R/predict.cv.cpernet.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.cpernet object
#'
#' This function makes predictions from a cross-validated cpernet model, using
#' the fitted \code{cv.cpernet} object, and the optimal value chosen for
#' \code{lambda}.
#'
#' @param object fitted \code{\link{cv.cpernet}} 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.cpernet}.
#' @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{cpernet}} objects.
#'
#' @author Yuwen Gu and Hui Zou\cr
#'
#'   Maintainer: Yuwen Gu <yuwen.gu@uconn.edu>
#'
#' @seealso \code{\link{cv.cpernet}}, \code{\link{coef.cv.cpernet}},
#'   \code{\link{plot.cv.cpernet}}
#'
#' @keywords models regression
#'
#' @examples
#'
#' set.seed(1)
#' n <- 100
#' p <- 400
#' x <- matrix(rnorm(n * p), n, p)
#' y <- rnorm(n)
#' tau <- 0.30
#' pf <- abs(rnorm(p))
#' pf2 <- abs(rnorm(p))
#' w <- 2.0
#' lambda2 <- 1
#' m2.cv <- cv.cpernet(y = y, x = x, w = w, tau = tau, eps = 1e-8,
#'                     pf.mean = pf, pf.scale = pf2,
#'                     standardize = FALSE, lambda2 = lambda2)
#' as.vector(predict(m2.cv, newx = x, s = "lambda.min"))
#'
#' @export
predict.cv.cpernet <- 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$cpernet.fit, newx, s = lambda, ...)
}