insight (version 0.16.0)

get_loglikelihood: Log-Likelihood

Description

A robust function to compute the log-likelihood of a model, as well as individual log-likelihoods (for each observation) whenever possible. Can be used as a replacement for stats::logLik() out of the box, as the returned object is of the same class (and it gives the same results by default).

Usage

get_loglikelihood(x, ...)
loglikelihood(x, ...)
# S3 method for lm
get_loglikelihood(x, estimator = "ML", REML = FALSE, verbose = TRUE, ...)

Arguments

x

A model.

...

Passed down to logLik(), if possible.

estimator

Corresponds to the different estimators for the standard deviation of the errors. If estimator="ML" (default), the scaling is done by n (the biased ML estimator), which is then equivalent to using stats::logLik(). If estimator="OLS", it returns the unbiased OLS estimator.

REML

Only for linear models. This argument is present for compatibility with stats::logLik(). Setting it to TRUE will overwrite the estimator argument and is thus equivalent to setting estimator="REML". It will give the same results as stats::logLik(..., REML=TRUE). Note that individual log-likelihoods are not available under REML.

verbose

Toggle warnings and messages.

Value

An object of class "logLik", also containing the log-likelihoods for each observation as a per_observation attribute (attributes(get_loglikelihood(x))$per_observation) when possible. The code was partly inspired from the nonnest2 package.

Examples

# NOT RUN {
x <- lm(Sepal.Length ~ Petal.Width + Species, data = iris)

get_loglikelihood(x, estimator = "ML") # Equivalent to stats::logLik(x)
get_loglikelihood(x, estimator = "REML") # Equivalent to stats::logLik(x, REML=TRUE)
get_loglikelihood(x, estimator = "OLS")
# }