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. Type 'q()' to quit R. > x <- array(list(46 + ,310232.86 + ,41.761 + ,0.939 + ,0.923 + ,0.869 + ,38 + ,1330141.29 + ,6.2 + ,0.623 + ,0.843 + ,0.618 + ,24 + ,139390.2 + ,13.611 + ,0.784 + ,0.77 + ,0.713 + ,29 + ,62348.45 + ,32.147 + ,0.815 + ,0.949 + ,0.832 + ,11 + ,81644.45 + ,32.255 + ,0.928 + ,0.953 + ,0.838 + ,11 + ,64768.39 + ,29.578 + ,0.87 + ,0.971 + ,0.819 + ,13 + ,48636.07 + ,25.493 + ,0.934 + ,0.956 + ,0.808 + ,7 + ,126804.43 + ,29.692 + ,0.883 + ,1.000 + ,0.827 + ,7 + ,21515.75 + ,34.259 + ,0.981 + ,0.976 + ,0.837 + ,8 + ,60748.96 + ,26.578 + ,0.856 + ,0.976 + ,0.799 + ,8 + ,9992.34 + ,16.896 + ,0.866 + ,0.858 + ,0.732 + ,6 + ,16783.09 + ,36.358 + ,0.931 + ,0.958 + ,0.845 + ,6 + ,45415.6 + ,5.737 + ,0.858 + ,0.765 + ,0.591 + ,7 + ,15460.48 + ,10.452 + ,0.834 + ,0.742 + ,0.668 + ,6 + ,4252.28 + ,24.706 + ,1.000 + ,0.957 + ,0.783 + ,3 + ,46505.96 + ,27.066 + ,0.874 + ,0.969 + ,0.799 + ,3 + ,201103.33 + ,9.414 + ,0.663 + ,0.844 + ,0.662 + ,4 + ,76923.3 + ,10.496 + ,0.64 + ,0.836 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,dim=c(6 + ,172) + ,dimnames=list(c('Gold' + ,'POP' + ,'GDP/cap' + ,'Education' + ,'LifeExpectancy' + ,'GNI/cap') + ,1:172)) > y <- array(NA,dim=c(6,172),dimnames=list(c('Gold','POP','GDP/cap','Education','LifeExpectancy','GNI/cap'),1:172)) > 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 = '1' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '1' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Gold" > x[,par1] [1] 46 38 24 29 11 11 13 7 7 8 8 6 6 7 6 3 3 4 4 4 1 2 2 2 2 [26] 2 3 2 3 3 1 1 2 1 2 1 2 2 1 1 1 1 0 1 1 1 1 0 1 1 [51] 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [76] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [126] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [151] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 0 1 2 3 4 6 7 8 11 13 24 29 38 46 121 18 10 5 3 3 3 2 2 1 1 1 1 1 > colnames(x) [1] "Gold" "POP" "GDP.cap" "Education" [5] "LifeExpectancy" "GNI.cap" > colnames(x)[par1] [1] "Gold" > x[,par1] [1] 46 38 24 29 11 11 13 7 7 8 8 6 6 7 6 3 3 4 4 4 1 2 2 2 2 [26] 2 3 2 3 3 1 1 2 1 2 1 2 2 1 1 1 1 0 1 1 1 1 0 1 1 [51] 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [76] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [126] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [151] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 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/1ut1r1355682290.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: Gold Inputs: POP, GDP.cap, Education, LifeExpectancy, GNI.cap Number of observations: 172 1) POP <= 44205.29; criterion = 1, statistic = 40.468 2) Education <= 0.806; criterion = 1, statistic = 25.445 3) Education <= 0.763; criterion = 0.978, statistic = 8.073 4) POP <= 29671.6; criterion = 0.999, statistic = 13.485 5)* weights = 97 4) POP > 29671.6 6)* weights = 7 3) Education > 0.763 7)* weights = 14 2) Education > 0.806 8)* weights = 27 1) POP > 44205.29 9)* weights = 27 > postscript(file="/var/wessaorg/rcomp/tmp/2swbo1355682290.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/3r14k1355682290.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 46 7.85185185 38.14814815 2 38 7.85185185 30.14814815 3 24 7.85185185 16.14814815 4 29 7.85185185 21.14814815 5 11 7.85185185 3.14814815 6 11 7.85185185 3.14814815 7 13 7.85185185 5.14814815 8 7 7.85185185 -0.85185185 9 7 2.07407407 4.92592593 10 8 7.85185185 0.14814815 11 8 2.07407407 5.92592593 12 6 2.07407407 3.92592593 13 6 7.85185185 -1.85185185 14 7 2.07407407 4.92592593 15 6 2.07407407 3.92592593 16 3 7.85185185 -4.85185185 17 3 7.85185185 -4.85185185 18 4 7.85185185 -3.85185185 19 4 0.85714286 3.14285714 20 4 2.07407407 1.92592593 21 1 2.07407407 -1.07407407 22 2 0.85714286 1.14285714 23 2 0.71428571 1.28571429 24 2 2.07407407 -0.07407407 25 2 2.07407407 -0.07407407 26 2 0.07216495 1.92783505 27 3 7.85185185 -4.85185185 28 2 2.07407407 -0.07407407 29 3 7.85185185 -4.85185185 30 3 0.85714286 2.14285714 31 1 2.07407407 -1.07407407 32 1 0.71428571 0.28571429 33 2 7.85185185 -5.85185185 34 1 2.07407407 -1.07407407 35 2 2.07407407 -0.07407407 36 1 7.85185185 -6.85185185 37 2 2.07407407 -0.07407407 38 2 2.07407407 -0.07407407 39 1 2.07407407 -1.07407407 40 1 0.85714286 0.14285714 41 1 0.85714286 0.14285714 42 1 2.07407407 -1.07407407 43 0 7.85185185 -7.85185185 44 1 0.07216495 0.92783505 45 1 0.07216495 0.92783505 46 1 0.07216495 0.92783505 47 1 0.07216495 0.92783505 48 0 0.07216495 -0.07216495 49 1 2.07407407 -1.07407407 50 1 0.71428571 0.28571429 51 1 0.85714286 0.14285714 52 0 2.07407407 -2.07407407 53 0 7.85185185 -7.85185185 54 1 0.71428571 0.28571429 55 1 0.07216495 0.92783505 56 0 0.07216495 -0.07216495 57 0 2.07407407 -2.07407407 58 0 7.85185185 -7.85185185 59 0 2.07407407 -2.07407407 60 0 0.85714286 -0.85714286 61 0 2.07407407 -2.07407407 62 0 7.85185185 -7.85185185 63 0 0.07216495 -0.07216495 64 0 0.07216495 -0.07216495 65 0 0.85714286 -0.85714286 66 0 0.07216495 -0.07216495 67 0 2.07407407 -2.07407407 68 0 0.07216495 -0.07216495 69 0 0.85714286 -0.85714286 70 0 0.07216495 -0.07216495 71 0 0.07216495 -0.07216495 72 0 0.07216495 -0.07216495 73 0 0.07216495 -0.07216495 74 0 0.07216495 -0.07216495 75 0 2.07407407 -2.07407407 76 0 0.71428571 -0.71428571 77 0 0.07216495 -0.07216495 78 0 0.07216495 -0.07216495 79 0 0.07216495 -0.07216495 80 0 0.07216495 -0.07216495 81 0 0.07216495 -0.07216495 82 0 2.07407407 -2.07407407 83 0 7.85185185 -7.85185185 84 0 0.07216495 -0.07216495 85 0 0.07216495 -0.07216495 86 0 0.07216495 -0.07216495 87 0 0.07216495 -0.07216495 88 0 0.07216495 -0.07216495 89 0 0.07216495 -0.07216495 90 0 0.07216495 -0.07216495 91 0 0.07216495 -0.07216495 92 0 0.07216495 -0.07216495 93 0 0.07216495 -0.07216495 94 0 0.07216495 -0.07216495 95 0 0.07216495 -0.07216495 96 0 0.85714286 -0.85714286 97 0 0.07216495 -0.07216495 98 0 0.07216495 -0.07216495 99 0 0.07216495 -0.07216495 100 0 0.07216495 -0.07216495 101 0 0.07216495 -0.07216495 102 0 0.07216495 -0.07216495 103 0 7.85185185 -7.85185185 104 0 0.07216495 -0.07216495 105 0 0.07216495 -0.07216495 106 0 0.07216495 -0.07216495 107 0 0.07216495 -0.07216495 108 0 0.85714286 -0.85714286 109 0 0.07216495 -0.07216495 110 0 0.07216495 -0.07216495 111 0 0.07216495 -0.07216495 112 0 0.07216495 -0.07216495 113 0 0.07216495 -0.07216495 114 0 0.07216495 -0.07216495 115 0 0.07216495 -0.07216495 116 0 2.07407407 -2.07407407 117 0 0.07216495 -0.07216495 118 0 2.07407407 -2.07407407 119 0 0.07216495 -0.07216495 120 0 0.07216495 -0.07216495 121 0 0.07216495 -0.07216495 122 0 0.07216495 -0.07216495 123 0 0.07216495 -0.07216495 124 0 0.07216495 -0.07216495 125 0 0.07216495 -0.07216495 126 0 0.07216495 -0.07216495 127 0 0.85714286 -0.85714286 128 0 0.07216495 -0.07216495 129 0 0.07216495 -0.07216495 130 0 0.07216495 -0.07216495 131 0 0.07216495 -0.07216495 132 0 0.85714286 -0.85714286 133 0 0.07216495 -0.07216495 134 0 0.07216495 -0.07216495 135 0 0.07216495 -0.07216495 136 0 0.07216495 -0.07216495 137 0 0.07216495 -0.07216495 138 0 0.07216495 -0.07216495 139 0 0.07216495 -0.07216495 140 0 0.07216495 -0.07216495 141 0 7.85185185 -7.85185185 142 0 7.85185185 -7.85185185 143 0 0.07216495 -0.07216495 144 0 0.07216495 -0.07216495 145 0 0.07216495 -0.07216495 146 0 0.07216495 -0.07216495 147 0 7.85185185 -7.85185185 148 0 0.07216495 -0.07216495 149 0 0.07216495 -0.07216495 150 0 0.07216495 -0.07216495 151 0 0.07216495 -0.07216495 152 0 0.07216495 -0.07216495 153 0 0.07216495 -0.07216495 154 0 0.07216495 -0.07216495 155 0 0.07216495 -0.07216495 156 0 0.07216495 -0.07216495 157 0 0.07216495 -0.07216495 158 0 0.07216495 -0.07216495 159 0 0.71428571 -0.71428571 160 0 0.07216495 -0.07216495 161 0 0.07216495 -0.07216495 162 0 0.71428571 -0.71428571 163 0 0.07216495 -0.07216495 164 0 0.07216495 -0.07216495 165 0 0.85714286 -0.85714286 166 0 0.07216495 -0.07216495 167 0 0.07216495 -0.07216495 168 0 0.07216495 -0.07216495 169 0 0.07216495 -0.07216495 170 0 7.85185185 -7.85185185 171 0 0.07216495 -0.07216495 172 0 0.07216495 -0.07216495 > 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/41xej1355682290.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/5qt5o1355682290.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/61f041355682290.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/7rvim1355682290.tab") + } > > try(system("convert tmp/2swbo1355682290.ps tmp/2swbo1355682290.png",intern=TRUE)) character(0) > try(system("convert tmp/3r14k1355682290.ps tmp/3r14k1355682290.png",intern=TRUE)) character(0) > try(system("convert tmp/41xej1355682290.ps tmp/41xej1355682290.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.872 0.430 5.282