glmmbootFit {glmmML}R Documentation

Generalized Linear Models with fixed effects grouping

Description

'glmmbootFit' is the workhorse in the function glmmboot. It is suitable to call instead of 'glmmboot', e.g. in simulations.

Usage

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.e-8, maxit = 200, trace
= FALSE), boot = 0)

Arguments

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 binomial and poisson. The binomial family allows for the logit and cloglog links.
control A list. 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

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 boot.
bootP The bootstrap p value.
variance The variance-covariance matrix of the fixed effects (no intercept).
sd The standard errors of the coefficients.
boot_rep The number of bootstrap replicates.

Note

A profiling approach is used to estimate the cluster effects.

Author(s)

Göran Broström

See Also

glmmboot

Examples

## 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 = 2000)
summary(fit)
## End(Not run)
## Should show no effects.

[Package glmmML version 0.81-4 Index]