logLik.mixturecure: Log-likelihood for fitted mixture cure model

In hdcuremodels: High-Dimensional Cure Models

Log-likelihood for fitted mixture cure model

Description

This function returns the log-likelihood for a user-specified model criterion or step for a curegmifs, cureem, cv_curegmifs or cv_cureem fitted object.

Usage

## S3 method for class 'mixturecure'
logLik(object, model_select = "AIC", ...)

Arguments

object	a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, cv_cureem.
model_select	either a case-sensitive parameter for models fit using curegmifs or cureem or any numeric step along the solution path can be selected. The default is model_select = "AIC" which calculates the predicted values using the coefficients from the model achieving the minimum AIC. The complete list of options are: "AIC" for the minimum AIC (default). "mAIC" for the minimum modified AIC. "cAIC" for the minimum corrected AIC. "BIC", for the minimum BIC. "mBIC" for the minimum modified BIC. "EBIC" for the minimum extended BIC. "logLik" for the step that maximizes the log-likelihood. n where n is any numeric value from the solution path. This option has no effect for objects fit using cv_curegmifs or cv_cureem.
...	other arguments.

Value

log-likelihood of the fitted mixture cure model using the specified criteria.

Examples

library(survival)
withr::local_seed(1234)
temp <- generate_cure_data(n = 100, j = 10, n_true = 10, a = 1.8)
training <- temp$training
fit <- curegmifs(Surv(Time, Censor) ~ .,
  data = training, x_latency = training,
  model = "weibull", thresh = 1e-4, maxit = 2000,
  epsilon = 0.01, verbose = FALSE
)
logLik(fit, model_select = "AIC")

hdcuremodels documentation built on Aug. 8, 2025, 7:38 p.m.