sampler.likelihood_model: Estimate the sampling distribution of the MLE for a...

In likelihood.model: Likelihood-Based Statistical Inference in the Fisherian Tradition

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

Description

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.

Usage

## 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

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.

Value

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

likelihood.model documentation built on March 19, 2026, 9:07 a.m.