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, random.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, random.only = FALSE, \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
fixed.only
; indicates
whether random components only are sought. (Trying to specify
fixed.only
and random.only
at the same time
will produce an error.)vcov
, indicates whether the correlation matrix as well as
the variance-covariance matrix is desired;
for summary.merMod
, indicates whether the correlation matrix
should be computed and stored along w
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$aic()
-2*logLik(object)
}
This table requires two caveats:
Gamma
) thenobject@resp$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()
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