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. 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+ ,7 + ,13 + ,17 + ,78 + ,47 + ,36 + ,34 + ,12 + ,6 + ,13 + ,11 + ,71 + ,44 + ,33 + ,32 + ,16 + ,9 + ,13 + ,13 + ,72 + ,45 + ,37 + ,33 + ,12 + ,10 + ,12 + ,17 + ,68 + ,44 + ,34 + ,33 + ,14 + ,11 + ,12 + ,15 + ,67 + ,43 + ,35 + ,37 + ,16 + ,12 + ,9 + ,21 + ,75 + ,43 + ,31 + ,32 + ,14 + ,8 + ,9 + ,18 + ,62 + ,40 + ,37 + ,34 + ,13 + ,11 + ,15 + ,15 + ,67 + ,41 + ,35 + ,30 + ,4 + ,3 + ,10 + ,8 + ,83 + ,52 + ,27 + ,30 + ,15 + ,11 + ,14 + ,12 + ,64 + ,38 + ,34 + ,38 + ,11 + ,12 + ,15 + ,12 + ,68 + ,41 + ,40 + ,36 + ,11 + ,7 + ,7 + ,22 + ,62 + ,39 + ,29 + ,32 + ,14 + ,9 + ,14 + ,12 + ,72 + ,43) + ,dim=c(8 + ,264) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Sport1' + ,'Sport2') + ,1:264)) > y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2'),1:264)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'yes' > par3 = '' > par2 = 'none' > par1 = '5' > par4 <- 'yes' > par3 <- '' > par2 <- 'none' > par1 <- '5' > #'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] "Happiness" > x[,par1] [1] 14 18 11 12 16 18 14 14 15 15 17 19 10 16 18 14 14 17 14 16 18 11 14 12 17 [26] 9 16 14 15 11 16 13 17 15 14 16 9 15 17 13 15 16 16 12 15 11 15 15 17 13 [51] 16 14 11 12 12 15 16 15 12 12 8 13 11 14 15 10 11 12 15 15 14 16 15 15 13 [76] 12 17 13 15 13 15 15 16 15 14 15 14 13 7 17 13 15 14 13 16 12 14 17 15 17 [101] 12 16 11 15 9 16 15 10 10 15 11 13 14 18 16 14 14 14 14 12 14 15 15 15 13 [126] 17 17 19 15 13 9 15 15 15 16 11 14 11 15 13 15 16 14 15 16 16 11 12 9 16 [151] 13 16 12 9 13 13 14 19 13 12 13 10 14 16 10 11 14 12 9 9 11 16 9 13 16 [176] 13 9 12 16 11 14 13 15 14 16 13 14 15 13 11 11 14 15 11 15 12 14 14 8 13 [201] 9 15 17 13 15 15 14 16 13 16 9 16 11 10 11 15 17 14 8 15 11 16 10 15 9 [226] 16 19 12 8 11 14 9 15 13 16 11 12 13 10 11 12 8 12 12 15 11 13 14 10 12 [251] 15 13 13 13 12 12 9 9 15 10 14 15 7 14 > 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]) 7 8 9 10 11 12 13 14 15 16 17 18 19 2 5 16 11 25 26 33 39 51 33 14 5 4 > colnames(x) [1] "Connected" "Separate" "Learning" "Software" "Happiness" [6] "Depression" "Sport1" "Sport2" > colnames(x)[par1] [1] "Happiness" > x[,par1] [1] 14 18 11 12 16 18 14 14 15 15 17 19 10 16 18 14 14 17 14 16 18 11 14 12 17 [26] 9 16 14 15 11 16 13 17 15 14 16 9 15 17 13 15 16 16 12 15 11 15 15 17 13 [51] 16 14 11 12 12 15 16 15 12 12 8 13 11 14 15 10 11 12 15 15 14 16 15 15 13 [76] 12 17 13 15 13 15 15 16 15 14 15 14 13 7 17 13 15 14 13 16 12 14 17 15 17 [101] 12 16 11 15 9 16 15 10 10 15 11 13 14 18 16 14 14 14 14 12 14 15 15 15 13 [126] 17 17 19 15 13 9 15 15 15 16 11 14 11 15 13 15 16 14 15 16 16 11 12 9 16 [151] 13 16 12 9 13 13 14 19 13 12 13 10 14 16 10 11 14 12 9 9 11 16 9 13 16 [176] 13 9 12 16 11 14 13 15 14 16 13 14 15 13 11 11 14 15 11 15 12 14 14 8 13 [201] 9 15 17 13 15 15 14 16 13 16 9 16 11 10 11 15 17 14 8 15 11 16 10 15 9 [226] 16 19 12 8 11 14 9 15 13 16 11 12 13 10 11 12 8 12 12 15 11 13 14 10 12 [251] 15 13 13 13 12 12 9 9 15 10 14 15 7 14 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/1abp51385553610.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Happiness Inputs: Connected, Separate, Learning, Software, Depression, Sport1, Sport2 Number of observations: 264 1) Depression <= 15; criterion = 1, statistic = 89.37 2) Depression <= 11; criterion = 0.998, statistic = 13.033 3)* weights = 81 2) Depression > 11 4)* weights = 121 1) Depression > 15 5)* weights = 62 > postscript(file="/var/wessaorg/rcomp/tmp/2xlpq1385553610.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/wessaorg/rcomp/tmp/3gn6f1385553610.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 14 13.85950 0.14049587 2 18 14.95062 3.04938272 3 11 13.85950 -2.85950413 4 12 13.85950 -1.85950413 5 16 10.95161 5.04838710 6 18 13.85950 4.14049587 7 14 10.95161 3.04838710 8 14 14.95062 -0.95061728 9 15 14.95062 0.04938272 10 15 13.85950 1.14049587 11 17 14.95062 2.04938272 12 19 14.95062 4.04938272 13 10 13.85950 -3.85950413 14 16 13.85950 2.14049587 15 18 14.95062 3.04938272 16 14 13.85950 0.14049587 17 14 13.85950 0.14049587 18 17 14.95062 2.04938272 19 14 14.95062 -0.95061728 20 16 13.85950 2.14049587 21 18 14.95062 3.04938272 22 11 13.85950 -2.85950413 23 14 13.85950 0.14049587 24 12 13.85950 -1.85950413 25 17 14.95062 2.04938272 26 9 14.95062 -5.95061728 27 16 14.95062 1.04938272 28 14 13.85950 0.14049587 29 15 13.85950 1.14049587 30 11 13.85950 -2.85950413 31 16 14.95062 1.04938272 32 13 13.85950 -0.85950413 33 17 14.95062 2.04938272 34 15 14.95062 0.04938272 35 14 13.85950 0.14049587 36 16 14.95062 1.04938272 37 9 10.95161 -1.95161290 38 15 13.85950 1.14049587 39 17 14.95062 2.04938272 40 13 14.95062 -1.95061728 41 15 14.95062 0.04938272 42 16 13.85950 2.14049587 43 16 14.95062 1.04938272 44 12 13.85950 -1.85950413 45 15 14.95062 0.04938272 46 11 13.85950 -2.85950413 47 15 14.95062 0.04938272 48 15 14.95062 0.04938272 49 17 13.85950 3.14049587 50 13 14.95062 -1.95061728 51 16 14.95062 1.04938272 52 14 13.85950 0.14049587 53 11 10.95161 0.04838710 54 12 13.85950 -1.85950413 55 12 14.95062 -2.95061728 56 15 13.85950 1.14049587 57 16 13.85950 2.14049587 58 15 14.95062 0.04938272 59 12 14.95062 -2.95061728 60 12 13.85950 -1.85950413 61 8 10.95161 -2.95161290 62 13 13.85950 -0.85950413 63 11 13.85950 -2.85950413 64 14 13.85950 0.14049587 65 15 13.85950 1.14049587 66 10 14.95062 -4.95061728 67 11 10.95161 0.04838710 68 12 13.85950 -1.85950413 69 15 13.85950 1.14049587 70 15 13.85950 1.14049587 71 14 13.85950 0.14049587 72 16 13.85950 2.14049587 73 15 13.85950 1.14049587 74 15 14.95062 0.04938272 75 13 14.95062 -1.95061728 76 12 10.95161 1.04838710 77 17 13.85950 3.14049587 78 13 10.95161 2.04838710 79 15 13.85950 1.14049587 80 13 14.95062 -1.95061728 81 15 14.95062 0.04938272 82 15 14.95062 0.04938272 83 16 13.85950 2.14049587 84 15 13.85950 1.14049587 85 14 13.85950 0.14049587 86 15 13.85950 1.14049587 87 14 13.85950 0.14049587 88 13 13.85950 -0.85950413 89 7 10.95161 -3.95161290 90 17 13.85950 3.14049587 91 13 13.85950 -0.85950413 92 15 13.85950 1.14049587 93 14 13.85950 0.14049587 94 13 13.85950 -0.85950413 95 16 14.95062 1.04938272 96 12 10.95161 1.04838710 97 14 14.95062 -0.95061728 98 17 14.95062 2.04938272 99 15 14.95062 0.04938272 100 17 14.95062 2.04938272 101 12 10.95161 1.04838710 102 16 13.85950 2.14049587 103 11 14.95062 -3.95061728 104 15 10.95161 4.04838710 105 9 10.95161 -1.95161290 106 16 14.95062 1.04938272 107 15 10.95161 4.04838710 108 10 13.85950 -3.85950413 109 10 10.95161 -0.95161290 110 15 13.85950 1.14049587 111 11 13.85950 -2.85950413 112 13 14.95062 -1.95061728 113 14 13.85950 0.14049587 114 18 13.85950 4.14049587 115 16 14.95062 1.04938272 116 14 13.85950 0.14049587 117 14 13.85950 0.14049587 118 14 14.95062 -0.95061728 119 14 13.85950 0.14049587 120 12 13.85950 -1.85950413 121 14 13.85950 0.14049587 122 15 14.95062 0.04938272 123 15 14.95062 0.04938272 124 15 14.95062 0.04938272 125 13 14.95062 -1.95061728 126 17 14.95062 2.04938272 127 17 14.95062 2.04938272 128 19 14.95062 4.04938272 129 15 13.85950 1.14049587 130 13 14.95062 -1.95061728 131 9 10.95161 -1.95161290 132 15 14.95062 0.04938272 133 15 13.85950 1.14049587 134 15 13.85950 1.14049587 135 16 13.85950 2.14049587 136 11 10.95161 0.04838710 137 14 13.85950 0.14049587 138 11 10.95161 0.04838710 139 15 14.95062 0.04938272 140 13 13.85950 -0.85950413 141 15 14.95062 0.04938272 142 16 13.85950 2.14049587 143 14 13.85950 0.14049587 144 15 13.85950 1.14049587 145 16 14.95062 1.04938272 146 16 13.85950 2.14049587 147 11 13.85950 -2.85950413 148 12 14.95062 -2.95061728 149 9 10.95161 -1.95161290 150 16 13.85950 2.14049587 151 13 10.95161 2.04838710 152 16 14.95062 1.04938272 153 12 13.85950 -1.85950413 154 9 10.95161 -1.95161290 155 13 10.95161 2.04838710 156 13 13.85950 -0.85950413 157 14 13.85950 0.14049587 158 19 14.95062 4.04938272 159 13 14.95062 -1.95061728 160 12 10.95161 1.04838710 161 13 10.95161 2.04838710 162 10 10.95161 -0.95161290 163 14 13.85950 0.14049587 164 16 10.95161 5.04838710 165 10 10.95161 -0.95161290 166 11 10.95161 0.04838710 167 14 13.85950 0.14049587 168 12 13.85950 -1.85950413 169 9 10.95161 -1.95161290 170 9 10.95161 -1.95161290 171 11 10.95161 0.04838710 172 16 13.85950 2.14049587 173 9 13.85950 -4.85950413 174 13 10.95161 2.04838710 175 16 13.85950 2.14049587 176 13 14.95062 -1.95061728 177 9 10.95161 -1.95161290 178 12 10.95161 1.04838710 179 16 14.95062 1.04938272 180 11 13.85950 -2.85950413 181 14 14.95062 -0.95061728 182 13 14.95062 -1.95061728 183 15 14.95062 0.04938272 184 14 14.95062 -0.95061728 185 16 14.95062 1.04938272 186 13 10.95161 2.04838710 187 14 13.85950 0.14049587 188 15 13.85950 1.14049587 189 13 13.85950 -0.85950413 190 11 10.95161 0.04838710 191 11 13.85950 -2.85950413 192 14 14.95062 -0.95061728 193 15 13.85950 1.14049587 194 11 10.95161 0.04838710 195 15 13.85950 1.14049587 196 12 13.85950 -1.85950413 197 14 10.95161 3.04838710 198 14 13.85950 0.14049587 199 8 10.95161 -2.95161290 200 13 13.85950 -0.85950413 201 9 10.95161 -1.95161290 202 15 13.85950 1.14049587 203 17 14.95062 2.04938272 204 13 13.85950 -0.85950413 205 15 14.95062 0.04938272 206 15 13.85950 1.14049587 207 14 13.85950 0.14049587 208 16 13.85950 2.14049587 209 13 13.85950 -0.85950413 210 16 13.85950 2.14049587 211 9 10.95161 -1.95161290 212 16 14.95062 1.04938272 213 11 10.95161 0.04838710 214 10 13.85950 -3.85950413 215 11 10.95161 0.04838710 216 15 13.85950 1.14049587 217 17 14.95062 2.04938272 218 14 13.85950 0.14049587 219 8 10.95161 -2.95161290 220 15 13.85950 1.14049587 221 11 13.85950 -2.85950413 222 16 13.85950 2.14049587 223 10 10.95161 -0.95161290 224 15 14.95062 0.04938272 225 9 10.95161 -1.95161290 226 16 14.95062 1.04938272 227 19 14.95062 4.04938272 228 12 13.85950 -1.85950413 229 8 10.95161 -2.95161290 230 11 13.85950 -2.85950413 231 14 13.85950 0.14049587 232 9 10.95161 -1.95161290 233 15 14.95062 0.04938272 234 13 10.95161 2.04838710 235 16 14.95062 1.04938272 236 11 13.85950 -2.85950413 237 12 10.95161 1.04838710 238 13 13.85950 -0.85950413 239 10 14.95062 -4.95061728 240 11 13.85950 -2.85950413 241 12 14.95062 -2.95061728 242 8 10.95161 -2.95161290 243 12 10.95161 1.04838710 244 12 10.95161 1.04838710 245 15 13.85950 1.14049587 246 11 10.95161 0.04838710 247 13 13.85950 -0.85950413 248 14 10.95161 3.04838710 249 10 10.95161 -0.95161290 250 12 10.95161 1.04838710 251 15 13.85950 1.14049587 252 13 10.95161 2.04838710 253 13 14.95062 -1.95061728 254 13 13.85950 -0.85950413 255 12 10.95161 1.04838710 256 12 13.85950 -1.85950413 257 9 10.95161 -1.95161290 258 9 10.95161 -1.95161290 259 15 13.85950 1.14049587 260 10 14.95062 -4.95061728 261 14 13.85950 0.14049587 262 15 13.85950 1.14049587 263 7 10.95161 -3.95161290 264 14 13.85950 0.14049587 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/40oee1385553610.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/wessaorg/rcomp/tmp/5i7mp1385553610.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/wessaorg/rcomp/tmp/66ax61385553611.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/wessaorg/rcomp/tmp/72kww1385553611.tab") + } > > try(system("convert tmp/2xlpq1385553610.ps tmp/2xlpq1385553610.png",intern=TRUE)) character(0) > try(system("convert tmp/3gn6f1385553610.ps tmp/3gn6f1385553610.png",intern=TRUE)) character(0) > try(system("convert tmp/40oee1385553610.ps tmp/40oee1385553610.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.636 2.462 15.215