"glm"
, "lm"
, "nls"
and
"Arima"
. Packages contain methods for other classes, such as
"fitdistr"
, "negbin"
and "polr"
in package
\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}MASSMASS, "multinom"
in package \href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}nnetnnet and
"gls"
, "gnls"
"lme"
and others in package
\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}nlmenlme.
logLik(object, ...)
"logLik"(object, REML = FALSE, ...)
TRUE
the restricted
log-likelihood is returned, else, if FALSE
, the
log-likelihood is returned. Defaults to FALSE
.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 print
method for "logLik"
objects.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
).
logLik
is most commonly used for a model fitted by maximum
likelihood, and some uses, e.g.\ifelse{latex}{\out{~}}{ } by AIC
, assume
this. So care is needed where other fit criteria have been used, for
example REML (the default for "lme"
). For a "glm"
fit the family
does not have to
specify how to calculate the log-likelihood, so this is based on using
the family's aic()
function to compute the AIC. For the
gaussian
, Gamma
and
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
likelihood for "glm"
fits from the Gamma and inverse gaussian
families, as the estimate of dispersion used is not the MLE.
For "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
single-response fits.
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 \href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}nlmenlme, etc.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)
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