object and a response
module of a class that inherits from class
. A model with a
response has class lmerMod
; a
response has class glmerMod
; and a
response has class nlmerMod
.# S3 method for merMod
anova(object, ..., refit = TRUE, model.names=NULL)
# S3 method for merMod
coef(object, ...)
# S3 method for merMod
deviance(object, REML = NULL, ...)
REMLcrit(object)
# S3 method for merMod
extractAIC(fit, scale = 0, k = 2, ...)
# S3 method for merMod
family(object, ...)
# S3 method for merMod
formula(x, fixed.only = FALSE, random.only = FALSE, ...)
# S3 method for merMod
fitted(object, ...)
# S3 method for merMod
logLik(object, REML = NULL, ...)
# S3 method for merMod
nobs(object, ...)
# S3 method for merMod
ngrps(object, ...)
# S3 method for merMod
terms(x, fixed.only = TRUE, random.only = FALSE, …)
# S3 method for merMod
vcov(object, correlation = TRUE, sigm = sigma(object),
use.hessian = NULL, …)
# S3 method for merMod
model.frame(formula, fixed.only = FALSE, ...)
# S3 method for merMod
model.matrix(object, type = c("fixed", "random", "randomListRaw"), ...)
# S3 method for 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 merMod
summary(object, correlation = , use.hessian = NULL, …)
# S3 method for 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 merMod
update(object, formula., ..., evaluate = TRUE)
# S3 method for merMod
weights(object, type = c("prior", "working"), ...)
merMod
or summary.merMod
,
respectively, the latter resulting from summary(<merMod>)
.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 fixed effects,
whose likelihoods are not directly comparable.extractAIC
).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 with the covariance;
for print.summary.merMod
, indicates whether the correlation
matrix of the fixed-effects parameters should be printed. In the
latter case, when NULL
(the default), the correlation matrix
is printed when it has been computed by summary(.)
, and when
\(p <= 20\).getME(.,"RX")
).
The default is to to use the Hessian whenever the
fixed effect parameters are arguments to the deviance
function (i.e. for GLMMs with nAGQ>0
), and to use
getME(.,"RX")
whenever the fixed effect parameters are
profiled out (i.e. for GLMMs with nAGQ==0
or LMMs). use.hessian=FALSE
is backward-compatible with older versions
of lme4
, but may give less accurate SE estimates when the
estimates of the fixed-effect (see getME(.,"beta")
)
and random-effect (see getME(.,"theta")
) parameters
are correlated.
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 algorithm. For model.matrix
, type of model matrix to
return (one of fixed
giving the fixed effects model matrix,
random
giving the random effects model matrix, or
randomListRaw
giving a list of the raw random effects model
matrices associated with each random effects term).anova
:lmerMod
the default behavior is to refit the models
with LM if fitted with REML = TRUE
, this can be controlled via the
refit
argument. See also anova
.coef
:extractAIC
:isREML(fit)
, then fit
is
refitted using maximum likelihood.family
:family
of fitted
GLMM. (Warning: this accessor may not work properly with
customized families/link functions.)fitted
:predict.merMod
.logLik
:logLik
. model.frame
:frame
slot of
.model.matrix
:nobs
, ngrps
:ngrps
.summary
:print
method,
see also summary
.print.summary
:vcov
:vcov
.update
:update
.glmerMod
object: conditional | unconditional | |
relative | deviance(object) |
NA in lme4 |
Gamma
) then object@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. misc/logLikGLMM
directory in the package source.
resp
:lmResp-class
).Gp
:getME
.call
:frame
:flist
:getME
.cnms
:getME
.lower
:getME
.theta
:beta
:u
:devcomp
:getME
.pp
:merPredD-class
).optinfo
:lmer
, glmer
,
nlmer
,
,
,
,
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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