merModclass {lme4}  R Documentation 
Class "merMod" of Fitted MixedEffect Models
Description
A mixedeffects model is represented as a
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
.
Usage
## S3 method for class 'merMod'
anova(object, ..., refit = TRUE, model.names=NULL)
## S3 method for class 'merMod'
as.function(x, ...)
## 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, ...)
## 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.",
ranef.corr = any(ranef.comp == "Std.Dev."), ...)
## S3 method for class 'merMod'
summary(object, correlation = , use.hessian = NULL, ...)
## 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."),
ranef.corr = any(ranef.comp == "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"), ...)
Arguments
object 
an R object of class 
x 
an R object of class 
fit 
an R object of class 
formula 
in the case of 
refit 
logical indicating if objects of class 
model.names 
character vectors of model names to be used in the anova table. 
scale 
Not currently used (see 
k 
see 
REML 
Logical. If 
fixed.only 
logical indicating if only the fixed effects components (terms or formula elements) are sought. If false, all components, including random ones, are returned. 
random.only 
complement of 
correlation 
(logical)
for 
use.hessian 
(logical) indicates whether to use the
finitedifference Hessian of the deviance function to compute
standard errors of the fixed effects, rather estimating
based on internal information about the inverse of
the model matrix (see

sigm 
the residual standard error; by default 
digits 
number of significant digits for printing 
symbolic.cor 
should a symbolic encoding of the fixedeffects correlation
matrix be printed? If so, the 
signif.stars 
(logical) should significance stars be used? 
ranef.comp 
character vector of length one or two, indicating if randomeffects parameters should be reported on the variance and/or standard deviation scale. 
show.resids 
should the quantiles of the scaled residuals be printed? 
formula. 
see 
evaluate 
see 
type 
For

ranef.corr 
(logical) print correlations (rather than covariances) of random effects? 
... 
potentially further arguments passed from other methods. 
Objects from the Class
Objects of class merMod
are created by calls to
lmer
, glmer
or nlmer
.
S3 methods
The following S3 methods with arguments given above exist (this list is currently not complete):
anova
:returns the sequential decomposition of the contributions of fixedeffects terms or, for multiple arguments, model comparison statistics. For objects of class
lmerMod
the default behavior is to refit the models with ML if fitted withREML = TRUE
, this can be controlled via therefit
argument. See alsoanova
.as.function
:returns the deviance function, the same as
lmer(*, devFunOnly=TRUE)
, andmkLmerDevfun()
ormkGlmerDevfun()
, respectively.coef
:Computes the sum of the random and fixed effects coefficients for each explanatory variable for each level of each grouping factor.
extractAIC
:Computes the (generalized) Akaike An Information Criterion. If
isREML(fit)
, thenfit
is refitted using maximum likelihood.family
:family
of fitted GLMM. (Warning: this accessor may not work properly with customized families/link functions.)fitted
:Fitted values, given the conditional modes of the random effects. For more flexible access to fitted values, use
predict.merMod
.logLik
:Loglikelihood at the fitted value of the parameters. Note that for GLMMs, the returned value is only proportional to the log probability density (or distribution) of the response variable. See
logLik
.model.frame
:returns the
frame
slot ofmerMod
.model.matrix
:returns the fixed effects model matrix.
nobs
,ngrps
:Number of observations and vector of the numbers of levels in each grouping factor. See
ngrps
.summary
:Computes and returns a list of summary statistics of the fitted model, the amount of output can be controlled via the
print
method, see alsosummary
.print.summary
:Controls the output for the summary method.
vcov
:Calculate variancecovariance matrix of the fixed effect terms, see also
vcov
.update
:See
update
.
Deviance and loglikelihood of GLMMs
One must be careful when defining the deviance of a GLM. For example, should the deviance be defined as minus twice the loglikelihood or does it involve subtracting the deviance for a saturated model? To distinguish these two possibilities we refer to absolute deviance (minus twice the loglikelihood) and relative deviance (relative to a saturated model, e.g. Section 2.3.1 in McCullagh and Nelder 1989).
With GLMMs however, there is an additional complication involving the
distinction between the likelihood and the conditional likelihood.
The latter is the likelihood obtained by conditioning on the estimates
of the conditional modes of the spherical random effects coefficients,
whereas the likelihood itself (i.e. the unconditional likelihood)
involves integrating out these coefficients. The following table
summarizes how to extract the various types of deviance for a
glmerMod
object:
conditional  unconditional  
relative  deviance(object)  NA in lme4 
absolute  object@resp$aic()  2*logLik(object)

This table requires two caveats:
If the link function involves a scale parameter (e.g.
Gamma
) thenobject@resp$aic()  2 * getME(object, "devcomp")$dims["useSc"]
is required for the absoluteconditional case.If adaptive GaussHermite quadrature is used, then
logLik(object)
is currently only proportional to the absoluteunconditional loglikelihood.
For more information about this topic see the misc/logLikGLMM
directory in the package source.
Slots
resp
:A reference class object for an lme4 response module (
lmRespclass
).Gp
:See
getME
.call
:The matched call.
frame
:The model frame containing all of the variables required to parse the model formula.
flist
:See
getME
.cnms
:See
getME
.lower
:See
getME
.theta
:Covariance parameter vector.
beta
:Fixed effects coefficients.
u
:Conditional model of spherical random effects coefficients.
devcomp
:See
getME
.pp
:A reference class object for an lme4 predictor module (
merPredDclass
).optinfo
:List containing information about the nonlinear optimization.
See Also
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
.
Examples
showClass("merMod")
methods(class="merMod")## over 30 (S3) methods available
m1 < lmer(Reaction ~ Days + (Days  Subject), sleepstudy)
print(m1, ranef.corr = TRUE) ## print correlations of REs
print(m1, ranef.corr = FALSE) ## print covariances of REs
## > example(lmer) for an example of vcov.merMod()