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Evaluate model performance on test data

test_eval.Rd

Evaluates model performance on a test dataset using either the log-partial-likelihood loss or the concordance index (C-index).

This function accepts either:

  • test_z and betahat, which will be multiplied to obtain risk scores; or

  • test_RS, a pre-computed numeric vector of risk scores.

Usage

test_eval(
  test_z = NULL,
  test_RS = NULL,
  test_delta,
  test_time,
  test_stratum = NULL,
  betahat = NULL,
  criteria = c("loss", "CIndex")
)

Arguments

test_z

Optional numeric matrix or data frame of covariates for the test dataset. Required if test_RS is not provided.

test_RS

Optional numeric vector of pre-computed risk scores (e.g., linear predictors). If provided, test_z and betahat are ignored.

test_delta

Numeric vector of event indicators (1 = event, 0 = censored).

test_time

Numeric vector of survival times for the test dataset.

test_stratum

Optional vector indicating stratum membership for each test observation. If NULL, all observations are assumed to belong to a single stratum.

betahat

Optional numeric vector of estimated regression coefficients. Required if test_RS is not provided.

criteria

Character string specifying the evaluation criterion; one of:

  • "loss": negative twice the log–partial-likelihood.

  • "CIndex": concordance index.

Value

A numeric value representing either:

  • if criteria = "loss": the negative twice log–partial-likelihood on the test data.

  • if criteria = "CIndex": the concordance index on the test data.

Details

Prior to evaluation, observations are sorted by (stratum, time) to ensure correct risk-set construction. For stratified C-index computation, the provided test_stratum is used; otherwise all test data are treated as a single stratum.

You may supply either covariates and coefficients (test_z with betahat) or a precomputed risk score vector (test_RS). When test_RS is provided, test_z and betahat are ignored.