xuzjie
我是前天才知道的R软件，可现在就要用MASS package中glmPQL功能，我看说明书看眼都花了，有没有大虾救人于危难，glmPQL功能怎么用啊，以下是对该功能的说明，，狂汗~~~
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.
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))
