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Data X:
1418 210907 79 2 869 120982 58 0 1530 176508 60 0 2172 179321 108 4 901 123185 49 0 463 52746 0 0 3201 385534 121 0 371 33170 1 0 1192 101645 20 0 1583 149061 43 0 1439 165446 69 0 1764 237213 78 0 1495 173326 86 -4 1373 133131 44 4 2187 258873 104 4 1491 180083 63 0 4041 324799 158 0 1706 230964 102 -1 2152 236785 77 0 1036 135473 82 0 1882 202925 115 0 1929 215147 101 0 2242 344297 80 1 1220 153935 50 0 1289 132943 83 0 2515 174724 123 0 2147 174415 73 3 2352 225548 81 0 1638 223632 105 -1 1222 124817 47 0 1812 221698 105 0 1677 210767 94 0 1579 170266 44 0 1731 260561 114 0 807 84853 38 0 2452 294424 107 4 829 101011 30 0 1940 215641 71 0 2662 325107 84 3 186 7176 0 0 1499 167542 59 0 865 106408 33 1 1793 96560 42 0 2527 265769 96 -2 2747 269651 106 -3 1324 149112 56 -4 2702 175824 57 0 1383 152871 59 2 1179 111665 39 0 2099 116408 34 0 4308 362301 76 2 918 78800 20 0 1831 183167 91 -4 3373 277965 115 3 1713 150629 85 0 1438 168809 76 0 496 24188 8 0 2253 329267 79 0 744 65029 21 0 1161 101097 30 0 2352 218946 76 2 2144 244052 101 2 4691 341570 94 0 1112 103597 27 0 2694 233328 92 5 1973 256462 123 0 1769 206161 75 -2 3148 311473 128 0 2474 235800 105 0 2084 177939 55 0 1954 207176 56 -2 1226 196553 41 -3 1389 174184 72 0 1496 143246 67 2 2269 187559 75 0 1833 187681 114 0 1268 119016 118 0 1943 182192 77 2 893 73566 22 0 1762 194979 66 2 1403 167488 69 0 1425 143756 105 4 1857 275541 116 4 1840 243199 88 0 1502 182999 73 0 1441 135649 99 0 1420 152299 62 2 1416 120221 53 0 2970 346485 118 0 1317 145790 30 0 1644 193339 100 2 870 80953 49 0 1654 122774 24 0 1054 130585 67 -4 937 112611 46 3 3004 286468 57 0 2008 241066 75 0 2547 148446 135 3 1885 204713 68 0 1626 182079 124 2 1468 140344 33 0 2445 220516 98 0 1964 243060 58 -1 1381 162765 68 -3 1369 182613 81 0 1659 232138 131 0 2888 265318 110 0 1290 85574 37 0 2845 310839 130 0 1982 225060 93 1 1904 232317 118 0 1391 144966 39 0 602 43287 13 0 1743 155754 74 0 1559 164709 81 0 2014 201940 109 0 2143 235454 151 0 2146 220801 51 0 874 99466 28 0 1590 92661 40 0 1590 133328 56 -3 1210 61361 27 0 2072 125930 37 0 1281 100750 83 3 1401 224549 54 0 834 82316 27 0 1105 102010 28 0 1272 101523 59 0 1944 243511 133 0 391 22938 12 0 761 41566 0 0 1605 152474 106 0 530 61857 23 0 1988 99923 44 0 1386 132487 71 3 2395 317394 116 -3 387 21054 4 0 1742 209641 62 0 620 22648 12 0 449 31414 18 0 800 46698 14 0 1684 131698 60 0 1050 91735 7 0 2699 244749 98 0 1606 184510 64 -4 1502 79863 29 0 1204 128423 32 2 1138 97839 25 -1 568 38214 16 0 1459 151101 48 0 2158 272458 100 0 1111 172494 46 3 1421 108043 45 0 2833 328107 129 0 1955 250579 130 0 2922 351067 136 0 1002 158015 59 0 1060 98866 25 0 956 85439 32 0 2186 229242 63 2 3604 351619 95 5 1035 84207 14 0 1417 120445 36 0 3261 324598 113 2 1587 131069 47 0 1424 204271 92 0 1701 165543 70 0 1249 141722 19 0 946 116048 50 0 1926 250047 41 0 3352 299775 91 0 1641 195838 111 -2 2035 173260 41 0 2312 254488 120 0 1369 104389 135 0 1577 136084 27 0 2201 199476 87 3 961 92499 25 -2 1900 224330 131 0 1254 135781 45 0 1335 74408 29 0 1597 81240 58 0 207 14688 4 0 1645 181633 47 6 2429 271856 109 -3 151 7199 7 0 474 46660 12 0 141 17547 0 0 1639 133368 37 0 872 95227 37 3 1318 152601 46 0 1018 98146 15 0 1383 79619 42 0 1314 59194 7 0 1335 139942 54 0 1403 118612 54 -2 910 72880 14 0 616 65475 16 1 1407 99643 33 0 771 71965 32 0 766 77272 21 0 473 49289 15 0 1376 135131 38 0 1232 108446 22 0 1521 89746 28 0 572 44296 10 0 1059 77648 31 0 1544 181528 32 0 1230 134019 32 0 1206 124064 43 0 1205 92630 27 0 1255 121848 37 2 613 52915 20 0 721 81872 32 0 1109 58981 0 0 740 53515 5 0 1126 60812 26 0 728 56375 10 0 689 65490 27 0 592 80949 11 0 995 76302 29 2 1613 104011 25 0 2048 98104 55 -3 705 67989 23 0 301 30989 5 -2 1803 135458 43 0 799 73504 23 0 861 63123 34 0 1186 61254 36 0 1451 74914 35 0 628 31774 0 1 1161 81437 37 0 1463 87186 28 0 742 50090 16 0 979 65745 26 0 675 56653 38 0 1241 158399 23 0 676 46455 22 0 1049 73624 30 0 620 38395 16 0 1081 91899 18 0 1688 139526 28 0 736 52164 32 0 617 51567 21 0 812 70551 23 0 1051 84856 29 0 1656 102538 50 0 705 86678 12 0 945 85709 21 0 554 34662 18 0 1597 150580 27 -4 982 99611 41 0 222 19349 13 0 1212 99373 12 0 1143 86230 21 0 435 30837 8 0 532 31706 26 0 882 89806 27 0 608 62088 13 0 459 40151 16 0 578 27634 2 0 826 76990 42 0 509 37460 5 0 717 54157 37 0 637 49862 17 0 857 84337 38 0 830 64175 37 0 652 59382 29 1 707 119308 32 0 954 76702 35 0 1461 103425 17 0 672 70344 20 0 778 43410 7 0 1141 104838 46 0 680 62215 24 0 1090 69304 40 0 616 53117 3 0 285 19764 10 0 1145 86680 37 0 733 84105 17 0 888 77945 28 0 849 89113 19 0 1182 91005 29 0 528 40248 8 0 642 64187 10 0 947 50857 15 0 819 56613 15 0 757 62792 28 0 894 72535 17 0
Names of X columns:
page time blog score
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
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Raw Output
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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