cat_cox_tune: Catalytic Cox Proportional-Hazards Model (COX) Fitting...

In catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

Catalytic Cox Proportional-Hazards Model (COX) Fitting Function by Tuning tau from a Sequence of tau Values

Description

This function tunes a catalytic Cox proportional-hazards model (COX) by performing cross-validation to estimate the optimal value of the tuning parameter tau. It finally uses the optimal tau value in the cat_cox function for model fitting.

Usage

cat_cox_tune(
  formula,
  cat_init,
  method = c("CRE", "WME"),
  tau_seq = NULL,
  cross_validation_fold_num = 5,
  ...
)

Arguments

formula	A formula specifying the Cox model. Should at least include response variables (e.g. ~.).
cat_init	A list generated from cat_cox_initialization.
method	The estimation method, either "CRE" (Catalytic-regularized Estimator) or "WME" (Weighted Mixture Estimator).
tau_seq	A numeric vector specifying the sequence of tau values to be tested. If NULL, a default sequence is generated based on the number of predictors.
cross_validation_fold_num	An integer representing the number of folds for cross-validation. Defaults to 5.
...	Additional arguments passed to the cat_cox function for model fitting.

Value

A list containing the values of all the arguments and the following components:

tau	he optimal tau value determined from cross-validation.
model	The fitted lmer model object by using the optimal tau value.
coefficients	Coefficients of the fitted model by using the optimal tau value.
likelihood_list	Average likelihood value for each tau value.

Examples

library(survival)
data("cancer")
cancer$status[cancer$status == 1] <- 0
cancer$status[cancer$status == 2] <- 1

cat_init <- cat_cox_initialization(
  formula = Surv(time, status) ~ 1, # formula for simple model
  data = cancer,
  syn_size = 100, # Synthetic data size
  hazard_constant = 0.1, # Hazard rate value
  entry_points = rep(0, nrow(cancer)), # Entry points of each observation
  x_degree = rep(1, ncol(cancer) - 2), # Degrees for polynomial expansion of predictors
  resample_only = FALSE, # Whether to perform resampling only
  na_replace = stats::na.omit # How to handle NA values in data
)

cat_model <- cat_cox_tune(
  formula = ~., # Should at least include response variables
  cat_init = cat_init, # Only accept object generated from `cat_cox_initialization`
  tau_seq = c(1, 2), # Vector of weights for synthetic data
  cross_validation_fold_num = 5 # number of folds for cross-validation
)
cat_model

catalytic documentation built on April 4, 2025, 5:51 a.m.