Description Usage Arguments Details Value Note Author(s) References See Also Examples
Fits grouped GLMs with fixed group effects. The significance of the grouping is tested by simulation, with a bootstrap approach.
1 2 3 
formula 
a symbolic description of the model to be fit. The details of model specification are given below. 
family 
Currently, the only valid values are 
data 
an optional data frame containing the variables in the model. By default the variables are taken from ‘environment(formula)’, typically the environment from which ‘glmmML’ is called. 
cluster 
Factor indicating which items are correlated. 
weights 
Case weights. 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
See glm. 
offset 
this can be used to specify an a priori known component to be included in the linear predictor during fitting. 
start.coef 
starting values for the parameters in the linear predictor. Defaults to zero. 
control 
Controls the convergence criteria. See

boot 
number of bootstrap replicates. If equal to zero, no test of significance of the grouping factor is performed. 
The simulation is performed by
simulating new response vectors from the fitted probabilities without
clustering, and comparing the maximized log likelihoods. The
maximizations are performed by profiling out the grouping factor. It is
a very fast procedure, compared to glm
, when the grouping
factor has many levels.
The return value is a list, an object of class 'glmmboot'.
coefficients 
Estimated regression coefficients 
logLik 
the max log likelihood 
cluster.null.deviance 
Deviance without the clustering 
frail 
The estimated cluster effects 
bootLog 
The logLik values from the bootstrap samples 
bootP 
Bootstrap p value 
variance 
Variance covariance matrix 
sd 
Standard error of regression parameters 
boot_rep 
No. of bootstrap replicates 
mixed 
Logical 
deviance 
Deviance 
df.residual 
Its degrees of freedom 
aic 
AIC 
boot 
Logical 
call 
The function call 
There is no overall intercept for this model; each cluster has its
own intercept. See frail
Göran Broström and Henrik Holmberg
Broström, G. and Holmberg, H. (2011). Generalized linear models with clustered data: Fixed and random effects models. Computational Statistics and Data Analysis 55:31233134.
link{glmmML}
, optim
,
lmer
in Matrix
, and
glmmPQL
in MASS
.
1 2 3 4 5 6 7 8  ## Not run:
id < factor(rep(1:20, rep(5, 20)))
y < rbinom(100, prob = rep(runif(20), rep(5, 20)), size = 1)
x < rnorm(100)
dat < data.frame(y = y, x = x, id = id)
res < glmmboot(y ~ x, cluster = id, data = dat, boot = 5000)
## End(Not run)
##system.time(res.glm < glm(y ~ x + id, family = binomial))

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