glmmboot {glmmML} | R Documentation |
Fits grouped GLMs with fixed group effects. The significance of the grouping is tested by simulation, with a bootstrap approach.
glmmboot(formula, family = binomial, data, cluster, subset, na.action, offset, conditional = FALSE, start.coef = NULL, control = glm.control(epsilon = 1e-08, maxit = 100, trace = FALSE), boot = 0)
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, but can only be represented as binary data. |
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. |
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. |
conditional |
Is the bootstap performed conditional on the total numer of successes? |
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. |
The simulation is performed by making random permutations of the
grouping factor and comparing the maximized loglikelihoods. 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.
The return value is a list, an object of class 'glmmboot'.
This is a preliminary version and not well tested.
Göran Broström
~put references to the literature/web site here ~
link{glmmML}
, optim
,
glmm
in Lindsey's
repeated
package, GLMM
in lme4
and
glmmPQL
in MASS
.
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) ##system.time(res.glm <- glm(y ~ x + id, family = binomial))