glmmPQL
From MASS v7.3-18
by Brian Ripley
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 used as the first place to find
variables in the formulae,
weightsand 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": seelmeObject.
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
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
<testonly># an example of offset
summary(glmmPQL(y ~ trt + week, random = ~ 1 | ID,
family = binomial, data = bacteria))
summary(glmmPQL(y ~ trt + week + offset(week), random = ~ 1 | ID,
family = binomial, data = bacteria))</testonly>Community examples
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