slice_rcpp

A slice sampler for the latent lengths r

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

slice_rcpp function

<dl><dt>X1</dt>
<dd>A model matrix for component I.</dd>
<dt>X2</dt>
<dd>A model matrix for component II.</dd>
<dt>theta</dt>
<dd>A vector with the circular dependent variable.</dd>
<dt>beta1</dt>
<dd>A matrix containing the coefficients of component I for the current iteration.</dd>
<dt>beta2</dt>
<dd>A matrix containing the coefficients of component II for the current iteration.</dd>
<dt>n</dt>
<dd>An integer indicating the sample size of the data.</dd>
<dt>r</dt>
<dd>A matrix with the estimates of r of the previous iteration.</dd></dl>

Arguments

A slice sampler for the latent lengths r — slice_rcpp

<dl>

<dt>X1</dt>
<dd>A model matrix for component I.</dd>


<dt>X2</dt>
<dd>A model matrix for component II.</dd>


<dt>theta</dt>
<dd>A vector with the circular dependent variable.</dd>


<dt>beta1</dt>
<dd>A matrix containing the coefficients of component I for the current iteration.</dd>


<dt>beta2</dt>
<dd>A matrix containing the coefficients of component II for the current iteration.</dd>


<dt>n</dt>
<dd>An integer indicating the sample size of the data.</dd>


<dt>r</dt>
<dd>A matrix with the estimates of r of the previous iteration.</dd>

</dl>

slice_rcpp: A slice sampler for the latent lengths r

Description

Usage

Arguments