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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.