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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 = '2' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 17 15 15 20 18 16 16 19 16 17 17 16 [26] 15 14 15 12 14 16 14 10 14 16 16 16 14 20 14 14 11 15 16 14 16 14 12 16 9 [51] 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 16 16 13 16 16 [76] 20 16 15 15 16 14 15 12 17 16 15 13 16 16 16 16 14 16 16 20 15 16 13 17 16 [101] 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15 16 15 17 15 12 16 10 16 14 [126] 15 13 15 11 12 16 15 17 16 10 18 13 15 16 16 14 10 17 13 15 16 12 13 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 9 10 11 12 13 14 15 16 17 18 19 20 1 5 2 10 13 20 22 51 10 7 3 4 > colnames(x) [1] "Gender" "Learning" "Concern" "Doubts" "Expectations" [6] "Standards" "Organization" > colnames(x)[par1] [1] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 17 15 15 20 18 16 16 19 16 17 17 16 [26] 15 14 15 12 14 16 14 10 14 16 16 16 14 20 14 14 11 15 16 14 16 14 12 16 9 [51] 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 16 16 13 16 16 [76] 20 16 15 15 16 14 15 12 17 16 15 13 16 16 16 16 14 16 16 20 15 16 13 17 16 [101] 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15 16 15 17 15 12 16 10 16 14 [126] 15 13 15 11 12 16 15 17 16 10 18 13 15 16 16 14 10 17 13 15 16 12 13 > 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/1l19v1292528857.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: Learning Inputs: Gender, Concern, Doubts, Expectations, Standards, Organization Number of observations: 148 1) Doubts <= 11; criterion = 0.998, statistic = 13.301 2) Organization <= 23; criterion = 0.997, statistic = 12.143 3)* weights = 58 2) Organization > 23 4)* weights = 36 1) Doubts > 11 5)* weights = 54 > postscript(file="/var/www/html/freestat/rcomp/tmp/2wtqx1292528857.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/3wtqx1292528857.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 13 16.36111 -3.3611111 2 16 16.36111 -0.3611111 3 19 15.08621 3.9137931 4 15 14.16667 0.8333333 5 14 15.08621 -1.0862069 6 13 15.08621 -2.0862069 7 19 15.08621 3.9137931 8 15 16.36111 -1.3611111 9 14 15.08621 -1.0862069 10 15 14.16667 0.8333333 11 16 14.16667 1.8333333 12 16 15.08621 0.9137931 13 16 14.16667 1.8333333 14 17 16.36111 0.6388889 15 15 14.16667 0.8333333 16 15 15.08621 -0.0862069 17 20 16.36111 3.6388889 18 18 15.08621 2.9137931 19 16 16.36111 -0.3611111 20 16 14.16667 1.8333333 21 19 15.08621 3.9137931 22 16 14.16667 1.8333333 23 17 14.16667 2.8333333 24 17 15.08621 1.9137931 25 16 15.08621 0.9137931 26 15 14.16667 0.8333333 27 14 16.36111 -2.3611111 28 15 15.08621 -0.0862069 29 12 14.16667 -2.1666667 30 14 15.08621 -1.0862069 31 16 15.08621 0.9137931 32 14 16.36111 -2.3611111 33 10 14.16667 -4.1666667 34 14 14.16667 -0.1666667 35 16 16.36111 -0.3611111 36 16 14.16667 1.8333333 37 16 14.16667 1.8333333 38 14 15.08621 -1.0862069 39 20 16.36111 3.6388889 40 14 14.16667 -0.1666667 41 14 14.16667 -0.1666667 42 11 14.16667 -3.1666667 43 15 15.08621 -0.0862069 44 16 15.08621 0.9137931 45 14 15.08621 -1.0862069 46 16 14.16667 1.8333333 47 14 15.08621 -1.0862069 48 12 15.08621 -3.0862069 49 16 14.16667 1.8333333 50 9 15.08621 -6.0862069 51 14 15.08621 -1.0862069 52 16 15.08621 0.9137931 53 16 15.08621 0.9137931 54 15 15.08621 -0.0862069 55 16 15.08621 0.9137931 56 12 14.16667 -2.1666667 57 16 16.36111 -0.3611111 58 16 16.36111 -0.3611111 59 14 16.36111 -2.3611111 60 16 14.16667 1.8333333 61 17 16.36111 0.6388889 62 18 15.08621 2.9137931 63 18 14.16667 3.8333333 64 12 14.16667 -2.1666667 65 16 16.36111 -0.3611111 66 10 14.16667 -4.1666667 67 14 14.16667 -0.1666667 68 18 16.36111 1.6388889 69 18 16.36111 1.6388889 70 16 16.36111 -0.3611111 71 16 16.36111 -0.3611111 72 16 14.16667 1.8333333 73 13 15.08621 -2.0862069 74 16 15.08621 0.9137931 75 16 14.16667 1.8333333 76 20 16.36111 3.6388889 77 16 15.08621 0.9137931 78 15 15.08621 -0.0862069 79 15 16.36111 -1.3611111 80 16 16.36111 -0.3611111 81 14 14.16667 -0.1666667 82 15 14.16667 0.8333333 83 12 15.08621 -3.0862069 84 17 15.08621 1.9137931 85 16 16.36111 -0.3611111 86 15 14.16667 0.8333333 87 13 14.16667 -1.1666667 88 16 15.08621 0.9137931 89 16 16.36111 -0.3611111 90 16 16.36111 -0.3611111 91 16 16.36111 -0.3611111 92 14 15.08621 -1.0862069 93 16 14.16667 1.8333333 94 16 14.16667 1.8333333 95 20 16.36111 3.6388889 96 15 16.36111 -1.3611111 97 16 14.16667 1.8333333 98 13 15.08621 -2.0862069 99 17 16.36111 0.6388889 100 16 14.16667 1.8333333 101 12 14.16667 -2.1666667 102 16 15.08621 0.9137931 103 16 14.16667 1.8333333 104 17 15.08621 1.9137931 105 13 14.16667 -1.1666667 106 12 15.08621 -3.0862069 107 18 16.36111 1.6388889 108 14 14.16667 -0.1666667 109 14 14.16667 -0.1666667 110 13 14.16667 -1.1666667 111 16 15.08621 0.9137931 112 13 14.16667 -1.1666667 113 16 15.08621 0.9137931 114 13 15.08621 -2.0862069 115 16 16.36111 -0.3611111 116 15 14.16667 0.8333333 117 16 14.16667 1.8333333 118 15 15.08621 -0.0862069 119 17 16.36111 0.6388889 120 15 15.08621 -0.0862069 121 12 15.08621 -3.0862069 122 16 15.08621 0.9137931 123 10 14.16667 -4.1666667 124 16 15.08621 0.9137931 125 14 15.08621 -1.0862069 126 15 16.36111 -1.3611111 127 13 14.16667 -1.1666667 128 15 15.08621 -0.0862069 129 11 14.16667 -3.1666667 130 12 14.16667 -2.1666667 131 16 16.36111 -0.3611111 132 15 14.16667 0.8333333 133 17 15.08621 1.9137931 134 16 16.36111 -0.3611111 135 10 14.16667 -4.1666667 136 18 15.08621 2.9137931 137 13 14.16667 -1.1666667 138 15 15.08621 -0.0862069 139 16 15.08621 0.9137931 140 16 14.16667 1.8333333 141 14 14.16667 -0.1666667 142 10 14.16667 -4.1666667 143 17 15.08621 1.9137931 144 13 15.08621 -2.0862069 145 15 15.08621 -0.0862069 146 16 16.36111 -0.3611111 147 12 15.08621 -3.0862069 148 13 15.08621 -2.0862069 > 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/4o2801292528857.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/5sl661292528857.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/6d35u1292528857.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/7gmli1292528857.tab") + } > > try(system("convert tmp/2wtqx1292528857.ps tmp/2wtqx1292528857.png",intern=TRUE)) character(0) > try(system("convert tmp/3wtqx1292528857.ps tmp/3wtqx1292528857.png",intern=TRUE)) character(0) > try(system("convert tmp/4o2801292528857.ps tmp/4o2801292528857.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.462 0.771 4.676