erred option, but one
could also consider to bootstrap residuals.
Jeroen
From: e.olof...@lumc.nl [mailto:e.olof...@lumc.nl]
Sent: Monday, March 31, 2014 13:08
To: Elassaiss - Schaap, J (Jeroen); gej1...@cam.ac.uk; nmusers@globomaxnm.com
Subject: RE: [NMusers] NONMEM vs SPSS
Hi Jeroen,
The BOOT
, but one
could also consider to bootstrap residuals.
Jeroen
From: e.olof...@lumc.nl [mailto:e.olof...@lumc.nl]
Sent: Monday, March 31, 2014 13:08
To: Elassaiss - Schaap, J (Jeroen); gej1...@cam.ac.uk; nmusers@globomaxnm.com
Subject: RE: [NMusers] NONMEM vs SPSS
Hi Jeroen,
The BOOTSTRAP option
- Schaap, J (Jeroen) [jeroen.elassaiss-sch...@merck.com]
Sent: Monday, March 31, 2014 12:23 PM
To: Gavin Jarvis; nmusers@globomaxnm.com
Subject: RE: [NMusers] NONMEM vs SPSS
Dear Gavin,
Reading back your original post, if your data are really N=1 and you have this
perfect fit phenomenon there is
approaches use all sampling over
subjects. (There are other ways of doing a bootstrap)
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Gavin Jarvis
Sent: Monday, March 31, 2014 11:33
To: nmusers@globomaxnm.com
Subject: RE: [NMusers] NONMEM vs SPSS
Dear All
m.com
Subject: RE: [NMusers] NONMEM vs SPSS
I concur with Ken’s statement, and I also prefer to use MATRIX=R as the first
choice for covariance assessment. On occasion, MATRIX=S can be used if there
are numerical difficulties in assessing the R matrix, and if there are enough
subjects re
; 'Gavin Jarvis'; nmusers@globomaxnm.com
Subject: RE: [NMusers] NONMEM vs SPSS
I concur with Ken’s statement, and I also prefer to use MATRIX=R as the first
choice for covariance assessment. On occasion, MATRIX=S can be used if there
are numerical difficulties in assessing the R matrix
to:robert.ba...@iconplc.com>
Web: www.iconplc.com<http://www.iconplc.com/>
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Ken Kowalski
Sent: Saturday, March 29, 2014 3:44 PM
To: 'Gavin Jarvis'; nmusers@globomaxnm.com
Subject: RE: [NMusers] NO
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
s
Gavin,
NONMEM has been noted (Senn et al 2012) to produce smaller SE (R-1 S R-1
method) compared to estimates from Mathcad, SAS, GenStat and R. The
Mathcad estimates were identical to SAS, Genstat and R when using
numerical derivatives and larger when based on the expected Fisher
information
Dear Gavin,
Perhaps an idea is to compare the different MATRIX options of the covariance
step of NONMEM, and with the bootstrap, to assess their relative properties.
Erik
From: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] on behalf of
Gavin Jarvis
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