merMod-class: Class "merMod" of Fitted Mixed-Effect Models

Description

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.

Usage

## 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"), ...)

Arguments

object

an Robject of class merMod, i.e., as resulting from lmer(), or glmer(), etc.

an Robject of class merMod or summary.merMod, respectively, the latter resulting from summary().

fit

an Robject of class merMod.

formula

in the case of model.frame, a merMod object.

refit

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

model.names

character vectors of model names to be used in the anova table.

scale

Not currently used (see extractAIC).

see extractAIC.

REML

Logical. If TRUE, return the restricted log-likelihood rather than the log-likelihood. If NULL (the default), set REML to isREML(object) (see isREML

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 specify fixed.only and random.only at the same time will produce an error.)

correlation

(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 w

use.hessian

(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

sigm

the residual standard error; by default sigma(object).

digits

number of significant digits for printing

symbolic.cor

should a symbolic encoding of the fixed-effects 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 random-effects 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 alg

...

potentially further arguments passed from other methods.

Objects from the Class

Objects of class merMod are created by calls to lmer, glmer or nlmer.

Deviance and log-likelihood of GLMMs

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:

lrr{ 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 absolute-conditional case.
If adaptive Gauss-Hermite quadrature is used, thenlogLik(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.

Examples

Run this code

showClass("merMod")
methods(class="merMod")## over 30  (S3) methods available

## -> example(lmer)  for an example of vcov.merMod()

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