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, weights, subset, na.action, offset, start.coef = NULL, control = list(epsilon = 1e-08, maxit = 200, 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. |
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. |
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.
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 |
There is no overall intercept for this model; each cluster has its
own intercept. See frail
Göran Broström
link{glmmML}
, optim
,
glmm
in
repeated
, lmer
in Matrix
, and
glmmPQL
in MASS
.
## 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))