AIC
Akaike's Information Criterion
Description
Calculate Akaike's Information Criterion (AIC)
or the Bayesian Information Criterion (BIC)
for a fitted model with a log-likelihood.
Usage
AIC(object, ..., k = 2)
## Default S3 method:
AIC(object, ..., k = 2)
## S3 method for class 'logLik':
AIC(object, ..., k = 2)
BIC(object, ...)
## Default S3 method:
BIC(object, ...)
## S3 method for class 'logLik':
BIC(object, ...)
Arguments
| object |
an object inheriting from fitted model or class,
which has a log-likelihood value that can be extracted.
|
| ... |
additional arguments to be passed to or from other functions.
|
| k |
a numeric value, use as penalty coefficient for number of parameters
in the fitted model.
|
Details
AIC is computed as -2*log-likelihood + k*npar,
where npar represents the number of parameters in the fitted model.
BIC is computed as log-likelihood + npar*log(nobs),
where npar represents the number of parameters
and nobs the number of observations in the fitted model.
Value
a numeric value with the corresponding AIC or BIC value
if just one object is provided to AIC or BIC.
If more than one object is provided, a data frame is returned
with row names corresponding to the objects
and columns with df (degrees of freedom) and AIC or BIC.
AIC.logLik, BIC.logLik
return the AIC or BIC value computed from the provided log-likelihood.
References
Sakamoto, Y., Ishiguro, M., and Kitagawa G. 1986. Akaike Information Criterion Statistics. Tokyo, JP: KTK Scientific Publishers.
See Also
Examples
m1 <- lm(Fuel ~ Disp. + Weight, data=Sdatasets::fuel.frame)
AIC(m1)
BIC(m1)
m2 <- lm(Fuel ~ Disp. + Weight + Type, data=Sdatasets::fuel.frame)
AIC(m1, m2)
BIC(m1, m2)
AIC(logLik(m1))
BIC(logLik(m1))