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1 <tool id="lda_analy1" name="Perform LDA" version="1.0.1">
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2 <description>Linear Discriminant Analysis</description>
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3 <command interpreter="sh">r_wrapper.sh $script_file</command>
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4 <inputs>
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5 <param format="tabular" name="input" type="data" label="Source file"/>
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6 <param name="cond" size="30" type="integer" value="3" label="Number of principal components" help="See TIP below">
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7 <validator type="empty_field" message="Enter a valid number of principal components, see syntax below for examples"/>
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8 </param>
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9
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10 </inputs>
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11 <outputs>
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12 <data format="txt" name="output" />
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13 </outputs>
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14
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15 <tests>
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16 <test>
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17 <param name="input" value="matrix_generator_for_pc_and_lda_output.tabular"/>
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18 <output name="output" file="lda_analy_output.txt"/>
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19 <param name="cond" value="2"/>
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20
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21 </test>
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22 </tests>
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23
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24 <configfiles>
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25 <configfile name="script_file">
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26
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27 rm(list = objects() )
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28
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29 ############# FORMAT X DATA #########################
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30 format<-function(data) {
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31 ind=NULL
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32 for(i in 1 : ncol(data)){
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33 if (is.na(data[nrow(data),i])) {
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34 ind<-c(ind,i)
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35 }
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36 }
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37 #print(is.null(ind))
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38 if (!is.null(ind)) {
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39 data<-data[,-c(ind)]
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40 }
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41
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42 data
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43 }
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44
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45 ########GET RESPONSES ###############################
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46 get_resp<- function(data) {
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47 resp1<-as.vector(data[,ncol(data)])
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48 resp=numeric(length(resp1))
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49 for (i in 1:length(resp1)) {
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50 if (resp1[i]=="Y ") {
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51 resp[i] = 0
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52 }
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53 if (resp1[i]=="X ") {
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54 resp[i] = 1
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55 }
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56 }
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57 return(resp)
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58 }
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59
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60 ######## CHARS TO NUMBERS ###########################
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61 f_to_numbers<- function(F) {
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62 ind<-NULL
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63 G<-matrix(0,nrow(F), ncol(F))
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64 for (i in 1:nrow(F)) {
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65 for (j in 1:ncol(F)) {
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66 G[i,j]<-as.integer(F[i,j])
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67 }
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68 }
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69 return(G)
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70 }
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71
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72 ###################NORMALIZING#########################
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73 norm <- function(M, a=NULL, b=NULL) {
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74 C<-NULL
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75 ind<-NULL
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76
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77 for (i in 1: ncol(M)) {
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78 if (sd(M[,i])!=0) {
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79 M[,i]<-(M[,i]-mean(M[,i]))/sd(M[,i])
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80 }
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81 # else {print(mean(M[,i]))}
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82 }
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83 return(M)
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84 }
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85
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86 ##### LDA DIRECTIONS #################################
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87 lda_dec <- function(data, k){
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88 priors=numeric(k)
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89 grandmean<-numeric(ncol(data)-1)
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90 means=matrix(0,k,ncol(data)-1)
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91 B = matrix(0, ncol(data)-1, ncol(data)-1)
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92 N=nrow(data)
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93 for (i in 1:k){
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94 priors[i]=sum(data[,1]==i)/N
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95 grp=subset(data,data\$group==i)
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96 means[i,]=mean(grp[,2:ncol(data)])
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97 #print(means[i,])
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98 #print(priors[i])
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99 #print(priors[i]*means[i,])
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100 grandmean = priors[i]*means[i,] + grandmean
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101 }
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102
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103 for (i in 1:k) {
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104 B= B + priors[i]*((means[i,]-grandmean)%*%t(means[i,]-grandmean))
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105 }
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106
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107 W = var(data[,2:ncol(data)])
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108 svdW = svd(W)
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109 inv_sqrtW =solve(svdW\$v %*% diag(sqrt(svdW\$d)) %*% t(svdW\$v))
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110 B_star= t(inv_sqrtW)%*%B%*%inv_sqrtW
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111 B_star_decomp = svd(B_star)
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112 directions = inv_sqrtW%*%B_star_decomp\$v
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113 return( list(directions, B_star_decomp\$d) )
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114 }
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115
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116 ################ NAIVE BAYES FOR 1D SIR OR LDA ##############
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117 naive_bayes_classifier <- function(resp, tr_data, test_data, k=2, tau) {
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118 tr_data=data.frame(resp=resp, dir=tr_data)
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119 means=numeric(k)
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120 #print(k)
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121 cl=numeric(k)
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122 predclass=numeric(length(test_data))
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123 for (i in 1:k) {
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124 grp = subset(tr_data, resp==i)
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125 means[i] = mean(grp\$dir)
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126 #print(i, means[i])
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127 }
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128 cutoff = tau*means[1]+(1-tau)*means[2]
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129 #print(tau)
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130 #print(means)
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131 #print(cutoff)
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132 if (cutoff>means[1]) {
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133 cl[1]=1
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134 cl[2]=2
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135 }
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136 else {
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137 cl[1]=2
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138 cl[2]=1
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139 }
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140
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141 for (i in 1:length(test_data)) {
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142
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143 if (test_data[i] <= cutoff) {
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144 predclass[i] = cl[1]
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145 }
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146 else {
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147 predclass[i] = cl[2]
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148 }
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149 }
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150 #print(means)
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151 #print(mean(means))
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152 #X11()
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153 #plot(test_data,pch=predclass, col=resp)
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154 predclass
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155 }
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156
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157 ################# EXTENDED ERROR RATES #################
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158 ext_error_rate <- function(predclass, actualclass,msg=c("you forgot the message"), pr=1) {
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159 er=sum(predclass != actualclass)/length(predclass)
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160
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161 matr<-data.frame(predclass=predclass,actualclass=actualclass)
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162 escapes = subset(matr, actualclass==1)
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163 subjects = subset(matr, actualclass==2)
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164 er_esc=sum(escapes\$predclass != escapes\$actualclass)/length(escapes\$predclass)
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165 er_subj=sum(subjects\$predclass != subjects\$actualclass)/length(subjects\$predclass)
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166
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167 if (pr==1) {
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168 # print(paste(c(msg, 'overall : ', (1-er)*100, "%."),collapse=" "))
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169 # print(paste(c(msg, 'within escapes : ', (1-er_esc)*100, "%."),collapse=" "))
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170 # print(paste(c(msg, 'within subjects: ', (1-er_subj)*100, "%."),collapse=" "))
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171 }
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172 return(c((1-er)*100, (1-er_esc)*100, (1-er_subj)*100))
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173 }
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174
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175 ## Main Function ##
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176
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177 files<-matrix("${input}", 1,1, byrow=T)
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178
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179 d<-"${cond}" # Number of PC
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180
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181 tau<-seq(0,1, by=0.005)
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182 #tau<-seq(0,1, by=0.1)
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183 for_curve=matrix(-10, 3,length(tau))
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184
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185 ##############################################################
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186
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187 test_data_whole_X <-read.delim(files[1,1], row.names=1)
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188
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189 #### FORMAT TRAINING DATA ####################################
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190 # get only necessary columns
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191
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192 test_data_whole_X<-format(test_data_whole_X)
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193 oligo_labels<-test_data_whole_X[1:(nrow(test_data_whole_X)-1),ncol(test_data_whole_X)]
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194 test_data_whole_X<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)]
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195
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196 X_names<-colnames(test_data_whole_X)[1:ncol(test_data_whole_X)]
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197 test_data_whole_X<-t(test_data_whole_X)
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198 resp<-get_resp(test_data_whole_X)
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199 ldaqda_resp = resp + 1
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200 a<-sum(resp) # Number of Subject
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201 b<-length(resp) - a # Number of Escape
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202 ## FREQUENCIES #################################################
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203 F<-test_data_whole_X[,1:(ncol(test_data_whole_X)-1)]
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204 F<-f_to_numbers(F)
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205 FN<-norm(F, a, b)
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206 ss<-svd(FN)
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207 eigvar<-NULL
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208 eig<-ss\$d^2
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209
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210 for ( i in 1:length(ss\$d)) {
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211 eigvar[i]<-sum(eig[1:i])/sum(eig)
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212 }
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213
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214 #print(paste(c("Variance explained : ", eigvar[d]*100, "%"), collapse=""))
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215
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216 Z<-F%*%ss\$v
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217
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218 ldaqda_data <- data.frame(group=ldaqda_resp,Z[,1:d])
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219 lda_dir<-lda_dec(ldaqda_data,2)
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220 train_lda_pred <-Z[,1:d]%*%lda_dir[[1]]
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221
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222 ############# NAIVE BAYES CROSS-VALIDATION #############
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223 ### LDA #####
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224
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225 y<-ldaqda_resp
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226 X<-F
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227 cv<-matrix(c(rep('NA',nrow(test_data_whole_X))), nrow(test_data_whole_X), length(tau))
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228 for (i in 1:nrow(test_data_whole_X)) {
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229 # print(i)
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230 resp<-y[-i]
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231 p<-matrix(X[-i,], dim(X)[1]-1, dim(X)[2])
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232 testdata<-matrix(X[i,],1,dim(X)[2])
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233 p1<-norm(p)
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234 sss<-svd(p1)
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235 pred<-(p%*%sss\$v)[,1:d]
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236 test<- (testdata%*%sss\$v)[,1:d]
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237 lda <- lda_dec(data.frame(group=resp,pred),2)
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238 pred <- pred[,1:d]%*%lda[[1]][,1]
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239 test <- test%*%lda[[1]][,1]
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240 test<-matrix(test, 1, length(test))
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241 for (t in 1:length(tau)) {
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242 cv[i, t] <- naive_bayes_classifier (resp, pred, test,k=2, tau[t])
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243 }
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244 }
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245
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246 for (t in 1:length(tau)) {
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247 tr_err<-ext_error_rate(cv[,t], ldaqda_resp , c("CV"), 1)
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248 for_curve[1:3,t]<-tr_err
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249 }
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250
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251 dput(for_curve, file="${output}")
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252
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253
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254 </configfile>
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255 </configfiles>
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256
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257 <help>
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258
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259 .. class:: infomark
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260
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261 **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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262
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263 -----
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264
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265 .. class:: infomark
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266
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267 **What it does**
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268
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269 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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270
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271 *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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272
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273 -----
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274
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275 .. class:: warningmark
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276
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277 **Note**
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278
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279 - Output from "Generate A Matrix" tool is used as input file for this tool
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280 - 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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281
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282
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283 </help>
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284
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285 </tool>
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