a symbolic description of the model to be fit. The details of
model specification are given below.
family
Currently, the only valid values are binomial and
poisson. The binomial family allows for the logit and
cloglog links.
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
glm.control for details.
boot
number of bootstrap replicates. If equal to zero, no test
of significance of the grouping factor is performed.
Details
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.
Value
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
Note
There is no overall intercept for this model; each cluster has its
own intercept. See frail
Author(s)
Göran Broström and Henrik Holmberg
References
Broström, G. and Holmberg, H. (2011). Generalized linear models with
clustered data: Fixed and random effects models. Computational
Statistics and Data Analysis 55:3123-3134.
See Also
link{glmmML}, optim,
lmer in Matrix, and
glmmPQL in MASS.
Examples
## 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))