| getVarCov {nlme} | R Documentation |
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, ...)
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 |
A variance-covariance matrix or a list of variance-covariance matrices.
Mary Lindstrom lindstro@biostat.wisc.edu
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)