as_draws: Posterior draws in the posterior package's format

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Posterior draws in the posterior package's format

Description

Convert a fit's posterior draws to a posterior draws object. as_draws() returns the draws_array shape; as_draws_array(), as_draws_matrix(), as_draws_df() and as_draws_rvars() return theirs. When the posterior package is installed these are also registered against its generics, so posterior::as_draws(fit) works.

Usage

as_draws(x, ...)

## S3 method for class 'tulpa_fit'
as_draws(x, n_draws = NULL, seed = NULL, ...)

as_draws_array(x, ...)

## S3 method for class 'tulpa_fit'
as_draws_array(x, n_draws = NULL, seed = NULL, ...)

as_draws_matrix(x, ...)

## S3 method for class 'tulpa_fit'
as_draws_matrix(x, n_draws = NULL, seed = NULL, ...)

as_draws_df(x, ...)

## S3 method for class 'tulpa_fit'
as_draws_df(x, n_draws = NULL, seed = NULL, ...)

as_draws_rvars(x, ...)

## S3 method for class 'tulpa_fit'
as_draws_rvars(x, n_draws = NULL, seed = NULL, ...)

Arguments

x	A tulpa_fit object.
...	Passed to the corresponding posterior converter.
n_draws	Number of draws to synthesize from the Gaussian approximation for a fit that carries none. NULL (default) errors on such a fit rather than silently approximating. Ignored, with a warning, when the fit already carries draws.
seed	Optional integer seed for the synthesis. The RNG state is restored afterwards.

Details

Fits differ in whether they carry draws at all. Sampler and nested-Laplace fits do, and convert directly. A Gaussian-approximation fit (mode = "laplace", mode = "eb") carries a mode and a precision instead, and converting it means drawing from the approximation – which is a modelling decision, not a format change, because every downstream posterior summary would then treat a normal approximation as a posterior sample. So it is opt-in: pass n_draws to synthesize that many draws from N(coef(object), vcov(object)), or get an error naming the alternative. Synthesized draws form a single chain and cover the fixed effects only.

Value

A posterior draws object of the requested shape.

See Also

tulpa_draws_array() for the base R array without the dependency, posterior_sample() for the raw matrix.

Examples

set.seed(1)
df <- data.frame(x = rnorm(80))
df$y <- rpois(80, exp(0.5 + 0.3 * df$x))
fit <- tulpa(y ~ x, data = df, family = "poisson")
if (requireNamespace("posterior", quietly = TRUE)) {
  posterior::summarise_draws(as_draws(fit))
}

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.