summe

Compute summary and model fit statistics for the circular mixed-effects
regression model

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

summe function

<dl><dt>output</dt>
<dd>from a mixed-effects model</dd>
<dt>mm</dt>
<dd>output from the function mmme()</dd></dl>

Arguments

Compute summary and model fit statistics for the circular mixed-effects
regression model — summe

<dl>

<dt>output</dt>
<dd>from a mixed-effects model</dd>


<dt>mm</dt>
<dd>output from the function mmme()</dd>

</dl>

summe: Compute summary and model fit statistics for the circular mixed-effects regression model

Description

Usage

Arguments