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Data X:
1845 162687 95 595 115 0 48 21 82 73 20465 6200 23975 39 37 1797 201906 63 545 76 1 58 20 80 56 33629 10265 85634 46 43 192 7215 18 72 1 0 0 0 0 0 1423 603 1929 0 0 2444 146367 97 679 155 0 67 27 84 63 25629 8874 36294 54 54 3567 257045 139 1201 125 0 83 31 124 116 54002 20323 72255 93 86 6953 528532 268 1975 278 1 137 36 140 138 151036 26258 189748 198 181 1873 191582 60 602 93 1 65 26 100 76 33287 10165 61834 42 42 1740 195674 60 496 59 0 86 30 115 107 31172 8247 68167 59 59 2079 177020 45 670 87 0 62 30 109 50 28113 8683 38462 49 46 3120 330255 100 1047 130 1 72 27 108 81 57803 16957 101219 83 77 1946 121844 75 634 158 2 50 24 63 58 49830 8058 43270 49 49 2370 203938 72 743 120 0 88 30 118 91 52143 20488 76183 83 79 1962 116737 108 692 87 0 62 22 71 41 21055 7945 31476 39 37 3198 220751 120 1086 264 4 79 28 112 100 47007 13448 62157 93 92 1496 173259 65 420 51 4 56 18 63 61 28735 5389 46261 31 31 1574 156326 89 474 85 3 54 22 86 74 59147 6185 50063 29 28 1808 145178 59 442 100 0 81 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10615 43461 55 55 658 48188 28 205 37 0 12 22 82 39 22346 2102 14812 14 14 2013 169355 71 506 52 0 104 23 87 69 30874 12396 37819 44 44 2616 325322 77 830 89 0 86 27 97 93 68701 18717 102738 115 113 2072 241518 80 694 73 0 57 30 107 76 35728 9724 54509 57 55 1912 195583 60 691 49 1 67 33 126 117 29010 9863 62956 48 46 1775 159913 57 547 77 8 44 12 43 31 23110 8374 55411 40 39 1943 223936 71 547 58 0 53 26 96 65 38844 8030 50611 51 51 1047 101694 26 329 75 1 26 26 100 78 27084 7509 26692 32 31 1190 157258 68 427 32 0 67 23 91 87 35139 14146 60056 36 36 2932 211586 101 993 59 10 36 38 136 85 57476 7768 25155 47 47 1868 181076 66 564 71 6 56 32 128 119 33277 13823 42840 51 53 2255 150518 84 836 91 0 52 21 83 65 31141 7230 39358 37 38 1392 141491 64 376 87 11 54 22 74 60 61281 10170 47241 52 52 1355 130108 40 471 48 3 61 26 96 67 25820 7573 49611 42 37 1321 166351 38 431 63 0 27 28 102 94 23284 5753 41833 11 11 1526 124197 43 483 41 0 58 33 122 100 35378 9791 48930 47 45 2335 195043 71 504 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145043 41 524 132 0 57 25 90 61 28263 10226 41029 40 40 1208 95827 48 393 49 0 54 22 87 55 17215 3456 12416 36 36 1866 173924 59 574 71 0 38 26 99 55 48140 8895 51158 64 60 2727 241957 238 672 102 0 63 33 132 124 62897 22557 79935 117 114 1208 115367 115 284 74 0 58 24 96 73 22883 6900 26552 40 39 1427 118689 65 452 49 7 46 24 91 73 41622 8620 25807 46 45 1610 164078 54 653 74 0 46 21 77 67 40715 7820 50620 61 59 1865 158931 42 684 59 5 51 28 104 66 65897 12112 61467 59 59 2413 184139 83 706 91 1 87 28 100 77 76542 13178 65292 94 93 1238 152856 58 417 68 0 39 25 94 83 37477 7028 55516 36 35 1468 146159 61 551 81 0 28 15 60 55 53216 6616 42006 51 47 974 62535 43 394 33 0 26 13 46 27 40911 9570 26273 39 36 2319 245196 117 730 166 0 52 36 135 115 57021 14612 90248 62 59 1890 199841 71 571 97 0 96 27 99 85 73116 11219 61476 79 79 223 19349 12 67 15 0 13 1 2 0 3895 786 9604 14 14 2527 247280 109 877 105 3 43 24 96 83 46609 11252 45108 45 42 2105 164457 86 865 61 0 42 31 109 90 29351 9289 47232 43 41 778 72128 30 306 11 0 30 4 15 4 2325 593 3439 8 8 1194 104253 26 382 45 0 59 21 68 60 31747 6562 30553 41 41 1424 151090 57 435 89 0 73 27 102 74 32665 8208 24751 25 24 1386 147990 68 348 72 1 40 26 93 55 19249 7488 34458 22 22 839 87448 42 227 27 1 36 12 46 24 15292 4574 24649 18 18 596 27676 22 194 59 0 2 16 59 17 5842 522 2342 3 1 1684 170326 52 413 127 0 103 29 116 105 33994 12840 52739 54 53 1168 132148 38 273 48 1 30 26 29 20 13018 1350 6245 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1106 95778 34 343 58 0 46 25 91 51 98177 10623 35381 50 49 1149 109001 68 376 57 0 25 21 76 76 37941 5322 19595 33 33 1485 158833 46 495 60 0 59 24 86 61 31032 7987 50848 54 50 1529 150013 66 448 77 1 60 21 84 70 32683 10566 39443 63 64 962 89887 63 313 71 0 36 21 65 38 34545 1900 27023 56 53 78 3616 5 14 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1184 199005 45 410 70 0 45 23 84 81 27525 10698 61022 49 48 1672 160930 93 606 76 0 79 33 114 78 66856 14884 63528 90 90 2142 177948 102 593 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27749 24 22 1305 88837 38 269 44 0 21 26 89 52 61849 1661 47555 34 34 81 7131 4 27 0 1 0 0 0 0 0 0 0 0 0 262 9056 15 99 18 0 0 4 12 1 2179 548 1336 10 10 1104 88589 29 306 52 1 18 19 60 49 8019 3080 11017 16 16 1290 144470 53 327 56 0 53 22 84 72 39644 13400 55184 93 93 1248 111408 20 459 50 1 17 22 88 56 23494 8181 43485 28 22
Names of X columns:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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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