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Data X:
0 13 26 9 15 25 25 0 16 20 9 15 25 24 0 19 21 9 14 19 21 1 15 31 14 10 18 23 0 14 21 8 10 18 17 0 13 18 8 12 22 19 0 19 26 11 18 29 18 0 15 22 10 12 26 27 0 14 22 9 14 25 23 0 15 29 15 18 23 23 1 16 15 14 9 23 29 0 16 16 11 11 23 21 1 16 24 14 11 24 26 0 17 17 6 17 30 25 1 15 19 20 8 19 25 1 15 22 9 16 24 23 0 20 31 10 21 32 26 1 18 28 8 24 30 20 0 16 38 11 21 29 29 1 16 26 14 14 17 24 0 19 25 11 7 25 23 0 16 25 16 18 26 24 1 17 29 14 18 26 30 0 17 28 11 13 25 22 1 16 15 11 11 23 22 0 15 18 12 13 21 13 1 14 21 9 13 19 24 0 15 25 7 18 35 17 1 12 23 13 14 19 24 0 14 23 10 12 20 21 0 16 19 9 9 21 23 1 14 18 9 12 21 24 1 10 26 16 5 23 24 1 14 18 12 10 19 23 0 16 18 6 11 17 26 1 16 28 14 11 24 24 1 16 17 14 12 15 21 0 14 29 10 12 25 23 1 20 12 4 15 27 28 1 14 25 12 12 29 23 0 14 28 12 16 27 22 0 11 20 14 14 18 24 0 15 17 9 17 25 21 0 16 17 9 13 22 23 1 14 20 10 10 26 23 0 16 31 14 17 23 20 1 14 21 10 12 16 23 1 12 19 9 13 27 21 0 16 23 14 13 25 27 1 9 15 8 11 14 12 0 14 24 9 13 19 15 0 16 28 8 12 20 22 0 16 16 9 12 16 21 1 15 19 9 12 18 21 0 16 21 9 9 22 20 1 12 21 15 7 21 24 1 16 20 8 17 22 24 0 16 16 10 12 22 29 0 14 25 8 12 32 25 0 16 30 14 9 23 14 1 17 29 11 9 31 30 0 18 22 10 13 18 19 1 18 19 12 10 23 29 0 12 33 14 11 26 25 1 16 17 9 12 24 25 1 10 9 13 10 19 25 0 14 14 15 13 14 16 0 18 15 8 6 20 25 1 18 12 7 7 22 28 1 16 21 10 13 24 24 0 16 20 10 11 25 25 0 16 29 13 18 21 21 1 13 33 11 9 28 22 1 16 21 8 9 24 20 1 16 15 12 11 20 25 1 20 19 9 11 21 27 0 16 23 10 15 23 21 1 15 20 11 8 13 13 0 15 20 11 11 24 26 0 16 18 10 14 21 26 1 14 31 16 14 21 25 0 15 18 16 12 17 22 0 12 13 8 12 14 19 0 17 9 6 8 29 23 0 16 20 11 11 25 25 0 15 18 12 10 16 15 0 13 23 14 17 25 21 0 16 17 9 16 25 23 0 16 17 11 13 21 25 0 16 16 8 15 23 24 1 16 31 8 11 22 24 1 14 15 7 12 19 21 0 16 28 16 16 24 24 1 16 26 13 20 26 22 0 20 20 8 16 25 24 1 15 19 11 11 20 28 0 16 25 14 15 22 21 1 13 18 10 15 14 17 0 17 20 10 12 20 28 1 16 33 14 9 32 24 0 12 24 14 24 21 10 0 16 22 10 15 22 20 0 16 32 12 18 28 22 0 17 31 9 17 25 19 1 13 13 16 12 17 22 0 12 18 8 15 21 22 1 18 17 9 11 23 26 0 14 29 16 11 27 24 0 14 22 13 15 22 22 0 13 18 13 12 19 20 0 16 22 8 14 20 20 0 13 25 14 11 17 15 0 16 20 11 20 24 20 0 13 20 9 11 21 20 0 16 17 8 12 21 24 0 15 21 13 17 23 22 0 16 26 13 12 24 29 1 15 10 10 11 19 23 0 17 15 8 10 22 24 0 15 20 7 11 26 22 0 12 14 11 12 17 16 1 16 16 11 9 17 23 1 10 23 14 8 19 27 0 16 11 6 6 15 16 1 14 19 10 12 17 21 0 15 30 9 15 27 26 1 13 21 12 13 19 22 1 15 20 11 17 21 23 0 11 22 14 14 25 19 0 12 30 12 16 19 18 0 16 28 8 16 18 24 1 15 23 14 11 20 29 0 17 23 8 11 15 22 1 16 21 11 16 20 24 0 10 30 12 15 29 22 0 18 22 9 14 19 12 1 13 32 16 9 29 26 0 15 22 11 13 24 18 1 16 15 11 11 23 22 0 16 21 12 14 22 24 0 14 27 15 11 23 21 0 10 22 13 12 22 15 0 17 9 6 8 29 23 0 13 29 11 7 26 22 0 15 20 7 11 26 22 0 16 16 8 13 21 24 0 12 16 8 9 18 23 0 13 16 9 12 10 13
Names of X columns:
Gender Learning Concern Doubts Expectations Standards Organization
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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