glmmML (version 0.65-2)

glmmboot: 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.

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

encoding

UTF-8

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.

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))