Posterior Object Summary

Compute and display posterior summaries. This includes the point estimate (posterior mean) and equal-tailed credible intervals.

Usage

# S3 method for class 'inz_dbeta'
summary(object, ...)

# S3 method for class 'inz_ddir'
summary(object, ...)

# S3 method for class 'inz_dNIG'
summary(object, ...)

# S3 method for class 'inz_dNIG_reg'
summary(object, ...)

Arguments

object

A posterior object returned by calculate_posterior.

...

Currently no additional arguments.

Value

An object of class summary.inz_*, which is used by the corresponding print method to automatically print the output.

Details

Beta-Binomial (inz_dbeta):

The point estimates are calculated using the expectation formula of the Beta distribution: $$\frac{\alpha}{\alpha+\beta}$$

The credible intervals are calculated using the quantile function qbeta.

Dirichlet-Multinomial (inz_ddir):

The point estimates are calculated using the expectation formula of the Dirichlet distribution: $$\frac{\alpha_i}{\alpha_0}$$

The marginal distribution of Dirichlet follows a Beta distribution. Hence, the credible intervals are calculated using the quantile function qbeta.

Normal-Inverse-Gamma Prior, Normal Likelihood (inz_dNIG or inz_dNIG_reg):

The posterior mean computed in calculate_posterior (\(m_n\) or \(\boldsymbol{\mu}_n\) for regression) are the point estimates.

The marginal posterior distribution of the mean follows a t-distribution. Hence, the credible intervals are calculated using the quantile function qt.

See also

calculate_posterior

Examples

# Beta-Binomial example
if (FALSE) { # \dontrun{
lik <- inz_lbinom(surf_data, Gender)
prior <- inz_dbeta(likelihood = lik)
posterior <- calculate_posterior(prior = prior)
summary(posterior)
} # }

# Regression example (Normal-Inverse-Gamma)
if (FALSE) { # \dontrun{
lik <- inz_lnorm(surf_data, Income, Hours)
prior <- inz_dNIG(likelihood = lik)
posterior <- calculate_posterior(prior = prior)
summary(posterior)
} # }