Generate prior samples and transform them using the same matrix transformation as posterior samples. This is the correct approach for visualizing priors on the original (unscaled) scale, especially for the intercept which depends on contributions from multiple coefficient priors.
transform_prior_samples(
fit,
n_samples = 10000,
seed = NULL,
formula_scale = NULL
)a fitted model object with prior_list and optionally
formula_scale attributes
number of samples to generate (default: 10000)
random seed for reproducibility (optional)
optional nested list containing standardization information.
If not provided, extracted from fit attribute.
A matrix of prior samples on the original (unscaled) scale, with columns matching the structure of posterior samples.
When models use auto-scaling (standardizing predictors), the posterior samples are on the standardized scale. To correctly visualize priors on the original scale, we cannot simply apply a linear transformation to individual priors because the intercept on the original scale is a weighted sum of multiple priors:
$$\beta_0^{orig} = \beta_0^* - \sum_i \frac{\mu_i}{\sigma_i} \beta_i^*$$
This function generates samples from ALL priors simultaneously and applies the same matrix transformation used for posterior samples, which correctly handles the intercept and all other parameters.
# With a fitted model that used formula_scale:
# prior_samples <- transform_prior_samples(fit, n_samples = 10000)
# This can then be used with density() or for custom plotting