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From: Ken Kowalski <ken.kowalski>
Date: Sat, 29 Mar 2014 15:43:46 -0400

Dear Gavin,


This is most likely because most nonlinear regression programs invert the
Hessian (second derivative matrix of the model with respect to the
parameters) to obtain the covariance matrix. This corresponds to the R
matrix in NONMEM. However, the default method that NONMEM uses is a
sandwich estimator involving both the Hessian (R) and the square of the
first derivatives matrix (S). I suspect that if you use the MATRIX=R option
on the $COV step you will find that the standard errors will now be in
agreement with SPSS (NLR). I know Stu Beal made the sandwich estimator the
default as it is supposed to be more robust to non-normality but I would
have preferred the MATRIX=R option to be the default to be more consistent
with other nonlinear regression software implementations.




From: owner-nmusers
Behalf Of Gavin Jarvis
Sent: Saturday, March 29, 2014 12:55 PM
To: nmusers
Subject: [NMusers] NONMEM vs SPSS


Dear NONMEM Users


Does anyone have a view on the relative merits/reliability/accuracy of
NONMEM ($COV step) vs SPSS (NLR) with respect to their derived values of the
parameter standard errors and parameter correlation matrices?


The data I am analysing are single subject (not population). Parameter
estimates from the two programs are, to all intents and purposes, identical.
However, the SE values from NONMEM $COV are consistently smaller by


Any thoughts?






Dr Gavin E Jarvis MA PhD VetMB MRCVS

University Lecturer in Veterinary Anatomy

Department of Physiology, Development & Neuroscience

Physiological Laboratory

Downing Street



Tel: +44 (0) 1223 333745


Fellow and College Lecturer in Pharmacology

Selwyn College



Tel: +44 (0) 1223 761303


Email: <mailto:gej1000

Web: <>



Received on Sat Mar 29 2014 - 15:43:46 EDT

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