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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,28 + ,6 + ,1 + ,27 + ,22 + ,20 + ,25 + ,24 + ,18 + ,4 + ,2 + ,24 + ,20 + ,12 + ,28 + ,25 + ,25 + ,4 + ,2 + ,24 + ,22 + ,16 + ,28 + ,21 + ,23 + ,4 + ,2 + ,21 + ,18 + ,16 + ,20 + ,21 + ,21 + ,5 + ,1 + ,18 + ,16 + ,18 + ,25 + ,23 + ,20 + ,6 + ,1 + ,16 + ,16 + ,16 + ,19 + ,27 + ,25 + ,16 + ,1 + ,22 + ,16 + ,13 + ,25 + ,23 + ,22 + ,6 + ,1 + ,20 + ,16 + ,17 + ,22 + ,18 + ,21 + ,6 + ,2 + ,18 + ,17 + ,13 + ,18 + ,16 + ,16 + ,4 + ,1 + ,20 + ,18 + ,17 + ,20 + ,16 + ,18 + ,4) + ,dim=c(8 + ,162) + ,dimnames=list(c('G' + ,'I1' + ,'I2' + ,'I3' + ,'E1' + ,'E2' + ,'E3' + ,'A') + ,1:162)) > y <- array(NA,dim=c(8,162),dimnames=list(c('G','I1','I2','I3','E1','E2','E3','A'),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 = '3' > par2 = 'none' > par1 = '2' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '2' > #'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] "I1" > x[,par1] [1] 26 20 19 19 20 25 25 22 26 22 17 22 19 24 26 21 13 26 20 22 14 21 7 23 17 [26] 25 25 19 20 23 22 22 21 15 20 22 18 20 28 22 18 23 20 25 26 15 17 23 21 13 [51] 18 19 22 16 24 18 20 24 14 22 24 18 21 23 17 22 24 21 22 16 21 23 22 24 24 [76] 16 16 21 26 15 25 18 23 20 17 25 24 17 19 20 15 27 22 23 16 19 25 19 19 26 [101] 21 20 24 22 20 18 18 24 24 22 23 22 20 18 25 18 16 20 19 15 19 19 16 17 28 [126] 23 25 20 17 23 16 23 11 18 24 23 21 16 24 23 18 20 9 24 25 20 21 25 22 21 [151] 21 22 27 24 24 21 18 16 22 20 18 20 > 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 9 11 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 1 1 2 2 5 10 8 14 12 19 14 20 14 16 12 7 2 2 > colnames(x) [1] "G" "I1" "I2" "I3" "E1" "E2" "E3" "A" > colnames(x)[par1] [1] "I1" > x[,par1] [1] 26 20 19 19 20 25 25 22 26 22 17 22 19 24 26 21 13 26 20 22 14 21 7 23 17 [26] 25 25 19 20 23 22 22 21 15 20 22 18 20 28 22 18 23 20 25 26 15 17 23 21 13 [51] 18 19 22 16 24 18 20 24 14 22 24 18 21 23 17 22 24 21 22 16 21 23 22 24 24 [76] 16 16 21 26 15 25 18 23 20 17 25 24 17 19 20 15 27 22 23 16 19 25 19 19 26 [101] 21 20 24 22 20 18 18 24 24 22 23 22 20 18 25 18 16 20 19 15 19 19 16 17 28 [126] 23 25 20 17 23 16 23 11 18 24 23 21 16 24 23 18 20 9 24 25 20 21 25 22 21 [151] 21 22 27 24 24 21 18 16 22 20 18 20 > 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/1t6061354808539.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: I1 Inputs: G, I2, I3, E1, E2, E3, A Number of observations: 162 1) I2 <= 21; criterion = 1, statistic = 57.813 2) I3 <= 10; criterion = 1, statistic = 18.734 3)* weights = 32 2) I3 > 10 4) E1 <= 20; criterion = 1, statistic = 17.525 5)* weights = 19 4) E1 > 20 6) I3 <= 18; criterion = 0.976, statistic = 8.562 7)* weights = 60 6) I3 > 18 8)* weights = 7 1) I2 > 21 9) I3 <= 19; criterion = 0.987, statistic = 9.668 10)* weights = 29 9) I3 > 19 11)* weights = 15 > postscript(file="/var/fisher/rcomp/tmp/2rs981354808539.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/3yy2f1354808539.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 26 23.28571 2.7142857 2 20 20.65000 -0.6500000 3 19 20.65000 -1.6500000 4 19 17.78947 1.2105263 5 20 17.62500 2.3750000 6 25 22.82759 2.1724138 7 25 17.62500 7.3750000 8 22 23.28571 -1.2857143 9 26 20.65000 5.3500000 10 22 20.65000 1.3500000 11 17 17.62500 -0.6250000 12 22 20.65000 1.3500000 13 19 20.65000 -1.6500000 14 24 17.62500 6.3750000 15 26 25.20000 0.8000000 16 21 20.65000 0.3500000 17 13 17.62500 -4.6250000 18 26 25.20000 0.8000000 19 20 22.82759 -2.8275862 20 22 20.65000 1.3500000 21 14 17.62500 -3.6250000 22 21 23.28571 -2.2857143 23 7 17.62500 -10.6250000 24 23 20.65000 2.3500000 25 17 20.65000 -3.6500000 26 25 22.82759 2.1724138 27 25 22.82759 2.1724138 28 19 17.62500 1.3750000 29 20 17.62500 2.3750000 30 23 20.65000 2.3500000 31 22 22.82759 -0.8275862 32 22 20.65000 1.3500000 33 21 17.78947 3.2105263 34 15 17.78947 -2.7894737 35 20 17.62500 2.3750000 36 22 20.65000 1.3500000 37 18 17.62500 0.3750000 38 20 20.65000 -0.6500000 39 28 25.20000 2.8000000 40 22 22.82759 -0.8275862 41 18 20.65000 -2.6500000 42 23 17.62500 5.3750000 43 20 22.82759 -2.8275862 44 25 22.82759 2.1724138 45 26 22.82759 3.1724138 46 15 17.62500 -2.6250000 47 17 17.62500 -0.6250000 48 23 17.62500 5.3750000 49 21 20.65000 0.3500000 50 13 17.62500 -4.6250000 51 18 20.65000 -2.6500000 52 19 20.65000 -1.6500000 53 22 20.65000 1.3500000 54 16 17.62500 -1.6250000 55 24 22.82759 1.1724138 56 18 17.62500 0.3750000 57 20 20.65000 -0.6500000 58 24 17.62500 6.3750000 59 14 17.62500 -3.6250000 60 22 22.82759 -0.8275862 61 24 17.62500 6.3750000 62 18 17.78947 0.2105263 63 21 22.82759 -1.8275862 64 23 22.82759 0.1724138 65 17 17.78947 -0.7894737 66 22 22.82759 -0.8275862 67 24 25.20000 -1.2000000 68 21 20.65000 0.3500000 69 22 20.65000 1.3500000 70 16 17.62500 -1.6250000 71 21 22.82759 -1.8275862 72 23 22.82759 0.1724138 73 22 20.65000 1.3500000 74 24 23.28571 0.7142857 75 24 25.20000 -1.2000000 76 16 17.78947 -1.7894737 77 16 17.78947 -1.7894737 78 21 22.82759 -1.8275862 79 26 25.20000 0.8000000 80 15 17.62500 -2.6250000 81 25 22.82759 2.1724138 82 18 20.65000 -2.6500000 83 23 20.65000 2.3500000 84 20 20.65000 -0.6500000 85 17 20.65000 -3.6500000 86 25 25.20000 -0.2000000 87 24 20.65000 3.3500000 88 17 17.78947 -0.7894737 89 19 20.65000 -1.6500000 90 20 20.65000 -0.6500000 91 15 17.62500 -2.6250000 92 27 22.82759 4.1724138 93 22 20.65000 1.3500000 94 23 23.28571 -0.2857143 95 16 20.65000 -4.6500000 96 19 20.65000 -1.6500000 97 25 20.65000 4.3500000 98 19 20.65000 -1.6500000 99 19 20.65000 -1.6500000 100 26 25.20000 0.8000000 101 21 20.65000 0.3500000 102 20 22.82759 -2.8275862 103 24 20.65000 3.3500000 104 22 22.82759 -0.8275862 105 20 20.65000 -0.6500000 106 18 17.78947 0.2105263 107 18 17.78947 0.2105263 108 24 23.28571 0.7142857 109 24 20.65000 3.3500000 110 22 22.82759 -0.8275862 111 23 17.62500 5.3750000 112 22 17.62500 4.3750000 113 20 17.78947 2.2105263 114 18 17.78947 0.2105263 115 25 25.20000 -0.2000000 116 18 20.65000 -2.6500000 117 16 20.65000 -4.6500000 118 20 20.65000 -0.6500000 119 19 22.82759 -3.8275862 120 15 17.62500 -2.6250000 121 19 20.65000 -1.6500000 122 19 22.82759 -3.8275862 123 16 17.62500 -1.6250000 124 17 17.78947 -0.7894737 125 28 22.82759 5.1724138 126 23 20.65000 2.3500000 127 25 25.20000 -0.2000000 128 20 20.65000 -0.6500000 129 17 17.78947 -0.7894737 130 23 22.82759 0.1724138 131 16 17.78947 -1.7894737 132 23 25.20000 -2.2000000 133 11 17.62500 -6.6250000 134 18 20.65000 -2.6500000 135 24 25.20000 -1.2000000 136 23 20.65000 2.3500000 137 21 22.82759 -1.8275862 138 16 20.65000 -4.6500000 139 24 25.20000 -1.2000000 140 23 23.28571 -0.2857143 141 18 17.62500 0.3750000 142 20 20.65000 -0.6500000 143 9 17.62500 -8.6250000 144 24 20.65000 3.3500000 145 25 25.20000 -0.2000000 146 20 17.62500 2.3750000 147 21 20.65000 0.3500000 148 25 22.82759 2.1724138 149 22 20.65000 1.3500000 150 21 20.65000 0.3500000 151 21 20.65000 0.3500000 152 22 20.65000 1.3500000 153 27 25.20000 1.8000000 154 24 20.65000 3.3500000 155 24 22.82759 1.1724138 156 21 17.78947 3.2105263 157 18 20.65000 -2.6500000 158 16 17.78947 -1.7894737 159 22 20.65000 1.3500000 160 20 20.65000 -0.6500000 161 18 17.78947 0.2105263 162 20 17.78947 2.2105263 > 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/4r9g31354808539.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/58tg91354808539.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/6zfnm1354808539.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/71ula1354808539.tab") + } > > try(system("convert tmp/2rs981354808539.ps tmp/2rs981354808539.png",intern=TRUE)) character(0) > try(system("convert tmp/3yy2f1354808539.ps tmp/3yy2f1354808539.png",intern=TRUE)) character(0) > try(system("convert tmp/4r9g31354808539.ps tmp/4r9g31354808539.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.137 0.573 5.699