get_samps can be used to randomly
select states from a fitted model object of class epimodel.
The object must have been fit using Markov chain Monte Carlo, i.e.
using algorithm = "sampling" in the call to epim.
The states are sampled uniformly at random without replacement, across
all chains and not including the warmup period.
get_samps(prefit, n)
| prefit | An object of class |
|---|---|
| n | A positive integer. This specifies the number of states to sample. |
A list of length \(n\). Each element in the list is itself a named
list, with elements corresponding to sample parameters. The result can be
passed directly as the init argument in epim.
This function can be used to specify the initial state for
sampling based on states from another sampling run. This
is particularly useful, for example, when you wish to fit a model using
pop_adjust = T, as this makes the posterior geometry difficult to
explore. Using a "prefit" run with pop_adjust = F is useful for
finding good states that can be used as initial states for the run with
the population adjustment.