Estimate the sampling distribution of the MLE for a likelihood model.

We use the bootstrap method. In other words, we treat the data as an empirical distribution and sample from it to get a new dataset, then we fit the model to that dataset and return the MLE. We do this R times and return the R MLEs.

# S3 method for class 'likelihood_model'
sampler(x, df, par, ..., nthreads = 1L)

Arguments

x

The likelihood model

df

Data frame to bootstrap from

par

Initial parameter values

...

Additional arguments to pass into the likelihood model

nthreads

The number of threads to use for parallelization

Value

A function that returns a bootstrapped sampling distribution of an MLE (fisher_boot object).

Details

This is the default method, but if you want to use a different method, you should define your own method for your likelihood model.