R: Extract the data covariance matrix from an lme object

extract.lme.cov {mgcv} | R Documentation

## Extract the data covariance matrix from an lme object

### Description

This is a service routine for `gamm`. 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. `extract.lme.cov` forms the full matrix explicitly: `extract.lme.cov2` tries to be more economical than this.

### Usage

    extract.lme.cov(b,data,start.level=1)
    extract.lme.cov2(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.

### 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. These routines 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 `extract.lme.cov` calculation is not optimally efficient, since it forms the full matrix, which may in fact be sparse. `extract.lme.cov2` is more efficient. If the covariance matrix is diagonal, then only the leading diagonal is returned; if it can be written as a block diagonal matrix (under some permutation of the original data) then a list of matrices defining the non-zero blocks is returned along with an index indicating which row of the original data each row/column of the block diagonal matrix relates to. The block sizes are defined by the coarsest level of grouping in the random effect structure.

`gamm` uses `extract.lme.cov2`.

`extract.lme.cov` does not currently deal with the situation in which the grouping factors for a correlation structure are finer than those for the random effects. `extract.lme.cov2` does deal with this situation.

### Value

For `extract.lme.cov` an estimated covariance matrix.
For `extract.lme.cov2` a list containing the estimated covariance matrix and an indexing array. The covariance matrix is stored as the elements on the leading diagonal, a list of the matrices defining a block diagonal matrix, or a full matrix if the previous two options are not possible.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### 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. (2006) Low rank scale invariant tensor product smooths for Generalized Additive Mixed Models. Biometrics 62(4):1025-1036

or

Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.

http://www.maths.bath.ac.uk/~sw283/

### See Also

`gamm`, `formXtViX`

### Examples

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

[Package mgcv version 1.4-1.1 Index]