It can be done if you add extra $cov statement after each estimation method record Thank you Leonid
On Sat, Jul 27, 2024, 12:47 PM <kgkowalsk...@gmail.com> wrote: > Dear Santosh, > > > > There is a good reason for this. Wald (1943) has shown that the inverse > of the Hessian (R matrix) evaluated at the maximum likelihood estimates is > a consistent estimator of the covariance matrix. It is based on Wald’s > approximation that the likelihood surface locally near the maximum > likelihood estimates can be approximated by a quadratic function in the > parameters. This theory does not hold for any set of parameter estimates > along the algorithm’s search path prior to convergence to the maximum > likelihood estimates. Moreover, inverting the Hessian evaluated at an > interim step prior to convergence would likely be a poor approximation > especially early in the search path where the gradients are large (i.e., > large changes in OFV for a given change in the parameters would probably > have substantial curvature and not be well approximated by a quadratic > model in the parameters). > > > > Thus, the COV step in NONMEM is only applied once convergence is obtained > during the EST step. > > > > Wald, A. “Tests of statistical hypotheses concerning several parameters > when the number of observations is large.” *Trans. Amer. Math. Soc.* > 1943;54:426. > > > > Best, > > > > Ken > > > > Kenneth G. Kowalski > > President > > Kowalski PMetrics Consulting, LLC > > Email: kgkowalsk...@gmail.com > > Cell: 248-207-5082 > > > > > > *From:* owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> *On > Behalf Of *Santosh > *Sent:* Friday, July 26, 2024 3:38 AM > *To:* nmusers@globomaxnm.com > *Subject:* [NMusers] Obtaining RSE% > > > > Dear esteemed experts! > > When using one or more estimation methods & covariance step in a NONMEM > control stream, the resulting ext file contains final estimate (for all > estimation steps) & standard error (only for the last estimation step). > > > > Is there a way that standard error is generated for every estimation step? > > > > TIA > > Santosh >
