glmmML (version 0.25)

glmmboot: Generalized Linear Models with fixed effects grouping

Description

Fits grouped GLMs with fixed group effects. The significance of the grouping is tested by simulation, with a bootstrap approach.

Usage

glmmboot(formula, family = binomial, data, cluster, subset, na.action,
offset, start.coef = NULL,
control = glm.control(epsilon = 1e-08, maxit = 100, trace = FALSE), boot = 0)

Arguments

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 binomial and poisson. The binomial family allows for the logit and cloglog links, but can only be represented as binary data.

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.

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.

Value

• The return value is a list, an object of class 'glmmboot'.

Details

The simulation is performed by making random permutations of the grouping factor and comparing the maximized loglikelihoods. 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.

References

~put references to the literature/web site here ~

See Also

link{glmmML}, optim, glmm in Lindsey's repeated package, GLMM in lme4and glmmPQL in MASS.

Examples

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)
##system.time(res.glm <- glm(y ~ x + id, family = binomial))