R/plot.gds.R

In hdme: High-Dimensional Regression with Measurement Error

#' @title Plot the estimates returned by gds
#' @description Plot the number of nonzero coefficients at the given lambda.
#' @param x An object of class gds
#' @param ... Other arguments to plot (not used).
#' @examples
#' set.seed(1)
#' # Example with logistic regression
#' # Number of samples
#' n <- 1000
#' # Number of covariates
#' p <- 10
#' # True (latent) variables (Design matrix)
#' X <- matrix(rnorm(n * p), nrow = n)
#' # True regression coefficients
#' beta <- c(seq(from = 0.1, to = 1, length.out = 5), rep(0, p-5))
#' # Binomially distributed response
#' y <- rbinom(n, 1, (1 + exp(-X %*% beta))^(-1))
#' # Fit the generalized Dantzig Selector
#' gds <- gds(X, y, family = "binomial")
#' # Plot the estimated coefficients at the chosen lambda
#' plot(gds)
#'
#' @export
plot.gds <- function(x, ...) {
  x$delta <- x$lambda
  plot.gmus(x)
}