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Data X:
1 1 23 17 23 1 1 24 17 20 1 1 22 18 20 1 2 20 21 21 1 1 24 20 24 1 1 27 28 22 1 2 28 19 23 1 1 27 22 20 1 1 24 16 25 1 1 23 18 23 1 2 24 25 27 1 2 27 17 27 1 1 27 14 22 1 1 28 11 24 1 1 27 27 25 1 2 23 20 22 1 1 24 22 28 1 2 28 22 28 1 2 27 21 27 1 1 25 23 25 1 2 19 17 16 1 1 24 24 28 1 1 20 14 21 1 2 28 17 24 1 1 26 23 27 1 1 23 24 14 1 1 23 24 14 1 1 20 8 27 1 2 11 22 20 1 1 24 23 21 1 2 25 25 22 1 1 23 21 21 1 1 18 24 12 1 2 20 15 20 1 2 20 22 24 1 2 24 21 19 1 1 23 25 28 1 2 25 16 23 1 2 28 28 27 1 1 26 23 22 1 1 26 21 27 1 1 23 21 26 1 1 22 26 22 1 2 24 22 21 1 2 21 21 19 1 1 20 18 24 1 2 22 12 19 1 2 20 25 26 1 1 25 17 22 1 2 20 24 28 1 1 22 15 21 1 1 23 13 23 1 1 25 26 28 1 1 23 16 10 1 2 23 24 24 1 1 22 21 21 1 1 24 20 21 1 1 25 14 24 1 2 21 25 24 1 2 12 25 25 1 1 17 20 25 1 1 20 22 23 1 1 23 20 21 1 2 23 26 16 1 1 20 18 17 1 2 28 22 25 1 2 24 24 24 1 2 24 17 23 1 1 24 24 25 1 1 24 20 23 1 1 28 19 28 1 2 25 20 26 1 2 21 15 22 1 1 25 23 19 1 1 25 26 26 1 1 18 22 18 1 1 17 20 18 1 2 26 24 25 1 2 28 26 27 1 2 21 21 12 1 2 27 25 15 1 1 22 13 21 1 0 21 20 23 1 1 25 22 22 1 2 22 23 21 1 2 23 28 24 1 1 26 22 27 1 1 19 20 22 1 1 25 6 28 1 1 21 21 26 1 1 13 20 10 1 2 24 18 19 1 1 25 23 22 1 1 26 20 21 1 1 25 24 24 1 1 25 22 25 1 2 22 21 21 1 1 21 18 20 1 2 23 21 21 0 2 25 23 24 0 1 24 23 23 0 2 21 15 18 0 1 21 21 24 0 1 25 24 24 0 2 22 23 19 0 1 20 21 20 0 2 20 21 18 0 1 23 20 20 0 1 28 11 27 0 1 23 22 23 0 1 28 27 26 0 1 24 25 23 0 1 18 18 17 0 1 20 20 21 0 1 28 24 25 0 2 21 10 23 0 1 21 27 27 0 1 25 21 24 0 2 19 21 20 0 1 18 18 27 0 1 21 15 21 0 1 22 24 24 0 1 24 22 21 0 1 15 14 15 0 1 28 28 25 0 2 26 18 25 0 1 23 26 22 0 1 26 17 24 0 2 20 19 21 0 2 22 22 22 0 1 20 18 23 0 2 23 24 22 0 2 22 15 20 0 2 24 18 23 0 2 23 26 25 0 1 22 11 23 0 1 26 26 22 0 2 23 21 25 0 2 27 23 26 0 1 23 23 22 0 1 21 15 24 0 1 26 22 24 0 1 23 26 25 0 2 21 16 20 0 1 27 20 26 0 1 19 18 21 0 2 23 22 26 0 2 25 16 21 0 2 23 19 22 0 2 22 20 16 0 1 22 19 26 0 1 25 23 28 0 1 25 24 18 0 2 28 25 25 0 2 28 21 23 0 2 20 21 21 0 1 25 23 20 0 1 19 27 25 0 1 25 23 22 0 1 22 18 21 0 2 18 16 16 0 1 20 16 18
Names of X columns:
Pop Gender E1 E2 E3
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
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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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