glmmbootFit  R Documentation 
'glmmbootFit' is the workhorse in the function glmmboot
. It is
suitable to call instead of 'glmmboot', e.g. in simulations.
glmmbootFit(X, Y, weights = rep(1, NROW(Y)),
start.coef = NULL, cluster = rep(1, length(Y)),
offset = rep(0, length(Y)), family = binomial(),
control = list(epsilon = 1.e8, maxit = 200, trace
= FALSE), boot = 0)
X 
The design matrix (n * p). 
Y 
The response vector of length n. 
weights 
Case weights. 
start.coef 
start values for the parameters in the linear predictor (except the intercept). 
cluster 
Factor indicating which items are correlated. 
offset 
this can be used to specify an a priori known component to be included in the linear predictor during fitting. 
family 
Currently, the only valid values are 
control 
A list. Controls the convergence criteria. See

boot 
number of bootstrap replicates. If equal to zero, no test of significance of the grouping factor is performed. If nonzero, it should be large, at least, say, 2000. 
A list with components
coefficients 
Estimated regression coefficients (note: No intercept). 
logLik 
The maximised log likelihood. 
cluster.null.deviance 
deviance from a moddel without cluster. 
frail 
The estimated cluster effects. 
bootLog 
The maximised bootstrap log likelihood values. A vector
of length 
bootP 
The bootstrap p value. 
variance 
The variancecovariance matrix of the fixed effects (no intercept). 
sd 
The standard errors of the 
boot_rep 
The number of bootstrap replicates. 
A profiling approach is used to estimate the cluster effects.
Göran Broström
glmmboot
## Not run
x < matrix(rnorm(1000), ncol = 1)
id < rep(1:100, rep(10, 100))
y < rbinom(1000, size = 1, prob = 0.4)
fit < glmmbootFit(x, y, cluster = id, boot = 200)
summary(fit)
## End(Not run)
## Should show no effects. And boot too small.
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