A Gibbs sampler for a projected normal regression model

Fitting Bayesian multiple and mixed-effect regression models for
circular data based on the projected normal distribution. Both continuous
and categorical predictors can be included. Sampling from the posterior is
performed via an MCMC algorithm. Posterior descriptives of all parameters,
model fit statistics and Bayes factors for hypothesis tests for inequality
constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018)
<doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014)
<doi:10.1016/j.csda.2012.07.025>.

Jolien Cremers

bpnreg

Bayesian Projected Normal Regression Models for Circular Data

pnr function

<dl><dt>theta</dt>
<dd>A vector with the circular dependent variable.</dd>
<dt>X1</dt>
<dd>A model matrix for component I.</dd>
<dt>X2</dt>
<dd>A model matrix for component II.</dd>
<dt>its</dt>
<dd>An integer specifying the number of iterations</dd>
<dt>lag</dt>
<dd>An integer specifying the amount of lag.</dd>
<dt>burn</dt>
<dd>An integer specifying the number of burn-in iterations.</dd></dl>

Arguments

A Gibbs sampler for a projected normal regression model — pnr

<dl>

<dt>theta</dt>
<dd>A vector with the circular dependent variable.</dd>


<dt>X1</dt>
<dd>A model matrix for component I.</dd>


<dt>X2</dt>
<dd>A model matrix for component II.</dd>


<dt>its</dt>
<dd>An integer specifying the number of iterations</dd>


<dt>lag</dt>
<dd>An integer specifying the amount of lag.</dd>


<dt>burn</dt>
<dd>An integer specifying the number of burn-in iterations.</dd>

</dl>

pnr: A Gibbs sampler for a projected normal regression model

Description

Usage

Arguments