plot.coxkl: Plot Model Performance vs Eta for 'coxkl'

In survkl: Estimate Survival Data with Data Integration

Plot Model Performance vs Eta for coxkl

Description

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

Usage

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

Arguments

x	A fitted model object of class "coxkl".
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).

Details

When criteria = "loss" and no test data are supplied, the plotted values are (-2 * x$likelihood) / n, where n is the number of rows in the (training) data. When test data are provided, performance is computed via test_eval(..., criteria = "loss") and divided by the test sample size. For criteria = "CIndex", performance is computed via test_eval(..., criteria = "CIndex") on the chosen dataset. The plot adds a dotted horizontal reference line at the value corresponding to eta = 0 (closest point on the eta grid).

Value

A ggplot object showing the performance curve.

Examples

data(ExampleData_lowdim)

train_dat_lowdim  <- ExampleData_lowdim$train
test_dat_lowdim   <- ExampleData_lowdim$test
beta_external_good_lowdim <- ExampleData_lowdim$beta_external_good
eta_grid <- generate_eta(method = "exponential", n = 100, max_eta = 30)

model <- coxkl(z = train_dat_lowdim$z,
               delta = train_dat_lowdim$status,
               time = train_dat_lowdim$time,
               stratum = train_dat_lowdim$stratum,
               beta = beta_external_good_lowdim,
               etas = eta_grid)
plot(model,
     test_z = test_dat_lowdim$z, 
     test_time = test_dat_lowdim$time, 
     test_delta = test_dat_lowdim$status, 
     test_stratum = test_dat_lowdim$stratum, 
     criteria = "loss")

survkl documentation built on April 22, 2026, 1:08 a.m.