BayesTools (version 0.3.0)

transform_prior_samples: Transform prior samples to original scale

Description

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.

Usage

transform_prior_samples(
  fit,
  n_samples = 10000,
  seed = NULL,
  formula_scale = NULL
)

Value

A matrix of prior samples on the original (unscaled) scale, with columns matching the structure of posterior samples.

Arguments

fit

a fitted model object with prior_list and optionally formula_scale attributes

n_samples

number of samples to generate (default: 10000)

seed

random seed for reproducibility (optional)

formula_scale

optional nested list containing standardization information. If not provided, extracted from fit attribute.

Details

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.

See Also

transform_scale_samples() plot_posterior()

Examples

# 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