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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,152) + ,dimnames=list(c('Gender' + ,'Anxiety' + ,'Mistakes' + ,'Doubts' + ,'Expectations' + ,'Critism' + ,'Pstandards' + ,'Organization') + ,1:152)) > y <- array(NA,dim=c(8,152),dimnames=list(c('Gender','Anxiety','Mistakes','Doubts','Expectations','Critism','Pstandards','Organization'),1:152)) > 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 = '0' > par2 = 'none' > par1 = '6' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Critism" > x[,par1] [1] 6 6 13 8 7 9 5 8 9 11 8 11 12 8 7 9 12 20 7 8 8 16 10 6 8 [26] 9 9 11 12 8 7 8 9 4 8 8 8 6 8 4 7 14 10 9 6 8 11 8 8 10 [51] 8 10 7 8 7 9 5 7 7 7 9 5 8 8 8 9 6 8 6 4 6 4 12 6 11 [76] 8 10 10 4 8 9 9 7 7 11 8 8 7 5 7 9 8 6 8 10 10 8 11 8 8 [101] 6 20 6 12 9 5 10 5 6 10 6 10 5 13 7 9 11 8 5 4 9 7 5 5 4 [126] 7 9 8 8 11 10 9 12 10 10 7 10 6 6 11 8 9 9 13 11 4 9 5 4 9 [151] 8 9 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 16 20 9 11 16 18 36 23 15 11 6 3 1 1 2 > colnames(x) [1] "Gender" "Anxiety" "Mistakes" "Doubts" "Expectations" [6] "Critism" "Pstandards" "Organization" > colnames(x)[par1] [1] "Critism" > x[,par1] [1] 6 6 13 8 7 9 5 8 9 11 8 11 12 8 7 9 12 20 7 8 8 16 10 6 8 [26] 9 9 11 12 8 7 8 9 4 8 8 8 6 8 4 7 14 10 9 6 8 11 8 8 10 [51] 8 10 7 8 7 9 5 7 7 7 9 5 8 8 8 9 6 8 6 4 6 4 12 6 11 [76] 8 10 10 4 8 9 9 7 7 11 8 8 7 5 7 9 8 6 8 10 10 8 11 8 8 [101] 6 20 6 12 9 5 10 5 6 10 6 10 5 13 7 9 11 8 5 4 9 7 5 5 4 [126] 7 9 8 8 11 10 9 12 10 10 7 10 6 6 11 8 9 9 13 11 4 9 5 4 9 [151] 8 9 > 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/1g4ah1292055384.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Critism Inputs: Gender, Anxiety, Mistakes, Doubts, Expectations, Pstandards, Organization Number of observations: 152 1) Expectations <= 17; criterion = 1, statistic = 47.742 2) Expectations <= 7; criterion = 1, statistic = 20.244 3)* weights = 7 2) Expectations > 7 4) Expectations <= 13; criterion = 0.988, statistic = 9.838 5)* weights = 92 4) Expectations > 13 6)* weights = 40 1) Expectations > 17 7)* weights = 13 > postscript(file="/var/www/rcomp/tmp/2g4ah1292055384.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/3g4ah1292055384.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 6 8.975000 -2.9750000 2 6 8.975000 -2.9750000 3 13 8.975000 4.0250000 4 8 7.684783 0.3152174 5 7 7.684783 -0.6847826 6 9 7.684783 1.3152174 7 5 12.230769 -7.2307692 8 8 7.684783 0.3152174 9 9 8.975000 0.0250000 10 11 12.230769 -1.2307692 11 8 7.684783 0.3152174 12 11 7.684783 3.3152174 13 12 7.684783 4.3152174 14 8 8.975000 -0.9750000 15 7 7.684783 -0.6847826 16 9 8.975000 0.0250000 17 12 12.230769 -0.2307692 18 20 12.230769 7.7692308 19 7 12.230769 -5.2307692 20 8 8.975000 -0.9750000 21 8 5.000000 3.0000000 22 16 12.230769 3.7692308 23 10 12.230769 -2.2307692 24 6 7.684783 -1.6847826 25 8 7.684783 0.3152174 26 9 7.684783 1.3152174 27 9 7.684783 1.3152174 28 11 12.230769 -1.2307692 29 12 8.975000 3.0250000 30 8 7.684783 0.3152174 31 7 7.684783 -0.6847826 32 8 7.684783 0.3152174 33 9 7.684783 1.3152174 34 4 5.000000 -1.0000000 35 8 7.684783 0.3152174 36 8 7.684783 0.3152174 37 8 7.684783 0.3152174 38 6 7.684783 -1.6847826 39 8 7.684783 0.3152174 40 4 8.975000 -4.9750000 41 7 7.684783 -0.6847826 42 14 8.975000 5.0250000 43 10 8.975000 1.0250000 44 9 8.975000 0.0250000 45 6 7.684783 -1.6847826 46 8 7.684783 0.3152174 47 11 8.975000 2.0250000 48 8 7.684783 0.3152174 49 8 7.684783 0.3152174 50 10 7.684783 2.3152174 51 8 7.684783 0.3152174 52 10 7.684783 2.3152174 53 7 7.684783 -0.6847826 54 8 7.684783 0.3152174 55 7 7.684783 -0.6847826 56 9 7.684783 1.3152174 57 5 5.000000 0.0000000 58 7 8.975000 -1.9750000 59 7 7.684783 -0.6847826 60 7 7.684783 -0.6847826 61 9 7.684783 1.3152174 62 5 7.684783 -2.6847826 63 8 7.684783 0.3152174 64 8 7.684783 0.3152174 65 8 7.684783 0.3152174 66 9 7.684783 1.3152174 67 6 7.684783 -1.6847826 68 8 7.684783 0.3152174 69 6 5.000000 1.0000000 70 4 5.000000 -1.0000000 71 6 7.684783 -1.6847826 72 4 7.684783 -3.6847826 73 12 12.230769 -0.2307692 74 6 7.684783 -1.6847826 75 11 7.684783 3.3152174 76 8 7.684783 0.3152174 77 10 7.684783 2.3152174 78 10 8.975000 1.0250000 79 4 7.684783 -3.6847826 80 8 7.684783 0.3152174 81 9 8.975000 0.0250000 82 9 8.975000 0.0250000 83 7 7.684783 -0.6847826 84 7 7.684783 -0.6847826 85 11 7.684783 3.3152174 86 8 7.684783 0.3152174 87 8 7.684783 0.3152174 88 7 8.975000 -1.9750000 89 5 8.975000 -3.9750000 90 7 7.684783 -0.6847826 91 9 8.975000 0.0250000 92 8 7.684783 0.3152174 93 6 7.684783 -1.6847826 94 8 8.975000 -0.9750000 95 10 12.230769 -2.2307692 96 10 8.975000 1.0250000 97 8 7.684783 0.3152174 98 11 8.975000 2.0250000 99 8 8.975000 -0.9750000 100 8 7.684783 0.3152174 101 6 7.684783 -1.6847826 102 20 12.230769 7.7692308 103 6 8.975000 -2.9750000 104 12 12.230769 -0.2307692 105 9 8.975000 0.0250000 106 5 7.684783 -2.6847826 107 10 8.975000 1.0250000 108 5 7.684783 -2.6847826 109 6 7.684783 -1.6847826 110 10 8.975000 1.0250000 111 6 7.684783 -1.6847826 112 10 8.975000 1.0250000 113 5 7.684783 -2.6847826 114 13 12.230769 0.7692308 115 7 7.684783 -0.6847826 116 9 7.684783 1.3152174 117 11 8.975000 2.0250000 118 8 7.684783 0.3152174 119 5 7.684783 -2.6847826 120 4 7.684783 -3.6847826 121 9 7.684783 1.3152174 122 7 7.684783 -0.6847826 123 5 7.684783 -2.6847826 124 5 7.684783 -2.6847826 125 4 5.000000 -1.0000000 126 7 7.684783 -0.6847826 127 9 8.975000 0.0250000 128 8 7.684783 0.3152174 129 8 8.975000 -0.9750000 130 11 8.975000 2.0250000 131 10 8.975000 1.0250000 132 9 8.975000 0.0250000 133 12 8.975000 3.0250000 134 10 7.684783 2.3152174 135 10 7.684783 2.3152174 136 7 8.975000 -1.9750000 137 10 8.975000 1.0250000 138 6 8.975000 -2.9750000 139 6 7.684783 -1.6847826 140 11 7.684783 3.3152174 141 8 7.684783 0.3152174 142 9 8.975000 0.0250000 143 9 7.684783 1.3152174 144 13 7.684783 5.3152174 145 11 7.684783 3.3152174 146 4 5.000000 -1.0000000 147 9 7.684783 1.3152174 148 5 7.684783 -2.6847826 149 4 7.684783 -3.6847826 150 9 7.684783 1.3152174 151 8 7.684783 0.3152174 152 9 7.684783 1.3152174 > 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/4rva21292055384.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/5nn8b1292055384.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/6gw7d1292055384.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/71xnj1292055384.tab") + } > > try(system("convert tmp/2g4ah1292055384.ps tmp/2g4ah1292055384.png",intern=TRUE)) character(0) > try(system("convert tmp/3g4ah1292055384.ps tmp/3g4ah1292055384.png",intern=TRUE)) character(0) > try(system("convert tmp/4rva21292055384.ps tmp/4rva21292055384.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.880 0.450 3.318