RE: Describing variability

From:"Kowalski, Ken" Subject:RE: [NMusers] Describing variability Date:Tue, 1 Apr 2003 14:13:00 -0500 I agree rounding errors are a bigger concern but that still doesn't diminish the importance of the $COV step and the diagnostics that a successful $COV step provides (even if one doesn't plan to use the standard errors for making inference). Moreover, a successful $COV step is not the end in itself. I've seen situations where an over-parameterized model resulted in convergence and a successful $COV step and yet, NONMEM will report correlations between some parameters to 1.000 (to three decimal places). Such a model fit results in a numerically non-singular Hessian but it is extremely ill-conditioned even though the $COV step ran successfully. I suspect the situation that Diane describes below may be an example of this. Changing compilers which may have different numerical accuracies or changing starting values, etc. to get the $COV step to run successfully should not be the end goal. In this setting chances are the model is still ill-conditioned regardless of whether the $COV step ran. It is important to inspect the correlation matrix and its eigenvalues (PRINT=E option on $COV) to assess the stability of the model rather than to simply acknowledge that the $COV step ran. I know I come across as too rigid but I'd rather err on that side as opposed to dismissing the $COV step as if it were something we could do without. In my opinion, ignoring $COV step failures should be the exception to the rule and not the rule itself. Regards, Ken