glmmML: glmmboot – R documentation

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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, 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.

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

Generalized Linear Models with Clustering

v1.1.1

GPL (>= 3)

Authors

Göran Broström [aut, cre], Jianming Jin [ctb], Henrik Holmberg [ctb]

Initial release

2020-05-27