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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 = 'yes' > par3 = '2' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Trades" > x[,par1] [1] 1081 309 458 588 299 156 481 323 452 109 115 110 239 247 497 [16] 103 109 502 248 373 119 84 102 295 105 64 267 129 37 361 [31] 28 85 44 49 22 155 91 81 79 145 816 61 226 105 62 [46] 24 26 322 84 33 108 150 115 162 158 97 9 66 107 101 [61] 47 38 34 84 79 947 74 53 94 63 58 49 34 11 35 [76] 17 47 43 117 171 26 73 59 18 15 72 86 14 64 11 [91] 52 41 99 75 45 43 8 198 22 11 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 8 9 11 14 15 17 18 22 24 26 28 33 34 35 37 38 1 1 3 1 1 1 1 2 1 2 1 1 2 1 1 1 41 43 44 45 47 49 52 53 58 59 61 62 63 64 66 72 1 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 73 74 75 79 81 84 85 86 91 94 97 99 101 102 103 105 1 1 1 2 1 3 1 1 1 1 1 1 1 1 1 2 107 108 109 110 115 117 119 129 145 150 155 156 158 162 171 198 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 226 239 247 248 267 295 299 309 322 323 361 373 452 458 481 497 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 502 588 816 947 1081 1 1 1 1 1 > colnames(x) [1] "Group" "Costs" "GrCosts" "Trades" "GrTrades" "Dividends" [7] "GrDiv" "TrDiv" "Wealth." > colnames(x)[par1] [1] "Trades" > x[,par1] [1] 1081 309 458 588 299 156 481 323 452 109 115 110 239 247 497 [16] 103 109 502 248 373 119 84 102 295 105 64 267 129 37 361 [31] 28 85 44 49 22 155 91 81 79 145 816 61 226 105 62 [46] 24 26 322 84 33 108 150 115 162 158 97 9 66 107 101 [61] 47 38 34 84 79 947 74 53 94 63 58 49 34 11 35 [76] 17 47 43 117 171 26 73 59 18 15 72 86 14 64 11 [91] 52 41 99 75 45 43 8 198 22 11 > 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/13qir1293220556.tab") + } + } > m Conditional inference tree with 8 terminal nodes Response: Trades Inputs: Group, Costs, GrCosts, GrTrades, Dividends, GrDiv, TrDiv, Wealth. Number of observations: 100 1) TrDiv <= 44112712; criterion = 1, statistic = 69.83 2) Costs <= 25830; criterion = 1, statistic = 52.673 3) TrDiv <= 16885896; criterion = 1, statistic = 45.85 4) TrDiv <= 6823104; criterion = 1, statistic = 25.692 5) Costs <= 8568; criterion = 1, statistic = 21.142 6) TrDiv <= 4160835; criterion = 1, statistic = 22.485 7) TrDiv <= 1540875; criterion = 0.967, statistic = 8.198 8)* weights = 10 7) TrDiv > 1540875 9)* weights = 16 6) TrDiv > 4160835 10)* weights = 17 5) Costs > 8568 11)* weights = 7 4) TrDiv > 6823104 12)* weights = 22 3) TrDiv > 16885896 13)* weights = 11 2) Costs > 25830 14)* weights = 8 1) TrDiv > 44112712 15)* weights = 9 > postscript(file="/var/www/html/rcomp/tmp/23qir1293220556.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/33qir1293220556.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 1081 608.66667 472.3333333 2 309 320.75000 -11.7500000 3 458 608.66667 -150.6666667 4 588 320.75000 267.2500000 5 299 320.75000 -21.7500000 6 156 320.75000 -164.7500000 7 481 608.66667 -127.6666667 8 323 608.66667 -285.6666667 9 452 320.75000 131.2500000 10 109 113.71429 -4.7142857 11 115 198.54545 -83.5454545 12 110 113.71429 -3.7142857 13 239 113.71429 125.2857143 14 247 320.75000 -73.7500000 15 497 608.66667 -111.6666667 16 103 103.77273 -0.7727273 17 109 198.54545 -89.5454545 18 502 608.66667 -106.6666667 19 248 320.75000 -72.7500000 20 373 608.66667 -235.6666667 21 119 103.77273 15.2272727 22 84 113.71429 -29.7142857 23 102 103.77273 -1.7727273 24 295 198.54545 96.4545455 25 105 103.77273 1.2272727 26 64 113.71429 -49.7142857 27 267 320.75000 -53.7500000 28 129 113.71429 15.2857143 29 37 15.10000 21.9000000 30 361 198.54545 162.4545455 31 28 37.31250 -9.3125000 32 85 103.77273 -18.7727273 33 44 37.31250 6.6875000 34 49 61.47059 -12.4705882 35 22 61.47059 -39.4705882 36 155 103.77273 51.2272727 37 91 103.77273 -12.7727273 38 81 103.77273 -22.7727273 39 79 103.77273 -24.7727273 40 145 103.77273 41.2272727 41 816 608.66667 207.3333333 42 61 113.71429 -52.7142857 43 226 198.54545 27.4545455 44 105 103.77273 1.2272727 45 62 61.47059 0.5294118 46 24 37.31250 -13.3125000 47 26 61.47059 -35.4705882 48 322 198.54545 123.4545455 49 84 103.77273 -19.7727273 50 33 37.31250 -4.3125000 51 108 103.77273 4.2272727 52 150 198.54545 -48.5454545 53 115 198.54545 -83.5454545 54 162 198.54545 -36.5454545 55 158 198.54545 -40.5454545 56 97 61.47059 35.5294118 57 9 15.10000 -6.1000000 58 66 61.47059 4.5294118 59 107 103.77273 3.2272727 60 101 103.77273 -2.7727273 61 47 61.47059 -14.4705882 62 38 37.31250 0.6875000 63 34 37.31250 -3.3125000 64 84 103.77273 -19.7727273 65 79 61.47059 17.5294118 66 947 608.66667 338.3333333 67 74 61.47059 12.5294118 68 53 61.47059 -8.4705882 69 94 103.77273 -9.7727273 70 63 61.47059 1.5294118 71 58 37.31250 20.6875000 72 49 37.31250 11.6875000 73 34 37.31250 -3.3125000 74 11 15.10000 -4.1000000 75 35 37.31250 -2.3125000 76 17 15.10000 1.9000000 77 47 61.47059 -14.4705882 78 43 37.31250 5.6875000 79 117 103.77273 13.2272727 80 171 198.54545 -27.5454545 81 26 37.31250 -11.3125000 82 73 61.47059 11.5294118 83 59 103.77273 -44.7727273 84 18 15.10000 2.9000000 85 15 15.10000 -0.1000000 86 72 61.47059 10.5294118 87 86 103.77273 -17.7727273 88 14 15.10000 -1.1000000 89 64 61.47059 2.5294118 90 11 15.10000 -4.1000000 91 52 61.47059 -9.4705882 92 41 37.31250 3.6875000 93 99 61.47059 37.5294118 94 75 103.77273 -28.7727273 95 45 37.31250 7.6875000 96 43 37.31250 5.6875000 97 8 15.10000 -7.1000000 98 198 103.77273 94.2272727 99 22 37.31250 -15.3125000 100 11 15.10000 -4.1000000 > 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/4d00c1293220556.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/5rsfl1293220556.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/62jf61293220556.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/7ojdu1293220556.tab") + } > > try(system("convert tmp/23qir1293220556.ps tmp/23qir1293220556.png",intern=TRUE)) character(0) > try(system("convert tmp/33qir1293220556.ps tmp/33qir1293220556.png",intern=TRUE)) character(0) > try(system("convert tmp/4d00c1293220556.ps tmp/4d00c1293220556.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.943 0.666 14.167