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Data X:
1 0 32 31 13 12 15 1 1 33 34 8 8 11 1 0 38 27 14 12 12 1 0 34 24 14 11 9 1 1 41 34 13 11 14 1 1 39 35 16 13 16 1 1 35 27 14 11 15 1 1 34 30 13 10 16 1 0 47 31 15 7 7 1 1 32 31 13 10 13 1 0 28 28 16 12 15 1 1 44 48 20 15 20 1 1 40 40 17 12 16 1 1 29 31 15 12 16 1 0 30 27 16 12 15 1 1 41 37 16 10 15 1 1 32 29 12 10 17 1 0 33 34 9 8 12 1 0 33 33 15 11 15 1 0 40 37 17 14 13 1 1 38 35 12 12 9 0 1 37 34 10 11 14 1 0 41 35 11 6 16 0 0 32 33 16 12 9 1 1 29 29 16 14 14 1 0 38 31 15 11 14 0 1 35 37 13 8 15 1 0 40 31 14 12 14 1 1 43 40 19 15 17 1 1 31 41 16 13 15 1 0 34 29 17 11 12 1 1 26 34 10 12 16 1 1 28 41 15 7 14 1 1 31 34 14 11 14 0 1 32 36 14 7 14 0 0 29 30 16 12 15 1 1 32 36 17 12 15 1 1 35 31 15 12 16 1 0 31 35 17 13 14 1 0 37 35 14 12 14 1 1 34 33 10 9 17 1 0 35 31 14 9 10 1 0 36 31 16 11 10 1 0 45 35 18 14 12 1 1 39 35 15 12 16 1 1 32 28 16 15 14 1 1 39 27 16 12 17 1 1 34 33 10 6 12 1 0 34 33 8 5 16 0 1 34 35 17 13 15 1 1 37 30 14 11 14 1 1 27 29 12 11 15 1 0 43 30 10 6 14 1 1 40 42 14 12 16 1 1 40 36 12 10 16 1 1 35 36 16 6 17 1 1 37 33 16 12 15 1 1 39 34 15 14 15 1 0 26 33 11 6 6 0 1 29 30 16 11 14 1 0 34 25 8 6 12 1 1 32 40 17 14 10 1 1 38 36 16 12 12 0 1 39 33 15 12 14 1 0 27 35 8 8 18 0 1 40 25 13 10 12 0 1 37 39 14 11 15 0 1 34 32 13 7 8 1 1 36 34 16 12 11 0 0 34 38 12 9 16 0 1 36 29 19 13 14 1 1 32 39 19 14 16 1 1 43 36 12 6 7 1 1 47 32 14 12 16 0 0 24 38 15 6 9 1 1 40 39 13 14 8 1 0 33 32 16 12 15 0 0 38 31 10 10 10 0 1 33 31 15 10 12 0 1 36 30 16 12 11 1 1 39 44 15 11 14 1 0 37 28 11 10 18 1 0 38 36 9 7 12 1 1 36 30 16 12 17 0 1 30 31 12 12 16 1 0 36 32 14 12 11 1 1 41 32 14 10 9 1 1 32 35 13 10 18 0 0 35 33 15 12 14 1 0 41 32 17 12 13 1 0 36 32 14 12 16 1 0 34 27 9 9 10 0 0 35 28 11 8 13 0 1 36 36 9 10 16 1 0 43 35 7 5 9 1 1 36 27 13 10 12 1 0 36 34 15 10 10 0 1 34 31 12 12 16 1 0 36 33 15 11 12 0 0 32 32 14 9 16 0 1 27 33 15 15 15 0 0 32 35 9 8 8 1 1 41 31 16 12 17 1 1 40 33 16 12 13 1 1 30 30 14 10 16 0 0 37 28 14 11 13 0 0 35 31 13 10 15 0 1 39 31 14 11 13 0 0 35 30 16 12 16 0 1 27 38 16 11 14 0 1 37 35 13 10 18 0 0 37 28 12 9 10 0 0 38 37 16 9 13 0 1 38 36 16 11 14 0 1 41 34 16 12 18 0 0 38 27 10 7 9 0 0 39 29 14 12 15 0 0 31 30 12 11 15 0 0 39 35 12 12 11 0 1 32 32 12 6 17 0 1 35 32 12 9 10 0 1 45 39 19 15 13 0 0 29 27 14 10 14 0 1 26 34 13 11 16 0 1 35 31 17 14 17 0 0 40 30 16 12 16 0 1 39 36 15 12 16 0 1 35 35 12 12 13 0 1 34 33 8 11 14 0 1 35 36 10 9 13 0 1 33 36 16 11 16 0 0 37 28 10 6 7 0 0 35 31 16 12 15 0 1 38 33 10 12 14 0 1 35 42 18 14 12 0 1 29 35 12 8 7 0 0 0 5 16 10 14 0 0 30 28 10 9 15 0 0 32 31 15 9 10 0 1 43 41 17 11 17 0 0 37 27 14 10 12 0 0 33 32 12 9 13 0 0 41 30 11 10 13 0 0 39 30 15 12 12 0 1 39 33 7 11 11
Names of X columns:
Pop Gender Connected Separate Learning Software Happiness
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
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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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