R/coef_tune_xrnet.R

In xrnet: Hierarchical Regularized Regression

#' Get coefficient estimates from "tune_xrnet" model object
#'
#' @description Returns coefficients from 'xrnet' model. Note that we currently only support returning
#' coefficient estimates that are in the original path(s).
#'
#' @param object A \code{\link{tune_xrnet}} object
#' @param p vector of penalty values to apply to predictor variables.
#' Default is optimal value in tune_xrnet object.
#' @param pext vector of penalty values to apply to external data variables.
#' Default is optimal value in tune_xrnet object.
#' @param ... pass other arguments to xrnet function (if needed)
#'
#' @return A list with coefficient estimates at each of the requested penalty combinations
#' \item{beta0}{matrix of first-level intercepts indexed by penalty values, NULL if no first-level intercept in original model fit}
#' \item{betas}{3-dimensional array of first-level penalized coefficients indexed by penalty values}
#' \item{gammas}{3-dimensional array of first-level non-penalized coefficients indexed by penalty values, NULL if unpen NULL in original model fit}
#' \item{alpha0}{matrix of second-level intercepts indexed by penalty values, NULL if no second-level intercept in original model fit}
#' \item{alphas}{3-dimensional array of second-level external data coefficients indexed by penalty values, NULL if external NULL in original model fit}
#' @examples
#' ## cross validation of hierarchical linear regression model
#' data(GaussianExample)
#'
#' ## 5-fold cross validation
#' cv_xrnet <- tune_xrnet(
#'     x = x_linear,
#'     y = y_linear,
#'     external = ext_linear,
#'     family = "gaussian",
#'     control = xrnet.control(tolerance = 1e-6)
#'  )
#'
#'  ## Get coefficient estimates at optimal penalty combination
#'  coef_opt <- coef(cv_xrnet)
#'

#' @export
coef.tune_xrnet <- function(object,
                          p = "opt",
                          pext = "opt",
                          ...) {

    if (p == "opt")
        p <- object$opt_penalty
    if (pext == "opt")
        pext <- object$opt_penalty_ext

    predict(
        object$fitted_model,
        newdata = NULL,
        newdata_fixed = NULL,
        p = p,
        pext = pext,
        type = "coefficients",
        ...
    )

}