AIC {stats} | R Documentation |

## Akaike's An Information Criterion

### Description

Generic function calculating the Akaike information criterion for
one or several fitted model objects for which a log-likelihood value
can be obtained, according to the formula
*-2*log-likelihood + k*npar*,
where *npar* represents the number of parameters in the
fitted model, and *k = 2* for the usual AIC, or *k = log(n)*
(*n* the number of observations) for the so-called BIC or SBC
(Schwarz's Bayesian criterion).

### Usage

AIC(object, ..., k = 2)

### 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

The default method for `AIC`

, `AIC.default()`

entirely
relies on the existence of a `logLik`

method
computing the log-likelihood for the given class.

When comparing fitted objects, the smaller the AIC, the better the fit.

### Value

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

);
if more than one object are provided, returns a `data.frame`

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

) and the AIC.

### Author(s)

Jose Pinheiro and Douglas Bates

### References

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

### See Also

`extractAIC`

, `logLik`

.

### Examples

lm1 <- lm(Fertility ~ . , data = swiss)
AIC(lm1)
stopifnot(all.equal(AIC(lm1),
AIC(logLik(lm1))))
## a version of BIC or Schwarz' BC :
AIC(lm1, k = log(nrow(swiss)))

[Package

*stats* version 2.2.1

Index]