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Data X:
9.1 4.5 1.0 -1.0 1989.3 9.0 4.3 1.0 3.0 2097.8 9.0 4.3 1.3 2.0 2154.9 8.9 4.2 1.1 3.0 2152.2 8.8 4.0 0.8 5.0 2250.3 8.7 3.8 0.7 5.0 2346.9 8.5 4.1 0.7 3.0 2525.6 8.3 4.2 0.9 2.0 2409.4 8.1 4.0 1.3 1.0 2394.4 7.9 4.3 1.4 -4.0 2401.3 7.8 4.7 1.6 1.0 2354.3 7.6 5.0 2.1 1.0 2450.4 7.4 5.1 0.3 6.0 2504.7 7.2 5.4 2.1 3.0 2661.4 7.0 5.4 2.5 2.0 2880.4 7.0 5.4 2.3 2.0 3064.4 6.8 5.5 2.4 2.0 3141.1 6.8 5.8 3.0 -8.0 3327.7 6.7 5.7 1.7 0.0 3565.0 6.8 5.5 3.5 -2.0 3403.1 6.7 5.6 4.0 3.0 3149.9 6.7 5.6 3.7 5.0 3006.8 6.7 5.5 3.7 8.0 3230.7 6.5 5.5 3.0 8.0 3361.1 6.3 5.7 2.7 9.0 3484.7 6.3 5.6 2.5 11.0 3411.1 6.3 5.6 2.2 13.0 3288.2 6.5 5.4 2.9 12.0 3280.4 6.6 5.2 3.1 13.0 3174.0 6.5 5.1 3.0 15.0 3165.3 6.3 5.1 2.8 13.0 3092.7 6.3 5.0 2.5 16.0 3053.1 6.5 5.3 1.9 10.0 3182.0 7.0 5.4 1.9 14.0 2999.9 7.1 5.3 1.8 14.0 3249.6 7.3 5.1 2.0 15.0 3210.5 7.3 5.0 2.6 13.0 3030.3 7.4 5.0 2.5 8.0 2803.5 7.4 4.6 2.5 7.0 2767.6 7.3 4.8 1.6 3.0 2882.6 7.4 5.1 1.4 3.0 2863.4 7.5 5.1 0.8 4.0 2897.1 7.7 5.1 1.1 4.0 3012.6 7.7 5.4 1.3 0.0 3143.0 7.7 5.3 1.2 -4.0 3032.9 7.7 5.3 1.3 -14.0 3045.8 7.7 5.1 1.1 -18.0 3110.5 7.8 4.9 1.3 -8.0 3013.2 8.0 4.7 1.2 -1.0 2987.1 8.1 4.4 1.6 1.0 2995.6 8.1 4.6 1.7 2.0 2833.2 8.2 4.5 1.5 0.0 2849.0 8.2 4.2 0.9 1.0 2794.8 8.2 4.0 1.5 0.0 2845.3 8.1 3.9 1.4 -1.0 2915.0 8.1 4.1 1.6 -3.0 2892.6 8.2 4.1 1.7 -3.0 2604.4 8.3 3.7 1.4 -3.0 2641.7 8.3 3.8 1.8 -4.0 2659.8 8.4 4.1 1.7 -8.0 2638.5 8.5 4.1 1.4 -9.0 2720.3 8.5 4.0 1.2 -13.0 2745.9 8.4 4.3 1.0 -18.0 2735.7 8.0 4.4 1.7 -11.0 2811.7 7.9 4.2 2.4 -9.0 2799.4 8.1 4.2 2.0 -10.0 2555.3 8.5 4.0 2.1 -13.0 2305.0 8.8 4.0 2.0 -11.0 2215.0 8.8 4.3 1.8 -5.0 2065.8 8.6 4.4 2.7 -15.0 1940.5 8.3 4.4 2.3 -6.0 2042.0 8.3 4.3 1.9 -6.0 1995.4 8.3 4.1 2.0 -3.0 1946.8 8.4 4.1 2.3 -1.0 1765.9 8.4 3.9 2.8 -3.0 1635.3 8.5 3.8 2.4 -4.0 1833.4 8.6 3.7 2.3 -6.0 1910.4 8.6 3.5 2.7 0.0 1959.7 8.6 3.7 2.7 -4.0 1969.6 8.6 3.7 2.9 -2.0 2061.4 8.6 3.5 3.0 -2.0 2093.5 8.5 3.3 2.2 -6.0 2120.9 8.4 3.2 2.3 -7.0 2174.6 8.4 3.3 2.8 -6.0 2196.7 8.4 3.1 2.8 -6.0 2350.4 8.5 3.2 2.8 -3.0 2440.3 8.5 3.4 2.2 -2.0 2408.6 8.6 3.5 2.6 -5.0 2472.8 8.6 3.3 2.8 -11.0 2407.6 8.4 3.5 2.5 -11.0 2454.6 8.2 3.5 2.4 -11.0 2448.1 8.0 3.8 2.3 -10.0 2497.8 8.0 4.0 1.9 -14.0 2645.6 8.0 4.0 1.7 -8.0 2756.8 8.0 4.1 2.0 -9.0 2849.3 7.9 4.0 2.1 -5.0 2921.4 7.9 3.8 1.7 -1.0 2981.9 7.8 3.7 1.8 -2.0 3080.6 7.8 3.8 1.8 -5.0 3106.2 8.0 3.7 1.8 -4.0 3119.3 7.8 4.0 1.3 -6.0 3061.3 7.4 4.2 1.3 -2.0 3097.3 7.2 4.0 1.3 -2.0 3161.7 7.0 4.1 1.2 -2.0 3257.2 7.0 4.2 1.4 -2.0 3277.0 7.2 4.5 2.2 2.0 3295.3 7.2 4.6 2.9 1.0 3364.0 7.2 4.5 3.1 -8.0 3494.2 7.0 4.5 3.5 -1.0 3667.0 6.9 4.5 3.6 1.0 3813.1 6.8 4.4 4.4 -1.0 3918.0 6.8 4.3 4.1 2.0 3895.5 6.8 4.5 5.1 2.0 3801.1 6.9 4.1 5.8 1.0 3570.1 7.2 4.1 5.9 -1.0 3701.6 7.2 4.3 5.4 -2.0 3862.3 7.2 4.4 5.5 -2.0 3970.1 7.1 4.7 4.8 -1.0 4138.5 7.2 5.0 3.2 -8.0 4199.8 7.3 4.7 2.7 -4.0 4290.9 7.5 4.5 2.1 -6.0 4443.9 7.6 4.5 1.9 -3.0 4502.6 7.7 4.5 0.6 -3.0 4357.0 7.7 5.5 0.7 -7.0 4591.3 7.7 4.5 -0.2 -9.0 4697.0 7.8 4.4 -1.0 -11.0 4621.4 8.0 4.2 -1.7 -13.0 4562.8 8.1 3.9 -0.7 -11.0 4202.5 8.1 3.9 -1.0 -9.0 4296.5 8.0 4.2 -0.9 -17.0 4435.2 8.1 4.0 0.0 -22.0 4105.2 8.2 3.8 0.3 -25.0 4116.7 8.3 3.7 0.8 -20.0 3844.5 8.4 3.7 0.8 -24.0 3721.0 8.4 3.7 1.9 -24.0 3674.4 8.4 3.7 2.1 -22.0 3857.6 8.5 3.7 2.5 -19.0 3801.1 8.5 3.8 2.7 -18.0 3504.4 8.6 3.7 2.4 -17.0 3032.6 8.6 3.5 2.4 -11.0 3047.0 8.5 3.5 2.9 -11.0 2962.3 8.5 3.1 3.1 -12.0 2197.8
Names of X columns:
Werkloosheid rente inflatie consumer Bel20
Endogenous Variable (Column Number)
Categorization
none
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
yes
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') }
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Raw Input
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Raw Output
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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