RE: Estimation method using ITS and IMP iterations
Dear Nathalie
In answer to your question; yes, it is usual to see this "unstability" in
the final few iteration OFVs.
When using the IMP method, I often include two sequential $EST commands. The
first command will perform optimisation of parameter estimates until a
global minimum is found. The second command will then take those parameter
estimates and calculate more precise estimates of the objective function
value. The second $EST command will have a higher ISAMPLE to reduce the
Monte Carlo noise, and have ETYPE=1 (no optimisation of parameter values).
I suspect that the number of samples that you are using may not be enough,
giving large Monte Carlo noise in the OFV estimate. I suggest that you
perform another run with the parameter values set to their final estimates,
and with:
$EST METHOD=IMP ISAMPLE=10000 INTERACTION LAPLACE NITER=5 SIG=3 PRINT=1
SIGL=6 EONLY=1 NOHABORT RANMETHOD=3S2
The higher number of samples should give a more stable result (although the
run time of each iteration will increase significantly). Taking the average
OFV of these 5 iterations will give a more accurate estimation of the final
OFV.
Jon
Jon Moss, PhD
Modeller
BAST Inc Limited
Loughborough Innovation Centre
Charnwood Wing
Holywell Park
Ashby Road
Loughborough, LE11 3AQ, UK
Tel: +44 (0)1509 222908
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of [email protected]
Sent: 10 November 2016 07:06
To: [email protected]
Subject: [NMusers] Estimation method using ITS and IMP iterations
Dear NONMEM users,
I am building a relatively complex PKPD model (with 47 parameters and 11
differential equations).
I had problems using FOCE so I am trying this estimation method :
$EST METHOD=ITS INTERACTION LAPLACE NITER=200 SIG=3 PRINT=1 SIGL=6 NOHABORT
CTYPE=3 NUMERICAL SLOW
$EST METHOD=IMPMAP ISAMPLE=1000 INTERACTION LAPLACE NITER=1000 SIG=3 PRINT=1
SIGL=6 NOHABORT CTYPE=3 IACCEPT=0.4 MAPITER=0 RANMETHOD=3S2
$COV UNCONDITIONAL MATRIX=S TOL=12 SIGL=12 SLOW
The iteration for the ITS step seems to be quite stable with some artefacts:
iteration 175 OBJ= 4693.4674554341409
iteration 176 OBJ= 4694.2296104065535
iteration 177 OBJ= 4693.7753507970829
iteration 178 OBJ= 4693.9600270372885
iteration 179 OBJ= 4693.5732455834705
iteration 180 OBJ= 4693.6386423202493
iteration 181 OBJ= 4693.6215390721527
iteration 182 OBJ= 4693.6006496138452
iteration 183 OBJ= 4693.7877620448235
iteration 184 OBJ= 4694.1591757809929
iteration 185 OBJ= 4693.2614956897451
iteration 186 OBJ= 4693.5641640401127
iteration 187 OBJ= 4693.5575289919379
iteration 188 OBJ= 4495.6489907149398
iteration 189 OBJ= 4693.7711764252363
iteration 190 OBJ= 4693.6281175153035
iteration 191 OBJ= 4694.1171774559862
iteration 192 OBJ= 4693.7908707845536
iteration 193 OBJ= 4693.7709264605819
iteration 194 OBJ= 4495.9262902940209
iteration 195 OBJ= 4693.3321354894242
iteration 196 OBJ= 4694.3177205227348
iteration 197 OBJ= 4694.1301486616576
iteration 198 OBJ= 4694.2898587322170
iteration 199 OBJ= 4693.8304358341920
iteration 200 OBJ= 4691.6818293505230
#TERM:
OPTIMIZATION WAS NOT COMPLETED
The IMP step seems less stable :
iteration 120 OBJ= 4314.8310660241377 eff.= 446. Smpl.=
1000. Fit.= 0.96389
iteration 121 OBJ= 4326.9079856676717 eff.= 448. Smpl.=
1000. Fit.= 0.96409
iteration 122 OBJ= 4164.6649529423103 eff.= 479. Smpl.=
1000. Fit.= 0.96392
iteration 123 OBJ= 4299.9887619753636 eff.= 432. Smpl.=
1000. Fit.= 0.96395
iteration 124 OBJ= 4303.9571213327054 eff.= 399. Smpl.=
1000. Fit.= 0.96349
iteration 125 OBJ= 4328.9835950930074 eff.= 417. Smpl.=
1000. Fit.= 0.96423
iteration 126 OBJ= 4304.3861595488252 eff.= 550. Smpl.=
1000. Fit.= 0.96392
iteration 127 OBJ= 4291.0862736663648 eff.= 422. Smpl.=
1000. Fit.= 0.96430
iteration 128 OBJ= 4326.2378678645500 eff.= 407. Smpl.=
1000. Fit.= 0.96409
iteration 129 OBJ= 4157.5352046539456 eff.= 406. Smpl.=
1000. Fit.= 0.96404
iteration 130 OBJ= 4332.6894073732456 eff.= 399. Smpl.=
1000. Fit.= 0.96399
iteration 131 OBJ= 4357.5343346793761 eff.= 493. Smpl.=
1000. Fit.= 0.96414
Convergence achieved
iteration 131 OBJ= 4336.1893012015007 eff.= 417. Smpl.=
1000. Fit.= 0.96369
#TERM:
OPTIMIZATION WAS COMPLETED
I think the ITS step is OK with an objective function ~ 4690.
The "unstability" of the IMP step is it usual ? Nonmem is completed at the
end..
I want to trust in this model, but am I right ?
Thanks in advance for your answers.
Nathalie