glmmboot: Generalized Linear Models with fixed effects grouping

In glmmML: Generalized Linear Models with Clustering

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, contrasts = NULL, 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.
contrasts	an optional list. See the 'contrasts.arg' of 'model.matrix.default'.
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)

G\"oran Brostr\"om and Henrik Holmberg

References

Brostr\"om, G. and Holmberg, H. (2011). Generalized linear models with clustered data: Fixed and random effects models. Computational Statistics and Data Analysis 55:3123-3134.

See Also

link{glmmML}, optim, 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))

glmmML documentation built on Sept. 8, 2023, 5:10 p.m.