DIC_reg

Compute Model Fit Measures Regression Model

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

DIC_reg function

<dl><dt>theta</dt>
<dd>circular outcome values</dd>
<dt>beta1</dt>
<dd>regression coefficients for the second component for each mcmc iteration from pnr function</dd>
<dt>beta2</dt>
<dd>regression coefficients for the second component for each mcmc iteration from pnr function</dd>
<dt>Likelihood</dt>
<dd>likelihood values for each individual and mcmc itertion from pnr function</dd>
<dt>X1</dt>
<dd>model matrix for the first component</dd>
<dt>X2</dt>
<dd>model matrix for the second component</dd></dl>

Arguments

Compute Model Fit Measures Regression Model — DIC_reg

<dl>

<dt>theta</dt>
<dd>circular outcome values</dd>


<dt>beta1</dt>
<dd>regression coefficients for the second component for each mcmc iteration from pnr function</dd>


<dt>beta2</dt>
<dd>regression coefficients for the second component for each mcmc iteration from pnr function</dd>


<dt>Likelihood</dt>
<dd>likelihood values for each individual and mcmc itertion from pnr function</dd>


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


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

</dl>

DIC_reg: Compute Model Fit Measures Regression Model

Description

Usage

Arguments