Type == "Graduate", 1, 0) donations$LGDate<-as.numeric(donations$LGDate) donations$Class <- as.numeric(donations$Class) donations$Class1 <- 2015- donations$Class donations$FY1314 <- donations$FY13*donations$FY14 donations$log13 <- log(donations$FY13 + 1) donations$log14 <- log(donations$FY14 + 1) donations$logLast <- log(donations$Last + 1) Lg<-as.numeric(donations$LGDate) Cu<-as.Date("15Sep2015", "%d%b%Y") donations$newdate=Cu-Lg donations$newdate <- as.numeric(donations$newdate) currentdate <- as.Date("15Sep2015", "%d%b%Y") donations$TG <- as.character(donations$TestGroup1) donations$TestGroup1B <- ifelse(donations$TG == "B", 1, 0) donations$TestGroup1C <- ifelse(donations$TG == "C", 1, 0) donations$TestGroup1D <- ifelse(donations$TG == "D", 1, 0) # data transformation before generating train and test # Spliting the Data (Test & Train) set.seed(1234) Partition <- rbinom(n = nrow(donations) ,size = 1, prob = 0.7) donations <- data.frame(donations, Partition) train <- donations[donations$Partition==1, ] test <- donations[donations$Partition==0, ] # logistic model fit.logit <- glm(data = train, formula = Gifts ~ logLast+FY1314+log14+log13+newdate, family = binomial(),na.action = na.exclude) summary(fit.logit) # test the model prob <- predict.glm(fit.logit, test, type = "response") gifts <- as.numeric(test$Gifts) mean(gifts*log(prob) + (1 - gifts)*log(1-prob)) # regression model train2 <- train[train$CnGf_1_Amount > 0, ] test2 <- test[test$CnGf_1_Amount > 0, ] fit.reg <- lm(data = train2, formula = CnGf_1_Amount ~ Last+log14+log13+Class1+Left+newdate) summary(fit.reg) # test the model pred <- predict.lm(object = fit.reg, newdata = test2) mean(abs(pred-test2$CnGf_1_Amount)) # test the logit model #attach(donations) donations$Gifts <- as.numeric(donations$Gifts) donations$Gender <- as.numeric(donations$Gender) prob = .335 donations$Baselinell <- (donations$Gifts*log(prob) + (1 - donations$Gifts)*log(1-prob)) summary(donations) # # test the regression model test <- donations[donations$CnGf_1_Amount > 0, ] test$Baseline <- (abs( test$CnGf_1_Amount -test$Last)) summary(test) # model # Logistic Model ## Gift = 1.049e+01 +(-1.234e-01)logLast+(-1.678e-05)FY1314 + 5.006e-01log14 + 3.414e-01log13 + (-7.925e-04)newdate # # Regression # Donation Amount= 129.010335 + 0.906707Last + (-0.334862)log14 + 0.746060log13 + (-0.130387)Class1 + 0.020227Left + (-0.007011)newdate

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/character_classes.R \docType{data} \name{alnum} \alias{alnum} \title{Alpha-numeric} \format{An object of class \code{character} of length 62.} \usage{ alnum } \description{ Alpha-numeric } \keyword{datasets}

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test_polydata.R

library("ncdf4") context("NCDF SG polygonData tests") # data prep. # library(rgdal) # shapeData<-readOGR(dsn = "data/Yahara_alb/Yahara_River_HRUs_alb_eq.shp", # layer = "Yahara_River_HRUs_alb_eq", # stringsAsFactors = FALSE) # saveRDS(shapeData,file="data/yahara_shapefile_data.rds") test_that("A whole shapefile can be written", { polygonData <- readRDS("data/yahara_shapefile_data.rds") nc_file <- ToNCDFSG(nc_file=tempfile(), geomData = polygonData) nc<-nc_open(nc_file) crs <- list(grid_mapping_name = "albers_conical_equal_area", longitude_of_central_meridian = -96, latitude_of_projection_origin = 23, false_easting = 0.0, false_northing = 0.0, standard_parallel = c(29.5, 45.5), semi_major_axis = 6378137.0, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) expect_equal(ncatt_get(nc, pkg.env$crs_var_name)[names(crs)], crs) expect_equal(as.numeric(polygonData@data$GRIDCODE),as.numeric(ncvar_get(nc, varid = "GRIDCODE"))) expect_equal(length(nc$dim$instance$vals), length(polygonData@polygons)) for(var in names(polygonData@data)) { expect_equal(ncatt_get(nc, var, pkg.env$geometry_container_att_name)$value, pkg.env$geom_container_var_name) } coords<-polygonData@polygons[[1]]@Polygons[[1]]@coords expect_equal(as.numeric(coords[nrow(coords):1,1]),as.numeric(ncvar_get(nc, varid = "x", start = c(1), count = c(118)))) expect_equal(as.numeric(coords[nrow(coords):1,2]),as.numeric(ncvar_get(nc, varid = "y", start = c(1), count = c(118)))) # Check to make sure a hole is encoded correctly. node_count <- ncvar_get(nc, pkg.env$node_count_var_name) part_node_count <- ncvar_get(nc, pkg.env$part_node_count_var_name) part_type <- ncvar_get(nc, pkg.env$part_type_var_name) expect_equal(length(polygonData@polygons), length(node_count)) p <- 1 for(i in 1:length(node_count)) { nCount <- 0 for(j in 1:length(polygonData@polygons[[i]]@Polygons)) { if(polygonData@polygons[[i]]@Polygons[[j]]@hole) expect_equal(part_type[p], pkg.env$hole_val) expect_equal(length(polygonData@polygons[[i]]@Polygons[[j]]@coords[,1]), part_node_count[p]) nCount <- nCount + part_node_count[p] p <- p + 1 } expect_equal(nCount, node_count[i]) } checkAllPoly(polygonData, ncvar_get(nc,pkg.env$node_count_var_name), ncvar_get(nc,pkg.env$part_node_count_var_name), ncvar_get(nc,pkg.env$part_type_var_name)) returnPolyData<-FromNCDFSG(nc_file) compareSP(polygonData, returnPolyData) for(name in names(polygonData@data)) { expect_equal(as.character(polygonData@data[name]), as.character(returnPolyData@data[name])) } for(i in 1:length(returnPolyData@polygons)) { expect_equal(length(returnPolyData@polygons[[i]]@Polygons), length(polygonData@polygons[[i]]@Polygons)) for(j in 1:length(returnPolyData@polygons[[i]]@Polygons)) { expect_equal(length(returnPolyData@polygons[[i]]@Polygons[[j]]@coords), length(polygonData@polygons[[i]]@Polygons[[j]]@coords)) } } # writePolyShape(returnPolyData, "yaharaData_test") })

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rm(list=ls()) # clawling & wordcloud2 ## 01.clawling # install.packages('rvest') library(rvest) ### data road from wed(movie review) all_reviews<-c() test<-paste0('https://movie.daum.net/moviedb/grade?movieId=109924&type=netizen&page=', 2) test ### read html htxt=read_html(test) htxt table=html_nodes(htxt, '.review_info') table content=html_nodes(table, '.desc_review') content reviews=html_text(content) reviews reviews<-gsub('\n', '', reviews) reviews reviews<-gsub('\t', '', reviews) reviews writeLines(reviews, 'C:/Users/ktm/reviews2.txt') ## 02. clawling with for repeat all_url<-c() for (i in 1:20){ ### road url test<-paste0('https://movie.daum.net/moviedb/grade?movieId=46092&type=netizen&page=',i) all_url=c(all_url,test) } all_url all_reviews=c() for (i in 1:20){ ### read html htxt=read_html(all_url[i]) #리뷰영역 가져오기 table=html_nodes(htxt, '.review_info') #리뷰 가져오기 content=html_nodes(table, '.desc_review') reviews=html_text(content) reviews<-gsub('\n', '', reviews) # 불필요 부호 제거 reviews<-gsub('\t', '', reviews) # 불필요 부호 제거 all_reviews<-c(all_reviews, reviews) } all_reviews ## wordcloud ### extract noun nouns<-sapply(all_reviews, extractNoun, USE.NAMES = F) str(nouns) nouns<-unlist(nouns) str(nouns) ### Delete useless noun, confirm frequency nouns<-gsub('\\d+', '', nouns) nouns<-gsub('\t', '', nouns) nouns<-gsub('[a-zA-Z]', '', nouns) nouns<-nouns[nchar(nouns)>=2] nouns wordFreq<-

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operator_multiply.R

## Do this in a separate file to see the generated help: #library(devtools) #document() #load_all(as.package("../../onlineforecast")) #?"%**%" #' Multiplication of each element in a list (x) with y #' #' Each element of x is multiplied with y using the usual elementwise '*' operator. #' #' Typical use is when a function, e.g. \code{\link{bspline}()}, returns a list of matrices (e.g. one for each base spline) and they should individually be multiplied with y (a vector, matrix, etc.). #' #' Since this is intended to be used for forecast models in the transformation stage #' then there are some percularities: #' #' If the number of columns or the names of the columns are not equal for one element in x #' and y, then only the columns with same names are used, hence the resulting matrices can be #' of lower dimensions. #' #' See the example \url{https://onlineforecasting.org/examples/solar-power-forecasting.html} where the operator is used. #' #' @title Multiplication of list with y, elementwise #' @param x a list of matrices, data.frames, etc. #' @param y a vector, data.frame or matrix #' @return A list of same length of x #' @examples #' #' x <- list(matrix(1:9,3), matrix(9:1,3)) #' x #' #' y <- matrix(2,3,3) #' y #' #' x %**% y #' #' y <- 1:3 #' #' x %**% y #' #' # Naming percularity #' nams(x[[1]]) <- c("k1","k2","k3") #' nams(x[[2]]) <- c("k2","k3","k4") #' y <- matrix(2,3,3) #' nams(y) <- c("k1","k3","k7") #' #' # Now the only the horizons matching will be used #' x %**% y #' #' @export "%**%" <- function(x, y) { if( is.null(dim(y)) ){ ## y is not matrix like lapply(x, function(xx) { xx * y }) }else{ ## y is matrix like lapply(x, function(xx) { ## Check if different horizon k columns colmatch <- TRUE if (ncol(xx) != ncol(y)) { colmatch <- FALSE }else if(any(nams(xx) != nams(y))){ colmatch <- FALSE } if(!colmatch){ ## Not same columns, take only the k in both nms <- nams(xx)[nams(xx) %in% nams(y)] xx <- xx[, nms] y <- y[, nms] } ## Now multiply val <- xx * y ## Must be data.frame if( is.null(dim(val)) ){ val <- data.frame(val) nams(val) <- nms } return(val) }) } }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/recent_changes.R \name{recent_changes} \alias{recent_changes} \title{Retrieves entries from the RecentChanges feed} \usage{ recent_changes(language = NULL, project = NULL, domain = NULL, properties = c("user", "userid", "comment", "parsedcomment", "flags", "timestamp", "title", "ids", "sizes", "redirect", "loginfo", "tags", "sha1"), type = c("edit", "external", "new", "log"), tag = NULL, dir = "newer", limit = 50, top = FALSE, clean_response = FALSE, ...) } \arguments{ \item{language}{The language code of the project you wish to query, if appropriate.} \item{project}{The project you wish to query ("wikiquote"), if appropriate. Should be provided in conjunction with \code{language}.} \item{domain}{as an alternative to a \code{language} and \code{project} combination, you can also provide a domain ("rationalwiki.org") to the URL constructor, allowing for the querying of non-Wikimedia MediaWiki instances.} \item{properties}{Properties you're trying to retrieve about each entry, Options include "user" (the username of the person responsible for that entry), "userid" (the userID of said person), "comment" (the edit summary associated with the entry), "parsedcomment" (the same, but parsed, generating HTML from any wikitext in that comment), "flags" (whether the revision was 'minor' or not), "timestamp", "title" (the name of the page the entry affected), "ids" (the page id, along with the old and new revision IDs when applicable) "sizes" (the size, in uncompressed bytes, of the entry, and, in the case of revisions, the size of the edit it displaced), "tags" (any tags associated with the revision) and "loginfo" (applicable only to log entries, and consisting of log ID numbers, log types and actions, and so on) and "sha1" (the SHA-1 hash of the revision text).} \item{type}{The type of entry you want to retrieve; can be any permutation of "edit" (edits to existing pages), "external" (external actions that impact on the project - primarily wikidata changes), "new" (the creation of new pages) and "log" (log entries). By default, all of these entry types are included.} \item{tag}{Only return items with particular "tags", such as "mobile edit". NULL by default.} \item{dir}{Should it go from newest to oldest ("newer"), or oldest to newest ("older")? By default, set to "newer".} \item{limit}{The number of entries you'd like to return. By default, set to 50, which is also the maximum number per-request for logged-out users.} \item{top}{Should the request only return "top" entries - in other words, the most recent entry on a page? Set to FALSE by default.} \item{clean_response}{whether to do some basic sanitising of the resulting data structure. Set to FALSE by default.} \item{...}{further arguments to pass to httr's GET.} } \description{ wiki_recentchanges retrieves a stream of entries from Special:RecentChanges, with a variety of associated metadata and filtering (of both entries *and* that metadata. }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/getExperimentContainers.R \name{getExperimentContainers} \alias{getExperimentContainers} \title{getExperimentContainers - Gets experiment containers from experiment identified by barcode.} \usage{ getExperimentContainers(coreApi, experimentType, barcode, useVerbose = FALSE) } \arguments{ \item{coreApi}{coreApi object with valid jsessionid} \item{experimentType}{experiment entity type to get} \item{barcode}{barcode of experiment to query} \item{useVerbose}{TRUE or FALSE to indicate if verbose options should be used in http POST} } \value{ returns a list $entity contains barcodes of the containers, $response contains the entire http response } \description{ \code{getExperimentContainers} Gets experiment contaniers from experiment identified by experiment barcode. } \details{ \code{getExperimentContainers} Gets experiment containers from experiment identified by barcode. } \examples{ \dontrun{ api<-CoreAPIV2::CoreAPI("PATH TO JSON FILE") login<- CoreAPIV2::authBasic(api) exptCaontainerBarcodes <- CoreAPIV2::getExperimentContainers(login$coreApi,"entityType","barcode") CoreAPIV2:logOut(login$coreApi) } } \author{ Craig Parman ngsAnalytics, ngsanalytics.com }

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library(UsingR) # Carregar os dados ------------------------------------------------------- #Dados utilizados por Galton em 1885 data("galton") library(reshape) long = melt(galton) g = ggplot(long, aes(x = value, fill = variable)) + geom_histogram(colour = "black", binwidth = 1) + facet_grid(.~variable)

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PAF <- function(pop, cn, mat){ ##!! hard coding of indices: 1=sex, 2=age or age_cat paf <- apply(mat,1,function(x)sum(pop[[cn]][pop[[1]]==x[1]&pop[[2]]==x[2]])) paf }

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% Generated by roxygen2 (4.0.1): do not edit by hand \name{do_the_perms} \alias{do_the_perms} \title{function to do the permutations} \usage{ do_the_perms(kg_file, trio_file, meta_file, REPS = 1000, ItalianCommas = FALSE) } \description{ function to do the permutations }

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library(seewave) ### Name: simspec ### Title: Similarity between two frequency spectra ### Aliases: simspec ### Keywords: dplot ts ### ** Examples a<-noisew(f=8000,d=1) b<-synth(f=8000,d=1,cf=2000) c<-synth(f=8000,d=1,cf=1000) d<-noisew(f=8000,d=1) speca<-spec(a,f=8000,at=0.5,plot=FALSE) specb<-spec(b,f=8000,at=0.5,plot=FALSE) specc<-spec(c,f=8000,at=0.5,plot=FALSE) specd<-spec(d,f=8000,at=0.5,plot=FALSE) simspec(speca,speca) simspec(speca,specb) simspec(speca,specc,plot=TRUE) simspec(specb,specc,plot=TRUE) #[1] 12.05652 simspec(speca,specd,plot=TRUE) ## mel scale require(tuneR) data(orni) data(tico) orni.mel <- melfcc(orni, nbands = 256, dcttype = "t3", fbtype = "htkmel", spec_out=TRUE) orni.mel.mean <- apply(orni.mel$aspectrum, MARGIN=2, FUN=mean) tico.mel <- melfcc(tico, nbands = 256, dcttype = "t3", fbtype = "htkmel", spec_out=TRUE) tico.mel.mean <- apply(tico.mel$aspectrum, MARGIN=2, FUN=mean) simspec(orni.mel.mean, tico.mel.mean, f=22050, mel=TRUE, plot=TRUE)