R/predict.cv.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

#' Make predictions from a cv.ernet object
#'
#' This function makes predictions from a cross-validated ernet model, using
#' the fitted \code{cv.ernet} object, and the optimal value chosen for
#' \code{lambda}.
#'
#' @param object fitted \code{\link{cv.ernet}} object.
#' @param newx matrix of new values for \code{x} at which predictions are to be
#'   made. Must be a matrix. See documentation for \code{predict.ernet}.
#' @param s value(s) of the penalty parameter \code{lambda} at which predictions
#'   are to be made. Default is the value \code{s = "lambda.1se"} stored on the
#'   CV object. Alternatively \code{s = "lambda.min"} can be used. If \code{s}
#'   is numeric, it is taken as the value(s) of \code{lambda} to be used.
#' @param \dots not used. Other arguments to predict.
#'
#' @details This function makes it easier to use the results of cross-validation
#'   to make a prediction.
#'
#' @return The object returned depends the \dots{} argument which is passed on
#'   to the \code{\link{predict}} method for \code{\link{ernet}} objects.
#'
#' @author Yuwen Gu and Hui Zou\cr
#'
#'   Maintainer: Yuwen Gu <yuwen.gu@uconn.edu>
#'
#' @seealso \code{\link{cv.ernet}}, \code{\link{coef.cv.ernet}},
#'   \code{\link{plot.cv.ernet}}
#'
#' @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.cv <- cv.ernet(y = y, x = x, tau = tau, eps = 1e-8, pf = pf,
#'                   pf2 = pf2, standardize = FALSE, intercept = FALSE,
#'                   lambda2 = lambda2)
#' as.vector(predict(m1.cv, newx = x, s = "lambda.min"))
#'
#' @export
predict.cv.ernet <- function(object, newx, s = c("lambda.1se", "lambda.min"),
                             ...) {
  if (is.numeric(s))
    lambda <- s else if (is.character(s)) {
                  s <- match.arg(s)
                  lambda <- object[[s]]
                } else stop("Invalid form for s")
  predict(object$ernet.fit, newx, s = lambda, ...)
}