merModclass
Class "merMod" of Fitted MixedEffect Models
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
.
 Keywords
 classes
Usage
# S3 method for merMod
anova(object, ..., refit = TRUE, model.names=NULL)
# S3 method for merMod
as.function(x, ...)
# 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"), ...)
Arguments
 object
an R object of class
'>merMod
, i.e., as resulting fromlmer()
, orglmer()
, etc. x
an R object of class
merMod
orsummary.merMod
, respectively, the latter resulting fromsummary(<merMod>)
. fit
 formula
 refit
logical indicating if objects of class
lmerMod
should be refitted with ML before comparing models. The default isTRUE
to prevent the common mistake of inappropriately comparing REMLfitted models with different fixed effects, whose likelihoods are not directly comparable. model.names
character vectors of model names to be used in the anova table.
 scale
Not currently used (see
extractAIC
). k
see
extractAIC
. REML
Logical. If
TRUE
, return the restricted loglikelihood rather than the loglikelihood. IfNULL
(the default), setREML
toisREML(object)
(seeisREML
). 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
fixed.only
; indicates whether random components only are sought. (Trying to specifyfixed.only
andrandom.only
at the same time will produce an error.) correlation
(logical) for
vcov
, indicates whether the correlation matrix as well as the variancecovariance matrix is desired; forsummary.merMod
, indicates whether the correlation matrix should be computed and stored along with the covariance; forprint.summary.merMod
, indicates whether the correlation matrix of the fixedeffects parameters should be printed. In the latter case, whenNULL
(the default), the correlation matrix is printed when it has been computed bysummary(.)
, and when \(p <= 20\). 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
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 withnAGQ>0
), and to usegetME(.,"RX")
whenever the fixed effect parameters are profiled out (i.e. for GLMMs withnAGQ==0
or LMMs).use.hessian=FALSE
is backwardcompatible with older versions oflme4
, but may give less accurate SE estimates when the estimates of the fixedeffect (seegetME(.,"beta")
) and randomeffect (seegetME(.,"theta")
) parameters are correlated. sigm
the residual standard error; by default
sigma(object)
. digits
number of significant digits for printing
 symbolic.cor
should a symbolic encoding of the fixedeffects correlation matrix be printed? If so, the
symnum
function is used. 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
update.formula
. evaluate
see
update
. type
For
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. Formodel.matrix
, type of model matrix to return (one offixed
giving the fixed effects model matrix,random
giving the random effects model matrix, orrandomListRaw
giving a list of the raw random effects model matrices associated with each random effects term). …
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):

%% TODO: document differences between update and update.merMod
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
: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 
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
# NOT RUN {
showClass("merMod")
methods(class="merMod")## over 30 (S3) methods available
## > example(lmer) for an example of vcov.merMod()
# }