# glmmPQL

##### Fit Generalized Linear Mixed Models via PQL

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

- Keywords
- models

##### 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, list or environment 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

##### Examples

```
# NOT RUN {
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
# }
```

*Documentation reproduced from package MASS, version 7.3-53, License: GPL-2 | GPL-3*