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Data X:
1 1 41 38 13 12 14 1 1 39 32 16 11 18 1 1 30 35 19 15 11 1 0 31 33 15 6 12 1 1 34 37 14 13 16 1 1 35 29 13 10 18 1 1 39 31 19 12 14 1 1 34 36 15 14 14 1 1 36 35 14 12 15 1 1 37 38 15 9 15 1 0 38 31 16 10 17 1 1 36 34 16 12 19 1 0 38 35 16 12 10 1 1 39 38 16 11 16 1 1 33 37 17 15 18 1 0 32 33 15 12 14 1 0 36 32 15 10 14 1 1 38 38 20 12 17 1 0 39 38 18 11 14 1 1 32 32 16 12 16 1 0 32 33 16 11 18 1 1 31 31 16 12 11 1 1 39 38 19 13 14 1 1 37 39 16 11 12 1 0 39 32 17 12 17 1 1 41 32 17 13 9 1 0 36 35 16 10 16 1 1 33 37 15 14 14 1 1 33 33 16 12 15 1 0 34 33 14 10 11 1 1 31 31 15 12 16 1 0 27 32 12 8 13 1 1 37 31 14 10 17 1 1 34 37 16 12 15 1 0 34 30 14 12 14 1 0 32 33 10 7 16 1 0 29 31 10 9 9 1 0 36 33 14 12 15 1 1 29 31 16 10 17 1 0 35 33 16 10 13 1 0 37 32 16 10 15 1 1 34 33 14 12 16 1 0 38 32 20 15 16 1 0 35 33 14 10 12 1 1 38 28 14 10 15 1 1 37 35 11 12 11 1 1 38 39 14 13 15 1 1 33 34 15 11 15 1 1 36 38 16 11 17 1 0 38 32 14 12 13 1 1 32 38 16 14 16 1 0 32 30 14 10 14 1 0 32 33 12 12 11 1 1 34 38 16 13 12 1 0 32 32 9 5 12 1 1 37 35 14 6 15 1 1 39 34 16 12 16 1 1 29 34 16 12 15 1 0 37 36 15 11 12 1 1 35 34 16 10 12 1 0 30 28 12 7 8 1 0 38 34 16 12 13 1 1 34 35 16 14 11 1 1 31 35 14 11 14 1 1 34 31 16 12 15 1 0 35 37 17 13 10 1 1 36 35 18 14 11 1 0 30 27 18 11 12 1 1 39 40 12 12 15 1 0 35 37 16 12 15 1 0 38 36 10 8 14 1 1 31 38 14 11 16 1 1 34 39 18 14 15 1 0 38 41 18 14 15 1 0 34 27 16 12 13 1 1 39 30 17 9 12 1 1 37 37 16 13 17 1 1 34 31 16 11 13 1 0 28 31 13 12 15 1 0 37 27 16 12 13 1 0 33 36 16 12 15 1 1 35 37 16 12 15 1 0 37 33 15 12 16 1 1 32 34 15 11 15 1 1 33 31 16 10 14 1 0 38 39 14 9 15 1 1 33 34 16 12 14 1 1 29 32 16 12 13 1 1 33 33 15 12 7 1 1 31 36 12 9 17 1 1 36 32 17 15 13 1 1 35 41 16 12 15 1 1 32 28 15 12 14 1 1 29 30 13 12 13 1 1 39 36 16 10 16 1 1 37 35 16 13 12 1 1 35 31 16 9 14 1 0 37 34 16 12 17 1 0 32 36 14 10 15 1 1 38 36 16 14 17 1 0 37 35 16 11 12 1 1 36 37 20 15 16 1 0 32 28 15 11 11 1 1 33 39 16 11 15 1 0 40 32 13 12 9 1 1 38 35 17 12 16 1 0 41 39 16 12 15 1 0 36 35 16 11 10 1 1 43 42 12 7 10 1 1 30 34 16 12 15 1 1 31 33 16 14 11 1 1 32 41 17 11 13 1 1 37 34 12 10 18 1 0 37 32 18 13 16 1 1 33 40 14 13 14 1 1 34 40 14 8 14 1 1 33 35 13 11 14 1 1 38 36 16 12 14 1 0 33 37 13 11 12 1 1 31 27 16 13 14 1 1 38 39 13 12 15 1 1 37 38 16 14 15 1 1 36 31 15 13 15 1 1 31 33 16 15 13 1 0 39 32 15 10 17 1 1 44 39 17 11 17 1 1 33 36 15 9 19 1 1 35 33 12 11 15 1 0 32 33 16 10 13 1 0 28 32 10 11 9 1 1 40 37 16 8 15 1 0 27 30 12 11 15 1 0 37 38 14 12 15 1 1 32 29 15 12 16 1 0 28 22 13 9 11 1 0 34 35 15 11 14 1 1 30 35 11 10 11 1 1 35 34 12 8 15 1 0 31 35 11 9 13 1 1 32 34 16 8 15 1 0 30 37 15 9 16 1 1 30 35 17 15 14 1 0 31 23 16 11 15 1 1 40 31 10 8 16 1 1 32 27 18 13 16 1 0 36 36 13 12 11 1 0 32 31 16 12 12 1 0 35 32 13 9 9 1 1 38 39 10 7 16 1 1 42 37 15 13 13 1 0 34 38 16 9 16 1 1 35 39 16 6 12 1 1 38 34 14 8 9 1 1 33 31 10 8 13 1 1 32 37 13 6 14 1 1 33 36 15 9 19 1 1 34 32 16 11 13 1 1 32 38 12 8 12 0 0 27 26 13 10 10 0 0 31 26 12 8 14 0 0 38 33 17 14 16 0 1 34 39 15 10 10 0 0 24 30 10 8 11 0 0 30 33 14 11 14 0 1 26 25 11 12 12 0 1 34 38 13 12 9 0 0 27 37 16 12 9 0 0 37 31 12 5 11 0 1 36 37 16 12 16 0 0 41 35 12 10 9 0 1 29 25 9 7 13 0 1 36 28 12 12 16 0 0 32 35 15 11 13 0 1 37 33 12 8 9 0 0 30 30 12 9 12 0 1 31 31 14 10 16 0 1 38 37 12 9 11 0 1 36 36 16 12 14 0 0 35 30 11 6 13 0 0 31 36 19 15 15 0 0 38 32 15 12 14 0 1 22 28 8 12 16 0 1 32 36 16 12 13 0 0 36 34 17 11 14 0 1 39 31 12 7 15 0 0 28 28 11 7 13 0 0 32 36 11 5 11 0 1 32 36 14 12 11 0 1 38 40 16 12 14 0 1 32 33 12 3 15 0 1 35 37 16 11 11 0 1 32 32 13 10 15 0 0 37 38 15 12 12 0 1 34 31 16 9 14 0 1 33 37 16 12 14 0 0 33 33 14 9 8 0 0 30 30 16 12 9 0 0 24 30 14 10 15 0 0 34 31 11 9 17 0 0 34 32 12 12 13 0 1 33 34 15 8 15 0 1 34 36 15 11 15 0 1 35 37 16 11 14 0 0 35 36 16 12 16 0 0 36 33 11 10 13 0 0 34 33 15 10 16 0 1 34 33 12 12 9 0 0 41 44 12 12 16 0 0 32 39 15 11 11 0 0 30 32 15 8 10 0 1 35 35 16 12 11 0 0 28 25 14 10 15 0 1 33 35 17 11 17 0 1 39 34 14 10 14 0 0 36 35 13 8 8 0 1 36 39 15 12 15 0 0 35 33 13 12 11 0 0 38 36 14 10 16 0 1 33 32 15 12 10 0 0 31 32 12 9 15 0 1 32 36 8 6 16 0 0 31 32 14 10 19 0 0 33 34 14 9 12 0 0 34 33 11 9 8 0 0 34 35 12 9 11 0 1 34 30 13 6 14 0 0 33 38 10 10 9 0 0 32 34 16 6 15 0 1 41 33 18 14 13 0 1 34 32 13 10 16 0 0 36 31 11 10 11 0 0 37 30 4 6 12 0 0 36 27 13 12 13 0 1 29 31 16 12 10 0 0 37 30 10 7 11 0 0 27 32 12 8 12 0 0 35 35 12 11 8 0 0 28 28 10 3 12 0 0 35 33 13 6 12 0 0 29 35 12 8 11 0 0 32 35 14 9 13 0 1 36 32 10 9 14 0 1 19 21 12 8 10 0 1 21 20 12 9 12 0 0 31 34 11 7 15 0 0 33 32 10 7 13 0 1 36 34 12 6 13 0 1 33 32 16 9 13 0 0 37 33 12 10 12 0 0 34 33 14 11 12 0 0 35 37 16 12 9 0 1 31 32 14 8 9 0 1 37 34 13 11 15 0 1 35 30 4 3 10 0 1 27 30 15 11 14 0 0 34 38 11 12 15 0 0 40 36 11 7 7 0 0 29 32 14 9 14 0 0 38 34 15 12 8 0 1 34 33 14 8 10 0 0 21 27 13 11 13 0 0 36 32 11 8 13 0 1 38 34 15 10 13 0 0 30 29 11 8 8 0 0 35 35 13 7 12 0 1 30 27 13 8 13 0 1 36 33 16 10 12 0 0 34 38 13 8 10 0 1 35 36 16 12 13 0 0 34 33 16 14 12 0 0 32 39 12 7 9 0 1 33 29 7 6 15 0 0 33 32 16 11 13 0 1 26 34 5 4 13 0 0 35 38 16 9 13 0 0 21 17 4 5 15 0 0 38 35 12 9 15 0 0 35 32 15 11 14 0 1 33 34 14 12 15 0 0 37 36 11 9 11 0 0 38 31 16 12 15 0 1 34 35 15 10 14 0 0 27 29 12 9 13 0 1 16 22 6 6 12 0 0 40 41 16 10 16 0 0 36 36 10 9 16 0 1 42 42 15 13 9 0 1 30 33 14 12 14
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
view raw input (R code)
Raw Output
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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