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+ ,11 + ,9 + ,26 + ,22 + ,1 + ,28 + ,16 + ,16 + ,8 + ,24 + ,24 + ,1 + ,38 + ,11 + ,21 + ,7 + ,29 + ,29 + ,0 + ,22 + ,9 + ,14 + ,6 + ,19 + ,12 + ,1 + ,20 + ,11 + ,20 + ,13 + ,24 + ,20 + ,0 + ,17 + ,9 + ,13 + ,6 + ,19 + ,21 + ,1 + ,28 + ,14 + ,11 + ,8 + ,24 + ,24 + ,1 + ,22 + ,13 + ,15 + ,10 + ,22 + ,22 + ,0 + ,31 + ,16 + ,19 + ,16 + ,17 + ,20) + ,dim=c(7 + ,159) + ,dimnames=list(c('gender' + ,'ConcernoverMistakes' + ,'Doubtsaboutactions' + ,'ParentalExpectations' + ,'ParentalCriticism' + ,'PersonalStandards' + ,'Organization') + ,1:159)) > y <- array(NA,dim=c(7,159),dimnames=list(c('gender','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]) + } + } > par4 = 'no' > par3 = '' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "ConcernoverMistakes" > x[,par1] [1] 24 25 17 18 18 16 20 16 18 17 23 30 23 18 15 12 21 15 20 31 27 34 21 31 19 [26] 16 20 21 22 17 24 25 26 25 17 32 33 13 32 25 29 22 18 17 20 15 20 33 29 23 [51] 26 18 20 6 8 13 10 8 7 15 9 10 12 13 10 11 8 9 13 11 8 9 9 15 9 [76] 10 14 12 12 11 14 6 12 8 14 11 10 14 12 10 14 5 11 10 9 10 16 13 9 10 [101] 10 7 9 8 14 14 8 9 14 14 8 8 8 7 6 8 6 11 14 11 11 11 14 8 20 [126] 11 8 11 10 14 11 9 9 8 10 13 13 12 8 13 14 12 14 15 13 16 9 9 9 8 [151] 7 16 11 9 11 9 14 13 16 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 19 20 21 22 23 24 25 26 27 29 30 31 1 4 4 16 16 12 14 8 10 15 6 7 5 6 1 7 3 2 3 2 4 2 1 2 1 2 32 33 34 2 2 1 > colnames(x) [1] "gender" "ConcernoverMistakes" "Doubtsaboutactions" [4] "ParentalExpectations" "ParentalCriticism" "PersonalStandards" [7] "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 6 8 13 10 8 7 15 9 10 12 13 10 11 8 9 13 11 8 9 9 15 9 [76] 10 14 12 12 11 14 6 12 8 14 11 10 14 12 10 14 5 11 10 9 10 16 13 9 10 [101] 10 7 9 8 14 14 8 9 14 14 8 8 8 7 6 8 6 11 14 11 11 11 14 8 20 [126] 11 8 11 10 14 11 9 9 8 10 13 13 12 8 13 14 12 14 15 13 16 9 9 9 8 [151] 7 16 11 9 11 9 14 13 16 > 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/1s14u1292081340.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: ConcernoverMistakes Inputs: gender, Doubtsaboutactions, ParentalExpectations, ParentalCriticism, PersonalStandards, Organization Number of observations: 159 1) Organization <= 12; criterion = 1, statistic = 97.885 2) gender <= 22; criterion = 0.972, statistic = 7.986 3)* weights = 70 2) gender > 22 4)* weights = 37 1) Organization > 12 5) Doubtsaboutactions <= 13; criterion = 0.996, statistic = 11.702 6)* weights = 38 5) Doubtsaboutactions > 13 7)* weights = 14 > postscript(file="/var/www/html/rcomp/tmp/2s14u1292081340.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/33s4g1292081340.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 26.14286 -2.14285714 2 25 20.68421 4.31578947 3 17 20.68421 -3.68421053 4 18 20.68421 -2.68421053 5 18 20.68421 -2.68421053 6 16 20.68421 -4.68421053 7 20 20.68421 -0.68421053 8 16 20.68421 -4.68421053 9 18 26.14286 -8.14285714 10 17 20.68421 -3.68421053 11 23 20.68421 2.31578947 12 30 20.68421 9.31578947 13 23 20.68421 2.31578947 14 18 20.68421 -2.68421053 15 15 20.68421 -5.68421053 16 12 20.68421 -8.68421053 17 21 20.68421 0.31578947 18 15 10.18571 4.81428571 19 20 20.68421 -0.68421053 20 31 26.14286 4.85714286 21 27 26.14286 0.85714286 22 34 26.14286 7.85714286 23 21 20.68421 0.31578947 24 31 26.14286 4.85714286 25 19 20.68421 -1.68421053 26 16 20.68421 -4.68421053 27 20 20.68421 -0.68421053 28 21 20.68421 0.31578947 29 22 20.68421 1.31578947 30 17 20.68421 -3.68421053 31 24 20.68421 3.31578947 32 25 26.14286 -1.14285714 33 26 20.68421 5.31578947 34 25 20.68421 4.31578947 35 17 20.68421 -3.68421053 36 32 26.14286 5.85714286 37 33 20.68421 12.31578947 38 13 26.14286 -13.14285714 39 32 20.68421 11.31578947 40 25 20.68421 4.31578947 41 29 26.14286 2.85714286 42 22 20.68421 1.31578947 43 18 20.68421 -2.68421053 44 17 20.68421 -3.68421053 45 20 20.68421 -0.68421053 46 15 20.68421 -5.68421053 47 20 26.14286 -6.14285714 48 33 26.14286 6.85714286 49 29 20.68421 8.31578947 50 23 26.14286 -3.14285714 51 26 26.14286 -0.14285714 52 18 20.68421 -2.68421053 53 20 20.68421 -0.68421053 54 6 10.18571 -4.18571429 55 8 12.02703 -4.02702703 56 13 12.02703 0.97297297 57 10 10.18571 -0.18571429 58 8 10.18571 -2.18571429 59 7 10.18571 -3.18571429 60 15 10.18571 4.81428571 61 9 10.18571 -1.18571429 62 10 10.18571 -0.18571429 63 12 10.18571 1.81428571 64 13 10.18571 2.81428571 65 10 10.18571 -0.18571429 66 11 12.02703 -1.02702703 67 8 12.02703 -4.02702703 68 9 10.18571 -1.18571429 69 13 10.18571 2.81428571 70 11 10.18571 0.81428571 71 8 12.02703 -4.02702703 72 9 10.18571 -1.18571429 73 9 12.02703 -3.02702703 74 15 12.02703 2.97297297 75 9 12.02703 -3.02702703 76 10 12.02703 -2.02702703 77 14 10.18571 3.81428571 78 12 10.18571 1.81428571 79 12 10.18571 1.81428571 80 11 12.02703 -1.02702703 81 14 12.02703 1.97297297 82 6 10.18571 -4.18571429 83 12 10.18571 1.81428571 84 8 10.18571 -2.18571429 85 14 12.02703 1.97297297 86 11 10.18571 0.81428571 87 10 10.18571 -0.18571429 88 14 10.18571 3.81428571 89 12 12.02703 -0.02702703 90 10 10.18571 -0.18571429 91 14 12.02703 1.97297297 92 5 10.18571 -5.18571429 93 11 10.18571 0.81428571 94 10 10.18571 -0.18571429 95 9 10.18571 -1.18571429 96 10 12.02703 -2.02702703 97 16 12.02703 3.97297297 98 13 12.02703 0.97297297 99 9 10.18571 -1.18571429 100 10 10.18571 -0.18571429 101 10 12.02703 -2.02702703 102 7 10.18571 -3.18571429 103 9 10.18571 -1.18571429 104 8 10.18571 -2.18571429 105 14 12.02703 1.97297297 106 14 12.02703 1.97297297 107 8 10.18571 -2.18571429 108 9 12.02703 -3.02702703 109 14 12.02703 1.97297297 110 14 10.18571 3.81428571 111 8 10.18571 -2.18571429 112 8 12.02703 -4.02702703 113 8 10.18571 -2.18571429 114 7 12.02703 -5.02702703 115 6 10.18571 -4.18571429 116 8 10.18571 -2.18571429 117 6 10.18571 -4.18571429 118 11 10.18571 0.81428571 119 14 12.02703 1.97297297 120 11 10.18571 0.81428571 121 11 12.02703 -1.02702703 122 11 10.18571 0.81428571 123 14 10.18571 3.81428571 124 8 10.18571 -2.18571429 125 20 10.18571 9.81428571 126 11 10.18571 0.81428571 127 8 10.18571 -2.18571429 128 11 10.18571 0.81428571 129 10 10.18571 -0.18571429 130 14 12.02703 1.97297297 131 11 10.18571 0.81428571 132 9 10.18571 -1.18571429 133 9 10.18571 -1.18571429 134 8 10.18571 -2.18571429 135 10 10.18571 -0.18571429 136 13 12.02703 0.97297297 137 13 10.18571 2.81428571 138 12 10.18571 1.81428571 139 8 10.18571 -2.18571429 140 13 10.18571 2.81428571 141 14 12.02703 1.97297297 142 12 12.02703 -0.02702703 143 14 12.02703 1.97297297 144 15 10.18571 4.81428571 145 13 10.18571 2.81428571 146 16 12.02703 3.97297297 147 9 12.02703 -3.02702703 148 9 10.18571 -1.18571429 149 9 10.18571 -1.18571429 150 8 10.18571 -2.18571429 151 7 10.18571 -3.18571429 152 16 12.02703 3.97297297 153 11 12.02703 -1.02702703 154 9 10.18571 -1.18571429 155 11 10.18571 0.81428571 156 9 10.18571 -1.18571429 157 14 12.02703 1.97297297 158 13 10.18571 2.81428571 159 16 12.02703 3.97297297 > 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/4ej301292081340.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/5z2jo1292081340.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/6dv3p1292081341.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/76m2a1292081341.tab") + } > > try(system("convert tmp/2s14u1292081340.ps tmp/2s14u1292081340.png",intern=TRUE)) character(0) > try(system("convert tmp/33s4g1292081340.ps tmp/33s4g1292081340.png",intern=TRUE)) character(0) > try(system("convert tmp/4ej301292081340.ps tmp/4ej301292081340.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.037 0.576 8.932