Estimating multiple EPS with SAEM

Hi all, I am having some issues when estimating two residual error parameters with the SAEM algorithm (NM7.1.2, PsN 3.2.4). Below is my example, where each EPS is turned on/off by an indicator variable, not sure what the behavior is like for error models such as Y=IPRD*EXP(EPS(1)) + EPS(2), but I suspect it works fine. I have two subject populations, TYPE = 2 and TYPE = 3. Good reason to suspect TYPE = 3 is more variable, so I implemented the following in $ERROR of my PD model Y=(IPRD+ERR(1))*(3-TYPE)+(IPRD+ERR(2))*(TYPE-2) $SIGMA 30 ; EPS type2 30 ; EPS type3 This is the estimation sequence $ESTIMATION METHOD=ITS PRINT=10 NOABORT NITER=100 NSIG=3 SIGL=6 CTYPE=2 $ESTIMATION METHOD=SAEM ISAMPLE=2 NBURN=500 NITER=2000 PRINT=10 NSIG=3 SIGL=6 SEED=123345 CTYPE=3 $ESTIMATION METHOD=IMPMAP EONLY=1 NITER=10 ISAMPLE=1000 PRINT=1 NSIG=3 SIGL=6 SEED=123345 CTYPE=3 CITER=10 CALPHA=0.05 Results from ITS are shown below, these estimates make sense. ITS: EPS1 EPS2 EPS1 + 2.17E+01 EPS2 + 0.00E+00 2.51E+01 After SAEM (which converges before end of burn in), the estimate of SIGMA2 has blown up (at least I think it has, the "E" is missing, which I think is a result of truncation since the exponent has 3 digits). All other THETA and OMEGA estimates are reasonable and consistent with prior models. SAEM: EPS1 EPS2 EPS1 + 2.23E+01 EPS2 + 0.00E+00 2.95+304 I do not understand why this, would have thought the study has a reasonable number of TYPE2:TYPE3 to estimate the two values (70:37), ITS think so.... I would like to confirm if it is a data issue or a NONMEM issue. Consequently, the IMPMAP (EONLY=1) estimation goes off the rails with very large OBJ (which is usually in the range of 3000-4000), largely driven by several clusters of subjects with very high individual contributions to IMPMAP OBJ. #METH: Objective Function Evaluation by Importance/MAP Sampling EM/BAYES SETUP THETAS THAT ARE MU MODELED: 1 2 4 5 6 7 8 9 10 11 12 13 14 THETAS THAT ARE SIGMA-LIKE: MONITORING OF SEARCH: iteration 0 OBJ= 178895.840910174 iteration 1 OBJ= 178911.252369277 iteration 2 OBJ= 178904.447527235 iteration 3 OBJ= 178901.025144545 iteration 4 OBJ= 178911.754485815 iteration 5 OBJ= 178902.859882922 iteration 6 OBJ= 178904.436625787 iteration 7 OBJ= 178909.875233486 iteration 8 OBJ= 178905.268884384 iteration 9 OBJ= 178908.849070488 Elapsed estimation time in seconds: 5830.79 iteration 10 OBJ= 178909.810873438 Any thoughts? Cheers Brendan