Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
210907 56 396 3 79 30 112285 120982 56 297 4 58 28 84786 176508 54 559 12 60 38 83123 179321 89 967 2 108 30 101193 123185 40 270 1 49 22 38361 52746 25 143 3 0 26 68504 385534 92 1562 0 121 25 119182 33170 18 109 0 1 18 22807 101645 63 371 0 20 11 17140 149061 44 656 5 43 26 116174 165446 33 511 0 69 25 57635 237213 84 655 0 78 38 66198 173326 88 465 7 86 44 71701 133131 55 525 7 44 30 57793 258873 60 885 3 104 40 80444 180083 66 497 9 63 34 53855 324799 154 1436 0 158 47 97668 230964 53 612 4 102 30 133824 236785 119 865 3 77 31 101481 135473 41 385 0 82 23 99645 202925 61 567 7 115 36 114789 215147 58 639 0 101 36 99052 344297 75 963 1 80 30 67654 153935 33 398 5 50 25 65553 132943 40 410 7 83 39 97500 174724 92 966 0 123 34 69112 174415 100 801 0 73 31 82753 225548 112 892 5 81 31 85323 223632 73 513 0 105 33 72654 124817 40 469 0 47 25 30727 221698 45 683 0 105 33 77873 210767 60 643 3 94 35 117478 170266 62 535 4 44 42 74007 260561 75 625 1 114 43 90183 84853 31 264 4 38 30 61542 294424 77 992 2 107 33 101494 101011 34 238 0 30 13 27570 215641 46 818 0 71 32 55813 325107 99 937 0 84 36 79215 7176 17 70 0 0 0 1423 167542 66 507 2 59 28 55461 106408 30 260 1 33 14 31081 96560 76 503 0 42 17 22996 265769 146 927 2 96 32 83122 269651 67 1269 10 106 30 70106 149112 56 537 6 56 35 60578 175824 107 910 0 57 20 39992 152871 58 532 5 59 28 79892 111665 34 345 4 39 28 49810 116408 61 918 1 34 39 71570 362301 119 1635 2 76 34 100708 78800 42 330 2 20 26 33032 183167 66 557 0 91 39 82875 277965 89 1178 8 115 39 139077 150629 44 740 3 85 33 71595 168809 66 452 0 76 28 72260 24188 24 218 0 8 4 5950 329267 259 764 8 79 39 115762 65029 17 255 5 21 18 32551 101097 64 454 3 30 14 31701 218946 41 866 1 76 29 80670 244052 68 574 5 101 44 143558 341570 168 1276 1 94 21 117105 103597 43 379 1 27 16 23789 233328 132 825 5 92 28 120733 256462 105 798 0 123 35 105195 206161 71 663 12 75 28 73107 311473 112 1069 8 128 38 132068 235800 94 921 8 105 23 149193 177939 82 858 8 55 36 46821 207176 70 711 8 56 32 87011 196553 57 503 2 41 29 95260 174184 53 382 0 72 25 55183 143246 103 464 5 67 27 106671 187559 121 717 8 75 36 73511 187681 62 690 2 114 28 92945 119016 52 462 5 118 23 78664 182192 52 657 12 77 40 70054 73566 32 385 6 22 23 22618 194979 62 577 7 66 40 74011 167488 45 619 2 69 28 83737 143756 46 479 0 105 34 69094 275541 63 817 4 116 33 93133 243199 75 752 3 88 28 95536 182999 88 430 6 73 34 225920 135649 46 451 2 99 30 62133 152299 53 537 0 62 33 61370 120221 37 519 1 53 22 43836 346485 90 1000 0 118 38 106117 145790 63 637 5 30 26 38692 193339 78 465 2 100 35 84651 80953 25 437 0 49 8 56622 122774 45 711 0 24 24 15986 130585 46 299 5 67 29 95364 112611 41 248 0 46 20 26706 286468 144 1162 1 57 29 89691 241066 82 714 0 75 45 67267 148446 91 905 1 135 37 126846 204713 71 649 1 68 33 41140 182079 63 512 2 124 33 102860 140344 53 472 6 33 25 51715 220516 62 905 1 98 32 55801 243060 63 786 4 58 29 111813 162765 32 489 2 68 28 120293 182613 39 479 3 81 28 138599 232138 62 617 0 131 31 161647 265318 117 925 10 110 52 115929 85574 34 351 0 37 21 24266 310839 92 1144 9 130 24 162901 225060 93 669 7 93 41 109825 232317 54 707 0 118 33 129838 144966 144 458 0 39 32 37510 43287 14 214 4 13 19 43750 155754 61 599 4 74 20 40652 164709 109 572 0 81 31 87771 201940 38 897 0 109 31 85872 235454 73 819 0 151 32 89275 220801 75 720 1 51 18 44418 99466 50 273 0 28 23 192565 92661 61 508 1 40 17 35232 133328 55 506 0 56 20 40909 61361 77 451 0 27 12 13294 125930 75 699 4 37 17 32387 100750 72 407 0 83 30 140867 224549 50 465 4 54 31 120662 82316 32 245 4 27 10 21233 102010 53 370 3 28 13 44332 101523 42 316 0 59 22 61056 243511 71 603 0 133 42 101338 22938 10 154 0 12 1 1168 41566 35 229 5 0 9 13497 152474 65 577 0 106 32 65567 61857 25 192 4 23 11 25162 99923 66 617 0 44 25 32334 132487 41 411 0 71 36 40735 317394 86 975 1 116 31 91413 21054 16 146 0 4 0 855 209641 42 705 5 62 24 97068 22648 19 184 0 12 13 44339 31414 19 200 0 18 8 14116 46698 45 274 0 14 13 10288 131698 65 502 0 60 19 65622 91735 35 382 0 7 18 16563 244749 95 964 2 98 33 76643 184510 49 537 7 64 40 110681 79863 37 438 1 29 22 29011 128423 64 369 8 32 38 92696 97839 38 417 2 25 24 94785 38214 34 276 0 16 8 8773 151101 32 514 2 48 35 83209 272458 65 822 0 100 43 93815 172494 52 389 0 46 43 86687 108043 62 466 1 45 14 34553 328107 65 1255 3 129 41 105547 250579 83 694 0 130 38 103487 351067 95 1024 3 136 45 213688 158015 29 400 0 59 31 71220 98866 18 397 0 25 13 23517 85439 33 350 0 32 28 56926 229242 247 719 4 63 31 91721 351619 139 1277 4 95 40 115168 84207 29 356 11 14 30 111194 120445 118 457 0 36 16 51009 324598 110 1402 0 113 37 135777 131069 67 600 4 47 30 51513 204271 42 480 0 92 35 74163 165543 65 595 1 70 32 51633 141722 94 436 0 19 27 75345 116048 64 230 0 50 20 33416 250047 81 651 0 41 18 83305 299775 95 1367 9 91 31 98952 195838 67 564 1 111 31 102372 173260 63 716 3 41 21 37238 254488 83 747 10 120 39 103772 104389 45 467 5 135 41 123969 136084 30 671 0 27 13 27142 199476 70 861 2 87 32 135400 92499 32 319 0 25 18 21399 224330 83 612 1 131 39 130115 135781 31 433 2 45 14 24874 74408 67 434 4 29 7 34988 81240 66 503 0 58 17 45549 14688 10 85 0 4 0 6023 181633 70 564 2 47 30 64466 271856 103 824 1 109 37 54990 7199 5 74 0 7 0 1644 46660 20 259 0 12 5 6179 17547 5 69 0 0 1 3926 133368 36 535 1 37 16 32755 95227 34 239 0 37 32 34777 152601 48 438 2 46 24 73224 98146 40 459 0 15 17 27114 79619 43 426 3 42 11 20760 59194 31 288 6 7 24 37636 139942 42 498 0 54 22 65461 118612 46 454 2 54 12 30080 72880 33 376 0 14 19 24094 65475 18 225 2 16 13 69008 99643 55 555 1 33 17 54968 71965 35 252 1 32 15 46090 77272 59 208 2 21 16 27507 49289 19 130 1 15 24 10672 135131 66 481 0 38 15 34029 108446 60 389 1 22 17 46300 89746 36 565 3 28 18 24760 44296 25 173 0 10 20 18779 77648 47 278 0 31 16 21280 181528 54 609 0 32 16 40662 134019 53 422 0 32 18 28987 124064 40 445 1 43 22 22827 92630 40 387 4 27 8 18513 121848 39 339 0 37 17 30594 52915 14 181 0 20 18 24006 81872 45 245 0 32 16 27913 58981 36 384 7 0 23 42744 53515 28 212 2 5 22 12934 60812 44 399 0 26 13 22574 56375 30 229 7 10 13 41385 65490 22 224 3 27 16 18653 80949 17 203 0 11 16 18472 76302 31 333 0 29 20 30976 104011 55 384 6 25 22 63339 98104 54 636 2 55 17 25568 67989 21 185 0 23 18 33747 30989 14 93 0 5 17 4154 135458 81 581 3 43 12 19474 73504 35 248 0 23 7 35130 63123 43 304 1 34 17 39067 61254 46 344 1 36 14 13310 74914 30 407 0 35 23 65892 31774 23 170 1 0 17 4143 81437 38 312 0 37 14 28579 87186 54 507 0 28 15 51776 50090 20 224 0 16 17 21152 65745 53 340 0 26 21 38084 56653 45 168 0 38 18 27717 158399 39 443 0 23 18 32928 46455 20 204 0 22 17 11342 73624 24 367 0 30 17 19499 38395 31 210 0 16 16 16380 91899 35 335 0 18 15 36874 139526 151 364 0 28 21 48259 52164 52 178 0 32 16 16734 51567 30 206 2 21 14 28207 70551 31 279 0 23 15 30143 84856 29 387 1 29 17 41369 102538 57 490 1 50 15 45833 86678 40 238 0 12 15 29156 85709 44 343 0 21 10 35944 34662 25 232 0 18 6 36278 150580 77 530 0 27 22 45588 99611 35 291 0 41 21 45097 19349 11 67 0 13 1 3895 99373 63 397 1 12 18 28394 86230 44 467 0 21 17 18632 30837 19 178 0 8 4 2325 31706 13 175 0 26 10 25139 89806 42 299 0 27 16 27975 62088 38 154 1 13 16 14483 40151 29 106 0 16 9 13127 27634 20 189 0 2 16 5839 76990 27 194 0 42 17 24069 37460 20 135 0 5 7 3738 54157 19 201 0 37 15 18625 49862 37 207 0 17 14 36341 84337 26 280 0 38 14 24548 64175 42 260 0 37 18 21792 59382 49 227 0 29 12 26263 119308 30 239 0 32 16 23686 76702 49 333 0 35 21 49303 103425 67 428 1 17 19 25659 70344 28 230 0 20 16 28904 43410 19 292 0 7 1 2781 104838 49 350 1 46 16 29236 62215 27 186 0 24 10 19546 69304 30 326 6 40 19 22818 53117 22 155 3 3 12 32689 19764 12 75 1 10 2 5752 86680 31 361 2 37 14 22197 84105 20 261 0 17 17 20055 77945 20 299 0 28 19 25272 89113 39 300 0 19 14 82206 91005 29 450 3 29 11 32073 40248 16 183 1 8 4 5444 64187 27 238 0 10 16 20154 50857 21 165 0 15 20 36944 56613 19 234 1 15 12 8019 62792 35 176 0 28 15 30884 72535 14 329 0 17 16 19540
Names of X columns:
time_in_rfc logins compendium_views_info shared_compendiums blogged_computations compendiums_reviewed totsize
Endogenous Variable (Column Number)
Categorization
Do not include Seasonal Dummies
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
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
Click here to blog (archive) this computation