| bread.glmerMod | R Documentation | 
This function calculates the bread component of the Huber-White 
sandwich estimator (variance covariance matrix multiplied by 
the number of clusters) for a generalized linear mixed effects model 
of class glmerMod-class.  
## S3 method for class 'glmerMod'
bread(x, ...)
x | 
 An object of class   | 
... | 
 additional arguments,
including   | 
A p by p "bread" matrix for the Huber-White sandwich estimator 
(variance-covariance matrix based on observed Fisher information
multiplied by the number of clusters), where
p represents the number of parameters.  
If full = FALSE, returns the 
variance-covariance matrix of only fixed effect 
parameters. If full = TRUE , returns the variance-covariance matrix 
for all fitted parameters (including fixed effect parameters, 
random effect (co)variances, and residual variance.
If ranpar = "var", the random effects are parameterized as 
variance/covariance; If ranpar = "sd", the random effects are 
parameterized as standard deviation/correlation; If ranpar = "theta", 
the random effects are parameterized as components of Cholesky
decomposition.  
Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v067.i01")}.
Zeileis, A. (2006). Object-Oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1-16. https://www.jstatsoft.org/v16/i09/
## Not run: 
# The cbpp example
data(finance, package = "smdata")
lme4fit <- glmer(corr ~ jmeth + (1 | item), data = finance,
                 family = binomial, nAGQ = 20)
# bread component for all parameters
bread(lme4fit, full = TRUE, ranpar = "var")
## End(Not run)
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