Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
1844 162687 94 595 115 0 48 21 82 73 20465 6200 23975 39 37 1796 201906 62 545 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 2443 146367 96 679 155 0 67 27 84 63 25629 8874 36294 54 54 3566 257045 138 1201 125 0 83 31 124 116 54002 20323 72255 93 86 6917 524450 265 1967 278 1 136 36 140 138 151036 26258 189748 198 181 1840 188294 58 595 89 1 65 23 88 71 33287 10165 61834 42 42 1739 195674 59 496 59 0 86 30 115 107 31172 8247 68167 59 59 2078 177020 44 670 87 0 62 30 109 50 28113 8683 38462 49 46 3097 325899 98 1039 130 1 71 27 108 81 57803 16957 101219 83 77 1946 121844 75 634 158 2 50 24 63 58 49830 8058 43270 49 49 2369 203938 71 743 120 0 88 30 118 91 52143 20488 76183 83 79 1880 107220 104 681 87 0 50 22 71 41 21055 7945 31476 39 37 3198 220751 120 1086 264 4 79 28 112 100 47007 13448 62157 93 92 1490 172905 62 419 51 4 56 18 63 61 28735 5389 46261 31 31 1573 156326 88 474 85 3 54 22 86 74 59147 6185 50063 29 28 1807 145178 58 442 100 0 81 37 148 147 78950 24369 64483 104 103 1309 89171 61 373 72 5 13 15 54 45 13497 70 2341 2 2 2820 172624 88 899 147 0 74 34 134 110 46154 17327 48149 46 48 756 32443 25 235 49 0 14 18 57 41 53249 3878 12743 27 25 1162 87927 62 399 40 0 31 15 59 37 10726 3149 18743 16 16 2817 241285 102 850 99 0 99 30 113 84 83700 20517 97057 108 106 1760 195820 72 642 127 1 38 25 96 67 40400 2570 17675 36 35 2315 146946 56 717 164 1 59 34 96 69 33797 5162 33106 33 33 1994 159763 89 619 41 1 54 21 78 58 36205 5299 53311 46 45 1805 207078 33 657 160 0 63 21 80 60 30165 7233 42754 65 64 2152 212394 166 691 92 0 66 25 93 88 58534 15657 59056 80 73 1457 201536 95 366 59 0 90 31 109 75 44663 15329 101621 81 78 3000 394662 121 994 89 0 72 31 115 98 92556 14881 118120 69 63 2234 217808 44 929 90 0 61 20 79 67 40078 16318 79572 69 69 1684 182286 44 490 76 0 61 28 103 84 34711 9556 42744 37 36 1625 181740 46 553 116 2 61 22 71 62 31076 10462 65931 45 41 2256 137978 106 738 92 4 53 17 66 35 74608 7192 38575 62 59 3373 255929 130 1028 361 0 118 25 100 74 58092 4362 28795 33 33 2571 236489 55 844 85 1 73 25 100 93 42009 14349 94440 77 76 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2142 230761 64 1000 63 0 54 31 121 87 36022 10881 38229 34 27 1848 130843 53 619 138 3 53 14 51 39 23333 8022 31972 44 44 2190 157118 49 532 270 9 46 35 119 101 53349 13073 40071 43 43 2186 253254 68 811 64 0 83 34 136 135 92596 26641 132480 117 104 2532 269329 71 837 96 2 106 22 84 76 49598 14426 62797 125 120 1823 161273 61 682 62 0 44 34 136 118 44093 15604 40429 49 44 1095 107181 33 400 35 2 27 23 84 76 84205 9184 45545 76 71 2162 195891 79 804 59 1 64 24 92 65 63369 5989 57568 81 78 1365 139667 51 419 56 2 71 26 103 97 60132 11270 39019 111 106 1244 171101 98 334 41 2 44 23 85 70 37403 13958 53866 61 61 755 81407 32 216 49 1 23 35 106 63 24460 7162 38345 56 53 2417 247563 104 786 121 0 78 24 96 96 46456 13275 50210 54 51 2327 239807 90 752 113 1 60 31 124 112 66616 21224 80947 47 46 2786 172743 59 964 190 8 73 30 106 82 41554 10615 43461 55 55 658 48188 28 205 37 0 12 22 82 39 22346 2102 14812 14 14 2012 169355 70 506 52 0 104 23 87 69 30874 12396 37819 44 44 2602 315622 76 830 89 0 83 27 97 93 68701 18717 102738 115 113 2071 241518 79 694 73 0 57 30 107 76 35728 9724 54509 57 55 1911 195583 59 691 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 1918 220241 69 538 58 0 53 26 96 65 38844 8030 50611 51 51 1046 101694 25 329 75 1 26 26 100 78 27084 7509 26692 32 31 1178 151985 67 421 32 0 67 23 91 87 35139 14146 60056 36 36 2890 202536 99 972 59 10 36 38 136 85 57476 7768 25155 47 47 1836 173505 64 541 71 6 56 32 128 119 33277 13823 42840 51 53 2254 150518 83 836 91 0 52 21 83 65 31141 7230 39358 37 38 1389 141273 61 376 87 11 54 22 74 60 61281 10170 47241 52 52 1325 125612 38 467 48 3 57 26 96 67 25820 7573 49611 42 37 1317 166049 36 430 63 0 27 28 102 94 23284 5753 41833 11 11 1525 124197 42 483 41 0 58 33 122 100 35378 9791 48930 47 45 2335 195043 71 504 86 8 76 36 144 135 74990 19365 110600 59 59 2897 138708 65 887 152 2 93 25 90 71 29653 9422 52235 82 82 1118 116552 40 271 49 0 59 25 97 78 64622 12310 53986 49 49 340 31970 15 101 40 0 5 21 78 42 4157 1283 4105 6 6 2969 255587 114 1093 135 3 56 19 72 42 29245 6372 59331 83 81 1449 151184 78 469 83 1 42 12 45 8 50008 5413 47796 56 56 1550 135926 68 528 62 2 88 30 120 86 52338 10837 38302 114 105 1684 119629 72 475 91 1 53 21 59 41 13310 3394 14063 46 46 2728 171518 71 698 95 0 81 39 150 131 92901 12964 54414 46 46 1574 108949 45 425 82 2 35 32 117 91 10956 3495 9903 2 2 2413 183471 60 709 112 1 102 28 123 102 34241 11580 53987 51 51 2563 159966 98 824 70 0 71 29 114 91 75043 9970 88937 96 95 1079 93786 34 336 78 0 28 21 75 46 21152 4911 21928 20 18 1234 84971 71 395 105 0 34 31 114 60 42249 10138 29487 57 55 966 88797 75 228 49 0 54 26 94 69 42005 14697 35334 49 48 2246 304603 65 830 60 0 49 29 116 95 41152 8464 57596 51 48 1075 75101 29 334 49 1 30 23 86 17 14399 4204 29750 40 39 1637 145043 40 524 132 0 57 25 90 61 28263 10226 41029 40 40 1207 95827 47 393 49 0 54 22 87 55 17215 3456 12416 36 36 1865 173924 58 574 71 0 38 26 99 55 48140 8895 51158 64 60 2726 241957 237 672 102 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 1419 118408 64 450 49 7 46 24 91 73 41622 8620 25807 46 45 1609 164078 53 653 74 0 46 21 77 67 40715 7820 50620 61 59 1864 158931 41 684 59 5 51 28 104 66 65897 12112 61467 59 59 2412 184139 82 706 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 1462 144014 59 549 81 0 28 15 60 55 53216 6616 42006 51 47 973 62535 42 394 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 2526 247280 108 877 105 3 43 24 96 83 46609 11252 45108 45 42 2072 159408 83 856 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 1193 104253 25 382 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 1327 137382 65 336 67 1 39 23 84 52 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 1671 165507 50 410 127 0 102 29 116 105 33994 12840 52739 54 53 1167 132148 37 273 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 1105 95778 33 343 58 0 46 25 91 51 98177 10623 35381 50 49 1148 109001 67 376 57 0 25 21 76 76 37941 5322 19595 33 33 1484 158833 45 495 60 0 59 24 86 61 31032 7987 50848 54 50 1526 147690 63 448 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 1671 160930 92 606 76 0 79 33 114 78 66856 14884 63528 90 90 2142 177948 102 593 124 2 30 32 132 76 28549 6852 34835 51 46 1015 136061 39 312 56 0 43 23 92 89 38610 6873 37172 29 29 778 43410 19 292 63 0 7 1 3 3 2781 4 13 1 1 1856 184277 74 547 92 1 80 29 109 87 41211 9188 62548 68 64 1056 108858 43 302 58 0 32 20 81 55 22698 5141 31334 29 29 2234 141744 58 632 64 8 81 33 121 73 41194 4260 20839 27 27 731 60493 40 174 29 3 3 12 48 32 32689 443 5084 4 4 285 19764 12 75 19 1 10 2 8 4 5752 2416 9927 10 10 1872 177559 56 572 64 3 47 21 80 70 26757 9831 53229 47 47 1181 140281 35 389 79 0 35 28 107 102 22527 5953 29877 44 44 1725 164249 54 562 104 0 54 35 140 109 44810 9435 37310 53 51 256 11796 9 79 22 0 1 2 8 1 0 0 0 0 0 98 10674 9 33 7 0 0 0 0 0 0 0 0 0 0 1435 151322 59 487 37 0 46 18 56 39 100674 7642 50067 40 38 41 6836 3 11 5 0 0 1 4 0 0 0 0 0 0 1930 174712 67 664 48 6 51 21 70 45 57786 6837 47708 57 57 42 5118 3 6 1 0 5 0 0 0 0 0 0 0 0 528 40248 16 183 34 1 8 4 14 7 5444 775 6012 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1121 127628 50 342 53 0 38 29 104 86 28470 8191 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 1099 87957 26 305 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:
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10 Var11 Var12 Var13 Var14 Var15
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation