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Type 'q()' to quit R. > x <- array(list(0 + ,13 + ,26 + ,9 + ,15 + ,25 + ,25 + ,0 + ,16 + ,20 + ,9 + ,15 + ,25 + ,24 + ,0 + ,19 + ,21 + ,9 + ,14 + ,19 + ,21 + ,1 + ,15 + ,31 + ,14 + ,10 + ,18 + ,23 + ,0 + ,14 + ,21 + ,8 + ,10 + ,18 + ,17 + ,0 + ,13 + ,18 + ,8 + ,12 + ,22 + ,19 + ,0 + ,19 + ,26 + ,11 + ,18 + ,29 + ,18 + ,0 + ,15 + ,22 + ,10 + ,12 + ,26 + ,27 + ,0 + ,14 + ,22 + ,9 + ,14 + ,25 + ,23 + ,0 + ,15 + ,29 + ,15 + ,18 + ,23 + ,23 + ,1 + ,16 + ,15 + ,14 + ,9 + ,23 + ,29 + ,0 + ,16 + ,16 + ,11 + ,11 + ,23 + ,21 + ,1 + ,16 + ,24 + ,14 + ,11 + ,24 + ,26 + ,0 + ,17 + ,17 + ,6 + ,17 + ,30 + ,25 + ,1 + ,15 + ,19 + ,20 + ,8 + ,19 + ,25 + ,1 + ,15 + ,22 + ,9 + ,16 + ,24 + ,23 + ,0 + ,20 + ,31 + ,10 + ,21 + ,32 + ,26 + ,1 + ,18 + ,28 + ,8 + ,24 + ,30 + ,20 + ,0 + ,16 + ,38 + ,11 + ,21 + ,29 + ,29 + ,1 + ,16 + ,26 + ,14 + ,14 + ,17 + ,24 + ,0 + ,19 + ,25 + ,11 + ,7 + ,25 + ,23 + ,0 + ,16 + ,25 + ,16 + ,18 + ,26 + ,24 + ,1 + ,17 + ,29 + ,14 + ,18 + ,26 + ,30 + ,0 + ,17 + ,28 + ,11 + ,13 + 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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 = '5' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Expectations" > x[,par1] [1] 15 15 14 10 10 12 18 12 14 18 9 11 11 17 8 16 21 24 21 14 7 18 18 13 11 [26] 13 13 18 14 12 9 12 5 10 11 11 12 12 15 12 16 14 17 13 10 17 12 13 13 11 [51] 13 12 12 12 9 7 17 12 12 9 9 13 10 11 12 10 13 6 7 13 11 18 9 9 11 [76] 11 15 8 11 14 14 12 12 8 11 10 17 16 13 15 11 12 16 20 16 11 15 15 12 9 [101] 24 15 18 17 12 15 11 11 15 12 14 11 20 11 12 17 12 11 10 11 12 9 8 6 12 [126] 15 13 17 14 16 16 11 11 16 15 14 9 13 11 14 11 12 8 7 11 13 9 12 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 24 1 2 4 5 11 8 25 26 14 11 12 8 8 7 2 2 2 > colnames(x) [1] "Gender" "Learning" "Concern" "Doubts" "Expectations" [6] "Standards" "Organization" > colnames(x)[par1] [1] "Expectations" > x[,par1] [1] 15 15 14 10 10 12 18 12 14 18 9 11 11 17 8 16 21 24 21 14 7 18 18 13 11 [26] 13 13 18 14 12 9 12 5 10 11 11 12 12 15 12 16 14 17 13 10 17 12 13 13 11 [51] 13 12 12 12 9 7 17 12 12 9 9 13 10 11 12 10 13 6 7 13 11 18 9 9 11 [76] 11 15 8 11 14 14 12 12 8 11 10 17 16 13 15 11 12 16 20 16 11 15 15 12 9 [101] 24 15 18 17 12 15 11 11 15 12 14 11 20 11 12 17 12 11 10 11 12 9 8 6 12 [126] 15 13 17 14 16 16 11 11 16 15 14 9 13 11 14 11 12 8 7 11 13 9 12 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/1zid71292539969.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Expectations Inputs: Gender, Learning, Concern, Doubts, Standards, Organization Number of observations: 148 1) Concern <= 16; criterion = 0.999, statistic = 15.168 2)* weights = 27 1) Concern > 16 3) Standards <= 28; criterion = 0.964, statistic = 7.513 4)* weights = 109 3) Standards > 28 5)* weights = 12 > postscript(file="/var/www/html/freestat/rcomp/tmp/2zid71292539969.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/freestat/rcomp/tmp/3zid71292539969.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 15 12.97248 2.02752294 2 15 12.97248 2.02752294 3 14 12.97248 1.02752294 4 10 12.97248 -2.97247706 5 10 12.97248 -2.97247706 6 12 12.97248 -0.97247706 7 18 15.41667 2.58333333 8 12 12.97248 -0.97247706 9 14 12.97248 1.02752294 10 18 12.97248 5.02752294 11 9 10.66667 -1.66666667 12 11 10.66667 0.33333333 13 11 12.97248 -1.97247706 14 17 15.41667 1.58333333 15 8 12.97248 -4.97247706 16 16 12.97248 3.02752294 17 21 15.41667 5.58333333 18 24 15.41667 8.58333333 19 21 15.41667 5.58333333 20 14 12.97248 1.02752294 21 7 12.97248 -5.97247706 22 18 12.97248 5.02752294 23 18 12.97248 5.02752294 24 13 12.97248 0.02752294 25 11 10.66667 0.33333333 26 13 12.97248 0.02752294 27 13 12.97248 0.02752294 28 18 15.41667 2.58333333 29 14 12.97248 1.02752294 30 12 12.97248 -0.97247706 31 9 12.97248 -3.97247706 32 12 12.97248 -0.97247706 33 5 12.97248 -7.97247706 34 10 12.97248 -2.97247706 35 11 12.97248 -1.97247706 36 11 12.97248 -1.97247706 37 12 12.97248 -0.97247706 38 12 12.97248 -0.97247706 39 15 10.66667 4.33333333 40 12 15.41667 -3.41666667 41 16 12.97248 3.02752294 42 14 12.97248 1.02752294 43 17 12.97248 4.02752294 44 13 12.97248 0.02752294 45 10 12.97248 -2.97247706 46 17 12.97248 4.02752294 47 12 12.97248 -0.97247706 48 13 12.97248 0.02752294 49 13 12.97248 0.02752294 50 11 10.66667 0.33333333 51 13 12.97248 0.02752294 52 12 12.97248 -0.97247706 53 12 10.66667 1.33333333 54 12 12.97248 -0.97247706 55 9 12.97248 -3.97247706 56 7 12.97248 -5.97247706 57 17 12.97248 4.02752294 58 12 10.66667 1.33333333 59 12 15.41667 -3.41666667 60 9 12.97248 -3.97247706 61 9 15.41667 -6.41666667 62 13 12.97248 0.02752294 63 10 12.97248 -2.97247706 64 11 12.97248 -1.97247706 65 12 12.97248 -0.97247706 66 10 10.66667 -0.66666667 67 13 10.66667 2.33333333 68 6 10.66667 -4.66666667 69 7 10.66667 -3.66666667 70 13 12.97248 0.02752294 71 11 12.97248 -1.97247706 72 18 12.97248 5.02752294 73 9 12.97248 -3.97247706 74 9 12.97248 -3.97247706 75 11 10.66667 0.33333333 76 11 12.97248 -1.97247706 77 15 12.97248 2.02752294 78 8 12.97248 -4.97247706 79 11 12.97248 -1.97247706 80 14 12.97248 1.02752294 81 14 12.97248 1.02752294 82 12 12.97248 -0.97247706 83 12 10.66667 1.33333333 84 8 10.66667 -2.66666667 85 11 12.97248 -1.97247706 86 10 12.97248 -2.97247706 87 17 12.97248 4.02752294 88 16 12.97248 3.02752294 89 13 12.97248 0.02752294 90 15 10.66667 4.33333333 91 11 12.97248 -1.97247706 92 12 10.66667 1.33333333 93 16 12.97248 3.02752294 94 20 12.97248 7.02752294 95 16 12.97248 3.02752294 96 11 12.97248 -1.97247706 97 15 12.97248 2.02752294 98 15 12.97248 2.02752294 99 12 12.97248 -0.97247706 100 9 15.41667 -6.41666667 101 24 12.97248 11.02752294 102 15 12.97248 2.02752294 103 18 12.97248 5.02752294 104 17 12.97248 4.02752294 105 12 10.66667 1.33333333 106 15 12.97248 2.02752294 107 11 12.97248 -1.97247706 108 11 12.97248 -1.97247706 109 15 12.97248 2.02752294 110 12 12.97248 -0.97247706 111 14 12.97248 1.02752294 112 11 12.97248 -1.97247706 113 20 12.97248 7.02752294 114 11 12.97248 -1.97247706 115 12 12.97248 -0.97247706 116 17 12.97248 4.02752294 117 12 12.97248 -0.97247706 118 11 10.66667 0.33333333 119 10 10.66667 -0.66666667 120 11 12.97248 -1.97247706 121 12 10.66667 1.33333333 122 9 10.66667 -1.66666667 123 8 12.97248 -4.97247706 124 6 10.66667 -4.66666667 125 12 12.97248 -0.97247706 126 15 12.97248 2.02752294 127 13 12.97248 0.02752294 128 17 12.97248 4.02752294 129 14 12.97248 1.02752294 130 16 12.97248 3.02752294 131 16 12.97248 3.02752294 132 11 12.97248 -1.97247706 133 11 12.97248 -1.97247706 134 16 12.97248 3.02752294 135 15 15.41667 -0.41666667 136 14 12.97248 1.02752294 137 9 15.41667 -6.41666667 138 13 12.97248 0.02752294 139 11 10.66667 0.33333333 140 14 12.97248 1.02752294 141 11 12.97248 -1.97247706 142 12 12.97248 -0.97247706 143 8 10.66667 -2.66666667 144 7 12.97248 -5.97247706 145 11 12.97248 -1.97247706 146 13 10.66667 2.33333333 147 9 10.66667 -1.66666667 148 12 10.66667 1.33333333 > 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/freestat/rcomp/tmp/4a9ca1292539969.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/freestat/rcomp/tmp/5619i1292539969.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/freestat/rcomp/tmp/6zarl1292539969.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/freestat/rcomp/tmp/72ap91292539969.tab") + } > > try(system("convert tmp/2zid71292539969.ps tmp/2zid71292539969.png",intern=TRUE)) character(0) > try(system("convert tmp/3zid71292539969.ps tmp/3zid71292539969.png",intern=TRUE)) character(0) > try(system("convert tmp/4a9ca1292539969.ps tmp/4a9ca1292539969.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.419 0.744 4.586