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Data X:
1418 210907 56 396 869 120982 56 297 1530 176508 54 559 2172 179321 89 967 901 123185 40 270 463 52746 25 143 3201 385534 92 1562 371 33170 18 109 1192 101645 63 371 1583 149061 44 656 1439 165446 33 511 1764 237213 84 655 1495 173326 88 465 1373 133131 55 525 2187 258873 60 885 1491 180083 66 497 4041 324799 154 1436 1706 230964 53 612 2152 236785 119 865 1036 135473 41 385 1882 202925 61 567 1929 215147 58 639 2242 344297 75 963 1220 153935 33 398 1289 132943 40 410 2515 174724 92 966 2147 174415 100 801 2352 225548 112 892 1638 223632 73 513 1222 124817 40 469 1812 221698 45 683 1677 210767 60 643 1579 170266 62 535 1731 260561 75 625 807 84853 31 264 2452 294424 77 992 829 101011 34 238 1940 215641 46 818 2662 325107 99 937 186 7176 17 70 1499 167542 66 507 865 106408 30 260 1793 96560 76 503 2527 265769 146 927 2747 269651 67 1269 1324 149112 56 537 2702 175824 107 910 1383 152871 58 532 1179 111665 34 345 2099 116408 61 918 4308 362301 119 1635 918 78800 42 330 1831 183167 66 557 3373 277965 89 1178 1713 150629 44 740 1438 168809 66 452 496 24188 24 218 2253 329267 259 764 744 65029 17 255 1161 101097 64 454 2352 218946 41 866 2144 244052 68 574 4691 341570 168 1276 1112 103597 43 379 2694 233328 132 825 1973 256462 105 798 1769 206161 71 663 3148 311473 112 1069 2474 235800 94 921 2084 177939 82 858 1954 207176 70 711 1226 196553 57 503 1389 174184 53 382 1496 143246 103 464 2269 187559 121 717 1833 187681 62 690 1268 119016 52 462 1943 182192 52 657 893 73566 32 385 1762 194979 62 577 1403 167488 45 619 1425 143756 46 479 1857 275541 63 817 1840 243199 75 752 1502 182999 88 430 1441 135649 46 451 1420 152299 53 537 1416 120221 37 519 2970 346485 90 1000 1317 145790 63 637 1644 193339 78 465 870 80953 25 437 1654 122774 45 711 1054 130585 46 299 937 112611 41 248 3004 286468 144 1162 2008 241066 82 714 2547 148446 91 905 1885 204713 71 649 1626 182079 63 512 1468 140344 53 472 2445 220516 62 905 1964 243060 63 786 1381 162765 32 489 1369 182613 39 479 1659 232138 62 617 2888 265318 117 925 1290 85574 34 351 2845 310839 92 1144 1982 225060 93 669 1904 232317 54 707 1391 144966 144 458 602 43287 14 214 1743 155754 61 599 1559 164709 109 572 2014 201940 38 897 2143 235454 73 819 2146 220801 75 720 874 99466 50 273 1590 92661 61 508 1590 133328 55 506 1210 61361 77 451 2072 125930 75 699 1281 100750 72 407 1401 224549 50 465 834 82316 32 245 1105 102010 53 370 1272 101523 42 316 1944 243511 71 603 391 22938 10 154 761 41566 35 229 1605 152474 65 577 530 61857 25 192 1988 99923 66 617 1386 132487 41 411 2395 317394 86 975 387 21054 16 146 1742 209641 42 705 620 22648 19 184 449 31414 19 200 800 46698 45 274 1684 131698 65 502 1050 91735 35 382 2699 244749 95 964 1606 184510 49 537 1502 79863 37 438 1204 128423 64 369 1138 97839 38 417 568 38214 34 276 1459 151101 32 514 2158 272458 65 822 1111 172494 52 389 1421 108043 62 466 2833 328107 65 1255 1955 250579 83 694 2922 351067 95 1024 1002 158015 29 400 1060 98866 18 397 956 85439 33 350 2186 229242 247 719 3604 351619 139 1277 1035 84207 29 356 1417 120445 118 457 3261 324598 110 1402 1587 131069 67 600 1424 204271 42 480 1701 165543 65 595 1249 141722 94 436 946 116048 64 230 1926 250047 81 651 3352 299775 95 1367 1641 195838 67 564 2035 173260 63 716 2312 254488 83 747 1369 104389 45 467 1577 136084 30 671 2201 199476 70 861 961 92499 32 319 1900 224330 83 612 1254 135781 31 433 1335 74408 67 434 1597 81240 66 503 207 14688 10 85 1645 181633 70 564 2429 271856 103 824 151 7199 5 74 474 46660 20 259 141 17547 5 69 1639 133368 36 535 872 95227 34 239 1318 152601 48 438 1018 98146 40 459 1383 79619 43 426 1314 59194 31 288 1335 139942 42 498 1403 118612 46 454 910 72880 33 376 616 65475 18 225 1407 99643 55 555 771 71965 35 252 766 77272 59 208 473 49289 19 130 1376 135131 66 481 1232 108446 60 389 1521 89746 36 565 572 44296 25 173 1059 77648 47 278 1544 181528 54 609 1230 134019 53 422 1206 124064 40 445 1205 92630 40 387 1255 121848 39 339 613 52915 14 181 721 81872 45 245 1109 58981 36 384 740 53515 28 212 1126 60812 44 399 728 56375 30 229 689 65490 22 224 592 80949 17 203 995 76302 31 333 1613 104011 55 384 2048 98104 54 636 705 67989 21 185 301 30989 14 93 1803 135458 81 581 799 73504 35 248 861 63123 43 304 1186 61254 46 344 1451 74914 30 407 628 31774 23 170 1161 81437 38 312 1463 87186 54 507 742 50090 20 224 979 65745 53 340 675 56653 45 168 1241 158399 39 443 676 46455 20 204 1049 73624 24 367 620 38395 31 210 1081 91899 35 335 1688 139526 151 364 736 52164 52 178 617 51567 30 206 812 70551 31 279 1051 84856 29 387 1656 102538 57 490 705 86678 40 238 945 85709 44 343 554 34662 25 232 1597 150580 77 530 982 99611 35 291 222 19349 11 67 1212 99373 63 397 1143 86230 44 467 435 30837 19 178 532 31706 13 175 882 89806 42 299 608 62088 38 154 459 40151 29 106 578 27634 20 189 826 76990 27 194 509 37460 20 135 717 54157 19 201 637 49862 37 207 857 84337 26 280 830 64175 42 260 652 59382 49 227 707 119308 30 239 954 76702 49 333 1461 103425 67 428 672 70344 28 230 778 43410 19 292 1141 104838 49 350 680 62215 27 186 1090 69304 30 326 616 53117 22 155 285 19764 12 75 1145 86680 31 361 733 84105 20 261 888 77945 20 299 849 89113 39 300 1182 91005 29 450 528 40248 16 183 642 64187 27 238 947 50857 21 165 819 56613 19 234 757 62792 35 176 894 72535 14 329
Names of X columns:
pageviews time logins Compviewinfo
Endogenous Variable (Column Number)
Categorization
none
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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