Determine the estimated variance-covariance matrix of observations \(y\).
getV(obj)(VCA) object with additional elements in the 'Matrices' element, including matrix \(V\).
(VCA) object
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
A linear mixed model can be written as \(y = Xb + Zg + e\), where \(y\) is the column vector of observations, \(X\) and \(Z\) are design matrices assigning fixed (\(b\)), respectively, random (\(g\)) effects to observations, and \(e\) is the column vector of residual errors. The variance-covariance matrix of \(y\) is equal to \(Var(y) = ZGZ^{-T} + R\), where \(R\) is the variance-covariance matrix of \(e\) and \(G\) is the variance-covariance matrix of \(g\). Here, \(G\) is assumed to be a diagonal matrix, i.e. all random effects \(g\) are mutually independent (uncorrelated).