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Data X:
0 13 13 14 13 3 0 12 12 8 13 5 1 15 10 12 16 6 1 12 9 7 12 6 1 10 10 10 11 5 1 12 12 7 12 3 0 15 13 16 18 8 1 9 12 11 11 4 0 12 12 14 14 4 0 11 6 6 9 4 1 11 5 16 14 6 0 11 12 11 12 6 0 15 11 16 11 5 1 7 14 12 12 4 0 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 0 14 11 15 16 6 0 10 11 7 9 4 0 6 7 9 11 4 0 11 9 7 13 2 0 15 11 14 15 7 0 11 11 15 10 5 0 12 12 7 11 4 1 14 12 15 13 6 1 15 11 17 16 6 0 9 11 15 15 7 1 13 8 14 14 5 1 13 9 14 14 6 1 16 12 8 14 4 1 13 10 8 8 4 0 12 10 14 13 7 1 14 12 14 15 7 1 11 8 8 13 4 0 9 12 11 11 4 0 16 11 16 15 6 1 12 12 10 15 6 0 10 7 8 9 5 1 13 11 14 13 6 1 16 11 16 16 7 1 14 12 13 13 6 1 15 9 5 11 3 1 5 15 8 12 3 0 8 11 10 12 4 0 11 11 8 12 6 1 16 11 13 14 7 1 17 11 15 14 5 1 9 15 6 8 4 1 9 11 12 13 5 1 13 12 16 16 6 1 10 12 5 13 6 0 6 9 15 11 6 1 12 12 12 14 5 1 8 12 8 13 4 1 14 13 13 13 5 1 12 11 14 13 5 0 11 9 12 12 4 0 16 9 16 16 6 1 8 11 10 15 2 0 15 11 15 15 8 1 7 12 8 12 3 0 16 12 16 14 6 1 14 9 19 12 6 1 16 11 14 15 6 1 9 9 6 12 5 0 14 12 13 13 5 1 11 12 15 12 6 1 13 12 7 12 5 1 15 12 13 13 6 0 5 14 4 5 2 1 15 11 14 13 5 0 13 12 13 13 5 0 11 11 11 14 5 1 11 6 14 17 6 0 12 10 12 13 6 0 12 12 15 13 6 0 12 13 14 12 5 1 12 8 13 13 5 1 14 12 8 14 4 1 6 12 6 11 2 0 7 12 7 12 4 0 14 6 13 12 6 0 14 11 13 16 6 1 10 10 11 12 5 0 13 12 5 12 3 0 12 13 12 12 6 0 9 11 8 10 4 1 12 7 11 15 5 1 16 11 14 15 8 0 10 11 9 12 4 1 14 11 10 16 6 0 10 11 13 15 6 1 16 12 16 16 7 0 15 10 16 13 6 1 12 11 11 12 5 0 10 12 8 11 4 0 8 7 4 13 6 1 8 13 7 10 3 1 11 8 14 15 5 0 13 12 11 13 6 1 16 11 17 16 7 0 16 12 15 15 7 1 14 14 17 18 6 0 11 10 5 13 3 1 4 10 4 10 2 1 14 13 10 16 8 1 9 10 11 13 3 0 14 11 15 15 8 0 8 10 10 14 3 1 8 7 9 15 4 1 11 10 12 14 5 1 12 8 15 13 7 1 11 12 7 13 6 1 14 12 13 15 6 1 15 12 12 16 7 1 16 11 14 14 6 16 12 14 14 6 11 12 8 16 6 14 12 15 14 6 14 11 12 12 4 12 12 12 13 4 14 11 16 12 5 8 11 9 12 4 13 13 15 14 6 16 12 15 14 6 12 12 6 14 5 16 12 14 16 8 12 12 15 13 6 11 8 10 14 5 4 8 6 4 4 16 12 14 16 8 15 11 12 13 6 10 12 8 16 4 13 13 11 15 6 15 12 13 14 6 12 12 9 13 4 14 11 15 14 6 7 12 13 12 3 19 12 15 15 6 12 10 14 14 5 12 11 16 13 4 13 12 14 14 6 15 12 14 16 4 8 10 10 6 4 12 12 10 13 4 10 13 4 13 6 8 12 8 14 5 10 15 15 15 6 15 11 16 14 6 16 12 12 15 8 13 11 12 13 7 16 12 15 16 7 9 11 9 12 4 14 10 12 15 6 14 11 14 12 6 12 11 11 14 2
Names of X columns:
Gender Popularity FindingFriends KnowingPeople Liked Celebrity
Endogenous Variable (Column Number)


Categorization


Number of categories (only if categorization<>none)


Cross-Validation? (only if categorization<>none)


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+2par3,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(detcoefdetcoef,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') }

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in list(0, 13, 13, 14, 13, 3, 0, 12, 12, 8, 13, 5, 1, 15, 10, 12, : argument 697 is empty Calls: array Execution halted