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Posterior draws in the posterior package's format

Source: R/methods_interop.R
as_draws.Rd

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.

Value

A posterior draws object of the requested shape.

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.

See also

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

Examples

# \donttest{
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))
}
# }