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Data X:
56 396 81 3 79 30 115 56 297 55 4 58 28 109 54 559 50 12 60 38 146 89 967 125 2 108 30 116 40 270 40 1 49 22 68 25 143 37 3 0 26 101 92 1562 63 0 121 25 96 18 109 44 0 1 18 67 63 371 88 0 20 11 44 44 656 66 5 43 26 100 33 511 57 0 69 25 93 84 655 74 0 78 38 140 88 465 49 7 86 44 166 55 525 52 7 44 30 99 60 885 88 3 104 40 139 66 497 36 9 63 34 130 154 1436 108 0 158 47 181 53 612 43 4 102 30 116 119 865 75 3 77 31 116 41 385 32 0 82 23 88 61 567 44 7 115 36 139 58 639 85 0 101 36 135 75 963 86 1 80 30 108 33 398 56 5 50 25 89 40 410 50 7 83 39 156 92 966 135 0 123 34 129 100 801 63 0 73 31 118 112 892 81 5 81 31 118 73 513 52 0 105 33 125 40 469 44 0 47 25 95 45 683 113 0 105 33 126 60 643 39 3 94 35 135 62 535 73 4 44 42 154 75 625 48 1 114 43 165 31 264 33 4 38 30 113 77 992 59 2 107 33 127 34 238 41 0 30 13 52 46 818 69 0 71 32 121 99 937 64 0 84 36 136 17 70 1 0 0 0 0 66 507 59 2 59 28 108 30 260 32 1 33 14 46 76 503 129 0 42 17 54 146 927 37 2 96 32 124 67 1269 31 10 106 30 115 56 537 65 6 56 35 128 107 910 107 0 57 20 80 58 532 74 5 59 28 97 34 345 54 4 39 28 104 61 918 76 1 34 39 59 119 1635 715 2 76 34 125 42 330 57 2 20 26 82 66 557 66 0 91 39 149 89 1178 106 8 115 39 149 44 740 54 3 85 33 122 66 452 32 0 76 28 118 24 218 20 0 8 4 12 259 764 71 8 79 39 144 17 255 21 5 21 18 67 64 454 70 3 30 14 52 41 866 112 1 76 29 108 68 574 66 5 101 44 166 168 1276 190 1 94 21 80 43 379 66 1 27 16 60 132 825 165 5 92 28 107 105 798 56 0 123 35 127 71 663 61 12 75 28 107 112 1069 53 8 128 38 146 94 921 127 8 105 23 84 82 858 63 8 55 36 141 70 711 38 8 56 32 123 57 503 50 2 41 29 111 53 382 52 0 72 25 98 103 464 42 5 67 27 105 121 717 76 8 75 36 135 62 690 67 2 114 28 107 52 462 50 5 118 23 85 52 657 53 12 77 40 155 32 385 39 6 22 23 88 62 577 50 7 66 40 155 45 619 77 2 69 28 104 46 479 57 0 105 34 132 63 817 73 4 116 33 127 75 752 34 3 88 28 108 88 430 39 6 73 34 129 46 451 46 2 99 30 116 53 537 63 0 62 33 122 37 519 35 1 53 22 85 90 1000 106 0 118 38 147 63 637 43 5 30 26 99 78 465 47 2 100 35 87 25 437 31 0 49 8 28 45 711 162 0 24 24 90 46 299 57 5 67 29 109 41 248 36 0 46 20 78 144 1162 263 1 57 29 111 82 714 78 0 75 45 158 91 905 63 1 135 37 141 71 649 54 1 68 33 122 63 512 63 2 124 33 124 53 472 77 6 33 25 93 62 905 79 1 98 32 124 63 786 110 4 58 29 112 32 489 56 2 68 28 108 39 479 56 3 81 28 99 62 617 43 0 131 31 117 117 925 111 10 110 52 199 34 351 71 0 37 21 78 92 1144 62 9 130 24 91 93 669 56 7 93 41 158 54 707 74 0 118 33 126 144 458 60 0 39 32 122 14 214 43 4 13 19 71 61 599 68 4 74 20 75 109 572 53 0 81 31 115 38 897 87 0 109 31 119 73 819 46 0 151 32 124 75 720 105 1 51 18 72 50 273 32 0 28 23 91 61 508 133 1 40 17 45 55 506 79 0 56 20 78 77 451 51 0 27 12 39 75 699 207 4 37 17 68 72 407 67 0 83 30 119 50 465 47 4 54 31 117 32 245 34 4 27 10 39 53 370 66 3 28 13 50 42 316 76 0 59 22 88 71 603 65 0 133 42 155 10 154 9 0 12 1 0 35 229 42 5 0 9 36 65 577 45 0 106 32 123 25 192 25 4 23 11 32 66 617 115 0 44 25 99 41 411 97 0 71 36 136 86 975 53 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7 28 66 503 89 0 58 17 68 10 85 0 0 4 0 0 70 564 48 2 47 30 110 103 824 91 1 109 37 147 5 74 0 0 7 0 0 20 259 7 0 12 5 15 5 69 3 0 0 1 4 36 535 54 1 37 16 64 34 239 70 0 37 32 111 48 438 36 2 46 24 85 40 459 37 0 15 17 68 43 426 123 3 42 11 40 31 288 247 6 7 24 80 42 498 46 0 54 22 88 46 454 72 2 54 12 48 33 376 41 0 14 19 76 18 225 24 2 16 13 51 55 555 45 1 33 17 67 35 252 33 1 32 15 59 59 208 27 2 21 16 61 19 130 36 1 15 24 76 66 481 87 0 38 15 60 60 389 90 1 22 17 68 36 565 114 3 28 18 71 25 173 31 0 10 20 76 47 278 45 0 31 16 62 54 609 69 0 32 16 61 53 422 51 0 32 18 67 40 445 34 1 43 22 88 40 387 60 4 27 8 30 39 339 45 0 37 17 64 14 181 54 0 20 18 68 45 245 25 0 32 16 64 36 384 38 7 0 23 91 28 212 52 2 5 22 88 44 399 67 0 26 13 52 30 229 74 7 10 13 49 22 224 38 3 27 16 62 17 203 30 0 11 16 61 31 333 26 0 29 20 76 55 384 67 6 25 22 88 54 636 132 2 55 17 66 21 185 42 0 23 18 71 14 93 35 0 5 17 68 81 581 118 3 43 12 48 35 248 68 0 23 7 25 43 304 43 1 34 17 68 46 344 76 1 36 14 41 30 407 64 0 35 23 90 23 170 48 1 0 17 66 38 312 64 0 37 14 54 54 507 56 0 28 15 59 20 224 71 0 16 17 60 53 340 75 0 26 21 77 45 168 39 0 38 18 68 39 443 42 0 23 18 72 20 204 39 0 22 17 67 24 367 93 0 30 17 64 31 210 38 0 16 16 63 35 335 60 0 18 15 59 151 364 71 0 28 21 84 52 178 52 0 32 16 64 30 206 27 2 21 14 56 31 279 59 0 23 15 54 29 387 40 1 29 17 67 57 490 79 1 50 15 58 40 238 44 0 12 15 59 44 343 65 0 21 10 40 25 232 10 0 18 6 22 77 530 124 0 27 22 83 35 291 81 0 41 21 81 11 67 15 0 13 1 2 63 397 92 1 12 18 72 44 467 42 0 21 17 61 19 178 10 0 8 4 15 13 175 24 0 26 10 32 42 299 64 0 27 16 62 38 154 45 1 13 16 58 29 106 22 0 16 9 36 20 189 56 0 2 16 59 27 194 94 0 42 17 68 20 135 19 0 5 7 21 19 201 35 0 37 15 55 37 207 32 0 17 14 54 26 280 35 0 38 14 55 42 260 48 0 37 18 72 49 227 49 0 29 12 41 30 239 48 0 32 16 61 49 333 62 0 35 21 67 67 428 96 1 17 19 76 28 230 45 0 20 16 64 19 292 63 0 7 1 3 49 350 71 1 46 16 63 27 186 26 0 24 10 40 30 326 48 6 40 19 69 22 155 29 3 3 12 48 12 75 19 1 10 2 8 31 361 45 2 37 14 52 20 261 45 0 17 17 66 20 299 67 0 28 19 76 39 300 30 0 19 14 43 29 450 36 3 29 11 39 16 183 34 1 8 4 14 27 238 36 0 10 16 61 21 165 34 0 15 20 71 19 234 37 1 15 12 44 35 176 46 0 28 15 60 14 329 44 0 17 16 64
Names of X columns:
logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1
Endogenous Variable (Column Number)
Categorization
Do not include Seasonal Dummies
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
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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