vcovVC: Calculate Variance-Covariance Matrix of Variance Components...

In VCA: Variance Component Analysis

Calculate Variance-Covariance Matrix of Variance Components of 'VCA' objects

Description

This function computes the variance-covariance matrix of variance components (VC) either applying the approach given in the 1^{st} reference ('method="scm"') or using the approximation given in the 2^{nd} reference ('method="gb"').

Usage

vcovVC(obj, method = NULL, quiet = FALSE)

Arguments

obj	(VCA) object
method	(character) string, optionally specifying whether to use the algorithm given in the 1st reference ("scm") or in the 2nd refernce ("gb"). If not not supplied, the option is used coming with the 'VCA' object.
quiet	(logical) TRUE = will suppress any warning, which will be issued otherwise

Details

This function is called on a 'VCA' object, which can be the sole argument. In this case the value assigned to element 'VarVC.method' of the 'VCA' object will be used.

Value

(matrix) corresponding to variance-covariance matrix of variance components

Author(s)

Andre Schuetzenmeister [e-mail], Florian Dufey [e-mail]

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

Examples

## Not run: 
data(realData)
dat1 <- realData[realData$PID==1,]
fit  <- anovaVCA(y~lot/calibration/day/run, dat1) 
vcovVC(fit)
vcovVC(fit, "scm")		# Searle-Casella-McCulloch method (1st reference)
vcovVC(fit, "gb")		# Giesbrecht and Burns method (2nd reference)

## End(Not run)

VCA documentation built on May 29, 2024, 1:48 a.m.