myAIC

This function calculates the Akaike Information Criterion (AIC) for a given
model.

An implementation of multiple regression models for
count data. These include various forms of the negative binomial (NB-1,
NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other
compound Poisson distributions, the Generalized Waring model, etc.
Information on the different forms of the negative binomial are
described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For
treatises on count models, see Cameron and Trivedi (2013)
<doi:10.1017/CBO9781139013567> and Hilbe (2012)
<doi:10.1017/CBO9780511973420>. For the implementation of
under-reporting in count models, see Wood et al. (2016)
<doi:10.1016/j.aap.2016.06.013>. For prediction methods in random
parameter models, see Wood and Gayah (2025)
<doi:10.1016/j.aap.2025.108147>. For estimating random parameters using
maximum simulated likelihood, see Greene and Hill (2010)
<doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996)
<doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015)
<doi:10.1017/CBO9781316136232>.

Jonathan Wood

flexCountReg

Estimation of a Variety of Count Regression Models

Guillermo Basulto-Elias

myAIC function

<dl><dt>LL</dt>
<dd>Numeric value representing the log-likelihood of the model.</dd>
<dt>nparam</dt>
<dd>Numeric value representing the number of parameters in the
model.</dd></dl>

Arguments

Calculate Akaike Information Criterion (AIC) — myAIC

<dl>

<dt>LL</dt>
<dd>Numeric value representing the log-likelihood of the model.</dd>


<dt>nparam</dt>
<dd>Numeric value representing the number of parameters in the
model.</dd>

</dl>

myAIC: Calculate Akaike Information Criterion (AIC)

Description

Usage

Value

Arguments

Details

Examples