R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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 + ,1 + ,41 + ,38 + ,13 + ,12 + ,14 + ,1 + ,1 + ,39 + ,32 + ,16 + ,11 + ,18 + ,1 + ,1 + ,30 + ,35 + ,19 + ,15 + ,11 + ,1 + ,0 + ,31 + ,33 + ,15 + ,6 + ,12 + ,1 + ,1 + ,34 + ,37 + ,14 + ,13 + ,16 + ,1 + ,1 + ,35 + ,29 + ,13 + ,10 + ,18 + ,1 + ,1 + ,39 + ,31 + ,19 + ,12 + ,14 + ,1 + ,1 + ,34 + ,36 + ,15 + ,14 + ,14 + ,1 + ,1 + ,36 + ,35 + ,14 + ,12 + ,15 + ,1 + ,1 + ,37 + ,38 + ,15 + ,9 + ,15 + ,1 + ,0 + ,38 + ,31 + ,16 + ,10 + ,17 + ,1 + ,1 + ,36 + ,34 + ,16 + ,12 + ,19 + ,1 + ,0 + ,38 + ,35 + ,16 + ,12 + ,10 + ,1 + ,1 + ,39 + ,38 + ,16 + ,11 + ,16 + ,1 + ,1 + ,33 + ,37 + ,17 + ,15 + ,18 + ,1 + ,0 + ,32 + ,33 + ,15 + ,12 + ,14 + ,1 + ,0 + ,36 + ,32 + ,15 + ,10 + ,14 + ,1 + ,1 + ,38 + ,38 + ,20 + ,12 + ,17 + ,1 + ,0 + ,39 + ,38 + ,18 + ,11 + ,14 + ,1 + ,1 + ,32 + ,32 + ,16 + ,12 + ,16 + ,1 + ,0 + ,32 + ,33 + ,16 + ,11 + ,18 + ,1 + ,1 + ,31 + ,31 + ,16 + ,12 + ,11 + ,1 + ,1 + ,39 + ,38 + ,19 + ,13 + ,14 + ,1 + ,1 + ,37 + ,39 + ,16 + ,11 + ,12 + ,1 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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 = '5' > 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 Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) 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] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 16 17 15 15 20 18 16 16 16 19 16 17 [26] 17 16 15 16 14 15 12 14 16 14 10 10 14 16 16 16 14 20 14 14 11 14 15 16 14 [51] 16 14 12 16 9 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 [76] 17 16 16 13 16 16 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 16 [101] 16 20 15 16 13 17 16 16 12 16 16 17 12 18 14 14 13 16 13 16 13 16 15 16 15 [126] 17 15 12 16 10 16 12 14 15 13 15 11 12 11 16 15 17 16 10 18 13 16 13 10 15 [151] 16 16 14 10 13 15 16 12 13 12 17 15 10 14 11 13 16 12 16 12 9 12 15 12 12 [176] 14 12 16 11 19 15 8 16 17 12 11 11 14 16 12 16 13 15 16 16 14 16 14 11 12 [201] 15 15 16 16 11 15 12 12 15 15 16 14 17 14 13 15 13 14 15 12 8 14 14 11 12 [226] 13 10 16 18 13 11 4 13 16 10 12 12 10 13 12 14 10 12 12 11 10 12 16 12 14 [251] 16 14 13 4 15 11 11 14 15 14 13 11 15 11 13 13 16 13 16 16 12 7 16 5 16 [276] 4 12 15 14 11 16 15 12 6 16 10 15 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]) 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 3 1 1 1 2 2 14 17 35 26 38 39 81 13 8 4 3 > colnames(x) [1] "Pop" "Gender" "Connected" "Separate" "Learning" "Software" [7] "Happiness" > colnames(x)[par1] [1] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 16 17 15 15 20 18 16 16 16 19 16 17 [26] 17 16 15 16 14 15 12 14 16 14 10 10 14 16 16 16 14 20 14 14 11 14 15 16 14 [51] 16 14 12 16 9 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 [76] 17 16 16 13 16 16 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 16 [101] 16 20 15 16 13 17 16 16 12 16 16 17 12 18 14 14 13 16 13 16 13 16 15 16 15 [126] 17 15 12 16 10 16 12 14 15 13 15 11 12 11 16 15 17 16 10 18 13 16 13 10 15 [151] 16 16 14 10 13 15 16 12 13 12 17 15 10 14 11 13 16 12 16 12 9 12 15 12 12 [176] 14 12 16 11 19 15 8 16 17 12 11 11 14 16 12 16 13 15 16 16 14 16 14 11 12 [201] 15 15 16 16 11 15 12 12 15 15 16 14 17 14 13 15 13 14 15 12 8 14 14 11 12 [226] 13 10 16 18 13 11 4 13 16 10 12 12 10 13 12 14 10 12 12 11 10 12 16 12 14 [251] 16 14 13 4 15 11 11 14 15 14 13 11 15 11 13 13 16 13 16 16 12 7 16 5 16 [276] 4 12 15 14 11 16 15 12 6 16 10 15 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/12r9y1322165640.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: Learning Inputs: Pop, Gender, Connected, Separate, Software, Happiness Number of observations: 288 1) Software <= 9; criterion = 1, statistic = 123.006 2) Software <= 7; criterion = 1, statistic = 21.585 3) Separate <= 30; criterion = 0.984, statistic = 8.982 4)* weights = 12 3) Separate > 30 5)* weights = 22 2) Software > 7 6)* weights = 57 1) Software > 9 7) Software <= 12; criterion = 1, statistic = 33.618 8) Pop <= 0; criterion = 0.986, statistic = 9.207 9)* weights = 66 8) Pop > 0 10)* weights = 95 7) Software > 12 11) Software <= 14; criterion = 0.978, statistic = 8.437 12)* weights = 28 11) Software > 14 13)* weights = 8 > postscript(file="/var/wessaorg/rcomp/tmp/2c2gx1322165640.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/3p1q01322165640.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 13 15.063158 -2.06315789 2 16 15.063158 0.93684211 3 19 18.125000 0.87500000 4 15 11.681818 3.31818182 5 14 16.250000 -2.25000000 6 13 15.063158 -2.06315789 7 19 15.063158 3.93684211 8 15 16.250000 -1.25000000 9 14 15.063158 -1.06315789 10 15 12.947368 2.05263158 11 16 15.063158 0.93684211 12 16 15.063158 0.93684211 13 16 15.063158 0.93684211 14 16 15.063158 0.93684211 15 17 18.125000 -1.12500000 16 15 15.063158 -0.06315789 17 15 15.063158 -0.06315789 18 20 15.063158 4.93684211 19 18 15.063158 2.93684211 20 16 15.063158 0.93684211 21 16 15.063158 0.93684211 22 16 15.063158 0.93684211 23 19 16.250000 2.75000000 24 16 15.063158 0.93684211 25 17 15.063158 1.93684211 26 17 16.250000 0.75000000 27 16 15.063158 0.93684211 28 15 16.250000 -1.25000000 29 16 15.063158 0.93684211 30 14 15.063158 -1.06315789 31 15 15.063158 -0.06315789 32 12 12.947368 -0.94736842 33 14 15.063158 -1.06315789 34 16 15.063158 0.93684211 35 14 15.063158 -1.06315789 36 10 11.681818 -1.68181818 37 10 12.947368 -2.94736842 38 14 15.063158 -1.06315789 39 16 15.063158 0.93684211 40 16 15.063158 0.93684211 41 16 15.063158 0.93684211 42 14 15.063158 -1.06315789 43 20 18.125000 1.87500000 44 14 15.063158 -1.06315789 45 14 15.063158 -1.06315789 46 11 15.063158 -4.06315789 47 14 16.250000 -2.25000000 48 15 15.063158 -0.06315789 49 16 15.063158 0.93684211 50 14 15.063158 -1.06315789 51 16 16.250000 -0.25000000 52 14 15.063158 -1.06315789 53 12 15.063158 -3.06315789 54 16 16.250000 -0.25000000 55 9 11.681818 -2.68181818 56 14 11.681818 2.31818182 57 16 15.063158 0.93684211 58 16 15.063158 0.93684211 59 15 15.063158 -0.06315789 60 16 15.063158 0.93684211 61 12 8.416667 3.58333333 62 16 15.063158 0.93684211 63 16 16.250000 -0.25000000 64 14 15.063158 -1.06315789 65 16 15.063158 0.93684211 66 17 16.250000 0.75000000 67 18 16.250000 1.75000000 68 18 15.063158 2.93684211 69 12 15.063158 -3.06315789 70 16 15.063158 0.93684211 71 10 12.947368 -2.94736842 72 14 15.063158 -1.06315789 73 18 16.250000 1.75000000 74 18 16.250000 1.75000000 75 16 15.063158 0.93684211 76 17 12.947368 4.05263158 77 16 16.250000 -0.25000000 78 16 15.063158 0.93684211 79 13 15.063158 -2.06315789 80 16 15.063158 0.93684211 81 16 15.063158 0.93684211 82 16 15.063158 0.93684211 83 15 15.063158 -0.06315789 84 15 15.063158 -0.06315789 85 16 15.063158 0.93684211 86 14 12.947368 1.05263158 87 16 15.063158 0.93684211 88 16 15.063158 0.93684211 89 15 15.063158 -0.06315789 90 12 12.947368 -0.94736842 91 17 18.125000 -1.12500000 92 16 15.063158 0.93684211 93 15 15.063158 -0.06315789 94 13 15.063158 -2.06315789 95 16 15.063158 0.93684211 96 16 16.250000 -0.25000000 97 16 12.947368 3.05263158 98 16 15.063158 0.93684211 99 14 15.063158 -1.06315789 100 16 16.250000 -0.25000000 101 16 15.063158 0.93684211 102 20 18.125000 1.87500000 103 15 15.063158 -0.06315789 104 16 15.063158 0.93684211 105 13 15.063158 -2.06315789 106 17 15.063158 1.93684211 107 16 15.063158 0.93684211 108 16 15.063158 0.93684211 109 12 11.681818 0.31818182 110 16 15.063158 0.93684211 111 16 16.250000 -0.25000000 112 17 15.063158 1.93684211 113 12 15.063158 -3.06315789 114 18 16.250000 1.75000000 115 14 16.250000 -2.25000000 116 14 12.947368 1.05263158 117 13 15.063158 -2.06315789 118 16 15.063158 0.93684211 119 13 15.063158 -2.06315789 120 16 16.250000 -0.25000000 121 13 15.063158 -2.06315789 122 16 16.250000 -0.25000000 123 15 16.250000 -1.25000000 124 16 18.125000 -2.12500000 125 15 15.063158 -0.06315789 126 17 15.063158 1.93684211 127 15 12.947368 2.05263158 128 12 15.063158 -3.06315789 129 16 15.063158 0.93684211 130 10 15.063158 -5.06315789 131 16 12.947368 3.05263158 132 12 15.063158 -3.06315789 133 14 15.063158 -1.06315789 134 15 15.063158 -0.06315789 135 13 12.947368 0.05263158 136 15 15.063158 -0.06315789 137 11 15.063158 -4.06315789 138 12 12.947368 -0.94736842 139 11 12.947368 -1.94736842 140 16 12.947368 3.05263158 141 15 12.947368 2.05263158 142 17 18.125000 -1.12500000 143 16 15.063158 0.93684211 144 10 12.947368 -2.94736842 145 18 16.250000 1.75000000 146 13 15.063158 -2.06315789 147 16 15.063158 0.93684211 148 13 12.947368 0.05263158 149 10 11.681818 -1.68181818 150 15 16.250000 -1.25000000 151 16 12.947368 3.05263158 152 16 11.681818 4.31818182 153 14 12.947368 1.05263158 154 10 12.947368 -2.94736842 155 13 11.681818 1.31818182 156 15 12.947368 2.05263158 157 16 15.063158 0.93684211 158 12 12.947368 -0.94736842 159 13 14.181818 -1.18181818 160 12 12.947368 -0.94736842 161 17 16.250000 0.75000000 162 15 14.181818 0.81818182 163 10 12.947368 -2.94736842 164 14 14.181818 -0.18181818 165 11 14.181818 -3.18181818 166 13 14.181818 -1.18181818 167 16 14.181818 1.81818182 168 12 11.681818 0.31818182 169 16 14.181818 1.81818182 170 12 14.181818 -2.18181818 171 9 8.416667 0.58333333 172 12 14.181818 -2.18181818 173 15 14.181818 0.81818182 174 12 12.947368 -0.94736842 175 12 12.947368 -0.94736842 176 14 14.181818 -0.18181818 177 12 12.947368 -0.94736842 178 16 14.181818 1.81818182 179 11 8.416667 2.58333333 180 19 18.125000 0.87500000 181 15 14.181818 0.81818182 182 8 14.181818 -6.18181818 183 16 14.181818 1.81818182 184 17 14.181818 2.81818182 185 12 11.681818 0.31818182 186 11 8.416667 2.58333333 187 11 11.681818 -0.68181818 188 14 14.181818 -0.18181818 189 16 14.181818 1.81818182 190 12 11.681818 0.31818182 191 16 14.181818 1.81818182 192 13 14.181818 -1.18181818 193 15 14.181818 0.81818182 194 16 12.947368 3.05263158 195 16 14.181818 1.81818182 196 14 12.947368 1.05263158 197 16 14.181818 1.81818182 198 14 14.181818 -0.18181818 199 11 12.947368 -1.94736842 200 12 14.181818 -2.18181818 201 15 12.947368 2.05263158 202 15 14.181818 0.81818182 203 16 14.181818 1.81818182 204 16 14.181818 1.81818182 205 11 14.181818 -3.18181818 206 15 14.181818 0.81818182 207 12 14.181818 -2.18181818 208 12 14.181818 -2.18181818 209 15 14.181818 0.81818182 210 15 12.947368 2.05263158 211 16 14.181818 1.81818182 212 14 14.181818 -0.18181818 213 17 14.181818 2.81818182 214 14 14.181818 -0.18181818 215 13 12.947368 0.05263158 216 15 14.181818 0.81818182 217 13 14.181818 -1.18181818 218 14 14.181818 -0.18181818 219 15 14.181818 0.81818182 220 12 12.947368 -0.94736842 221 8 11.681818 -3.68181818 222 14 14.181818 -0.18181818 223 14 12.947368 1.05263158 224 11 12.947368 -1.94736842 225 12 12.947368 -0.94736842 226 13 8.416667 4.58333333 227 10 14.181818 -4.18181818 228 16 11.681818 4.31818182 229 18 16.250000 1.75000000 230 13 14.181818 -1.18181818 231 11 14.181818 -3.18181818 232 4 8.416667 -4.41666667 233 13 14.181818 -1.18181818 234 16 14.181818 1.81818182 235 10 8.416667 1.58333333 236 12 12.947368 -0.94736842 237 12 14.181818 -2.18181818 238 10 8.416667 1.58333333 239 13 11.681818 1.31818182 240 12 12.947368 -0.94736842 241 14 12.947368 1.05263158 242 10 12.947368 -2.94736842 243 12 12.947368 -0.94736842 244 12 12.947368 -0.94736842 245 11 11.681818 -0.68181818 246 10 11.681818 -1.68181818 247 12 11.681818 0.31818182 248 16 12.947368 3.05263158 249 12 14.181818 -2.18181818 250 14 14.181818 -0.18181818 251 16 14.181818 1.81818182 252 14 12.947368 1.05263158 253 13 14.181818 -1.18181818 254 4 8.416667 -4.41666667 255 15 14.181818 0.81818182 256 11 14.181818 -3.18181818 257 11 11.681818 -0.68181818 258 14 12.947368 1.05263158 259 15 14.181818 0.81818182 260 14 12.947368 1.05263158 261 13 14.181818 -1.18181818 262 11 12.947368 -1.94736842 263 15 14.181818 0.81818182 264 11 12.947368 -1.94736842 265 13 11.681818 1.31818182 266 13 12.947368 0.05263158 267 16 14.181818 1.81818182 268 13 12.947368 0.05263158 269 16 14.181818 1.81818182 270 16 16.250000 -0.25000000 271 12 11.681818 0.31818182 272 7 8.416667 -1.41666667 273 16 14.181818 1.81818182 274 5 11.681818 -6.68181818 275 16 12.947368 3.05263158 276 4 8.416667 -4.41666667 277 12 12.947368 -0.94736842 278 15 14.181818 0.81818182 279 14 14.181818 -0.18181818 280 11 12.947368 -1.94736842 281 16 14.181818 1.81818182 282 15 14.181818 0.81818182 283 12 12.947368 -0.94736842 284 6 8.416667 -2.41666667 285 16 14.181818 1.81818182 286 10 12.947368 -2.94736842 287 15 16.250000 -1.25000000 288 14 14.181818 -0.18181818 > 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/4j6p01322165640.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5hqq11322165640.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/631bi1322165640.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/7su6b1322165640.tab") + } > > try(system("convert tmp/2c2gx1322165640.ps tmp/2c2gx1322165640.png",intern=TRUE)) character(0) > try(system("convert tmp/3p1q01322165640.ps tmp/3p1q01322165640.png",intern=TRUE)) character(0) > try(system("convert tmp/4j6p01322165640.ps tmp/4j6p01322165640.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.242 0.236 5.484