Thanks Leonid & Ken for quick responses. I did try with multiple $COV steps and submitted the jobs with Perl-speaks-NONMEM (PsN).. PsN reorganized the NONMEM blocks are changed the order of $COV steps.. I’ll try with nmfe way…
Hope PsN developers look into this issue and preserve the order of the lines of codes where they matter, especially the sequence of $ESTIMATION & $COVARIANCE steps. TIA Santosh On Sat, Jul 27, 2024 at 6:44 PM <kgkowalsk...@gmail.com> wrote: > Aah – I see that I misunderstood Santosh’s question. I thought Santosh > was asking about reporting standard errors at each iteration step within > the estimation algorithm. > > > > Best, > > > > Ken > > > > *From:* Leonid Gibiansky <lgibian...@quantpharm.com> > *Sent:* Saturday, July 27, 2024 4:18 PM > *To:* Ken Kowalski <kgkowalsk...@gmail.com> > *Cc:* Santosh <santosh2...@gmail.com>; nmusers <nmusers@globomaxnm.com> > *Subject:* Re: [NMusers] Obtaining RSE% > > > > 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 > >
