| logLik {stats} | R Documentation |
This function is generic; method functions can be written to handle
specific classes of objects. Classes which already have methods for
this function include: glm, lm, nls
and gls, lme and others in package nlme.
logLik(object, ...) ## S3 method for class 'lm': logLik(object, REML = FALSE, ...)
object |
any object from which a log-likelihood value, or a contribution to a log-likelihood value, can be extracted. |
... |
some methods for this generic function require additional arguments. |
REML |
an optional logical value. If TRUE the restricted
log-likelihood is returned, else, if FALSE, the
log-likelihood is returned. Defaults to FALSE. |
For a glm fit the family does not have to specify
how to calculate the log-likelihood, so this is based on the
family's function to compute
the AIC. For gaussian, Gamma and
inverse.gaussian
families it assumed that the dispersion of the GLM is estimated and
has been included in the AIC, and for all other families it is assumed
that the dispersion is known.
Note that this procedure is not completely accurate for the gamma and inverse gaussian families, as the estimate of dispersion used is not the MLE.
Returns an object, say r, of class logLik which is a
number with attributes, attr(r, "df") (degrees of
freedom) giving the number of parameters in the model.
There's a simple print method for logLik objects.
The details depend on the method function used; see the appropriate
documentation.
Jose Pinheiro and Douglas Bates
For logLik.lm:
Harville, D.A. (1974). Bayesian inference for variance components using only error contrasts. Biometrika, 61, 383–385.
logLik.gls, logLik.lme, in
package nlme, etc.
x <- 1:5
lmx <- lm(x ~ 1)
logLik(lmx) # using print.logLik() method
str(logLik(lmx))
## lm method
(fm1 <- lm(rating ~ ., data = attitude))
logLik(fm1)
logLik(fm1, REML = TRUE)
res <- try(data(Orthodont, package="nlme"))
if(!inherits(res, "try-error")) {
fm1 <- lm(distance ~ Sex * age, Orthodont)
print(logLik(fm1))
print(logLik(fm1, REML = TRUE))
}