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Data X:
14 11 23 8 1 6 7 22 24 4 2 5 22 23 24 7 2 20 12 21 21 4 2 12 15 19 21 4 2 11 9 12 19 5 2 12 20 24 12 15 1 11 10 21 21 5 1 9 12 21 25 7 2 13 23 26 27 4 2 9 10 18 21 4 1 14 11 21 27 7 1 12 20 22 20 8 1 18 11 26 16 4 2 9 22 20 26 8 1 15 19 20 24 4 2 12 20 26 25 5 2 12 16 27 25 16 1 12 12 27 27 7 1 15 14 16 23 4 2 11 14 26 22 6 1 13 9 20 10 4 1 10 19 25 25 5 2 17 17 16 18 4 1 13 14 20 21 4 1 17 19 20 20 6 1 15 20 24 18 4 1 13 20 24 25 4 1 17 9 22 28 4 1 21 10 18 27 8 1 12 6 21 20 5 2 12 15 17 20 4 1 15 9 15 20 10 2 8 24 28 27 4 2 15 11 23 23 4 1 16 4 19 23 4 2 9 12 15 22 5 2 13 22 26 26 5 1 11 16 20 21 4 1 9 14 11 17 6 1 15 13 17 27 4 2 9 13 16 16 4 2 15 10 21 26 4 1 14 12 18 17 4 1 8 13 17 24 4 2 11 16 21 23 4 2 14 18 18 20 6 1 14 10 16 10 4 1 12 12 13 21 5 1 15 9 28 25 4 1 11 7 25 28 4 1 11 16 24 25 5 2 9 12 15 20 10 2 8 15 21 20 10 1 13 15 11 27 4 1 12 8 27 26 4 1 24 14 23 19 4 2 11 13 21 26 8 1 11 18 16 20 4 2 16 11 20 22 14 1 12 12 21 19 4 2 18 12 10 23 5 2 12 24 18 28 4 2 14 11 20 22 8 2 16 5 21 27 4 2 24 17 24 14 4 1 13 9 26 25 5 1 11 20 23 22 8 1 14 17 22 24 7 1 16 14 13 23 4 1 12 23 27 25 4 1 21 10 24 28 9 2 11 19 19 28 4 1 6 5 17 16 4 2 9 16 16 25 5 1 14 19 20 21 4 1 16 5 8 27 4 1 18 15 16 21 6 2 9 18 17 22 6 1 13 20 23 26 4 2 17 17 18 21 6 1 11 19 24 24 4 1 16 11 17 24 6 1 11 12 20 23 4 1 11 13 22 26 8 2 11 7 22 21 5 1 20 8 20 24 8 1 10 15 18 23 7 1 12 13 21 21 4 2 11 18 23 20 6 1 14 19 28 22 4 1 12 12 19 26 5 1 12 12 22 23 6 1 12 17 17 23 4 2 10 17 25 22 4 2 12 11 22 25 4 2 10 11 21 21 8 2 10 17 15 21 9 1 13 5 20 25 4 1 12 8 25 26 12 2 13 17 21 21 4 1 9 18 24 24 8 1 14 17 23 21 8 2 14 17 22 23 4 1 12 10 14 24 4 1 18 8 11 24 4 1 17 9 22 24 15 1 12 13 22 25 3 1 15 14 6 28 8 1 8 5 15 18 4 2 8 16 26 28 5 1 12 22 26 22 4 1 10 15 20 28 3 1 18 14 26 22 11 1 15 8 15 24 6 1 16 10 25 27 4 2 11 18 22 21 5 2 10 18 20 26 4 2 7 9 18 24 16 1 17 15 23 25 8 1 7 9 22 20 4 2 14 15 23 21 4 1 12 21 17 23 4 1 15 9 20 23 5 1 13 16 21 19 8 2 10 15 23 22 4 1 16 10 25 15 4 2 11 4 25 24 4 2 7 12 21 18 8 2 15 14 22 18 8 1 18 14 18 23 4 1 11 18 18 17 18 1 13 19 18 19 4 2 11 16 21 21 5 2 13 7 21 12 4 2 12 12 25 25 4 2 11 18 24 25 4 1 11 13 24 24 7 1 13 21 28 24 4 2 8 24 24 24 6 2 12 17 22 22 4 2 9 12 22 22 4 1 14 12 20 21 6 1 18 10 25 23 5 1 15 14 13 21 4 1 9 14 21 24 8 1 11 13 23 22 6 1 17 17 18 25 5 2 12
Names of X columns:
I/Exp.Stimulation E/Introjected E/Ext.Regulation Amotivation gender PE
Endogenous Variable (Column Number)
Categorization
quantiles
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
yes
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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