GLME fit from glmList Object

DESCRIPTION:

If the random effects names defined in random are a subset of the glmList object coefficient names, initial estimates for the covariance matrix of the random effects are obtained (overwriting any values given in random). formula(fixed) and the data argument in the calling sequence used to obtain fixed 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.

USAGE:

glme(fixed, data, random, family, correlation, weights, dispersion,
     start, subset, method, na.action, control, verbose)

REQUIRED ARGUMENTS:

fixed
an object inheriting from class glmList, representing a list of glm fits with a common model.
data
this argument is included for consistency with the generic function. It is ignored in this method function.
random
an optional one-sided linear formula with no conditioning expression, or a pdMat object with a formula attribute. Multiple levels of grouping are not allowed with this method function. Defaults to a formula consisting of the right hand side of formula(fixed).
other arguments
identical to the arguments in the generic function call. See the documentation on glme.

VALUE:

an object of class 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.

DETAILS:

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).

REFERENCES:

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.

SEE ALSO:

, , .

EXAMPLES:

Clinic.gp <- groupedData(resp~trt | clinic, data=Clinic)
fm1 <- glmList(Clinic.gp, family=binomial)
fm2 <- glme(fm1)