[R-sig-ME] [lme4 package] Does the order of rows(trials) in a data frame (long format) affect the results of the lmer model (maybe somehow)?

[Zhaohong]

Dear Dr. Bolker,

Thank you so much for your response and your very helpful suggestions. Here
are a few follow-up points:

1. I did forgot to rewrite the variable names in model 2. I rewrote the
variable names in model one but forgot to do so in model 2, so that was not
a concern for different results:)

2. The reordering in my post was actually random (I randomly reordered the
data according to first subject number, and then item number), and the
results were different (The model before reordering was able to converge,
and wasn't after reordering). The reason for me doing so was to demonstrate
that reordering can change the results. My original methodological
reordering motivation was to create a new variable/column based on an
existing variable/column ordered according to two other variables/columns.
The model wasn't able to converge after methodological reordering. For
demonstration simplicity I posted the random reordering model results. But
both ways of reordering changed the results such that the model in the
reordered ones failed to converge even though model summaries were very
similar.

3. For the lmer model fit in the methodological reordered dataset that
could not converge, I first followed your suggestion of trying different
optimizers (built-in N-M and bobyqa; nlminb and L-BFGS-B from base R, via
the optimx package; and the nloptr versions of N-M and bobyqa), but
unfortunately none of the optimizers were able to make the model converge
in this case. I then followed your suggestion of doing the experiment of
inputing the results from each case as starting values. LUCKILY the model
was able to converge (even though the verbose reports are different, the
model summary of the updated model is the same as the original model)!

Model 1 and Model 3 were able to converge and have the same model
summaries. They do have different verbose reports. Model 2 failed to
converge, and has slightly different SEs, but the same t-values.

I am wondering if this means that able-to-converge model invalidate the
warning message of the unable-to-converge model or otherwise?

Here are the verbose results for each of the models:

Model 1: The model that was able to converge in the original dataset before
methodological reordering:

npt = 77 , n =  75
rhobeg =  0.2 , rhoend =  2e-07
   0.020:  86:     -783.903;0.492578 -0.0462110 -0.0363706 -0.0207690
-0.00431843 0.00131644 -0.00481740 -0.00308283 0.502089 -0.0211198
-0.0336018 -0.0237567 -0.00155808 -0.0307396 -0.000390386 0.354830
-0.0308058 -0.0351383 -0.0137964 -0.0238208 -0.0133714 0.305507 -0.0174871
-0.0328382 -0.00155512 0.00996183 0.646703 -0.0258383 -0.00140789
-0.00146090 0.593574 -0.0176632 -0.0351208 0.611448 0.000434827 0.819591
0.735976 -0.105154 0.553306 0.948775 -0.00950248 -0.00976528 -0.0113362
-0.00949456 -0.00980388 -0.0100146 -0.00779947 0.946928 -0.00984788
-0.0101504 -0.00957164 -0.00986887 -0.00987130 -0.00960832 0.946926
-0.0101303 -0.0103410 -0.00976277 -0.00978197 -0.00937871 0.947412
-0.00988796 -0.0100200 -0.0103097 -0.0103002 0.949607 -0.0106541
-0.00943222 -0.0111189 0.947582 -0.0104333 -0.00883065 0.949409 -0.0119380
0.954371
  0.0020: 1266:     -1011.94;0.450579 -0.0561922 -0.0390931 0.00888335
0.0594143 0.0967819 0.0600815 0.0674752 0.220653 0.00904686 -0.0695420
-0.0628733 0.117864 -0.0596009 0.0799141 0.229251 -0.0664593 -0.123684
0.0490019 -0.0235120 -0.0541726 0.118324 -0.0741990 -0.0809683 0.0958857
0.268214 0.249829 0.0324988 0.114487 0.128747 0.0173838 0.0167991
-0.00732449 0.0501891 0.0850320 0.157297 0.746053 -0.130440 0.331480
0.293937 0.0743482 0.0377308 -0.139008 0.0682751 0.0331050 -0.0866526
-0.0383130 0.0117850 -0.0240952 0.0400393 0.0809940 -0.0429156 -0.159851
0.0126200 0.0106298 -0.0212809 0.00295026 -0.00943432 0.0602099 -0.0644152
 0.00000 0.0531561 0.00504124 0.107394 -0.0116981 0.177808 -0.0275010
0.0632146 -0.0964148 0.0448545 0.0930555 -0.0329402 0.233815 0.111024
0.684505
 0.00020: 2829:     -1024.14;0.452709 -0.0579625 -0.0344969 0.0120118
0.0734510 0.0896992 0.0499010 0.0954442 0.229133 0.00559951 -0.0696637
-0.0242122 0.111817 -0.0789206 0.0837580 0.222271 -0.0611380 -0.112126
0.0329055 -0.0277847 -0.00797160 0.120486 -0.0643358 -0.0845771 0.0736635
0.288591 0.257914 -0.00181955 0.110898 0.147722  0.00000 0.000172809
0.00919736 0.0110891 0.0152384 0.00828677 0.748922 -0.123172 0.330259
0.206775 0.0476522 0.0280362 -0.101461 0.0463152 0.0216698 0.0248654
0.170800 0.0176330 -0.0380416 0.0317560 0.142628 -0.0190826 -0.0671519
-0.00752021  0.00000 -0.0188540 0.0493985 -0.0278924 0.112063 -0.127799
 0.00000 0.0437870 -0.0209737 0.0658322 -0.0777777 0.0965694 -0.0408791
0.127360 -0.174456  0.00000 -0.000274027 -0.0130361 2.24477e-05 9.26975e-05
0.0325827
 2.0e-05: 5773:     -1024.50;0.453311 -0.0585468 -0.0356171 0.0124542
0.0734133 0.0881771 0.0507491 0.0961264 0.230039 0.00643291 -0.0684440
-0.0227084 0.110856 -0.0735117 0.0881374 0.222234 -0.0600243 -0.108611
0.0331596 -0.0250605 -0.00237720 0.120825 -0.0585146 -0.0847106 0.0774464
0.290086 0.261511 -3.75224e-05 0.115623 0.142874  0.00000 0.000183608
0.000936919 0.000697002 0.000660236 0.00131320 0.749940 -0.124134 0.330879
0.212274 0.0477193 0.0297672 -0.103379 0.0384115 0.0255082 0.0174492
0.192128 0.0200405 -0.0419245 0.0218963 0.182316 -0.0385164 0.00556693
-0.0984504 0.00132058 -0.0348845 0.0586401 -0.0444801 0.190766 -0.212638
 0.00000 0.000668956 -0.000403147 0.00142120 -0.00175281  0.00000
0.000166863 -0.00106768 0.000914863 0.000467674 -0.00274150 0.00346480
0.00114500 -0.000318531 0.00111171
 2.0e-06: 9248:     -1024.51;0.453348 -0.0584731 -0.0355746 0.0125052
0.0734903 0.0881831 0.0507601 0.0960166 0.230127 0.00645362 -0.0684865
-0.0230384 0.110978 -0.0737085 0.0876321 0.222118 -0.0599844 -0.108724
0.0331173 -0.0250216 -0.00259363 0.120773 -0.0589481 -0.0846303 0.0772936
0.289112 0.261172 -0.000167296 0.115547 0.144054 9.37833e-07 -2.73741e-05
-4.00219e-05 1.07567e-05 3.85261e-05 3.10490e-05 0.750009 -0.124225
0.330861 0.212679 0.0477609 0.0298412 -0.103074 0.0371887 0.0264153
0.0144220 0.197224 0.0205217 -0.0404589 0.0134622 0.191228 -0.0472840
0.0469882 -0.142964 0.0109281 -0.0403486 0.0179550 -0.0346377 0.183949
-0.183214 0.00214351 -0.00362890 0.00276037 -0.0118387 0.0131118  0.00000
5.56133e-05 -0.000257805 0.000368822  0.00000 0.000199027 -0.000336293
0.000352925 -0.000454433 1.25530e-05
 2.0e-07: 15764:     -1024.51;0.453351 -0.0584700 -0.0355723 0.0124965
0.0734787 0.0881846 0.0507616 0.0960397 0.230123 0.00645416 -0.0684847
-0.0230381 0.110983 -0.0737035 0.0876777 0.222123 -0.0599873 -0.108719
0.0331109 -0.0250217 -0.00255763 0.120773 -0.0589676 -0.0846268 0.0772896
0.289120 0.261188 -0.000185005 0.115550 0.144119 2.65656e-06 -6.82831e-06
-2.76466e-05  0.00000 1.83385e-05 7.87228e-07 0.750014 -0.124224 0.330867
0.212670 0.0477592 0.0298445 -0.103084 0.0371768 0.0264019 0.0144639
0.197132 0.0205263 -0.0404874 0.0135545 0.191202 -0.0472145 0.0466104
-0.142610 0.0108195 -0.0403564 0.0185109 -0.0348318 0.184403 -0.183997
0.000797901 -0.00138066 0.00103545 -0.00442881 0.00490172  0.00000
7.51833e-06 -3.87226e-05 6.04092e-05  0.00000 3.63510e-06 -4.49410e-06
1.42910e-06 5.74795e-06 5.03001e-07
At return
30433:    -1024.5110: 0.453352 -0.0584699 -0.0355712 0.0124974 0.0734792
0.0881850 0.0507622 0.0960430 0.230123 0.00645469 -0.0684872 -0.0230377
0.110983 -0.0737051 0.0876725 0.222122 -0.0599864 -0.108715 0.0331096
-0.0250228 -0.00255738 0.120773 -0.0589583 -0.0846263 0.0772945 0.289133
0.261192 -0.000180489 0.115545 0.144090  0.00000 -1.01443e-06 -2.72331e-06
4.36692e-09 2.82744e-07 1.08531e-07 0.750012 -0.124223 0.330865 0.212669
0.0477582 0.0298448 -0.103086 0.0371794 0.0264002 0.0144699 0.197126
0.0205267 -0.0404964 0.0135892 0.191188 -0.0471837 0.0464530 -0.142440
0.0107821 -0.0403469 0.0186941 -0.0348872 0.184494 -0.184176 0.000119925
-0.000204327 0.000154724 -0.000663551 0.000735230 6.17347e-06 -3.20551e-06
1.16271e-05 -1.43359e-05  0.00000 1.40493e-06 -2.16589e-06 1.27346e-06
-2.57135e-06 2.74006e-08


Model 2: The model that failed to converge in the methodologically
reordered dataset:

npt = 77 , n =  75
rhobeg =  0.2 , rhoend =  2e-07
   0.020:  86:     -783.903;0.492578 -0.0462110 -0.0363706 -0.0207690
-0.00431843 0.00131644 -0.00481740 -0.00308283 0.502089 -0.0211198
-0.0336018 -0.0237567 -0.00155808 -0.0307396 -0.000390386 0.354830
-0.0308058 -0.0351383 -0.0137964 -0.0238208 -0.0133714 0.305507 -0.0174871
-0.0328382 -0.00155512 0.00996183 0.646703 -0.0258383 -0.00140789
-0.00146090 0.593574 -0.0176632 -0.0351208 0.611448 0.000434827 0.819591
0.735976 -0.105154 0.553306 0.948775 -0.00950248 -0.00976528 -0.0113362
-0.00949456 -0.00980388 -0.0100146 -0.00779947 0.946928 -0.00984788
-0.0101504 -0.00957164 -0.00986887 -0.00987130 -0.00960832 0.946926
-0.0101303 -0.0103410 -0.00976277 -0.00978197 -0.00937871 0.947412
-0.00988796 -0.0100200 -0.0103097 -0.0103002 0.949607 -0.0106541
-0.00943222 -0.0111189 0.947582 -0.0104333 -0.00883065 0.949409 -0.0119380
0.954371
  0.0020: 1172:     -1014.44;0.447624 -0.0669099 -0.0320947 0.0167277
0.0912024 0.0719610 0.0630252 0.0938361 0.222104 0.00464982 -0.0604313
-0.0253292 0.105348 -0.0903103 0.145056 0.219771 -0.0684526 -0.0980628
0.0509629 -0.0193132 -0.0425260 0.111319 -0.0674473 -0.0826473 0.0645061
0.308971 0.275658 0.00898888 0.0991931 0.0808576 0.0383104 -0.0420248
-0.0892487 0.0548139 0.207690 0.159669 0.757029 -0.120768 0.331302 0.267592
0.0676536 0.0201904 -0.126278 0.0562606 0.0250859 -0.0697141 0.0129532
 0.00000 -0.00388083 0.0181382 -0.0313332 0.0263182 -0.113085 0.0409921
0.0244835 -0.00143217 -0.109671 0.0390520 -0.102010 0.215959 0.0189336
0.0559744 -0.00894316 -0.0905837 0.00493369 0.131495 -0.0369109 0.00188335
-0.0600454 0.0130668 -0.000123405 0.0735400 0.299381 0.100145 0.406015
 0.00020: 2316:     -1024.17;0.452651 -0.0576679 -0.0363522 0.0142167
0.0737818 0.0886794 0.0505187 0.0968153 0.230282 0.00656979 -0.0700034
-0.0229655 0.112017 -0.0729715 0.0846494 0.222177 -0.0601802 -0.109146
0.0348149 -0.0255153 -0.0102024 0.119204 -0.0593872 -0.0844258 0.0784315
0.290692 0.265046 0.000206971 0.114277 0.137916 0.00347211 -0.00262445
-0.0181085 0.0191210 0.0499287 0.0236533 0.750671 -0.124422 0.332829
0.208668 0.0474820 0.0264901 -0.103312 0.0506237 0.0221234 0.0282738
0.161618 0.00724535 -0.0234226 0.0364098 0.0571019 0.00927926 -0.128524
0.0884775 0.0357219 -0.00762950 -0.178271 0.0554376 -0.101325 0.194006
0.0146784 -0.00596418 0.0138789 -0.0792504 0.0696503 0.0233231 -0.0134198
0.0447087 -0.0595230 0.00147059 -0.0191394 0.0156479 0.0305705 -0.0303258
0.0440939
 2.0e-05: 6667:     -1024.50;0.453333 -0.0585376 -0.0356834 0.0125678
0.0734487 0.0882151 0.0506785 0.0962391 0.230149 0.00646481 -0.0684075
-0.0229736 0.110955 -0.0735249 0.0887876 0.222150 -0.0599851 -0.108783
0.0331485 -0.0249883 -0.00231090 0.120743 -0.0588238 -0.0846573 0.0773961
0.289167 0.261248 -0.000202959 0.115370 0.143609 0.000375922 -0.000227722
-0.00210776 0.000249744 0.000200904 0.00155220 0.750066 -0.124132 0.330857
0.212715 0.0477406 0.0299282 -0.103651 0.0375423 0.0260087 0.0168784
0.195044 0.0201795 -0.0417657 0.0213221 0.183624 -0.0393913 0.00908481
-0.103410  0.00000 0.0341739 -0.0558429 0.0434061 -0.187388 0.206720
0.00602252 -0.0104587 0.00784840 -0.0335030 0.0373768  0.00000 0.000810863
-0.00406451 0.00531339 0.000623899 -0.00298601 0.00364139 0.00127020
-0.00249732 0.000136553
 2.0e-06: 7951:     -1024.50;0.453363 -0.0585214 -0.0355937 0.0125148
0.0734771 0.0881768 0.0507700 0.0960643 0.230135 0.00647050 -0.0684990
-0.0229578 0.110975 -0.0736253 0.0877978 0.222128 -0.0599846 -0.108632
0.0331081 -0.0249697 -0.00240205 0.120748 -0.0589092 -0.0846212 0.0773143
0.289105 0.261252 -0.000171769 0.115543 0.144025 7.68975e-06 -9.93934e-05
-0.000158060 2.14420e-06 -0.000108389 4.40368e-06 0.749999 -0.124185
0.330849 0.212710 0.0477984 0.0298783 -0.103537 0.0378475 0.0258748
0.0167651 0.194737 0.0200724 -0.0418390 0.0216548 0.183097 -0.0389119
0.00712609 -0.101166  0.00000 0.0340977 -0.0575895 0.0438893 -0.187939
0.208423 0.00597793 -0.00995945 0.00765416 -0.0329048 0.0363802 9.49403e-06
-1.55017e-05 4.43789e-05 -5.32016e-06 2.81165e-05 -0.000129310 0.000211690
3.15843e-05 -7.69581e-05 2.67395e-06
 2.0e-07: 8903:     -1024.50;0.453360 -0.0585275 -0.0355925 0.0125155
0.0734845 0.0881790 0.0507774 0.0960783 0.230139 0.00647101 -0.0684924
-0.0229629 0.110969 -0.0736363 0.0877776 0.222135 -0.0599849 -0.108668
0.0331033 -0.0249925 -0.00249774 0.120746 -0.0589032 -0.0846192 0.0773104
0.289164 0.261220 -0.000165898 0.115538 0.144051 2.68516e-06 -8.50514e-06
-1.59307e-05 1.31307e-06 -1.15980e-05 1.25947e-05 0.749998 -0.124188
0.330852 0.212669 0.0477858 0.0298613 -0.103532 0.0378953 0.0258498
0.0168472 0.194461 0.0200718 -0.0418512 0.0216547 0.183128 -0.0389264
0.00718263 -0.101254  0.00000 0.0341018 -0.0575525 0.0438835 -0.187975
0.208542 0.00595594 -0.0100401 0.00766203 -0.0328283 0.0364101 5.20909e-05
-2.37566e-05 7.39340e-05 -0.000105120 2.19095e-06 -9.43038e-06 1.25759e-05
 0.00000 1.09861e-05 1.18396e-05
At return
10078:    -1024.5030: 0.453360 -0.0585273 -0.0355919 0.0125149 0.0734832
0.0881784 0.0507752 0.0960809 0.230139 0.00647089 -0.0684925 -0.0229637
0.110970 -0.0736367 0.0877742 0.222135 -0.0599852 -0.108670 0.0331028
-0.0249919 -0.00249632 0.120746 -0.0589016 -0.0846198 0.0773097 0.289162
0.261220 -0.000166264 0.115537 0.144045  0.00000 -1.19720e-07 1.53556e-06
3.08499e-07 -2.54427e-06 1.83151e-06 0.749999 -0.124188 0.330852 0.212667
0.0477863 0.0298606 -0.103533 0.0379029 0.0258461 0.0168572 0.194439
0.0200716 -0.0418515 0.0216519 0.183133 -0.0389304 0.00720182 -0.101281
 0.00000 0.0341025 -0.0575332 0.0438776 -0.187972 0.208527 0.00595515
-0.0100495 0.00766287 -0.0328261 0.0364180 6.42615e-06 -3.40726e-06
1.16423e-05 -1.43135e-05 1.68240e-06 -7.84610e-06 9.54898e-06 8.95258e-08
-1.45106e-06 7.29374e-09
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 2.40355 (tol = 0.002, component
65)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge: degenerate  Hessian with 1 negative eigenvalues

(3) The updated model of (2) using (1) theta:

E.maximal.model.RRT.rep.updated <-
update(E.maximal.model.RRT.rep,start=getME(E.maximal.model.RRT,"theta"))

npt = 77 , n =  75
rhobeg =  0.1500024 , rhoend =  1.500024e-07
   0.015:  78:     -1007.25;0.453352 -0.0584699 -0.0355712 0.0124974
0.0734792 0.0881850 0.0507622 0.0960430 0.230123 0.00645469 -0.0684872
-0.0230377 0.110983 -0.0737051 0.0876725 0.222122 -0.0599864 -0.108715
0.0331096 -0.0250228 -0.00255738 0.150002 -0.0589583 -0.0846263 0.0772945
0.289133 0.261192 -0.000180489 0.115545 0.144090  0.00000 -1.01443e-06
-2.72331e-06 0.150002 2.82744e-07 0.150002 0.750012 -0.124223 0.330865
0.212669 0.0477582 0.0298448 -0.103086 0.0371794 0.0264002 0.0144699
0.197126 0.150002 -0.0404964 0.0135892 0.191188 -0.0471837 0.0464530
-0.142440 0.150002 -0.0403469 0.0186941 -0.0348872 0.184494 -0.184176
0.150002 -0.000204327 0.000154724 -0.000663551 0.150738 0.150002
-3.20551e-06 1.16271e-05 -1.43359e-05  0.00000 1.40493e-06 -2.16589e-06
0.150002 -2.57135e-06 0.150002
  0.0015: 274:     -1022.80;0.452074 -0.0566838 -0.0335530 0.0105914
0.0650464 0.0888558 0.0454422 0.0818484 0.235260 0.00370672 -0.0607487
-0.0170701 0.112566 -0.0772648 0.0912295 0.218476 -0.0619023 -0.112771
0.0219813 -0.0268611 -0.00187945 0.122432 -0.0469394 -0.0843561 0.0856558
0.287286 0.244826 -0.00158542 0.113384 0.156312 0.00346480 0.00953380
0.0145042 0.0610715 0.0340415 0.133814 0.742904 -0.118794 0.327997 0.209131
0.0405832 0.0273765 -0.0978607 0.0438295 0.0244138 0.00520580 0.190993
0.00849323 -0.0492413 0.0159085 0.196088 -0.0479540 0.0673088 -0.132961
0.0114834 -0.0414148 -0.00971450 -0.0242264 0.186732 -0.160444 0.0116101
0.00253052 0.00599139 -0.0153175 0.0741567 0.0938898 -0.0160072 0.0283886
-0.00636907 0.00202612 0.00303601 0.0144939 0.0831271 -0.0668251 0.113080
 0.00015: 883:     -1024.39;0.453665 -0.0585067 -0.0360498 0.0123685
0.0732151 0.0885324 0.0508982 0.0923064 0.230682 0.00673961 -0.0680962
-0.0233385 0.111270 -0.0730391 0.0959433 0.223503 -0.0594483 -0.107672
0.0320622 -0.0249977 0.000572442 0.120297 -0.0596571 -0.0848154 0.0760521
0.286619 0.262302 -0.00181074 0.116938 0.147450 0.000607578 0.00258809
0.00295064 0.00870774 0.0110953 0.0513588 0.751154 -0.124160 0.330323
0.212719 0.0478004 0.0297213 -0.102591 0.0370312 0.0263823 0.0156825
0.194049 0.0207475 -0.0401990 0.0116376 0.188745 -0.0486827 0.0560793
-0.145942 0.0143298 -0.0398109 0.00324054 -0.0290687 0.172062 -0.160556
0.00815574 -0.00979049 0.00919127 -0.0416405 0.0436646 0.0192964
-0.00983632 0.0248093 -0.0310215 0.000459667 0.00130757 -0.000901646
0.0342696 -0.0376915 0.0306594
 1.5e-05: 3284:     -1024.51;0.453385 -0.0584484 -0.0356090 0.0124758
0.0734563 0.0881546 0.0508248 0.0962059 0.230125 0.00646632 -0.0685329
-0.0233102 0.111124 -0.0737367 0.0872650 0.222137 -0.0600659 -0.108960
0.0332052 -0.0249734 -0.00297287 0.120747 -0.0596915 -0.0846255 0.0770361
0.288639 0.260991 -0.000252664 0.115696 0.144715  0.00000 0.000212990
0.000133828 1.60752e-05 -0.000197350  0.00000 0.750004 -0.124227 0.330932
0.212779 0.0478032 0.0298216 -0.103062 0.0372814 0.0263856 0.0143016
0.197110 0.0205353 -0.0399386 0.0118496 0.191628 -0.0486431 0.0544321
-0.150492 0.0128156 -0.0404642 0.00802782 -0.0314552 0.177600 -0.172395
0.00727224 -0.0124290 0.00934080 -0.0404360 0.0448702 0.00474710
-0.00245309 0.00790385 -0.0103348  0.00000 0.000371469 -0.000983666
 0.00000 0.000141958 0.000421746
 1.5e-06: 7178:     -1024.51;0.453352 -0.0584690 -0.0355774 0.0124997
0.0734840 0.0881869 0.0507670 0.0960555 0.230123 0.00645470 -0.0684869
-0.0230435 0.110983 -0.0737021 0.0876813 0.222121 -0.0599796 -0.108712
0.0331053 -0.0250164 -0.00249269 0.120771 -0.0589597 -0.0846281 0.0772928
0.289162 0.261192 -0.000190203 0.115552 0.144091 2.01629e-06 -4.08502e-05
-1.90946e-05  0.00000 -9.66359e-05 1.04936e-05 0.750011 -0.124226 0.330863
0.212665 0.0477618 0.0298406 -0.103103 0.0372181 0.0263686 0.0145848
0.196972 0.0205089 -0.0405674 0.0139592 0.190847 -0.0468184 0.0446604
-0.140577 0.0105124 -0.0395898 0.0187035 -0.0343555 0.181272 -0.181164
0.00664697 -0.0113702 0.00859624 -0.0368000 0.0407681  0.00000 1.76746e-05
-9.88034e-05 1.88689e-05  0.00000 5.17909e-05 -8.02859e-05 3.64008e-05
-6.60707e-05 5.93081e-06
 1.5e-07: 16007:     -1024.51;0.453353 -0.0584700 -0.0355713 0.0124974
0.0734791 0.0881842 0.0507617 0.0960368 0.230124 0.00645341 -0.0684879
-0.0230369 0.110985 -0.0737046 0.0876622 0.222121 -0.0599878 -0.108716
0.0331093 -0.0250229 -0.00255615 0.120773 -0.0589587 -0.0846260 0.0772924
0.289138 0.261189 -0.000183022 0.115548 0.144100  0.00000 -2.77640e-06
1.38614e-06 1.83196e-06 -2.21543e-06 1.76626e-06 0.750012 -0.124222
0.330866 0.212674 0.0477582 0.0298468 -0.103088 0.0371797 0.0263991
0.0144792 0.197126 0.0205267 -0.0405065 0.0136319 0.191163 -0.0471447
0.0462561 -0.142231 0.0107401 -0.0403131 0.0188609 -0.0349189 0.184476
-0.184251 0.000942279 -0.00163288 0.00122564 -0.00523554 0.00580441
1.43220e-05 -9.76379e-06 4.38830e-05 -3.89789e-05 1.56950e-05 -7.46413e-05
9.71269e-05 4.58216e-05 -4.96008e-05 2.15804e-06
At return
39183:    -1024.5110: 0.453351 -0.0584699 -0.0355713 0.0124976 0.0734794
0.0881849 0.0507626 0.0960419 0.230123 0.00645454 -0.0684866 -0.0230371
0.110983 -0.0737039 0.0876768 0.222122 -0.0599866 -0.108715 0.0331095
-0.0250227 -0.00255809 0.120773 -0.0589560 -0.0846266 0.0772956 0.289136
0.261192 -0.000179848 0.115546 0.144084 1.74822e-07 -3.48518e-07
-2.06009e-06 7.14056e-07 3.68010e-06 3.77125e-07 0.750012 -0.124223
0.330865 0.212669 0.0477582 0.0298451 -0.103087 0.0371793 0.0263998
0.0144727 0.197124 0.0205267 -0.0405008 0.0136068 0.191179 -0.0471682
0.0463709 -0.142355 0.0107635 -0.0403371 0.0187756 -0.0349080 0.184511
-0.184236 0.000261844 -0.000451348 0.000339652 -0.00145181 0.00160760
4.66708e-06 -2.02023e-06 6.29582e-06 -6.76577e-06  0.00000 4.54581e-06
-4.78681e-06 1.56292e-07 1.35805e-06 4.87490e-06

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