kr-vcovAdj: Adjusted covariance matrix for linear mixed models according...

In pbkrtest: Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Adjusted covariance matrix for linear mixed models according to Kenward and Roger

Description

Kenward and Roger (1997) describe an improved small sample approximation to the covariance matrix estimate of the fixed parameters in a linear mixed model.

Usage

vcovAdj(object, details = 0)

## S3 method for class 'lmerMod'
vcovAdj(object, details = 0)

Arguments

object	An lmer model
details	If larger than 0 some timing details are printed.

Value

phiA	the estimated covariance matrix, this has attributed P, a list of matrices used in KR_adjust and the estimated matrix W of the variances of the covariance parameters of the random effects
SigmaG	list: Sigma: the covariance matrix of Y; G: the G matrices that sum up to Sigma; n.ggamma: the number (called M in the article) of G matrices)

Note

If $N$ is the number of observations, then the vcovAdj() function involves inversion of an $N x N$ matrix, so the computations can be relatively slow.

Author(s)

Ulrich Halekoh [e-mail], Søren Højsgaard [e-mail]

References

Ulrich Halekoh, Søren Højsgaard (2014)., A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest., Journal of Statistical Software, 58(10), 1-30., https://www.jstatsoft.org/v59/i09/

Kenward, M. G. and Roger, J. H. (1997), Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood, Biometrics 53: 983-997.

See Also

getKR, KRmodcomp, lmer, PBmodcomp, vcovAdj

Examples

fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML=TRUE)
class(fm1)

set.seed(123)
sleepstudy2 <- sleepstudy[sample(nrow(sleepstudy), size=120), ]

fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy2, REML=TRUE)

## Here the adjusted and unadjusted covariance matrices are identical,
## but that is not generally the case:

v1 <- vcov(fm1)
v1a <- vcovAdj(fm1, details=0)
v1a / v1

v2 <- vcov(fm2)
v2a <- vcovAdj(fm2, details=0)
v2a / v2

# For comparison, an alternative estimate of the
# variance-covariance matrix is based on parametric bootstrap (and
# this is easily parallelized):

pbkrtest documentation built on Aug. 8, 2025, 6:06 p.m.