Unexpected influence of parameter order on estimation results

Dear NMusers, I always thought that the order in which parameters are declared in the control stream has no impact on the estimation outcomes, but the following results seem to contradict this. The PK of drug X was modeled with a linear 3-compartment model using a proportional residual variability model. Inter-individual variability was estimated on elimination clearance and central volume of distribution. The magnitude of residual variability was estimated using a THETA and a SIGMA fixed to 1 as follows: $ERROR IPRED=F CV=THETA(x) W=CV*IPRED Y=IPRED+W*EPS(1) Two versions of this model were created with slight differences in the order of declaration of the theta parameters: the theta used to estimate the RV was basically moved from the third to the last position and the $PK and the $ERROR blocks were updated accordingly. Both models were run with NONMEM 6.2.0 on opensuse 11.1 (with the gfortran compiler). One of the models converged successfully while the other stopped at an early iteration and returned some estimation warnings and a 'S matrix singular' message. The strange thing is that gradients appears identical until the 10th iteration, at which point the two models take different search paths (see below). I would be very interested to know the opinion of the group on this puzzling result. Thanks Sebastien ----------------------------------------------------------------------------------- Model 1 (RV theta in the 1st position) 1 MONITORING OF SEARCH: 0ITERATION NO.: 0 OBJECTIVE VALUE: 0.25863E+04 NO. OF FUNC. EVALS.: 9 CUMULATIVE NO. OF FUNC. EVALS.: 9 PARAMETER: 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 GRADIENT: -0.9523E+02 0.3303E+03 -0.2730E+04 0.6103E+03 -0.1044E+04 0.2780E+03 -0.6146E+03 -0.9406E+02 -0.3231E+03 0ITERATION NO.: 5 OBJECTIVE VALUE: 0.10396E+04 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 59 PARAMETER: 0.1919E+01 -0.5699E+00 0.4872E+00 -0.1661E+01 0.1040E+01 -0.3113E+00 -0.8783E-01 0.1274E+01 -0.6898E-01 GRADIENT: 0.1511E+02 -0.2036E+02 -0.3532E+03 -0.3176E+02 -0.4009E+02 -0.9733E+02 0.4026E+02 -0.2917E+02 -0.4199E+02 0ITERATION NO.: 10 OBJECTIVE VALUE: 0.88163E+03 NO. OF FUNC. EVALS.:12 CUMULATIVE NO. OF FUNC. EVALS.: 127 PARAMETER: 0.1643E+01 -0.4360E+00 0.9125E+00 -0.1429E+01 0.1009E+01 0.2690E+00 0.1835E+00 0.1894E+01 -0.3302E+00 GRADIENT: 0.7310E+01 0.2031E+02 -0.3379E+02 0.1896E+02 -0.6428E+02 -0.5519E+01 0.2288E+02 0.8420E+01 -0.3893E+02 0ITERATION NO.: 15 OBJECTIVE VALUE: 0.85825E+03 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 179 PARAMETER: 0.7899E+00 -0.5002E+00 0.1014E+01 -0.1314E+01 0.1104E+01 -0.4181E-01 -0.2654E+00 0.1545E+01 0.3062E+00 GRADIENT: 0.8389E+01 0.8285E+01 0.5404E+01 0.2172E+02 -0.9433E+01 -0.2633E+02 0.7059E+01 0.2790E+01 -0.1023E+01 0ITERATION NO.: 20 OBJECTIVE VALUE: 0.85807E+03 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 275 PARAMETER: 0.7816E+00 -0.5006E+00 0.1013E+01 -0.1314E+01 0.1104E+01 -0.4133E-01 -0.2649E+00 0.1477E+01 0.3305E+00 GRADIENT: 0.9405E+01 0.7846E+01 0.5605E+01 0.2021E+02 -0.9587E+01 -0.2640E+02 0.6285E+01 0.2135E-01 -0.1198E-02 0ITERATION NO.: 25 OBJECTIVE VALUE: 0.84968E+03 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 344 PARAMETER: -0.2358E+00 -0.5888E+00 0.1008E+01 -0.1312E+01 0.1114E+01 0.8588E-01 -0.2390E+00 0.9860E+00 0.3144E+00 GRADIENT: -0.2043E+01 -0.4198E+01 -0.1418E+00 0.1786E+02 0.1856E+00 -0.2873E+01 -0.6265E+01 0.8535E+00 -0.2022E+00 0ITERATION NO.: 30 OBJECTIVE VALUE: 0.84767E+03 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 396 PARAMETER: -0.9020E-02 -0.5500E+00 0.1022E+01 -0.1312E+01 0.1258E+01 0.3517E+00 0.7877E-01 0.9016E+00 0.3574E+00 GRADIENT: -0.1566E+00 -0.1616E+00 -0.3990E+00 0.2010E+02 0.5696E+00 -0.5633E+00 0.4708E+00 -0.3923E+00 0.1648E-01 0ITERATION NO.: 35 OBJECTIVE VALUE: 0.84766E+03 NO. OF FUNC. EVALS.:17 CUMULATIVE NO. OF FUNC. EVALS.: 469 PARAMETER: -0.2786E-02 -0.5413E+00 0.1025E+01 -0.1312E+01 0.1252E+01 0.3551E+00 0.7957E-01 0.9105E+00 0.3546E+00 GRADIENT: -0.1860E-02 0.2683E-01 -0.1566E-01 0.1869E+02 0.3775E-02 -0.6239E-02 0.5749E-02 0.1541E-01 -0.6128E-03 0ITERATION NO.: 40 OBJECTIVE VALUE: 0.84590E+03 NO. OF FUNC. EVALS.:17 CUMULATIVE NO. OF FUNC. EVALS.: 568 PARAMETER: -0.1454E+00 -0.5904E+00 0.9921E+00 -0.1483E+01 0.1287E+01 0.2158E+00 0.8236E-01 0.9660E+00 0.3228E+00 GRADIENT: -0.7465E-01 -0.3447E+00 -0.4737E+00 0.2200E+01 0.2029E+01 -0.1106E+01 -0.5923E+00 -0.8064E-01 0.1356E+00 0ITERATION NO.: 45 OBJECTIVE VALUE: 0.84585E+03 NO. OF FUNC. EVALS.:14 CUMULATIVE NO. OF FUNC. EVALS.: 650 PARAMETER: -0.1440E+00 -0.5825E+00 0.9933E+00 -0.1493E+01 0.1261E+01 0.2183E+00 0.8561E-01 0.9659E+00 0.3136E+00 GRADIENT: -0.5281E-04 -0.5273E-03 0.1878E-03 -0.9052E-03 0.1615E-03 0.1004E-02 -0.8575E-03 -0.1004E-03 -0.1273E-03 0MINIMIZATION SUCCESSFUL NO. OF FUNCTION EVALUATIONS USED: 650 NO. OF SIG. DIGITS IN FINAL EST.: 4.7 ETABAR IS THE ARITHMETIC MEAN OF THE ETA-ESTIMATES, AND THE P-VALUE IS GIVEN FOR THE NULL HYPOTHESIS THAT THE TRUE MEAN IS 0. ETABAR: -0.46E-02 0.39E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 SE: 0.21E+00 0.91E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 P VAL.: 0.98E+00 0.97E+00 0.10E+01 0.10E+01 0.10E+01 0.10E+01 0.10E+01 ---------------------------------------------------------------------------------- Model 2 (RV theta in the 7th position) 1 MONITORING OF SEARCH: 0ITERATION NO.: 0 OBJECTIVE VALUE: 0.25863E+04 NO. OF FUNC. EVALS.: 9 CUMULATIVE NO. OF FUNC. EVALS.: 9 PARAMETER: 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 0.1000E+00 GRADIENT: -0.9523E+02 0.3303E+03 0.6103E+03 -0.1044E+04 0.2780E+03 -0.6146E+03 -0.2730E+04 -0.9406E+02 -0.3231E+03 0ITERATION NO.: 5 OBJECTIVE VALUE: 0.10396E+04 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 59 PARAMETER: 0.1919E+01 -0.5699E+00 -0.1661E+01 0.1040E+01 -0.3113E+00 -0.8783E-01 0.4872E+00 0.1274E+01 -0.6898E-01 GRADIENT: 0.1511E+02 -0.2036E+02 -0.3176E+02 -0.4009E+02 -0.9733E+02 0.4026E+02 -0.3532E+03 -0.2917E+02 -0.4199E+02 0ITERATION NO.: 10 OBJECTIVE VALUE: 0.88167E+03 NO. OF FUNC. EVALS.:12 CUMULATIVE NO. OF FUNC. EVALS.: 127 PARAMETER: 0.1642E+01 -0.4358E+00 -0.1429E+01 0.1009E+01 0.2691E+00 0.1839E+00 0.9126E+00 0.1895E+01 -0.3306E+00 GRADIENT: 0.7304E+01 0.2046E+02 0.1895E+02 -0.6432E+02 -0.5543E+01 0.2291E+02 -0.3381E+02 0.8433E+01 -0.3897E+02 0ITERATION NO.: 15 OBJECTIVE VALUE: 0.85827E+03 NO. OF FUNC. EVALS.:10 CUMULATIVE NO. OF FUNC. EVALS.: 179 PARAMETER: 0.8105E+00 -0.5381E+00 -0.1334E+01 0.1062E+01 -0.1712E-02 -0.2072E+00 0.1000E+01 0.1570E+01 0.2716E+00 GRADIENT: 0.8146E+01 -0.2087E+01 0.2338E+02 -0.2616E+02 -0.2205E+02 0.1064E+02 0.4212E+01 0.3944E+01 -0.2221E+01 0ITERATION NO.: 20 OBJECTIVE VALUE: 0.85775E+03 NO. OF FUNC. EVALS.:39 CUMULATIVE NO. OF FUNC. EVALS.: 317 RESET HESSIAN, TYPE I PARAMETER: 0.7924E+00 -0.5386E+00 -0.1335E+01 0.1073E+01 -0.2161E-02 -0.2085E+00 0.1001E+01 0.1558E+01 0.2793E+00 GRADIENT: 0.8121E+01 -0.1709E+01 0.2187E+02 -0.2257E+02 -0.2142E+02 0.9023E+01 0.4553E+01 0.3690E+01 -0.1895E+01 0ITERATION NO.: 24 OBJECTIVE VALUE: 0.85768E+03 NO. OF FUNC. EVALS.:24 CUMULATIVE NO. OF FUNC. EVALS.: 386 PARAMETER: 0.7924E+00 -0.5386E+00 -0.1335E+01 0.1076E+01 -0.2161E-02 -0.2085E+00 0.1001E+01 0.1556E+01 0.2805E+00 GRADIENT: -0.7820E+04 -0.5761E+04 -0.4623E+04 0.5748E+04 0.6202E+05 -0.2974E+05 0.3099E+04 0.1998E+04 0.2212E+05 0MINIMIZATION SUCCESSFUL HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. NO. OF FUNCTION EVALUATIONS USED: 386 NO. OF SIG. DIGITS IN FINAL EST.: 3.3 ETABAR IS THE ARITHMETIC MEAN OF THE ETA-ESTIMATES, AND THE P-VALUE IS GIVEN FOR THE NULL HYPOTHESIS THAT THE TRUE MEAN IS 0. ETABAR: -0.61E+00 0.15E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 SE: 0.31E+00 0.92E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 P VAL.: 0.48E-01 0.87E+00 0.10E+01 0.10E+01 0.10E+01 0.10E+01 0.10E+01 0S MATRIX ALGORITHMICALLY SINGULAR 0S MATRIX IS OUTPUT 0INVERSE COVARIANCE MATRIX SET TO RS*R, WHERE S* IS A PSEUDO INVERSE OF S