lik_reg

Compute the Likelihood of the PN distribution (regression)

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

lik_reg function

<dl><dt>X1</dt>
<dd>the model matrix of the first component</dd>
<dt>X2</dt>
<dd>the model matrix of the second component</dd>
<dt>theta</dt>
<dd>a circular outcome value</dd>
<dt>beta1</dt>
<dd>estimated linear coefficients of the first component</dd>
<dt>beta2</dt>
<dd>estimated linear coefficients of the second component</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Compute the Likelihood of the PN distribution (regression) — lik_reg

<dl>

<dt>X1</dt>
<dd>the model matrix of the first component</dd>


<dt>X2</dt>
<dd>the model matrix of the second component</dd>


<dt>theta</dt>
<dd>a circular outcome value</dd>


<dt>beta1</dt>
<dd>estimated linear coefficients of the first component</dd>


<dt>beta2</dt>
<dd>estimated linear coefficients of the second component</dd>


<dt>n</dt>
<dd>sample size</dd>

</dl>

lik_reg: Compute the Likelihood of the PN distribution (regression)

Description

Usage

Arguments