schools

Dataset as seen in Rubin (1981) which was an analysis of coaching
effects on SAT scores from eight schools.

datasets

We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

Joseph Kelly

Rgbp

Hierarchical Modeling and Frequency Method Checking on
Overdispersed Gaussian, Poisson, and Binomial Data

schools function

A dataset of 8 schools containing<dl class="dl-horizontal">
 <dt><code>y</code></dt><dd>The observed coaching effect of each school</dd>
 <dt><code>se</code></dt><dd>The standard error of the coaching effect of each school.</dd>
 </dl>

Format

Eight Schools Data — schools

A dataset of 8 schools containing<dl class='dl-horizontal'>
 <dt><code>y</code></dt><dd>The observed coaching effect of each school</dd>
 <dt><code>se</code></dt><dd>The standard error of the coaching effect of each school.</dd>
 </dl>

schools: Eight Schools Data

Description

Usage

Arguments

Format

Examples