R/plot.gmus.R

In hdme: High-Dimensional Regression with Measurement Error

#' @title Plot the estimates returned by gmus and mus
#' @description Plot the number of nonzero coefficients along a range of delta
#'   values if delta has length larger than 1, or the estimated coefficients if
#'   delta has length 1.
#' @param x An object of class gmus
#' @param ... Other arguments to plot (not used).
#' @examples
#' # Example with linear regression
#' set.seed(1)
#' # Number of samples
#' n <- 100
#' # Number of covariates
#' p <- 50
#' # True (latent) variables
#' X <- matrix(rnorm(n * p), nrow = n)
#' # Measurement matrix (this is the one we observe)
#' W <- X + matrix(rnorm(n*p, sd = 0.4), nrow = n, ncol = p)
#' # Coefficient vector
#' beta <- c(seq(from = 0.1, to = 1, length.out = 5), rep(0, p-5))
#' # Response
#' y <- X %*% beta + rnorm(n, sd = 1)
#' # Run the MU Selector
#' mus1 <- mus(W, y)
#' # Draw an elbow plot to select delta
#' plot(mus1)
#'
#' # Now, according to the "elbow rule", choose the final
#' # delta where the curve has an "elbow".
#' # In this case, the elbow is at about delta = 0.08, so
#' # we use this to compute the final estimate:
#' mus2 <- mus(W, y, delta = 0.08)
#' # Plot the coefficients
#' plot(mus2)
#'
#' @export
plot.gmus <- function(x, ...) {
  if(length(x$delta) > 1) {
    df <- data.frame(delta = x$delta, nonzero = x$num_non_zero)
    ggplot2::ggplot(df, ggplot2::aes(x = .data$delta, y = .data$nonzero)) +
      ggplot2::geom_line() +
      ggplot2::labs(x = "delta", y = "Nonzero coefficients", title = "Elbow plot")
  } else {
    df <- data.frame(index = seq_along(x$beta), beta = x$beta)

    ggplot2::ggplot(df, ggplot2::aes(x = .data$index, y = .data$beta)) +
      ggplot2::geom_point() +
      ggplot2::labs(x = "Coefficient number", y = "Coefficient value", title = "Estimated coefficients")
  }

}