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Data X:
317 310232.86 41.761 0.939 0.923 0.869 267 1330141.29 6.2 0.623 0.843 0.618 204 139390.2 13.611 0.784 0.77 0.713 198 62348.45 32.147 0.815 0.949 0.832 107 81644.45 32.255 0.928 0.953 0.838 89 64768.39 29.578 0.87 0.971 0.819 88 48636.07 25.493 0.934 0.956 0.808 80 126804.43 29.692 0.883 1.000 0.827 79 21515.75 34.259 0.981 0.976 0.837 69 60748.96 26.578 0.856 0.976 0.799 53 9992.34 16.896 0.866 0.858 0.732 50 16783.09 36.358 0.931 0.958 0.845 49 45415.6 5.737 0.858 0.765 0.591 42 15460.48 10.452 0.834 0.742 0.668 39 4252.28 24.706 1.000 0.957 0.783 39 46505.96 27.066 0.874 0.969 0.799 34 201103.33 9.414 0.663 0.844 0.662 33 76923.3 10.496 0.64 0.836 0.662 32 2847.23 6.931 0.768 0.838 0.598 29 10201.71 22.098 0.924 0.91 0.769 27 33759.74 34.567 0.927 0.962 0.84 25 9612.63 11.841 0.776 0.794 0.702 23 40046.57 1.428 0.582 0.586 0.387 22 21959.28 10.794 0.831 0.851 0.674 21 5515.57 32.252 0.924 0.928 0.836 20 8303.51 8.752 0.671 0.8 0.639 20 88013.49 848 0.237 0.619 0.326 20 38463.69 16.705 0.822 0.885 0.739 20 49109.11 9.333 0.705 0.517 0.652 19 4486.88 16.338 0.778 0.893 0.724 16 9074.06 32.314 0.904 0.969 0.842 15 44205.29 8.136 0.667 0.847 0.633 15 77804.12 11.209 0.583 0.851 0.689 14 4600.82 4.335 0.839 0.848 0.554 14 3545.32 15.011 0.883 0.824 0.729 14 112468.85 12.429 0.726 0.898 0.7 14 7623.44 36.954 0.872 0.983 0.858 13 4676.31 47.676 0.985 0.964 0.883 10 4622.92 36.278 0.963 0.955 0.814 9 41343.2 13.202 0.806 0.882 0.713 9 7344.85 9.967 0.79 0.86 0.663 9 2003.14 24.806 0.933 0.936 0.79 8 1173108.02 2.993 0.45 0.717 0.508 8 1228.69 23.221 0.712 0.791 0.782 8 10525.04 7.512 0.645 0.86 0.614 8 27865.74 2.611 0.711 0.762 0.486 7 9823.82 7.658 0.616 0.842 0.629 7 3086.92 3.198 0.722 0.765 0.505 6 2217.97 12.847 0.873 0.841 0.711 5 34586.18 7.421 0.652 0.838 0.621 5 107.82 7.593 0.779 0.883 0.608 5 5470.31 19.202 0.875 0.875 0.759 5 66336.26 7.26 0.597 0.854 0.622 5 33398.68 1.105 0.475 0.538 0.347 5 27223.23 11.19 0.692 0.858 0.669 4 2966.8 4.794 0.76 0.856 0.566 4 10423.49 32.395 0.882 0.947 0.832 4 80471.87 5.151 0.56 0.84 0.568 4 5255.07 30.784 0.877 0.946 0.828 3 7148.78 11.456 0.802 0.842 0.678 3 1291.17 16.132 0.916 0.865 0.734 3 242968.34 3.813 0.584 0.779 0.518 3 28274.73 12.724 0.73 0.855 0.704 2 2029.31 12.154 0.693 0.523 0.698 2 1102.68 25.759 0.798 0.94 0.79 2 1545.26 13.094 0.66 0.674 0.689 2 10749.94 26.482 0.861 0.945 0.783 2 13550.44 4.286 0.438 0.807 0.534 2 666.73 10.022 0.802 0.861 0.665 2 10735.76 21.37 0.739 0.938 0.763 2 840.93 82.978 0.623 0.921 1.000 2 4317.48 2.592 0.716 0.778 0.49 2 4701.07 45.978 0.751 0.964 0.897 1 29121.29 1.20 0.367 0.452 0.38 1 7089.7 39.255 0.837 0.99 0.874 1 31627.43 4.081 0.447 0.823 0.535 1 25731.78 21.321 0.689 0.85 0.781 1 7487.49 1.791 0.704 0.75 0.425 0 2986.95 7.449 0.721 0.898 0.624 0 13068.16 5.278 0.422 0.49 0.557 0 86.75 17.052 0.744 0.83 0.723 0 8214.16 34.673 0.858 0.96 0.842 0 156118.46 1.286 0.415 0.772 0.391 0 314.52 6.019 0.663 0.884 0.582 0 9056.01 1.369 0.365 0.569 0.374 0 699.85 4.643 0.336 0.744 0.568 0 9947.42 4.013 0.749 0.735 0.53 0 4621.6 7.266 0.723 0.878 0.621 0 16241.81 1.078 0.187 0.559 0.349 0 9863.12 356 0.353 0.48 0.186 0 14453.68 1.739 0.502 0.68 0.418 0 19294.15 2.002 0.52 0.499 0.431 0 508.66 3.309 0.425 0.854 0.505 0 4844.93 688 0.321 0.448 0.28 0 10543.46 1.181 0.219 0.466 0.344 0 16746.49 13.057 0.797 0.932 0.701 0 773.41 1.074 0.368 0.648 0.341 0 4125.92 3.848 0.523 0.59 0.49 0 4516.22 10.085 0.659 0.936 0.667 0 21058.8 1.545 0.304 0.558 0.377 0 740.53 2.106 0.294 0.598 0.451 0 72.81 8.066 0.67 0.907 0.626 0 69851.29 290 0.356 0.448 0.147 0 14790.61 7.508 0.686 0.877 0.62 0 6052.06 6.02 0.637 0.823 0.585 0 650.7 28.857 0.427 0.49 0.741 0 5792.98 527 0.271 0.656 0.24 0 875.98 4.11 0.786 0.777 0.533 0 1755.46 1.285 0.334 0.607 0.365 0 24339.84 1.41 0.574 0.698 0.396 0 10324.02 951 0.246 0.538 0.309 0 1565.13 973 0.302 0.444 0.329 0 748.49 2.942 0.65 0.787 0.496 0 9648.92 1.045 0.406 0.664 0.346 0 7989.41 3.488 0.574 0.838 0.507 0 308.91 33.98 0.912 0.975 0.814 0 29671.6 3.222 0.491 0.774 0.495 0 7353.98 25.474 0.907 0.972 0.796 0 6407.09 5.082 0.71 0.842 0.569 0 99.48 2.209 0.647 0.759 0.494 0 5508.63 2.073 0.716 0.753 0.432 0 6368.16 2.048 0.432 0.749 0.445 0 4125.25 11.868 0.695 0.83 0.698 0 1919.55 1.333 0.507 0.445 0.403 0 3685.08 360 0.439 0.58 0.14 0 6461.45 14.985 0.731 0.864 0.693 0 497.54 68.853 0.771 0.946 0.892 0 21281.84 912 0.497 0.737 0.302 0 15447.5 721 0.41 0.54 0.289 0 395.65 4.972 0.568 0.897 0.568 0 13796.35 1.077 0.27 0.496 0.346 0 406.77 21.987 0.797 0.941 0.769 0 3205.06 1.751 0.366 0.609 0.419 0 1294.1 11.658 0.659 0.842 0.696 0 107.15 2.804 0.689 0.773 0.484 0 22417.45 804 0.222 0.477 0.314 0 2128.47 5.821 0.617 0.67 0.591 0 28951.85 1.049 0.356 0.77 0.351 0 5604.45 2.398 0.525 0.852 0.457 0 15878.27 626 0.177 0.547 0.266 0 152217.34 2.001 0.442 0.503 0.434 0 184404.79 2.369 0.386 0.717 0.464 0 3410.68 11.857 0.743 0.885 0.69 0 6064.52 2.072 0.335 0.675 0.447 0 6375.83 4.107 0.643 0.828 0.552 0 28947.97 7.836 0.704 0.852 0.634 0 99900.18 3.216 0.684 0.769 0.508 0 11055.98 1.032 0.407 0.559 0.348 0 175.81 1.653 0.452 0.705 0.413 0 160.92 8.722 0.693 0.862 0.632 0 192 4 0.75 0.827 0.526 0 12323.25 1.65 0.385 0.62 0.406 0 88.34 17.786 0.747 0.845 0.733 0 5245.69 734 0.304 0.438 0.286 0 559.2 2.312 0.427 0.755 0.413 0 21083.83 4.333 0.68 0.867 0.559 0 49.9 13.191 0.693 0.838 0.684 0 104.22 8.312 0.712 0.825 0.628 0 43939.6 2.007 0.247 0.654 0.421 0 1354.05 4.539 0.578 0.453 0.545 0 22198.11 4.295 0.534 0.881 0.537 0 41892.89 1.237 0.454 0.603 0.37 0 1154.62 731 0.371 0.67 0.487 0 6587.24 772 0.473 0.585 0.297 0 105.63 4.055 0.79 0.825 0.535 0 4940.92 6.576 0.739 0.71 0.615 0 4975.59 52.435 0.741 0.892 0.916 0 3301.08 11.977 0.763 0.899 0.7 0 221.55 4.03 0.554 0.805 0.527 0 89571.13 2.682 0.503 0.87 0.478 0 23495.36 2.243 0.31 0.718 0.444 0 13460.31 1.299 0.48 0.458 0.362
Names of X columns:
Gold POP GDP/cap Education LifeExpectancy GNI/cap
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
par4 <- 'no' par3 <- '3' par2 <- 'none' par1 <- '1' 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') }
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Raw Input
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Raw Output
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Big Analytics Cloud Computing Center
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