cvl_tramnet: Cross validation for '"tramnet"' models

In tramnet: Penalized Transformation Models

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))
}

tramnet documentation built on April 1, 2023, 12:20 a.m.