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,9 + ,12 + ,10 + ,13) + ,dim=c(7 + ,148) + ,dimnames=list(c('Gender' + ,'Learning' + ,'Concern' + ,'Doubts' + ,'Expectations' + ,'Standards' + ,'Organization') + ,1:148)) > y <- array(NA,dim=c(7,148),dimnames=list(c('Gender','Learning','Concern','Doubts','Expectations','Standards','Organization'),1:148)) > 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 = '2' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Organization" > x[,par1] [1] 25 24 21 23 17 19 18 27 23 23 29 21 26 25 25 23 26 20 29 24 23 24 30 22 22 [26] 13 24 17 24 21 23 24 24 23 26 24 21 23 28 23 22 24 21 23 23 20 23 21 27 12 [51] 15 22 21 21 20 24 24 29 25 14 30 19 29 25 25 25 16 25 28 24 25 21 22 20 25 [76] 27 21 13 26 26 25 22 19 23 25 15 21 23 25 24 24 21 24 22 24 28 21 17 28 24 [101] 10 20 22 19 22 22 26 24 22 20 20 15 20 20 24 22 29 23 24 22 16 23 27 16 21 [126] 26 22 23 19 18 24 29 22 24 22 12 26 18 22 24 21 15 23 22 22 24 23 13 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 1 4 3 3 3 5 9 15 19 19 24 13 8 4 4 6 2 > colnames(x) [1] "Gender" "Learning" "Concern" "Doubts" "Expectations" [6] "Standards" "Organization" > colnames(x)[par1] [1] "Organization" > x[,par1] [1] 25 24 21 23 17 19 18 27 23 23 29 21 26 25 25 23 26 20 29 24 23 24 30 22 22 [26] 13 24 17 24 21 23 24 24 23 26 24 21 23 28 23 22 24 21 23 23 20 23 21 27 12 [51] 15 22 21 21 20 24 24 29 25 14 30 19 29 25 25 25 16 25 28 24 25 21 22 20 25 [76] 27 21 13 26 26 25 22 19 23 25 15 21 23 25 24 24 21 24 22 24 28 21 17 28 24 [101] 10 20 22 19 22 22 26 24 22 20 20 15 20 20 24 22 29 23 24 22 16 23 27 16 21 [126] 26 22 23 19 18 24 29 22 24 22 12 26 18 22 24 21 15 23 22 22 24 23 13 > 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/1yzlj1292541059.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Organization Inputs: Gender, Learning, Concern, Doubts, Expectations, Standards Number of observations: 148 1) Standards <= 16; criterion = 1, statistic = 18.545 2)* weights = 12 1) Standards > 16 3) Gender <= 0; criterion = 0.999, statistic = 14.669 4)* weights = 86 3) Gender > 0 5)* weights = 50 > postscript(file="/var/www/html/rcomp/tmp/2yzlj1292541059.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/3yzlj1292541059.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 21.90698 3.09302326 2 24 21.90698 2.09302326 3 21 21.90698 -0.90697674 4 23 24.32000 -1.32000000 5 17 21.90698 -4.90697674 6 19 21.90698 -2.90697674 7 18 21.90698 -3.90697674 8 27 21.90698 5.09302326 9 23 21.90698 1.09302326 10 23 21.90698 1.09302326 11 29 24.32000 4.68000000 12 21 21.90698 -0.90697674 13 26 24.32000 1.68000000 14 25 21.90698 3.09302326 15 25 24.32000 0.68000000 16 23 24.32000 -1.32000000 17 26 21.90698 4.09302326 18 20 24.32000 -4.32000000 19 29 21.90698 7.09302326 20 24 24.32000 -0.32000000 21 23 21.90698 1.09302326 22 24 21.90698 2.09302326 23 30 24.32000 5.68000000 24 22 21.90698 0.09302326 25 22 24.32000 -2.32000000 26 13 21.90698 -8.90697674 27 24 24.32000 -0.32000000 28 17 21.90698 -4.90697674 29 24 24.32000 -0.32000000 30 21 21.90698 -0.90697674 31 23 21.90698 1.09302326 32 24 24.32000 -0.32000000 33 24 24.32000 -0.32000000 34 23 24.32000 -1.32000000 35 26 21.90698 4.09302326 36 24 24.32000 -0.32000000 37 21 17.33333 3.66666667 38 23 21.90698 1.09302326 39 28 24.32000 3.68000000 40 23 24.32000 -1.32000000 41 22 21.90698 0.09302326 42 24 21.90698 2.09302326 43 21 21.90698 -0.90697674 44 23 21.90698 1.09302326 45 23 24.32000 -1.32000000 46 20 21.90698 -1.90697674 47 23 17.33333 5.66666667 48 21 24.32000 -3.32000000 49 27 21.90698 5.09302326 50 12 17.33333 -5.33333333 51 15 21.90698 -6.90697674 52 22 21.90698 0.09302326 53 21 17.33333 3.66666667 54 21 24.32000 -3.32000000 55 20 21.90698 -1.90697674 56 24 24.32000 -0.32000000 57 24 24.32000 -0.32000000 58 29 21.90698 7.09302326 59 25 21.90698 3.09302326 60 14 21.90698 -7.90697674 61 30 24.32000 5.68000000 62 19 21.90698 -2.90697674 63 29 24.32000 4.68000000 64 25 21.90698 3.09302326 65 25 24.32000 0.68000000 66 25 24.32000 0.68000000 67 16 17.33333 -1.33333333 68 25 21.90698 3.09302326 69 28 24.32000 3.68000000 70 24 24.32000 -0.32000000 71 25 21.90698 3.09302326 72 21 21.90698 -0.90697674 73 22 24.32000 -2.32000000 74 20 24.32000 -4.32000000 75 25 24.32000 0.68000000 76 27 24.32000 2.68000000 77 21 21.90698 -0.90697674 78 13 17.33333 -4.33333333 79 26 21.90698 4.09302326 80 26 21.90698 4.09302326 81 25 24.32000 0.68000000 82 22 21.90698 0.09302326 83 19 17.33333 1.66666667 84 23 21.90698 1.09302326 85 25 21.90698 3.09302326 86 15 17.33333 -2.33333333 87 21 21.90698 -0.90697674 88 23 21.90698 1.09302326 89 25 21.90698 3.09302326 90 24 21.90698 2.09302326 91 24 24.32000 -0.32000000 92 21 24.32000 -3.32000000 93 24 21.90698 2.09302326 94 22 24.32000 -2.32000000 95 24 21.90698 2.09302326 96 28 24.32000 3.68000000 97 21 21.90698 -0.90697674 98 17 17.33333 -0.33333333 99 28 21.90698 6.09302326 100 24 24.32000 -0.32000000 101 10 21.90698 -11.90697674 102 20 21.90698 -1.90697674 103 22 21.90698 0.09302326 104 19 21.90698 -2.90697674 105 22 24.32000 -2.32000000 106 22 21.90698 0.09302326 107 26 24.32000 1.68000000 108 24 21.90698 2.09302326 109 22 21.90698 0.09302326 110 20 21.90698 -1.90697674 111 20 21.90698 -1.90697674 112 15 21.90698 -6.90697674 113 20 21.90698 -1.90697674 114 20 21.90698 -1.90697674 115 24 21.90698 2.09302326 116 22 21.90698 0.09302326 117 29 21.90698 7.09302326 118 23 24.32000 -1.32000000 119 24 21.90698 2.09302326 120 22 21.90698 0.09302326 121 16 21.90698 -5.90697674 122 23 24.32000 -1.32000000 123 27 24.32000 2.68000000 124 16 17.33333 -1.33333333 125 21 24.32000 -3.32000000 126 26 21.90698 4.09302326 127 22 24.32000 -2.32000000 128 23 24.32000 -1.32000000 129 19 21.90698 -2.90697674 130 18 21.90698 -3.90697674 131 24 21.90698 2.09302326 132 29 24.32000 4.68000000 133 22 17.33333 4.66666667 134 24 24.32000 -0.32000000 135 22 21.90698 0.09302326 136 12 21.90698 -9.90697674 137 26 24.32000 1.68000000 138 18 21.90698 -3.90697674 139 22 24.32000 -2.32000000 140 24 21.90698 2.09302326 141 21 21.90698 -0.90697674 142 15 21.90698 -6.90697674 143 23 21.90698 1.09302326 144 22 21.90698 0.09302326 145 22 21.90698 0.09302326 146 24 21.90698 2.09302326 147 23 21.90698 1.09302326 148 13 17.33333 -4.33333333 > 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/4rr241292541059.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/5urjs1292541059.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/6ga0g1292541059.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/78jzj1292541059.tab") + } > > try(system("convert tmp/2yzlj1292541059.ps tmp/2yzlj1292541059.png",intern=TRUE)) character(0) > try(system("convert tmp/3yzlj1292541059.ps tmp/3yzlj1292541059.png",intern=TRUE)) character(0) > try(system("convert tmp/4rr241292541059.ps tmp/4rr241292541059.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.774 0.629 7.938