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,10 + ,13) + ,dim=c(7 + ,150) + ,dimnames=list(c('Gender' + ,'Learning' + ,'Concern' + ,'Doubts' + ,'Criticism' + ,'Standards' + ,'Organization') + ,1:150)) > y <- array(NA,dim=c(7,150),dimnames=list(c('Gender','Learning','Concern','Doubts','Criticism','Standards','Organization'),1:150)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'yes' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Organization" > x[,par1] [1] 25 24 21 23 17 19 18 27 23 23 29 21 26 25 25 23 26 20 29 24 23 24 30 22 22 [26] 13 24 17 24 21 23 24 24 24 23 26 24 21 23 28 23 22 24 21 23 23 20 23 21 27 [51] 12 15 22 21 21 20 24 24 29 25 14 30 19 29 25 25 25 16 25 28 24 25 21 22 20 [76] 25 27 21 13 26 26 25 22 19 23 25 15 21 23 25 24 24 21 24 22 24 28 21 17 28 [101] 24 10 20 22 19 22 22 26 24 22 20 20 15 20 20 24 22 29 23 24 22 16 23 27 16 [126] 21 26 22 23 19 18 24 24 29 22 24 22 12 26 18 22 24 21 15 23 22 22 24 23 13 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 26 13 8 4 4 6 2 > colnames(x) [1] "Gender" "Learning" "Concern" "Doubts" "Criticism" [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 24 23 26 24 21 23 28 23 22 24 21 23 23 20 23 21 27 [51] 12 15 22 21 21 20 24 24 29 25 14 30 19 29 25 25 25 16 25 28 24 25 21 22 20 [76] 25 27 21 13 26 26 25 22 19 23 25 15 21 23 25 24 24 21 24 22 24 28 21 17 28 [101] 24 10 20 22 19 22 22 26 24 22 20 20 15 20 20 24 22 29 23 24 22 16 23 27 16 [126] 21 26 22 23 19 18 24 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/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/1mxtb1292268942.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Organization Inputs: Gender, Learning, Concern, Doubts, Criticism, Standards Number of observations: 150 1) Standards <= 16; criterion = 1, statistic = 18.836 2)* weights = 12 1) Standards > 16 3) Gender <= 0; criterion = 0.999, statistic = 15.077 4)* weights = 86 3) Gender > 0 5)* weights = 52 > postscript(file="/var/www/rcomp/tmp/2mxtb1292268942.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/3mxtb1292268942.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 24.30769 -1.30769231 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 24.30769 4.69230769 12 21 21.90698 -0.90697674 13 26 24.30769 1.69230769 14 25 21.90698 3.09302326 15 25 24.30769 0.69230769 16 23 24.30769 -1.30769231 17 26 21.90698 4.09302326 18 20 24.30769 -4.30769231 19 29 21.90698 7.09302326 20 24 24.30769 -0.30769231 21 23 21.90698 1.09302326 22 24 21.90698 2.09302326 23 30 24.30769 5.69230769 24 22 21.90698 0.09302326 25 22 24.30769 -2.30769231 26 13 21.90698 -8.90697674 27 24 24.30769 -0.30769231 28 17 21.90698 -4.90697674 29 24 24.30769 -0.30769231 30 21 21.90698 -0.90697674 31 23 21.90698 1.09302326 32 24 24.30769 -0.30769231 33 24 24.30769 -0.30769231 34 24 24.30769 -0.30769231 35 23 24.30769 -1.30769231 36 26 21.90698 4.09302326 37 24 24.30769 -0.30769231 38 21 17.33333 3.66666667 39 23 21.90698 1.09302326 40 28 24.30769 3.69230769 41 23 24.30769 -1.30769231 42 22 21.90698 0.09302326 43 24 21.90698 2.09302326 44 21 21.90698 -0.90697674 45 23 21.90698 1.09302326 46 23 24.30769 -1.30769231 47 20 21.90698 -1.90697674 48 23 17.33333 5.66666667 49 21 24.30769 -3.30769231 50 27 21.90698 5.09302326 51 12 17.33333 -5.33333333 52 15 21.90698 -6.90697674 53 22 21.90698 0.09302326 54 21 17.33333 3.66666667 55 21 24.30769 -3.30769231 56 20 21.90698 -1.90697674 57 24 24.30769 -0.30769231 58 24 24.30769 -0.30769231 59 29 21.90698 7.09302326 60 25 21.90698 3.09302326 61 14 21.90698 -7.90697674 62 30 24.30769 5.69230769 63 19 21.90698 -2.90697674 64 29 24.30769 4.69230769 65 25 21.90698 3.09302326 66 25 24.30769 0.69230769 67 25 24.30769 0.69230769 68 16 17.33333 -1.33333333 69 25 21.90698 3.09302326 70 28 24.30769 3.69230769 71 24 24.30769 -0.30769231 72 25 21.90698 3.09302326 73 21 21.90698 -0.90697674 74 22 24.30769 -2.30769231 75 20 24.30769 -4.30769231 76 25 24.30769 0.69230769 77 27 24.30769 2.69230769 78 21 21.90698 -0.90697674 79 13 17.33333 -4.33333333 80 26 21.90698 4.09302326 81 26 21.90698 4.09302326 82 25 24.30769 0.69230769 83 22 21.90698 0.09302326 84 19 17.33333 1.66666667 85 23 21.90698 1.09302326 86 25 21.90698 3.09302326 87 15 17.33333 -2.33333333 88 21 21.90698 -0.90697674 89 23 21.90698 1.09302326 90 25 21.90698 3.09302326 91 24 21.90698 2.09302326 92 24 24.30769 -0.30769231 93 21 24.30769 -3.30769231 94 24 21.90698 2.09302326 95 22 24.30769 -2.30769231 96 24 21.90698 2.09302326 97 28 24.30769 3.69230769 98 21 21.90698 -0.90697674 99 17 17.33333 -0.33333333 100 28 21.90698 6.09302326 101 24 24.30769 -0.30769231 102 10 21.90698 -11.90697674 103 20 21.90698 -1.90697674 104 22 21.90698 0.09302326 105 19 21.90698 -2.90697674 106 22 24.30769 -2.30769231 107 22 21.90698 0.09302326 108 26 24.30769 1.69230769 109 24 21.90698 2.09302326 110 22 21.90698 0.09302326 111 20 21.90698 -1.90697674 112 20 21.90698 -1.90697674 113 15 21.90698 -6.90697674 114 20 21.90698 -1.90697674 115 20 21.90698 -1.90697674 116 24 21.90698 2.09302326 117 22 21.90698 0.09302326 118 29 21.90698 7.09302326 119 23 24.30769 -1.30769231 120 24 21.90698 2.09302326 121 22 21.90698 0.09302326 122 16 21.90698 -5.90697674 123 23 24.30769 -1.30769231 124 27 24.30769 2.69230769 125 16 17.33333 -1.33333333 126 21 24.30769 -3.30769231 127 26 21.90698 4.09302326 128 22 24.30769 -2.30769231 129 23 24.30769 -1.30769231 130 19 21.90698 -2.90697674 131 18 21.90698 -3.90697674 132 24 24.30769 -0.30769231 133 24 21.90698 2.09302326 134 29 24.30769 4.69230769 135 22 17.33333 4.66666667 136 24 24.30769 -0.30769231 137 22 21.90698 0.09302326 138 12 21.90698 -9.90697674 139 26 24.30769 1.69230769 140 18 21.90698 -3.90697674 141 22 24.30769 -2.30769231 142 24 21.90698 2.09302326 143 21 21.90698 -0.90697674 144 15 21.90698 -6.90697674 145 23 21.90698 1.09302326 146 22 21.90698 0.09302326 147 22 21.90698 0.09302326 148 24 21.90698 2.09302326 149 23 21.90698 1.09302326 150 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/rcomp/tmp/4w7te1292268942.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/5ag851292268942.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/63q8q1292268942.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/7o8ov1292268942.tab") + } > > try(system("convert tmp/2mxtb1292268942.ps tmp/2mxtb1292268942.png",intern=TRUE)) character(0) > try(system("convert tmp/3mxtb1292268942.ps tmp/3mxtb1292268942.png",intern=TRUE)) character(0) > try(system("convert tmp/4w7te1292268942.ps tmp/4w7te1292268942.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.730 0.860 3.738