R/print.bayesQR.summary.r

In bayesQR: Bayesian Quantile Regression

print.bayesQR.summary <- function(x, digits=3, ...){

	# Number of quantile regression summaries to print
	nqr <- length(x)

	# Loop trough every quantile regression 
	for (i in 1:nqr){
		QRsub <- x[[i]]
		cat("\n")
		if (QRsub$method=="QRc"){
			cat("Type of dependent variable: continuous\n")
			cat("Lasso variable selection: no\n")
		} else if (QRsub$method=="QRc.AL"){
			cat("Type of dependent variable: continuous\n")
			cat("Lasso variable selection: yes\n")
		} else if (QRsub$method=="QRb"){
			cat("Type of dependent variable: binary\n")
			cat("Lasso variable selection: no\n")
		} else if (QRsub$method=="QRb.AL"){
			cat("Type of dependent variable: binary\n")
			cat("Lasso variable selection: yes\n")
		}
		if (QRsub$normal.approx){
			cat("Normal approximation of posterior: yes\n")
		} else {
			cat("Normal approximation of posterior: no\n")
		}
		cat(paste("Estimated quantile: ",QRsub$quantile),"\n")
		cat(paste("Lower credible bound: ",QRsub$credint[1]),"\n")
		cat(paste("Upper credible bound: ",QRsub$credint[2]),"\n")
		cat(paste("Number of burnin draws: ",QRsub$burnin),"\n")
		cat(paste("Number of retained draws: ",QRsub$retained),"\n")
		cat("\n")
		cat("\n")
		cat("Summary of the estimated beta:\n")
		cat("\n")
		print(QRsub$betadraw,digits=digits)
		cat("\n")
		if ((QRsub$method %in% c("QRc","QRc.AL"))&(!QRsub$normal.approx)){
			cat("\n")
			cat("Summary of the estimated sigma:\n")
			cat("\n")
			print(QRsub$sigmadraw,digits=digits)
			cat("\n")
		}
		if ((nqr>1)&(i<nqr)) cat("*****************************************\n")
	}
}