coef_circ

<code>coef_circ</code> gives posterior summaries of the circular coefficients.

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

coef_circ function

<dl><dt>object</dt>
<dd>an object used to select a method.</dd>
<dt>type</dt>
<dd>one of <code>c("continuous", "categorical")</code> to get either the
coefficients for the continuous or categorical predictor variables.</dd>
<dt>units</dt>
<dd>one of <code>c("degrees", "radians")</code> to get categorical
coefficients estimates and estimates for <code>$ac$, $bc$, AS and SAM</code> in degrees or
radians.</dd></dl>

Arguments

Circular coefficients — coef_circ

<dl>

<dt>object</dt>
<dd>an object used to select a method.</dd>


<dt>type</dt>
<dd>one of <code>c("continuous", "categorical")</code> to get either the
coefficients for the continuous or categorical predictor variables.</dd>


<dt>units</dt>
<dd>one of <code>c("degrees", "radians")</code> to get categorical
coefficients estimates and estimates for <code>$ac$, $bc$, AS and SAM</code> in degrees or
radians.</dd>

</dl>

coef_circ: Circular coefficients

Description

Usage

Arguments

Details

Examples