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Data X:
5675 51 50 22 4 0 1 0 2800 36 47 14 5 0 1 0 2750 51 50 29 4 0 1 0 2925 47 40 25 4 0 1 0 2625 27 52 9 2 0 0 0 2825 31 45 9 2 0 0 1 2825 52 38 30 4 0 0 0 5500 43 60 9 5 0 0 0 2750 51 38 29 4 0 1 0 2800 47 41 25 3 0 1 0 2625 60 45 39 3 1 0 0 1750 54 19 32 4 0 0 1 5500 52 38 30 4 0 0 1 4500 61 38 38 2 0 0 0 2300 32 40 8 3 0 1 0 3250 35 50 13 2 0 1 0 2425 36 57 14 2 0 0 1 2750 42 40 20 5 0 1 0 4500 33 50 9 2 0 0 0 3425 46 43 24 6 0 0 1 3125 28 57 5 2 0 0 0 7750 53 40 31 5 0 0 0 2300 28 38 6 1 0 0 0 2750 32 60 9 2 0 0 0 1800 27 43 3 2 0 1 0 2925 28 35 5 2 0 0 0 4000 26 45 3 6 0 0 0 4925 64 52 42 3 0 1 0 2625 28 45 5 2 0 0 0 2425 27 45 4 3 0 0 1 1800 25 33 2 2 0 1 0 2750 26 42 4 4 0 1 0 5500 42 40 19 5 0 0 0 2625 27 40 4 1 0 0 0 2425 29 40 7 2 0 1 0 2425 26 44 4 1 0 0 0 3125 35 38 10 1 0 0 0 4625 60 38 38 5 0 0 0 7000 28 70 5 2 0 0 0 2125 24 40 2 4 0 0 1 2125 26 40 4 3 0 0 0 2925 25 52 2 4 0 0 0 2325 24 40 2 2 0 0 0 3500 24 40 1 1 0 0 0 3325 32 70 8 1 0 0 0 3625 35 50 13 4 0 0 0 2300 25 44 1 4 0 0 0 6500 39 50 17 4 0 0 0 3625 36 38 13 4 0 0 0 2325 28 45 5 2 0 0 0 2425 34 38 11 3 0 0 0 2625 34 40 8 2 0 0 0 4000 31 38 8 2 0 0 0 2125 26 57 4 5 0 0 0 1925 28 40 4 5 0 0 0 7750 33 40 9 2 0 0 0 1925 28 40 5 2 0 0 0 2925 31 40 8 1 0 0 0 2825 29 43 5 2 0 0 0 2625 29 50 7 1 0 0 0 2625 28 38 10 2 0 0 0 2925 27 38 4 3 0 0 0 1250 31 50 9 3 0 1 0 2300 36 38 9 1 0 0 1 3250 37 38 13 5 0 0 1 1750 68 38 38 2 0 0 0 2125 26 38 2 4 0 0 0 5500 42 40 19 2 0 0 0 4825 47 50 21 5 0 0 0 1925 26 45 3 2 0 0 0 2325 25 40 3 1 0 0 0 2250 36 55 13 2 0 1 0 1800 41 20 19 4 0 1 0 3250 49 45 26 5 0 1 0 1925 24 43 1 1 0 0 0 2300 35 38 11 4 0 0 0 4000 29 50 5 1 0 0 0 2250 33 60 9 3 0 1 0 1925 46 33 21 4 0 1 0 2825 27 44 4 2 0 0 0 2925 54 38 29 4 0 1 0 5325 29 40 7 3 0 0 0 2800 37 50 15 3 0 1 0 4825 48 55 25 5 0 0 0 2425 32 42 7 1 0 0 0 3125 29 62 3 2 0 0 0 2625 28 45 5 2 0 0 1 1825 24 38 2 1 0 0 0 2300 49 30 27 2 0 0 1 4125 44 40 22 5 0 0 0 2250 27 48 5 2 0 0 0 2750 39 40 17 4 0 1 0 3125 36 38 13 4 0 0 0 1925 24 38 2 1 0 0 1 3125 31 60 10 2 0 0 0 1800 26 52 4 3 0 0 0 2825 33 45 9 3 0 0 0 3625 52 45 29 4 0 0 0 2300 30 38 7 3 0 0 1 3325 47 38 25 3 0 1 0 2800 51 38 26 5 0 1 0 2250 30 60 6 2 1 0 0 2250 35 45 11 1 0 1 0 2750 47 42 24 6 0 1 0 2800 45 38 23 5 0 1 0 2750 37 38 15 1 0 0 1 1300 24 32 0 1 0 0 0 1625 24 38 1 3 0 0 0 4825 55 38 31 2 0 0 0 2125 23 42 1 5 0 0 0 300 25 22 0 2 0 1 0 5000 45 40 20 4 0 0 0 2250 27 38 5 2 0 0 1 2425 34 38 3 4 0 1 0 4625 54 45 32 4 0 0 0 7750 50 60 25 5 0 0 0 7750 45 60 23 5 0 0 0 1925 32 40 8 3 0 0 0 2425 35 50 11 1 0 0 0 3250 50 38 16 1 1 0 0 2250 47 28 24 5 0 0 1 2625 28 45 5 2 0 0 0 4125 36 38 11 3 0 0 0 2925 33 50 10 3 0 0 0 2925 29 45 6 2 0 0 0 1925 45 28 22 4 0 0 1 3250 46 40 24 3 0 0 1 2300 31 38 8 3 0 0 1 2750 31 38 8 1 1 0 0 6750 27 50 1 6 0 0 0 1925 39 38 3 2 0 0 1 2425 26 38 4 1 0 0 0 2125 28 50 5 2 0 0 0 2250 47 40 23 4 0 1 0 1925 26 38 3 2 0 0 1 1925 28 40 3 1 0 0 0 2425 26 45 3 2 0 0 0 3425 58 40 33 4 1 0 0 2925 35 50 13 1 0 0 1 2425 28 60 5 1 0 0 0 4125 27 70 3 6 0 0 0 1925 27 38 5 4 0 0 0 1625 23 40 0 4 0 0 0 2825 22 45 0 5 1 0 0 1925 28 38 5 1 0 0 0 2625 30 50 7 3 0 0 0 2125 34 32 11 4 0 0 0 3125 33 38 8 4 0 0 0 1925 26 45 3 1 1 0 0 3325 29 50 7 1 0 0 0 3325 30 42 6 4 0 0 0 2625 29 38 7 1 0 0 0 4125 29 38 5 2 0 0 0 3125 39 55 8 1 0 0 0 3125 28 42 5 2 0 0 0 2625 28 43 6 2 0 0 0 1925 29 42 5 2 0 0 0 3500 27 42 5 2 0 0 0 2325 26 50 1 5 0 0 0 4500 29 44 4 2 0 0 0 2300 40 40 17 3 0 0 0 2250 27 38 4 2 0 0 1 2300 30 38 6 3 0 0 0 2300 37 38 14 4 0 0 1 3000 28 38 6 3 0 0 0 6000 35 50 10 1 0 0 0 2625 25 47 1 3 0 0 0 3825 32 70 7 2 0 0 1 2125 24 38 2 4 0 0 0 1800 25 40 3 1 0 1 0 3125 36 38 13 1 0 0 0 2300 30 38 7 1 0 0 0 2250 37 38 13 4 0 1 0 1925 25 45 3 2 0 0 0 1800 25 40 3 4 0 0 1 3750 47 38 24 3 1 0 0 7750 53 70 31 4 1 0 0 2425 30 38 7 2 0 0 0 6075 44 40 22 4 0 0 0 3625 50 38 26 4 0 0 0 3750 39 38 15 3 1 0 0 2250 45 38 21 3 0 0 0 2300 30 38 7 3 0 0 1 2125 27 38 4 2 0 0 0 5875 36 40 14 5 0 0 0 7750 60 45 37 2 0 0 0 4125 36 40 13 3 0 0 0 3325 37 40 13 4 0 0 0
Names of X columns:
totinkomeneur leeftijd toturenarbeid ancienniteit gezinsleden zelfstandig onderwijs overheid
Endogenous Variable (Column Number)
Categorization
quantiles
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
yes
no
yes
Chart options
R Code
par4 <- 'yes' par3 <- '3' par2 <- 'quantiles' 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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