panelGompertzLikelihood

Evaluates the likelihood function for a panel Gompertz model,
using a format convenient for maximization by optim() to obtain a maximum
likelihood estimate. Specifically, estimated and fixed parameters are
supplied by two different arguments.

Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.

Jesse Wheeler

panelPomp

Inference for Panel Partially Observed Markov Processes

Carles Breto

Aaron Abkemeier

panelGompertzLikelihood function

<dl><dt>x</dt>
<dd>named vector for a subset of parameters, corresponding to
those being estimated.</dd>
<dt>panelPompObject</dt>
<dd>a panel Gompertz model.</dd>
<dt>params</dt>
<dd>named vector containing all the parameters of the panel Gompertz model. Estimated parameters are overwritten by x.</dd></dl>

Arguments

Author

Likelihood for a panel Gompertz model via a Kalman filter — panelGompertzLikelihood

<dl>

<dt>x</dt>
<dd>named vector for a subset of parameters, corresponding to
those being estimated.</dd>


<dt>panelPompObject</dt>
<dd>a panel Gompertz model.</dd>


<dt>params</dt>
<dd>named vector containing all the parameters of the panel Gompertz model. Estimated parameters are overwritten by x.</dd>

</dl>

panelGompertzLikelihood: Likelihood for a panel Gompertz model via a Kalman filter

Description

Usage

Value

Arguments

Author

Examples