## ----setup, include = FALSE---------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----whole_analysis------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") data<-loadFile(fileInput) scaledI<-rescaleI(data,samples=1000, scalingUpTo="MaxMin") fn = file.path(tempdir(),"output.csv",fsep = .Platform$file.sep) saveFile(fn,scaledI) if (file.exists(fn)) #Delete file if it exists file.remove(fn) ## ----Rho Correction------------------------------------------------------ fileInput <- system.file("testdata", "chen.csv", package="Irescale") data <- loadFile(fileInput) rectifiedI<-rectifyIrho(data,1000) fn = file.path(tempdir(),"output.csv",fsep = .Platform$file.sep) saveFile(fn,rectifiedI) if (file.exists(fn)) #Delete file if it exists file.remove(fn) ## ------------------------------------------------------------------------ fileInput<-system.file("testdata", "chen.csv", package="Irescale") head(read.csv(fileInput)) ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) head(input$data) head(input$varOfInterest) ## ------------------------------------------------------------------------ library(Irescale) fileInput<-"../inst/testdata/chessboard.csv" input<-loadChessBoard(fileInput) head(input$data) head(input$varOfInterest) ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) distM[1:5,1:5] ## ------------------------------------------------------------------------ library(Irescale) fileInput<-"../inst/testdata/chessboard.csv" input<-loadChessBoard(fileInput) distM<-calculateManhattanDistance(input$data) distM[1:5,1:5] ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) distW<-calculateWeightedDistMatrix(distM) distW[1:5,1:5] ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest) I ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest) vI<-resamplingI(distM, input$varOfInterest) # This is the permutation statsVI<-summaryVector(vI) statsVI ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest) vI<-resamplingI(distM, input$varOfInterest) # This is the permutation statsVI<-summaryVector(vI) plotHistogramOverlayNormal(vI,statsVI, main=colnames(input$varOfInterest)) ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest) vI<-resamplingI(distM, input$varOfInterest) # This is the permutation statsVI<-summaryVector(vI) corrections<-iCorrection(I,vI) corrections$newI ## ------------------------------------------------------------------------ fileInput <- system.file("testdata", "chen.csv", package="Irescale") data <- loadFile(fileInput) distM<-calculateEuclideanDistance(data$data) vI<-resamplingI(distM,data$varOfInterest,n = 100000) rectifiedI<- ItoPearsonCorrelation(vI, length(data)) rectifiedI$newI ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) distM<-calculateEuclideanDistance(input$data) I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest) vI<-resamplingI(distM, input$varOfInterest) # This is the permutation statsVI<-summaryVector(vI) corrections<-iCorrection(I,vI) pvalueIscaled<-calculatePvalue(corrections$scaledData,corrections$newI,corrections$summaryScaledD$mean) pvalueIscaled ## ------------------------------------------------------------------------ library(Irescale) fileInput<-system.file("testdata", "chen.csv", package="Irescale") input<-loadFile(fileInput) resultsChen<-buildStabilityTable(data=input, times=10, samples=10^2, plots=TRUE) ## ------------------------------------------------------------------------ fileInput <- system.file("testdata", "chen.csv", package="Irescale") data <- loadFile(fileInput) resultsChen<-buildStabilityTableForCorrelation(data=data,times=10,samples=10^2,plots=TRUE)