glmmML (version 0.30-2)

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, start.coef = NULL, start.sigma = NULL, mixed = TRUE,
cluster = NULL, offset = rep(0, nobs), family = binomial(), n.points =
16, control = list(epsilon = 1.e-8, maxit = 200, trace = FALSE), method,
intercept = TRUE, boot = 0)

Arguments

X

Design matrix of covariates

Y

Response vector

start.coef

Starting values for the coefficients.

start.sigma

Starting value for the mixing standard deviation.

mixed

Logical. If FALSE, an ordinary glm is fitted.

cluster

The clustring 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.

n.points

Number of points in the Gauss-hermite quadrature.

control

Control of the iterations. See glm.control

method

Which optimizer? Only choice is "vmmin".

intercept

Logical. If TRUE, an intercept is fitted.

boot

Integer. If > 0, bootstrapping with boot replicates.

Value

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

Details

"vmmin" is followed by some Newton-Raphson steps, until convergence. As a by-product we get the estimated variance-covariance matrix.

References

Brostr�m (2003)

See Also

glmmML, glmmPQL, and glmm

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, mixed = TRUE, method = 1)