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Data X:
317 310232.86 0.939 0.923 0.869 14991300 267 1330141.29 0.623 0.843 0.618 7318499.27 204 139390.2 0.784 0.77 0.713 1857769.676 198 62348.45 0.815 0.949 0.832 2445408.065 0 86.75 0.744 0.83 0.723 1118.317379 107 81644.45 0.928 0.953 0.838 3600833.333 89 64768.39 0.87 0.971 0.819 2773032.125 88 48636.07 0.934 0.956 0.808 1116247.397 80 126804.43 0.883 1 0.827 5867154.492 79 21515.75 0.981 0.976 0.837 1379382.222 69 60748.96 0.856 0.976 0.799 2193971.063 1 1180.08 0.747 0.868 0.808 NA 53 9992.34 0.866 0.858 0.732 140029.3445 50 16783.09 0.931 0.958 0.845 836073.6111 49 45415.6 0.858 0.765 0.591 165245.01 42 15460.48 0.834 0.742 0.668 188049.9864 0 314.52 0.663 0.884 0.582 1447.5 39 4252.28 1 0.957 0.783 159705.7488 0 699.85 0.336 0.744 0.568 1732.186153 39 46505.96 0.874 0.969 0.799 1476881.944 34 201103.33 0.663 0.844 0.662 2476652.19 32 2847.23 0.768 0.838 0.598 14439.3314 29 10201.71 0.924 0.91 0.769 217026.5537 27 33759.74 0.927 0.962 0.84 1736050.505 25 9612.63 0.776 0.794 0.702 55132.0804 23 40046.57 0.582 0.586 0.387 33620.68402 22 21959.28 0.831 0.851 0.674 179793.5123 21 5515.57 0.924 0.928 0.836 333616.0149 20 8303.51 0.671 0.8 0.639 63403.65075 20 88013.49 0.237 0.619 0.326 30247.35964 20 38463.69 0.822 0.885 0.739 514496.4568 20 49109.11 0.705 0.517 0.652 408236.7523 19 4486.88 0.778 0.893 0.724 62493.22067 16 9074.06 0.904 0.969 0.842 539681.6641 15 44205.29 0.667 0.847 0.633 333371.9379 15 77804.12 0.583 0.851 0.689 774983.418 0 773.41 0.368 0.648 0.341 610.3726974 14 4600.82 0.839 0.848 0.554 14366.52768 14 112468.85 0.726 0.898 0.7 1153343.069 14 7623.44 0.872 0.983 0.858 659307.9208 13 4676.31 0.985 0.964 0.883 485803.3929 37 11098.42 0.876 0.933 0.572 NA 10 4622.92 0.963 0.955 0.814 217274.9513 9 41343.2 0.806 0.882 0.713 446044.1436 9 7344.85 0.79 0.86 0.663 45819.56102 0 740.53 0.294 0.598 0.451 NA 0 72.81 0.67 0.907 0.626 484.13716 9 2003.14 0.933 0.936 0.79 49539.27111 8 1173108.02 0.45 0.717 0.508 1847976.749 8 1228.69 0.712 0.791 0.782 22483.11587 8 10525.04 0.645 0.86 0.614 45863.8048 8 27865.74 0.711 0.762 0.486 45359.43236 7 9823.82 0.616 0.842 0.629 55611.24562 7 3086.92 0.722 0.765 0.505 8761.426371 6 2217.97 0.873 0.841 0.711 28252.49885 5 34586.18 0.652 0.838 0.621 188681.0992 5 310.43 0.671 0.877 0.779 7787.514 5 5470.31 0.875 0.875 0.759 95994.1479 5 66336.26 0.597 0.854 0.622 345672.2321 5 33398.68 0.475 0.538 0.347 16809.62349 0 1755.46 0.334 0.607 0.365 898.2828659 5 27223.23 0.692 0.858 0.669 316482.1908 4 2966.8 0.76 0.856 0.566 10247.78888 4 10423.49 0.882 0.947 0.832 513661.1111 4 80471.87 0.56 0.84 0.568 229530.5683 4 5255.07 0.877 0.946 0.828 263011.1111 5 107.82 0.779 0.883 0.608 816.0544067 3 7148.78 0.802 0.842 0.678 53514.38073 3 1291.17 0.916 0.865 0.734 22154.72222 0 1565.13 0.302 0.444 0.329 973.4274565 3 242968.34 0.584 0.779 0.518 846832.2829 3 28274.73 0.73 0.855 0.704 287936.9716 2 2029.31 0.693 0.523 0.698 17327.51003 2 1102.68 0.798 0.94 0.79 24689.60245 2 1545.26 0.66 0.674 0.689 17051.61675 2 10749.94 0.861 0.945 0.783 289627.3629 2 13550.44 0.438 0.807 0.534 46900.00026 2 666.73 0.802 0.861 0.665 4495.775882 33 76923.3 0.64 0.836 0.662 NA 2 10735.76 0.739 0.938 0.763 237373.6111 2 840.93 0.623 0.921 1 172981.5884 2 4317.48 0.716 0.778 0.49 7000.325747 2 4701.07 0.751 0.964 0.897 239699.5985 1 29121.29 0.367 0.452 0.38 19176.77204 1 7089.7 0.837 0.99 0.874 248611.8962 1 2543.17 0.577 0.861 0.884 176590.0752 1 31627.43 0.447 0.823 0.535 100221.002 1 25731.78 0.689 0.85 0.781 576824 0 99.48 0.647 0.759 0.494 166.6901846 1 7487.49 0.704 0.75 0.425 6522.200291 0 2986.95 0.721 0.898 0.624 12959.5639 0 13068.16 0.422 0.49 0.557 104331.6133 0 8214.16 0.858 0.96 0.842 417656.1625 0 156118.46 0.415 0.772 0.391 111879.1217 0 285.65 0.747 0.896 0.743 3685 0 3685.08 0.439 0.58 0.14 1545.46166 0 9056.01 0.365 0.569 0.374 7294.865847 14 3545.32 0.883 0.824 0.729 NA 0 9947.42 0.749 0.735 0.53 23948.67062 0 4621.6 0.723 0.878 0.621 18088.23805 0 395.03 0.733 0.915 0.877 16359.79569 0 16241.81 0.187 0.559 0.349 10187.2117 0 9863.12 0.353 0.48 0.186 2325.972144 0 14453.68 0.502 0.68 0.418 12829.54114 0 19294.15 0.52 0.499 0.431 25235.74721 0 508.66 0.425 0.854 0.505 1901.13623 0 4844.93 0.321 0.448 0.28 2194.720004 0 10543.46 0.219 0.466 0.344 9485.741541 0 107.15 0.689 0.773 0.484 310.2875193 0 16746.49 0.797 0.932 0.701 248585.4999 0 4125.92 0.523 0.59 0.49 14425.60679 0 4516.22 0.659 0.936 0.667 40869.76852 0 21058.8 0.304 0.558 0.377 24073.81283 0 69851.29 0.356 0.448 0.147 15653.63404 0 14790.61 0.686 0.877 0.62 65945.43231 0 6052.06 0.637 0.823 0.585 23054.1 0 650.7 0.427 0.49 0.741 19789.8014 0 5792.98 0.271 0.656 0.24 2608.715447 0 875.98 0.786 0.777 0.533 3818.121194 0 24339.84 0.574 0.698 0.396 39199.65605 0 10324.02 0.246 0.538 0.309 5089.487834 0 748.49 0.65 0.787 0.496 2576.731667 0 9648.92 0.406 0.664 0.346 7346.156703 0 20.88 0.89 0.817 0.656 165.5173142 0 7989.41 0.574 0.838 0.507 17426.57443 0 308.91 0.912 0.975 0.814 14026.17228 0 29671.6 0.491 0.774 0.495 115388.469 0 7353.98 0.907 0.972 0.796 242928.7311 0 6407.09 0.71 0.842 0.569 28840.19702 0 5508.63 0.716 0.753 0.432 5918.610958 0 6368.16 0.432 0.749 0.445 8297.664741 0 4125.25 0.695 0.83 0.698 40094.32836 0 1919.55 0.507 0.445 0.403 2426.200017 0 6461.45 0.731 0.864 0.693 42725.40405 0 497.54 0.771 0.946 0.892 59200.83333 0 21281.84 0.497 0.737 0.302 9911.781297 0 15447.5 0.41 0.54 0.289 5621.000678 0 175.81 0.452 0.705 0.413 248.2867782 0 160.92 0.693 0.862 0.632 1259.078382 0 192 0.75 0.827 0.526 640.8769533 0 395.65 0.568 0.897 0.568 2050.135788 0 13796.35 0.27 0.496 0.346 10589.92535 0 406.77 0.797 0.941 0.769 8886.572143 0 88.34 0.747 0.845 0.733 1007.186292 0 3205.06 0.366 0.609 0.419 4075.675053 0 1294.1 0.659 0.842 0.696 11259.8563 0 22417.45 0.222 0.477 0.314 12797.75423 0 2128.47 0.617 0.67 0.591 12300.6989 0 559.2 0.427 0.755 0.413 838.0221049 0 28951.85 0.356 0.77 0.351 18884.49563 0 5604.45 0.525 0.852 0.457 9316.75554 0 15878.27 0.177 0.547 0.266 6016.960988 0 49.9 0.693 0.838 0.684 697.2787078 0 104.22 0.712 0.825 0.628 687.9937926 0 152217.34 0.442 0.503 0.434 243985.8123 0 486.62 0.636 0.798 0.619 NA 0 2967.72 0.539 0.836 0.778 71781.53504 0 184404.79 0.386 0.717 0.464 210216.1979 0 3410.68 0.743 0.885 0.69 26778.1 0 22198.11 0.534 0.881 0.537 NA 0 6064.52 0.335 0.675 0.447 12937.18322 0 6375.83 0.643 0.828 0.552 23836.76972 0 28947.97 0.704 0.852 0.634 176925.3419 0 1154.62 0.371 0.67 0.487 1054 0 99900.18 0.684 0.769 0.508 224753.5798 0 105.63 0.79 0.825 0.535 433.8792945 0 11055.98 0.407 0.559 0.348 6374.877468 0 12323.25 0.385 0.62 0.406 14291.45685 0 5245.69 0.304 0.438 0.286 2242.960927 0 21083.83 0.68 0.867 0.559 59172.1353 0 43939.6 0.247 0.654 0.421 19171.96665 0 1354.05 0.578 0.453 0.545 3977.75436 0 41892.89 0.454 0.603 0.37 23874.16505 0 6587.24 0.473 0.585 0.297 3620.169607 0 4940.92 0.739 0.71 0.615 28061.75439 0 4975.59 0.741 0.892 0.916 360245.075 0 221.55 0.554 0.805 0.527 760.0397903 0 3301.08 0.763 0.899 0.7 46709.79768 0 89571.13 0.503 0.87 0.478 123600.1414 0 23495.36 0.31 0.718 0.444 33757.50332 0 13460.31 0.48 0.458 0.362 19206.04493 0 11651.86 0.566 0.495 0.19 9656.199514
Names of X columns:
TotalPoints POP Education2011 LifeExpectancy2011 GNI/cap2011 GDP
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
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