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