getVarCov function

Extract variance-covariance matrix

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

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

Returns

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, individuals = "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)