Calculate Variance-Covariance Matrix of Variance Components of 'VCA' objects
This function computes the variance-covariance matrix of variance components (VC) either applying the approach given in the 1st reference ('method="scm"') or using the approximation given in the 2nd reference ('method="gb"').
vcovVC(obj, method = NULL, quiet = FALSE)
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 |
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.
(matrix) corresponding to variance-covariance matrix of variance components
Andre Schuetzenmeister andre.schuetzenmeister@roche.com, Florian Dufey florian.dufey@contractors.roche.com
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
## 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)
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