R/print-msaenet.R

In road2stat/msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

#' Print msaenet Model Information
#'
#' Print msaenet model objects (currently, only
#' printing the model information of the final step).
#'
#' @param x An object of class \code{msaenet}.
#' @param ... Additional parameters for \code{\link{print}} (not used).
#'
#' @method print msaenet
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @importFrom utils capture.output
#'
#' @export
#'
#' @examples
#' dat <- msaenet.sim.gaussian(
#'   n = 150, p = 500, rho = 0.6,
#'   coef = rep(1, 5), snr = 2, p.train = 0.7,
#'   seed = 1001
#' )
#'
#' msaenet.fit <- msaenet(
#'   dat$x.tr, dat$y.tr,
#'   alphas = seq(0.2, 0.8, 0.2),
#'   nsteps = 3L, seed = 1003
#' )
#'
#' print(msaenet.fit)
print.msaenet <- function(x, ...) {
  cat(paste("Call:", paste(capture.output(x$"call"), collapse = "\n")), "\n")

  if (.is.ncvreg(x$"model")) {
    model.info <- data.frame(
      .df(x$"model"),
      x$"model"$"lambda",
      x$"model"$"gamma",
      x$"model"$"alpha"
    )
    names(model.info) <- c("Df", "Lambda", "Gamma", "Alpha")
    print(model.info)
  }

  if (.is.glmnet(x$"model")) {
    model.info <- data.frame(
      .df(x$"model"),
      x$"model"$"dev.ratio",
      x$"model"$"lambda"
    )
    names(model.info) <- c("Df", "%Dev", "Lambda")
    print(model.info)
  }
}