AIC.pmrm_fit: Akaike information criterion (AIC)

In pmrm: Progression Models for Repeated Measures

Akaike information criterion (AIC)

Description

Extract the Akaike information criterion (AIC) of a progression model for repeated measures (PMRM).

Usage

## S3 method for class 'pmrm_fit'
AIC(object, ..., k = NULL)

Arguments

object	A fitted model object of class "pmrm_fit".
...	Not used.
k	Not used. Must be NULL.

Value

Numeric scalar, the Akaike information criterion (AIC) of the fitted model.

See Also

Other model comparison: BIC.pmrm_fit(), confint.pmrm_fit(), deviance.pmrm_fit(), glance.pmrm_fit(), logLik.pmrm_fit(), summary.pmrm_fit()

Examples

  set.seed(0L)
  simulation <- pmrm_simulate_decline_proportional(
    visit_times = seq_len(5L) - 1,
    gamma = c(1, 2)
  )
  fit <- pmrm_model_decline_proportional(
    data = simulation,
    outcome = "y",
    time = "t",
    patient = "patient",
    visit = "visit",
    arm = "arm",
    covariates = ~ w_1 + w_2
  )
  AIC(fit)

pmrm documentation built on March 12, 2026, 5:07 p.m.