This function is generic; method functions can be written to handle
specific classes of objects. Classes which have methods for this
"Arima". Packages contain methods for other classes, such as
"polr" in package
"multinom" in package
"lme" and others in package
## S3 method for class 'lm': logLik(object, REML = FALSE, \dots)
- 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.
- an optional logical value. If
TRUEthe restricted log-likelihood is returned, else, if
FALSE, the log-likelihood is returned. Defaults to
logLik is most commonly used for a model fitted by maximum
likelihood, and some uses, e.g.
this. So care is needed where other fit criteria have been used, for
example REML (the default for
"glm" fit the
family does not have to
specify how to calculate the log-likelihood, so this is based on using
aic() function to compute the AIC. For the
inverse.gaussian families it assumed that the dispersion
of the GLM is estimated and has been counted as a parameter in the AIC
value, and for all other families it is assumed that the dispersion is
known. Note that this procedure does not give the maximized
"glm" fits from the Gamma and inverse gaussian
families, as the estimate of dispersion used is not the MLE.
"lm" fits it is assumed that the scale has been estimated
(by maximum likelihood or REML), and all the constants in the
log-likelihood are included. That method is only applicable to
- Returns an object of class
logLik. This is a number with at least one attribute,
"df"(degrees of freedom), giving the number of (estimated) parameters in the model.
There is a simple
There may be other attributes depending on the method used: see the appropriate documentation. One that is used by several methods is
"nobs", the number of observations used in estimation (after the restrictions if
REML = TRUE).
Harville, D.A. (1974). Bayesian inference for variance components using only error contrasts. Biometrika, 61, 383--385.
x <- 1:5 lmx <- lm(x ~ 1) logLik(lmx) # using print.logLik() method utils::str(logLik(lmx)) ## lm method (fm1 <- lm(rating ~ ., data = attitude)) logLik(fm1) logLik(fm1, REML = TRUE) utils::data(Orthodont, package = "nlme") fm1 <- lm(distance ~ Sex * age, Orthodont) logLik(fm1) logLik(fm1, REML = TRUE)