eha (version 2.4-6)

glmmML.fit: 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".

Value

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

Details

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

References

Brostr<U+001ADF7B>Bridgewater

See Also

glmmML, glmmPQL in the package MASS, and lmer in the package lme4.

Examples

# NOT RUN {
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)


# }