R/print.ernet.R

In SALES: The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms

#' Print an ernet object
#'
#' Print a summary of the ernet path at each step along the path.
#'
#' @param x fitted \code{\link{ernet}} object.
#' @param digits significant digits in the output.
#' @param \dots additional print arguments.
#'
#' @details The call that produced the \code{\link{ernet}} object is printed,
#'   followed by a two-column matrix with columns \code{Df} and \code{Lambda}.
#'   The \code{Df} column is the number of nonzero coefficients.
#'
#' @return a two-column matrix, the first columns is the number of nonzero
#'   coefficients and the second column is \code{Lambda}.
#'
#' @author Yuwen Gu and Hui Zou\cr
#'
#'   Maintainer: Yuwen Gu <yuwen.gu@uconn.edu>
#'
#' @keywords models regression
#'
#' @examples
#'
#' set.seed(1)
#' n <- 100
#' p <- 400
#' x <- matrix(rnorm(n * p), n, p)
#' y <- rnorm(n)
#' tau <- 0.90
#' pf <- abs(rnorm(p))
#' pf2 <- abs(rnorm(p))
#' lambda2 <- 1
#' m1 <- ernet(y = y, x = x, tau = tau, eps = 1e-8, pf = pf,
#'             pf2 = pf2, standardize = FALSE, intercept = FALSE,
#'             lambda2 = lambda2)
#' print(m1)
#'
#' @method print ernet
#' @export
print.ernet <- function(x, digits = max(3, getOption("digits") - 3), ...) {
    cat("\nCall: ", deparse(x$call), "\n\n")
    print(cbind(Df = x$df, Lambda = signif(x$lambda, digits)))
}