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 class 'merMod':
anova(object, ..., refit = TRUE, model.names=NULL)
## S3 method for class 'merMod':
terms(x, fixed.only = TRUE, \dots)
## S3 method for class 'merMod':
vcov(object, correlation = TRUE, sigm = sigma(object),
use.hessian = NULL, ...)## 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':
weights(object, type = c("prior", "working"), ...)
merMod
or summary.merMod
,
respectively, the latter resulting from summary()
.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 fixedterms
are sought, defaults to true. If false, all
terms, including random ones are returned.vcov
, indicates whether the
correlation matrix as well as the variance-covariance matrix is
desired; for print.summary.merMod
, indicates whether the
correlation matrix of the fixed-effects parameters sho
sigma(object)
.symnum
function is used."prior"
for
the initially supplied weights or "working"
for the weights
at the final iteration of the penalized iteratively reweighted least
squares algorithm.lmer
, glmer
,
nlmer
, merPredD
,
lmerResp
,
glmResp
,
nlsResp
showClass("merMod")
methods(class="merMod")## over 30 (S3) methods available
## -> example(lmer) for an example of vcov.merMod()
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