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#' Predict function for "tune_xrnet" object
#'
#' @description Extract coefficients or predict response in new data using fitted model from a \code{\link{tune_xrnet}} object.
#' Note that we currently only support returning results that are in the original path(s).
#'
#' @param object A \code{\link{tune_xrnet}} object
#' @param newdata matrix with new values for penalized variables
#' @param newdata_fixed matrix with new values for unpenalized variables
#' @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 type type of prediction to make using the xrnet model, options include:
#' \itemize{
#' \item response
#' \item link (linear predictor)
#' \item coefficients
#' }
#' @param ... pass other arguments to xrnet function (if needed)
#'
#' @return The object returned is based on the value of type as follows:
#' \itemize{
#' \item response: An array with the response predictions based on the data for each penalty combination
#' \item link: An array with linear predictions based on the data for each penalty combination
#' \item coefficients: A list with the coefficient estimates for each penalty combination. See \code{\link{coef.xrnet}}.
#' }
#'
#' @examples
#' 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 coefficients and predictions at optimal penalty combination
#' coef_xrnet <- predict(cv_xrnet, type = "coefficients")
#' pred_xrnet <- predict(cv_xrnet, newdata = x_linear, type = "response")
#'
#' @export
predict.tune_xrnet <- function(object,
newdata = NULL,
newdata_fixed = NULL,
p = "opt",
pext = "opt",
type = c("response", "link", "coefficients"),
...)
{
if (p == "opt")
p <- object$opt_penalty
if (pext == "opt")
pext <- object$opt_penalty_ext
predict(object$fitted_model,
newdata = newdata,
newdata_fixed = newdata_fixed,
p = p,
pext = pext,
type = type,
...
)
}
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