R/print.graper.R

In graper: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes

#' @title Print a graper object
#' @name print.graper
#' @description Function to print a fitted graper model.
#' @param x fitted graper model as obtained from  \code{\link{graper}}
#' @param ... additional print arguments
#' @return Print output.
#' @importFrom methods is
#' @export
#' @examples
#' # create data
#' dat <- makeExampleData()
#' # fit the graper model
#' fit <- graper(dat$X, dat$y, dat$annot)
#' # print a summary of the fitted model
#' print(fit)

print.graper <- function(x, ...){
    # sanity check
    if(!is(x, "graper")) {
        stop("object needs to be a graper object.")
    }

    cat(ifelse(x$Options$spikeslab, "Sparse", "Dense"), "graper object for a",
        ifelse(x$Options$family == "gaussian", "linear", "logistic"),
        "regression model with", nrow(x$EW_beta),
        "predictors in", length(unique(x$annot)), "groups.\n",
        "Group-wise shrinkage:\n",
        paste(unique(as.character(x$annot)), collapse="\t"), "\n",
        paste(round(x$EW_gamma, 2), collapse="\t"), "\n")

    if(x$Options$spikeslab) {
        cat("Group-wise sparsity (1 = dense, 0 = sparse):\n")
        cat(paste(unique(as.character(x$annot)), collapse="\t"), "\n")
        cat(paste(round(x$EW_pi, 2), collapse="\t"))
    }

}