R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(695 + ,0 + ,641 + ,696 + ,729 + ,839 + ,627 + ,608 + ,638 + ,0 + ,695 + ,641 + ,696 + ,729 + ,696 + ,651 + ,762 + ,0 + ,638 + ,695 + ,641 + ,696 + ,825 + ,691 + ,635 + ,0 + ,762 + ,638 + ,695 + ,641 + ,677 + ,627 + ,721 + ,0 + ,635 + ,762 + ,638 + ,695 + ,656 + ,634 + ,854 + ,0 + ,721 + ,635 + ,762 + ,638 + ,785 + ,731 + ,418 + ,0 + ,854 + ,721 + ,635 + ,762 + ,412 + ,475 + ,367 + ,0 + ,418 + ,854 + ,721 + ,635 + ,352 + ,337 + ,824 + ,0 + ,367 + ,418 + ,854 + ,721 + ,839 + ,803 + ,687 + ,0 + ,824 + ,367 + ,418 + ,854 + ,729 + ,722 + ,601 + ,0 + ,687 + ,824 + ,367 + ,418 + ,696 + ,590 + ,676 + ,0 + ,601 + ,687 + ,824 + ,367 + ,641 + ,724 + ,740 + ,0 + ,676 + ,601 + ,687 + ,824 + ,695 + ,627 + ,691 + ,0 + ,740 + ,676 + ,601 + ,687 + ,638 + ,696 + ,683 + ,0 + ,691 + ,740 + ,676 + ,601 + ,762 + ,825 + ,594 + ,0 + ,683 + ,691 + ,740 + ,676 + ,635 + ,677 + ,729 + ,0 + ,594 + ,683 + ,691 + ,740 + ,721 + ,656 + ,731 + ,0 + ,729 + ,594 + ,683 + ,691 + ,854 + ,785 + ,386 + ,0 + ,731 + ,729 + ,594 + ,683 + ,418 + ,412 + ,331 + ,0 + ,386 + ,731 + ,729 + ,594 + ,367 + ,352 + ,706 + ,0 + ,331 + ,386 + ,731 + ,729 + ,824 + ,839 + ,715 + ,0 + ,706 + ,331 + ,386 + ,731 + ,687 + ,729 + ,657 + ,0 + ,715 + ,706 + ,331 + ,386 + ,601 + ,696 + ,653 + ,0 + ,657 + ,715 + ,706 + ,331 + ,676 + ,641 + ,642 + ,0 + ,653 + ,657 + ,715 + ,706 + ,740 + ,695 + ,643 + ,0 + ,642 + ,653 + ,657 + ,715 + ,691 + ,638 + ,718 + ,0 + ,643 + ,642 + ,653 + ,657 + ,683 + ,762 + ,654 + ,0 + ,718 + ,643 + ,642 + ,653 + ,594 + ,635 + ,632 + ,0 + ,654 + ,718 + ,643 + ,642 + ,729 + ,721 + ,731 + ,0 + ,632 + ,654 + ,718 + ,643 + ,731 + ,854 + ,392 + ,1 + ,731 + ,632 + ,654 + ,718 + ,386 + ,418 + ,344 + ,1 + ,392 + ,731 + ,632 + ,654 + ,331 + ,367 + ,792 + ,1 + ,344 + ,392 + ,731 + ,632 + ,706 + ,824 + ,852 + ,1 + ,792 + ,344 + ,392 + ,731 + ,715 + ,687 + ,649 + ,1 + ,852 + ,792 + ,344 + ,392 + ,657 + ,601 + ,629 + ,1 + ,649 + ,852 + ,792 + ,344 + ,653 + ,676 + ,685 + ,1 + ,629 + ,649 + ,852 + ,792 + ,642 + ,740 + ,617 + ,1 + ,685 + ,629 + ,649 + ,852 + ,643 + ,691 + ,715 + ,1 + ,617 + ,685 + ,629 + ,649 + ,718 + ,683 + ,715 + ,1 + ,715 + ,617 + ,685 + ,629 + ,654 + ,594 + ,629 + ,1 + ,715 + ,715 + ,617 + ,685 + ,632 + ,729 + ,916 + ,1 + ,629 + ,715 + ,715 + ,617 + ,731 + ,731 + ,531 + ,1 + ,916 + ,629 + ,715 + ,715 + ,392 + ,386 + ,357 + ,1 + ,531 + ,916 + ,629 + ,715 + ,344 + ,331 + ,917 + ,1 + ,357 + ,531 + ,916 + ,629 + ,792 + ,706 + ,828 + ,1 + ,917 + ,357 + ,531 + ,916 + ,852 + ,715 + ,708 + ,1 + ,828 + ,917 + ,357 + ,531 + ,649 + ,657 + ,858 + ,1 + ,708 + ,828 + ,917 + ,357 + ,629 + ,653 + ,775 + ,1 + ,858 + ,708 + ,828 + ,917 + ,685 + ,642 + ,785 + ,1 + ,775 + ,858 + ,708 + ,828 + ,617 + ,643 + ,1006 + ,1 + ,785 + ,775 + ,858 + ,708 + ,715 + ,718 + ,789 + ,1 + ,1006 + ,785 + ,775 + ,858 + ,715 + ,654 + ,734 + ,1 + ,789 + ,1006 + ,785 + ,775 + ,629 + ,632 + ,906 + ,1 + ,734 + ,789 + ,1006 + ,785 + ,916 + ,731 + ,532 + ,1 + ,906 + ,734 + ,789 + ,1006 + ,531 + ,392 + ,387 + ,1 + ,532 + ,906 + ,734 + ,789 + ,357 + ,344 + ,991 + ,1 + ,387 + ,532 + ,906 + ,734 + ,917 + ,792 + ,841 + ,1 + ,991 + ,387 + ,532 + ,906 + ,828 + ,852 + ,892 + ,1 + ,841 + ,991 + ,387 + ,532 + ,708 + ,649 + ,782 + ,1 + ,892 + ,841 + ,991 + ,387 + ,858 + ,629) + ,dim=c(8 + ,60) + ,dimnames=list(c('faillissement' + ,'crisis' + ,'t-1' + ,'t-2' + ,'t-3' + ,'t-4' + ,'t-12' + ,'t-24 ') + ,1:60)) > y <- array(NA,dim=c(8,60),dimnames=list(c('faillissement','crisis','t-1','t-2','t-3','t-4','t-12','t-24 '),1:60)) > 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 = '1' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "faillissement" > x[,par1] [1] 695 638 762 635 721 854 418 367 824 687 601 676 740 691 683 [16] 594 729 731 386 331 706 715 657 653 642 643 718 654 632 731 [31] 392 344 792 852 649 629 685 617 715 715 629 916 531 357 917 [46] 828 708 858 775 785 1006 789 734 906 532 387 991 841 892 782 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 331 344 357 367 386 387 392 418 531 532 594 601 617 629 632 635 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 638 642 643 649 653 654 657 676 683 685 687 691 695 706 708 715 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 718 721 729 731 734 740 762 775 782 785 789 792 824 828 841 852 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 854 858 892 906 916 917 991 1006 1 1 1 1 1 1 1 1 > colnames(x) [1] "faillissement" "crisis" "t.1" "t.2" [5] "t.3" "t.4" "t.12" "t.24." > colnames(x)[par1] [1] "faillissement" > x[,par1] [1] 695 638 762 635 721 854 418 367 824 687 601 676 740 691 683 [16] 594 729 731 386 331 706 715 657 653 642 643 718 654 632 731 [31] 392 344 792 852 649 629 685 617 715 715 629 916 531 357 917 [46] 828 708 858 775 785 1006 789 734 906 532 387 991 841 892 782 > 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/1bu1r1292955770.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: faillissement Inputs: crisis, t.1, t.2, t.3, t.4, t.12, t.24. Number of observations: 60 1) t.12 <= 531; criterion = 1, statistic = 42.767 2)* weights = 10 1) t.12 > 531 3) t.12 <= 696; criterion = 0.999, statistic = 14.307 4)* weights = 26 3) t.12 > 696 5) crisis <= 0; criterion = 0.992, statistic = 10.592 6)* weights = 11 5) crisis > 0 7)* weights = 13 > postscript(file="/var/www/html/rcomp/tmp/24l0c1292955770.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/34l0c1292955770.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 1 695 685.1923 9.8076923 2 638 685.1923 -47.1923077 3 762 725.5455 36.4545455 4 635 685.1923 -50.1923077 5 721 685.1923 35.8076923 6 854 725.5455 128.4545455 7 418 404.5000 13.5000000 8 367 404.5000 -37.5000000 9 824 725.5455 98.4545455 10 687 725.5455 -38.5454545 11 601 685.1923 -84.1923077 12 676 685.1923 -9.1923077 13 740 685.1923 54.8076923 14 691 685.1923 5.8076923 15 683 725.5455 -42.5454545 16 594 685.1923 -91.1923077 17 729 725.5455 3.4545455 18 731 725.5455 5.4545455 19 386 404.5000 -18.5000000 20 331 404.5000 -73.5000000 21 706 725.5455 -19.5454545 22 715 685.1923 29.8076923 23 657 685.1923 -28.1923077 24 653 685.1923 -32.1923077 25 642 725.5455 -83.5454545 26 643 685.1923 -42.1923077 27 718 685.1923 32.8076923 28 654 685.1923 -31.1923077 29 632 725.5455 -93.5454545 30 731 725.5455 5.4545455 31 392 404.5000 -12.5000000 32 344 404.5000 -60.5000000 33 792 863.6154 -71.6153846 34 852 863.6154 -11.6153846 35 649 685.1923 -36.1923077 36 629 685.1923 -56.1923077 37 685 685.1923 -0.1923077 38 617 685.1923 -68.1923077 39 715 863.6154 -148.6153846 40 715 685.1923 29.8076923 41 629 685.1923 -56.1923077 42 916 863.6154 52.3846154 43 531 404.5000 126.5000000 44 357 404.5000 -47.5000000 45 917 863.6154 53.3846154 46 828 863.6154 -35.6153846 47 708 685.1923 22.8076923 48 858 685.1923 172.8076923 49 775 685.1923 89.8076923 50 785 685.1923 99.8076923 51 1006 863.6154 142.3846154 52 789 863.6154 -74.6153846 53 734 685.1923 48.8076923 54 906 863.6154 42.3846154 55 532 404.5000 127.5000000 56 387 404.5000 -17.5000000 57 991 863.6154 127.3846154 58 841 863.6154 -22.6153846 59 892 863.6154 28.3846154 60 782 863.6154 -81.6153846 > 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/4xvhx1292955770.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/5bn0y1292955771.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/6w6h41292955771.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/7h6xs1292955771.tab") + } > > try(system("convert tmp/24l0c1292955770.ps tmp/24l0c1292955770.png",intern=TRUE)) character(0) > try(system("convert tmp/34l0c1292955770.ps tmp/34l0c1292955770.png",intern=TRUE)) character(0) > try(system("convert tmp/4xvhx1292955770.ps tmp/4xvhx1292955770.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.274 0.579 5.075