R: Fit Generalized Linear Mixed Models via PQL

glmmPQL {MASS} | R Documentation

## Fit Generalized Linear Mixed Models via PQL

### Description

Fit a GLMM model with multivariate normal random effects, using Penalized Quasi-Likelihood.

### Usage

    glmmPQL(fixed, random, family, data, correlation, weights,
            control, niter = 10, verbose = TRUE, ...)

### Arguments

`fixed` | a two-sided linear formula giving fixed-effects part of the model.
`random` | a formula or list of formulae describing the random effects.
`family` | a GLM family.
`data` | an optional data frame used as the first place to find variables in the formulae, `weights` and if present in `...`, `subset`.
`correlation` | an optional correlation structure.
`weights` | optional case weights as in `glm`.
`control` | an optional argument to be passed to `lme`.
`niter` | maximum number of iterations.
`verbose` | logical: print out record of iterations?
`...` | Further arguments for `lme`.

### Details

`glmmPQL` works by repeated calls to `lme`, so package `nlme` will be loaded at first use if necessary.

### Value

A object of class `"lme"`: see `lmeObject`.

### References

Schall, R. (1991) Estimation in generalized linear models with random effects. Biometrika 78, 719–727.

Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.

Wolfinger, R. and O'Connell, M. (1993) Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 48, 233–243.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### See Also

`lme`

### Examples

    library(nlme) # will be loaded automatically if omitted
    summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                    family = binomial, data = bacteria))

[Package MASS version 7.3-50 Index]