circ_coef_rcpp

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

circ_coef_rcpp function

<dl><dt>a1</dt>
<dd>intercept estimate of component I.</dd>
<dt>a2</dt>
<dd>intercept estimate of component I.</dd>
<dt>b1</dt>
<dd>slope estimate of component I.</dd>
<dt>b2</dt>
<dd>slope estimate of component I.</dd></dl>

Arguments

Compute circular coefficients — circ_coef_rcpp

<dl>

<dt>a1</dt>
<dd>intercept estimate of component I.</dd>


<dt>a2</dt>
<dd>intercept estimate of component I.</dd>


<dt>b1</dt>
<dd>slope estimate of component I.</dd>


<dt>b2</dt>
<dd>slope estimate of component I.</dd>

</dl>

circ_coef_rcpp: Compute circular coefficients

Description

Usage

Arguments