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Data X:
158258 48 18 63 20465 23975 186930 53 20 56 33629 85634 7215 0 0 0 1423 1929 128162 51 27 63 25629 36294 226974 76 31 116 54002 72255 500344 125 36 138 151036 189748 171007 59 23 71 33287 61834 179835 80 30 107 31172 68167 154581 55 30 50 28113 38462 278960 67 26 79 57803 101219 121844 50 24 58 49830 43270 183086 77 30 91 52143 76183 98796 44 22 41 21055 31476 209322 79 25 91 47007 62157 157125 51 18 61 28735 46261 154565 54 22 74 59147 50063 134198 75 33 131 78950 64483 69128 2 15 45 13497 2341 150680 73 34 110 46154 48149 27997 13 18 41 53249 12743 69919 19 15 37 10726 18743 233044 93 30 84 83700 97057 195820 38 25 67 40400 17675 127994 48 34 69 33797 33106 145433 50 21 58 36205 53311 170864 48 21 60 30165 42754 199655 60 25 88 58534 59056 188633 81 31 75 44663 101621 354266 60 31 98 92556 118120 192399 52 20 67 40078 79572 165753 50 28 84 34711 42744 173721 60 20 58 31076 65931 126739 53 17 35 74608 38575 224762 76 25 74 58092 28795 219428 63 24 89 42009 94440 0 0 0 0 0 0 217267 54 27 75 36022 38229 99706 44 14 39 23333 31972 136733 36 35 101 53349 40071 249965 83 34 135 92596 132480 232951 105 22 76 49598 62797 143755 37 34 118 44093 40429 95734 25 23 76 84205 45545 191416 63 24 65 63369 57568 114820 55 26 97 60132 39019 157625 41 22 67 37403 53866 81293 23 35 63 24460 38345 210040 63 24 96 46456 50210 223771 54 31 112 66616 80947 160344 68 26 75 41554 43461 48188 12 22 39 22346 14812 145235 84 21 63 30874 37819 287839 66 27 93 68701 102738 235223 56 30 76 35728 54509 195583 67 33 117 29010 62956 145942 40 11 30 23110 55411 207309 53 26 65 38844 50611 93764 26 26 78 27084 26692 151985 67 23 87 35139 60056 190545 36 38 85 57476 25155 146414 50 30 111 33277 42840 130794 48 19 60 31141 39358 124234 46 19 53 61281 47241 112718 53 26 67 25820 49611 160817 27 26 90 23284 41833 99070 38 33 100 35378 48930 178653 68 36 135 74990 110600 138708 93 25 71 29653 52235 114408 59 24 75 64622 53986 31970 5 21 42 4157 4105 224494 53 19 42 29245 59331 123328 36 12 8 50008 47796 113504 72 30 86 52338 38302 105932 49 21 41 13310 14063 162203 81 34 118 92901 54414 100098 27 32 91 10956 9903 174768 94 28 102 34241 53987 156752 71 28 89 75043 88937 77269 18 21 46 21152 21928 84971 34 31 60 42249 29487 80522 54 26 69 42005 35334 276525 44 29 95 41152 57596 62974 26 23 17 14399 29750 120296 44 25 61 28263 41029 75555 35 22 55 17215 12416 157988 32 26 55 48140 51158 223247 55 33 124 62897 79935 115019 58 24 73 22883 26552 99602 44 24 73 41622 25807 151804 39 21 67 40715 50620 146005 49 28 66 65897 61467 163444 72 27 75 76542 65292 151517 39 25 83 37477 55516 133686 28 15 55 53216 42006 58128 24 13 27 40911 26273 234325 49 36 115 57021 90248 195576 96 24 76 73116 61476 19349 13 1 0 3895 9604 213189 32 24 83 46609 45108 151672 41 31 90 29351 47232 59117 24 4 4 2325 3439 71931 52 20 56 31747 30553 126653 57 23 63 32665 24751 113552 28 23 52 19249 34458 85338 36 12 24 15292 24649 27676 2 16 17 5842 2342 138522 80 29 105 33994 52739 122417 29 26 20 13018 6245 0 0 0 0 0 0 87592 46 25 51 98177 35381 107205 25 21 76 37941 19595 144664 51 23 59 31032 50848 136540 59 21 70 32683 39443 71894 36 21 38 34545 27023 3616 0 0 0 0 0 0 0 0 0 0 0 175055 38 23 81 27525 61022 144618 68 33 78 66856 63528 152826 28 28 67 28549 34835 113245 36 23 89 38610 37172 43410 7 1 3 2781 13 175762 70 29 87 41211 62548 93634 30 17 48 22698 31334 117426 59 31 66 41194 20839 60493 3 12 32 32689 5084 19764 10 2 4 5752 9927 164062 46 21 70 26757 53229 128144 34 26 94 22527 29877 154959 54 29 91 44810 37310 11796 1 2 1 0 0 10674 0 0 0 0 0 138547 35 18 39 100674 50067 6836 0 1 0 0 0 154135 48 21 45 57786 47708 5118 5 0 0 0 0 40248 8 4 7 5444 6012 0 0 0 0 0 0 120460 36 25 75 28470 27749 88837 21 26 52 61849 47555 7131 0 0 0 0 0 9056 0 4 1 2179 1336 68916 15 17 49 8019 11017 132697 50 21 69 39644 55184 100681 17 22 56 23494 43485
Names of X columns:
A B C D E F
Endogenous Variable (Column Number)
Categorization
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
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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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