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Data X:
1 12 2.4 3.8 0.12 0 6 1.1 1.4 -0.14 0 4 0.6 0.8 0.14 0 9 1.8 2.4 0.06 0 5 0.5 1 0.53 1 19 5.9 9.6 0.41 0 5 0.4 0.3 -0.58 0 4 0.6 1.8 0.97 0 6 0.9 1.3 0.09 1 19 0.8 2 0.42 1 4 0.6 1.3 0.96 0 6 0.4 0.7 0.25 0 5 0.4 0.7 0.05 0 4 0.3 0.5 0.13 1 10 5.9 8.6 0.16 1 9 1.5 2.7 0.12 1 11 1 1.7 0.11 0 16 4.8 6.6 0.23 0 3 1.1 1 -0.16 0 3 0.5 0.7 -0.07 0 8 0.9 1.3 0.28 0 6 0.8 1.2 0.08 0 5 1.1 2 0.11 0 7 0.8 1.6 0.02 0 5 0.6 1.1 0.01 1 3 0.5 0.5 -0.39 1 8 0.7 1.7 0.5 0 10 1 0.9 -0.18 0 5 0.4 0.8 0.12 0 5 1.2 1 -0.51 0 9 1 0.9 -0.21 0 9 1.2 2 0.34 1 17 4.7 7.8 0.58 0 5 0.3 1.7 1.8 1 10 8.1 12.5 0.32 0 7 1.6 3.5 0.36 0 5 1 1.5 0.15 0 10 1.4 2.2 0.23 0 5 0.7 0.5 -0.2 0 5 0.9 1.1 -0.08 1 6 0.7 3.1 1.61 1 21 6.4 8.7 0.19 0 7 2.4 2.2 -0.19 1 7 0.7 1.7 0.28 1 6 2.5 6.8 0.91 0 7 0.6 1.6 0.46 0 6 1 1.6 0.31 0 6 2.3 2.3 -0.06 1 4 0.9 0.6 -0.43 0 8 1.2 1.9 0.34 1 4 0.6 1.2 0.43 0 4 0.7 1.2 0.08 0 5 0.7 0.9 0.17 0 5 0.7 0.9 0.09 0 4 0.9 0.7 -0.41 0 8 0.8 1.6 0.8 0 5 0.3 1 0.69 0 5 1 1.4 -0.02 0 5 0.5 0.7 0.04 1 4 0.7 1.1 0.38 0 5 0.9 2.1 0.21 0 5 0.4 0.9 0.73 1 5 0.8 1.5 0.42 0 5 0.9 1.4 -0.09 1 6 0.8 1.5 0.17 0 6 0.8 0.6 -0.38 0 9 1.1 2.1 0.21 0 7 1.3 1.3 -0.02 0 7 1.1 1.1 -0.32 0 5 0.9 1.4 -0.16 0 3 0.5 1.5 0.6 0 6 0.7 1.6 0.14 0 8 1.2 1.6 0.21 1 11 2.4 5.5 0.19 1 7 0.7 2.2 0.78 1 9 1.1 1.7 0.44 0 6 1.8 2.4 -0.02 0 4 1 1.1 -0.42 0 6 0.8 1 -0.05 0 9 0.8 1.2 0.29 1 8 0.9 2.1 0.75 1 5 0.9 1.1 0.01 0 6 0.7 3.3 1.49 0 12 9 15.4 0.46 0 5 1.4 2.6 0.14 0 5 1.1 0.6 -0.44 0 7 1 1.8 0.34 0 5 1 0.7 -0.31 0 6 0.9 1.2 0.08 1 11 2.3 4.3 0.13 1 15 6.2 14.5 1.08 0 6 0.6 1.2 0.06 0 4 0.3 0.4 -0.04 1 13 4.9 7.6 0.46 0 3 0.7 0.7 -0.41 0 5 1.7 1.4 -0.28 0 6 0.7 0.8 -0.06 0 7 1.6 2.3 0.28
Names of X columns:
ggananT Taille60 RatioSg60 RatioSg120 IRglobal
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') } } print(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') }
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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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