
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).
get_loglikelihood_adjustment()
can be used to correct the log-likelihood
for models with transformed response variables. The adjustment value can
be added to the log-likelihood to get the corrected value. This is done
automatically in get_loglikelihood()
if check_response = TRUE
.
get_loglikelihood(x, ...)loglikelihood(x, ...)
get_loglikelihood_adjustment(x)
# S3 method for lm
get_loglikelihood(
x,
estimator = "ML",
REML = FALSE,
check_response = FALSE,
verbose = TRUE,
...
)
get_loglikelihood()
returns 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.
get_loglikelihood_adjustment()
returns the adjustment value to be added to
the log-likelihood to correct for transformed response variables, or NULL
if the adjustment could not be computed.
A model.
Passed down to logLik()
, if possible.
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. estimator="REML"
will give same results as
logLik(..., REML=TRUE)
.
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.
Logical, if TRUE
, checks if the response variable
is transformed (like log()
or sqrt()
), and if so, returns a corrected
log-likelihood. To get back to the original scale, the likelihood of the
model is multiplied by the Jacobian/derivative of the transformation.
Toggle warnings and messages.
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")
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