full covariance matrix

From: "Rik Schoemaker" <rs@chdr.nl> Subject: full covariance matrix Date: Mon, 11 Jan 1999 22:44:32 +0100 Dear fellow nmusers, If I were to analyse a model say with 3 parameters and use a constant CV inter-individual error model: P1 = THETA(1)*EXP(ETA(1)) P2 = THETA(2)*EXP(ETA(2)) P3 = THETA(3)*EXP(ETA(3)) and I would like to estimate the full covariance matrix: $OMEGA BLOCK(3) .1 .01 .1 .01 .01 .1 then what is the meaning of the off-diagonal estimates (the ¡covariances¢) and can they be translated back to for instance correlation coefficients? Can anyone comment on whether I *should* use a full covariance matrix (provided the data can support all the extra parameters)? Thanks a lot, Rik Schoemaker Centre for Human Drug Research, Leiden, Netherlands rs@chdr.nl