hmodeC

Estimate the mode by finding the highest posterior density interval

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

hmodeC function

<dl><dt>x</dt>
<dd>a sample from which to estimate the interval</dd>
<dt>cip</dt>
<dd>bandwidth for the algorithm, ranging from 0 to 1</dd></dl>

Arguments

Estimate the mode by finding the highest posterior density interval — hmodeC

<dl>

<dt>x</dt>
<dd>a sample from which to estimate the interval</dd>


<dt>cip</dt>
<dd>bandwidth for the algorithm, ranging from 0 to 1</dd>

</dl>

hmodeC: Estimate the mode by finding the highest posterior density interval

Description

Usage

Value

Arguments