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)

Arguments

The design matrix (n * p).

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
coefficientsEstimated regression coefficients (note: No intercept).
logLikThe maximised log likelihood.
cluster.null.deviancedeviance from a moddel without cluster.
frailThe estimated cluster effects.
bootLogThe maximised bootstrap log likelihood values. A vector of length boot.
bootPThe bootstrap p value.
varianceThe variance-covariance matrix of the fixed effects (no intercept).
sdThe standard errors of the coefficients.
boot_repThe number of bootstrap replicates.

encoding

UTF-8

Examples

Run this code

## 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.