[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
wzhmelly at gmail.com
Thu Jan 8 01:13:24 CET 2015
*I believe that the order shouldn't matter, but I don't know why I got
different results after fitting the same model in a data frame that is
ordered differently. *
*Here are my R codes. Did I use the order() function wrongly such that I
got different results?*
*The lmer model fitted with the data frame before it is ordered (There were
no warning messages, and the model was able to converge.):*
*Model1<-lmer(Stimulus.RT~
1+A*B*C+(1+A|Subject)+(1+A*B*C|Item)+(1+A*B*C|Category),
data=edata,verbose=2,control=lmerControl(optCtrl=list(maxfun=500000)))*
*The verbose results are as follows:*
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
*The model summary is as follows:*
Linear mixed model fit by REML ['lmerMod']
Formula: Stimulus.RRT ~ 1 + A * B * C + (1 + A | Subject) + (1 + A * B
* C | Item) +
(1 + A * B * C | Category)
Data: edata
Control: lmerControl(optCtrl = list(maxfun = 5e+05))
REML criterion at convergence: -1024.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.1135 -0.6290 -0.0239 0.6658 3.4528
Random effects:
Groups Name Variance Std.Dev. Corr
Item (Intercept) 0.0079856 0.08936
A1 0.0021904 0.04680 -0.25
B1 0.0019678 0.04436 -0.16 0.07
C1 0.0008949 0.02991 0.08 -0.46 -0.42
A1:B1 0.0034753 0.05895 0.25 -0.14 -0.40 0.04
A1:C1 0.0011016 0.03319 0.52 0.51 0.13 -0.73 0.10
B1:C1 0.0010864 0.03296 0.30 -0.50 -0.21 0.65 0.68
-0.39
A1:B1:C1 0.0047121 0.06864 0.28 0.18 -0.04 0.57 0.25 -0.11
0.64
Subject (Intercept) 0.0218561 0.14784
A1 0.0048530 0.06966 -0.35
Category (Intercept) 0.0017573 0.04192
A1 0.0001050 0.01025 0.92
B1 0.0001028 0.01014 0.58 0.22
C1 0.0004833 0.02198 -0.92 -0.80 -0.71
A1:B1 0.0014875 0.03857 0.19 0.56 -0.64 -0.09
A1:C1 0.0001609 0.01268 0.41 0.09 0.70 -0.27 -0.69
B1:C1 0.0014145 0.03761 0.08 0.17 0.05 -0.39 0.34
-0.67
A1:B1:C1 0.0036161 0.06013 0.65 0.41 0.62 -0.44 -0.39 0.93
-0.65
Residual 0.0388540 0.19711
Number of obs: 4064, groups: Item, 68; Subject, 64; Category, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.897455 0.027600 -32.52
A1 0.002163 0.012815 0.17
B1 -0.085623 0.038088 -2.25
C1 0.004287 0.014429 0.30
A1:B1 -0.005849 0.027516 -0.21
A1:C1 0.112263 0.075240 1.49
B1:C1 0.014424 0.026637 0.54
A1:B1:C1 -0.275033 0.152135 -1.81
Correlation of Fixed Effects:
(Intr) A1 B1 C1 A1:B1 A1:C B1:C1
A1 -0.017
B1 0.030 0.012
C1 -0.349 -0.216 -0.064
A1:B1 0.093 0.091 -0.271 -0.028
A1:C1 0.028 0.014 0.006 -0.230 -0.027
B1:C1 0.044 0.000 -0.001 -0.114 0.138 -0.029
A1:B1:C1 0.071 0.025 0.010 -0.036 -0.033 0.010 -0.279
*However, if I reorder the dataset, the model failed to converge:*
*edata.reordered<-edata[order(edata$Subject,edata$Item),]*
*Model2<-lmer(Stimulus.RT~
1+1+A*B*C+(1+A|Subject)+(1+A*B*C|Item)+(1+A*B*C|Category),
data=edata.reordered,verbose=2,control=lmerControl(optCtrl=list(maxfun=500000)))*
*The model failed to converge, with the following warning messages given.
The verbose results are as follows:*
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: 1139: -1011.00;0.450616 -0.0596810 -0.0402272 0.0225926
0.0713467 0.103440 0.0602312 0.0721565 0.235672 0.00831575 -0.0664559
-0.0410181 0.131944 -0.0611257 0.0909471 0.234925 -0.0677961 -0.0928579
0.0491887 -0.0160378 -0.0609188 0.111951 -0.0797617 -0.0797398 0.105130
0.265564 0.270820 0.0170410 0.124769 0.199425 0.00278578 0.0268160
-0.00875111 0.0510349 0.110946 0.171222 0.758551 -0.121379 0.340244
0.289164 0.0340301 0.0299344 -0.127966 0.0945493 -0.0521814 -0.0136706
0.0808901 0.00103150 0.00478129 -0.0168369 -0.0419573 0.0154279 -0.0318584
-0.0284232 0.0113707 -0.00386646 -0.00455879 0.0266343 -0.0767034 0.0166990
0.0104083 -0.0298060 0.0336969 -0.0939880 -0.0530341 0.234749 -0.0630262
0.0236229 -0.0798100 0.0822270 -0.0883803 0.0469641 0.374075 0.0619410
0.530030
0.00020: 2754: -1024.04;0.453973 -0.0580443 -0.0341541 0.0128765
0.0743429 0.0876562 0.0513254 0.0961985 0.231256 0.00668522 -0.0689680
-0.0198556 0.111337 -0.0720035 0.0878909 0.222762 -0.0592635 -0.109626
0.0323470 -0.0244422 -0.00152948 0.121270 -0.0558188 -0.0840473 0.0772543
0.292009 0.262116 0.000440403 0.116106 0.141180 0.00136894 -0.00339338
-0.00197460 0.00348747 0.0105328 0.0115974 0.749943 -0.123029 0.330931
0.211777 0.0482195 0.0264188 -0.104392 0.0473197 0.0235507 0.0216781
0.184163 0.00000 0.0428526 -0.0310683 -0.175357 0.0368789 0.00217481
0.0939259 0.00000 0.0261004 -0.0313647 0.0268754 -0.122563 0.125346
0.000144427 -0.00203618 -0.000134387 -0.00154909 -0.000171925 0.0680554
-0.0371162 0.127778 -0.153718 0.0109013 -0.0471354 0.0631039 0.0408311
-0.0496230 0.0145855
2.0e-05: 5427: -1024.35;0.453500 -0.0588233 -0.0356824 0.0125644
0.0734100 0.0880990 0.0508688 0.0959347 0.230738 0.00618009 -0.0685843
-0.0222441 0.111017 -0.0734275 0.0869347 0.222257 -0.0600188 -0.109157
0.0332115 -0.0247808 -0.00219908 0.120720 -0.0585300 -0.0847411 0.0775846
0.290674 0.261498 -0.000106363 0.115210 0.143180 9.04281e-05 0.000517393
-0.000649503 0.000192640 0.000530472 0.00197453 0.749767 -0.124101 0.330961
0.212375 0.0487225 0.0278073 -0.102167 0.0463200 0.0243823 0.0162048
0.190955 0.00000 0.0439928 -0.0318335 -0.176979 0.0374637 0.00573705
0.0905893 0.00202642 0.0324678 -0.0734993 0.0480481 -0.190881 0.220526
0.000424046 -0.000319556 0.000485731 -0.00232483 0.00320987 0.00183945
-0.000969938 0.00300023 -0.00391019 0.00000 0.00377342 -0.00500665
0.000579337 -0.00215229 0.00124792
2.0e-06: 6708: -1024.35;0.453448 -0.0588097 -0.0355748 0.0125304
0.0734757 0.0881016 0.0508944 0.0958161 0.230754 0.00612699 -0.0684914
-0.0222696 0.110948 -0.0734877 0.0875664 0.222282 -0.0600764 -0.109357
0.0332592 -0.0250735 -0.00216874 0.120772 -0.0592389 -0.0846883 0.0772328
0.289264 0.261555 -0.000223966 0.115482 0.143552 1.82883e-06 8.95088e-05
0.000234107 4.38268e-06 -0.000100557 0.000268633 0.750021 -0.124161
0.330928 0.212373 0.0486257 0.0278246 -0.102272 0.0464224 0.0241039
0.0167711 0.190122 0.00000 0.0439680 -0.0319783 -0.176695 0.0374161
0.00595413 0.0898001 0.00213386 0.0324226 -0.0734879 0.0479364 -0.190868
0.220519 0.000515865 -0.000947565 0.000694358 -0.00286088 0.00324899
0.00000 9.25677e-05 -0.000493308 0.000515791 0.00000 3.22998e-05
1.34428e-06 0.000265348 -0.000330170 0.00000
2.0e-07: 8013: -1024.35;0.453452 -0.0588158 -0.0355775 0.0125249
0.0734807 0.0881052 0.0508818 0.0958264 0.230755 0.00613428 -0.0684918
-0.0222859 0.110940 -0.0735018 0.0875183 0.222287 -0.0600648 -0.109338
0.0332510 -0.0250861 -0.00212940 0.120774 -0.0592879 -0.0846939 0.0772195
0.289318 0.261524 -0.000225946 0.115515 0.143581 2.96479e-06 4.74658e-07
-4.67167e-06 6.32876e-07 -1.68484e-05 1.97228e-05 0.750021 -0.124164
0.330935 0.212387 0.0486304 0.0278256 -0.102275 0.0464074 0.0241141
0.0168024 0.190120 0.00000 0.0439746 -0.0319812 -0.176684 0.0373949
0.00599897 0.0897709 0.00214402 0.0324037 -0.0735321 0.0479302 -0.190866
0.220458 0.000500648 -0.000957101 0.000681481 -0.00281640 0.00318786
3.86116e-05 -1.87433e-05 6.35711e-05 -8.10232e-05 2.14600e-06 -9.10076e-06
1.52841e-05 1.48079e-05 -2.33962e-05 4.43378e-06
At return
9308: -1024.3493: 0.453452 -0.0588161 -0.0355779 0.0125253 0.0734813
0.0881053 0.0508814 0.0958291 0.230755 0.00613352 -0.0684914 -0.0222835
0.110941 -0.0734998 0.0875198 0.222286 -0.0600641 -0.109337 0.0332509
-0.0250859 -0.00212406 0.120774 -0.0592844 -0.0846942 0.0772196 0.289324
0.261524 -0.000224812 0.115519 0.143582 0.00000 6.69837e-07 2.90331e-06
5.81219e-07 -6.03136e-07 3.60563e-06 0.750021 -0.124164 0.330934 0.212389
0.0486307 0.0278253 -0.102276 0.0464083 0.0241164 0.0168007 0.190123
0.00000 0.0439749 -0.0319808 -0.176684 0.0373947 0.00599638 0.0897735
0.00214274 0.0324038 -0.0735262 0.0479285 -0.190864 0.220444 0.000501759
-0.000958368 0.000680990 -0.00282345 0.00319184 6.93762e-06 -2.82172e-06
7.79924e-06 -1.11704e-05 1.20920e-08 2.23002e-06 -2.13124e-06 2.39686e-06
-2.38538e-06 8.82173e-08
*Warning messages:*
*1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :*
* Model failed to converge with max|grad| = 0.761054 (tol = 0.002,
component 54)*
*2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :*
* Model failed to converge: degenerate Hessian with 1 negative
eigenvalues*
*The model summary is as follows:*
Linear mixed model fit by REML ['lmerMod']
Formula: Stimulus.RRT ~ 1 + RanOrCat * Direction * Running + (1 + RanOrCat
| Subject) + (1 + RanOrCat * Direction * Running | Item) +
(1 + RanOrCat * Direction * Running | Category)
Data: Edata.Correct.NoBadItems.reordered
Control: lmerControl(optCtrl = list(maxfun = 5e+05))
REML criterion at convergence: -1024.3
Scaled residuals:
Min 1Q Median 3Q Max
-4.1137 -0.6293 -0.0207 0.6657 3.4540
Random effects:
Groups Name Variance Std.Dev. Corr
Item (Intercept) 7.989e-03 0.089382
RanOrCat1 2.203e-03 0.046939 -0.25
Direction1 1.970e-03 0.044390 -0.16 0.07
Running1 8.953e-04 0.029921 0.08 -0.46
-0.42
RanOrCat1:Direction1 3.488e-03 0.059055 0.25 -0.13
-0.40 0.04
RanOrCat1:Running1 1.101e-03 0.033188 0.52 0.51
0.13 -0.73 0.11
Direction1:Running1 1.085e-03 0.032941 0.30 -0.50
-0.21 0.65 0.67 -0.39
RanOrCat1:Direction1:Running1 4.708e-03 0.068615 0.28 0.18
-0.04 0.57 0.25 -0.11 0.64
Subject (Intercept) 2.186e-02 0.147840
RanOrCat1 4.854e-03 0.069672 -0.35
Category (Intercept) 1.753e-03 0.041865
RanOrCat1 9.189e-05 0.009586 1.00
Direction1 1.054e-04 0.010266 0.53 0.53
Running1 4.870e-04 0.022067 -0.91 -0.91
-0.72
RanOrCat1:Direction1 1.507e-03 0.038816 0.24 0.24
-0.65 -0.07
RanOrCat1:Running1 1.662e-04 0.012892 0.37 0.37
0.71 -0.29 -0.70
Direction1:Running1 1.428e-03 0.037790 0.09 0.09
0.03 -0.38 0.36 -0.68
RanOrCat1:Direction1:Running1 3.606e-03 0.060051 0.62 0.62
0.61 -0.44 -0.39 0.93 -0.66
Residual 3.885e-02 0.197114
Number of obs: 4064, groups: Item, 68; Subject, 64; Category, 6
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.897433 0.027587 -32.53
RanOrCat1 0.002115 0.012737 0.17
Direction1 -0.085626 0.038095 -2.25
Running1 0.004295 0.014451 0.30
RanOrCat1:Direction1 -0.005863 0.027579 -0.21
RanOrCat1:Running1 0.112270 0.075247 1.49
Direction1:Running1 0.014433 0.026681 0.54
RanOrCat1:Direction1:Running1 -0.275019 0.152130 -1.81
Correlation of Fixed Effects:
(Intr) RnOrC1 Drctn1 Rnnng1 RnOC1:D1 ROC1:R Dr1:R1
RanOrCat1 -0.014
Direction1 0.028 0.023
Running1 -0.345 -0.229 -0.065
RnOrCt1:Dr1 0.109 0.028 -0.272 -0.020
RnOrCt1:Rn1 0.027 0.020 0.007 -0.231 -0.028
Drctn1:Rnn1 0.049 -0.016 -0.002 -0.111 0.145 -0.030
RnOC1:D1:R1 0.068 0.034 0.010 -0.036 -0.032 0.010 -0.279
*If we compare the two model summaries, we can see that the model summaries
are really similar, with very little differences in values. Nevertheless,
because Model2 failed to converge, I don't know if the model summary is
still reliable and ok to report.*
*Thank you for your time and I truly welcome and appreciate your comments.*
Best,
Zhaohong
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list