brm_simulate_prior {brms.mmrm}

Prior predictive draws.

Description

Simulate the outcome variable from the prior predictive distribution of an MMRM using brms.

Usage

brm_simulate_prior(
  data,
  formula,
  prior = brms.mmrm::brm_prior_simple(data = data, formula = formula),
  ...
)

Arguments

data

A tidy data frame with one row per patient per discrete time point.

formula

An object of class "brmsformula" from brm_formula() or brms::brmsformula(). Should include the full parameterization of the model, including fixed effects, residual correlation, and heterogeneity in the discrete-time-specific residual variance components.

prior

A valid brms prior object with proper priors for parameters b (model coefficients), b_sigma (log residual standard deviations for each time point), and cortime (residual correlations among time points within patients). See the brm_prior_simple() function for an example.

...

Arguments to brms::brm() other than data, formula, and prior.

Details

brm_simulate_prior() calls brms::brm() with sample_prior = "only", which sets the default intercept prior using the outcome variable and requires at least some elements of the outcome variable to be non-missing in advance. So to provide feasible and consistent output, brm_simulate_prior() temporarily sets the outcome variable to all zeros before invoking brms::brm().

Value

A list with the following elements:

• data: a classed tibble with the outcome variable simulated as a draw from the prior predictive distribution (the final row of outcome in the output). If you simulated a missingness pattern with brm_simulate_outline(), then that missingness pattern is applied so that the appropriate values of the outcome variable are set to NA.

• model: the brms model fit object.

• model_matrix: the model matrix of the fixed effects, obtained from brms::make_standata().

• outcome: a numeric matrix with one column per row of data and one row per saved prior predictive draw.

• parameters: a tibble of saved parameter draws from the prior predictive distribution.

See Also

Other simulation: brm_simulate_categorical(), brm_simulate_continuous(), brm_simulate_outline(), brm_simulate_simple()

Examples

if (identical(Sys.getenv("BRM_EXAMPLES", unset = ""), "true")) {
set.seed(0L)
data <- brm_simulate_outline()
data <- brm_simulate_continuous(data, names = c("age", "biomarker"))
formula <- brm_formula(
  data = data,
  baseline = FALSE,
  baseline_time = FALSE
)
tmp <- utils::capture.output(
  suppressMessages(
    suppressWarnings(
      out <- brm_simulate_prior(
        data = data,
        formula = formula
      )
    )
  )
)
out$data
}