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286 lines
9.3 KiB
XML
286 lines
9.3 KiB
XML
<tool id="lda_analy1" name="Perform LDA" version="1.0.1">
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<description>Linear Discriminant Analysis</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="tabular" name="input" type="data" label="Source file"/>
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<param name="cond" size="30" type="integer" value="3" label="Number of principal components" help="See TIP below">
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<validator type="empty_field" message="Enter a valid number of principal components, see syntax below for examples"/>
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</param>
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</inputs>
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<outputs>
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<data format="txt" name="output" />
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</outputs>
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<tests>
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<test>
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<param name="input" value="matrix_generator_for_pc_and_lda_output.tabular"/>
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<output name="output" file="lda_analy_output.txt"/>
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<param name="cond" value="2"/>
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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]=="Y ") {
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resp[i] = 0
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}
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if (resp1[i]=="X ") {
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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<-matrix("${input}", 1,1, byrow=T)
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d<-"${cond}" # Number of PC
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tau<-seq(0,1, by=0.005)
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#tau<-seq(0,1, by=0.1)
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for_curve=matrix(-10, 3,length(tau))
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##############################################################
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test_data_whole_X <-read.delim(files[1,1], row.names=1)
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#### FORMAT TRAINING DATA ####################################
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# get only necessary columns
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test_data_whole_X<-format(test_data_whole_X)
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oligo_labels<-test_data_whole_X[1:(nrow(test_data_whole_X)-1),ncol(test_data_whole_X)]
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test_data_whole_X<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)]
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X_names<-colnames(test_data_whole_X)[1:ncol(test_data_whole_X)]
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test_data_whole_X<-t(test_data_whole_X)
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resp<-get_resp(test_data_whole_X)
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ldaqda_resp = resp + 1
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a<-sum(resp) # Number of Subject
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b<-length(resp) - a # Number of Escape
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## FREQUENCIES #################################################
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F<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)]
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F<-f_to_numbers(F)
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FN<-norm(F, a, b)
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ss<-svd(FN)
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eigvar<-NULL
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eig<-ss\$d^2
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for ( i in 1:length(ss\$d)) {
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eigvar[i]<-sum(eig[1:i])/sum(eig)
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}
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#print(paste(c("Variance explained : ", eigvar[d]*100, "%"), collapse=""))
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Z<-F%*%ss\$v
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ldaqda_data <- data.frame(group=ldaqda_resp,Z[,1:d])
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lda_dir<-lda_dec(ldaqda_data,2)
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train_lda_pred <-Z[,1:d]%*%lda_dir[[1]]
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############# NAIVE BAYES CROSS-VALIDATION #############
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### LDA #####
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y<-ldaqda_resp
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X<-F
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cv<-matrix(c(rep('NA',nrow(test_data_whole_X))), nrow(test_data_whole_X), length(tau))
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for (i in 1:nrow(test_data_whole_X)) {
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# print(i)
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resp<-y[-i]
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p<-matrix(X[-i,], dim(X)[1]-1, dim(X)[2])
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testdata<-matrix(X[i,],1,dim(X)[2])
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p1<-norm(p)
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sss<-svd(p1)
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pred<-(p%*%sss\$v)[,1:d]
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test<- (testdata%*%sss\$v)[,1:d]
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lda <- lda_dec(data.frame(group=resp,pred),2)
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pred <- pred[,1:d]%*%lda[[1]][,1]
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test <- test%*%lda[[1]][,1]
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test<-matrix(test, 1, length(test))
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for (t in 1:length(tau)) {
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cv[i, t] <- naive_bayes_classifier (resp, pred, test,k=2, tau[t])
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}
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}
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for (t in 1:length(tau)) {
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tr_err<-ext_error_rate(cv[,t], ldaqda_resp , c("CV"), 1)
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for_curve[1:3,t]<-tr_err
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}
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dput(for_curve, file="${output}")
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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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**TIP:** If you want to perform Principal Component Analysis (PCA) on the give numeric input data (which corresponds to the "Source file First in "Generate A Matrix" tool), please use *Multivariate Analysis/Principal Component Analysis*
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-----
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.. class:: infomark
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**What it does**
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This tool consists of the module to perform the Linear Discriminant Analysis as described 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 "Generate A Matrix" tool is used as input file for this tool
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- Output of this tool contains LDA classification success rates for different values of the turning parameter tau (from 0 to 1 with 0.005 interval). This output file will be used to establish the ROC plot, and you can obtain more detail information from this plot.
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</help>
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</tool>
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