glmmPQL

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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, 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

Aliases
  • glmmPQL
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 7.3-51.3, License: GPL-2 | GPL-3

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