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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 = '' > par2 = 'none' > par1 = '7' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Pstandards" > x[,par1] [1] 25 25 19 18 18 22 29 26 25 23 23 23 24 30 19 24 32 30 29 17 25 26 26 25 23 [26] 21 19 35 19 20 21 21 24 23 19 17 24 15 25 27 29 27 18 25 22 26 23 16 27 25 [51] 14 19 20 16 18 22 21 22 22 32 23 31 18 23 26 24 19 14 20 22 24 25 21 28 24 [76] 20 21 23 13 24 21 21 17 14 29 25 16 25 25 21 23 22 19 24 26 25 20 22 14 20 [101] 32 21 22 28 25 17 21 23 27 22 19 20 17 24 21 21 23 24 19 22 26 17 17 19 15 [126] 17 27 19 21 25 19 22 18 20 15 20 29 19 29 24 23 22 23 22 29 26 26 21 18 10 [151] 19 10 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 10 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 35 2 1 4 3 3 8 7 15 9 15 14 14 12 15 9 5 2 7 2 1 3 1 > colnames(x) [1] "Gender" "Anxiety" "Mistakes" "Doubts" "Expectations" [6] "Critism" "Pstandards" "Organization" > colnames(x)[par1] [1] "Pstandards" > x[,par1] [1] 25 25 19 18 18 22 29 26 25 23 23 23 24 30 19 24 32 30 29 17 25 26 26 25 23 [26] 21 19 35 19 20 21 21 24 23 19 17 24 15 25 27 29 27 18 25 22 26 23 16 27 25 [51] 14 19 20 16 18 22 21 22 22 32 23 31 18 23 26 24 19 14 20 22 24 25 21 28 24 [76] 20 21 23 13 24 21 21 17 14 29 25 16 25 25 21 23 22 19 24 26 25 20 22 14 20 [101] 32 21 22 28 25 17 21 23 27 22 19 20 17 24 21 21 23 24 19 22 26 17 17 19 15 [126] 17 27 19 21 25 19 22 18 20 15 20 29 19 29 24 23 22 23 22 29 26 26 21 18 10 [151] 19 10 > 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/1vpqk1292958941.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Pstandards Inputs: Gender, Anxiety, Mistakes, Doubts, Expectations, Critism, Organization Number of observations: 152 1) Mistakes <= 24; criterion = 1, statistic = 27.189 2) Organization <= 17; criterion = 1, statistic = 21.413 3)* weights = 15 2) Organization > 17 4)* weights = 92 1) Mistakes > 24 5)* weights = 45 > postscript(file="/var/www/html/rcomp/tmp/2oz8o1292958941.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/3oz8o1292958941.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 25 25.17778 -0.1777778 2 25 21.64130 3.3586957 3 19 21.64130 -2.6413043 4 18 25.17778 -7.1777778 5 18 16.20000 1.8000000 6 22 21.64130 0.3586957 7 29 25.17778 3.8222222 8 26 21.64130 4.3586957 9 25 21.64130 3.3586957 10 23 25.17778 -2.1777778 11 23 21.64130 1.3586957 12 23 21.64130 1.3586957 13 24 21.64130 2.3586957 14 30 21.64130 8.3586957 15 19 21.64130 -2.6413043 16 24 21.64130 2.3586957 17 32 25.17778 6.8222222 18 30 25.17778 4.8222222 19 29 25.17778 3.8222222 20 17 25.17778 -8.1777778 21 25 25.17778 -0.1777778 22 26 25.17778 0.8222222 23 26 25.17778 0.8222222 24 25 25.17778 -0.1777778 25 23 21.64130 1.3586957 26 21 16.20000 4.8000000 27 19 21.64130 -2.6413043 28 35 25.17778 9.8222222 29 19 21.64130 -2.6413043 30 20 21.64130 -1.6413043 31 21 21.64130 -0.6413043 32 21 21.64130 -0.6413043 33 24 21.64130 2.3586957 34 23 25.17778 -2.1777778 35 19 21.64130 -2.6413043 36 17 21.64130 -4.6413043 37 24 25.17778 -1.1777778 38 15 21.64130 -6.6413043 39 25 25.17778 -0.1777778 40 27 21.64130 5.3586957 41 29 25.17778 3.8222222 42 27 25.17778 1.8222222 43 18 21.64130 -3.6413043 44 25 21.64130 3.3586957 45 22 21.64130 0.3586957 46 26 21.64130 4.3586957 47 23 25.17778 -2.1777778 48 16 21.64130 -5.6413043 49 27 21.64130 5.3586957 50 25 21.64130 3.3586957 51 14 16.20000 -2.2000000 52 19 16.20000 2.8000000 53 20 25.17778 -5.1777778 54 16 21.64130 -5.6413043 55 18 21.64130 -3.6413043 56 22 21.64130 0.3586957 57 21 21.64130 -0.6413043 58 22 21.64130 0.3586957 59 22 21.64130 0.3586957 60 32 25.17778 6.8222222 61 23 25.17778 -2.1777778 62 31 25.17778 5.8222222 63 18 21.64130 -3.6413043 64 23 21.64130 1.3586957 65 26 25.17778 0.8222222 66 24 21.64130 2.3586957 67 19 21.64130 -2.6413043 68 14 16.20000 -2.2000000 69 20 21.64130 -1.6413043 70 22 21.64130 0.3586957 71 24 21.64130 2.3586957 72 25 21.64130 3.3586957 73 21 25.17778 -4.1777778 74 28 25.17778 2.8222222 75 24 21.64130 2.3586957 76 20 21.64130 -1.6413043 77 21 21.64130 -0.6413043 78 23 21.64130 1.3586957 79 13 16.20000 -3.2000000 80 24 21.64130 2.3586957 81 21 21.64130 -0.6413043 82 21 25.17778 -4.1777778 83 17 21.64130 -4.6413043 84 14 21.64130 -7.6413043 85 29 21.64130 7.3586957 86 25 21.64130 3.3586957 87 16 16.20000 -0.2000000 88 25 21.64130 3.3586957 89 25 21.64130 3.3586957 90 21 21.64130 -0.6413043 91 23 21.64130 1.3586957 92 22 25.17778 -3.1777778 93 19 21.64130 -2.6413043 94 24 25.17778 -1.1777778 95 26 25.17778 0.8222222 96 25 21.64130 3.3586957 97 20 21.64130 -1.6413043 98 22 25.17778 -3.1777778 99 14 16.20000 -2.2000000 100 20 21.64130 -1.6413043 101 32 25.17778 6.8222222 102 21 16.20000 4.8000000 103 22 21.64130 0.3586957 104 28 25.17778 2.8222222 105 25 25.17778 -0.1777778 106 17 21.64130 -4.6413043 107 21 21.64130 -0.6413043 108 23 21.64130 1.3586957 109 27 25.17778 1.8222222 110 22 21.64130 0.3586957 111 19 21.64130 -2.6413043 112 20 21.64130 -1.6413043 113 17 25.17778 -8.1777778 114 24 21.64130 2.3586957 115 21 21.64130 -0.6413043 116 21 21.64130 -0.6413043 117 23 21.64130 1.3586957 118 24 25.17778 -1.1777778 119 19 21.64130 -2.6413043 120 22 21.64130 0.3586957 121 26 21.64130 4.3586957 122 17 16.20000 0.8000000 123 17 21.64130 -4.6413043 124 19 21.64130 -2.6413043 125 15 16.20000 -1.2000000 126 17 21.64130 -4.6413043 127 27 25.17778 1.8222222 128 19 21.64130 -2.6413043 129 21 21.64130 -0.6413043 130 25 21.64130 3.3586957 131 19 25.17778 -6.1777778 132 22 25.17778 -3.1777778 133 18 25.17778 -7.1777778 134 20 21.64130 -1.6413043 135 15 21.64130 -6.6413043 136 20 21.64130 -1.6413043 137 29 25.17778 3.8222222 138 19 16.20000 2.8000000 139 29 25.17778 3.8222222 140 24 21.64130 2.3586957 141 23 21.64130 1.3586957 142 22 21.64130 0.3586957 143 23 25.17778 -2.1777778 144 22 16.20000 5.8000000 145 29 21.64130 7.3586957 146 26 25.17778 0.8222222 147 26 21.64130 4.3586957 148 21 21.64130 -0.6413043 149 18 21.64130 -3.6413043 150 10 16.20000 -6.2000000 151 19 21.64130 -2.6413043 152 10 16.20000 -6.2000000 > 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/4h7781292958941.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/5k85e1292958941.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/65rm21292958941.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/7g0351292958941.tab") + } > > try(system("convert tmp/2oz8o1292958941.ps tmp/2oz8o1292958941.png",intern=TRUE)) character(0) > try(system("convert tmp/3oz8o1292958941.ps tmp/3oz8o1292958941.png",intern=TRUE)) character(0) > try(system("convert tmp/4h7781292958941.ps tmp/4h7781292958941.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.006 0.556 12.139