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 shosigma(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()Run the code above in your browser using DataLab