The function can be called in pva to fit the Gompertz growth model
to a given population time series assuming both
with and without observation error. When assuming the presence of
observation error, either the Normal
or the Poisson observation error model must be assumed within the
state-space model formulation (Nadeem and Lele, 2012).
The Gompertz growth model is defined as follows:
$$x_{t} = a + x_{t-1} + b x_{t-1} + \epsilon_{t}$$
where $x_{t}$ is log abundance at time $t$ and
$\epsilon_{t} \sim Normal(0, \sigma^2)$.
Observation error models are described in the help page of
pva.
The argument 'fixed' can be used to fit the model assuming
a priori values of a subset of the parameters.
The number of parameters that should be fixed at most is $p-1$,
where $p$ is the dimension of the full model. See examples
below and in pva and model.select.
References
Nadeem, K., Lele S. R., 2012.
Likelihood based population viability analysis in the presence of
observation error. Oikos. doi: 10.1111/j.1600-0706.2011.20010.x