Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
-25 37 -16 -33 -28 -23 33 -15 -32 -26 -24 36 -16 -32 -27 -24 37 -14 -31 -26 -25 39 -14 -31 -27 -25 39 -14 -32 -27 -24 37 -16 -32 -27 -24 37 -17 -33 -28 -22 36 -15 -31 -26 1 23 -9 -21 -13 -5 21 -9 -17 -13 -10 24 -7 -14 -14 -10 25 -4 -10 -12 -15 29 -9 -13 -16 -13 24 -8 -19 -16 -11 22 -6 -10 -12 -15 28 -5 -13 -15 -15 39 -7 -11 -18 -16 36 -6 -9 -17 -4 32 -1 -1 -10 -5 27 -2 -3 -9 -9 33 -1 -7 -13 -14 36 -3 -6 -15 -11 34 -2 -1 -12 -7 34 -2 -11 -13 -7 31 -1 -3 -10 -9 37 -2 -1 -13 -5 36 -1 -2 -11 -10 35 0 -2 -12 -9 32 1 -2 -10 -10 35 -1 -4 -13 -8 36 -1 -1 -12 -9 35 0 0 -11 -10 32 0 -3 -11 -10 28 1 -4 -11 -5 24 1 -4 -8 -6 25 2 -2 -7 -10 29 1 -3 -10 -10 28 2 4 -8 -9 25 1 3 -8 -10 22 0 3 -7 -8 22 2 -1 -7 -8 22 1 5 -6 -8 23 0 -2 -8 -4 22 1 2 -6 2 14 3 -1 -3 3 7 2 6 1 2 9 4 4 0 -3 12 1 -2 -3 -1 9 4 4 0 1 6 2 3 0 2 8 3 0 -1 -4 10 2 7 -1 0 8 3 5 0 5 9 5 3 1 -1 11 5 9 0 3 6 3 7 2 6 6 4 8 3 7 9 5 8 2 7 7 5 10 4 3 8 4 11 3 8 2 6 5 4 3 2 5 9 3 0 7 4 7 1 1 6 4 8 2 4 4 7 12 4 4 8 8 10 3 1 9 5 10 2 -17 11 4 8 -4 -16 14 1 11 -5 -13 18 2 10 -5 -15 23 0 8 -7 -31 25 -2 5 -13 -26 31 -1 12 -11 -5 18 2 10 -3 -5 19 3 8 -3 -6 23 2 8 -5 -5 24 2 10 -4 -5 25 5 12 -4 -7 26 4 13 -4 -6 27 5 7 -5 -8 23 2 13 -4 -6 27 6 11 -5 -12 34 7 13 -6 -15 34 1 11 -9 -15 37 1 10 -10 -16 41 0 15 -11 -19 43 -2 11 -13 -23 38 -1 10 -13 -23 39 -1 12 -13 -21 35 1 14 -11 -21 38 0 11 -12 -25 40 0 8 -14 -34 49 -1 3 -20 -30 51 -1 15 -17 -27 48 -1 11 -16 -40 54 -4 0 -24 -40 56 -6 4 -24 -34 56 -3 7 -22 -43 61 -7 12 -25 -39 57 -4 5 -24 -40 57 -5 2 -25 -40 52 -3 0 -24 -40 58 -5 5 -25 -35 60 -6 4 -24 -43 62 -7 7 -26 -44 48 -6 0 -25 -38 50 -8 -1 -24 -37 50 -5 3 -22 -31 48 -5 2 -20 -20 40 -3 7 -14 -22 35 -2 6 -13 -9 33 -1 3 -10 -11 34 1 3 -10 -8 34 -1 1 -11 -3 28 -1 8 -6 3 26 3 10 -2 6 23 2 6 -3 -3 20 4 11 -2 -8 20 3 6 -4 -8 26 1 6 -7 -10 28 0 3 -8 -9 29 2 10 -7 -7 25 2 12 -4 -12 27 2 9 -7 -9 24 3 12 -5 -8 26 2 10 -6 -19 38 1 6 -12 -21 38 0 8 -12 -24 45 -4 11 -16 -30 53 -9 11 -20 -28 44 -6 11 -16 -27 43 -7 14 -16 -26 47 -6 8 -18 -27 40 -6 12 -15 -23 34 -3 11 -12 -26 38 -3 14 -13 -23 39 -4 15 -13 -21 35 -5 15 -12 -20 35 -4 14 -11 -14 36 -3 16 -9 -16 25 -5 9 -9 -17 24 -3 13 -8 -18 29 -2 15 -8 -25 44 -3 14 -15 -26 43 -5 11 -16 -36 57 -3 14 -21 -35 56 -3 10 -21 -27 47 -4 13 -16 -22 41 -2 15 -13 -25 38 -3 20 -12 -17 33 -2 19 -8 -14 36 -3 16 -9 -7 22 2 22 -1 -12 27 1 19 -5 -17 32 -1 16 -9 -8 21 2 23 -1 -2 14 5 23 3 -1 10 3 16 2 1 14 3 23 3 0 12 3 30 5 -2 10 1 31 5 -5 12 3 24 3 -4 9 1 20 2 -9 14 2 24 1 -16 23 2 23 -4 -7 17 1 25 1 -7 16 2 25 1 3 7 4 23 6 -2 9 3 21 3 -3 9 3 16 2 -6 14 3 26 2 -7 12 2 23 2 -24 23 -1 15 -8 -13 12 1 23 0 -14 15 3 20 -2 -7 6 4 22 3 -1 6 4 24 5 5 1 6 22 8 6 3 4 24 8 5 -1 6 24 9 5 -4 6 29 11 9 -6 8 29 13 10 -9 4 25 12 14 -13 8 16 13 19 -13 10 18 15 18 -10 9 13 13 16 -12 12 22 16 8 -9 9 15 10 10 -15 11 20 14 12 -14 11 19 14 13 -18 11 18 15 15 -13 11 13 13 3 -2 11 17 8 2 -1 9 17 7 -2 5 8 13 3 1 8 6 14 3 1 6 7 13 4 -1 7 8 17 4 -6 15 6 17 0 -13 23 5 15 -4 -25 43 2 9 -14 -26 60 3 10 -18 -9 36 3 9 -8 1 28 7 14 -1 3 23 8 18 1 6 23 7 18 2 2 22 7 12 0 5 22 6 16 1 5 24 6 12 0 0 32 7 19 -1 -5 27 5 13 -3 -4 27 5 12 -3 -2 27 5 13 -3 -1 29 4 11 -4 -8 38 4 10 -8 -16 40 4 16 -9 -19 45 1 12 -13 -28 50 -1 6 -18 -11 43 3 8 -11 -4 44 4 6 -9 -9 44 3 8 -10 -12 49 2 8 -13 -10 42 1 9 -11 -2 36 4 13 -5 -13 57 3 8 -15 0 42 5 11 -6 0 39 6 8 -6 4 33 6 10 -3 7 32 6 15 -1 5 34 6 12 -3 2 37 6 13 -4 -2 38 5 12 -6 6 28 6 15 0 -3 31 5 13 -4 1 28 6 13 -2 0 30 5 16 -2 -7 39 7 14 -6 -6 38 4 12 -7 -4 39 5 15 -6 -4 38 6 14 -6 -2 37 6 19 -3 2 32 5 16 -2 -5 32 3 16 -5 -15 44 2 11 -11 -16 43 3 13 -11 -18 42 3 12 -11 -13 38 2 11 -10 -23 37 0 6 -14 -10 35 4 9 -8 -10 37 4 6 -9 -6 33 5 15 -5 -3 24 6 17 -1 -4 24 6 13 -2 -7 31 5 12 -5 -7 25 5 13 -4 -7 28 3 10 -6 -3 24 5 14 -2 0 25 5 13 -2 -5 16 5 10 -2 -3 17 3 11 -2 3 11 6 12 2 2 12 6 7 1 -7 39 4 11 -8 -1 19 6 9 -1 0 14 5 13 1 -3 15 4 12 -1 4 7 5 5 2 2 12 5 13 2 3 12 4 11 1 0 14 3 8 -1 -10 9 2 8 -2 -10 8 3 8 -2 -9 4 2 8 -1 -22 7 -1 0 -8 -16 3 0 3 -4 -18 5 -2 0 -6 -14 0 1 -1 -3 -12 -2 -2 -1 -3 -17 6 -2 -4 -7 -23 11 -2 1 -9 -28 9 -6 -1 -11 -31 17 -4 0 -13 -21 21 -2 -1 -11 -19 21 0 6 -9 -22 41 -5 0 -17 -22 57 -4 -3 -22 -25 65 -5 -3 -25 -16 68 -1 4 -20 -22 73 -2 1 -24 -21 71 -4 0 -24 -10 71 -1 -4 -22 -7 70 1 -2 -19 -5 69 1 3 -18 -4 65 -2 2 -17 7 57 1 5 -11 6 57 1 6 -11 3 57 3 6 -12 10 55 3 3 -10 0 65 1 4 -15 -2 65 1 7 -15 -1 64 0 5 -15 2 60 2 6 -13 8 43 2 1 -8 -6 47 -1 3 -13 -4 40 1 6 -9 4 31 0 0 -7 7 27 1 3 -4 3 24 1 4 -4 3 23 3 7 -2 8 17 2 6 0 3 16 0 6 -2 -3 15 0 6 -3 4 8 3 6 1 -5 5 -2 2 -2 -1 6 0 2 -1 5 5 1 2 1 0 12 -1 3 -3 -6 8 -2 -1 -4 -13 17 -1 -4 -9 -15 22 -1 4 -9 -8 24 1 5 -7 -20 36 -2 3 -14
Names of X columns:
VooruitzichtenEconomischeSituatie VooruitzichtenWerkloosheid VooruitzichtenFinanciƫleSituatieGezinnen VooruitzichtenSpaarvermogenGezinnen IndicatorConsumentenvertrouwen
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
Click here to blog (archive) this computation