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, start.coef = NULL, start.sigma = NULL, mixed = FALSE, cluster = NULL, offset = rep(0, nobs), family = binomial(), n.points = 16, control = glm.control(), method, intercept = TRUE)

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.

Details

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

Value

A list. For details, see the code.

Note

A preliminary version, with high potential for bugs. However, when it works, it is very fast, compared to glmm and glmmPQL

Author(s)

Göran Broström

References

Broström (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)


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