# glmmML.fit: Generalized Linear Model with random intercept In glmmML: Generalized Linear Models with Clustering

 glmmML.fit 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`.

Göran Broström

### References

Broström (2003)

`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)

``````

glmmML documentation built on Sept. 8, 2023, 5:10 p.m.