merPredD
object and a response
module of a class that inherits from class
lmResp
. A model with a
lmerResp
response has class lmerMod
; a
glmResp
response has class glmerMod
; and a
nlsResp
response has class nlmerMod
.## S3 method for class 'merMod':
anova(object, ..., refit = TRUE, model.names=NULL)
## S3 method for class 'merMod':
coef(object, ...)
## S3 method for class 'merMod':
deviance(object, REML = NULL, ...)
REMLcrit(object)
## S3 method for class 'merMod':
extractAIC(fit, scale = 0, k = 2, ...)
## S3 method for class 'merMod':
family(object, ...)
## S3 method for class 'merMod':
formula(x, fixed.only = FALSE, ...)
## S3 method for class 'merMod':
fitted(object, ...)
## S3 method for class 'merMod':
logLik(object, REML = NULL, ...)
## S3 method for class 'merMod':
nobs(object, ...)
## S3 method for class 'merMod':
ngrps(object, ...)
## S3 method for class 'merMod':
terms(x, fixed.only = TRUE, \dots)
## S3 method for class 'merMod':
vcov(object, correlation = TRUE, sigm = sigma(object),
use.hessian = NULL, ...)
## S3 method for class 'merMod':
model.frame(formula, fixed.only = FALSE, ...)
## S3 method for class 'merMod':
model.matrix(object, type = c("fixed", "random", "randomListRaw"), ...)
## S3 method for class 'merMod':
print(x, digits = max(3, getOption("digits") - 3),
correlation = NULL, symbolic.cor = FALSE,
signif.stars = getOption("show.signif.stars"), ranef.comp = "Std.Dev.", ...)## S3 method for class 'merMod':
summary(object, correlation = , use.hessian = NULL, \dots)
## S3 method for class 'summary.merMod':
print(x, digits = max(3, getOption("digits") - 3),
correlation = NULL, symbolic.cor = FALSE,
signif.stars = getOption("show.signif.stars"),
ranef.comp = c("Variance", "Std.Dev."), show.resids = TRUE, ...)
## S3 method for class 'merMod':
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'merMod':
weights(object, type = c("prior", "working"), ...)
merMod
or summary.merMod
,
respectively, the latter resulting from summary()
.merMod
.model.frame
, a
merMod
object.lmerMod
should be
refitted with ML before comparing models. The default is
TRUE
to prevent the common mistake of inappropriately
comparing REML-fitted models with different fixedextractAIC
).extractAIC
.TRUE
, return the restricted log-likelihood
rather than the log-likelihood. If NULL
(the default),
set REML
to isREML(object)
(see isREML
terms
are sought, defaults to true. If false, all
terms, including random ones are returned.vcov
, indicates whether the
correlation matrix as well as the variance-covariance matrix is
desired; for print.summary.merMod
, indicates whether the
correlation matrix of the fixed-effects parameters sho
sigma(object)
.symnum
function is used.update.formula
.update
.weights
, type of weights to be returned; either "prior"
for
the initially supplied weights or "working"
for the weights
at the final iteration of the penalized iteratively reweighted least
squares algglmerMod
object. deviance(object)
NA in lme4
absolute object@resp$aic()
-2*logLik(object)
}
This table requires two caveat:
Gamma
) thenobject@resp$aic() - 2 * getME(object,
"devcomp")$dims["useSc"]
is required for the absolute-conditional
case.logLik(object)
is currently only proportional to the
absolute-unconditional log-likelihood. For more information about this topic see the misc/logLikGLMM
directory in the package source.
lmer
, glmer
,
nlmer
, merPredD
,
lmerResp
,
glmResp
,
nlsResp
Other methods for merMod
objects documented elsewhere include:
fortify.merMod
, drop1.merMod
,
isLMM.merMod
, isGLMM.merMod
,
isNLMM.merMod
, isREML.merMod
,
plot.merMod
, predict.merMod
,
profile.merMod
, ranef.merMod
,
refit.merMod
, refitML.merMod
,
residuals.merMod
, sigma.merMod
,
simulate.merMod
, summary.merMod
.
showClass("merMod")
methods(class="merMod")## over 30 (S3) methods available
## -> example(lmer) for an example of vcov.merMod()
Run the code above in your browser using DataLab