R version 2.12.0 (2010-10-15) Copyright (C) 2010 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. 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,9 + ,16 + ,2 + ,30 + ,35 + ,17 + ,15 + ,14 + ,1 + ,31 + ,23 + ,16 + ,11 + ,15 + ,2 + ,40 + ,31 + ,10 + ,8 + ,16 + ,2 + ,32 + ,27 + ,18 + ,13 + ,16 + ,1 + ,36 + ,36 + ,13 + ,12 + ,11 + ,1 + ,32 + ,31 + ,16 + ,12 + ,12 + ,1 + ,35 + ,32 + ,13 + ,9 + ,9 + ,2 + ,38 + ,39 + ,10 + ,7 + ,16 + ,2 + ,42 + ,37 + ,15 + ,13 + ,13 + ,1 + ,34 + ,38 + ,16 + ,9 + ,16 + ,2 + ,35 + ,39 + ,16 + ,6 + ,12 + ,2 + ,35 + ,34 + ,14 + ,8 + ,9 + ,2 + ,33 + ,31 + ,10 + ,8 + ,13 + ,2 + ,36 + ,32 + ,17 + ,15 + ,13 + ,2 + ,32 + ,37 + ,13 + ,6 + ,14 + ,2 + ,33 + ,36 + ,15 + ,9 + ,19 + ,2 + ,34 + ,32 + ,16 + ,11 + ,13 + ,2 + ,32 + ,35 + ,12 + ,8 + ,12 + ,2 + ,34 + ,36 + ,13 + ,8 + ,13) + ,dim=c(6 + ,162) + ,dimnames=list(c('gender' + ,'connected' + ,'separate' + ,'learning' + ,'software' + ,'happiness') + ,1:162)) > y <- array(NA,dim=c(6,162),dimnames=list(c('gender','connected','separate','learning','software','happiness'),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 = '5' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "software" > x[,par1] [1] 12 11 15 6 13 10 12 14 12 6 10 12 12 11 15 12 10 12 11 12 11 12 13 11 9 [26] 13 10 14 12 10 12 8 10 12 12 7 6 12 10 10 10 12 15 10 10 12 13 11 11 12 [51] 14 10 12 13 5 6 12 12 11 10 7 12 14 11 12 13 14 11 12 12 8 11 14 14 12 [76] 9 13 11 12 12 12 12 12 12 11 10 9 12 12 12 9 15 12 12 12 10 13 9 12 10 [101] 14 11 15 11 11 12 12 12 11 7 12 14 11 11 10 13 13 8 11 12 11 13 12 14 13 [126] 15 10 11 9 11 10 11 8 11 12 12 9 11 10 8 9 8 9 15 11 8 13 12 12 9 [151] 7 13 9 6 8 8 15 6 9 11 8 8 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 5 6 7 8 9 10 11 12 13 14 15 1 6 4 11 12 20 28 48 14 10 8 > colnames(x) [1] "gender" "connected" "separate" "learning" "software" "happiness" > colnames(x)[par1] [1] "software" > x[,par1] [1] 12 11 15 6 13 10 12 14 12 6 10 12 12 11 15 12 10 12 11 12 11 12 13 11 9 [26] 13 10 14 12 10 12 8 10 12 12 7 6 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 12 11 10 9 12 12 12 9 15 12 12 12 10 13 9 12 10 [101] 14 11 15 11 11 12 12 12 11 7 12 14 11 11 10 13 13 8 11 12 11 13 12 14 13 [126] 15 10 11 9 11 10 11 8 11 12 12 9 11 10 8 9 8 9 15 11 8 13 12 12 9 [151] 7 13 9 6 8 8 15 6 9 11 8 8 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/www/rcomp/tmp/10od81323868650.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: software Inputs: gender, connected, separate, learning, happiness Number of observations: 162 1) learning <= 12; criterion = 1, statistic = 47.92 2)* weights = 22 1) learning > 12 3) learning <= 16; criterion = 1, statistic = 20.966 4)* weights = 115 3) learning > 16 5)* weights = 25 > postscript(file="/var/www/rcomp/tmp/2hz3p1323868650.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/www/rcomp/tmp/33rqr1323868650.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 11.086957 0.91304348 2 11 11.086957 -0.08695652 3 15 12.880000 2.12000000 4 6 11.086957 -5.08695652 5 13 11.086957 1.91304348 6 10 11.086957 -1.08695652 7 12 12.880000 -0.88000000 8 14 11.086957 2.91304348 9 12 11.086957 0.91304348 10 6 11.086957 -5.08695652 11 10 11.086957 -1.08695652 12 12 11.086957 0.91304348 13 12 11.086957 0.91304348 14 11 11.086957 -0.08695652 15 15 12.880000 2.12000000 16 12 11.086957 0.91304348 17 10 11.086957 -1.08695652 18 12 12.880000 -0.88000000 19 11 12.880000 -1.88000000 20 12 11.086957 0.91304348 21 11 11.086957 -0.08695652 22 12 11.086957 0.91304348 23 13 12.880000 0.12000000 24 11 11.086957 -0.08695652 25 9 12.880000 -3.88000000 26 13 12.880000 0.12000000 27 10 11.086957 -1.08695652 28 14 11.086957 2.91304348 29 12 11.086957 0.91304348 30 10 11.086957 -1.08695652 31 12 11.086957 0.91304348 32 8 8.818182 -0.81818182 33 10 11.086957 -1.08695652 34 12 11.086957 0.91304348 35 12 11.086957 0.91304348 36 7 8.818182 -1.81818182 37 6 8.818182 -2.81818182 38 12 11.086957 0.91304348 39 10 11.086957 -1.08695652 40 10 11.086957 -1.08695652 41 10 11.086957 -1.08695652 42 12 11.086957 0.91304348 43 15 12.880000 2.12000000 44 10 11.086957 -1.08695652 45 10 11.086957 -1.08695652 46 12 8.818182 3.18181818 47 13 11.086957 1.91304348 48 11 11.086957 -0.08695652 49 11 11.086957 -0.08695652 50 12 11.086957 0.91304348 51 14 11.086957 2.91304348 52 10 11.086957 -1.08695652 53 12 8.818182 3.18181818 54 13 11.086957 1.91304348 55 5 8.818182 -3.81818182 56 6 11.086957 -5.08695652 57 12 11.086957 0.91304348 58 12 11.086957 0.91304348 59 11 11.086957 -0.08695652 60 10 11.086957 -1.08695652 61 7 8.818182 -1.81818182 62 12 11.086957 0.91304348 63 14 11.086957 2.91304348 64 11 11.086957 -0.08695652 65 12 11.086957 0.91304348 66 13 12.880000 0.12000000 67 14 12.880000 1.12000000 68 11 12.880000 -1.88000000 69 12 8.818182 3.18181818 70 12 11.086957 0.91304348 71 8 8.818182 -0.81818182 72 11 11.086957 -0.08695652 73 14 12.880000 1.12000000 74 14 12.880000 1.12000000 75 12 11.086957 0.91304348 76 9 12.880000 -3.88000000 77 13 11.086957 1.91304348 78 11 11.086957 -0.08695652 79 12 11.086957 0.91304348 80 12 11.086957 0.91304348 81 12 11.086957 0.91304348 82 12 12.880000 -0.88000000 83 12 11.086957 0.91304348 84 12 11.086957 0.91304348 85 11 11.086957 -0.08695652 86 10 11.086957 -1.08695652 87 9 11.086957 -2.08695652 88 12 11.086957 0.91304348 89 12 11.086957 0.91304348 90 12 11.086957 0.91304348 91 9 8.818182 0.18181818 92 15 12.880000 2.12000000 93 12 11.086957 0.91304348 94 12 11.086957 0.91304348 95 12 11.086957 0.91304348 96 10 11.086957 -1.08695652 97 13 11.086957 1.91304348 98 9 11.086957 -2.08695652 99 12 11.086957 0.91304348 100 10 11.086957 -1.08695652 101 14 11.086957 2.91304348 102 11 11.086957 -0.08695652 103 15 12.880000 2.12000000 104 11 11.086957 -0.08695652 105 11 11.086957 -0.08695652 106 12 11.086957 0.91304348 107 12 12.880000 -0.88000000 108 12 11.086957 0.91304348 109 11 11.086957 -0.08695652 110 7 8.818182 -1.81818182 111 12 11.086957 0.91304348 112 14 11.086957 2.91304348 113 11 12.880000 -1.88000000 114 11 11.086957 -0.08695652 115 10 8.818182 1.18181818 116 13 12.880000 0.12000000 117 13 11.086957 1.91304348 118 8 11.086957 -3.08695652 119 11 11.086957 -0.08695652 120 12 11.086957 0.91304348 121 11 11.086957 -0.08695652 122 13 11.086957 1.91304348 123 12 11.086957 0.91304348 124 14 11.086957 2.91304348 125 13 11.086957 1.91304348 126 15 11.086957 3.91304348 127 10 11.086957 -1.08695652 128 11 12.880000 -1.88000000 129 9 11.086957 -2.08695652 130 11 8.818182 2.18181818 131 10 11.086957 -1.08695652 132 11 8.818182 2.18181818 133 8 11.086957 -3.08695652 134 11 8.818182 2.18181818 135 12 11.086957 0.91304348 136 12 11.086957 0.91304348 137 9 11.086957 -2.08695652 138 11 11.086957 -0.08695652 139 10 8.818182 1.18181818 140 8 8.818182 -0.81818182 141 9 8.818182 0.18181818 142 8 11.086957 -3.08695652 143 9 11.086957 -2.08695652 144 15 12.880000 2.12000000 145 11 11.086957 -0.08695652 146 8 8.818182 -0.81818182 147 13 12.880000 0.12000000 148 12 11.086957 0.91304348 149 12 11.086957 0.91304348 150 9 11.086957 -2.08695652 151 7 8.818182 -1.81818182 152 13 11.086957 1.91304348 153 9 11.086957 -2.08695652 154 6 11.086957 -5.08695652 155 8 11.086957 -3.08695652 156 8 8.818182 -0.81818182 157 15 12.880000 2.12000000 158 6 11.086957 -5.08695652 159 9 11.086957 -2.08695652 160 11 11.086957 -0.08695652 161 8 8.818182 -0.81818182 162 8 11.086957 -3.08695652 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/rcomp/tmp/4ix401323868650.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/www/rcomp/tmp/52m5n1323868650.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/www/rcomp/tmp/6tq6l1323868650.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/www/rcomp/tmp/7sm281323868650.tab") + } > > try(system("convert tmp/2hz3p1323868650.ps tmp/2hz3p1323868650.png",intern=TRUE)) character(0) > try(system("convert tmp/33rqr1323868650.ps tmp/33rqr1323868650.png",intern=TRUE)) character(0) > try(system("convert tmp/4ix401323868650.ps tmp/4ix401323868650.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.550 0.190 2.728