glmmML.fit {glmmML}R Documentation

Generalized Linear Model with random intercept

Description

This function is called by glmmML, but it can also be called directly by the user.

Usage

glmmML.fit(X, Y, weights = rep(1, NROW(Y)), cluster.weights = rep(1, NROW(Y)),
start.coef = NULL, start.sigma = NULL,
fix.sigma = FALSE,
cluster = NULL, offset = rep(0, nobs), family = binomial(),
method = 1, n.points = 1,
control = list(epsilon = 1.e-8, maxit = 200, trace = FALSE),
intercept = TRUE, boot = 0, prior = 0) 

Arguments

X Design matrix of covariates.
Y Response vector. Or two-column matrix.
weights Case weights. Defaults to one.
cluster.weights Cluster weights. Defaults to one.
start.coef Starting values for the coefficients.
start.sigma Starting value for the mixing standard deviation.
fix.sigma Should sigma be fixed at start.sigma?
cluster The clustering variable.
offset The offset in the model.
family Family of distributions. Defaults to binomial with logit link. Other possibilities are binomial with cloglog link and poisson with log link.
method Laplace (1) or Gauss-hermite (0)?
n.points Number of points in the Gauss-Hermite quadrature. Default is n.points = 1, which is equivalent to Laplace approximation.
control Control of the iterations. See glm.control.
intercept Logical. If TRUE, an intercept is fitted.
boot Integer. If > 0, bootstrapping with boot replicates.
prior Which prior distribution? 0 for "gaussian", 1 for "logistic", 2 for "cauchy".

Details

In the optimisation, "vmmin" (in C code) is used.

Value

A list. For details, see the code, and glmmML.

Author(s)

Göran Broström

References

Broström (2003)

See Also

glmmML, glmmPQL, and lmer.

Examples

x <- cbind(rep(1, 14), rnorm(14))
y <- rbinom(14, prob = 0.5, size = 1)
id <- rep(1:7, 2)

glmmML.fit(x, y, cluster = id)


[Package glmmML version 0.81-4 Index]