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