AIC {stats} | R Documentation

## Akaike's An Information Criterion

### Description

Generic function calculating Akaike's 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 being the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).

### 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

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.

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

The log-likelihood and hence the AIC/BIC is only defined up to an additive constant. Different constants have conventionally be 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.

### Author(s)

Originally by José Pinheiro and Douglas Bates, more recent revisions by R-core.

### References

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

### See Also

`extractAIC`, `logLik`, `nobs`.

### Examples

    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)

[Package stats version 2.15.1 Index]