cvl_tramnet {tramnet}

Cross validation for "tramnet" models

Description

k-fold cross validation for "tramnet" objects over a grid of the tuning parameters based on out-of-sample log-likelihood.

Usage

cvl_tramnet(object, fold = 2, lambda = 0, alpha = 0, folds = NULL,
  fit_opt = FALSE)

Arguments

object

object of class "tramnet"

fold

number of folds for cross validation

lambda

values for lambda to iterate over

alpha

values for alpha to iterate over

folds

manually specify folds for comparison with other methods

fit_opt

If TRUE, returns the full model evaluated at optimal hyper parameters

Value

Returns out-of-sample logLik and coefficient estimates for corresponding folds and values of the hyperparameters as an object of class "cvl_tramnet"

Author(s)

Lucas Kook

Examples

set.seed(241068)
if (require("survival") & require("TH.data")) {
  data("GBSG2", package = "TH.data")
  X <- 1 * matrix(GBSG2$horTh == "yes", ncol = 1)
  colnames(X) <- "horThyes"
  GBSG2$surv <- with(GBSG2, Surv(time, cens))
  m <- Coxph(surv ~ 1, data = GBSG2, log_first = TRUE)
  mt <- tramnet(model = m, x = X, lambda = 0, alpha = 0)
  mc <- Coxph(surv ~ horTh, data = GBSG2)
  cvl_tramnet(mt, fold = 2, lambda = c(0, 1), alpha = c(0, 1))
}