extractAIC.egf

Extracts from a model object the generalized Akaike Information Criterion
(AIC).

Maximum likelihood estimation of nonlinear mixed effects models of
epidemic growth using Template Model Builder ('TMB'). Enables
joint estimation for collections of disease incidence time series,
including time series that describe multiple epidemic waves.
Supports a set of widely used phenomenological models: exponential,
logistic, Richards (generalized logistic), subexponential,
and Gompertz. Provides methods for interrogating model objects
and several auxiliary functions, including one for computing basic
reproduction numbers from fitted values of the initial exponential
growth rate.
Preliminary versions of this software were applied
in Ma et al. (2014) <doi:10.1007/s11538-013-9918-2> and
in Earn et al. (2020) <doi:10.1073/pnas.2004904117>.

Mikael Jagan

epigrowthfit

Nonlinear Mixed Effects Models of Epidemic Growth

Ben Bolker

Jonathan Dushoff

David Earn

Junling Ma

extractAIC.egf function

<dl><dt>fit</dt>
<dd>an <code>egf</code> object.</dd><dt>scale</dt>
<dd>unused argument, for generic consistency.</dd><dt>k</dt>
<dd>a number giving a weight for the equivalent degrees of freedom.
 <code>k=2</code> and <code>k=log(nobs(fit))</code> give the standard
 Akaike Information Criterion and Bayesian Information Criterion.</dd><dt>...</dt>
<dd>unused optional arguments.</dd></dl>

Arguments

Extract the (Generalized) AIC — extractAIC.egf

<dl>

<dt>fit</dt>
<dd>an <code>egf</code> object.</dd>

<dt>scale</dt>
<dd>unused argument, for generic consistency.</dd>

<dt>k</dt>
<dd>a number giving a weight for the equivalent degrees of freedom.
 <code>k=2</code> and <code>k=log(nobs(fit))</code> give the standard
 Akaike Information Criterion and Bayesian Information Criterion.</dd>

<dt>...</dt>
<dd>unused optional arguments.</dd>

</dl>

extractAIC.egf: Extract the (Generalized) AIC

Description

Usage

Value

Arguments

See Also