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Type 'q()' to quit R. > x <- array(list(2 + ,1 + ,22 + ,15 + ,16 + ,17 + ,10 + ,1 + ,2 + ,22 + ,23 + ,24 + ,42 + ,9 + ,1 + ,2 + ,22 + ,26 + ,22 + ,39 + ,30 + ,1 + ,2 + ,23 + ,19 + ,21 + ,22 + ,18 + ,1 + ,2 + ,21 + ,19 + ,23 + ,20 + ,16 + ,1 + ,2 + ,21 + ,16 + ,23 + ,31 + ,20 + ,1 + ,1 + ,24 + ,23 + ,21 + ,42 + ,20 + ,2 + ,1 + ,22 + ,22 + ,20 + ,30 + ,18 + ,1 + ,2 + ,21 + ,19 + ,22 + ,33 + ,21 + ,1 + ,2 + ,23 + ,24 + ,20 + ,29 + ,20 + ,1 + ,1 + ,20 + ,19 + ,12 + ,31 + ,20 + ,2 + ,1 + ,23 + ,25 + ,23 + ,39 + ,20 + ,1 + ,1 + ,20 + ,23 + ,23 + ,44 + ,29 + ,1 + ,2 + ,21 + ,31 + ,30 + ,40 + ,14 + ,2 + ,1 + ,22 + ,29 + ,22 + ,42 + ,25 + ,2 + ,2 + ,22 + ,18 + ,21 + ,28 + ,19 + ,2 + ,2 + ,21 + ,17 + ,21 + ,29 + ,19 + ,1 + ,1 + ,20 + ,22 + ,15 + ,35 + ,25 + ,1 + ,1 + ,21 + ,21 + ,22 + ,26 + ,25 + ,1 + ,2 + ,21 + ,24 + ,24 + ,42 + ,19 + ,1 + ,1 + ,20 + ,22 + ,23 + ,26 + ,19 + ,1 + ,1 + ,21 + ,16 + ,15 + ,30 + ,18 + ,1 + ,2 + ,23 + ,22 + ,24 + ,28 + ,24 + ,1 + ,1 + ,23 + ,21 + ,24 + ,24 + ,18 + ,2 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+ ,1 + ,23 + ,27 + ,24 + ,45 + ,17 + ,1 + ,1 + ,22 + ,25 + ,21 + ,37 + ,24 + ,2 + ,1 + ,22 + ,21 + ,24 + ,25 + ,21) + ,dim=c(7 + ,152) + ,dimnames=list(c('Roken' + ,'Geslacht' + ,'Leeftijd' + ,'PS' + ,'O' + ,'CMD' + ,'PEC') + ,1:152)) > y <- array(NA,dim=c(7,152),dimnames=list(c('Roken','Geslacht','Leeftijd','PS','O','CMD','PEC'),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 = 'yes' > par3 = '2' > par2 = 'quantiles' > par1 = '7' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "PEC" > x[,par1] [1] 10 9 30 18 16 20 20 18 21 20 20 20 29 14 25 19 19 25 25 19 19 18 24 18 26 [26] 26 24 29 26 28 18 19 21 13 19 26 17 19 28 15 16 18 25 15 24 24 14 19 20 27 [51] 20 25 16 19 15 17 22 19 44 19 19 23 19 28 17 22 25 44 19 21 25 28 19 33 19 [76] 12 15 23 21 29 15 23 25 18 24 20 19 21 33 17 19 19 23 21 20 19 16 20 18 11 [101] 18 23 19 21 16 24 23 20 23 25 24 19 15 20 14 34 24 26 23 18 11 28 15 26 20 [126] 21 21 15 30 21 12 23 30 22 21 19 22 18 16 19 20 12 21 14 23 30 21 25 13 17 [151] 24 21 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) [ 9,21) [21,44] 81 71 > colnames(x) [1] "Roken" "Geslacht" "Leeftijd" "PS" "O" "CMD" "PEC" > colnames(x)[par1] [1] "PEC" > x[,par1] [1] [ 9,21) [ 9,21) [21,44] [ 9,21) [ 9,21) [ 9,21) [ 9,21) [ 9,21) [21,44] [10] [ 9,21) [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [ 9,21) [ 9,21) [21,44] [19] [21,44] [ 9,21) [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [21,44] [21,44] [28] [21,44] [21,44] [21,44] [ 9,21) [ 9,21) [21,44] [ 9,21) [ 9,21) [21,44] [37] [ 9,21) [ 9,21) [21,44] [ 9,21) [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [46] [21,44] [ 9,21) [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [ 9,21) [ 9,21) [55] [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [ 9,21) [ 9,21) [21,44] [ 9,21) [64] [21,44] [ 9,21) [21,44] [21,44] [21,44] [ 9,21) [21,44] [21,44] [21,44] [73] [ 9,21) [21,44] [ 9,21) [ 9,21) [ 9,21) [21,44] [21,44] [21,44] [ 9,21) [82] [21,44] [21,44] [ 9,21) [21,44] [ 9,21) [ 9,21) [21,44] [21,44] [ 9,21) [91] [ 9,21) [ 9,21) [21,44] [21,44] [ 9,21) [ 9,21) [ 9,21) [ 9,21) [ 9,21) [100] [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [ 9,21) [21,44] [21,44] [ 9,21) [109] [21,44] [21,44] [21,44] [ 9,21) [ 9,21) [ 9,21) [ 9,21) [21,44] [21,44] [118] [21,44] [21,44] [ 9,21) [ 9,21) [21,44] [ 9,21) [21,44] [ 9,21) [21,44] [127] [21,44] [ 9,21) [21,44] [21,44] [ 9,21) [21,44] [21,44] [21,44] [21,44] [136] [ 9,21) [21,44] [ 9,21) [ 9,21) [ 9,21) [ 9,21) [ 9,21) [21,44] [ 9,21) [145] [21,44] [21,44] [21,44] [21,44] [ 9,21) [ 9,21) [21,44] [21,44] Levels: [ 9,21) [21,44] > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/1bepa1292362785.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 1 455 265 2 271 380 [1] 0.6319444 [1] 0.5837174 [1] 0.6090445 m.ct.x.pred m.ct.x.actu 1 2 1 40 50 2 35 24 [1] 0.4444444 [1] 0.4067797 [1] 0.4295302 > m Conditional inference tree with 2 terminal nodes Response: as.factor(PEC) Inputs: Roken, Geslacht, Leeftijd, PS, O, CMD Number of observations: 152 1) CMD <= 31; criterion = 0.973, statistic = 8.028 2)* weights = 80 1) CMD > 31 3)* weights = 72 > postscript(file="/var/www/rcomp/tmp/2bepa1292362785.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/rcomp/tmp/3bepa1292362785.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) + } > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } [,1] [,2] [1,] 1 1 [2,] 1 2 [3,] 2 2 [4,] 1 1 [5,] 1 1 [6,] 1 1 [7,] 1 2 [8,] 1 1 [9,] 2 2 [10,] 1 1 [11,] 1 1 [12,] 1 2 [13,] 2 2 [14,] 1 2 [15,] 2 2 [16,] 1 1 [17,] 1 1 [18,] 2 2 [19,] 2 1 [20,] 1 2 [21,] 1 1 [22,] 1 1 [23,] 2 1 [24,] 1 1 [25,] 2 1 [26,] 2 2 [27,] 2 2 [28,] 2 2 [29,] 2 2 [30,] 2 2 [31,] 1 1 [32,] 1 2 [33,] 2 1 [34,] 1 2 [35,] 1 1 [36,] 2 1 [37,] 1 1 [38,] 1 1 [39,] 2 2 [40,] 1 1 [41,] 1 1 [42,] 1 2 [43,] 2 2 [44,] 1 2 [45,] 2 2 [46,] 2 2 [47,] 1 1 [48,] 1 1 [49,] 1 1 [50,] 2 1 [51,] 1 1 [52,] 2 2 [53,] 1 2 [54,] 1 1 [55,] 1 2 [56,] 1 1 [57,] 2 1 [58,] 1 1 [59,] 2 2 [60,] 1 1 [61,] 1 1 [62,] 2 2 [63,] 1 2 [64,] 2 2 [65,] 1 1 [66,] 2 2 [67,] 2 1 [68,] 2 2 [69,] 1 2 [70,] 2 1 [71,] 2 2 [72,] 2 2 [73,] 1 2 [74,] 2 2 [75,] 1 1 [76,] 1 1 [77,] 1 2 [78,] 2 1 [79,] 2 1 [80,] 2 2 [81,] 1 2 [82,] 2 2 [83,] 2 1 [84,] 1 1 [85,] 2 2 [86,] 1 1 [87,] 1 1 [88,] 2 2 [89,] 2 1 [90,] 1 1 [91,] 1 1 [92,] 1 2 [93,] 2 2 [94,] 2 1 [95,] 1 2 [96,] 1 2 [97,] 1 1 [98,] 1 1 [99,] 1 1 [100,] 1 2 [101,] 1 2 [102,] 2 2 [103,] 1 1 [104,] 2 1 [105,] 1 1 [106,] 2 1 [107,] 2 2 [108,] 1 1 [109,] 2 2 [110,] 2 1 [111,] 2 1 [112,] 1 1 [113,] 1 2 [114,] 1 2 [115,] 1 1 [116,] 2 2 [117,] 2 2 [118,] 2 2 [119,] 2 2 [120,] 1 1 [121,] 1 1 [122,] 2 2 [123,] 1 2 [124,] 2 2 [125,] 1 2 [126,] 2 2 [127,] 2 1 [128,] 1 1 [129,] 2 2 [130,] 2 1 [131,] 1 2 [132,] 2 1 [133,] 2 2 [134,] 2 1 [135,] 2 2 [136,] 1 1 [137,] 2 1 [138,] 1 1 [139,] 1 1 [140,] 1 2 [141,] 1 1 [142,] 1 1 [143,] 2 1 [144,] 1 1 [145,] 2 1 [146,] 2 2 [147,] 2 2 [148,] 2 1 [149,] 1 1 [150,] 1 2 [151,] 2 2 [152,] 2 1 [ 9,21) [21,44] [ 9,21) 53 28 [21,44] 27 44 > postscript(file="/var/www/rcomp/tmp/4l6od1292362785.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/rcomp/tmp/5hymm1292362785.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/rcomp/tmp/6s7371292362785.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/rcomp/tmp/7wpkc1292362785.tab") + } > > try(system("convert tmp/2bepa1292362785.ps tmp/2bepa1292362785.png",intern=TRUE)) character(0) > try(system("convert tmp/3bepa1292362785.ps tmp/3bepa1292362785.png",intern=TRUE)) character(0) > try(system("convert tmp/4l6od1292362785.ps tmp/4l6od1292362785.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.75 0.58 3.31