Plot Model Performance vs Lambda for coxkl_enet

Plots model performance across the lambda sequence. Performance is loss (-2 times partial log-likelihood) or concordance index (C-index). If no test data are provided, the curve uses the training data stored in object$data.

Usage

# S3 method for class 'coxkl_enet'
plot(
  object,
  test_z = NULL,
  test_time = NULL,
  test_delta = NULL,
  test_stratum = NULL,
  criteria = c("loss", "CIndex"),
  ...
)

Arguments

object: A fitted model object of class "coxkl_enet".
test_z: Optional numeric matrix of test covariates.
test_time: Optional numeric vector of test survival times.
test_delta: Optional numeric vector of test event indicators.
test_stratum: Optional vector of test stratum membership.
criteria: Character string: "loss" or "CIndex".
...: Additional arguments (ignored).

Value

A ggplot object showing the performance curve.

Details

When criteria = "loss" and no test data are supplied, the plotted values are -2 * object$likelihood (no normalization). When test data are provided, performance is computed via test_eval(..., criteria). The x-axis is shown in decreasing lambda with a reversed log10 scale.

Examples

data(ExampleData_highdim) 

train_dat_highdim <- ExampleData_highdim$train
test_dat_highdim <- ExampleData_highdim$test
beta_external_highdim <- ExampleData_highdim$beta_external

model_enet <- coxkl_enet(z = train_dat_highdim$z,
                         delta = train_dat_highdim$status,
                         time = train_dat_highdim$time,
                         beta = beta_external_highdim,
                         eta = 1,
                         alpha = 1.0)
plot(model_enet,
     test_z = test_dat_highdim$z,
     test_time = test_dat_highdim$time,
     test_delta = test_dat_highdim$status,
     test_stratum = test_dat_highdim$stratum,
     criteria = "loss")