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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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > 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 = '3' > par2 = 'none' > par1 = '6' > 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] "Software" > x[,par1] [1] 12 11 15 6 13 10 12 14 12 9 10 12 12 11 15 12 10 12 11 12 11 12 13 11 12 [26] 13 10 14 12 10 12 8 10 12 12 7 9 12 10 10 10 12 15 10 10 12 13 11 11 12 [51] 14 10 12 13 5 6 12 12 11 10 7 12 14 11 12 13 14 11 12 12 8 11 14 14 12 [76] 9 13 11 12 12 12 12 12 11 10 9 12 12 12 9 15 12 12 12 10 13 9 12 10 14 [101] 11 15 11 11 12 12 12 11 7 12 14 11 10 13 13 8 11 12 11 13 12 14 13 15 10 [126] 11 9 11 10 11 8 11 12 12 9 11 10 8 9 8 9 15 11 8 13 12 12 9 7 13 [151] 9 6 8 8 6 9 11 8 10 8 14 10 8 11 12 12 12 5 12 10 7 12 11 8 9 [176] 10 9 12 6 15 12 12 12 11 7 7 5 12 12 3 11 10 12 9 12 9 12 10 9 12 [201] 8 11 11 12 10 10 12 12 11 8 12 10 11 10 8 12 12 10 12 9 6 10 9 9 9 [226] 6 10 6 14 10 10 6 12 12 7 8 11 3 6 8 9 9 8 9 7 7 6 9 10 11 [251] 12 8 11 3 11 12 7 9 12 8 11 8 10 8 7 8 10 8 12 14 7 6 11 4 9 [276] 5 9 11 12 9 12 10 9 6 10 9 13 12 > 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]) 3 4 5 6 7 8 9 10 11 12 13 14 15 3 1 4 13 13 25 32 40 42 79 15 13 8 > colnames(x) [1] "Pop" "Gender" "Connected" "Separate" "Learning" "Software" [7] "Happiness" > colnames(x)[par1] [1] "Software" > x[,par1] [1] 12 11 15 6 13 10 12 14 12 9 10 12 12 11 15 12 10 12 11 12 11 12 13 11 12 [26] 13 10 14 12 10 12 8 10 12 12 7 9 12 10 10 10 12 15 10 10 12 13 11 11 12 [51] 14 10 12 13 5 6 12 12 11 10 7 12 14 11 12 13 14 11 12 12 8 11 14 14 12 [76] 9 13 11 12 12 12 12 12 11 10 9 12 12 12 9 15 12 12 12 10 13 9 12 10 14 [101] 11 15 11 11 12 12 12 11 7 12 14 11 10 13 13 8 11 12 11 13 12 14 13 15 10 [126] 11 9 11 10 11 8 11 12 12 9 11 10 8 9 8 9 15 11 8 13 12 12 9 7 13 [151] 9 6 8 8 6 9 11 8 10 8 14 10 8 11 12 12 12 5 12 10 7 12 11 8 9 [176] 10 9 12 6 15 12 12 12 11 7 7 5 12 12 3 11 10 12 9 12 9 12 10 9 12 [201] 8 11 11 12 10 10 12 12 11 8 12 10 11 10 8 12 12 10 12 9 6 10 9 9 9 [226] 6 10 6 14 10 10 6 12 12 7 8 11 3 6 8 9 9 8 9 7 7 6 9 10 11 [251] 12 8 11 3 11 12 7 9 12 8 11 8 10 8 7 8 10 8 12 14 7 6 11 4 9 [276] 5 9 11 12 9 12 10 9 6 10 9 13 12 > 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/1eeyd1386623951.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: Software Inputs: Pop, Gender, Connected, Separate, Learning, Happiness Number of observations: 288 1) Learning <= 12; criterion = 1, statistic = 123.006 2) Learning <= 9; criterion = 1, statistic = 16.081 3)* weights = 10 2) Learning > 9 4)* weights = 66 1) Learning > 12 5) Learning <= 16; criterion = 1, statistic = 44.303 6) Learning <= 14; criterion = 1, statistic = 15.963 7)* weights = 64 6) Learning > 14 8)* weights = 120 5) Learning > 16 9)* weights = 28 > postscript(file="/var/fisher/rcomp/tmp/28wde1386623951.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/379d01386623951.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 12 10.20312 1.79687500 2 11 11.22500 -0.22500000 3 15 12.96429 2.03571429 4 6 11.22500 -5.22500000 5 13 10.20312 2.79687500 6 10 10.20312 -0.20312500 7 12 12.96429 -0.96428571 8 14 11.22500 2.77500000 9 12 10.20312 1.79687500 10 9 11.22500 -2.22500000 11 10 11.22500 -1.22500000 12 12 11.22500 0.77500000 13 12 11.22500 0.77500000 14 11 11.22500 -0.22500000 15 15 12.96429 2.03571429 16 12 11.22500 0.77500000 17 10 11.22500 -1.22500000 18 12 12.96429 -0.96428571 19 11 12.96429 -1.96428571 20 12 11.22500 0.77500000 21 11 11.22500 -0.22500000 22 12 11.22500 0.77500000 23 13 12.96429 0.03571429 24 11 11.22500 -0.22500000 25 12 12.96429 -0.96428571 26 13 12.96429 0.03571429 27 10 11.22500 -1.22500000 28 14 11.22500 2.77500000 29 12 11.22500 0.77500000 30 10 10.20312 -0.20312500 31 12 11.22500 0.77500000 32 8 8.69697 -0.69696970 33 10 10.20312 -0.20312500 34 12 11.22500 0.77500000 35 12 10.20312 1.79687500 36 7 8.69697 -1.69696970 37 9 8.69697 0.30303030 38 12 10.20312 1.79687500 39 10 11.22500 -1.22500000 40 10 11.22500 -1.22500000 41 10 11.22500 -1.22500000 42 12 10.20312 1.79687500 43 15 12.96429 2.03571429 44 10 10.20312 -0.20312500 45 10 10.20312 -0.20312500 46 12 8.69697 3.30303030 47 13 10.20312 2.79687500 48 11 11.22500 -0.22500000 49 11 11.22500 -0.22500000 50 12 10.20312 1.79687500 51 14 11.22500 2.77500000 52 10 10.20312 -0.20312500 53 12 8.69697 3.30303030 54 13 11.22500 1.77500000 55 5 6.00000 -1.00000000 56 6 10.20312 -4.20312500 57 12 11.22500 0.77500000 58 12 11.22500 0.77500000 59 11 11.22500 -0.22500000 60 10 11.22500 -1.22500000 61 7 8.69697 -1.69696970 62 12 11.22500 0.77500000 63 14 11.22500 2.77500000 64 11 10.20312 0.79687500 65 12 11.22500 0.77500000 66 13 12.96429 0.03571429 67 14 12.96429 1.03571429 68 11 12.96429 -1.96428571 69 12 8.69697 3.30303030 70 12 11.22500 0.77500000 71 8 8.69697 -0.69696970 72 11 10.20312 0.79687500 73 14 12.96429 1.03571429 74 14 12.96429 1.03571429 75 12 11.22500 0.77500000 76 9 12.96429 -3.96428571 77 13 11.22500 1.77500000 78 11 11.22500 -0.22500000 79 12 10.20312 1.79687500 80 12 11.22500 0.77500000 81 12 11.22500 0.77500000 82 12 11.22500 0.77500000 83 12 11.22500 0.77500000 84 11 11.22500 -0.22500000 85 10 11.22500 -1.22500000 86 9 10.20312 -1.20312500 87 12 11.22500 0.77500000 88 12 11.22500 0.77500000 89 12 11.22500 0.77500000 90 9 8.69697 0.30303030 91 15 12.96429 2.03571429 92 12 11.22500 0.77500000 93 12 11.22500 0.77500000 94 12 10.20312 1.79687500 95 10 11.22500 -1.22500000 96 13 11.22500 1.77500000 97 9 11.22500 -2.22500000 98 12 11.22500 0.77500000 99 10 10.20312 -0.20312500 100 14 11.22500 2.77500000 101 11 11.22500 -0.22500000 102 15 12.96429 2.03571429 103 11 11.22500 -0.22500000 104 11 11.22500 -0.22500000 105 12 10.20312 1.79687500 106 12 12.96429 -0.96428571 107 12 11.22500 0.77500000 108 11 11.22500 -0.22500000 109 7 8.69697 -1.69696970 110 12 11.22500 0.77500000 111 14 11.22500 2.77500000 112 11 12.96429 -1.96428571 113 10 8.69697 1.30303030 114 13 12.96429 0.03571429 115 13 10.20312 2.79687500 116 8 10.20312 -2.20312500 117 11 10.20312 0.79687500 118 12 11.22500 0.77500000 119 11 10.20312 0.79687500 120 13 11.22500 1.77500000 121 12 10.20312 1.79687500 122 14 11.22500 2.77500000 123 13 11.22500 1.77500000 124 15 11.22500 3.77500000 125 10 11.22500 -1.22500000 126 11 12.96429 -1.96428571 127 9 11.22500 -2.22500000 128 11 8.69697 2.30303030 129 10 11.22500 -1.22500000 130 11 8.69697 2.30303030 131 8 11.22500 -3.22500000 132 11 8.69697 2.30303030 133 12 10.20312 1.79687500 134 12 11.22500 0.77500000 135 9 10.20312 -1.20312500 136 11 11.22500 -0.22500000 137 10 8.69697 1.30303030 138 8 8.69697 -0.69696970 139 9 8.69697 0.30303030 140 8 11.22500 -3.22500000 141 9 11.22500 -2.22500000 142 15 12.96429 2.03571429 143 11 11.22500 -0.22500000 144 8 8.69697 -0.69696970 145 13 12.96429 0.03571429 146 12 10.20312 1.79687500 147 12 11.22500 0.77500000 148 9 10.20312 -1.20312500 149 7 8.69697 -1.69696970 150 13 11.22500 1.77500000 151 9 11.22500 -2.22500000 152 6 11.22500 -5.22500000 153 8 10.20312 -2.20312500 154 8 8.69697 -0.69696970 155 6 10.20312 -4.20312500 156 9 11.22500 -2.22500000 157 11 11.22500 -0.22500000 158 8 8.69697 -0.69696970 159 10 10.20312 -0.20312500 160 8 8.69697 -0.69696970 161 14 12.96429 1.03571429 162 10 11.22500 -1.22500000 163 8 8.69697 -0.69696970 164 11 10.20312 0.79687500 165 12 8.69697 3.30303030 166 12 10.20312 1.79687500 167 12 11.22500 0.77500000 168 5 8.69697 -3.69696970 169 12 11.22500 0.77500000 170 10 8.69697 1.30303030 171 7 6.00000 1.00000000 172 12 8.69697 3.30303030 173 11 11.22500 -0.22500000 174 8 8.69697 -0.69696970 175 9 8.69697 0.30303030 176 10 10.20312 -0.20312500 177 9 8.69697 0.30303030 178 12 11.22500 0.77500000 179 6 8.69697 -2.69696970 180 15 12.96429 2.03571429 181 12 11.22500 0.77500000 182 12 6.00000 6.00000000 183 12 11.22500 0.77500000 184 11 12.96429 -1.96428571 185 7 8.69697 -1.69696970 186 7 8.69697 -1.69696970 187 5 8.69697 -3.69696970 188 12 10.20312 1.79687500 189 12 11.22500 0.77500000 190 3 8.69697 -5.69696970 191 11 11.22500 -0.22500000 192 10 10.20312 -0.20312500 193 12 11.22500 0.77500000 194 9 11.22500 -2.22500000 195 12 11.22500 0.77500000 196 9 10.20312 -1.20312500 197 12 11.22500 0.77500000 198 10 10.20312 -0.20312500 199 9 8.69697 0.30303030 200 12 8.69697 3.30303030 201 8 11.22500 -3.22500000 202 11 11.22500 -0.22500000 203 11 11.22500 -0.22500000 204 12 11.22500 0.77500000 205 10 8.69697 1.30303030 206 10 11.22500 -1.22500000 207 12 8.69697 3.30303030 208 12 8.69697 3.30303030 209 11 11.22500 -0.22500000 210 8 11.22500 -3.22500000 211 12 11.22500 0.77500000 212 10 10.20312 -0.20312500 213 11 12.96429 -1.96428571 214 10 10.20312 -0.20312500 215 8 10.20312 -2.20312500 216 12 11.22500 0.77500000 217 12 10.20312 1.79687500 218 10 10.20312 -0.20312500 219 12 11.22500 0.77500000 220 9 8.69697 0.30303030 221 6 6.00000 0.00000000 222 10 10.20312 -0.20312500 223 9 10.20312 -1.20312500 224 9 8.69697 0.30303030 225 9 8.69697 0.30303030 226 6 10.20312 -4.20312500 227 10 8.69697 1.30303030 228 6 11.22500 -5.22500000 229 14 12.96429 1.03571429 230 10 10.20312 -0.20312500 231 10 8.69697 1.30303030 232 6 6.00000 0.00000000 233 12 10.20312 1.79687500 234 12 11.22500 0.77500000 235 7 8.69697 -1.69696970 236 8 8.69697 -0.69696970 237 11 8.69697 2.30303030 238 3 8.69697 -5.69696970 239 6 10.20312 -4.20312500 240 8 8.69697 -0.69696970 241 9 10.20312 -1.20312500 242 9 8.69697 0.30303030 243 8 8.69697 -0.69696970 244 9 8.69697 0.30303030 245 7 8.69697 -1.69696970 246 7 8.69697 -1.69696970 247 6 8.69697 -2.69696970 248 9 11.22500 -2.22500000 249 10 8.69697 1.30303030 250 11 10.20312 0.79687500 251 12 11.22500 0.77500000 252 8 10.20312 -2.20312500 253 11 10.20312 0.79687500 254 3 6.00000 -3.00000000 255 11 11.22500 -0.22500000 256 12 8.69697 3.30303030 257 7 8.69697 -1.69696970 258 9 10.20312 -1.20312500 259 12 11.22500 0.77500000 260 8 10.20312 -2.20312500 261 11 10.20312 0.79687500 262 8 8.69697 -0.69696970 263 10 11.22500 -1.22500000 264 8 8.69697 -0.69696970 265 7 10.20312 -3.20312500 266 8 10.20312 -2.20312500 267 10 11.22500 -1.22500000 268 8 10.20312 -2.20312500 269 12 11.22500 0.77500000 270 14 11.22500 2.77500000 271 7 8.69697 -1.69696970 272 6 6.00000 0.00000000 273 11 11.22500 -0.22500000 274 4 6.00000 -2.00000000 275 9 11.22500 -2.22500000 276 5 6.00000 -1.00000000 277 9 8.69697 0.30303030 278 11 11.22500 -0.22500000 279 12 10.20312 1.79687500 280 9 8.69697 0.30303030 281 12 11.22500 0.77500000 282 10 11.22500 -1.22500000 283 9 8.69697 0.30303030 284 6 6.00000 0.00000000 285 10 11.22500 -1.22500000 286 9 8.69697 0.30303030 287 13 11.22500 1.77500000 288 12 10.20312 1.79687500 > 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/4h3h71386623951.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/5hf1i1386623951.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6ce931386623951.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/7xo401386623951.tab") + } > > try(system("convert tmp/28wde1386623951.ps tmp/28wde1386623951.png",intern=TRUE)) character(0) > try(system("convert tmp/379d01386623951.ps tmp/379d01386623951.png",intern=TRUE)) character(0) > try(system("convert tmp/4h3h71386623951.ps tmp/4h3h71386623951.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.996 1.774 13.737