nlme (version 3.1-137)

getVarCov: 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 lme
getVarCov(obj, individuals,
    type = c("random.effects", "conditional", "marginal"), …)
# S3 method for 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.

See Also

lme, gls

Examples

# NOT RUN {
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)
# }