R: Ajusted covariance matrix for linear mixed models according...

vcovAdj {pbkrtest}

R Documentation

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

Description

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

Usage

vcovAdj(object, details=0)
LMM_Sigma_G(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 effetcs
`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

This functionality is not thoroughly tested and should be used with care. Please do report bugs etc.

Author(s)

Ulrich Halekoh ulrich.halekoh@agrsci.dk, Søren Højsgaard sorenh@math.aau.dk

References

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

Examples

(fmLarge <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
## removing Day
(vcovAdj(fmLarge,detail=0))

[Package pbkrtest version 0.3-8 Index]