Create model matrices circular regression

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

mmr function

<dl><dt>pred.I</dt>
<dd>model equation for effects of component 1</dd>
<dt>data</dt>
<dd>the dataframe used for analysis</dd>
<dt>pred.II</dt>
<dd>model equation for effects of component 2</dd></dl>

Arguments

Create model matrices circular regression — mmr

<dl>

<dt>pred.I</dt>
<dd>model equation for effects of component 1</dd>


<dt>data</dt>
<dd>the dataframe used for analysis</dd>


<dt>pred.II</dt>
<dd>model equation for effects of component 2</dd>

</dl>

mmr: Create model matrices circular regression

Description

Usage

Arguments