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Data X:
252101 3 92 34 131 124252 25695 147 148 134577 4 58 30 117 98956 19967 126 124 198520 14 62 38 146 98073 14338 108 108 189326 2 108 34 132 106816 34117 145 142 137449 1 55 25 80 41449 9713 68 66 65295 3 8 31 117 76173 10024 49 47 439387 0 134 29 112 177551 39981 171 163 33186 0 1 18 67 22807 1271 5 5 178368 5 64 30 116 126938 30207 106 106 186657 0 77 29 107 61680 18035 88 87 261949 0 86 38 140 72117 21609 145 141 191088 7 93 50 190 79738 19836 93 88 138866 7 44 33 109 57793 9028 60 60 296878 3 106 46 159 91677 21750 145 145 192648 9 63 38 146 64631 10038 95 95 333462 0 160 52 201 106385 30276 144 137 243571 4 104 32 124 161961 34972 179 177 263451 3 86 35 131 112669 19954 102 102 155679 3 93 25 96 114029 28113 157 151 227053 7 119 42 163 124550 18830 170 156 240028 0 107 40 151 105416 37144 140 140 388549 1 86 35 128 72875 17916 133 130 156540 5 50 25 89 81964 16186 74 71 148421 9 92 46 184 104880 19195 120 116 177732 0 123 36 136 76302 29124 134 129 191441 0 81 35 134 96740 29813 108 107 249893 5 93 38 146 93071 20270 132 128 236812 0 113 35 130 78912 26105 125 119 142329 0 52 28 105 35224 9155 66 62 259667 0 113 37 142 90694 18113 130 124 231625 3 112 40 155 125369 40546 143 140 176062 4 44 42 154 80849 10096 150 144 286683 1 123 44 169 104434 32338 152 150 87485 4 38 33 125 65702 2871 28 28 322865 2 111 35 135 108179 36592 190 177 247082 0 77 37 139 63583 4914 73 73 346011 0 92 39 145 95066 30190 115 111 191653 2 74 32 124 62486 18153 101 98 114673 1 33 17 55 31081 12558 41 41 284224 2 105 34 131 94584 32894 147 139 284195 10 108 33 125 87408 24138 107 107 155363 6 66 35 128 68966 16628 103 102 177306 5 69 32 107 88766 26369 84 80 144571 5 62 35 130 57139 14171 68 66 140319 1 50 45 73 90586 8500 52 51 405267 2 91 38 138 109249 11940 70 69 78800 2 20 26 82 33032 7935 21 21 201970 0 101 45 173 96056 19456 155 155 302674 9 129 44 169 146648 21347 165 163 164733 3 93 40 145 80613 24095 124 121 194221 0 89 33 134 87026 26204 121 118 24188 0 8 4 12 5950 2694 7 7 346142 8 80 41 151 131106 20366 161 154 65029 5 21 18 67 32551 3597 21 21 101097 3 30 14 52 31701 5296 35 35 246088 1 86 33 121 91072 29463 125 122 273108 5 116 49 186 159803 35838 157 152 282220 5 106 32 120 143950 42590 256 255 275505 0 127 37 135 112368 38665 192 177 214872 12 75 32 123 82124 19442 86 83 335121 9 138 41 158 144068 25515 164 164 267171 11 114 25 90 162627 51318 213 202 189637 9 55 42 165 55062 11807 80 77 229512 8 67 35 135 95329 24130 122 118 209798 2 45 33 125 105612 34053 122 123 201345 0 88 28 110 62853 22641 113 109 163833 6 67 31 121 125976 18898 128 126 204250 8 75 40 151 79146 24539 117 114 197813 2 114 32 123 108461 21664 162 161 132955 5 123 25 92 99971 21577 87 85 216092 13 86 42 162 77826 16643 103 101 73566 6 22 23 88 22618 3007 26 25 213198 7 67 42 163 84892 18798 104 102 181713 2 77 38 133 92059 24648 127 126 148698 0 105 34 132 77993 20286 132 130 300103 4 119 38 144 104155 23999 112 112 251437 3 88 32 124 109840 26813 155 150 197295 6 78 37 140 238712 14718 57 54 158163 2 112 34 132 67486 16963 109 106 155529 0 66 33 122 68007 16673 92 90 132672 1 58 25 97 48194 14646 57 55 377205 0 132 40 155 134796 31772 145 139 145905 5 30 26 99 38692 9648 38 38 223701 2 100 40 106 93587 23096 152 148 80953 0 49 8 28 56622 7905 59 58 130805 0 26 27 101 15986 4527 27 27 135082 5 67 32 120 113402 37432 104 104 305270 1 57 33 127 97967 21082 80 75 271806 0 95 50 178 74844 30437 76 73 150949 1 139 37 141 136051 36288 163 157 225805 1 73 33 122 50548 12369 89 87 197389 2 134 34 127 112215 23774 199 186 156583 6 37 28 102 59591 8108 89 88 222599 1 98 32 124 59938 15049 107 107 261601 4 58 32 124 137639 36021 137 131 178489 3 78 32 124 143372 30391 123 123 200657 3 88 31 111 138599 30910 152 149 259244 0 142 35 129 174110 40656 202 201 313075 11 127 58 223 135062 35070 159 145 346933 12 139 27 102 175681 47250 282 273 246440 8 108 45 174 130307 36236 111 111 252444 0 128 37 141 139141 29601 197 195 159965 0 62 32 122 44244 10443 72 69 43287 4 13 19 71 43750 7409 49 49 172239 4 89 22 81 48029 18213 82 82 185198 0 83 35 131 95216 40856 192 193 227681 0 116 36 139 92288 36471 102 102 260464 0 157 36 137 94588 26077 127 124 106288 0 28 23 91 197426 24797 60 59 109632 0 83 36 142 151244 6816 61 61 268905 4 72 36 133 139206 25527 106 102 266805 0 134 42 155 106271 22139 139 138 23623 0 12 1 0 1168 238 11 11 152474 0 106 32 123 71764 24459 114 114 61857 4 23 11 32 25162 3913 31 28 144889 0 83 40 149 45635 9895 132 101 346600 1 126 34 128 101817 25902 210 208 21054 0 4 0 0 855 338 4 4 224051 5 71 27 99 100174 12937 98 93 31414 0 18 8 25 14116 3988 39 39 261043 2 98 35 132 85008 23370 119 114 206108 7 66 44 167 124254 24015 107 104 154984 12 44 40 151 105793 3870 41 40 112933 2 29 28 103 117129 14648 97 94 38214 0 16 8 27 8773 1888 16 16 158671 2 56 35 131 94747 16768 65 64 302148 0 112 47 178 107549 33400 156 154 177918 0 46 46 177 97392 23770 158 145 350552 3 129 42 163 126893 34762 160 150 275578 0 139 48 187 118850 18793 161 156 368746 3 136 49 182 234853 48186 238 229 172464 0 66 35 135 74783 20140 85 84 94381 0 42 32 118 66089 8728 50 49 244295 4 70 36 140 95684 19060 106 101 382487 4 97 42 158 139537 26880 184 179 114525 14 49 35 132 144253 415 15 15 345884 0 113 42 156 153824 38902 155 154 147989 4 55 34 123 63995 17375 90 90 216638 0 100 36 134 84891 31360 133 133 192862 1 80 36 129 61263 15051 136 133 184818 0 29 32 125 106221 16785 109 97 336707 9 95 33 128 113587 15886 118 116 215836 1 114 35 129 113864 28548 222 221 173260 3 41 21 79 37238 2805 16 15 271773 10 128 40 154 119906 34012 162 157 130908 5 142 49 188 135096 19215 108 105 204009 2 88 33 122 151611 34177 153 150 245514 1 147 39 144 144645 32990 181 177 1 9 0 0 0 0 0 0 0 14688 0 4 0 0 6023 2065 5 5 98 0 0 0 0 0 0 0 0 455 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 195765 2 56 33 120 77457 17428 113 111 326038 1 121 42 168 62464 19912 165 165 0 0 0 0 0 0 0 0 0 203 0 0 0 0 0 0 0 0 7199 0 7 0 0 1644 556 6 6 46660 0 12 5 15 6179 2089 13 13 17547 0 0 1 4 3926 2658 3 3 107465 0 37 38 133 42087 1801 33 33 969 0 0 0 0 0 0 0 0 173102 2 47 28 101 87656 16541 67 63
Names of X columns:
Time Shared Blogged Reviewed Feedback_messages Characters Revisions 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)
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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