Sam:
The nature of the $COV step using classical NONMEM methods is it can
take longer than the $EST command at times.  There is no infinite loop,
it just has to evaluate OBJ often in cases of large problems.  The SIGL
does not help with this, and the ctrl-K and ctrl-E switches do not act
during evaluation of the $COV STEP.  advan13 is best used for the Monte
Carlo methods.  If you are not particular about getting $COV results by
classical NONMEM methods, you can ctrl-C.
 
Regarding BAYES analysis, the release notes, and the slides I gave at
the workshops (make stable programs section of slides), inform the user
that Monte Carlo methods can call the user model with very extreme
values.  Thus, insert protective code, the kind that avoid square
rooting negative numbers, exponentiating very large numbers, etc.
For new methods, use INTERACTION, even when error is homoscedastic.
 
 

Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics
ICON Development Solutions 

Tel: (215) 616-6428
Mob: (925) 286-0769
Email: [email protected]
Web: www.icondevsolutions.com 

 

 

 

________________________________

From: [email protected] [mailto:[email protected]]
On Behalf Of Sam Liao
Sent: Monday, October 26, 2009 11:36 AM
To: [email protected]; [email protected]
Subject: [NMusers] $COV UNCONDITIONAL PRINT=E 



Dear NONMEM team:

I have two nm7 questions.

 

I have a PKPD model using ADVAN13 to solve the ODE.  It took 12 hours to
complete the $EST step with rounding error.

But it's been over 15 hours running the $COV step with UNCONDITIONAL
option. 

 

This is much longer time than I expected.  Could this be an infinitive
loop?  Should I interrupt the run and try smaller  SIGL in $COV?  I
tried the CTL-K or CTL-E to exit, but it did not work for me. 

 

The second question related to BAYES method in $EST step.  The model run
successfully using FOCE method.  But when I tried the BAYES method with
NOABORT option, it terminated with 'OBJECTIVE FUNCTION IS INFINITE' in
Burn-in mode after 10 iterations.  The statement used as below.  How can
I get around this problem?

=======================

$SUBROUTINES ADVAN13 TOL=6


$EST METHOD=BAYES NBURN=100 NSAMPLE=3000 SIGL=6 NOABORT FILE=RUN136.TXT

 

Best regards,

Sam

 

From: [email protected] [mailto:[email protected]]
On Behalf Of Bauer, Robert
Sent: Sunday, October 25, 2009 11:29 PM
To: [email protected]; [email protected]
Subject: RE: [NMusers] method=ITS, OPTIMIZATION NOT TESTED (?!)

 

Pavel:

The objective function progress looks good.  You should expect some
Monte Carlo fluctuations.  You should also run more iterations (perhaps
NITER=200), and set CTYPE=3, which turns on the termination tester.  To
resume where you left off, rename your new control stream file, and put
in the following lines.

 

$EST METHOD=CHAIN NSAMPLE=0 ISAMPLE=50
FILE=my_old_control_stream_file.ext

$EST METHOD=IMP NITER=200 CTYPE=3 FILE=my_new_control_stream_file.ext

 

Make sure you are linear MU referencing to get the greatest efficiency.

 

 

Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics
ICON Development Solutions 

Tel: (215) 616-6428
Mob: (925) 286-0769
Email: [email protected] <mailto:[email protected]> 
Web: www.icondevsolutions.com 

 

 

 

 

________________________________

From: [email protected] [mailto:[email protected]]
On Behalf Of [email protected]
Sent: Saturday, October 24, 2009 8:03 PM
To: [email protected]
Subject: [NMusers] method=ITS, OPTIMIZATION NOT TESTED (?!)

Hello NONMEM Team,

I found method=imp useful when there are local maxima.  Nevertheless, at
the end of optimization it prints a message, which makes me feel
somewhat uncomfortable:  OPTIMIZATION NOT TESTED.  Also, the final
objective function is not always the lowest one.  An example is below.
How do we interpret the results in this case?  

 THETAS THAT ARE SIGMA-LIKE:
 MONITORING OF SEARCH:

 iteration            0 OBJ=    10625.663135214874     
 iteration            1 OBJ=    10601.188754983375     
 iteration            2 OBJ=    10537.895114114934     
 iteration            3 OBJ=    10471.674625518765     
 iteration            4 OBJ=    10430.297437731866     
 iteration            5 OBJ=    10461.973668565577     
 iteration            6 OBJ=    10462.638834406265     
 iteration            7 OBJ=    10423.464983371641     
 iteration            8 OBJ=    10417.959956991735     
 iteration            9 OBJ=    10417.594007447198     
 iteration           10 OBJ=    10414.708468642830     
 iteration           11 OBJ=    10427.810693855947     
 iteration           12 OBJ=    10412.889081059604     
 iteration           13 OBJ=    10411.980622268416     
 iteration           14 OBJ=    10424.501127174915     
 iteration           15 OBJ=    10416.332869468861     
 iteration           16 OBJ=    10416.622580251338     
 iteration           17 OBJ=    10412.401585537709     
 iteration           18 OBJ=    10415.117257355550     
 iteration           19 OBJ=    10415.302370961055     
 iteration           20 OBJ=    10409.066188189252     
 iteration           21 OBJ=    10413.780620468329     
 iteration           22 OBJ=    10410.787496174480     
 iteration           23 OBJ=    10410.633582415931     
 iteration           24 OBJ=    10409.970257443048     
 iteration           25 OBJ=    10409.702420124611     
 iteration           26 OBJ=    10409.213115058612     
 iteration           27 OBJ=    10409.690639357370     
 iteration           28 OBJ=    10410.016047785200     
 iteration           29 OBJ=    10408.157468814226     
 iteration           30 OBJ=    10407.779614704938     
 iteration           31 OBJ=    10410.164563157052     
 iteration           32 OBJ=    10408.364552302961     
 iteration           33 OBJ=    10407.431920727997     
 iteration           34 OBJ=    10408.286189641487     
 iteration           35 OBJ=    10407.907347050501     
 iteration           36 OBJ=    10407.451608770069     
 iteration           37 OBJ=    10407.189482360372     
 iteration           38 OBJ=    10406.484357336147     
 iteration           39 OBJ=    10409.167125968375     
 iteration           40 OBJ=    10406.840873883246     
 iteration           41 OBJ=    10407.679485561714     
 iteration           42 OBJ=    10405.341101045238     
 iteration           43 OBJ=    10404.704382334516     
 iteration           44 OBJ=    10405.348023082915     
 iteration           45 OBJ=    10405.347406984720     
 iteration           46 OBJ=    10401.873473651774     
 iteration           47 OBJ=    10404.036204419035     
 iteration           48 OBJ=    10405.072916975221     
 iteration           49 OBJ=    10402.976628923887     
 Elapsed estimation time in seconds: 30420.73
 iteration           50 OBJ=    10403.285958168881     

 #TERM:
 OPTIMIZATION NOT TESTED

Thanks,

Pavel

 

 

 

 

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