R: Extract variance-covariance matrix

getVarCov {nlme} | R Documentation

## Extract variance-covariance matrix

### Description

Extract the variance-covariance matrix from a fitted model, such as a mixed-effects model.

### Usage

    getVarCov(obj, ...)
    ## S3 method for class 'lme'
    getVarCov(obj, individuals,
        type = c("random.effects", "conditional", "marginal"), ...)
    ## S3 method for class 'gls'
    getVarCov(obj, individual = 1, ...)

### Arguments

`obj` | A fitted model. Methods are available for models fit by `lme` and by `gls`
`individuals` | For models fit by `lme` a vector of levels of the grouping factor can be specified for the conditional or marginal variance-covariance matrices.
`individual` | For models fit by `gls` the only type of variance-covariance matrix provided is the marginal variance-covariance of the responses by group. The optional argument `individual` specifies the group of responses.
`type` | For models fit by `lme` the `type` argument specifies the type of variance-covariance matrix, either `"random.effects"` for the random-effects variance-covariance (the default), or `"conditional"` for the conditional. variance-covariance of the responses or `"marginal"` for the the marginal variance-covariance of the responses.
`...` | Optional arguments for some methods, as described above

### Value

A variance-covariance matrix or a list of variance-covariance matrices.

### Author(s)

Mary Lindstrom lindstro@biostat.wisc.edu

### See Also

`lme`, `gls`

### Examples

    fm1 <- lme(distance ~ age, data = Orthodont, subset = Sex == "Female")
    getVarCov(fm1)
    getVarCov(fm1, individual = "F01", type = "marginal")
    getVarCov(fm1, type = "conditional")
    fm2 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
               correlation = corAR1(form = ~ 1 | Mare))
    getVarCov(fm2)

[Package nlme version 3.1-104 Index]