Glance at a PMRM.

Return a one-row tibble of model comparison metrics for a fitted PMRM.

Usage

# S3 method for class 'pmrm_fit'
glance(x, ...)

Arguments

x

A fitted model x of class "pmrm_fit".

...

Not used.

Value

A tibble with one row and columns with the following columns:

• model: "decline" or "slowing".

• parameterization: "proportional" or "nonproportional".

• n_observations: number of non-missing observations in the data.

• n_parameters: number of true model parameters.

• log_likelihood: maximized log likelihood of the model fit.

• deviance: deviance of the fitted model, defined here as -2 * log_likelihood.

• aic: Akaike information criterion.

• bic: Bayesian information criterion.

This format is designed for easy comparison of multiple fitted models.

See also

Other model comparison: AIC.pmrm_fit(), BIC.pmrm_fit(), confint.pmrm_fit(), deviance.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
  )
  glance(fit)
#> # A tibble: 1 × 8
#>   model   parameterization n_observations n_parameters log_likelihood deviance
#>   <chr>   <chr>                     <int>        <int>          <dbl>    <dbl>
#> 1 decline proportional               1500           24         -2114.    4229.
#> # ℹ 2 more variables: aic <dbl>, bic <dbl>