R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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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+ ,16 + ,12 + ,1 + ,36 + ,36 + ,13 + ,12 + ,11 + ,13 + ,1 + ,32 + ,31 + ,16 + ,12 + ,12 + ,11 + ,1 + ,35 + ,32 + ,13 + ,9 + ,9 + ,19 + ,2 + ,38 + ,39 + ,10 + ,7 + ,16 + ,12 + ,2 + ,42 + ,37 + ,15 + ,13 + ,13 + ,17 + ,1 + ,34 + ,38 + ,16 + ,9 + ,16 + ,9 + ,2 + ,35 + ,39 + ,16 + ,6 + ,12 + ,12 + ,2 + ,35 + ,34 + ,14 + ,8 + ,9 + ,19 + ,2 + ,33 + ,31 + ,10 + ,8 + ,13 + ,18 + ,2 + ,36 + ,32 + ,17 + ,15 + ,13 + ,15 + ,2 + ,32 + ,37 + ,13 + ,6 + ,14 + ,14 + ,2 + ,33 + ,36 + ,15 + ,9 + ,19 + ,11 + ,2 + ,34 + ,32 + ,16 + ,11 + ,13 + ,9 + ,2 + ,32 + ,35 + ,12 + ,8 + ,12 + ,18 + ,2 + ,34 + ,36 + ,13 + ,8 + ,13 + ,16) + ,dim=c(7 + ,162) + ,dimnames=list(c('gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression') + ,1:162)) > y <- array(NA,dim=c(7,162),dimnames=list(c('gender','Connected','Separate','Learning','Software','Happiness','Depression'),1:162)) > 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 = '4' > par4 <- 'no' > par3 <- '' > par2 <- 'none' > par1 <- '4' > #'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 object(s) 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(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 7 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 20 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 [101] 16 16 20 15 16 13 17 16 16 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15 [126] 16 15 17 15 12 16 10 16 12 14 15 13 15 11 12 8 16 15 17 16 10 18 13 16 13 [151] 10 15 16 16 14 10 17 13 15 16 12 13 > 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 20 1 1 1 6 2 11 14 21 22 58 11 7 3 4 > colnames(x) [1] "gender" "Connected" "Separate" "Learning" "Software" [6] "Happiness" "Depression" > 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 7 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 20 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 [101] 16 16 20 15 16 13 17 16 16 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15 [126] 16 15 17 15 12 16 10 16 12 14 15 13 15 11 12 8 16 15 17 16 10 18 13 16 13 [151] 10 15 16 16 14 10 17 13 15 16 12 13 > 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/1dec51355143450.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Learning Inputs: gender, Connected, Separate, Software, Happiness, Depression Number of observations: 162 1) Software <= 8; criterion = 1, statistic = 47.92 2)* weights = 22 1) Software > 8 3) Software <= 12; criterion = 1, statistic = 22.459 4) Connected <= 31; criterion = 0.978, statistic = 8.41 5)* weights = 19 4) Connected > 31 6)* weights = 89 3) Software > 12 7)* weights = 32 > postscript(file="/var/fisher/rcomp/tmp/24taz1355143450.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/3846a1355143450.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 15.21348 -2.2134831 2 16 15.21348 0.7865169 3 19 16.62500 2.3750000 4 15 12.36364 2.6363636 5 14 16.62500 -2.6250000 6 13 15.21348 -2.2134831 7 19 15.21348 3.7865169 8 15 16.62500 -1.6250000 9 14 15.21348 -1.2134831 10 15 12.36364 2.6363636 11 16 15.21348 0.7865169 12 16 15.21348 0.7865169 13 16 15.21348 0.7865169 14 16 15.21348 0.7865169 15 17 16.62500 0.3750000 16 15 15.21348 -0.2134831 17 15 15.21348 -0.2134831 18 20 15.21348 4.7865169 19 18 15.21348 2.7865169 20 16 15.21348 0.7865169 21 16 15.21348 0.7865169 22 16 13.89474 2.1052632 23 19 16.62500 2.3750000 24 16 15.21348 0.7865169 25 17 15.21348 1.7865169 26 17 16.62500 0.3750000 27 16 15.21348 0.7865169 28 15 16.62500 -1.6250000 29 16 15.21348 0.7865169 30 14 15.21348 -1.2134831 31 15 13.89474 1.1052632 32 12 12.36364 -0.3636364 33 14 15.21348 -1.2134831 34 16 15.21348 0.7865169 35 14 15.21348 -1.2134831 36 7 12.36364 -5.3636364 37 10 12.36364 -2.3636364 38 14 15.21348 -1.2134831 39 16 13.89474 2.1052632 40 16 15.21348 0.7865169 41 16 15.21348 0.7865169 42 14 15.21348 -1.2134831 43 20 16.62500 3.3750000 44 14 15.21348 -1.2134831 45 14 15.21348 -1.2134831 46 11 15.21348 -4.2134831 47 14 16.62500 -2.6250000 48 15 15.21348 -0.2134831 49 16 15.21348 0.7865169 50 14 15.21348 -1.2134831 51 16 16.62500 -0.6250000 52 14 15.21348 -1.2134831 53 12 15.21348 -3.2134831 54 16 16.62500 -0.6250000 55 9 12.36364 -3.3636364 56 14 12.36364 1.6363636 57 16 15.21348 0.7865169 58 16 13.89474 2.1052632 59 15 15.21348 -0.2134831 60 16 15.21348 0.7865169 61 12 12.36364 -0.3636364 62 16 15.21348 0.7865169 63 16 16.62500 -0.6250000 64 14 13.89474 0.1052632 65 16 15.21348 0.7865169 66 17 16.62500 0.3750000 67 18 16.62500 1.3750000 68 18 13.89474 4.1052632 69 12 15.21348 -3.2134831 70 16 15.21348 0.7865169 71 10 12.36364 -2.3636364 72 14 13.89474 0.1052632 73 18 16.62500 1.3750000 74 18 16.62500 1.3750000 75 16 15.21348 0.7865169 76 17 15.21348 1.7865169 77 16 16.62500 -0.6250000 78 16 15.21348 0.7865169 79 13 13.89474 -0.8947368 80 16 15.21348 0.7865169 81 16 15.21348 0.7865169 82 20 15.21348 4.7865169 83 16 15.21348 0.7865169 84 15 15.21348 -0.2134831 85 15 15.21348 -0.2134831 86 16 15.21348 0.7865169 87 14 15.21348 -1.2134831 88 16 15.21348 0.7865169 89 16 13.89474 2.1052632 90 15 15.21348 -0.2134831 91 12 13.89474 -1.8947368 92 17 16.62500 0.3750000 93 16 15.21348 0.7865169 94 15 15.21348 -0.2134831 95 13 13.89474 -0.8947368 96 16 15.21348 0.7865169 97 16 16.62500 -0.6250000 98 16 15.21348 0.7865169 99 16 15.21348 0.7865169 100 14 15.21348 -1.2134831 101 16 16.62500 -0.6250000 102 16 15.21348 0.7865169 103 20 16.62500 3.3750000 104 15 15.21348 -0.2134831 105 16 15.21348 0.7865169 106 13 15.21348 -2.2134831 107 17 15.21348 1.7865169 108 16 15.21348 0.7865169 109 16 15.21348 0.7865169 110 12 12.36364 -0.3636364 111 16 13.89474 2.1052632 112 16 16.62500 -0.6250000 113 17 15.21348 1.7865169 114 13 15.21348 -2.2134831 115 12 15.21348 -3.2134831 116 18 16.62500 1.3750000 117 14 16.62500 -2.6250000 118 14 12.36364 1.6363636 119 13 15.21348 -2.2134831 120 16 15.21348 0.7865169 121 13 15.21348 -2.2134831 122 16 16.62500 -0.6250000 123 13 15.21348 -2.2134831 124 16 16.62500 -0.6250000 125 15 16.62500 -1.6250000 126 16 16.62500 -0.6250000 127 15 15.21348 -0.2134831 128 17 15.21348 1.7865169 129 15 15.21348 -0.2134831 130 12 15.21348 -3.2134831 131 16 15.21348 0.7865169 132 10 13.89474 -3.8947368 133 16 12.36364 3.6363636 134 12 13.89474 -1.8947368 135 14 15.21348 -1.2134831 136 15 15.21348 -0.2134831 137 13 13.89474 -0.8947368 138 15 15.21348 -0.2134831 139 11 13.89474 -2.8947368 140 12 12.36364 -0.3636364 141 8 13.89474 -5.8947368 142 16 12.36364 3.6363636 143 15 13.89474 1.1052632 144 17 16.62500 0.3750000 145 16 13.89474 2.1052632 146 10 12.36364 -2.3636364 147 18 16.62500 1.3750000 148 13 15.21348 -2.2134831 149 16 15.21348 0.7865169 150 13 15.21348 -2.2134831 151 10 12.36364 -2.3636364 152 15 16.62500 -1.6250000 153 16 15.21348 0.7865169 154 16 12.36364 3.6363636 155 14 12.36364 1.6363636 156 10 12.36364 -2.3636364 157 17 16.62500 0.3750000 158 13 12.36364 0.6363636 159 15 15.21348 -0.2134831 160 16 15.21348 0.7865169 161 12 12.36364 -0.3636364 162 13 12.36364 0.6363636 > 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/4hlek1355143450.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/5hhkc1355143450.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/6gyv51355143450.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/7z6u81355143450.tab") + } > > try(system("convert tmp/24taz1355143450.ps tmp/24taz1355143450.png",intern=TRUE)) character(0) > try(system("convert tmp/3846a1355143450.ps tmp/3846a1355143450.png",intern=TRUE)) character(0) > try(system("convert tmp/4hlek1355143450.ps tmp/4hlek1355143450.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.169 0.594 5.748