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Data X:
1845 162687 95 595 115 0 48 1917 233285 67 580 79 1 75 192 7215 18 72 1 0 0 2665 164587 99 737 158 0 74 3709 283430 141 1255 127 0 92 7138 546996 275 2021 278 1 137 1888 192501 61 606 95 1 65 1909 213538 64 533 64 0 97 2140 182282 46 687 92 0 62 3168 336547 102 1074 130 1 72 1957 122275 77 637 158 2 50 2370 203938 72 743 120 0 88 1998 119300 110 701 87 0 68 3203 220796 122 1087 264 4 79 1505 174005 67 422 51 4 56 1574 156326 89 474 85 3 54 1965 164063 60 483 100 0 101 1314 90025 63 375 72 5 13 2921 179987 90 929 147 0 80 823 47066 29 262 49 0 19 1289 109572 64 437 40 0 33 2818 241285 103 850 99 0 99 1792 208339 77 652 127 1 38 2474 164166 59 754 166 1 68 1994 159763 89 619 41 1 54 1806 207078 34 657 160 0 63 2177 217028 169 695 92 0 66 1458 201536 96 366 59 0 90 3057 408960 124 1015 89 0 75 2487 250260 48 1029 104 0 68 1914 216527 46 576 81 0 69 1825 212949 51 656 116 2 80 2509 164248 110 812 105 4 59 3634 278911 136 1108 388 0 135 2608 238654 59 852 88 1 75 1 0 1 0 0 0 0 2157 233971 66 1009 63 0 54 1978 149649 55 658 138 3 62 2224 161703 52 547 270 9 46 2215 254893 70 826 64 0 83 2538 269492 73 838 96 2 106 1881 169526 62 704 62 0 51 1113 107893 35 404 35 2 27 2380 229714 83 848 66 1 78 1365 139667 51 419 56 2 71 1294 175983 102 349 46 2 44 756 81407 33 216 49 1 23 2465 251259 110 796 121 0 78 2327 239807 90 752 113 1 60 2787 172743 60 964 190 8 73 658 48188 28 205 37 0 12 2013 169355 71 506 52 0 104 2666 335398 78 841 89 0 95 2086 244729 81 699 73 0 57 2067 208286 62 746 61 1 68 1776 159913 58 547 77 8 44 2045 232137 72 561 63 0 62 1047 101694 26 329 75 1 26 1190 157258 68 427 32 0 67 2932 211586 101 993 59 10 36 1868 181076 66 564 71 6 56 2316 158024 86 858 92 0 55 1392 141491 64 376 87 11 54 1355 130108 40 471 48 3 61 1326 166420 39 432 63 0 27 1587 135509 45 500 41 0 64 2336 195043 72 504 86 8 76 2898 138708 66 887 152 2 93 1118 116552 40 271 49 0 59 340 31970 15 101 40 0 5 3224 291993 121 1203 148 3 62 1552 167825 82 506 86 1 47 1551 135926 69 528 62 2 88 1794 136647 77 501 96 1 57 2728 171518 71 698 95 0 81 1580 108980 46 426 83 2 35 2414 183471 61 709 112 1 102 2640 167426 101 847 77 0 73 1203 112510 49 367 78 0 32 1313 92421 77 413 114 0 34 1207 117169 84 272 55 0 80 2246 304603 65 830 60 0 49 1076 75101 30 334 49 1 30 1638 145043 41 524 132 0 57 1208 95827 48 393 49 0 54 1868 173931 60 574 71 0 38 2829 250424 252 695 102 0 63 1209 115367 116 284 74 0 58 1463 125839 66 462 49 7 49 1610 164078 54 653 74 0 46 1865 158931 42 684 59 5 51 2444 190382 85 714 91 1 90 1253 155226 59 420 68 0 45 1468 146159 61 551 81 0 28 979 62641 44 396 33 0 26 2365 258585 121 741 166 0 54 1890 199841 71 571 97 0 96 223 19349 12 67 15 0 13 2527 247280 109 877 105 3 43 2186 173152 88 885 61 0 46 778 72128 30 306 11 0 30 1194 104253 26 382 45 0 59 1424 151090 57 435 89 0 73 1386 147990 68 348 72 1 40 839 87448 42 227 27 1 36 596 27676 22 194 59 0 2 1684 170326 52 413 127 0 103 1168 132148 38 273 48 1 30 0 0 0 0 0 0 0 1315 133868 36 390 58 0 78 1149 109001 68 376 57 0 25 1485 158833 46 495 60 0 59 1529 150013 66 448 77 1 60 962 89887 63 313 71 0 36 78 3616 5 14 5 0 0 0 0 0 0 0 0 0 1295 216479 48 445 78 0 51 1751 177323 102 637 76 0 79 2142 177948 102 593 124 2 30 1070 140106 41 326 67 0 43 778 43410 19 292 63 0 7 1986 206059 76 573 92 1 92 1084 109873 45 315 58 0 32 2400 157084 61 683 65 10 84 731 60493 40 174 29 3 3 285 19764 12 75 19 1 10 1873 177559 57 572 64 3 47 1269 154169 36 414 79 0 44 1725 164249 54 562 104 0 54 256 11796 9 79 22 0 1 98 10674 9 33 7 0 0 1435 151322 59 487 37 0 46 41 6836 3 11 5 0 0 1931 174712 68 664 48 6 51 42 5118 3 6 1 0 5 528 40248 16 183 34 1 8 0 0 0 0 0 0 0 1122 127628 51 342 53 0 38 1305 88837 38 269 44 0 21 81 7131 4 27 0 1 0 262 9056 15 99 18 0 0 1165 97191 31 322 52 1 26 1405 157478 59 367 60 0 53 1409 125583 23 521 50 1 31
Names of X columns:
a b c d e f g
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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