Re: Obtaining RSE%
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
Quoted reply history
On Sat, Jul 27, 2024 at 6:44 PM <[email protected]> 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 <[email protected]>
> *Sent:* Saturday, July 27, 2024 4:18 PM
> *To:* Ken Kowalski <[email protected]>
> *Cc:* Santosh <[email protected]>; nmusers <[email protected]>
> *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 <[email protected]> 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: [email protected]
>
> Cell: 248-207-5082
>
>
>
>
>
> *From:* [email protected] <[email protected]> *On
> Behalf Of *Santosh
> *Sent:* Friday, July 26, 2024 3:38 AM
> *To:* [email protected]
> *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
>
>