% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/performance_aicc.R
\name{performance_aicc}
\alias{performance_aicc}
\alias{performance_aic}
\alias{performance_aic.default}
\alias{performance_aic.lmerMod}
\title{Compute the AIC or second-order AIC}
\usage{
performance_aicc(x, ...)

performance_aic(x, ...)

\method{performance_aic}{default}(x, estimator = "ML", verbose = TRUE, ...)

\method{performance_aic}{lmerMod}(x, estimator = "REML", verbose = TRUE, ...)
}
\arguments{
\item{x}{A model object.}

\item{...}{Currently not used.}

\item{estimator}{Only for linear models. Corresponds to the different
estimators for the standard deviation of the errors. If \code{estimator = "ML"}
(default, except for \code{performance_aic()} when the model object is of class
\code{lmerMod}), the scaling is done by \code{n} (the biased ML estimator), which is
then equivalent to using \code{AIC(logLik())}. Setting it to \code{"REML"} will give
the same results as \code{AIC(logLik(..., REML = TRUE))}.}

\item{verbose}{Toggle warnings.}
}
\value{
Numeric, the AIC or AICc value.
}
\description{
Compute the AIC or the second-order Akaike's information criterion (AICc).
\code{performance_aic()} is a small wrapper that returns the AIC, however, for
models with a transformed response variable, \code{performance_aic()} returns the
corrected AIC value (see 'Examples'). It is a generic function that also
works for some models that don't have a AIC method (like Tweedie models).
\code{performance_aicc()} returns the second-order (or "small sample") AIC that
incorporates a correction for small sample sizes.
}
\details{
\code{performance_aic()} correctly detects transformed response and,
unlike \code{stats::AIC()}, returns the "corrected" AIC value on the original
scale. To get back to the original scale, the likelihood of the model is
multiplied by the Jacobian/derivative of the transformation.

In case it is not possible to return the corrected AIC value, a warning
is given that the corrected log-likelihood value could not be computed.
}
\examples{
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
AIC(m)
performance_aicc(m)

# correct AIC for models with transformed response variable
data("mtcars")
mtcars$mpg <- floor(mtcars$mpg)
model <- lm(log(mpg) ~ factor(cyl), mtcars)

# wrong AIC, not corrected for log-transformation
AIC(model)

# performance_aic() correctly detects transformed response and
# returns corrected AIC
performance_aic(model)

\dontrun{
# there are a few exceptions where the corrected log-likelihood values
# cannot be returned. The following exampe gives a warning.
model <- lm(1 / mpg ~ factor(cyl), mtcars)
performance_aic(model)
}
}
\references{
\itemize{
\item Akaike, H. (1973) Information theory as an extension of the maximum
likelihood principle. In: Second International Symposium on Information
Theory, pp. 267-281. Petrov, B.N., Csaki, F., Eds, Akademiai Kiado, Budapest.
\item Hurvich, C. M., Tsai, C.-L. (1991) Bias of the corrected AIC criterion
for underfitted regression and time series models. Biometrika 78, 499â€“509.
}
}
