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Data X:
140824 186099 165 0 474 38 144 83899 110459 113854 132 3 421 34 133 62711 105079 99776 121 0 673 42 162 122597 112098 106194 145 3 1137 38 148 112249 43929 100792 71 2 333 27 88 56414 76173 47552 47 2 179 35 129 23297 187326 250931 177 8 2146 33 128 231677 22807 6853 5 0 111 18 67 26333 144408 115466 124 1 735 34 132 92356 66485 110896 92 1 585 33 120 100802 79089 169351 149 5 754 42 155 123523 81625 94853 93 5 650 55 210 141038 68788 72591 70 0 615 35 115 84032 103297 101345 148 0 1117 52 179 242821 69446 113713 100 0 599 42 158 98074 114948 165354 142 8 1638 59 223 204399 167949 164263 194 3 724 36 140 127837 125081 135213 113 1 1139 39 144 179805 125818 111669 162 7 491 29 111 57017 136588 134163 186 14 675 46 179 121853 112431 140303 147 1 819 45 171 128937 103037 150773 137 3 1203 39 144 274771 82317 111848 71 3 421 25 89 50114 118906 102509 123 5 601 52 208 87388 83515 96785 134 6 1156 41 153 103760 104581 116136 115 1 923 38 146 87587 103129 158376 138 9 1061 41 158 108822 83243 153990 125 7 593 39 142 109222 37110 64057 66 4 559 32 117 91858 113344 230054 137 7 1016 41 158 96751 139165 184531 152 3 886 45 175 87130 86652 114198 159 6 605 47 174 82994 112302 198299 159 2 779 48 185 120264 69652 33750 31 0 310 37 141 63967 119442 189723 185 14 1135 39 151 157208 69867 100826 78 0 1186 42 159 173124 101629 188355 117 4 1287 41 151 223454 70168 104470 109 3 585 36 139 103722 31081 58391 41 0 276 17 55 57078 103925 164808 149 9 1139 39 151 163531 92622 134097 123 0 1490 39 145 190081 79011 80238 103 1 590 38 138 77659 93487 133252 87 7 632 36 115 59631 64520 54518 71 2 464 42 157 118932 93473 121850 51 1 1022 45 73 31928 114360 79367 70 1 2005 38 138 366195 33032 56968 21 0 330 26 82 21832 96125 106314 155 0 648 52 201 101737 151911 191889 172 2 1305 47 181 131263 89256 104864 133 3 868 45 164 70659 95671 160791 125 3 554 40 158 52259 5950 15049 7 0 218 4 12 9139 149695 191179 158 7 832 44 163 181046 32551 25109 21 0 255 18 67 39920 31701 45824 35 0 454 14 52 55273 100087 129711 133 4 1074 37 134 139882 169707 210012 169 5 642 56 210 92206 150491 194679 256 1 1057 36 134 121210 120192 197680 190 17 992 41 150 124866 95893 81180 100 3 814 36 139 165693 151715 197765 171 0 1288 46 178 162900 176225 214738 267 12 1128 28 101 81448 59900 96252 80 3 1061 42 165 136139 104767 124527 126 4 897 42 163 130023 114799 153242 132 -1 557 37 139 75353 72128 145707 121 5 436 30 116 70320 143592 113963 156 2 562 35 137 73996 89626 134904 133 5 799 44 167 92795 131072 114268 199 1 826 36 135 115430 126817 94333 98 6 600 28 102 72458 81351 102204 109 2 863 45 173 137073 22618 23824 25 1 385 23 88 49742 88977 111563 113 2 712 45 175 130935 92059 91313 126 1 705 38 133 95854 81897 89770 137 3 606 38 148 88511 108146 100125 121 0 965 42 157 248935 126372 165278 178 5 992 36 140 157848 249771 181712 63 5 522 41 154 24347 71154 80906 109 3 648 38 148 104064 71571 75881 101 2 588 37 134 93109 55918 83963 61 2 622 28 109 69650 160141 175721 157 9 1197 45 175 253760 38692 68580 38 0 651 26 99 77339 102812 136323 159 4 656 44 122 144020 56622 55792 58 1 437 8 28 25161 15986 25157 27 0 792 27 101 122949 123534 100922 108 0 342 35 129 45855 108535 118845 83 5 1353 37 143 217209 93879 170492 88 4 891 57 206 136994 144551 81716 164 6 1038 41 155 96779 56750 115750 96 2 786 37 138 135716 127654 105590 192 13 618 38 141 125371 65594 92795 94 2 545 31 114 82449 59938 82390 107 0 1061 36 140 179104 146975 135599 144 6 928 36 140 166284 143372 111542 123 0 555 36 140 77710 168553 162519 170 6 552 35 127 59985 183500 211381 210 3 741 39 141 66789 165986 189944 193 15 1251 58 223 177779 184923 226168 297 10 1389 30 114 166178 140358 117495 125 0 833 51 198 167160 149959 195894 204 4 812 41 155 77748 57224 80684 70 3 640 36 138 106172 43750 19630 49 0 214 19 71 23657 48029 88634 82 0 713 23 84 96668 104978 139292 205 1 686 40 151 63796 100046 128602 111 1 1140 40 155 131090 101047 135848 135 4 1028 40 150 165608 197426 178377 59 1 349 30 112 -58408 160902 106330 70 0 892 41 161 46698 147172 178303 108 4 606 40 149 128649 109432 116938 141 1 682 45 164 180869 1168 5841 11 0 156 1 0 17782 83248 106020 130 0 656 36 139 69512 25162 24610 28 3 192 11 32 37247 45724 74151 101 31 457 45 169 89615 110529 232241 216 3 1162 38 140 151812 855 6622 4 0 146 0 0 14432 101382 127097 97 5 866 30 111 125708 14116 13155 39 0 200 8 25 18806 89506 160501 119 6 1163 39 146 134108 135356 91502 118 3 693 46 175 143567 116066 24469 41 1 485 48 181 150393 144244 88229 107 4 670 29 107 63814 8773 13983 16 0 276 8 27 24231 102153 80716 69 1 646 39 147 108735 117440 157384 160 2 993 47 178 187418 104128 122975 158 15 441 50 193 67968 134238 191469 161 10 1548 48 187 204691 134047 231257 165 7 819 48 187 82955 279488 258287 246 8 1151 50 186 138425 79756 122531 89 1 473 40 151 65461 66089 61394 49 1 401 36 131 41030 102070 86480 107 6 943 40 155 196912 146760 195791 182 5 1429 46 172 205469 154771 18284 16 0 529 39 148 117652 165933 147581 173 2 1654 42 156 225565 64593 72558 90 0 689 39 143 84871 92280 147341 140 0 528 41 151 89029 67150 114651 142 3 873 42 145 144308 128692 100187 126 15 601 32 125 114151 124089 130332 123 2 1545 39 145 232822 125386 134218 239 2 747 36 133 98121 37238 10901 15 1 716 21 79 162359 140015 145758 170 5 940 45 174 171918 150047 75767 123 9 720 50 192 93227 154451 134969 151 3 980 36 132 98324 156349 169216 194 4 815 44 159 132369 0 0 0 0 0 0 0 1 6023 7953 5 0 85 0 0 6735 0 0 0 0 0 0 0 98 0 0 0 0 0 0 0 455 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 84601 105406 122 3 662 37 133 111397 68946 174586 173 1 1041 47 185 190644 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 203 1644 4245 6 0 74 0 0 2954 6179 21509 13 0 259 5 15 25151 3926 7670 3 0 69 1 4 9877 52789 15673 35 0 285 43 152 101005 0 0 0 0 0 0 0 969 100350 75882 72 8 573 31 113 119710
Names of X columns:
Y X1 X2 X3 X4 X5 X6 X7
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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