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Last data update: 2014.03.03

R: Generalized Linear Models with fixed effects grouping

glmmbootFit

R Documentation

Generalized Linear Models with fixed effects grouping

Description

'glmmbootFit' is the workhorse in the function glmmboot. It is suitable to call instead of 'glmmboot', e.g. in simulations.

Usage

glmmbootFit(X, Y, weights = rep(1, NROW(Y)),
start.coef = NULL, cluster = rep(1, length(Y)),
offset = rep(0, length(Y)), family = binomial(),
control = list(epsilon = 1.e-8, maxit = 200, trace
= FALSE), boot = 0)

Arguments

`X`	The design matrix (n * p).
`Y`	The response vector of length n.
`weights`	Case weights.
`start.coef`	start values for the parameters in the linear predictor (except the intercept).
`cluster`	Factor indicating which items are correlated.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting.
`family`	Currently, the only valid values are `binomial` and `poisson`. The binomial family allows for the `logit` and `cloglog` links.
`control`	A list. 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. If non-zero, it should be large, at least, say, 2000.

Value

A list with components

`coefficients`	Estimated regression coefficients (note: No intercept).
`logLik`	The maximised log likelihood.
`cluster.null.deviance`	deviance from a moddel without cluster.
`frail`	The estimated cluster effects.
`bootLog`	The maximised bootstrap log likelihood values. A vector of length `boot`.
`bootP`	The bootstrap p value.
`variance`	The variance-covariance matrix of the fixed effects (no intercept).
`sd`	The standard errors of the `coefficients`.
`boot_rep`	The number of bootstrap replicates.

Note

A profiling approach is used to estimate the cluster effects.

Author(s)

GÃ¶ran BrostrÃ¶m

Examples

## Not run
x <- matrix(rnorm(1000), ncol = 1)
id <- rep(1:100, rep(10, 100))
y <- rbinom(1000, size = 1, prob = 0.4)
fit <- glmmbootFit(x, y, cluster = id, boot = 200)
summary(fit)
## End(Not run)
## Should show no effects. And boot too small.