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Data X:
2.7 8.4 4.3 1.5 2.2 2.1 2.5 7.5 3.1 1.7 2.3 2.2 2.2 4.0 5.7 1.6 2.1 2.2 2.9 8.5 6.7 1.7 2.8 2.7 3.1 7.6 9.5 1.8 3.1 3.1 3.0 5.5 9.0 1.7 2.9 3.2 2.8 3.3 6.9 2.2 2.6 3.1 2.5 1.4 7.5 2.7 2.7 3.1 1.9 -4.4 7.0 3.0 2.3 2.8 1.9 -6.5 9.3 2.8 2.3 3.0 1.8 -8.5 7.2 2.7 2.1 2.8 2.0 -6.7 6.6 2.7 2.2 2.7 2.6 -3.3 10.4 2.5 2.9 3.2 2.5 -5.1 8.7 2.0 2.6 3.1 2.5 -3.5 7.9 1.8 2.7 3.0 1.6 -3.6 4.1 1.4 1.8 2.0 1.4 -6.3 2.2 1.5 1.3 1.7 0.8 -8.0 -0.5 1.6 0.9 1.2 1.1 -5.3 1.7 1.3 1.3 1.4 1.3 -4.0 0.4 1.1 1.3 1.3 1.2 -4.0 2.6 0.8 1.3 1.3 1.3 0.1 0.7 1.1 1.3 1.1 1.1 -0.9 0.7 1.3 1.1 0.9 1.3 1.1 0.5 1.5 1.4 1.2 1.2 3.1 -2.3 1.8 1.2 0.9 1.6 5.7 0.3 2.7 1.7 1.3 1.7 6.2 -0.2 3.0 1.8 1.4 1.5 -2.2 0.6 3.2 1.5 1.5 0.9 -4.2 -0.6 3.2 1.0 1.1 1.5 -1.6 2.7 3.3 1.6 1.6 1.4 -1.9 2.3 3.2 1.5 1.5 1.6 0.2 4.3 2.9 1.8 1.6 1.7 -1.2 5.4 2.7 1.8 1.7 1.4 -2.4 2.6 2.6 1.6 1.6 1.8 0.8 2.9 2.3 1.9 1.7 1.7 -0.1 2.9 2.2 1.7 1.6 1.4 -1.5 2.9 2.1 1.6 1.6 1.2 -4.4 1.4 2.4 1.3 1.3 1.0 -4.2 1.1 2.5 1.1 1.1 1.7 3.5 1.9 2.4 1.9 1.6 2.4 10.0 2.8 2.3 2.6 1.9 2.0 8.6 1.4 2.1 2.3 1.6 2.1 9.5 0.7 2.3 2.4 1.7 2.0 9.9 -0.8 2.2 2.2 1.6 1.8 10.4 -3.1 2.1 2.0 1.4 2.7 16.0 0.1 2.0 2.9 2.1 2.3 12.7 1.0 2.1 2.6 1.9 1.9 10.2 1.9 2.1 2.3 1.7 2.0 8.9 -0.5 2.5 2.3 1.8 2.3 12.6 1.5 2.2 2.6 2.0 2.8 13.6 3.9 2.3 3.1 2.5 2.4 14.8 1.9 2.3 2.8 2.1 2.3 9.5 2.6 2.2 2.5 2.1 2.7 13.7 1.7 2.2 2.9 2.3 2.7 17.0 1.4 1.6 3.1 2.4 2.9 14.7 2.8 1.8 3.1 2.4 3.0 17.4 0.5 1.7 3.2 2.3 2.2 9.0 1.0 1.9 2.5 1.7 2.3 9.1 1.5 1.8 2.6 2.0 2.8 12.2 1.8 1.9 2.9 2.3 2.8 15.9 2.7 1.5 2.6 2.0 2.8 12.9 3.0 1.0 2.4 2.0 2.2 10.9 -0.3 0.8 1.7 1.3 2.6 10.6 1.1 1.1 2.0 1.7 2.8 13.2 1.7 1.5 2.2 1.9 2.5 9.6 1.6 1.7 1.9 1.7 2.4 6.4 3.0 2.3 1.6 1.6 2.3 5.8 3.3 2.4 1.6 1.7 1.9 -1.0 6.7 3.0 1.2 1.8 1.7 -0.2 5.6 3.0 1.2 1.9 2.0 2.7 6.0 3.2 1.5 1.9 2.1 3.6 4.8 3.2 1.6 1.9 1.7 -0.9 5.9 3.2 1.7 2.0 1.8 0.3 4.3 3.5 1.8 2.1 1.8 -1.1 3.7 4.0 1.8 1.9 1.8 -2.5 5.6 4.3 1.8 1.9 1.3 -3.4 1.7 4.1 1.3 1.3 1.3 -3.5 3.2 4.0 1.3 1.3 1.3 -3.9 3.6 4.1 1.4 1.4 1.2 -4.6 1.7 4.2 1.1 1.2 1.4 -0.1 0.5 4.5 1.5 1.3 2.2 4.3 2.1 5.6 2.2 1.8 2.9 10.2 1.5 6.5 2.9 2.2 3.1 8.7 2.7 7.6 3.1 2.6 3.5 13.3 1.4 8.5 3.5 2.8 3.6 15.0 1.2 8.7 3.6 3.1 4.4 20.7 2.3 8.3 4.4 3.9 4.1 20.7 1.6 8.3 4.2 3.7 5.1 26.4 4.7 8.5 5.2 4.6 5.8 31.2 3.5 8.7 5.8 5.1 5.9 31.4 4.4 8.7 5.9 5.2 5.4 26.6 3.9 8.5 5.4 4.9 5.5 26.6 3.5 7.9 5.5 5.1 4.8 19.2 3.0 7.0 4.7 4.8 3.2 6.5 1.6 5.8 3.1 3.9 2.7 3.1 2.2 4.5 2.6 3.5 2.1 -0.2 4.1 3.7 2.3 3.3 1.9 -4.0 4.3 3.1 1.9 2.8 0.6 -12.6 3.5 2.7 0.6 1.6 0.7 -13.0 1.8 2.3 0.6 1.5 -0.2 -17.6 0.6 1.8 -0.4 0.7 -1.0 -21.7 -0.4 1.5 -1.1 -0.1 -1.7 -23.2 -2.5 1.2 -1.7 -0.7 -0.7 -16.8 -1.6 1.0 -0.8 -0.2 -1.0 -19.8 -1.9 0.9 -1.2 -0.6 -0.9 -17.2 -1.6 0.6 -1.0 -0.6 0.0 -10.4 -0.7 0.6 -0.1 -0.3 0.3 -6.8 -1.1 0.7 0.3 -0.3 0.8 -2.9 0.3 0.5 0.6 -0.1 0.8 -1.9 1.3 0.5 0.7 0.1 1.9 7.0 3.3 0.5 1.7 0.9 2.1 9.8 2.4 0.5 1.8 1.1 2.5 12.5 2.0 0.8 2.3 1.6 2.7 13.7 3.9 0.8 2.5 2.0 2.4 13.7 4.2 1.1 2.6 2.2 2.4 9.7 4.9 1.2 2.3 2.1 2.9 14.0 5.8 1.5 2.9 2.6 3.1 15.3 4.8 1.7 3.0 2.5 3.0 13.4 4.4 1.8 2.9 2.5 3.4 17.1 5.3 1.8 3.1 2.6 3.7 15.7 2.1 2.1 3.2 2.7 3.5 18.3 2.0 2.2 3.4 2.8 3.5 18.1 -0.9 2.5 3.5 2.9 3.3 16.3 0.1 2.7 3.4 2.9 3.1 15.8 -0.5 3.0 3.3 2.9 3.4 17.3 -0.1 3.4 3.7 3.3 4.0 18.0 0.7 3.4 3.8 3.3 3.4 17.6 -0.4 3.5 3.6 3.1 3.4 18.4 -1.5 3.5 3.6 3.0 3.4 17.4 -0.3 3.4 3.6 3.1 3.7 17.9 1.0 3.6 3.8 3.4 3.2 13.5 0.4 3.8 3.5 3.2 3.3 13.7 0.3 3.5 3.6 3.4 3.3 12.6 1.8 3.5 3.7 3.4 3.1 10.4 3.0 3.5 3.4 3.1 2.9 8.8 2.2 3.2 3.2 3.0 2.6 5.4 3.4 2.9 2.8 2.7 2.2 2.1 3.4 2.5 2.3 2.2 2.0 2.8 3.1 2.3 2.3 2.2 2.6 5.6 4.5 2.7 2.9 2.6 2.6 4.8 4.6 3.0 2.8 2.4 2.6 4.5 5.7 3.3 2.8 2.5 2.2 1.5 4.3 3.2 2.3 2.2
Names of X columns:
HICP Energiedragers Niet-bewerkte_levensmiddelen Bewerkte_levensmiddelen Algemene_index Gezondheidsindex
Endogenous Variable (Column Number)
Categorization
Do not include Seasonal Dummies
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
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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