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Type 'q()' to quit R. > x <- array(list(1 + ,162556 + ,162556 + ,1081 + ,1081 + ,213118 + ,213118 + ,230380558 + ,6282929 + ,1 + ,29790 + ,29790 + ,309 + ,309 + ,81767 + ,81767 + ,25266003 + ,4324047 + ,1 + ,87550 + ,87550 + ,458 + ,458 + ,153198 + ,153198 + ,70164684 + ,4108272 + ,0 + ,84738 + ,0 + ,588 + ,0 + ,-26007 + ,0 + ,-15292116 + ,-1212617 + ,1 + ,54660 + ,54660 + ,299 + ,299 + ,126942 + ,126942 + ,37955658 + ,1485329 + ,1 + ,42634 + ,42634 + ,156 + ,156 + ,157214 + ,157214 + ,24525384 + ,1779876 + ,0 + ,40949 + ,0 + ,481 + ,0 + ,129352 + ,0 + ,62218312 + ,1367203 + ,1 + ,42312 + ,42312 + ,323 + ,323 + ,234817 + ,234817 + ,75845891 + ,2519076 + ,1 + ,37704 + ,37704 + ,452 + ,452 + ,60448 + ,60448 + ,27322496 + ,912684 + ,1 + ,16275 + ,16275 + ,109 + ,109 + ,47818 + ,47818 + ,5212162 + ,1443586 + ,0 + ,25830 + ,0 + ,115 + ,0 + ,245546 + ,0 + ,28237790 + ,1220017 + ,0 + ,12679 + ,0 + ,110 + ,0 + ,48020 + ,0 + ,5282200 + ,984885 + ,1 + ,18014 + ,18014 + ,239 + ,239 + ,-1710 + 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,0 + ,1540875 + ,416776 + ,1 + ,5692 + ,5692 + ,72 + ,72 + ,64239 + ,64239 + ,4625208 + ,357257 + ,0 + ,3642 + ,0 + ,86 + ,0 + ,90262 + ,0 + ,7762532 + ,458343 + ,0 + ,540 + ,0 + ,14 + ,0 + ,103960 + ,0 + ,1455440 + ,388386 + ,0 + ,2099 + ,0 + ,64 + ,0 + ,106611 + ,0 + ,6823104 + ,358934 + ,0 + ,567 + ,0 + ,11 + ,0 + ,103345 + ,0 + ,1136795 + ,407560 + ,0 + ,2001 + ,0 + ,52 + ,0 + ,95551 + ,0 + ,4968652 + ,392558 + ,1 + ,2949 + ,2949 + ,41 + ,41 + ,82903 + ,82903 + ,3399023 + ,373177 + ,0 + ,2253 + ,0 + ,99 + ,0 + ,63593 + ,0 + ,6295707 + ,428370 + ,1 + ,6533 + ,6533 + ,75 + ,75 + ,126910 + ,126910 + ,9518250 + ,369419 + ,0 + ,1889 + ,0 + ,45 + ,0 + ,37527 + ,0 + ,1688715 + ,358649 + ,1 + ,3055 + ,3055 + ,43 + ,43 + ,60247 + ,60247 + ,2590621 + ,376641 + ,0 + ,272 + ,0 + ,8 + ,0 + ,112995 + ,0 + ,903960 + ,467427 + ,1 + ,1414 + ,1414 + ,198 + ,198 + ,70184 + ,70184 + ,13896432 + ,364885 + ,0 + ,2564 + ,0 + ,22 + ,0 + ,130140 + ,0 + ,2863080 + ,436230 + ,1 + ,1383 + ,1383 + ,11 + ,11 + ,73221 + ,73221 + ,805431 + ,329118) + ,dim=c(9 + ,100) + ,dimnames=list(c('Group' + ,'Costs' + ,'GrCosts' + ,'Trades' + ,'GrTrades' + ,'Dividends' + ,'GrDiv' + ,'TrDiv' + ,'Wealth ') + ,1:100)) > y <- array(NA,dim=c(9,100),dimnames=list(c('Group','Costs','GrCosts','Trades','GrTrades','Dividends','GrDiv','TrDiv','Wealth '),1:100)) > 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 = 'none' > par1 = '9' > #'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] "Wealth." > x[,par1] [1] 6282929 4324047 4108272 -1212617 1485329 1779876 1367203 2519076 [9] 912684 1443586 1220017 984885 1457425 -572920 929144 1151176 [17] 790090 774497 990576 454195 876607 711969 702380 264449 [25] 450033 541063 588864 -37216 783310 467359 688779 608419 [33] 696348 597793 821730 377934 651939 697458 700368 225986 [41] 348695 373683 501709 413743 379825 336260 636765 481231 [49] 469107 211928 563925 511939 521016 543856 329304 423262 [57] 509665 455881 367772 406339 493408 232942 416002 337430 [65] 361517 360962 235561 408247 450296 418799 247405 378519 [73] 326638 328233 386225 283662 370225 269236 365732 420383 [81] 345811 431809 418876 297476 416776 357257 458343 388386 [89] 358934 407560 392558 373177 428370 369419 358649 376641 [97] 467427 364885 436230 329118 > 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]) -1212617 -572920 -37216 211928 225986 232942 235561 247405 1 1 1 1 1 1 1 1 264449 269236 283662 297476 326638 328233 329118 329304 1 1 1 1 1 1 1 1 336260 337430 345811 348695 357257 358649 358934 360962 1 1 1 1 1 1 1 1 361517 364885 365732 367772 369419 370225 373177 373683 1 1 1 1 1 1 1 1 376641 377934 378519 379825 386225 388386 392558 406339 1 1 1 1 1 1 1 1 407560 408247 413743 416002 416776 418799 418876 420383 1 1 1 1 1 1 1 1 423262 428370 431809 436230 450033 450296 454195 455881 1 1 1 1 1 1 1 1 458343 467359 467427 469107 481231 493408 501709 509665 1 1 1 1 1 1 1 1 511939 521016 541063 543856 563925 588864 597793 608419 1 1 1 1 1 1 1 1 636765 651939 688779 696348 697458 700368 702380 711969 1 1 1 1 1 1 1 1 774497 783310 790090 821730 876607 912684 929144 984885 1 1 1 1 1 1 1 1 990576 1151176 1220017 1367203 1443586 1457425 1485329 1779876 1 1 1 1 1 1 1 1 2519076 4108272 4324047 6282929 1 1 1 1 > colnames(x) [1] "Group" "Costs" "GrCosts" "Trades" "GrTrades" "Dividends" [7] "GrDiv" "TrDiv" "Wealth." > colnames(x)[par1] [1] "Wealth." > x[,par1] [1] 6282929 4324047 4108272 -1212617 1485329 1779876 1367203 2519076 [9] 912684 1443586 1220017 984885 1457425 -572920 929144 1151176 [17] 790090 774497 990576 454195 876607 711969 702380 264449 [25] 450033 541063 588864 -37216 783310 467359 688779 608419 [33] 696348 597793 821730 377934 651939 697458 700368 225986 [41] 348695 373683 501709 413743 379825 336260 636765 481231 [49] 469107 211928 563925 511939 521016 543856 329304 423262 [57] 509665 455881 367772 406339 493408 232942 416002 337430 [65] 361517 360962 235561 408247 450296 418799 247405 378519 [73] 326638 328233 386225 283662 370225 269236 365732 420383 [81] 345811 431809 418876 297476 416776 357257 458343 388386 [89] 358934 407560 392558 373177 428370 369419 358649 376641 [97] 467427 364885 436230 329118 > 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/1524c1293218872.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: Wealth. Inputs: Group, Costs, GrCosts, Trades, GrTrades, Dividends, GrDiv, TrDiv Number of observations: 100 1) GrCosts <= 26088; criterion = 1, statistic = 72.399 2) GrCosts <= 11819; criterion = 0.997, statistic = 12.485 3) Dividends <= 39039; criterion = 1, statistic = 24.95 4)* weights = 7 3) Dividends > 39039 5) Costs <= 5692; criterion = 1, statistic = 25.599 6) Dividends <= 82903; criterion = 1, statistic = 22.14 7)* weights = 20 6) Dividends > 82903 8) Dividends <= 131116; criterion = 0.993, statistic = 11.117 9)* weights = 27 8) Dividends > 131116 10)* weights = 7 5) Costs > 5692 11)* weights = 24 2) GrCosts > 11819 12)* weights = 7 1) GrCosts > 26088 13)* weights = 8 > postscript(file="/var/www/html/freestat/rcomp/tmp/2524c1293218872.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/3524c1293218872.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 6282929 2800348.625 3482580.375 2 4324047 2800348.625 1523698.375 3 4108272 2800348.625 1307923.375 4 -1212617 -7838.571 -1204778.429 5 1485329 2800348.625 -1315019.625 6 1779876 2800348.625 -1020472.625 7 1367203 597500.333 769702.667 8 2519076 2800348.625 -281272.625 9 912684 2800348.625 -1887664.625 10 1443586 968870.286 474715.714 11 1220017 597500.333 622516.667 12 984885 597500.333 387384.667 13 1457425 968870.286 488554.714 14 -572920 -7838.571 -565081.429 15 929144 968870.286 -39726.286 16 1151176 597500.333 553675.667 17 790090 597500.333 192589.667 18 774497 968870.286 -194373.286 19 990576 2800348.625 -1809772.625 20 454195 597500.333 -143305.333 21 876607 968870.286 -92263.286 22 711969 968870.286 -256901.286 23 702380 597500.333 104879.667 24 264449 597500.333 -333051.333 25 450033 597500.333 -147467.333 26 541063 597500.333 -56437.333 27 588864 968870.286 -380006.286 28 -37216 -7838.571 -29377.429 29 783310 -7838.571 791148.571 30 467359 597500.333 -130141.333 31 688779 449145.556 239633.444 32 608419 597500.333 10918.667 33 696348 597500.333 98847.667 34 597793 449145.556 148647.444 35 821730 566119.714 255610.286 36 377934 597500.333 -219566.333 37 651939 597500.333 54438.667 38 697458 449145.556 248312.444 39 700368 566119.714 134248.286 40 225986 335014.200 -109028.200 41 348695 597500.333 -248805.333 42 373683 597500.333 -223817.333 43 501709 449145.556 52563.444 44 413743 597500.333 -183757.333 45 379825 335014.200 44810.800 46 336260 449145.556 -112885.556 47 636765 566119.714 70645.286 48 481231 597500.333 -116269.333 49 469107 449145.556 19961.444 50 211928 335014.200 -123086.200 51 563925 449145.556 114779.444 52 511939 566119.714 -54180.714 53 521016 597500.333 -76484.333 54 543856 566119.714 -22263.714 55 329304 566119.714 -236815.714 56 423262 335014.200 88247.800 57 509665 335014.200 174650.800 58 455881 449145.556 6735.444 59 367772 449145.556 -81373.556 60 406339 597500.333 -191161.333 61 493408 449145.556 44262.444 62 232942 335014.200 -102072.200 63 416002 449145.556 -33143.556 64 337430 597500.333 -260070.333 65 361517 335014.200 26502.800 66 360962 597500.333 -236538.333 67 235561 335014.200 -99453.200 68 408247 449145.556 -40898.556 69 450296 449145.556 1150.444 70 418799 449145.556 -30346.556 71 247405 -7838.571 255243.571 72 378519 -7838.571 386357.571 73 326638 449145.556 -122507.556 74 328233 335014.200 -6781.200 75 386225 449145.556 -62920.556 76 283662 335014.200 -51352.200 77 370225 449145.556 -78920.556 78 269236 335014.200 -65778.200 79 365732 335014.200 30717.800 80 420383 449145.556 -28762.556 81 345811 335014.200 10796.800 82 431809 449145.556 -17336.556 83 418876 566119.714 -147243.714 84 297476 335014.200 -37538.200 85 416776 449145.556 -32369.556 86 357257 335014.200 22242.800 87 458343 449145.556 9197.444 88 388386 449145.556 -60759.556 89 358934 449145.556 -90211.556 90 407560 449145.556 -41585.556 91 392558 449145.556 -56587.556 92 373177 335014.200 38162.800 93 428370 335014.200 93355.800 94 369419 597500.333 -228081.333 95 358649 -7838.571 366487.571 96 376641 335014.200 41626.800 97 467427 449145.556 18281.444 98 364885 335014.200 29870.800 99 436230 449145.556 -12915.556 100 329118 335014.200 -5896.200 > 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/4gb3x1293218872.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/5u31o1293218872.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/65ui91293218872.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/freestat/rcomp/tmp/78vyx1293218872.tab") + } > > try(system("convert tmp/2524c1293218872.ps tmp/2524c1293218872.png",intern=TRUE)) character(0) > try(system("convert tmp/3524c1293218872.ps tmp/3524c1293218872.png",intern=TRUE)) character(0) > try(system("convert tmp/4gb3x1293218872.ps tmp/4gb3x1293218872.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.487 0.801 4.636