R/casl_lenet.R

In kimgannon/bis557: Contains Homework Assignments for BIS 557 2020

#' Compute linear elastic net using coordinate descent.
#'
#' @param X A numeric data matrix.
#' @param y Response vector.
#' @param lambda The penalty term.
#' @param alpha Value from 0 and 1; balance between l1/l2 penalty.
#' @param b Current value of the regression vector.
#' @param tol Numeric tolerance parameter.
#' @param maxit Integer maximum number of iterations.
#' @param W Vector of sample weights.
#'
#' @return Regression vector beta of length ncol(X).
#'
#' @author Taylor Arnold, Michael Kane, Bryan Lewis.
#'
#' @references
#'
#' Taylor Arnold, Michael Kane, and Bryan Lewis.
#' \emph{A Computational Approach to Statistical Learning}.
#' Chapman & Hall/CRC Texts in Statistical Science, 2019.
#'
#' @export
casl_lenet <-
  function(X, y, lambda, alpha = 1, b=matrix(0, nrow=ncol(X), ncol=1),
           tol = 1e-5, maxit=50L, W=rep(1, length(y))/length(y))
  {
    for (j in seq_along(lambda))
    {
      if (j > 1)
      {
        b[,j] <- b[, j-1, drop = FALSE]
      }

      # Update the slope coefficients until they converge.
      for (i in seq(1, maxit))
      {
        b_old <- b[, j]
        b[, j] <- casl_lenet_update_beta(X, y, lambda[j], alpha,
                                         b[, j], W)
        if (all(abs(b[, j] - b_old) < tol)) {
          break
        }
      }
      if (i == maxit)
      {
        warning("Function lenet did not converge.")
      }
    }
    b
  }