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Data X:
0.6000 1.0800 1.0100 1.6100 1.7700 1.3900 1.7700 0.6000 1.0900 1.0000 1.5800 1.7700 1.3500 1.9800 0.6000 1.1000 1.0000 1.6900 1.7700 1.3900 1.9400 0.6000 1.1000 1.0000 1.7800 1.7700 1.3700 1.8500 0.6000 1.1100 1.0600 1.7600 1.7400 1.3800 1.8400 0.6000 1.1000 1.2200 1.8300 1.7800 1.5100 1.8200 0.6000 1.1000 1.2400 1.8000 1.7800 1.5100 1.8300 0.6000 1.1100 1.3400 1.5700 1.7800 1.4500 1.9100 0.6100 1.1100 1.3000 1.4500 1.7800 1.3000 1.8500 0.6100 1.1100 1.0500 1.4000 1.8100 1.2900 1.8100 0.6100 1.1100 1.0000 1.5500 1.8400 1.4400 1.8300 0.6100 1.1100 1.0000 1.5800 1.8000 1.4600 1.7900 0.6100 1.1200 1.0100 1.5800 1.7800 1.5000 1.8000 0.6100 1.1100 1.0200 1.5900 1.7600 1.3900 1.8200 0.6200 1.1100 1.0600 1.8000 1.7400 1.4800 1.8800 0.6200 1.1200 1.0900 1.9900 1.7200 1.5200 2.0100 0.6200 1.1200 1.0900 2.0600 1.7300 1.6800 1.9700 0.6300 1.1100 1.1500 2.0600 1.7700 1.7400 1.9200 0.6300 1.1200 1.2500 2.0800 1.8100 1.7200 1.9800 0.6300 1.1100 1.3700 2.0000 1.8300 1.7400 2.0200 0.6300 1.1100 1.5100 1.8500 1.8700 1.8300 1.9000 0.6300 1.1000 1.3500 1.7700 1.8900 1.9900 1.9400 0.6300 1.1000 1.3200 1.7000 1.8200 1.8500 1.9600 0.6400 1.1000 1.3000 1.6600 1.7900 1.6800 1.8400 0.6300 1.1100 1.3900 1.6700 1.7900 1.6200 1.8700 0.6300 1.1000 1.4000 1.7300 1.8200 1.6200 1.8400 0.6300 1.1000 1.3900 1.9100 1.8200 1.6400 2.0700 0.6300 1.0900 1.4200 2.0200 1.8100 1.5900 2.0800 0.6300 1.1000 1.4400 2.0700 1.8100 1.6300 2.1400 0.6300 1.1000 1.4400 2.1500 1.7800 1.6800 2.1500 0.6400 1.1100 1.4500 2.1000 1.8000 1.5900 2.0500 0.6400 1.1300 1.3900 1.6800 1.7900 1.5400 2.0500 0.6400 1.1300 1.4800 1.6800 1.8300 1.5100 1.9500 0.6500 1.1300 1.3200 1.6500 1.8200 1.5000 2.0200 0.6500 1.1300 1.2900 1.7200 1.8000 1.7100 2.0200 0.6500 1.1400 1.3100 1.7300 1.8200 1.6000 1.8800 0.6500 1.1400 1.2700 1.7600 1.8400 1.5500 1.9600 0.6500 1.1400 1.3800 1.8400 1.8200 1.6300 1.9300 0.6500 1.1500 1.3800 1.9900 1.8100 1.6400 2.0300 0.6500 1.1500 1.4500 2.0500 1.7900 1.6800 2.1000 0.6500 1.1500 1.5000 2.1200 1.8700 1.7200 1.9500 0.6500 1.1500 1.6300 2.1300 1.8900 1.7600 2.0700 0.6600 1.1500 1.7300 2.0800 1.9200 1.8400 2.0900 0.6600 1.1500 1.8400 1.8800 1.9000 1.8900 2.0100 0.6600 1.1400 1.7500 1.8100 1.9100 1.8600 1.9200 0.6500 1.1400 1.3400 1.8100 1.9500 1.8100 1.9900 0.6500 1.1400 1.3600 1.8800 2.0400 1.8300 2.1100 0.6500 1.1300 1.3300 1.8700 1.9900 1.7200 2.0000 0.6500 1.1200 1.3700 1.8700 1.9400 1.5900 2.0900 0.6500 1.1300 1.3900 1.9000 1.9300 1.6600 2.0400 0.6500 1.1300 1.4000 2.0100 1.8900 1.5900 2.0900 0.6500 1.1300 1.4000 2.0500 1.8700 1.6000 2.0900 0.6600 1.1200 1.4300 2.1600 1.8900 1.5600 2.1300 0.6700 1.1300 1.5200 2.1800 1.9000 1.6000 2.1300 0.6600 1.1200 1.5400 2.1500 1.9300 1.6200 2.1700 0.6700 1.1200 1.8500 2.1200 1.9400 1.6000 2.1300 0.6600 1.1100 1.8300 2.0400 1.8800 1.6000 2.0000 0.6600 1.1100 1.2900 2.0400 1.8900 1.6800 2.0500 0.6600 1.1100 1.2000 2.0600 1.9200 1.7700 2.0800 0.6600 1.1100 1.2000 1.9300 1.9100 1.7500 2.0700 0.7100 1.1400 1.2100 1.8600 1.8900 1.7600 2.1200 0.7400 1.1500 1.2100 1.9400 1.8900 1.8900 2.1300 0.7500 1.1500 1.1900 2.3500 1.9800 1.8800 2.1600 0.7500 1.1600 1.1800 2.4600 2.0200 1.9000 2.2500 0.7500 1.1500 1.1700 2.5900 2.0200 1.9100 2.2600 0.7500 1.1600 1.2200 2.6600 1.9900 1.9100 2.3900 0.7000 1.1300 1.2500 2.4100 1.9700 1.8400 2.3600 0.6900 1.1300 1.3000 2.1800 1.9600 1.6900 2.2600 0.6900 1.1200 1.3300 2.1300 1.9500 1.6100 2.2600 0.6800 1.1200 1.1800 2.1100 1.9800 1.6700 2.2700 0.6800 1.1100 1.1800 2.1200 2.0000 1.8400 2.2900 0.6800 1.1100 1.1900 2.1600 2.0000 1.8400 2.2100
Names of X columns:
Mineraalwater Vruchtesappen Jonagold Sinaasappelen Citroenen Pompelmoezen Bananen
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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