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Data X:
210907 112285 30 79 4 2 120982 84786 28 58 2 3 176508 83123 38 60 4 2 179321 101193 30 108 NA NA 123185 38361 22 49 NA NA 52746 68504 26 0 2 3 385534 119182 25 121 3 4 33170 22807 18 1 NA NA 101645 17140 11 20 NA NA 149061 116174 26 43 4 4 165446 57635 25 69 NA NA 237213 66198 38 78 3 2 173326 71701 44 86 NA NA 133131 57793 30 44 4 2 258873 80444 40 104 NA NA 180083 53855 34 63 NA NA 324799 97668 47 158 4 2 230964 133824 30 102 4 4 236785 101481 31 77 2 2 135473 99645 23 82 NA NA 202925 114789 36 115 3 2 215147 99052 36 101 1 2 344297 67654 30 80 4 3 153935 65553 25 50 NA NA 132943 97500 39 83 NA NA 174724 69112 34 123 5 2 174415 82753 31 73 2 1 225548 85323 31 81 NA NA 223632 72654 33 105 4 2 124817 30727 25 47 4 2 221698 77873 33 105 3 1 210767 117478 35 94 NA NA 170266 74007 42 44 4 4 260561 90183 43 114 3 3 84853 61542 30 38 NA NA 294424 101494 33 107 1 2 101011 27570 13 30 4 4 215641 55813 32 71 NA NA 325107 79215 36 84 2 3 7176 1423 0 0 NA NA 167542 55461 28 59 NA NA 106408 31081 14 33 5 4 96560 22996 17 42 4 3 265769 83122 32 96 3 1 269651 70106 30 106 NA NA 149112 60578 35 56 4 1 175824 39992 20 57 2 4 152871 79892 28 59 3 4 111665 49810 28 39 NA NA 116408 71570 39 34 NA NA 362301 100708 34 76 4 2 78800 33032 26 20 NA NA 183167 82875 39 91 4 3 277965 139077 39 115 1 2 150629 71595 33 85 3 3 168809 72260 28 76 4 2 24188 5950 4 8 NA NA 329267 115762 39 79 NA NA 65029 32551 18 21 NA NA 101097 31701 14 30 NA NA 218946 80670 29 76 2 5 244052 143558 44 101 4 4 341570 117105 21 94 4 4 103597 23789 16 27 2 2 233328 120733 28 92 NA NA 256462 105195 35 123 NA NA 206161 73107 28 75 NA NA 311473 132068 38 128 3 4 235800 149193 23 105 NA NA 177939 46821 36 55 NA NA 207176 87011 32 56 NA NA 196553 95260 29 41 2 2 174184 55183 25 72 NA NA 143246 106671 27 67 4 1 187559 73511 36 75 4 1 187681 92945 28 114 4 4 119016 78664 23 118 3 1 182192 70054 40 77 NA NA 73566 22618 23 22 NA NA 194979 74011 40 66 NA NA 167488 83737 28 69 4 1 143756 69094 34 105 4 2 275541 93133 33 116 NA NA 243199 95536 28 88 1 4 182999 225920 34 73 2 4 135649 62133 30 99 4 1 152299 61370 33 62 3 2 120221 43836 22 53 4 4 346485 106117 38 118 5 4 145790 38692 26 30 NA NA 193339 84651 35 100 2 4 80953 56622 8 49 NA NA 122774 15986 24 24 3 2 130585 95364 29 67 5 2 112611 26706 20 46 4 4 286468 89691 29 57 3 3 241066 67267 45 75 2 4 148446 126846 37 135 4 4 204713 41140 33 68 2 3 182079 102860 33 124 2 2 140344 51715 25 33 2 1 220516 55801 32 98 2 2 243060 111813 29 58 5 4 162765 120293 28 68 2 1 182613 138599 28 81 NA NA 232138 161647 31 131 NA NA 265318 115929 52 110 4 4 85574 24266 21 37 2 4 310839 162901 24 130 5 4 225060 109825 41 93 5 4 232317 129838 33 118 NA NA 144966 37510 32 39 NA NA 43287 43750 19 13 NA NA 155754 40652 20 74 NA NA 164709 87771 31 81 2 2 201940 85872 31 109 2 2 235454 89275 32 151 2 3 220801 44418 18 51 3 2 99466 192565 23 28 5 5 92661 35232 17 40 3 5 133328 40909 20 56 2 4 61361 13294 12 27 2 2 125930 32387 17 37 2 2 100750 140867 30 83 1 1 224549 120662 31 54 NA NA 82316 21233 10 27 5 2 102010 44332 13 28 3 2 101523 61056 22 59 4 2 243511 101338 42 133 2 4 22938 1168 1 12 NA NA 41566 13497 9 0 NA NA 152474 65567 32 106 4 3 61857 25162 11 23 NA NA 99923 32334 25 44 4 1 132487 40735 36 71 2 1 317394 91413 31 116 4 2 21054 855 0 4 NA NA 209641 97068 24 62 NA NA 22648 44339 13 12 3 2 31414 14116 8 18 NA NA 46698 10288 13 14 4 4 131698 65622 19 60 4 3 91735 16563 18 7 2 2 244749 76643 33 98 2 2 184510 110681 40 64 NA NA 79863 29011 22 29 4 4 128423 92696 38 32 3 4 97839 94785 24 25 5 5 38214 8773 8 16 NA NA 151101 83209 35 48 NA NA 272458 93815 43 100 2 2 172494 86687 43 46 4 4 108043 34553 14 45 3 1 328107 105547 41 129 2 3 250579 103487 38 130 4 1 351067 213688 45 136 4 4 158015 71220 31 59 NA NA 98866 23517 13 25 NA NA 85439 56926 28 32 NA NA 229242 91721 31 63 4 1 351619 115168 40 95 NA NA 84207 111194 30 14 4 2 120445 51009 16 36 2 1 324598 135777 37 113 4 4 131069 51513 30 47 4 2 204271 74163 35 92 4 3 165543 51633 32 70 NA NA 141722 75345 27 19 5 2 116048 33416 20 50 NA NA 250047 83305 18 41 3 2 299775 98952 31 91 3 4 195838 102372 31 111 4 4 173260 37238 21 41 4 2 254488 103772 39 120 3 3 104389 123969 41 135 NA NA 136084 27142 13 27 NA NA 199476 135400 32 87 NA NA 92499 21399 18 25 3 3 224330 130115 39 131 4 3 135781 24874 14 45 3 2 74408 34988 7 29 4 3 81240 45549 17 58 5 3 14688 6023 0 4 NA NA 181633 64466 30 47 2 5 271856 54990 37 109 4 4 7199 1644 0 7 NA NA 46660 6179 5 12 NA NA 17547 3926 1 0 NA NA 133368 32755 16 37 2 2 95227 34777 32 37 4 2 152601 73224 24 46 4 2 98146 27114 17 15 3 3 79619 20760 11 42 NA NA 59194 37636 24 7 2 1 139942 65461 22 54 2 2 118612 30080 12 54 2 2 72880 24094 19 14 3 4 65475 69008 13 16 3 2 99643 54968 17 33 4 1 71965 46090 15 32 4 2 77272 27507 16 21 NA NA 49289 10672 24 15 NA NA 135131 34029 15 38 2 4 108446 46300 17 22 1 4 89746 24760 18 28 5 4 44296 18779 20 10 NA NA 77648 21280 16 31 NA NA 181528 40662 16 32 3 2 134019 28987 18 32 4 1 124064 22827 22 43 NA NA 92630 18513 8 27 3 2 121848 30594 17 37 4 5 52915 24006 18 20 NA NA 81872 27913 16 32 2 1 58981 42744 23 0 4 3 53515 12934 22 5 4 4 60812 22574 13 26 3 2 56375 41385 13 10 2 2 65490 18653 16 27 5 5 80949 18472 16 11 NA NA 76302 30976 20 29 5 4 104011 63339 22 25 4 2 98104 25568 17 55 3 2 67989 33747 18 23 NA NA 30989 4154 17 5 NA NA 135458 19474 12 43 2 2 73504 35130 7 23 NA NA 63123 39067 17 34 2 3 61254 13310 14 36 0 3 74914 65892 23 35 NA NA 31774 4143 17 0 4 2 81437 28579 14 37 4 2 87186 51776 15 28 4 4 50090 21152 17 16 NA NA 65745 38084 21 26 2 2 56653 27717 18 38 4 4 158399 32928 18 23 4 4 46455 11342 17 22 NA NA 73624 19499 17 30 4 2 38395 16380 16 16 NA NA 91899 36874 15 18 3 3 139526 48259 21 28 NA NA 52164 16734 16 32 NA NA 51567 28207 14 21 4 2 70551 30143 15 23 NA NA 84856 41369 17 29 NA NA 102538 45833 15 50 2 2 86678 29156 15 12 NA NA 85709 35944 10 21 NA NA 34662 36278 6 18 4 1 150580 45588 22 27 4 4 99611 45097 21 41 2 3 19349 3895 1 13 NA NA 99373 28394 18 12 4 4 86230 18632 17 21 3 3 30837 2325 4 8 5 5 31706 25139 10 26 4 1 89806 27975 16 27 NA NA 62088 14483 16 13 4 4 40151 13127 9 16 NA NA 27634 5839 16 2 NA NA 76990 24069 17 42 NA NA 37460 3738 7 5 NA NA 54157 18625 15 37 4 4 49862 36341 14 17 4 5 84337 24548 14 38 4 2 64175 21792 18 37 NA NA 59382 26263 12 29 4 4 119308 23686 16 32 2 2 76702 49303 21 35 4 3 103425 25659 19 17 NA NA 70344 28904 16 20 NA NA 43410 2781 1 7 NA NA 104838 29236 16 46 3 2 62215 19546 10 24 NA NA 69304 22818 19 40 4 1 53117 32689 12 3 3 3 19764 5752 2 10 NA NA 86680 22197 14 37 4 2 84105 20055 17 17 3 3 77945 25272 19 28 4 2 89113 82206 14 19 NA NA 91005 32073 11 29 NA NA 40248 5444 4 8 NA NA 64187 20154 16 10 2 4 50857 36944 20 15 NA NA 56613 8019 12 15 NA NA 62792 30884 15 28 NA NA 72535 19540 16 17 NA NA
Names of X columns:
TimeRFC TotSize CompRV CompBlog Q1_1 Q1_9
Endogenous Variable (Column Number)
Categorization
equal
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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