cat_check

<code>cat_check</code> checks whether a vector contains only 0 and 1 and thus is a
dummy variable.

internal

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

cat_check function

<dl><dt>x</dt>
<dd>vector containing values of a variable.</dd></dl>

Arguments

Check whether a variable is categorical — cat_check

<dl>

<dt>x</dt>
<dd>vector containing values of a variable.</dd>

</dl>

cat_check: Check whether a variable is categorical

Description

Usage

Value

Arguments