AIC.femlm: Aikake's an information criterion

In FENmlm: Fixed Effects Nonlinear Maximum Likelihood Models

Aikake's an information criterion

Description

This function computes the AIC (Aikake's, an information criterion) from a femlm estimation.

Usage

## S3 method for class 'femlm'
AIC(object, ..., k = 2)

Arguments

object	An object of class femlm. Typically the result of a femlm estimation.
...	Optionally, more fitted objects.
k	A numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC (i.e. AIC=-2*LL+k*nparams).

Details

The AIC is computed as:

AIC = -2\times LogLikelihood + k\times nbParams

with k the penalty parameter.

You can have more information on this crtierion on AIC.

Value

It return a numeric vector, with length the same as the number of objects taken as arguments.

Author(s)

Laurent Berge

See Also

femlm, AIC.femlm, logLik.femlm, nobs.femlm.

Examples

# two fitted models with different expl. variables:
res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
            Petal.Width | Species, iris)
res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris)

AIC(res1, res2)
BIC(res1, res2)

FENmlm documentation built on Aug. 22, 2023, 5:11 p.m.