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Type 'q()' to quit R. > x <- array(list(0 + ,69 + ,26 + ,9 + ,15 + ,6 + ,25 + ,25 + ,1 + ,53 + ,20 + ,9 + ,15 + ,6 + ,25 + ,24 + ,1 + ,43 + ,21 + ,9 + ,14 + ,13 + ,19 + ,21 + ,0 + ,60 + ,31 + ,14 + ,10 + ,8 + ,18 + ,23 + ,1 + ,49 + ,21 + ,8 + ,10 + ,7 + ,18 + ,17 + ,1 + ,62 + ,18 + ,8 + ,12 + ,9 + ,22 + ,19 + ,1 + ,45 + ,26 + ,11 + ,18 + ,5 + ,29 + ,18 + ,1 + ,50 + ,22 + ,10 + ,12 + ,8 + ,26 + ,27 + ,1 + ,75 + ,22 + ,9 + ,14 + ,9 + ,25 + ,23 + ,1 + ,82 + ,29 + ,15 + ,18 + ,11 + ,23 + ,23 + ,0 + ,60 + ,15 + ,14 + ,9 + ,8 + ,23 + ,29 + ,1 + ,59 + ,16 + ,11 + ,11 + ,11 + ,23 + ,21 + ,1 + ,21 + ,24 + ,14 + ,11 + ,12 + ,24 + ,26 + ,1 + ,62 + ,17 + ,6 + ,17 + ,8 + ,30 + ,25 + ,0 + ,54 + ,19 + ,20 + ,8 + ,7 + ,19 + ,25 + ,1 + ,47 + ,22 + ,9 + ,16 + ,9 + ,24 + ,23 + ,1 + ,59 + ,31 + ,10 + ,21 + ,12 + ,32 + ,26 + ,0 + ,37 + ,28 + ,8 + ,24 + ,20 + ,30 + ,20 + ,0 + ,43 + ,38 + ,11 + ,21 + ,7 + ,29 + ,29 + ,1 + ,48 + ,26 + ,14 + ,14 + ,8 + ,17 + ,24 + ,0 + ,79 + ,25 + ,11 + ,7 + ,8 + ,25 + ,23 + 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,152) + ,dimnames=list(c('Gender' + ,'Anxiety' + ,'Mistakes' + ,'Doubts' + ,'Expectations' + ,'Critism' + ,'Pstandards' + ,'Organization') + ,1:152)) > y <- array(NA,dim=c(8,152),dimnames=list(c('Gender','Anxiety','Mistakes','Doubts','Expectations','Critism','Pstandards','Organization'),1:152)) > 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 = '3' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Anxiety" > x[,par1] [1] 69 53 43 60 49 62 45 50 75 82 60 59 21 62 54 47 59 37 43 48 79 62 16 38 58 [26] 60 67 55 47 59 49 47 57 39 49 26 53 75 65 49 48 45 31 61 49 69 54 80 57 34 [51] 69 44 70 51 66 18 74 59 48 55 44 56 65 77 46 70 39 55 44 45 45 49 65 45 71 [76] 48 41 40 64 56 52 41 42 54 40 40 51 48 80 38 57 28 51 46 58 67 72 26 54 53 [101] 64 47 43 66 54 62 52 64 55 57 74 32 38 66 37 26 64 28 66 65 48 44 64 39 50 [126] 66 48 70 66 61 31 61 54 34 62 47 52 37 46 38 63 34 46 40 30 35 51 56 68 39 [151] 44 58 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 16 18 21 26 28 30 31 32 34 35 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 1 1 1 3 2 1 2 1 3 1 3 4 4 4 2 1 3 5 5 4 5 7 6 2 4 3 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 74 75 77 79 80 82 3 6 4 3 4 3 4 3 3 5 1 5 4 6 2 1 3 3 1 1 2 2 1 1 2 1 > colnames(x) [1] "Gender" "Anxiety" "Mistakes" "Doubts" "Expectations" [6] "Critism" "Pstandards" "Organization" > colnames(x)[par1] [1] "Anxiety" > x[,par1] [1] 69 53 43 60 49 62 45 50 75 82 60 59 21 62 54 47 59 37 43 48 79 62 16 38 58 [26] 60 67 55 47 59 49 47 57 39 49 26 53 75 65 49 48 45 31 61 49 69 54 80 57 34 [51] 69 44 70 51 66 18 74 59 48 55 44 56 65 77 46 70 39 55 44 45 45 49 65 45 71 [76] 48 41 40 64 56 52 41 42 54 40 40 51 48 80 38 57 28 51 46 58 67 72 26 54 53 [101] 64 47 43 66 54 62 52 64 55 57 74 32 38 66 37 26 64 28 66 65 48 44 64 39 50 [126] 66 48 70 66 61 31 61 54 34 62 47 52 37 46 38 63 34 46 40 30 35 51 56 68 39 [151] 44 58 > 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/11iwa1292053438.tab") + } + } > m Conditional inference tree with 1 terminal nodes Response: Anxiety Inputs: Gender, Mistakes, Doubts, Expectations, Critism, Pstandards, Organization Number of observations: 152 1)* weights = 152 > postscript(file="/var/www/html/freestat/rcomp/tmp/21iwa1292053438.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/31iwa1292053438.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 69 52.07237 16.92763158 2 53 52.07237 0.92763158 3 43 52.07237 -9.07236842 4 60 52.07237 7.92763158 5 49 52.07237 -3.07236842 6 62 52.07237 9.92763158 7 45 52.07237 -7.07236842 8 50 52.07237 -2.07236842 9 75 52.07237 22.92763158 10 82 52.07237 29.92763158 11 60 52.07237 7.92763158 12 59 52.07237 6.92763158 13 21 52.07237 -31.07236842 14 62 52.07237 9.92763158 15 54 52.07237 1.92763158 16 47 52.07237 -5.07236842 17 59 52.07237 6.92763158 18 37 52.07237 -15.07236842 19 43 52.07237 -9.07236842 20 48 52.07237 -4.07236842 21 79 52.07237 26.92763158 22 62 52.07237 9.92763158 23 16 52.07237 -36.07236842 24 38 52.07237 -14.07236842 25 58 52.07237 5.92763158 26 60 52.07237 7.92763158 27 67 52.07237 14.92763158 28 55 52.07237 2.92763158 29 47 52.07237 -5.07236842 30 59 52.07237 6.92763158 31 49 52.07237 -3.07236842 32 47 52.07237 -5.07236842 33 57 52.07237 4.92763158 34 39 52.07237 -13.07236842 35 49 52.07237 -3.07236842 36 26 52.07237 -26.07236842 37 53 52.07237 0.92763158 38 75 52.07237 22.92763158 39 65 52.07237 12.92763158 40 49 52.07237 -3.07236842 41 48 52.07237 -4.07236842 42 45 52.07237 -7.07236842 43 31 52.07237 -21.07236842 44 61 52.07237 8.92763158 45 49 52.07237 -3.07236842 46 69 52.07237 16.92763158 47 54 52.07237 1.92763158 48 80 52.07237 27.92763158 49 57 52.07237 4.92763158 50 34 52.07237 -18.07236842 51 69 52.07237 16.92763158 52 44 52.07237 -8.07236842 53 70 52.07237 17.92763158 54 51 52.07237 -1.07236842 55 66 52.07237 13.92763158 56 18 52.07237 -34.07236842 57 74 52.07237 21.92763158 58 59 52.07237 6.92763158 59 48 52.07237 -4.07236842 60 55 52.07237 2.92763158 61 44 52.07237 -8.07236842 62 56 52.07237 3.92763158 63 65 52.07237 12.92763158 64 77 52.07237 24.92763158 65 46 52.07237 -6.07236842 66 70 52.07237 17.92763158 67 39 52.07237 -13.07236842 68 55 52.07237 2.92763158 69 44 52.07237 -8.07236842 70 45 52.07237 -7.07236842 71 45 52.07237 -7.07236842 72 49 52.07237 -3.07236842 73 65 52.07237 12.92763158 74 45 52.07237 -7.07236842 75 71 52.07237 18.92763158 76 48 52.07237 -4.07236842 77 41 52.07237 -11.07236842 78 40 52.07237 -12.07236842 79 64 52.07237 11.92763158 80 56 52.07237 3.92763158 81 52 52.07237 -0.07236842 82 41 52.07237 -11.07236842 83 42 52.07237 -10.07236842 84 54 52.07237 1.92763158 85 40 52.07237 -12.07236842 86 40 52.07237 -12.07236842 87 51 52.07237 -1.07236842 88 48 52.07237 -4.07236842 89 80 52.07237 27.92763158 90 38 52.07237 -14.07236842 91 57 52.07237 4.92763158 92 28 52.07237 -24.07236842 93 51 52.07237 -1.07236842 94 46 52.07237 -6.07236842 95 58 52.07237 5.92763158 96 67 52.07237 14.92763158 97 72 52.07237 19.92763158 98 26 52.07237 -26.07236842 99 54 52.07237 1.92763158 100 53 52.07237 0.92763158 101 64 52.07237 11.92763158 102 47 52.07237 -5.07236842 103 43 52.07237 -9.07236842 104 66 52.07237 13.92763158 105 54 52.07237 1.92763158 106 62 52.07237 9.92763158 107 52 52.07237 -0.07236842 108 64 52.07237 11.92763158 109 55 52.07237 2.92763158 110 57 52.07237 4.92763158 111 74 52.07237 21.92763158 112 32 52.07237 -20.07236842 113 38 52.07237 -14.07236842 114 66 52.07237 13.92763158 115 37 52.07237 -15.07236842 116 26 52.07237 -26.07236842 117 64 52.07237 11.92763158 118 28 52.07237 -24.07236842 119 66 52.07237 13.92763158 120 65 52.07237 12.92763158 121 48 52.07237 -4.07236842 122 44 52.07237 -8.07236842 123 64 52.07237 11.92763158 124 39 52.07237 -13.07236842 125 50 52.07237 -2.07236842 126 66 52.07237 13.92763158 127 48 52.07237 -4.07236842 128 70 52.07237 17.92763158 129 66 52.07237 13.92763158 130 61 52.07237 8.92763158 131 31 52.07237 -21.07236842 132 61 52.07237 8.92763158 133 54 52.07237 1.92763158 134 34 52.07237 -18.07236842 135 62 52.07237 9.92763158 136 47 52.07237 -5.07236842 137 52 52.07237 -0.07236842 138 37 52.07237 -15.07236842 139 46 52.07237 -6.07236842 140 38 52.07237 -14.07236842 141 63 52.07237 10.92763158 142 34 52.07237 -18.07236842 143 46 52.07237 -6.07236842 144 40 52.07237 -12.07236842 145 30 52.07237 -22.07236842 146 35 52.07237 -17.07236842 147 51 52.07237 -1.07236842 148 56 52.07237 3.92763158 149 68 52.07237 15.92763158 150 39 52.07237 -13.07236842 151 44 52.07237 -8.07236842 152 58 52.07237 5.92763158 > 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/4taed1292053438.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/571u41292053438.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/6itt71292053438.tab") + } Warning message: In cor(result$Forecasts, result$Actuals) : the standard deviation is zero > 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/73t9d1292053438.tab") + } > > try(system("convert tmp/21iwa1292053438.ps tmp/21iwa1292053438.png",intern=TRUE)) character(0) > try(system("convert tmp/31iwa1292053438.ps tmp/31iwa1292053438.png",intern=TRUE)) character(0) > try(system("convert tmp/4taed1292053438.ps tmp/4taed1292053438.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.515 0.709 5.708