BFc.bpnr

Outputs Bayes Factors for the circular differences between several levels of
a categorical variable and the baseline.

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

BFc.bpnr function

<dl><dt>object</dt>
<dd>a <code>bpnr object</code> obtained from the function <code>bpnr()</code>.</dd>
<dt>hypothesis</dt>
<dd>the inequality constrained hypothesis to test.</dd>
<dt>type</dt>
<dd>type of hypothesis to test <code>c("anchor", "isotropic")</code>. As of
yet only anchor hypotheses can be tested.</dd></dl>

Arguments

Bayes Factors for a Bayesian circular regression model — BFc.bpnr

<dl>

<dt>object</dt>
<dd>a <code>bpnr object</code> obtained from the function <code>bpnr()</code>.</dd>


<dt>hypothesis</dt>
<dd>the inequality constrained hypothesis to test.</dd>


<dt>type</dt>
<dd>type of hypothesis to test <code>c("anchor", "isotropic")</code>. As of
yet only anchor hypotheses can be tested.</dd>

</dl>

BFc.bpnr: Bayes Factors for a Bayesian circular regression model

Description

Usage

Value

Arguments

Examples