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#' @title Plot the SparseStep path
#'
#' @description Plot the coefficients of the SparseStep path
#'
#' @param x a \code{sparsestep} object
#' @param \dots further argument to matplot
#'
#' @author
#' Gerrit J.J. van den Burg, Patrick J.F. Groenen, Andreas Alfons\cr
#' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
#'
#' @references
#' Van den Burg, G.J.J., Groenen, P.J.F. and Alfons, A. (2017).
#' \emph{SparseStep: Approximating the Counting Norm for Sparse Regularization},
#' arXiv preprint arXiv:1701.06967 [stat.ME].
#' URL \url{https://arxiv.org/abs/1701.06967}.
#'
#' @export
#' @aliases plot
#'
#' @importFrom graphics matplot
#'
#' @examples
#' x <- matrix(rnorm(100*20), 100, 20)
#' y <- rnorm(100)
#' fit <- sparsestep(x, y)
#' plot(fit)
#' pth <- path.sparsestep(x, y)
#' plot(pth)
plot.sparsestep <- function(x, ...)
{
index <- log(x$lambda)
coefs <- t(as.matrix(x$beta))
dotlist <- list(...)
type <- ifelse(is.null(dotlist$type), "s", dotlist$type)
lty <- ifelse(is.null(dotlist$lty), 1, dotlist$lty)
matplot(index, coefs, xlab="Log lambda", ylab="Coefficients",
type=type, lty=lty, ...)
}
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