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Data X:
11.5 8 350 165 3693 11 8 318 150 3436 10.5 8 302 140 3449 10 8 429 198 4341 8.5 8 440 215 4312 10 8 455 225 4425 10 8 383 170 3563 8 8 340 160 3609 10 8 455 225 3086 15 4 113 95 2372 15.5 6 199 97 2774 20.5 4 97 46 1835 17.5 4 110 87 2672 17.5 4 104 95 2375 12.5 4 121 113 2234 14 8 360 215 4615 15 8 307 200 4376 18.5 8 304 193 4732 14.5 4 97 88 2130 14 4 113 95 2228 15.5 6 250 100 3329 15.5 6 232 100 3288 12 8 350 165 4209 13 8 318 150 4096 12 8 400 170 4746 12 8 400 175 5140 19 4 140 72 2408 15 6 250 100 3282 14 4 122 86 2220 14 4 116 90 2123 14.5 4 88 76 2065 19 4 71 65 1773 19 4 97 60 1834 20.5 4 91 70 1955 17 4 97.5 80 2126 16.5 4 122 86 2226 12 8 350 165 4274 13.5 8 318 150 4135 13 8 351 153 4129 11 8 429 208 4633 13.5 8 350 155 4502 12.5 8 400 190 4422 13.5 3 70 97 2330 14 8 307 130 4098 16 8 302 140 4294 14.5 4 121 112 2933 18 4 121 76 2511 16 4 122 86 2395 14.5 4 120 97 2506 15 4 98 80 2164 13 8 350 175 4100 11.5 8 304 150 3672 14.5 8 302 137 4042 12.5 8 318 150 3777 12 8 400 150 4464 13 8 351 158 4363 11 8 440 215 4735 11 8 455 225 4951 16.5 6 225 105 3121 18 6 250 100 3278 16.5 6 250 88 3021 16 6 198 95 2904 14 8 400 150 4997 12.5 8 350 180 4499 15 6 232 100 2789 19.5 4 140 72 2401 16.5 4 108 94 2379 18.5 4 122 85 2310 14 6 155 107 2472 13 8 350 145 4082 9.5 8 400 230 4278 15.5 4 116 75 2158 14 4 114 91 2582 11 8 318 150 3399 14 4 121 110 2660 11 8 350 180 3664 16.5 6 198 95 3102 16 6 232 100 2901 16.5 4 122 80 2451 21 4 71 65 1836 17 6 250 100 3781 18 6 258 110 3632 14 8 302 140 4141 14.5 8 350 150 4699 16 8 302 140 4638 15.5 8 304 150 4257 15.5 4 79 67 1963 14.5 4 97 78 2300 19 4 83 61 2003 14.5 4 90 75 2125 14 4 116 75 2246 15 4 120 97 2489 16 4 79 67 2000 16 6 225 95 3264 19.5 6 250 72 3158 11.5 8 400 170 4668 14 8 350 145 4440 13.5 8 351 148 4657 21 6 231 110 3907 19 6 258 110 3730 19 6 225 95 3785 13.5 8 262 110 3221 12 8 302 129 3169 17 4 140 83 2639 16 6 232 100 2914 13.5 4 134 96 2702 16.5 4 90 71 2223 14.5 6 171 97 2984 15 4 115 95 2694 17 4 120 88 2957 13.5 4 121 115 2671 17.5 4 91 53 1795 16.9 4 116 81 2220 14.9 4 140 92 2572 15.3 4 101 83 2202 13 8 305 140 4215 13.9 8 304 120 3962 12.8 8 351 152 4215 14.5 6 250 105 3353 17.6 6 200 81 3012 22.2 4 85 52 2035 22.1 4 98 60 2164 17.7 6 225 100 3651 16.2 6 250 110 3645 17.8 6 258 95 3193 17 4 85 70 1990 16.4 4 97 75 2155 15.7 4 130 102 3150 13.2 8 318 150 3940 16.7 6 168 120 3820 12.1 8 350 180 4380 15 8 302 130 3870 14 8 318 150 3755 14.8 4 111 80 2155 18.6 4 79 58 1825 16.8 4 85 70 1945 12.5 8 305 145 3880 13.7 8 318 145 4140 16.9 6 231 105 3425 17.7 6 225 100 3630 11.1 8 400 180 4220 11.4 8 350 170 4165 14.5 8 351 149 4335 14.5 4 97 78 1940 18.2 4 97 75 2265 15.8 4 140 89 2755 15.9 4 98 83 2075 16.4 4 97 67 1985 14.5 6 146 97 2815 12.8 4 121 110 2600 21.5 4 90 48 1985 14.4 4 98 66 1800 18.6 4 85 70 2070 13.2 8 318 140 3735 12.8 8 302 139 3570 18.2 6 200 95 3155 15.8 6 200 85 2965 17.2 6 225 100 3430 17.2 6 232 90 3210 16.7 6 200 85 3070 18.7 6 225 110 3620 13.2 8 305 145 3425 13.4 6 231 165 3445 13.7 8 318 140 4080 16.5 4 98 68 2155 14.7 4 119 97 2300 14.5 4 105 75 2230 17.6 4 151 85 2855 15.9 5 131 103 2830 13.6 6 163 125 3140 15.8 6 163 133 3410 14.9 4 89 71 1990 16.6 4 98 68 2135 18.2 6 200 85 2990 17.3 4 140 88 2890 16.6 6 225 110 3360 15.4 8 305 130 3840 13.2 8 351 138 3955 15.2 8 318 135 3830 14.3 8 351 142 4054 15 8 267 125 3605 14 4 89 71 1925 15.2 4 86 65 1975 15 4 121 80 2670 24.8 4 141 71 3190 22.2 8 260 90 3420 14.9 4 105 70 2150 19.2 4 85 65 2020 16 4 151 90 2670 11.3 6 173 115 2595 13.2 4 151 90 2556 14.7 4 98 76 2144 15.5 4 98 70 2120 16.4 4 86 65 2019 18.1 4 140 88 2870 20.1 4 151 90 3003 15.8 4 97 78 2188 15.5 4 134 90 2711 15 4 119 92 2434 15.2 4 108 75 2265 14.4 4 156 105 2800 19.2 4 85 65 2110 19.9 5 121 67 2950 13.8 4 91 67 1850 15.3 4 89 62 1845 15.1 4 122 88 2500 15.7 4 135 84 2490 16.4 4 151 84 2635 12.6 6 173 110 2725 12.9 4 135 84 2385 16.4 4 86 64 1875 16.1 4 81 60 1760 19.4 4 85 65 1975 17.3 4 89 62 2050 14.9 4 105 63 2215 16.2 4 98 65 2045 14.2 4 105 74 2190 14.8 4 119 100 2615 20.4 4 141 80 3230 13.8 6 146 120 2930 15.8 6 231 110 3415 17.1 6 200 88 3060 16.6 6 225 85 3465 18.6 4 112 88 2640 18 4 112 88 2395 16 4 135 84 2525 18 4 151 90 2735 15.3 4 105 74 1980 17.6 4 91 68 1970 14.7 4 105 63 2125 14.5 4 120 88 2160 14.5 4 107 75 2205 15.7 4 91 67 1965 16.4 6 181 110 2945 17 6 262 85 3015 13.9 4 144 96 2665 17.3 4 151 90 2950 15.6 4 140 86 2790 11.6 4 135 84 2295 18.6 4 120 79 2625
Names of X columns:
acceleration cylinders enginedisplacement horesepower weight
Endogenous Variable (Column Number)
Categorization
quantiles
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
yes
no
yes
Chart options
R Code
library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] 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]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='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='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() 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) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') 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() 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$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$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='mytable.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='mytable2.tab') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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