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,21 + ,15 + ,7 + ,5 + ,21 + ,24 + ,21 + ,13 + ,17 + ,11 + ,23 + ,22 + ,29 + ,16 + ,11 + ,6 + ,27 + ,24 + ,31 + ,9 + ,17 + ,9 + ,25 + ,19 + ,20 + ,9 + ,11 + ,7 + ,21 + ,20 + ,16 + ,9 + ,12 + ,9 + ,10 + ,13 + ,22 + ,8 + ,14 + ,10 + ,20 + ,20 + ,20 + ,7 + ,11 + ,9 + ,26 + ,22 + ,28 + ,16 + ,16 + ,8 + ,24 + ,24 + ,38 + ,11 + ,21 + ,7 + ,29 + ,29 + ,22 + ,9 + ,14 + ,6 + ,19 + ,12 + ,20 + ,11 + ,20 + ,13 + ,24 + ,20 + ,17 + ,9 + ,13 + ,6 + ,19 + ,21 + ,28 + ,14 + ,11 + ,8 + ,24 + ,24 + ,22 + ,13 + ,15 + ,10 + ,22 + ,22 + ,31 + ,16 + ,19 + ,16 + ,17 + ,20) + ,dim=c(6 + ,159) + ,dimnames=list(c('ConcernoverMistakes' + ,'Doubtsaboutactions' + ,'ParentalExpectations' + ,'ParentalCriticism' + ,'PersonalStandards' + ,'Organization') + ,1:159)) > y <- array(NA,dim=c(6,159),dimnames=list(c('ConcernoverMistakes','Doubtsaboutactions','ParentalExpectations','ParentalCriticism','PersonalStandards','Organization'),1:159)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = 'no' > par3 = '' > par2 = 'none' > par1 = '1' > 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] "ConcernoverMistakes" > x[,par1] [1] 24 25 17 18 18 16 20 16 18 17 23 30 23 18 15 12 21 15 20 31 27 34 21 31 19 [26] 16 20 21 22 17 24 25 26 25 17 32 33 13 32 25 29 22 18 17 20 15 20 33 29 23 [51] 26 18 20 11 28 26 22 17 12 14 17 21 19 18 10 29 31 19 9 20 28 19 30 29 26 [76] 23 13 21 19 28 23 18 21 20 23 21 21 15 28 19 26 10 16 22 19 31 31 29 19 22 [101] 23 15 20 18 23 25 21 24 25 17 13 28 21 25 9 16 19 17 25 20 29 14 22 15 19 [126] 20 15 20 18 33 22 16 17 16 21 26 18 18 17 22 30 30 24 21 21 29 31 20 16 22 [151] 20 28 38 22 20 17 28 22 31 > 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 21 22 23 24 25 26 27 28 29 30 31 32 33 34 2 2 1 2 3 2 7 8 12 12 10 15 13 11 8 4 8 6 1 7 7 4 7 2 3 1 38 1 > colnames(x) [1] "ConcernoverMistakes" "Doubtsaboutactions" "ParentalExpectations" [4] "ParentalCriticism" "PersonalStandards" "Organization" > colnames(x)[par1] [1] "ConcernoverMistakes" > x[,par1] [1] 24 25 17 18 18 16 20 16 18 17 23 30 23 18 15 12 21 15 20 31 27 34 21 31 19 [26] 16 20 21 22 17 24 25 26 25 17 32 33 13 32 25 29 22 18 17 20 15 20 33 29 23 [51] 26 18 20 11 28 26 22 17 12 14 17 21 19 18 10 29 31 19 9 20 28 19 30 29 26 [76] 23 13 21 19 28 23 18 21 20 23 21 21 15 28 19 26 10 16 22 19 31 31 29 19 22 [101] 23 15 20 18 23 25 21 24 25 17 13 28 21 25 9 16 19 17 25 20 29 14 22 15 19 [126] 20 15 20 18 33 22 16 17 16 21 26 18 18 17 22 30 30 24 21 21 29 31 20 16 22 [151] 20 28 38 22 20 17 28 22 31 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/1rw4f1323613933.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: ConcernoverMistakes Inputs: Doubtsaboutactions, ParentalExpectations, ParentalCriticism, PersonalStandards, Organization Number of observations: 159 1) PersonalStandards <= 25; criterion = 1, statistic = 28.54 2) ParentalCriticism <= 7; criterion = 1, statistic = 22.429 3) PersonalStandards <= 22; criterion = 0.955, statistic = 6.782 4)* weights = 38 3) PersonalStandards > 22 5)* weights = 12 2) ParentalCriticism > 7 6) Doubtsaboutactions <= 12; criterion = 1, statistic = 15.524 7)* weights = 52 6) Doubtsaboutactions > 12 8)* weights = 28 1) PersonalStandards > 25 9) Doubtsaboutactions <= 10; criterion = 0.997, statistic = 12.056 10)* weights = 13 9) Doubtsaboutactions > 10 11)* weights = 16 > postscript(file="/var/wessaorg/rcomp/tmp/2zu5r1323613933.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/wessaorg/rcomp/tmp/368nl1323613933.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 24 25.07143 -1.07142857 2 25 20.42308 4.57692308 3 17 21.46154 -4.46153846 4 18 20.42308 -2.42307692 5 18 20.42308 -2.42307692 6 16 17.21053 -1.21052632 7 20 21.50000 -1.50000000 8 16 20.42308 -4.42307692 9 18 17.21053 0.78947368 10 17 17.21053 -0.21052632 11 23 25.07143 -2.07142857 12 30 20.42308 9.57692308 13 23 20.42308 2.57692308 14 18 20.42308 -2.42307692 15 15 20.42308 -5.42307692 16 12 21.46154 -9.46153846 17 21 20.42308 0.57692308 18 15 20.42308 -5.42307692 19 20 17.21053 2.78947368 20 31 25.07143 5.92857143 21 27 25.07143 1.92857143 22 34 25.07143 8.92857143 23 21 20.42308 0.57692308 24 31 25.07143 5.92857143 25 19 20.42308 -1.42307692 26 16 20.42308 -4.42307692 27 20 21.50000 -1.50000000 28 21 20.42308 0.57692308 29 22 20.42308 1.57692308 30 17 17.21053 -0.21052632 31 24 21.50000 2.50000000 32 25 29.81250 -4.81250000 33 26 29.81250 -3.81250000 34 25 21.46154 3.53846154 35 17 20.42308 -3.42307692 36 32 29.81250 2.18750000 37 33 29.81250 3.18750000 38 13 17.21053 -4.21052632 39 32 29.81250 2.18750000 40 25 29.81250 -4.81250000 41 29 29.81250 -0.81250000 42 22 20.42308 1.57692308 43 18 20.42308 -2.42307692 44 17 21.50000 -4.50000000 45 20 21.46154 -1.46153846 46 15 20.42308 -5.42307692 47 20 25.07143 -5.07142857 48 33 29.81250 3.18750000 49 29 20.42308 8.57692308 50 23 21.50000 1.50000000 51 26 21.50000 4.50000000 52 18 20.42308 -2.42307692 53 20 20.42308 -0.42307692 54 11 17.21053 -6.21052632 55 28 21.46154 6.53846154 56 26 25.07143 0.92857143 57 22 21.46154 0.53846154 58 17 21.50000 -4.50000000 59 12 17.21053 -5.21052632 60 14 25.07143 -11.07142857 61 17 20.42308 -3.42307692 62 21 21.50000 -0.50000000 63 19 21.50000 -2.50000000 64 18 25.07143 -7.07142857 65 10 17.21053 -7.21052632 66 29 29.81250 -0.81250000 67 31 20.42308 10.57692308 68 19 21.46154 -2.46153846 69 9 17.21053 -8.21052632 70 20 20.42308 -0.42307692 71 28 17.21053 10.78947368 72 19 17.21053 1.78947368 73 30 21.46154 8.53846154 74 29 25.07143 3.92857143 75 26 21.50000 4.50000000 76 23 20.42308 2.57692308 77 13 17.21053 -4.21052632 78 21 20.42308 0.57692308 79 19 20.42308 -1.42307692 80 28 21.50000 6.50000000 81 23 25.07143 -2.07142857 82 18 20.42308 -2.42307692 83 21 20.42308 0.57692308 84 20 20.42308 -0.42307692 85 23 17.21053 5.78947368 86 21 17.21053 3.78947368 87 21 21.46154 -0.46153846 88 15 25.07143 -10.07142857 89 28 29.81250 -1.81250000 90 19 17.21053 1.78947368 91 26 25.07143 0.92857143 92 10 17.21053 -7.21052632 93 16 17.21053 -1.21052632 94 22 17.21053 4.78947368 95 19 20.42308 -1.42307692 96 31 21.46154 9.53846154 97 31 25.07143 5.92857143 98 29 25.07143 3.92857143 99 19 17.21053 1.78947368 100 22 20.42308 1.57692308 101 23 20.42308 2.57692308 102 15 17.21053 -2.21052632 103 20 20.42308 -0.42307692 104 18 20.42308 -2.42307692 105 23 25.07143 -2.07142857 106 25 17.21053 7.78947368 107 21 17.21053 3.78947368 108 24 20.42308 3.57692308 109 25 25.07143 -0.07142857 110 17 17.21053 -0.21052632 111 13 17.21053 -4.21052632 112 28 20.42308 7.57692308 113 21 20.42308 0.57692308 114 25 21.46154 3.53846154 115 9 21.46154 -12.46153846 116 16 17.21053 -1.21052632 117 19 20.42308 -1.42307692 118 17 17.21053 -0.21052632 119 25 25.07143 -0.07142857 120 20 17.21053 2.78947368 121 29 29.81250 -0.81250000 122 14 17.21053 -3.21052632 123 22 25.07143 -3.07142857 124 15 17.21053 -2.21052632 125 19 17.21053 1.78947368 126 20 20.42308 -0.42307692 127 15 17.21053 -2.21052632 128 20 20.42308 -0.42307692 129 18 20.42308 -2.42307692 130 33 29.81250 3.18750000 131 22 20.42308 1.57692308 132 16 20.42308 -4.42307692 133 17 21.50000 -4.50000000 134 16 17.21053 -1.21052632 135 21 20.42308 0.57692308 136 26 29.81250 -3.81250000 137 18 17.21053 0.78947368 138 18 20.42308 -2.42307692 139 17 20.42308 -3.42307692 140 22 25.07143 -3.07142857 141 30 25.07143 4.92857143 142 30 29.81250 0.18750000 143 24 25.07143 -1.07142857 144 21 17.21053 3.78947368 145 21 25.07143 -4.07142857 146 29 29.81250 -0.81250000 147 31 20.42308 10.57692308 148 20 17.21053 2.78947368 149 16 20.42308 -4.42307692 150 22 20.42308 1.57692308 151 20 21.46154 -1.46153846 152 28 25.07143 2.92857143 153 38 29.81250 8.18750000 154 22 17.21053 4.78947368 155 20 20.42308 -0.42307692 156 17 17.21053 -0.21052632 157 28 25.07143 2.92857143 158 22 25.07143 -3.07142857 159 31 25.07143 5.92857143 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/44pmn1323613933.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5lblg1323613933.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/wessaorg/rcomp/tmp/6ky4d1323613933.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/wessaorg/rcomp/tmp/7tyzt1323613933.tab") + } > > try(system("convert tmp/2zu5r1323613933.ps tmp/2zu5r1323613933.png",intern=TRUE)) character(0) > try(system("convert tmp/368nl1323613933.ps tmp/368nl1323613933.png",intern=TRUE)) character(0) > try(system("convert tmp/44pmn1323613933.ps tmp/44pmn1323613933.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.582 0.211 3.820