# AIC

##### Akaike's An Information Criterion

Generic function calculating Akaike's ‘An Information Criterion’ for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula \(-2 \mbox{log-likelihood} + k n_{par}\), where \(n_{par}\) represents the number of parameters in the fitted model, and \(k = 2\) for the usual AIC, or \(k = \log(n)\) (\(n\) being the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).

- Keywords
- models

##### Usage

`AIC(object, …, k = 2)`BIC(object, …)

##### Arguments

- object
a fitted model object for which there exists a

`logLik`

method to extract the corresponding log-likelihood, or an object inheriting from class`logLik`

.- …
optionally more fitted model objects.

- k
numeric, the

*penalty*per parameter to be used; the default`k = 2`

is the classical AIC.

##### Details

When comparing models fitted by maximum likelihood to the same data, the smaller the AIC or BIC, the better the fit.

The theory of AIC requires that the log-likelihood has been maximized: whereas AIC can be computed for models not fitted by maximum likelihood, their AIC values should not be compared.

Examples of models not ‘fitted to the same data’ are where the response is transformed (accelerated-life models are fitted to log-times) and where contingency tables have been used to summarize data.

These are generic functions (with S4 generics defined in package
stats4): however methods should be defined for the
log-likelihood function `logLik`

rather than these
functions: the action of their default methods is to call `logLik`

on all the supplied objects and assemble the results. Note that in
several common cases `logLik`

does not return the value at
the MLE: see its help page.

The log-likelihood and hence the AIC/BIC is only defined up to an
additive constant. Different constants have conventionally been used
for different purposes and so `extractAIC`

and `AIC`

may give different values (and do for models of class `"lm"`

: see
the help for `extractAIC`

). Particular care is needed
when comparing fits of different classes (with, for example, a
comparison of a Poisson and gamma GLM being meaningless since one has
a discrete response, the other continuous).

`BIC`

is defined as
`AIC(object, …, k = log(nobs(object)))`

.
This needs the number of observations to be known: the default method
looks first for a `"nobs"`

attribute on the return value from the
`logLik`

method, then tries the `nobs`

generic, and if neither succeed returns BIC as `NA`

.

##### Value

If just one object is provided, a numeric value with the corresponding
AIC (or BIC, or …, depending on `k`

).

If multiple objects are provided, a `data.frame`

with rows
corresponding to the objects and columns representing the number of
parameters in the model (`df`

) and the AIC or BIC.

##### References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986).
*Akaike Information Criterion Statistics*.
D. Reidel Publishing Company.

##### See Also

`extractAIC`

, `logLik`

, `nobs`

.

##### Examples

`library(stats)`

```
# NOT RUN {
lm1 <- lm(Fertility ~ . , data = swiss)
AIC(lm1)
stopifnot(all.equal(AIC(lm1),
AIC(logLik(lm1))))
BIC(lm1)
lm2 <- update(lm1, . ~ . -Examination)
AIC(lm1, lm2)
BIC(lm1, lm2)
# }
```

*Documentation reproduced from package stats, version 3.6.0, License: Part of R 3.6.0*