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Data X:
210907 56 396 79 30 112285 120982 56 297 58 28 84786 176508 54 559 60 38 83123 179321 89 967 108 30 101193 123185 40 270 49 22 38361 52746 25 143 0 26 68504 385534 92 1562 121 25 119182 33170 18 109 1 18 22807 101645 63 371 20 11 17140 149061 44 656 43 26 116174 165446 33 511 69 25 57635 237213 84 655 78 38 66198 173326 88 465 86 44 71701 133131 55 525 44 30 57793 258873 60 885 104 40 80444 180083 66 497 63 34 53855 324799 154 1436 158 47 97668 230964 53 612 102 30 133824 236785 119 865 77 31 101481 135473 41 385 82 23 99645 202925 61 567 115 36 114789 215147 58 639 101 36 99052 344297 75 963 80 30 67654 153935 33 398 50 25 65553 132943 40 410 83 39 97500 174724 92 966 123 34 69112 174415 100 801 73 31 82753 225548 112 892 81 31 85323 223632 73 513 105 33 72654 124817 40 469 47 25 30727 221698 45 683 105 33 77873 210767 60 643 94 35 117478 170266 62 535 44 42 74007 260561 75 625 114 43 90183 84853 31 264 38 30 61542 294424 77 992 107 33 101494 101011 34 238 30 13 27570 215641 46 818 71 32 55813 325107 99 937 84 36 79215 7176 17 70 0 0 1423 167542 66 507 59 28 55461 106408 30 260 33 14 31081 96560 76 503 42 17 22996 265769 146 927 96 32 83122 269651 67 1269 106 30 70106 149112 56 537 56 35 60578 175824 107 910 57 20 39992 152871 58 532 59 28 79892 111665 34 345 39 28 49810 116408 61 918 34 39 71570 362301 119 1635 76 34 100708 78800 42 330 20 26 33032 183167 66 557 91 39 82875 277965 89 1178 115 39 139077 150629 44 740 85 33 71595 168809 66 452 76 28 72260 24188 24 218 8 4 5950 329267 259 764 79 39 115762 65029 17 255 21 18 32551 101097 64 454 30 14 31701 218946 41 866 76 29 80670 244052 68 574 101 44 143558 341570 168 1276 94 21 117105 103597 43 379 27 16 23789 233328 132 825 92 28 120733 256462 105 798 123 35 105195 206161 71 663 75 28 73107 311473 112 1069 128 38 132068 235800 94 921 105 23 149193 177939 82 858 55 36 46821 207176 70 711 56 32 87011 196553 57 503 41 29 95260 174184 53 382 72 25 55183 143246 103 464 67 27 106671 187559 121 717 75 36 73511 187681 62 690 114 28 92945 119016 52 462 118 23 78664 182192 52 657 77 40 70054 73566 32 385 22 23 22618 194979 62 577 66 40 74011 167488 45 619 69 28 83737 143756 46 479 105 34 69094 275541 63 817 116 33 93133 243199 75 752 88 28 95536 182999 88 430 73 34 225920 135649 46 451 99 30 62133 152299 53 537 62 33 61370 120221 37 519 53 22 43836 346485 90 1000 118 38 106117 145790 63 637 30 26 38692 193339 78 465 100 35 84651 80953 25 437 49 8 56622 122774 45 711 24 24 15986 130585 46 299 67 29 95364 112611 41 248 46 20 26706 286468 144 1162 57 29 89691 241066 82 714 75 45 67267 148446 91 905 135 37 126846 204713 71 649 68 33 41140 182079 63 512 124 33 102860 140344 53 472 33 25 51715 220516 62 905 98 32 55801 243060 63 786 58 29 111813 162765 32 489 68 28 120293 182613 39 479 81 28 138599 232138 62 617 131 31 161647 265318 117 925 110 52 115929 85574 34 351 37 21 24266 310839 92 1144 130 24 162901 225060 93 669 93 41 109825 232317 54 707 118 33 129838 144966 144 458 39 32 37510 43287 14 214 13 19 43750 155754 61 599 74 20 40652 164709 109 572 81 31 87771 201940 38 897 109 31 85872 235454 73 819 151 32 89275 220801 75 720 51 18 44418 99466 50 273 28 23 192565 92661 61 508 40 17 35232 133328 55 506 56 20 40909 61361 77 451 27 12 13294 125930 75 699 37 17 32387 100750 72 407 83 30 140867 224549 50 465 54 31 120662 82316 32 245 27 10 21233 102010 53 370 28 13 44332 101523 42 316 59 22 61056 243511 71 603 133 42 101338 22938 10 154 12 1 1168 41566 35 229 0 9 13497 152474 65 577 106 32 65567 61857 25 192 23 11 25162 99923 66 617 44 25 32334 132487 41 411 71 36 40735 317394 86 975 116 31 91413 21054 16 146 4 0 855 209641 42 705 62 24 97068 22648 19 184 12 13 44339 31414 19 200 18 8 14116 46698 45 274 14 13 10288 131698 65 502 60 19 65622 91735 35 382 7 18 16563 244749 95 964 98 33 76643 184510 49 537 64 40 110681 79863 37 438 29 22 29011 128423 64 369 32 38 92696 97839 38 417 25 24 94785 38214 34 276 16 8 8773 151101 32 514 48 35 83209 272458 65 822 100 43 93815 172494 52 389 46 43 86687 108043 62 466 45 14 34553 328107 65 1255 129 41 105547 250579 83 694 130 38 103487 351067 95 1024 136 45 213688 158015 29 400 59 31 71220 98866 18 397 25 13 23517 85439 33 350 32 28 56926 229242 247 719 63 31 91721 351619 139 1277 95 40 115168 84207 29 356 14 30 111194 120445 118 457 36 16 51009 324598 110 1402 113 37 135777 131069 67 600 47 30 51513 204271 42 480 92 35 74163 165543 65 595 70 32 51633 141722 94 436 19 27 75345 116048 64 230 50 20 33416 250047 81 651 41 18 83305 299775 95 1367 91 31 98952 195838 67 564 111 31 102372 173260 63 716 41 21 37238 254488 83 747 120 39 103772 104389 45 467 135 41 123969 136084 30 671 27 13 27142 199476 70 861 87 32 135400 92499 32 319 25 18 21399 224330 83 612 131 39 130115 135781 31 433 45 14 24874 74408 67 434 29 7 34988 81240 66 503 58 17 45549 14688 10 85 4 0 6023 181633 70 564 47 30 64466 271856 103 824 109 37 54990 7199 5 74 7 0 1644 46660 20 259 12 5 6179 17547 5 69 0 1 3926 133368 36 535 37 16 32755 95227 34 239 37 32 34777 152601 48 438 46 24 73224 98146 40 459 15 17 27114 79619 43 426 42 11 20760 59194 31 288 7 24 37636 139942 42 498 54 22 65461 118612 46 454 54 12 30080 72880 33 376 14 19 24094 65475 18 225 16 13 69008 99643 55 555 33 17 54968 71965 35 252 32 15 46090 77272 59 208 21 16 27507 49289 19 130 15 24 10672 135131 66 481 38 15 34029 108446 60 389 22 17 46300 89746 36 565 28 18 24760 44296 25 173 10 20 18779 77648 47 278 31 16 21280 181528 54 609 32 16 40662 134019 53 422 32 18 28987 124064 40 445 43 22 22827 92630 40 387 27 8 18513 121848 39 339 37 17 30594 52915 14 181 20 18 24006 81872 45 245 32 16 27913 58981 36 384 0 23 42744 53515 28 212 5 22 12934 60812 44 399 26 13 22574 56375 30 229 10 13 41385 65490 22 224 27 16 18653 80949 17 203 11 16 18472 76302 31 333 29 20 30976 104011 55 384 25 22 63339 98104 54 636 55 17 25568 67989 21 185 23 18 33747 30989 14 93 5 17 4154 135458 81 581 43 12 19474 73504 35 248 23 7 35130 63123 43 304 34 17 39067 61254 46 344 36 14 13310 74914 30 407 35 23 65892 31774 23 170 0 17 4143 81437 38 312 37 14 28579 87186 54 507 28 15 51776 50090 20 224 16 17 21152 65745 53 340 26 21 38084 56653 45 168 38 18 27717 158399 39 443 23 18 32928 46455 20 204 22 17 11342 73624 24 367 30 17 19499 38395 31 210 16 16 16380 91899 35 335 18 15 36874 139526 151 364 28 21 48259 52164 52 178 32 16 16734 51567 30 206 21 14 28207 70551 31 279 23 15 30143 84856 29 387 29 17 41369 102538 57 490 50 15 45833 86678 40 238 12 15 29156 85709 44 343 21 10 35944 34662 25 232 18 6 36278 150580 77 530 27 22 45588 99611 35 291 41 21 45097 19349 11 67 13 1 3895 99373 63 397 12 18 28394 86230 44 467 21 17 18632 30837 19 178 8 4 2325 31706 13 175 26 10 25139 89806 42 299 27 16 27975 62088 38 154 13 16 14483 40151 29 106 16 9 13127 27634 20 189 2 16 5839 76990 27 194 42 17 24069 37460 20 135 5 7 3738 54157 19 201 37 15 18625 49862 37 207 17 14 36341 84337 26 280 38 14 24548 64175 42 260 37 18 21792 59382 49 227 29 12 26263 119308 30 239 32 16 23686 76702 49 333 35 21 49303 103425 67 428 17 19 25659 70344 28 230 20 16 28904 43410 19 292 7 1 2781 104838 49 350 46 16 29236 62215 27 186 24 10 19546 69304 30 326 40 19 22818 53117 22 155 3 12 32689 19764 12 75 10 2 5752 86680 31 361 37 14 22197 84105 20 261 17 17 20055 77945 20 299 28 19 25272 89113 39 300 19 14 82206 91005 29 450 29 11 32073 40248 16 183 8 4 5444 64187 27 238 10 16 20154 50857 21 165 15 20 36944 56613 19 234 15 12 8019 62792 35 176 28 15 30884 72535 14 329 17 16 19540
Names of X columns:
time_in_rfc logins compendium_views_info blogged_computations compendiums_reviewed totsize
Endogenous Variable (Column Number)
Categorization
none
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
no
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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