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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 = '3' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Mistakes" > x[,par1] [1] 26 20 21 31 21 18 26 22 22 29 15 16 24 17 19 22 31 28 38 26 25 25 29 28 15 [26] 18 21 25 23 23 19 18 18 26 18 18 28 17 29 12 25 28 20 17 17 20 31 21 19 23 [51] 15 24 28 16 19 21 21 20 16 25 30 29 22 19 33 17 9 14 15 12 21 20 29 33 21 [76] 15 19 23 20 20 18 31 18 13 9 20 18 23 17 17 16 31 15 28 26 20 19 25 18 20 [101] 33 24 22 32 31 13 18 17 29 22 18 22 25 20 20 17 21 26 10 15 20 14 16 23 11 [126] 19 30 21 20 22 30 25 28 23 23 21 30 22 32 22 15 21 27 22 9 29 20 16 16 16 [151] 18 16 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 38 3 1 1 2 2 2 8 9 9 13 8 15 12 11 8 3 8 6 1 7 7 4 6 2 3 1 > colnames(x) [1] "Gender" "Anxiety" "Mistakes" "Doubts" "Expectations" [6] "Critism" "Pstandards" "Organization" > colnames(x)[par1] [1] "Mistakes" > x[,par1] [1] 26 20 21 31 21 18 26 22 22 29 15 16 24 17 19 22 31 28 38 26 25 25 29 28 15 [26] 18 21 25 23 23 19 18 18 26 18 18 28 17 29 12 25 28 20 17 17 20 31 21 19 23 [51] 15 24 28 16 19 21 21 20 16 25 30 29 22 19 33 17 9 14 15 12 21 20 29 33 21 [76] 15 19 23 20 20 18 31 18 13 9 20 18 23 17 17 16 31 15 28 26 20 19 25 18 20 [101] 33 24 22 32 31 13 18 17 29 22 18 22 25 20 20 17 21 26 10 15 20 14 16 23 11 [126] 19 30 21 20 22 30 25 28 23 23 21 30 22 32 22 15 21 27 22 9 29 20 16 16 16 [151] 18 16 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/html/rcomp/tmp/1zj0n1292055670.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: Mistakes Inputs: Gender, Anxiety, Doubts, Expectations, Critism, Pstandards, Organization Number of observations: 152 1) Pstandards <= 23; criterion = 1, statistic = 27.189 2) Critism <= 8; criterion = 1, statistic = 16.868 3)* weights = 60 2) Critism > 8 4) Doubts <= 11; criterion = 0.997, statistic = 12.43 5)* weights = 17 4) Doubts > 11 6)* weights = 18 1) Pstandards > 23 7) Doubts <= 7; criterion = 1, statistic = 17.235 8)* weights = 7 7) Doubts > 7 9) Pstandards <= 25; criterion = 0.999, statistic = 15.627 10)* weights = 27 9) Pstandards > 25 11)* weights = 23 > postscript(file="/var/www/html/rcomp/tmp/2ashq1292055670.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/html/rcomp/tmp/3ashq1292055670.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 22.59259 3.40740741 2 20 22.59259 -2.59259259 3 21 19.82353 1.17647059 4 31 18.43333 12.56666667 5 21 18.43333 2.56666667 6 18 19.82353 -1.82352941 7 26 28.34783 -2.34782609 8 22 28.34783 -6.34782609 9 22 22.59259 -0.59259259 10 29 25.05556 3.94444444 11 15 18.43333 -3.43333333 12 16 19.82353 -3.82352941 13 24 22.59259 1.40740741 14 17 16.00000 1.00000000 15 19 18.43333 0.56666667 16 22 22.59259 -0.59259259 17 31 28.34783 2.65217391 18 28 28.34783 -0.34782609 19 38 28.34783 9.65217391 20 26 18.43333 7.56666667 21 25 22.59259 2.40740741 22 25 28.34783 -3.34782609 23 29 28.34783 0.65217391 24 28 22.59259 5.40740741 25 15 18.43333 -3.43333333 26 18 25.05556 -7.05555556 27 21 19.82353 1.17647059 28 25 16.00000 9.00000000 29 23 25.05556 -2.05555556 30 23 18.43333 4.56666667 31 19 18.43333 0.56666667 32 18 18.43333 -0.43333333 33 18 22.59259 -4.59259259 34 26 18.43333 7.56666667 35 18 18.43333 -0.43333333 36 18 18.43333 -0.43333333 37 28 22.59259 5.40740741 38 17 18.43333 -1.43333333 39 29 22.59259 6.40740741 40 12 16.00000 -4.00000000 41 25 28.34783 -3.34782609 42 28 28.34783 -0.34782609 43 20 25.05556 -5.05555556 44 17 22.59259 -5.59259259 45 17 18.43333 -1.43333333 46 20 28.34783 -8.34782609 47 31 25.05556 5.94444444 48 21 18.43333 2.56666667 49 19 28.34783 -9.34782609 50 23 22.59259 0.40740741 51 15 18.43333 -3.43333333 52 24 19.82353 4.17647059 53 28 18.43333 9.56666667 54 16 18.43333 -2.43333333 55 19 18.43333 0.56666667 56 21 19.82353 1.17647059 57 21 18.43333 2.56666667 58 20 18.43333 1.56666667 59 16 18.43333 -2.43333333 60 25 28.34783 -3.34782609 61 30 25.05556 4.94444444 62 29 28.34783 0.65217391 63 22 18.43333 3.56666667 64 19 18.43333 0.56666667 65 33 28.34783 4.65217391 66 17 22.59259 -5.59259259 67 9 18.43333 -9.43333333 68 14 18.43333 -4.43333333 69 15 18.43333 -3.43333333 70 12 18.43333 -6.43333333 71 21 22.59259 -1.59259259 72 20 22.59259 -2.59259259 73 29 25.05556 3.94444444 74 33 28.34783 4.65217391 75 21 22.59259 -1.59259259 76 15 18.43333 -3.43333333 77 19 19.82353 -0.82352941 78 23 19.82353 3.17647059 79 20 18.43333 1.56666667 80 20 22.59259 -2.59259259 81 18 19.82353 -1.82352941 82 31 25.05556 5.94444444 83 18 18.43333 -0.43333333 84 13 18.43333 -5.43333333 85 9 16.00000 -7.00000000 86 20 22.59259 -2.59259259 87 18 18.43333 -0.43333333 88 23 22.59259 0.40740741 89 17 22.59259 -5.59259259 90 17 18.43333 -1.43333333 91 16 19.82353 -3.82352941 92 31 18.43333 12.56666667 93 15 18.43333 -3.43333333 94 28 22.59259 5.40740741 95 26 28.34783 -2.34782609 96 20 22.59259 -2.59259259 97 19 18.43333 0.56666667 98 25 25.05556 -0.05555556 99 18 18.43333 -0.43333333 100 20 18.43333 1.56666667 101 33 28.34783 4.65217391 102 24 25.05556 -1.05555556 103 22 18.43333 3.56666667 104 32 28.34783 3.65217391 105 31 22.59259 8.40740741 106 13 18.43333 -5.43333333 107 18 19.82353 -1.82352941 108 17 18.43333 -1.43333333 109 29 28.34783 0.65217391 110 22 25.05556 -3.05555556 111 18 18.43333 -0.43333333 112 22 19.82353 2.17647059 113 25 18.43333 6.56666667 114 20 22.59259 -2.59259259 115 20 18.43333 1.56666667 116 17 19.82353 -2.82352941 117 21 25.05556 -4.05555556 118 26 22.59259 3.40740741 119 10 18.43333 -8.43333333 120 15 18.43333 -3.43333333 121 20 16.00000 4.00000000 122 14 18.43333 -4.43333333 123 16 18.43333 -2.43333333 124 23 18.43333 4.56666667 125 11 18.43333 -7.43333333 126 19 18.43333 0.56666667 127 30 28.34783 1.65217391 128 21 18.43333 2.56666667 129 20 18.43333 1.56666667 130 22 22.59259 -0.59259259 131 30 25.05556 4.94444444 132 25 25.05556 -0.05555556 133 28 19.82353 8.17647059 134 23 25.05556 -2.05555556 135 23 19.82353 3.17647059 136 21 18.43333 2.56666667 137 30 28.34783 1.65217391 138 22 18.43333 3.56666667 139 32 28.34783 3.65217391 140 22 22.59259 -0.59259259 141 15 18.43333 -3.43333333 142 21 25.05556 -4.05555556 143 27 25.05556 1.94444444 144 22 25.05556 -3.05555556 145 9 16.00000 -7.00000000 146 29 28.34783 0.65217391 147 20 16.00000 4.00000000 148 16 18.43333 -2.43333333 149 16 18.43333 -2.43333333 150 16 19.82353 -3.82352941 151 18 18.43333 -0.43333333 152 16 19.82353 -3.82352941 > 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/html/rcomp/tmp/432zu1292055670.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/html/rcomp/tmp/56kfz1292055670.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/html/rcomp/tmp/6rlv51292055670.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/html/rcomp/tmp/7vlub1292055670.tab") + } > > try(system("convert tmp/2ashq1292055670.ps tmp/2ashq1292055670.png",intern=TRUE)) character(0) > try(system("convert tmp/3ashq1292055670.ps tmp/3ashq1292055670.png",intern=TRUE)) character(0) > try(system("convert tmp/432zu1292055670.ps tmp/432zu1292055670.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.124 0.760 11.339