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#' @title print method for class \code{BayesSUR}
#' @description
#' Print a short summary of a \code{BayesSUR} class object. It includes the
#' argument matching information, number of selected predictors based on
#' thresholding the posterior mean of the latent indicator variable at 0.5
#' by default.
#'
#' @name print.BayesSUR
#' @param x an object of class \code{BayesSUR}
#' @param Pmax threshold that truncates the estimated coefficients based on
#' thresholding the estimated latent indicator variable. Default is 0.5
#' @param ... other arguments
#'
#' @return Return a short summary from an object of class \code{BayesSUR},
#' including the number of selected predictors with mPIP>\code{Pmax} and the
#' expected log pointwise predictive density estimates (i.e., elpd.LOO and
#' elpd.WAIC).
#'
#' @examples
#' data("exampleEQTL", package = "BayesSUR")
#' hyperpar <- list(a_w = 2, b_w = 5)
#'
#' set.seed(9173)
#' fit <- BayesSUR(
#' Y = exampleEQTL[["blockList"]][[1]],
#' X = exampleEQTL[["blockList"]][[2]],
#' data = exampleEQTL[["data"]], outFilePath = tempdir(),
#' nIter = 10, burnin = 0, nChains = 1, gammaPrior = "hotspot",
#' hyperpar = hyperpar, tmpFolder = "tmp/", output_CPO = TRUE
#' )
#'
#' ## check output
#' # show the print information
#' print(fit)
#'
#' @export
print.BayesSUR <- function(x, Pmax = 0.5, ...) {
gamma <- as.matrix(read.table(paste(x$output$outFilePath,
x$output$gamma, sep = "")))
call.string <- unlist(strsplit(deparse(x$call), ","))
call.string[which(call.string == " ") + 1] <-
substr(call.string[which(call.string == " ") + 1], 4,
nchar(call.string[which(call.string == " ") + 1]))
call.string <- call.string[call.string != " "]
if (length(call.string) <= 3) {
cat("\nCall:\n ", paste(call.string,
c(rep(",", length(call.string) - 1), ""),
sep = "", collapse = ""), "\n", sep = "")
} else {
cat("\nCall:\n ", paste(call.string[1:3], c(",", ",", ", ...)"), sep = "",
collapse = ""), "\n", sep = "")
}
cat("\nNumber of selected predictors (mPIP > ", Pmax, "): ",
sum(gamma > Pmax), " of ", ncol(gamma), "x", nrow(gamma),
"\n", sep = "")
cat("\nExpected log pointwise predictive density (elpd):\n",
" elpd.LOO = ", elpd(x, method = "LOO"),
", elpd.WAIC = ", elpd(x, method = "WAIC"), "\n\n", sep = "")
}
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