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(7361 + ,493 + ,797 + ,48 + ,1.5 + ,105.0 + ,508643 + ,7391 + ,514 + ,840 + ,49 + ,1.6 + ,104.0 + ,527568 + ,7420 + ,522 + ,988 + ,59 + ,1.8 + ,109.8 + ,520008 + ,7406 + ,490 + ,819 + ,56 + ,1.5 + ,98.6 + ,498484 + ,7439 + ,484 + ,831 + ,47 + ,1.3 + ,93.5 + ,523917 + ,7512 + ,506 + ,904 + ,56 + ,1.6 + ,98.2 + ,553522 + ,7579 + ,501 + ,814 + ,50 + ,1.6 + ,88.0 + ,558901 + ,7520 + ,462 + ,798 + ,54 + ,1.8 + ,85.3 + ,548933 + ,7453 + ,465 + ,828 + ,79 + ,1.8 + ,96.8 + ,567013 + ,7462 + ,454 + ,789 + ,50 + ,1.6 + ,98.8 + ,551085 + ,7472 + ,464 + ,930 + ,54 + ,1.8 + ,110.3 + ,588245 + ,7443 + ,427 + ,744 + ,56 + ,2 + ,111.6 + ,605010 + ,7439 + ,460 + ,832 + ,50 + ,1.3 + ,111.2 + ,631572 + ,7460 + ,473 + ,826 + ,46 + ,1.1 + ,106.9 + ,639180 + ,7482 + ,465 + ,907 + ,47 + ,1 + ,117.6 + ,653847 + ,7442 + ,422 + ,776 + ,43 + ,1.2 + ,97.0 + ,657073 + ,7454 + ,415 + ,835 + ,52 + ,1.2 + ,97.3 + ,626291 + ,7536 + ,413 + ,715 + ,48 + ,1.3 + ,98.4 + ,625616 + ,7616 + ,420 + ,729 + ,36 + ,1.3 + ,87.6 + ,633352 + ,7548 + ,363 + ,733 + ,41 + ,1.4 + ,87.4 + ,672820 + ,7507 + ,376 + ,736 + ,34 + ,1.1 + ,94.7 + ,691369 + ,7515 + ,380 + ,712 + ,37 + ,0.9 + ,101.5 + ,702595 + ,7549 + ,384 + ,711 + ,37 + ,1 + ,110.4 + ,692241 + ,7540 + ,346 + ,667 + ,34 + ,1.1 + ,108.4 + ,718722 + ,7525 + ,389 + ,799 + ,55 + ,1.4 + ,109.7 + ,732297 + ,7575 + ,407 + ,661 + ,37 + ,1.5 + ,105.2 + ,721798 + ,7621 + ,393 + ,692 + ,27 + ,1.8 + ,111.1 + ,766192 + ,7589 + ,346 + ,649 + ,38 + ,1.8 + ,96.2 + ,788456 + ,7606 + ,348 + ,729 + ,43 + ,1.8 + ,97.3 + ,806132 + ,7722 + ,353 + ,622 + ,26 + ,1.7 + ,98.9 + ,813944 + ,7788 + ,364 + ,671 + ,32 + ,1.5 + ,91.7 + ,788025 + ,7735 + ,305 + ,635 + ,29 + ,1.1 + ,90.9 + ,765985 + ,7654 + ,307 + ,648 + ,41 + ,1.3 + ,98.8 + ,702684 + ,7678 + ,312 + ,745 + ,55 + ,1.6 + ,111.5 + ,730159 + ,7688 + ,312 + ,624 + ,50 + ,1.9 + ,119.0 + ,678942 + ,7653 + ,286 + ,477 + ,30 + ,1.9 + ,115.3 + ,672527 + ,7688 + ,324 + ,710 + ,35 + ,2 + ,116.3 + ,594783 + ,7734 + ,336 + ,515 + ,29 + ,2.2 + ,113.6 + ,594575 + ,7754 + ,327 + ,461 + ,22 + ,2.2 + ,115.1 + ,576299 + ,7760 + ,302 + ,590 + ,39 + ,2 + ,109.7 + ,530770 + ,7770 + ,299 + ,415 + ,24 + ,2.3 + ,97.6 + ,524491 + ,7867 + ,311 + ,554 + ,38 + ,2.6 + ,100.8 + ,456590 + ,7938 + ,315 + ,585 + ,30 + ,3.2 + ,94.0 + ,428448 + ,7860 + ,264 + ,513 + ,31 + ,3.2 + ,87.2 + ,444937 + ,7793 + ,278 + ,591 + ,39 + ,3.1 + ,102.9 + ,372206 + ,7829 + ,278 + ,561 + ,33 + ,2.8 + ,111.3 + ,317272 + ,7828 + ,287 + ,684 + ,57 + ,2.3 + ,106.6 + ,297604 + ,7789 + ,279 + ,668 + ,49 + ,1.9 + ,108.9 + ,288561 + ,7820 + ,324 + ,795 + ,74 + ,1.9 + ,108.2 + ,289287 + ,7850 + ,354 + ,776 + ,74 + ,2 + ,100.2 + ,258923 + ,7860 + ,354 + ,1043 + ,115 + ,2 + ,104.0 + ,255493 + ,7836 + ,360 + ,964 + ,67 + ,1.8 + ,90.0 + ,277992 + ,7844 + ,363 + ,762 + ,51 + ,1.6 + ,87.4 + ,295474 + ,7915 + ,385 + ,1030 + ,114 + ,1.4 + ,91.9 + ,291680 + ,7971 + ,412 + ,939 + ,70 + ,0.2 + ,89.3 + ,318736 + ,7890 + ,370 + ,779 + ,73 + ,0.3 + ,81.3 + ,338463 + ,7807 + ,389 + ,918 + ,77 + ,0.4 + ,94.9 + ,351963 + ,7797 + ,395 + ,839 + ,67 + ,0.7 + ,102.6 + ,347240 + ,7788 + ,417 + ,874 + ,60 + ,1 + ,107.2 + ,347081 + ,7779 + ,404 + ,840 + ,73 + ,1.1 + ,114.0 + ,383486) + ,dim=c(7 + ,60) + ,dimnames=list(c('Beroepsbevolking' + ,'Werkloosheid' + ,'Faillissementen' + ,'Faillissementennijverheid' + ,'Inflatie' + ,'Nijverheid' + ,'Beurswaarde ') + ,1:60)) > y <- array(NA,dim=c(7,60),dimnames=list(c('Beroepsbevolking','Werkloosheid','Faillissementen','Faillissementennijverheid','Inflatie','Nijverheid','Beurswaarde '),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 = '4' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Faillissementennijverheid" > x[,par1] [1] 48 49 59 56 47 56 50 54 79 50 54 56 50 46 47 43 52 48 36 [20] 41 34 37 37 34 55 37 27 38 43 26 32 29 41 55 50 30 35 29 [39] 22 39 24 38 30 31 39 33 57 49 74 74 115 67 51 114 70 73 77 [58] 67 60 73 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 22 24 26 27 29 30 31 32 33 34 35 36 37 38 39 41 43 46 47 48 1 1 1 1 2 2 1 1 1 2 1 1 3 2 2 2 2 1 2 2 49 50 51 52 54 55 56 57 59 60 67 70 73 74 77 79 114 115 2 4 1 1 2 2 3 1 1 1 2 1 2 2 1 1 1 1 > colnames(x) [1] "Beroepsbevolking" "Werkloosheid" [3] "Faillissementen" "Faillissementennijverheid" [5] "Inflatie" "Nijverheid" [7] "Beurswaarde." > colnames(x)[par1] [1] "Faillissementennijverheid" > x[,par1] [1] 48 49 59 56 47 56 50 54 79 50 54 56 50 46 47 43 52 48 36 [20] 41 34 37 37 34 55 37 27 38 43 26 32 29 41 55 50 30 35 29 [39] 22 39 24 38 30 31 39 33 57 49 74 74 115 67 51 114 70 73 77 [58] 67 60 73 > 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/1z3h11293379220.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: Faillissementennijverheid Inputs: Beroepsbevolking, Werkloosheid, Faillissementen, Inflatie, Nijverheid, Beurswaarde. Number of observations: 60 1) Faillissementen <= 736; criterion = 1, statistic = 35.702 2) Faillissementen <= 585; criterion = 0.963, statistic = 7.449 3)* weights = 8 2) Faillissementen > 585 4) Beurswaarde. <= 678942; criterion = 0.976, statistic = 8.241 5)* weights = 9 4) Beurswaarde. > 678942 6)* weights = 12 1) Faillissementen > 736 7) Beroepsbevolking <= 7678; criterion = 0.999, statistic = 13.555 8)* weights = 19 7) Beroepsbevolking > 7678 9)* weights = 12 > postscript(file="/var/www/html/rcomp/tmp/2z3h11293379220.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/3z3h11293379220.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 48 52.94737 -4.9473684 2 49 52.94737 -3.9473684 3 59 52.94737 6.0526316 4 56 52.94737 3.0526316 5 47 52.94737 -5.9473684 6 56 52.94737 3.0526316 7 50 52.94737 -2.9473684 8 54 52.94737 1.0526316 9 79 52.94737 26.0526316 10 50 52.94737 -2.9473684 11 54 52.94737 1.0526316 12 56 52.94737 3.0526316 13 50 52.94737 -2.9473684 14 46 52.94737 -6.9473684 15 47 52.94737 -5.9473684 16 43 52.94737 -9.9473684 17 52 52.94737 -0.9473684 18 48 43.77778 4.2222222 19 36 43.77778 -7.7777778 20 41 43.77778 -2.7777778 21 34 34.58333 -0.5833333 22 37 34.58333 2.4166667 23 37 34.58333 2.4166667 24 34 34.58333 -0.5833333 25 55 52.94737 2.0526316 26 37 34.58333 2.4166667 27 27 34.58333 -7.5833333 28 38 34.58333 3.4166667 29 43 34.58333 8.4166667 30 26 34.58333 -8.5833333 31 32 34.58333 -2.5833333 32 29 34.58333 -5.5833333 33 41 34.58333 6.4166667 34 55 52.94737 2.0526316 35 50 43.77778 6.2222222 36 30 29.62500 0.3750000 37 35 43.77778 -8.7777778 38 29 29.62500 -0.6250000 39 22 29.62500 -7.6250000 40 39 43.77778 -4.7777778 41 24 29.62500 -5.6250000 42 38 29.62500 8.3750000 43 30 29.62500 0.3750000 44 31 29.62500 1.3750000 45 39 43.77778 -4.7777778 46 33 29.62500 3.3750000 47 57 43.77778 13.2222222 48 49 43.77778 5.2222222 49 74 76.25000 -2.2500000 50 74 76.25000 -2.2500000 51 115 76.25000 38.7500000 52 67 76.25000 -9.2500000 53 51 76.25000 -25.2500000 54 114 76.25000 37.7500000 55 70 76.25000 -6.2500000 56 73 76.25000 -3.2500000 57 77 76.25000 0.7500000 58 67 76.25000 -9.2500000 59 60 76.25000 -16.2500000 60 73 76.25000 -3.2500000 > 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/4klgp1293379220.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/5o4ev1293379220.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/6r4vj1293379220.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/7kdul1293379220.tab") + } > > try(system("convert tmp/2z3h11293379220.ps tmp/2z3h11293379220.png",intern=TRUE)) character(0) > try(system("convert tmp/3z3h11293379220.ps tmp/3z3h11293379220.png",intern=TRUE)) character(0) > try(system("convert tmp/4klgp1293379220.ps tmp/4klgp1293379220.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.347 0.575 4.925