formula(fixed)
are used
to construct the fixed effects model formula.
This formula and the
groupedData
object
are passed as the
fixed
and
data
arguments
to
glme.formula
,
together with any other additional arguments in the function call.
See the documentation on
glme
for a description of that function.
glme(fixed, data, random, family, correlation, weights, dispersion, start, subset, method, na.action, control, verbose)
groupedData
.
glme
.
glme
,
also inheriting from class
lme
,
representing the generalized linear mixed-effects model fit.
Generic functions such as
print
,
plot
and
summary
have methods to show the results of the fit.
See
glmeObject
for the components of the fit.
The functions
resid
,
coef
,
fitted
,
fixef
, and
ranef
can be used to extract some of its components.
The model formulation and the computational and estimation methods are
described in Breslow and Clayton (1993)
and also in Wolfinger and O'Connell (1993).
The variance-covariance parametrizations are described
in Pinheiro and Bates (1996).
The different correlation structures available
for the
correlation
argument
are described in Box et al. (1994),
Littel et al. (1996),
and Venables and Ripley (1999).
The use of variance functions for linear and nonlinear mixed effects models is
presented in detail in Davidian and Giltinan (1995).
Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized mixed models. Journal of the American Statistical Association, 88, 9-25.
Box, G. E. P., Jenkins, G. M., and Reinsel G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd Edition. Holden-Day.
Davidian, M. and Giltinan, D. M. (1995). Nonlinear Mixed Effects Models for Repeated Measurement Data. Chapman and Hall.
Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R.D. (1996). SAS Systems for Mixed Models. SAS Institute.
Pinheiro, J. C. and Bates., D. M. (1996). Unconstrained parametrizations for variance-covariance matrices. Statistics and Computing, 6, 289-296.
Venables, W. N. and Ripley, B. D. (1999). Modern Applied Statistics with S-PLUS", 3rd Edition Springer-Verlag, New York.
Wolfinger, R. D. and O'Connell, M. (1993). Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computing and Simulation, 48, 233-243.
fm1 <- glme(Clinic, family=binomial)