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Data X:
13 15 2 9 42 12 12 18 1 9 51 15 15 11 1 9 42 14 12 16 1 8 46 10 10 12 2 14 41 10 12 17 2 14 49 9 15 15 1 15 47 18 9 19 1 11 33 11 11 18 1 8 47 12 11 10 2 14 42 11 11 14 1 9 32 15 15 18 1 6 53 17 7 18 2 14 41 14 11 14 2 8 41 24 11 14 1 11 33 7 10 12 1 16 37 18 14 16 2 11 43 11 6 13 2 13 33 14 11 16 1 7 49 18 15 14 2 9 42 12 11 9 1 15 43 11 12 9 2 16 37 5 14 17 1 10 43 12 15 13 2 14 42 11 9 15 2 12 43 10 13 17 1 6 46 11 13 16 2 4 33 15 16 12 1 12 42 16 13 11 1 14 40 14 12 16 2 13 44 8 14 17 1 9 42 13 11 17 2 14 52 18 9 16 1 14 44 17 16 13 2 10 45 10 12 12 1 14 46 13 10 12 2 8 36 11 13 16 1 8 45 12 16 14 1 10 49 12 14 12 2 9 43 12 15 12 1 9 43 9 5 14 1 11 37 18 8 8 2 15 32 7 11 15 1 9 45 14 16 14 2 9 45 16 17 11 1 10 45 12 9 13 2 8 45 17 9 14 1 8 31 12 13 15 1 14 33 9 10 16 1 10 44 12 6 10 2 11 49 9 12 11 2 9 44 13 8 12 2 12 41 10 14 14 2 13 44 10 12 15 1 14 38 11 11 16 1 15 33 13 16 9 1 11 47 13 8 11 2 9 37 13 15 15 1 8 48 6 7 15 2 7 40 7 16 13 2 10 50 13 14 17 1 10 54 21 16 17 1 10 43 11 9 15 1 9 54 9 14 13 1 13 44 18 11 15 2 11 47 9 13 13 2 8 33 9 15 15 1 10 45 15 5 10 2 14 33 9 15 15 1 11 44 11 13 14 1 10 47 14 11 15 2 16 45 14 11 16 2 11 43 8 12 7 1 16 43 12 12 13 1 6 33 8 12 15 1 11 46 11 14 13 1 14 47 17 6 16 1 9 47 16 7 16 2 9 0 11 14 12 1 11 43 13 13 15 2 12 46 11 12 14 2 20 36 8 9 11 2 11 42 11 12 14 1 12 44 13 16 15 1 9 47 13 10 9 2 10 41 15 14 15 1 14 47 15 10 17 1 8 46 12 16 16 1 10 47 12 15 14 1 8 46 15 12 15 2 7 46 12 10 16 1 11 36 21 8 10 1 14 30 24 8 17 2 8 48 11 11 15 2 14 45 12 13 15 1 10 49 15 16 13 1 9 55 17 14 14 2 16 11 12 11 16 1 8 52 16 4 11 2 12 33 13 14 18 1 8 47 15 9 14 1 16 33 11 14 14 1 13 44 15 8 14 1 13 42 12 8 14 1 8 55 14 11 15 1 9 42 12 12 14 1 11 46 20 14 15 1 9 46 17 15 15 2 8 47 12 16 12 1 14 33 11 16 19 1 7 53 11 14 13 2 11 42 9 12 15 1 11 44 12 14 17 2 10 55 11 8 9 2 14 40 8 16 15 2 10 46 12 12 16 1 9 53 15 12 17 1 8 44 10 11 11 1 14 35 14 4 15 1 12 40 16 16 11 1 12 44 18 15 15 1 6 46 6 10 17 1 16 45 16 13 14 1 8 53 11 15 12 2 13 45 20 12 14 1 12 48 10 14 15 2 11 46 16 7 16 1 12 55 15 19 16 1 9 47 14 12 14 1 11 43 7 12 11 2 16 38 9 8 14 2 10 40 12 12 13 1 13 47 12 10 13 1 11 47 13 8 14 2 11 42 17 10 16 2 9 53 11 14 16 2 11 43 11 16 12 1 12 44 14 13 11 1 10 42 13 16 13 1 13 51 12 9 15 1 9 54 11 14 13 2 14 41 15 14 16 2 14 51 11 12 13 1 8 51 13
Names of X columns:
Popularity Gender Belonging Happiness Doubtsaboutactions Parentalexpectations
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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