glmmboot {glmmML}R Documentation

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, weights, subset, na.action,
offset, start.coef = NULL,
control = list(epsilon = 1e-08, maxit = 200, 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.
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.
weights Case weights.
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.

Details

The simulation is performed by simulating new response vectors from the fitted probabilities without clustering, and comparing the maximized log likelihoods. 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.

Value

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

coefficients Estimated regression coefficients
logLik the max log likelihood
cluster.null.deviance Deviance without the clustering
frail The estimated cluster effects
bootLog The logLik values from the bootstrap samples
bootP Bootstrap p value
variance Variance covariance matrix
sd Standard error of regression parameters
boot_rep No. of bootstrap replicates
mixed Logical
deviance Deviance
df.residual Its degrees of freedom
aic AIC
boot Logical
call The function call

Note

There is no overall intercept for this model; each cluster has its own intercept. See frail

Author(s)

Göran Broström

See Also

link{glmmML}, optim, glmm in repeated, lmer in Matrix, and glmmPQL in MASS.

Examples

## Not run:
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)
## End(Not run)
##system.time(res.glm <- glm(y ~ x + id, family = binomial))

[Package glmmML version 0.81-4 Index]