[R-lang] Re: False convergence in mixed logit model
Laura Suttle
lsuttle@princeton.edu
Thu Nov 29 09:46:02 PST 2012
Hello again,
The deviance was not going down anymore when the model ended (and had been
stable for around 10 steps). Changing the start value doesn't do anything
as well.
Another potential issue: are subject random effects relevant when you are
only looking at between subjects fixed effects? I'm wondering if that's
part of the issue.
Here's the code with output for the simplest model I have with subjects
random effect (adding them in in any form causes this issue). ditrans is a
binary DV that categorizes speaker usages of a novel verb into double
object datives (1) or other sentence types (0). Condition is a categorical
variable that has three levels with a control condition as the baseline
(I've dummy coded these and there are no differences between running it
this way and with the dummy code, so I'm providing this one for the same of
simplicity).
> ditmodel3 <-lmer(ditrans~condition +
(1|subj),data=adultdata,family="binomial",verbose=T)
0: 814.04384: 0.391997 -0.472359 -0.840468 0.222898
1: 474.30352: 1.39176 -0.489556 -0.853831 0.221628
2: 446.52182: 1.60483 -1.29691 -1.39869 0.144777
3: 386.13626: 2.59735 -1.19061 -1.38968 0.204059
4: 383.11685: 2.69684 -1.19357 -1.39816 0.208801
5: 377.92258: 2.89551 -1.20228 -1.41725 0.218187
6: 376.11423: 2.97465 -1.20814 -1.42671 0.221868
7: 372.88540: 3.13258 -1.22176 -1.44712 0.229201
8: 371.72249: 3.19542 -1.22881 -1.45656 0.232118
9: 369.58988: 3.32079 -1.24417 -1.47647 0.237957
10: 369.58183: 3.32129 -1.24424 -1.47656 0.237981
11: 369.56574: 3.32229 -1.24439 -1.47674 0.238028
12: 369.55931: 3.32269 -1.24444 -1.47681 0.238046
13: 369.54645: 3.32349 -1.24456 -1.47695 0.238084
14: 369.54594: 3.32352 -1.24456 -1.47696 0.238085
15: 369.54183: 3.32377 -1.24460 -1.47700 0.238097
16: 369.54183: 3.32377 -1.24460 -1.47700 0.238097
17: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
18: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
19: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
20: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
21: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
22: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
23: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
24: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
25: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
26: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
27: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
28: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
29: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
30: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
31: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
32: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
33: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
34: 369.54182: 3.32377 -1.24460 -1.47700 0.238097
35: 369.54175: 3.32378 -1.24460 -1.47699 0.238081
36: 369.54062: 3.32390 -1.24460 -1.47681 0.237810
37: 369.52871: 3.32514 -1.24460 -1.47496 0.234970
38: 369.41249: 3.33754 -1.24460 -1.45648 0.206662
39: 368.48882: 3.45721 -1.24460 -1.27810 -0.0665675
40: 367.50882: 3.71632 -1.24460 -0.891867 -0.658143
41: 367.42619: 3.80235 -1.24460 -0.763626 -0.854531
42: 367.42069: 3.82935 -1.24460 -0.723378 -0.916133
43: 367.42061: 3.83197 -1.24460 -0.719473 -0.922078
44: 367.42056: 3.83302 -1.24460 -0.717885 -0.924450
45: 367.42030: 3.83643 -1.24460 -0.712740 -0.931948
46: 367.41976: 3.84096 -1.24460 -0.705833 -0.941589
47: 367.41821: 3.84903 -1.24460 -0.693317 -0.957909
48: 367.41433: 3.86190 -1.24460 -0.672862 -0.981743
49: 367.40420: 3.88359 -1.24460 -0.637123 -1.01631
50: 367.37911: 3.91871 -1.24460 -0.576118 -1.05854
51: 367.31987: 3.97369 -1.24460 -0.473099 -1.09171
52: 367.19878: 4.04676 -1.24460 -0.319173 -1.06129
53: 367.00997: 4.11079 -1.24460 -0.148079 -0.875985
54: 366.83505: 4.11105 -1.24460 -0.0694293 -0.533561
55: 366.76596: 4.05082 -1.24460 -0.131236 -0.273508
56: 366.75441: 4.00835 -1.24460 -0.205517 -0.224632
57: 366.75330: 3.99692 -1.24460 -0.231735 -0.238739
58: 366.75328: 3.99700 -1.24460 -0.232466 -0.242660
59: 366.75327: 3.99706 -1.24460 -0.232631 -0.243916
60: 366.75321: 3.99733 -1.24460 -0.233186 -0.248657
61: 366.75310: 3.99766 -1.24460 -0.233948 -0.254660
62: 366.75277: 3.99818 -1.24460 -0.235467 -0.265375
63: 366.75194: 3.99913 -1.24460 -0.237734 -0.282069
64: 366.74974: 4.00078 -1.24460 -0.241634 -0.309537
65: 366.74401: 4.00376 -1.24460 -0.248277 -0.353787
66: 366.72917: 4.00940 -1.24460 -0.260050 -0.425563
67: 366.69014: 4.02037 -1.24460 -0.282150 -0.541757
68: 366.58903: 4.04387 -1.24460 -0.323645 -0.729228
69: 366.33054: 4.09566 -1.24460 -0.407098 -1.03058
70: 365.69165: 4.21044 -1.24460 -0.582890 -1.50206
71: 364.22961: 4.45811 -1.24460 -0.955294 -2.19620
72: 363.88256: 4.50536 -1.24460 -1.03067 -2.28460
73: 363.78744: 4.51528 -1.24460 -1.04951 -2.29787
74: 363.76346: 4.51729 -1.24460 -1.05384 -2.29942
75: 363.71559: 4.52093 -1.24460 -1.06284 -2.30192
76: 363.50871: 4.53406 -1.24460 -1.10070 -2.30424
77: 362.67929: 4.58017 -1.24460 -1.25368 -2.28868
78: 362.34767: 4.59767 -1.24460 -1.31389 -2.27484
79: 362.28332: 4.60067 -1.24460 -1.32582 -2.27114
80: 362.15615: 4.60614 -1.24460 -1.34933 -2.26235
81: 362.10583: 4.60818 -1.24460 -1.35861 -2.25843
82: 361.98440: 4.61586 -1.24460 -1.37557 -2.24974
83: 361.94584: 4.61723 -1.24460 -1.38296 -2.24642
84: 361.93799: 4.61752 -1.24460 -1.38442 -2.24571
85: 361.87531: 4.61979 -1.24460 -1.39595 -2.23983
86: 361.87506: 4.61980 -1.24460 -1.39600 -2.23980
87: 361.87456: 4.61982 -1.24460 -1.39609 -2.23975
88: 361.87424: 4.61985 -1.24460 -1.39611 -2.23974
89: 361.87421: 4.61986 -1.24460 -1.39612 -2.23973
90: 361.87420: 4.61986 -1.24460 -1.39612 -2.23973
91: 361.87418: 4.61986 -1.24460 -1.39612 -2.23973
92: 361.87417: 4.61986 -1.24460 -1.39612 -2.23973
93: 361.87417: 4.61986 -1.24460 -1.39612 -2.23973
94: 361.87417: 4.61986 -1.24460 -1.39612 -2.23973
95: 361.87417: 4.61986 -1.24460 -1.39612 -2.23973
96: 361.87417: 4.61986 -1.24460 -1.39612 -2.23973
97: 361.61308: 4.63208 -1.24460 -1.31790 -2.07725
98: 360.78393: 4.70563 -1.24460 -1.01240 -1.42611
99: 360.30955: 4.80814 -1.24460 -0.794807 -0.933962
100: 359.83679: 4.94614 -1.24460 -0.694790 -0.658108
101: 358.66805: 5.34357 -1.24460 -0.659430 -0.362284
102: 357.21569: 5.94720 -1.24460 -0.865900 -0.407961
103: 355.87363: 6.65147 -1.24460 -1.34871 -0.894451
104: 354.79576: 7.33163 -1.24460 -2.04508 -1.75302
105: 354.79427: 7.33250 -1.24460 -2.04623 -1.75453
106: 354.78176: 7.33863 -1.24460 -2.05548 -1.76699
107: 354.78124: 7.33885 -1.24460 -2.05585 -1.76750
108: 354.78114: 7.33890 -1.24460 -2.05592 -1.76760
109: 354.78092: 7.33897 -1.24460 -2.05608 -1.76780
110: 354.78083: 7.33900 -1.24460 -2.05614 -1.76789
111: 354.78081: 7.33901 -1.24460 -2.05615 -1.76790
112: 354.78077: 7.33902 -1.24460 -2.05618 -1.76794
113: 354.78075: 7.33903 -1.24460 -2.05619 -1.76795
114: 354.78075: 7.33903 -1.24460 -2.05619 -1.76795
115: 354.78074: 7.33903 -1.24460 -2.05619 -1.76796
116: 354.78074: 7.33903 -1.24460 -2.05619 -1.76796
117: 354.78074: 7.33903 -1.24460 -2.05619 -1.76796
118: 354.78074: 7.33903 -1.24460 -2.05620 -1.76796
119: 354.78074: 7.33903 -1.24460 -2.05620 -1.76796
120: 354.78074: 7.33903 -1.24460 -2.05620 -1.76796
121: 354.78074: 7.33903 -1.24460 -2.05620 -1.76796
122: 354.78074: 7.33903 -1.24460 -2.05620 -1.76796
Warning message:
In mer_finalize(ans) : false convergence (8)
>
> summary(ditmodel3)
Generalized linear mixed model fit by the Laplace approximation
Formula: ditrans ~ condition + (1 | subj)
Data: adultdata
AIC BIC logLik deviance
362.8 381.7 -177.4 354.8
Random effects:
Groups Name Variance Std.Dev.
subj (Intercept) 53.861 7.339
Number of obs: 833, groups: subj, 48
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.245 1.951 -0.638 0.523
conditionDative -2.056 2.832 -0.726 0.468
conditionTrans -1.768 2.791 -0.633 0.526
Correlation of Fixed Effects:
(Intr) cndtnD
conditinDtv -0.689
conditnTrns -0.699 0.481
Thanks and sorry for the length,
Laura
On Thu, Nov 29, 2012 at 11:08 AM, Levy, Roger <rlevy@ucsd.edu> wrote:
> Yes -- the first column of the verbose output is the step number and the
> second column is the deviance. If the deviance was still going down and
> the model stopped, you probably need more iterations.
>
> It could be useful to change the starting value of the model parameters
> with the "start" argument of lmer and see if you wind up converging to
> the same parameter estimates regardless of starting value.
>
> More information about the dataset, and example code output, is, of
> course, always helpful.
>
> Best
>
> Roger
>
>
>
> On Nov 29, 2012, at 7:03 AM PST, Laura Suttle wrote:
>
> Hi Roger,
>
> Thanks for the other list suggestion, I'll cross post to there.
>
> Every variable in my data set is categorical, so I can't do that fix.
> I've tried playing around with the maxIter parameter before, but I'm not
> sure I was doing it right. Do you have any suggestions for where I can read
> more about how to interpret the verbose output? I found some things but
> they weren't very helpful.
>
> Thanks,
> Laura
>
>
> On Thu, Nov 29, 2012 at 1:34 AM, Levy, Roger <rlevy@ucsd.edu> wrote:
>
>> Hi Laura,
>>
>> This is a question that might be better answered on R-sig-ME, but
>> briefly: I would be cautious with a model that reports false convergence;
>> in my experience with this warning (and I am by no means expert on it), it
>> can indicate that the optimization routine that determines the best-fit
>> model parameters got stuck at a parameter estimate that is not near a true
>> optimum, perhaps due to numerical issues. You might try standardizing any
>> continuous predictor variables you and rerunning the lmer() call. It would
>> be helpful to set the msVerbose control parameter to TRUE to see what the
>> optimizer is doing. Also, upping the maxIter and/or maxFN control
>> parameters *might* be helpful.
>>
>> I do not think that this warning message alone would be justification
>> to omit a random effect.
>>
>> Best & hope that this helps,
>>
>> Roger
>>
>> On Nov 28, 2012, at 8:58 PM PST, Laura Suttle wrote:
>>
>> Hello all,
>>
>> I hope this question hasn't been asked before, but the internet isn't
>> being of much help to me.
>>
>> I am trying to run a mixed logit regression predicting whether
>> participants use a novel verb in a particular construction or not depending
>> on how they were exposed to that novel verb. I dummy coded the three
>> conditions of the experiment into two dummy variables and have added two
>> random effects, one for the motion used for the verb, the other for the
>> verb itself (since these were all counterbalanced).
>>
>> I can get this model to run fine, the problem is when I try to add any
>> kind of random effect for the subjects themselves. I then get this error
>> message:
>>
>> Warning message:
>> In mer_finalize(ans) : false convergence (8)
>>
>> And all of the effects I had of the exposure type go away.
>>
>> I've been trying to look up what this means and how to deal with it,
>> but there are no clear solutions or explanations that I can find, but
>> plenty of warning of how I should be skeptical of any output from a model
>> with this warning. One suggestion I did find was that the subjects variable
>> may be overfitting my data and there might be something to this: when
>> participants are exposed to the verb in a certain way, they tend to only
>> use the construction I'm looking for, with no variance in their responses.
>> That said, I'm not sure that's right and I'd love a second opinion on
>> either how I can fix this or whether I can use this as justification to not
>> include the subjects random effect.
>>
>> Thanks in advance for any help you can give,
>> Laura Suttle
>>
>>
>>
>
>
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