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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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,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 = '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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Costs" > x[,par1] [1] 162556 29790 87550 84738 54660 42634 40949 42312 37704 16275 [11] 25830 12679 18014 43556 24524 6532 7123 20813 37597 17821 [21] 12988 22330 13326 16189 7146 15824 26088 11326 8568 14416 [31] 3369 11819 6620 4519 2220 18562 10327 5336 2365 4069 [41] 7710 13718 4525 6869 4628 3653 1265 7489 4901 2284 [51] 3160 4150 7285 1134 4658 2384 3748 5371 1285 9327 [61] 5565 1528 3122 7317 2675 13253 880 2053 1424 4036 [71] 3045 5119 1431 554 1975 1286 1012 810 1280 666 [81] 1380 4608 876 814 514 5692 3642 540 2099 567 [91] 2001 2949 2253 6533 1889 3055 272 1414 2564 1383 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 272 514 540 554 567 666 810 814 876 880 1012 1 1 1 1 1 1 1 1 1 1 1 1134 1265 1280 1285 1286 1380 1383 1414 1424 1431 1528 1 1 1 1 1 1 1 1 1 1 1 1889 1975 2001 2053 2099 2220 2253 2284 2365 2384 2564 1 1 1 1 1 1 1 1 1 1 1 2675 2949 3045 3055 3122 3160 3369 3642 3653 3748 4036 1 1 1 1 1 1 1 1 1 1 1 4069 4150 4519 4525 4608 4628 4658 4901 5119 5336 5371 1 1 1 1 1 1 1 1 1 1 1 5565 5692 6532 6533 6620 6869 7123 7146 7285 7317 7489 1 1 1 1 1 1 1 1 1 1 1 7710 8568 9327 10327 11326 11819 12679 12988 13253 13326 13718 1 1 1 1 1 1 1 1 1 1 1 14416 15824 16189 16275 17821 18014 18562 20813 22330 24524 25830 1 1 1 1 1 1 1 1 1 1 1 26088 29790 37597 37704 40949 42312 42634 43556 54660 84738 87550 1 1 1 1 1 1 1 1 1 1 1 162556 1 > colnames(x) [1] "Group" "Costs" "GrCosts" "Trades" "GrTrades" "Dividends" [7] "GrDiv" "TrDiv" "Wealth." > colnames(x)[par1] [1] "Costs" > x[,par1] [1] 162556 29790 87550 84738 54660 42634 40949 42312 37704 16275 [11] 25830 12679 18014 43556 24524 6532 7123 20813 37597 17821 [21] 12988 22330 13326 16189 7146 15824 26088 11326 8568 14416 [31] 3369 11819 6620 4519 2220 18562 10327 5336 2365 4069 [41] 7710 13718 4525 6869 4628 3653 1265 7489 4901 2284 [51] 3160 4150 7285 1134 4658 2384 3748 5371 1285 9327 [61] 5565 1528 3122 7317 2675 13253 880 2053 1424 4036 [71] 3045 5119 1431 554 1975 1286 1012 810 1280 666 [81] 1380 4608 876 814 514 5692 3642 540 2099 567 [91] 2001 2949 2253 6533 1889 3055 272 1414 2564 1383 > 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/1t6f91293218168.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Costs Inputs: Group, GrCosts, Trades, GrTrades, Dividends, GrDiv, TrDiv, Wealth. Number of observations: 100 1) GrCosts <= 29790; criterion = 1, statistic = 74.278 2) Trades <= 226; criterion = 1, statistic = 28.76 3) Wealth. <= 700368; criterion = 1, statistic = 22.241 4)* weights = 69 3) Wealth. > 700368 5)* weights = 10 2) Trades > 226 6)* weights = 14 1) GrCosts > 29790 7)* weights = 7 > postscript(file="/var/www/rcomp/tmp/23gwc1293218168.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/33gwc1293218168.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 162556 66430.429 96125.571429 2 29790 26096.429 3693.571429 3 87550 66430.429 21119.571429 4 84738 26096.429 58641.571429 5 54660 66430.429 -11770.428571 6 42634 66430.429 -23796.428571 7 40949 26096.429 14852.571429 8 42312 66430.429 -24118.428571 9 37704 66430.429 -28726.428571 10 16275 12787.100 3487.900000 11 25830 12787.100 13042.900000 12 12679 12787.100 -108.100000 13 18014 26096.429 -8082.428571 14 43556 26096.429 17459.571429 15 24524 26096.429 -1572.428571 16 6532 12787.100 -6255.100000 17 7123 12787.100 -5664.100000 18 20813 26096.429 -5283.428571 19 37597 66430.429 -28833.428571 20 17821 26096.429 -8275.428571 21 12988 12787.100 200.900000 22 22330 12787.100 9542.900000 23 13326 12787.100 538.900000 24 16189 26096.429 -9907.428571 25 7146 4006.522 3139.478261 26 15824 4006.522 11817.478261 27 26088 26096.429 -8.428571 28 11326 4006.522 7319.478261 29 8568 12787.100 -4219.100000 30 14416 26096.429 -11680.428571 31 3369 4006.522 -637.521739 32 11819 4006.522 7812.478261 33 6620 4006.522 2613.478261 34 4519 4006.522 512.478261 35 2220 12787.100 -10567.100000 36 18562 4006.522 14555.478261 37 10327 4006.522 6320.478261 38 5336 4006.522 1329.478261 39 2365 4006.522 -1641.521739 40 4069 4006.522 62.478261 41 7710 26096.429 -18386.428571 42 13718 4006.522 9711.478261 43 4525 4006.522 518.478261 44 6869 4006.522 2862.478261 45 4628 4006.522 621.478261 46 3653 4006.522 -353.521739 47 1265 4006.522 -2741.521739 48 7489 26096.429 -18607.428571 49 4901 4006.522 894.478261 50 2284 4006.522 -1722.521739 51 3160 4006.522 -846.521739 52 4150 4006.522 143.478261 53 7285 4006.522 3278.478261 54 1134 4006.522 -2872.521739 55 4658 4006.522 651.478261 56 2384 4006.522 -1622.521739 57 3748 4006.522 -258.521739 58 5371 4006.522 1364.478261 59 1285 4006.522 -2721.521739 60 9327 4006.522 5320.478261 61 5565 4006.522 1558.478261 62 1528 4006.522 -2478.521739 63 3122 4006.522 -884.521739 64 7317 4006.522 3310.478261 65 2675 4006.522 -1331.521739 66 13253 26096.429 -12843.428571 67 880 4006.522 -3126.521739 68 2053 4006.522 -1953.521739 69 1424 4006.522 -2582.521739 70 4036 4006.522 29.478261 71 3045 4006.522 -961.521739 72 5119 4006.522 1112.478261 73 1431 4006.522 -2575.521739 74 554 4006.522 -3452.521739 75 1975 4006.522 -2031.521739 76 1286 4006.522 -2720.521739 77 1012 4006.522 -2994.521739 78 810 4006.522 -3196.521739 79 1280 4006.522 -2726.521739 80 666 4006.522 -3340.521739 81 1380 4006.522 -2626.521739 82 4608 4006.522 601.478261 83 876 4006.522 -3130.521739 84 814 4006.522 -3192.521739 85 514 4006.522 -3492.521739 86 5692 4006.522 1685.478261 87 3642 4006.522 -364.521739 88 540 4006.522 -3466.521739 89 2099 4006.522 -1907.521739 90 567 4006.522 -3439.521739 91 2001 4006.522 -2005.521739 92 2949 4006.522 -1057.521739 93 2253 4006.522 -1753.521739 94 6533 4006.522 2526.478261 95 1889 4006.522 -2117.521739 96 3055 4006.522 -951.521739 97 272 4006.522 -3734.521739 98 1414 4006.522 -2592.521739 99 2564 4006.522 -1442.521739 100 1383 4006.522 -2623.521739 > 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/rcomp/tmp/4e7vf1293218168.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/5hpbl1293218168.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/638a91293218168.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/76rrf1293218168.tab") + } > > try(system("convert tmp/23gwc1293218168.ps tmp/23gwc1293218168.png",intern=TRUE)) character(0) > try(system("convert tmp/33gwc1293218168.ps tmp/33gwc1293218168.png",intern=TRUE)) character(0) > try(system("convert tmp/4e7vf1293218168.ps tmp/4e7vf1293218168.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.470 0.420 2.872