R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1 + ,119.992 + ,0.06545 + ,0.02971 + ,0.00784 + ,2.301442 + ,0.414783 + ,1 + ,122.4 + ,0.09403 + ,0.04368 + ,0.00968 + ,2.486855 + ,0.458359 + ,1 + ,116.682 + ,0.0827 + ,0.0359 + ,0.0105 + ,2.342259 + ,0.429895 + ,1 + ,116.676 + ,0.08771 + ,0.03772 + ,0.00997 + ,2.405554 + ,0.434969 + ,1 + ,116.014 + ,0.1047 + ,0.04465 + ,0.01284 + ,2.33218 + ,0.417356 + ,1 + ,120.552 + ,0.06985 + ,0.03243 + ,0.00968 + ,2.18756 + ,0.415564 + ,1 + ,120.267 + ,0.02337 + ,0.01351 + ,0.00333 + ,1.854785 + ,0.59604 + ,1 + ,107.332 + ,0.02487 + ,0.01256 + ,0.0029 + ,2.064693 + ,0.63742 + ,1 + ,95.73 + ,0.03218 + ,0.01717 + ,0.00551 + ,2.322511 + ,0.615551 + ,1 + ,95.056 + ,0.04324 + ,0.02444 + ,0.00532 + ,2.432792 + ,0.547037 + ,1 + ,88.333 + ,0.03237 + ,0.01892 + ,0.00505 + ,2.407313 + ,0.611137 + ,1 + ,91.904 + ,0.04272 + ,0.02214 + ,0.0054 + ,2.642476 + ,0.58339 + ,1 + ,136.926 + ,0.01968 + ,0.0114 + ,0.00293 + ,2.041277 + ,0.4606 + ,1 + ,139.173 + ,0.02184 + 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,0.407567 + ,0 + ,198.764 + ,0.03794 + ,0.01588 + ,0.0074 + ,2.138608 + ,0.451221 + ,0 + ,214.289 + ,0.03078 + ,0.01373 + ,0.00567 + ,2.555477 + ,0.462803) + ,dim=c(7 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'Shimmer:DDA' + ,'MDVP:APQ' + ,'MDVP:Jitter(%)' + ,'D2' + ,'RPDE') + ,1:195)) > y <- array(NA,dim=c(7,195),dimnames=list(c('status','MDVP:Fo(Hz)','Shimmer:DDA','MDVP:APQ','MDVP:Jitter(%)','D2','RPDE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '' > par2 = 'none' > par1 = '1' > par4 <- 'no' > par3 <- '' > par2 <- 'none' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object is masked from 'package:survival': untangle.specials The following objects are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "status" > x[,par1] [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 [38] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$centers,1:par3),ncol=2) + clm <- clm[sort.list(clm[,1]),] + for (i in 1:par3) { + cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') + } + cl$cluster <- as.factor(cl$cluster) + print(cl$cluster) + x[,par1] <- cl$cluster + } > if (par2 == 'quantiles') { + x[,par1] <- cut2(x[,par1],g=par3) + } > if (par2 == 'hclust') { + hc <- hclust(dist(x[,par1])^2, 'cen') + print(hc) + memb <- cutree(hc, k = par3) + dum <- c(mean(x[memb==1,par1])) + for (i in 2:par3) { + dum <- c(dum, mean(x[memb==i,par1])) + } + hcm <- matrix(cbind(dum,1:par3),ncol=2) + hcm <- hcm[sort.list(hcm[,1]),] + for (i in 1:par3) { + memb[memb==hcm[i,2]] <- paste('C',i,sep='') + } + memb <- as.factor(memb) + print(memb) + x[,par1] <- memb + } > if (par2=='equal') { + ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) + x[,par1] <- as.factor(ed) + } > table(x[,par1]) 0 1 48 147 > colnames(x) [1] "status" "MDVP.Fo.Hz." "Shimmer.DDA" "MDVP.APQ" [5] "MDVP.Jitter..." "D2" "RPDE" > colnames(x)[par1] [1] "status" > x[,par1] [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 [38] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/1tce71386673501.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: status Inputs: MDVP.Fo.Hz., Shimmer.DDA, MDVP.APQ, MDVP.Jitter..., D2, RPDE Number of observations: 195 1) MDVP.Fo.Hz. <= 193.03; criterion = 1, statistic = 28.537 2) D2 <= 2.223719; criterion = 0.998, statistic = 12.805 3) Shimmer.DDA <= 0.02789; criterion = 0.966, statistic = 7.604 4) MDVP.Fo.Hz. <= 117.226; criterion = 0.994, statistic = 10.743 5)* weights = 13 4) MDVP.Fo.Hz. > 117.226 6)* weights = 12 3) Shimmer.DDA > 0.02789 7)* weights = 33 2) D2 > 2.223719 8)* weights = 96 1) MDVP.Fo.Hz. > 193.03 9) D2 <= 2.460791; criterion = 0.999, statistic = 13.947 10) MDVP.Jitter... <= 0.00298; criterion = 0.977, statistic = 8.343 11)* weights = 18 10) MDVP.Jitter... > 0.00298 12)* weights = 7 9) D2 > 2.460791 13)* weights = 16 > postscript(file="/var/fisher/rcomp/tmp/2e5011386673501.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3tbyp1386673501.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > if (par2 == 'none') { + forec <- predict(m) + result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) + colnames(result) <- c('Actuals','Forecasts','Residuals') + print(result) + } Actuals Forecasts Residuals 1 1 0.96875000 0.03125000 2 1 0.96875000 0.03125000 3 1 0.96875000 0.03125000 4 1 0.96875000 0.03125000 5 1 0.96875000 0.03125000 6 1 0.90909091 0.09090909 7 1 0.83333333 0.16666667 8 1 0.07692308 0.92307692 9 1 0.96875000 0.03125000 10 1 0.96875000 0.03125000 11 1 0.96875000 0.03125000 12 1 0.96875000 0.03125000 13 1 0.83333333 0.16666667 14 1 0.96875000 0.03125000 15 1 0.90909091 0.09090909 16 1 0.83333333 0.16666667 17 1 0.96875000 0.03125000 18 1 0.96875000 0.03125000 19 1 0.96875000 0.03125000 20 1 0.96875000 0.03125000 21 1 0.96875000 0.03125000 22 1 0.96875000 0.03125000 23 1 0.96875000 0.03125000 24 1 0.96875000 0.03125000 25 1 0.96875000 0.03125000 26 1 0.96875000 0.03125000 27 1 0.96875000 0.03125000 28 1 0.96875000 0.03125000 29 1 0.96875000 0.03125000 30 1 0.83333333 0.16666667 31 0 0.00000000 0.00000000 32 0 0.00000000 0.00000000 33 0 0.00000000 0.00000000 34 0 0.00000000 0.00000000 35 0 0.00000000 0.00000000 36 0 0.00000000 0.00000000 37 1 0.96875000 0.03125000 38 1 0.96875000 0.03125000 39 1 0.96875000 0.03125000 40 1 0.96875000 0.03125000 41 1 0.96875000 0.03125000 42 1 0.96875000 0.03125000 43 0 0.00000000 0.00000000 44 0 0.00000000 0.00000000 45 0 0.00000000 0.00000000 46 0 0.00000000 0.00000000 47 0 0.00000000 0.00000000 48 0 0.00000000 0.00000000 49 0 0.83333333 -0.83333333 50 0 0.83333333 -0.83333333 51 0 0.90909091 -0.90909091 52 0 0.96875000 -0.96875000 53 0 0.90909091 -0.90909091 54 0 0.90909091 -0.90909091 55 1 0.90909091 0.09090909 56 1 0.90909091 0.09090909 57 1 0.90909091 0.09090909 58 1 0.90909091 0.09090909 59 1 0.90909091 0.09090909 60 1 0.90909091 0.09090909 61 0 0.00000000 0.00000000 62 0 0.00000000 0.00000000 63 0 0.00000000 0.00000000 64 0 0.75000000 -0.75000000 65 0 0.00000000 0.00000000 66 0 0.00000000 0.00000000 67 1 0.96875000 0.03125000 68 1 0.96875000 0.03125000 69 1 0.90909091 0.09090909 70 1 0.90909091 0.09090909 71 1 0.96875000 0.03125000 72 1 0.96875000 0.03125000 73 1 0.90909091 0.09090909 74 1 0.90909091 0.09090909 75 1 0.96875000 0.03125000 76 1 0.90909091 0.09090909 77 1 0.90909091 0.09090909 78 1 0.90909091 0.09090909 79 1 0.90909091 0.09090909 80 1 0.96875000 0.03125000 81 1 0.96875000 0.03125000 82 1 0.96875000 0.03125000 83 1 0.96875000 0.03125000 84 1 0.90909091 0.09090909 85 1 0.96875000 0.03125000 86 1 0.96875000 0.03125000 87 1 0.96875000 0.03125000 88 1 0.96875000 0.03125000 89 1 0.96875000 0.03125000 90 1 0.96875000 0.03125000 91 1 0.96875000 0.03125000 92 1 0.96875000 0.03125000 93 1 0.96875000 0.03125000 94 1 0.90909091 0.09090909 95 1 0.90909091 0.09090909 96 1 0.96875000 0.03125000 97 1 0.96875000 0.03125000 98 1 0.96875000 0.03125000 99 1 0.96875000 0.03125000 100 1 0.96875000 0.03125000 101 1 0.96875000 0.03125000 102 1 0.96875000 0.03125000 103 1 0.96875000 0.03125000 104 1 0.83333333 0.16666667 105 1 0.83333333 0.16666667 106 1 0.83333333 0.16666667 107 1 0.83333333 0.16666667 108 1 0.96875000 0.03125000 109 1 0.96875000 0.03125000 110 1 0.96875000 0.03125000 111 1 0.75000000 0.25000000 112 1 0.75000000 0.25000000 113 1 0.14285714 0.85714286 114 1 0.75000000 0.25000000 115 1 0.75000000 0.25000000 116 1 0.96875000 0.03125000 117 1 0.96875000 0.03125000 118 1 0.96875000 0.03125000 119 1 0.96875000 0.03125000 120 1 0.75000000 0.25000000 121 1 0.96875000 0.03125000 122 1 0.96875000 0.03125000 123 1 0.90909091 0.09090909 124 1 0.90909091 0.09090909 125 1 0.90909091 0.09090909 126 1 0.90909091 0.09090909 127 1 0.90909091 0.09090909 128 1 0.90909091 0.09090909 129 1 0.96875000 0.03125000 130 1 0.83333333 0.16666667 131 1 0.96875000 0.03125000 132 1 0.96875000 0.03125000 133 1 0.96875000 0.03125000 134 1 0.83333333 0.16666667 135 1 0.96875000 0.03125000 136 1 0.96875000 0.03125000 137 1 0.96875000 0.03125000 138 1 0.96875000 0.03125000 139 1 0.96875000 0.03125000 140 1 0.96875000 0.03125000 141 1 0.96875000 0.03125000 142 1 0.75000000 0.25000000 143 1 0.75000000 0.25000000 144 1 0.75000000 0.25000000 145 1 0.75000000 0.25000000 146 1 0.75000000 0.25000000 147 1 0.96875000 0.03125000 148 1 0.96875000 0.03125000 149 1 0.96875000 0.03125000 150 1 0.75000000 0.25000000 151 1 0.96875000 0.03125000 152 1 0.96875000 0.03125000 153 1 0.75000000 0.25000000 154 1 0.90909091 0.09090909 155 1 0.96875000 0.03125000 156 1 0.96875000 0.03125000 157 1 0.96875000 0.03125000 158 1 0.96875000 0.03125000 159 1 0.96875000 0.03125000 160 1 0.90909091 0.09090909 161 1 0.90909091 0.09090909 162 1 0.96875000 0.03125000 163 1 0.96875000 0.03125000 164 1 0.90909091 0.09090909 165 1 0.96875000 0.03125000 166 0 0.00000000 0.00000000 167 0 0.14285714 -0.14285714 168 0 0.14285714 -0.14285714 169 0 0.75000000 -0.75000000 170 0 0.14285714 -0.14285714 171 0 0.14285714 -0.14285714 172 0 0.07692308 -0.07692308 173 0 0.07692308 -0.07692308 174 0 0.07692308 -0.07692308 175 0 0.07692308 -0.07692308 176 0 0.07692308 -0.07692308 177 0 0.07692308 -0.07692308 178 1 0.96875000 0.03125000 179 1 0.96875000 0.03125000 180 1 0.96875000 0.03125000 181 1 0.96875000 0.03125000 182 1 0.96875000 0.03125000 183 1 0.90909091 0.09090909 184 0 0.07692308 -0.07692308 185 0 0.07692308 -0.07692308 186 0 0.07692308 -0.07692308 187 0 0.07692308 -0.07692308 188 0 0.07692308 -0.07692308 189 0 0.07692308 -0.07692308 190 0 0.14285714 -0.14285714 191 0 0.96875000 -0.96875000 192 0 0.75000000 -0.75000000 193 0 0.96875000 -0.96875000 194 0 0.14285714 -0.14285714 195 0 0.75000000 -0.75000000 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/fisher/rcomp/tmp/4lfv91386673501.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/5uwqw1386673501.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/61ckk1386673502.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/7zljl1386673502.tab") + } > > try(system("convert tmp/2e5011386673501.ps tmp/2e5011386673501.png",intern=TRUE)) character(0) > try(system("convert tmp/3tbyp1386673501.ps tmp/3tbyp1386673501.png",intern=TRUE)) character(0) > try(system("convert tmp/4lfv91386673501.ps tmp/4lfv91386673501.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.199 2.146 14.326