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Data X:
1 1 14 41 38 13 12 1 1 18 39 32 16 11 1 1 11 30 35 19 15 1 0 12 31 33 15 6 1 1 16 34 37 14 13 1 1 18 35 29 13 10 1 1 14 39 31 19 12 1 1 14 34 36 15 14 1 1 15 36 35 14 12 1 1 15 37 38 15 9 1 0 17 38 31 16 10 1 1 19 36 34 16 12 1 0 10 38 35 16 12 1 1 16 39 38 16 11 1 1 18 33 37 17 15 1 0 14 32 33 15 12 1 0 14 36 32 15 10 1 1 17 38 38 20 12 1 0 14 39 38 18 11 1 1 16 32 32 16 12 1 0 18 32 33 16 11 1 1 11 31 31 16 12 1 1 14 39 38 19 13 1 1 12 37 39 16 11 1 0 17 39 32 17 12 1 1 9 41 32 17 13 1 0 16 36 35 16 10 1 1 14 33 37 15 14 1 1 15 33 33 16 12 1 0 11 34 33 14 10 1 1 16 31 31 15 12 1 0 13 27 32 12 8 1 1 17 37 31 14 10 1 1 15 34 37 16 12 1 0 14 34 30 14 12 1 0 16 32 33 10 7 1 0 9 29 31 10 9 1 0 15 36 33 14 12 1 1 17 29 31 16 10 1 0 13 35 33 16 10 1 0 15 37 32 16 10 1 1 16 34 33 14 12 1 0 16 38 32 20 15 1 0 12 35 33 14 10 1 1 15 38 28 14 10 1 1 11 37 35 11 12 1 1 15 38 39 14 13 1 1 15 33 34 15 11 1 1 17 36 38 16 11 1 0 13 38 32 14 12 1 1 16 32 38 16 14 1 0 14 32 30 14 10 1 0 11 32 33 12 12 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15 41 39 16 12 1 0 10 36 35 16 11 1 1 10 43 42 12 7 1 1 15 30 34 16 12 1 1 11 31 33 16 14 1 1 13 32 41 17 11 1 1 18 37 34 12 10 1 0 16 37 32 18 13 1 1 14 33 40 14 13 1 1 14 34 40 14 8 1 1 14 33 35 13 11 1 1 14 38 36 16 12 1 0 12 33 37 13 11 1 1 14 31 27 16 13 1 1 15 38 39 13 12 1 1 15 37 38 16 14 1 1 15 36 31 15 13 1 1 13 31 33 16 15 1 0 17 39 32 15 10 1 1 17 44 39 17 11 1 1 19 33 36 15 9 1 1 15 35 33 12 11 1 0 13 32 33 16 10 1 0 9 28 32 10 11 1 1 15 40 37 16 8 1 0 15 27 30 12 11 1 0 15 37 38 14 12 1 1 16 32 29 15 12 1 0 11 28 22 13 9 1 0 14 34 35 15 11 1 1 11 30 35 11 10 1 1 15 35 34 12 8 1 0 13 31 35 11 9 1 1 15 32 34 16 8 1 0 16 30 37 15 9 1 1 14 30 35 17 15 1 0 15 31 23 16 11 1 1 16 40 31 10 8 1 1 16 32 27 18 13 1 0 11 36 36 13 12 1 0 12 32 31 16 12 1 0 9 35 32 13 9 1 1 16 38 39 10 7 1 1 13 42 37 15 13 1 0 16 34 38 16 9 1 1 12 35 39 16 6 1 1 9 38 34 14 8 1 1 13 33 31 10 8 1 1 14 32 37 13 6 1 1 19 33 36 15 9 1 1 13 34 32 16 11 1 1 12 32 38 12 8 0 0 10 27 26 13 10 0 0 14 31 26 12 8 0 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0 8 36 35 13 8 0 1 15 36 39 15 12 0 0 11 35 33 13 12 0 0 16 38 36 14 10 0 1 10 33 32 15 12 0 0 15 31 32 12 9 0 1 16 32 36 8 6 0 0 19 31 32 14 10 0 0 12 33 34 14 9 0 0 8 34 33 11 9 0 0 11 34 35 12 9 0 1 14 34 30 13 6 0 0 9 33 38 10 10 0 0 15 32 34 16 6 0 1 13 41 33 18 14 0 1 16 34 32 13 10 0 0 11 36 31 11 10 0 0 12 37 30 4 6 0 0 13 36 27 13 12 0 1 10 29 31 16 12 0 0 11 37 30 10 7 0 0 12 27 32 12 8 0 0 8 35 35 12 11 0 0 12 28 28 10 3 0 0 12 35 33 13 6 0 0 11 29 35 12 8 0 0 13 32 35 14 9 0 1 14 36 32 10 9 0 1 10 19 21 12 8 0 1 12 21 20 12 9 0 0 15 31 34 11 7 0 0 13 33 32 10 7 0 1 13 36 34 12 6 0 1 13 33 32 16 9 0 0 12 37 33 12 10 0 0 12 34 33 14 11 0 0 9 35 37 16 12 0 1 9 31 32 14 8 0 1 15 37 34 13 11 0 1 10 35 30 4 3 0 1 14 27 30 15 11 0 0 15 34 38 11 12 0 0 7 40 36 11 7 0 0 14 29 32 14 9 0 0 8 38 34 15 12 0 1 10 34 33 14 8 0 0 13 21 27 13 11 0 0 13 36 32 11 8 0 1 13 38 34 15 10 0 0 8 30 29 11 8 0 0 12 35 35 13 7 0 1 13 30 27 13 8 0 1 12 36 33 16 10 0 0 10 34 38 13 8 0 1 13 35 36 16 12 0 0 12 34 33 16 14 0 0 9 32 39 12 7 0 1 15 33 29 7 6 0 0 13 33 32 16 11 0 1 13 26 34 5 4 0 0 13 35 38 16 9 0 0 15 21 17 4 5 0 0 15 38 35 12 9 0 0 14 35 32 15 11 0 1 15 33 34 14 12 0 0 11 37 36 11 9 0 0 15 38 31 16 12 0 1 14 34 35 15 10 0 0 13 27 29 12 9 0 1 12 16 22 6 6 0 0 16 40 41 16 10 0 0 16 36 36 10 9 0 1 9 42 42 15 13 0 1 14 30 33 14 12
Names of X columns:
Populatie Geslacht Happiness Connected Separate Learning Software
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
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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