coef_ran.bpnme

Gives posterior summaries of the circular or linear random effect variances.
The circular random intercept variance and circular random slope variance of
categorical predictors is computed as 1 - mean resultant length.

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_ran.bpnme function

<dl><dt>object</dt>
<dd>a <code>bpnme object</code> obtained from the function
<code>bpnme</code>.</dd>
<dt>type</dt>
<dd>one of <code>c("linear", "circular")</code> to get either the linear or
circular random effect variances.</dd></dl>

Arguments

Obtain random effect variances of a Bayesian circular mixed-effects model — coef_ran.bpnme

<dl>

<dt>object</dt>
<dd>a <code>bpnme object</code> obtained from the function
<code>bpnme</code>.</dd>


<dt>type</dt>
<dd>one of <code>c("linear", "circular")</code> to get either the linear or
circular random effect variances.</dd>

</dl>

coef_ran.bpnme: Obtain random effect variances of a Bayesian circular mixed-effects model

Description

Usage

Value

Arguments

Examples