Fit Generalized Linear Mixed Models via PQL
Fit a GLMM model with multivariate normal random effects, using Penalized Quasi-Likelihood.
glmmPQL(fixed, random, family, data, correlation, weights,
control, niter = 10, verbose = TRUE, ...)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, |
correlation |
an optional correlation structure. |
weights |
optional case weights as in |
control |
an optional argument to be passed to |
niter |
maximum number of iterations. |
verbose |
logical: print out record of iterations? |
... |
Further arguments for |
glmmPQL works by repeated calls to lme, so
package nlme will be loaded at first use if necessary.
A object of class "lme": see lmeObject.
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.
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.