b_samp

Sample subject specific random effects

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

b_samp function

<dl><dt>Omega</dt>
<dd>current covariance matrix</dd>
<dt>beta</dt>
<dd>current fixed effect coefficients vector</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>q</dt>
<dd>dimension Z(number of random effects)</dd>
<dt>Z</dt>
<dd>design matrix for random effects (differs per person)</dd>
<dt>ZtZ</dt>
<dd>transpose(Z)*Z</dd>
<dt>N</dt>
<dd>sample size at second level</dd></dl>

Arguments

Sample subject specific random effects — b_samp

<dl>

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


<dt>beta</dt>
<dd>current fixed effect coefficients vector</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>q</dt>
<dd>dimension Z(number of random effects)</dd>


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


<dt>ZtZ</dt>
<dd>transpose(Z)*Z</dd>


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

</dl>

b_samp: Sample subject specific random effects

Description

Usage

Arguments