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259 lines
8.4 KiB
XML
259 lines
8.4 KiB
XML
<tool id="plot_for_lda_output1" name="Draw ROC plot" version="1.0.1">
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<description>on "Perform LDA" output</description>
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<command interpreter="sh">r_wrapper.sh $script_file</command>
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<inputs>
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<param format="txt" name="input" type="data" label="Source file"> </param>
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<param name="my_title" size="30" type="text" value="My Figure" label="Title of your plot" help="See syntax below"> </param>
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<param name="X_axis" size="30" type="text" value="Text for X axis" label="Legend of X axis in your plot" help="See syntax below"> </param>
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<param name="Y_axis" size="30" type="text" value="Text for Y axis" label="Legend of Y axis in your plot" help="See syntax below"> </param>
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</inputs>
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<outputs>
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<data format="pdf" name="pdf_output" />
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</outputs>
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<tests>
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<test>
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<param name="input" value="lda_analy_output.txt"/>
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<param name="my_title" value="Test Plot1"/>
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<param name="X_axis" value="Test Plot2"/>
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<param name="Y_axis" value="Test Plot3"/>
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<output name="pdf_output" file="plot_for_lda_output.pdf"/>
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</test>
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</tests>
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<configfiles>
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<configfile name="script_file">
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rm(list = objects() )
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############# FORMAT X DATA #########################
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format<-function(data) {
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ind=NULL
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for(i in 1 : ncol(data)){
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if (is.na(data[nrow(data),i])) {
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ind<-c(ind,i)
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}
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}
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#print(is.null(ind))
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if (!is.null(ind)) {
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data<-data[,-c(ind)]
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}
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data
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}
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########GET RESPONSES ###############################
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get_resp<- function(data) {
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resp1<-as.vector(data[,ncol(data)])
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resp=numeric(length(resp1))
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for (i in 1:length(resp1)) {
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if (resp1[i]=="Control ") {
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resp[i] = 0
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}
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if (resp1[i]=="XLMR ") {
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resp[i] = 1
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}
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}
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return(resp)
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}
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######## CHARS TO NUMBERS ###########################
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f_to_numbers<- function(F) {
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ind<-NULL
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G<-matrix(0,nrow(F), ncol(F))
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for (i in 1:nrow(F)) {
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for (j in 1:ncol(F)) {
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G[i,j]<-as.integer(F[i,j])
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}
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}
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return(G)
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}
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###################NORMALIZING#########################
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norm <- function(M, a=NULL, b=NULL) {
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C<-NULL
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ind<-NULL
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for (i in 1: ncol(M)) {
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if (sd(M[,i])!=0) {
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M[,i]<-(M[,i]-mean(M[,i]))/sd(M[,i])
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}
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# else {print(mean(M[,i]))}
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}
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return(M)
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}
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##### LDA DIRECTIONS #################################
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lda_dec <- function(data, k){
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priors=numeric(k)
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grandmean<-numeric(ncol(data)-1)
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means=matrix(0,k,ncol(data)-1)
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B = matrix(0, ncol(data)-1, ncol(data)-1)
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N=nrow(data)
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for (i in 1:k){
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priors[i]=sum(data[,1]==i)/N
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grp=subset(data,data\$group==i)
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means[i,]=mean(grp[,2:ncol(data)])
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#print(means[i,])
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#print(priors[i])
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#print(priors[i]*means[i,])
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grandmean = priors[i]*means[i,] + grandmean
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}
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for (i in 1:k) {
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B= B + priors[i]*((means[i,]-grandmean)%*%t(means[i,]-grandmean))
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}
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W = var(data[,2:ncol(data)])
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svdW = svd(W)
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inv_sqrtW =solve(svdW\$v %*% diag(sqrt(svdW\$d)) %*% t(svdW\$v))
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B_star= t(inv_sqrtW)%*%B%*%inv_sqrtW
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B_star_decomp = svd(B_star)
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directions = inv_sqrtW%*%B_star_decomp\$v
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return( list(directions, B_star_decomp\$d) )
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}
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################ NAIVE BAYES FOR 1D SIR OR LDA ##############
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naive_bayes_classifier <- function(resp, tr_data, test_data, k=2, tau) {
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tr_data=data.frame(resp=resp, dir=tr_data)
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means=numeric(k)
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#print(k)
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cl=numeric(k)
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predclass=numeric(length(test_data))
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for (i in 1:k) {
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grp = subset(tr_data, resp==i)
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means[i] = mean(grp\$dir)
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#print(i, means[i])
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}
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cutoff = tau*means[1]+(1-tau)*means[2]
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#print(tau)
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#print(means)
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#print(cutoff)
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if (cutoff>means[1]) {
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cl[1]=1
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cl[2]=2
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}
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else {
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cl[1]=2
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cl[2]=1
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}
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for (i in 1:length(test_data)) {
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if (test_data[i] <= cutoff) {
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predclass[i] = cl[1]
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}
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else {
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predclass[i] = cl[2]
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}
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}
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#print(means)
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#print(mean(means))
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#X11()
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#plot(test_data,pch=predclass, col=resp)
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predclass
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}
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################# EXTENDED ERROR RATES #################
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ext_error_rate <- function(predclass, actualclass,msg=c("you forgot the message"), pr=1) {
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er=sum(predclass != actualclass)/length(predclass)
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matr<-data.frame(predclass=predclass,actualclass=actualclass)
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escapes = subset(matr, actualclass==1)
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subjects = subset(matr, actualclass==2)
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er_esc=sum(escapes\$predclass != escapes\$actualclass)/length(escapes\$predclass)
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er_subj=sum(subjects\$predclass != subjects\$actualclass)/length(subjects\$predclass)
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if (pr==1) {
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# print(paste(c(msg, 'overall : ', (1-er)*100, "%."),collapse=" "))
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# print(paste(c(msg, 'within escapes : ', (1-er_esc)*100, "%."),collapse=" "))
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# print(paste(c(msg, 'within subjects: ', (1-er_subj)*100, "%."),collapse=" "))
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}
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return(c((1-er)*100, (1-er_esc)*100, (1-er_subj)*100))
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}
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## Main Function ##
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files_alias<-c("${my_title}")
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tau=seq(0,1,by=0.005)
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nfiles=1
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f = c("${input}")
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rez_ext<-list()
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for (i in 1:nfiles) {
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rez_ext[[i]]<-dget(paste(f[i], sep="",collapse=""))
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}
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tau<-tau[1:(length(tau)-1)]
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for (i in 1:nfiles) {
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rez_ext[[i]]<-rez_ext[[i]][,1:(length(tau)-1)]
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}
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######## OPTIMAIL TAU ###########################
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#rez_ext
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rate<-c("Optimal tau","Tr total", "Tr Y", "Tr X")
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m_tr<-numeric(nfiles)
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m_xp22<-numeric(nfiles)
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m_x<-numeric(nfiles)
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for (i in 1:nfiles) {
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r<-rez_ext[[i]]
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#tr
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# rate<-rbind(rate, c(files_alias[i]," "," "," ") )
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mm<-which((r[3,])==max(r[3,]))
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m_tr[i]<-mm[1]
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rate<-rbind(rate,c(tau[m_tr[i]],r[,m_tr[i]]))
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}
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print(rate)
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pdf(file= paste("${pdf_output}"))
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plot(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlim=c(0,100), ylim=c(0,100), xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col="blue", xaxt='n', yaxt='n')
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for (i in 1:nfiles) {
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lines(rez_ext[[i]][2,]~rez_ext[[i]][3,], xlab="${X_axis} [1-FP(False Positive)]", ylab="${Y_axis} [1-FP(False Positive)]", type="l", lty=1, col=i)
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# pt=c(r,)
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points(x=rez_ext[[i]][3,m_tr[i]],y=rez_ext[[i]][2,m_tr[i]], pch=16, col=i)
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}
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title(main="${my_title}", adj=0, cex.main=1.1)
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axis(2, at=c(0,20,40,60,80,100), labels=c('0','20','40','60','80','100%'))
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axis(1, at=c(0,20,40,60,80,100), labels=c('0','20','40','60','80','100%'))
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#leg=c("10 kb","50 kb","100 kb")
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#legend("bottomleft",legend=leg , col=c(1,2,3), lty=c(1,1,1))
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#dev.off()
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</configfile>
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</configfiles>
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<help>
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.. class:: infomark
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**What it does**
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This tool generates a Receiver Operating Characteristic (ROC) plot that shows LDA classification success rates for different values of the tuning parameter tau as Figure 3 in Carrel et al., 2006 (PMID: 17009873).
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*Carrel L, Park C, Tyekucheva S, Dunn J, Chiaromonte F, et al. (2006) Genomic Environment Predicts Expression Patterns on the Human Inactive X Chromosome. PLoS Genet 2(9): e151. doi:10.1371/journal.pgen.0020151*
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-----
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.. class:: warningmark
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**Note**
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- Output from "Perform LDA" tool is used as input file for this tool.
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</help>
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</tool>
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