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Data X:
1418 210907 56 81 79 30 869 120982 56 55 58 28 1530 176508 54 50 60 38 2172 179321 89 125 108 30 901 123185 40 40 49 22 463 52746 25 37 0 26 3201 385534 92 63 121 25 371 33170 18 44 1 18 1192 101645 63 88 20 11 1583 149061 44 66 43 26 1439 165446 33 57 69 25 1764 237213 84 74 78 38 1495 173326 88 49 86 44 1373 133131 55 52 44 30 2187 258873 60 88 104 40 1491 180083 66 36 63 34 4041 324799 154 108 158 47 1706 230964 53 43 102 30 2152 236785 119 75 77 31 1036 135473 41 32 82 23 1882 202925 61 44 115 36 1929 215147 58 85 101 36 2242 344297 75 86 80 30 1220 153935 33 56 50 25 1289 132943 40 50 83 39 2515 174724 92 135 123 34 2147 174415 100 63 73 31 2352 225548 112 81 81 31 1638 223632 73 52 105 33 1222 124817 40 44 47 25 1812 221698 45 113 105 33 1677 210767 60 39 94 35 1579 170266 62 73 44 42 1731 260561 75 48 114 43 807 84853 31 33 38 30 2452 294424 77 59 107 33 829 101011 34 41 30 13 1940 215641 46 69 71 32 2662 325107 99 64 84 36 186 7176 17 1 0 0 1499 167542 66 59 59 28 865 106408 30 32 33 14 1793 96560 76 129 42 17 2527 265769 146 37 96 32 2747 269651 67 31 106 30 1324 149112 56 65 56 35 2702 175824 107 107 57 20 1383 152871 58 74 59 28 1179 111665 34 54 39 28 2099 116408 61 76 34 39 4308 362301 119 715 76 34 918 78800 42 57 20 26 1831 183167 66 66 91 39 3373 277965 89 106 115 39 1713 150629 44 54 85 33 1438 168809 66 32 76 28 496 24188 24 20 8 4 2253 329267 259 71 79 39 744 65029 17 21 21 18 1161 101097 64 70 30 14 2352 218946 41 112 76 29 2144 244052 68 66 101 44 4691 341570 168 190 94 21 1112 103597 43 66 27 16 2694 233328 132 165 92 28 1973 256462 105 56 123 35 1769 206161 71 61 75 28 3148 311473 112 53 128 38 2474 235800 94 127 105 23 2084 177939 82 63 55 36 1954 207176 70 38 56 32 1226 196553 57 50 41 29 1389 174184 53 52 72 25 1496 143246 103 42 67 27 2269 187559 121 76 75 36 1833 187681 62 67 114 28 1268 119016 52 50 118 23 1943 182192 52 53 77 40 893 73566 32 39 22 23 1762 194979 62 50 66 40 1403 167488 45 77 69 28 1425 143756 46 57 105 34 1857 275541 63 73 116 33 1840 243199 75 34 88 28 1502 182999 88 39 73 34 1441 135649 46 46 99 30 1420 152299 53 63 62 33 1416 120221 37 35 53 22 2970 346485 90 106 118 38 1317 145790 63 43 30 26 1644 193339 78 47 100 35 870 80953 25 31 49 8 1654 122774 45 162 24 24 1054 130585 46 57 67 29 937 112611 41 36 46 20 3004 286468 144 263 57 29 2008 241066 82 78 75 45 2547 148446 91 63 135 37 1885 204713 71 54 68 33 1626 182079 63 63 124 33 1468 140344 53 77 33 25 2445 220516 62 79 98 32 1964 243060 63 110 58 29 1381 162765 32 56 68 28 1369 182613 39 56 81 28 1659 232138 62 43 131 31 2888 265318 117 111 110 52 1290 85574 34 71 37 21 2845 310839 92 62 130 24 1982 225060 93 56 93 41 1904 232317 54 74 118 33 1391 144966 144 60 39 32 602 43287 14 43 13 19 1743 155754 61 68 74 20 1559 164709 109 53 81 31 2014 201940 38 87 109 31 2143 235454 73 46 151 32 2146 220801 75 105 51 18 874 99466 50 32 28 23 1590 92661 61 133 40 17 1590 133328 55 79 56 20 1210 61361 77 51 27 12 2072 125930 75 207 37 17 1281 100750 72 67 83 30 1401 224549 50 47 54 31 834 82316 32 34 27 10 1105 102010 53 66 28 13 1272 101523 42 76 59 22 1944 243511 71 65 133 42 391 22938 10 9 12 1 761 41566 35 42 0 9 1605 152474 65 45 106 32 530 61857 25 25 23 11 1988 99923 66 115 44 25 1386 132487 41 97 71 36 2395 317394 86 53 116 31 387 21054 16 2 4 0 1742 209641 42 52 62 24 620 22648 19 44 12 13 449 31414 19 22 18 8 800 46698 45 35 14 13 1684 131698 65 74 60 19 1050 91735 35 103 7 18 2699 244749 95 144 98 33 1606 184510 49 60 64 40 1502 79863 37 134 29 22 1204 128423 64 89 32 38 1138 97839 38 42 25 24 568 38214 34 52 16 8 1459 151101 32 98 48 35 2158 272458 65 99 100 43 1111 172494 52 52 46 43 1421 108043 62 29 45 14 2833 328107 65 125 129 41 1955 250579 83 106 130 38 2922 351067 95 95 136 45 1002 158015 29 40 59 31 1060 98866 18 140 25 13 956 85439 33 43 32 28 2186 229242 247 128 63 31 3604 351619 139 142 95 40 1035 84207 29 73 14 30 1417 120445 118 72 36 16 3261 324598 110 128 113 37 1587 131069 67 61 47 30 1424 204271 42 73 92 35 1701 165543 65 148 70 32 1249 141722 94 64 19 27 946 116048 64 45 50 20 1926 250047 81 58 41 18 3352 299775 95 97 91 31 1641 195838 67 50 111 31 2035 173260 63 37 41 21 2312 254488 83 50 120 39 1369 104389 45 105 135 41 1577 136084 30 69 27 13 2201 199476 70 46 87 32 961 92499 32 57 25 18 1900 224330 83 52 131 39 1254 135781 31 98 45 14 1335 74408 67 61 29 7 1597 81240 66 89 58 17 207 14688 10 0 4 0 1645 181633 70 48 47 30 2429 271856 103 91 109 37 151 7199 5 0 7 0 474 46660 20 7 12 5 141 17547 5 3 0 1 1639 133368 36 54 37 16 872 95227 34 70 37 32 1318 152601 48 36 46 24 1018 98146 40 37 15 17 1383 79619 43 123 42 11 1314 59194 31 247 7 24 1335 139942 42 46 54 22 1403 118612 46 72 54 12 910 72880 33 41 14 19 616 65475 18 24 16 13 1407 99643 55 45 33 17 771 71965 35 33 32 15 766 77272 59 27 21 16 473 49289 19 36 15 24 1376 135131 66 87 38 15 1232 108446 60 90 22 17 1521 89746 36 114 28 18 572 44296 25 31 10 20 1059 77648 47 45 31 16 1544 181528 54 69 32 16 1230 134019 53 51 32 18 1206 124064 40 34 43 22 1205 92630 40 60 27 8 1255 121848 39 45 37 17 613 52915 14 54 20 18 721 81872 45 25 32 16 1109 58981 36 38 0 23 740 53515 28 52 5 22 1126 60812 44 67 26 13 728 56375 30 74 10 13 689 65490 22 38 27 16 592 80949 17 30 11 16 995 76302 31 26 29 20 1613 104011 55 67 25 22 2048 98104 54 132 55 17 705 67989 21 42 23 18 301 30989 14 35 5 17 1803 135458 81 118 43 12 799 73504 35 68 23 7 861 63123 43 43 34 17 1186 61254 46 76 36 14 1451 74914 30 64 35 23 628 31774 23 48 0 17 1161 81437 38 64 37 14 1463 87186 54 56 28 15 742 50090 20 71 16 17 979 65745 53 75 26 21 675 56653 45 39 38 18 1241 158399 39 42 23 18 676 46455 20 39 22 17 1049 73624 24 93 30 17 620 38395 31 38 16 16 1081 91899 35 60 18 15 1688 139526 151 71 28 21 736 52164 52 52 32 16 617 51567 30 27 21 14 812 70551 31 59 23 15 1051 84856 29 40 29 17 1656 102538 57 79 50 15 705 86678 40 44 12 15 945 85709 44 65 21 10 554 34662 25 10 18 6 1597 150580 77 124 27 22 982 99611 35 81 41 21 222 19349 11 15 13 1 1212 99373 63 92 12 18 1143 86230 44 42 21 17 435 30837 19 10 8 4 532 31706 13 24 26 10 882 89806 42 64 27 16 608 62088 38 45 13 16 459 40151 29 22 16 9 578 27634 20 56 2 16 826 76990 27 94 42 17 509 37460 20 19 5 7 717 54157 19 35 37 15 637 49862 37 32 17 14 857 84337 26 35 38 14 830 64175 42 48 37 18 652 59382 49 49 29 12 707 119308 30 48 32 16 954 76702 49 62 35 21 1461 103425 67 96 17 19 672 70344 28 45 20 16 778 43410 19 63 7 1 1141 104838 49 71 46 16 680 62215 27 26 24 10 1090 69304 30 48 40 19 616 53117 22 29 3 12 285 19764 12 19 10 2 1145 86680 31 45 37 14 733 84105 20 45 17 17 888 77945 20 67 28 19 849 89113 39 30 19 14 1182 91005 29 36 29 11 528 40248 16 34 8 4 642 64187 27 36 10 16 947 50857 21 34 15 20 819 56613 19 37 15 12 757 62792 35 46 28 15 894 72535 14 44 17 16
Names of X columns:
pageviews RFC logins compendiumviews bloggedcomputations compendiumsreviewed
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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