mgcv (version 1.0-5)

extract.lme.cov: Extract the data covariance matrix from an lme object

Description

This is a service routine for gamm. It extracts the estimated covariance matrix of the data from an lme object, allowing the user control about which levels of random effects to include in this calculation.

Usage

extract.lme.cov(b,data,start.level=1)

Arguments

b

A fitted model object returned by a call to lme

data

The data frame/ model frame that was supplied to lme.

start.level

The level of nesting at which to start including random effects in the calculation. This is used to allow smooth terms to be estimated as random effects, but treated like fixed effects for variance calculations.

Value

• An estimated covariance matrix.

Details

The random effects, correlation structure and variance structure used for a linear mixed model combine to imply a covariance matrix for the response data being modelled. This routine extracts that covariance matrix. The process is slightly complicated, because different components of the fitted model object are stored in different orders (see function code for details!).

The calculation is not optimally efficient, since it forms the full matrix, which may in fact be sparse. In applications in which the main objective is to allow non-independent `errors' in GAMs this is unlikely to cause great computational losses.

References

For lme see:

Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer

For details of how GAMMs are set up here for estimation using lme see: Wood, S.N. (manuscript) Tensor product smooth interaction terms in Generalized Additive Mixed Models.

http://www.stats.gla.ac.uk/~simon/

See Also

gamm

Examples

library(nlme)
data(Rail)
b <- lme(travel~1,Rail,~1|Rail)
extract.lme.cov(b,Rail)