glmmML {glmmML}R Documentation

Generalized Linear Models with random intercept

Description

Fits GLMs with random intercept by Maximum Likelihood and numerical integration via Gauss-Hermite quadrature.

Usage

glmmML(formula, data = list(), cluster = NULL, family = binomial,
start.coef = NULL, start.sigma = NULL, offset = NULL, method = "vmmin",
control = glm.control(epsilon = 1e-08, maxit = 100, trace = FALSE),
n.points = 16) 

Arguments

formula a symbolic description of the model to be fit. The details of model specification are given below.
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
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.
start.coef starting values for the parameters in the linear predictor. Defaults to zero.
start.sigma starting value for the mixing standard deviation. Defaults to 0.5.
offset this can be used to specify an a priori known component to be included in the linear predictor during fitting.
method the method to be used in fitting the model. The default (and presently only) method `vmmin' uses the BFGS method in the 'optim' function.
control Controls the convergence criteria. See glm.control for details.
n.points Number of points in the Gauss-hermite quadrature. If n.points == 1, an ordinary glm is fitted.

Details

After the 'vmmin' function has converged, an ordinary Newton-Raphson procedure finishes the maximization. As a by-product, the variance-covariance is estimated.

Value

The return value is a list, an object of class 'glmmML'.

Note

This is a very preliminary version. The optimization may fail with the default value of start.sigma. In that case, try different start values for sigma.

Author(s)

Göran Broström

References

Broström (2003). Generalized linear models with random intercepts. http://www.stat.umu.se/forskning/glmmML.pdf

See Also

optim, glmm in Lindsey's repeated package, and glmmPQL in MASS.

Examples

x <- cbind(rep(1, 14), rnorm(14))
y <- rbinom(14, prob = 0.5, size = 1)
id <- rep(1:7, 2)
dat <- data.frame(y = y, x1 = x[, 2])
glmmML(y ~ x1, data = dat, cluster = id)

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