Giesbrecht & Burns Approximation of the Variance-Covariance Matrix of Variance Components.

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Description

Compute variance covariance matrix of variance components of a linear mixed model via the method stated in Giesbrecht and Burns (1985).

Usage

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getGB(obj, tol = 1e-12)

Arguments

obj

(object) with list-type structure, e.g. VCA object fitted by ANOVA or a premature VCA object fitted by REML

tol

(numeric) values < 'tol' will be considered being equal to zero

Details

This function is not intended to be called by users and therefore not exported.

Value

(matrix) corresponding to the Giesbrecht & Burns approximation of the variance-covariance matrix of variance components

Author(s)

Andre Schuetzenmeister andre.schuetzenmeister@roche.com

References

Searle, S.R, Casella, G., McCulloch, C.E. (1992), Variance Components, Wiley New York

Giesbrecht, F.G. and Burns, J.C. (1985), Two-Stage Analysis Based on a Mixed Model: Large-Sample Asymptotic Theory and Small-Sample Simulation Results, Biometrics 41, p. 477-486

See Also

vcovVC, remlVCA, remlMM

Examples

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## Not run: 
data(dataEP05A2_3)
fit <- anovaVCA(y~day/run, dataEP05A2_3)
fit <- solveMME(fit)		# some additional matrices required
getGB(fit)

## End(Not run)

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