glmmbootFit: Generalized Linear Models with fixed effects grouping In glmmML: Generalized Linear Models with Clustering

 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)

Göran Broström

`glmmboot`

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.
``````

glmmML documentation built on Sept. 8, 2023, 5:10 p.m.