cvl_tramnet: Cross-validating tramnet models

In tramnet: Penalized Transformation Models

Cross-validating 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 hyper-parameters as an object of class "cvl_tramnet"

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 Nov. 4, 2023, 3 p.m.