betaBlock

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

betaBlock function

<dl><dt>Omega</dt>
<dd>current covariance matrix</dd>
<dt>R</dt>
<dd>component of current outcome vector (Y.I=cos(theta)*R, Y.II=sin(theta)*R)</dd>
<dt>X</dt>
<dd>design matrix model parameters (differs per person)</dd>
<dt>Z</dt>
<dd>design matrix for random effects (differs per person)</dd>
<dt>p</dt>
<dd>dimension X (number of columns/variables+indicator variables)</dd>
<dt>A</dt>
<dd>prior variance of fixed effect coefficients</dd>
<dt>N</dt>
<dd>sample size at second level</dd></dl>

Arguments

Sample fixed effect coefficients — betaBlock

<dl>

<dt>Omega</dt>
<dd>current covariance matrix</dd>


<dt>R</dt>
<dd>component of current outcome vector (Y.I=cos(theta)*R, Y.II=sin(theta)*R)</dd>


<dt>X</dt>
<dd>design matrix model parameters (differs per person)</dd>


<dt>Z</dt>
<dd>design matrix for random effects (differs per person)</dd>


<dt>p</dt>
<dd>dimension X (number of columns/variables+indicator variables)</dd>


<dt>A</dt>
<dd>prior variance of fixed effect coefficients</dd>


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

</dl>

betaBlock: Sample fixed effect coefficients

Description

Usage

Arguments