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Data X:
144244 1685 152043 53 670 47 3 44 29 107 84 17416 88229 111 107 197426 1109 121726 66 356 44 0 28 30 112 83 24797 178377 60 59 86652 1829 204039 75 615 83 6 44 49 182 146 10971 114198 165 159 65594 1808 185890 62 590 86 9 48 31 114 90 8589 92795 96 94 101382 2137 252805 52 866 67 5 81 30 111 77 13326 127097 102 97 76173 631 70849 28 179 46 3 8 35 129 89 10024 47552 49 47 124089 3873 366774 116 1548 117 9 95 39 145 120 16378 130332 125 123 66089 1121 102424 42 401 55 0 42 36 131 100 8728 61394 50 49 22618 893 73566 32 385 39 6 22 23 88 67 3007 23824 26 25 149695 2531 372238 308 833 81 8 85 44 163 157 20867 191179 165 158 56622 870 80953 25 437 31 0 49 8 28 27 7905 55792 59 58 150047 1915 168994 57 720 122 5 147 50 192 179 21166 75767 132 123 151911 3942 334657 116 1342 116 10 142 47 181 165 22938 191889 174 172 25162 530 61857 25 192 25 4 23 11 32 30 3913 24610 31 28 105079 1844 222373 69 673 59 16 70 42 162 106 16346 99776 121 121 69446 1937 220700 82 621 46 10 71 43 162 87 11034 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147 36171 169216 198 194 108146 2314 359644 84 982 95 4 140 45 166 143 25217 100125 121 121 168553 1606 222504 50 552 72 5 94 35 127 127 39932 162519 176 170 144408 1997 207822 57 735 75 7 74 34 132 117 34416 115466 125 124 183500 2091 285198 78 771 61 0 158 39 141 104 43840 211381 213 210 104128 1288 196269 58 445 64 0 50 53 205 171 24959 122975 173 158 33032 918 78800 42 330 57 2 20 26 82 66 7935 56968 21 21 43929 1148 162874 50 348 48 1 61 27 88 64 10621 100792 73 71 56750 2312 251466 89 786 58 1 76 37 138 85 13841 115750 98 96 126372 2645 341637 90 1056 48 3 130 36 140 133 30927 165278 183 178 160141 3842 447353 114 1216 114 0 147 45 175 138 39361 175721 166 157 71571 1787 182231 64 644 67 0 83 37 134 110 17696 75881 103 101 125818 1438 176082 55 507 41 3 103 29 111 85 31219 111669 169 162 38692 1369 145943 69 653 45 5 30 26 99 99 9648 68580 38 38 95893 2175 252529 83 822 77 12 89 36 139 127 23923 81180 103 100 67150 2535 282399 94 897 192 1 110 46 158 124 16786 114651 145 142 110529 2949 384053 114 1162 63 1 135 38 140 102 27738 232241 219 216 59938 2958 261494 72 1061 89 1 128 36 140 135 15049 82390 107 107 81625 2128 237633 114 660 61 7 117 55 210 184 20648 94853 98 93 71154 2250 201783 61 690 58 2 133 38 148 134 18050 80906 112 109 104767 2610 264889 88 931 59 8 92 42 163 142 26584 124527 130 126 125386 2181 236660 88 759 67 1 121 39 145 126 31852 134218 241 239 165933 4060 383703 130 1698 146 0 127 46 172 161 42570 147581 175 173 64520 1714 173510 60 470 85 6 68 42 157 126 16747 54518 73 71 165986 3805 367807 145 1253 123 12 151 58 223 221 43146 189944 208 193 102812 2306 280343 103 656 57 2 117 44 122 92 26759 136323 163 159 81897 1940 191030 60 681 61 1 127 38 148 133 21588 89770 140 137 37110 1495 155915 51 559 53 0 57 32 117 99 9845 64057 70 66 146975 2473 314255 79 947 134 4 73 36 140 132 39038 135599 150 144 92059 1694 187167 52 705 94 2 79 38 133 90 24648 91313 127 126 144551 3085 179797 104 1044 72 1 165 45 171 159 39039 81716 170 164 184923 3705 397681 108 1415 73 13 165 30 114 106 50099 226168 307 297 79756 1250 187992 35 473 49 0 71 40 151 137 21654 122531 90 89 140015 2960 323545 99 955 56 11 145 45 174 136 38086 145758 175 170 89506 3397 311281 113 1211 153 3 106 39 146 137 24347 160501 125 119 64593 1830 157429 76 689 76 4 55 39 143 112 17672 72558 90 90 70168 1840 215710 81 611 67 2 79 36 139 89 19433 104470 112 109 134238 3553 403932 79 1564 134 4 137 48 187 167 37343 191469 171 161 101047 2649 301614 88 1030 55 0 169 40 150 124 28317 135848 139 135 92622 3246 324178 79 1490 42 10 123 39 145 122 26041 134097 123 123 14116 492 31961 22 200 22 0 18 8 25 9 3988 13155 39 39 15986 1966 150216 54 822 175 0 52 27 101 77 4527 25157 27 27 89256 2081 175523 54 868 68 3 99 45 164 146 25314 104864 136 133 150491 3574 323485 179 1079 220 5 115 39 145 137 42721 194679 257 256 140358 2676 287015 134 846 83 8 117 51 198 176 40312 117495 125 125 114948 4763 369889 176 1650 127 0 168 59 223 199 33433 165354 150 142 95671 1816 213060 73 559 48 0 94 40 158 137 28524 160791 128 125 176225 3160 303406 116 1143 146 12 139 28 101 73 53405 214738 279 267 93487 1735 195153 73 635 82 5 75 36 115 108 28395 133252 94 87 89626 2711 237323 143 824 89 9 85 44 167 148 27269 134904 138 133 66485 1758 213274 44 595 72 0 82 33 120 82 20318 110896 93 92 79089 2063 296074 106 776 79 0 92 43 158 139 24409 169351 154 149 55918 1678 153613 46 622 41 1 62 28 109 89 17326 83963 63 61 112302 2090 318563 83 779 53 1 133 48 185 178 34811 198299 161 159 104581 2591 207280 114 960 72 0 86 38 146 139 32580 116136 116 115 117440 2758 353021 81 1010 115 0 137 52 198 187 36809 157384 162 160 101629 3657 422946 119 1317 74 0 112 43 158 148 32344 188355 121 117 112098 2683 218443 104 1137 135 2 134 38 148 133 35926 106194 148 145 68946 3145 366745 142 1047 113 3 130 47 185 115 22124 174586 174 173 114799 1426 228595 66 557 58 2 52 37 139 125 37062 153242 131 132 119442 3091 369331 92 1198 72 4 132 39 151 148 38941 189723 199 185 100087 3027 279012 58 1108 125 1 97 37 134 120 32982 129711 137 133 139165 2272 278019 74 908 49 3 123 45 175 165 46201 184531 155 152 83243 1968 270750 85 617 73 0 117 43 154 148 27652 153990 132 125 123534 1337 156923 57 390 68 6 71 36 132 130 41517 100922 108 108 6179 474 46660 20 259 7 0 12 5 15 13 2089 21509 13 13 1644 151 7199 5 74 0 0 7 0 0 0 556 4245 6 6 6023 207 14688 10 85 0 0 4 0 0 0 2065 7953 5 5 120192 2588 338543 137 1039 63 0 146 42 153 150 41455 197680 207 190 83248 2205 195817 73 779 54 0 146 40 155 103 28830 106020 130 130 103925 3314 336047 189 1174 45 2 124 39 151 143 36524 164808 158 149 72128 1646 216027 65 436 58 0 96 30 116 87 25696 145707 126 121 112431 2471 271965 69 828 99 0 123 45 171 148 40085 140303 148 147 92280 1627 236370 46 528 83 1 104 41 151 135 34245 147341 140 140 83515 3202 219420 114 1196 150 1 138 41 153 144 31452 96785 140 134 48029 2146 185468 80 716 85 4 89 23 84 36 18213 88634 82 82 93879 2616 318651 112 907 91 0 130 57 206 122 36099 170492 92 88 855 387 21054 16 146 2 0 4 0 0 0 338 6622 4 4 100046 2549 259692 47 1140 110 0 128 40 155 125 39844 128602 112 111 31081 934 115469 32 276 36 1 33 17 55 46 12558 58391 41 41 104978 2130 219475 138 749 72 0 92 40 151 88 45873 139292 206 205 5950 496 24188 24 218 20 0 8 4 12 7 2694 15049 7 7 3926 141 17547 5 69 3 0 0 1 4 4 2658 7670 3 3
Names of X columns:
#karakters #PageViews #SecRFC #LogIns #CourseCompViews #CompViewsPR #shared #Blogs #Reviews #FBMinPR #FBMinPR+120 #revisions #seconden #hyperlinks #blogs
Endogenous Variable (Column Number)
Categorization
quantiles
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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