egf_has_converged

Performs simple diagnostic tests to assess whether the optimizer that
produced an estimated model actually converged to a local minimum point
of the negative log marginal likelihood function.

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

egf_has_converged function

<dl><dt>object</dt>
<dd>an <code>egf</code> object.</dd><dt>check</dt>
<dd>a logical. If <code>TRUE</code>, then an error is thrown if <code>object</code>
 does not actually inherit from class <code>egf</code>.</dd><dt>tol</dt>
<dd>a positive number. Convergence requires all gradient elements
 to be less than or equal to <code>tol</code> in absolute value.</dd></dl>

Arguments

Test for Convergence — egf_has_converged

<dl>

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

<dt>check</dt>
<dd>a logical. If <code>TRUE</code>, then an error is thrown if <code>object</code>
 does not actually inherit from class <code>egf</code>.</dd>

<dt>tol</dt>
<dd>a positive number. Convergence requires all gradient elements
 to be less than or equal to <code>tol</code> in absolute value.</dd>

</dl>

egf_has_converged: Test for Convergence

Description

Usage

Value

Arguments