adjCov can be used to adjust the covariance matrix of a fitted SemiParBIVProbit, copulaReg, copulaSampleSel, SemiParTRIV object.
adjCov(x, id)A fitted SemiParBIVProbit, copulaReg, copulaSampleSel, SemiParTRIV object as produced by the respective fitting function.
Cluster identifier.
This function returns a fitted object which is identical to that supplied in adjCov but with adjusted covariance matrix.
This correction may not be appropriate for models fitted using penalties.
This adjustment can be made when dealing with clustered data and the cluster structure is neglected when fitting the model. The basic idea is that the model is fitted as though observations were independent, and subsequently adjust the covariance matrix of the parameter estimates. Using the terminology of Liang and Zeger (1986), this would correspond to using an independence structure within the context of generalized estimating equations. The parameter estimators are still consistent but are inefficient as compared to a model which accounts for the correct cluster dependence structure. The covariance matrix of the independence estimators can be adjusted as described in Liang and Zeger (1986, Section 2).
Liang K.-Y. and Zeger S. (1986), Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.
SemiParBIVProbit-package, SemiParBIVProbit, summary.SemiParBIVProbit,
SemiParTRIV, summary.SemiParTRIV,
copulaReg, summary.copulaReg,
copulaSampleSel, summary.copulaSampleSel