A mixed-effects 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.
# 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.",
ranef.corr = any(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."),
ranef.corr = any(ranef.comp == "Std.Dev."), show.resids = TRUE, ...)
# S3 method for merMod
update(object, formula., ..., evaluate = TRUE)
# S3 method for merMod
weights(object, type = c("prior", "working"), ...)
an R object of class merMod, i.e.,
as resulting from lmer(), or glmer(),
etc.
an R object of class merMod or summary.merMod,
respectively, the latter resulting from summary(<merMod>).
an R object of class merMod.
in the case of model.frame, a
merMod object.
logical indicating if objects of class 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.
character vectors of model names to be used in the anova table.
Not currently used (see extractAIC).
see extractAIC.
Logical. If TRUE, return the restricted log-likelihood
rather than the log-likelihood. If NULL (the default),
set REML to isREML(object) (see isREML).
logical indicating if only the fixed effects components (terms or formula elements) are sought. If false, all components, including random ones, are returned.
complement of 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.)
(logical)
for 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
\(p <= 12\), and the cutoff 12 may be modified by
options(lme4.summary.cor.max = <n>)
(logical) indicates whether to use the
finite-difference 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 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.
the residual standard error; by default sigma(object).
number of significant digits for printing
should a symbolic encoding of the fixed-effects correlation
matrix be printed? If so, the symnum function is used.
(logical) should significance stars be used?
character vector of length one or two, indicating if random-effects parameters should be reported on the variance and/or standard deviation scale.
should the quantiles of the scaled residuals be printed?
see update.formula.
see update.
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 (PIRLS).
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.
(logical) print correlations (rather than covariances) of random effects?
potentially further arguments passed from other methods.
Objects of class merMod are created by calls to
lmer, glmer or nlmer.
The following S3 methods with arguments given above exist (this list is currently not complete):
anova:returns the sequential decomposition of the contributions of
fixed-effects 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 with REML = TRUE, this can be controlled via the
refit argument. See also anova.
as.function:returns the deviance function, the same as
lmer(*, devFunOnly=TRUE), and mkLmerDevfun()
or mkGlmerDevfun(), 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), 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:Fitted values, given the conditional modes of
the random effects. For more flexible access to fitted values, use
predict.merMod.
logLik:Log-likelihood 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 of merMod.
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 also summary.
print.summary:Controls the output for the summary method.
vcov:Calculate variance-covariance matrix of the fixed
effect terms, see also vcov.
update:See update.
One must be careful when defining the deviance of a GLM. For example, should the deviance be defined as minus twice the log-likelihood or does it involve subtracting the deviance for a saturated model? To distinguish these two possibilities we refer to absolute deviance (minus twice the log-likelihood) 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$aic() | -2*logLik(object) |
This table requires two caveats:
If the link function involves a scale parameter
(e.g. Gamma) then object@resp$aic() - 2 * getME(object,
"devcomp")$dims["useSc"] is required for the absolute-conditional
case.
If adaptive Gauss-Hermite quadrature is used, then
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.
resp:A reference class object for an lme4
response module (lmResp-class).
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 (merPredD-class).
optinfo:List containing information about the nonlinear optimization.
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
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()
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