Prior predictive simulation — prior

Draw datasets from the prior predictive distribution: parameters are sampled from their priors (no data conditioning) and pushed through the model's linear predictor and the family's simulator.

Useful for checking whether priors imply plausible data ranges before fitting.

Usage

prior_predict(
  formula,
  family,
  data,
  priors = NULL,
  n_draws = 100,
  seed = NULL,
  ...
)

Arguments

formula: A model formula (e.g., y ~ x + (1 | g)). For multi-process families, a list of formulas keyed by process name.
family: A tulpa_family object exposing a simulate_fn (see tulpa_family()). Model packages (tulpaRatio, tulpaObs) provide families; tests can build a minimal one with tulpa_family().
data: Data frame containing covariates and grouping factors. Used for dimensions and design matrices; the response column may be absent or NA.
priors: Prior specification (tulpa_priors()). If NULL, uses defaults.
n_draws: Number of prior parameter draws. Default 100.
seed: Optional integer seed for reproducibility.
...: Passed to family$simulate_fn.

Value

A tulpa_prior_predict object: a list with

y: list of length n_draws, each element the simulated response for that draw (matching whatever shape family$simulate_fn returns).
theta: list of length n_draws of parameter draws (beta, sigma, RE coefficients u, family-specific extras).
linpred: list of length n_draws, each a list of linear predictor vectors per process.
family: the family used.
n_draws, n_obs.

Examples

# Toy Gaussian family for illustration
fam <- tulpa_family(
  name = "gaussian",
  simulate_fn = function(eta, params, n_obs, ...) {
    rnorm(n_obs, eta[[1]], params$sigma_y)
  },
  extra_params = list(sigma_y = prior_half_normal(1))
)
df <- data.frame(y = rep(0, 20), x = rnorm(20))
pp <- prior_predict(y ~ x, fam, df, n_draws = 50, seed = 1)
length(pp$y)  # 50