glmmML (version 1.1.7)

glmmbootFit: 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)

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.

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. If non-zero, it should be large, at least, say, 2000.

Author

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 = 200)
summary(fit)
## End(Not run)
## Should show no effects. And boot too small.