omega_samp

internal

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

omega_samp function

<dl><dt>b</dt>
<dd>subject specific random effects vectors</dd>
<dt>B</dt>
<dd>prior sum of squares matrix, scale parameter Wishart distribution
(of b=random effect if prior close to 0--&gt;no random effect)</dd>
<dt>v</dt>
<dd>prior df=dimension Z</dd>
<dt>q</dt>
<dd>dimension Z(number of random effects)</dd>
<dt>N</dt>
<dd>sample size at second level</dd></dl>

Arguments

Sample precision matrix — omega_samp

<dl>

<dt>b</dt>
<dd>subject specific random effects vectors</dd>


<dt>B</dt>
<dd>prior sum of squares matrix, scale parameter Wishart distribution
(of b=random effect if prior close to 0--&gt;no random effect)</dd>


<dt>v</dt>
<dd>prior df=dimension Z</dd>


<dt>q</dt>
<dd>dimension Z(number of random effects)</dd>


<dt>N</dt>
<dd>sample size at second level</dd>

</dl>

omega_samp: Sample precision matrix

Description

Usage

Arguments