[R-sig-ME] likelihood-ratio tests in conflict with coefficiants in maximal random effect model
Levy, Roger
rlevy at ucsd.edu
Tue Mar 4 18:39:01 CET 2014
On Mar 4, 2014, at 3:10 AM, Emilia Ellsiepen <emilia.ellsiepen at gmail.com> wrote:
> Thank you for your support, Roger - actually the problem vanished when
> I used sum-coding (after installing the packages and reloading the
> data, I accidentally skipped this step yesterday). So, in fact with
> the old lme4.0 version, the model did converge. I pasted in all model
> summaries and LRTs for dummy and sum coding below.
> Would you recommend to always use the old version then?
Thanks so much, Emilia, for following up. This is very interesting. Something doesn’t quite add up, though, in the model summaries and LRTs that you pasted below, because the log-likelihoods in the model summaries are not the same as the log-likelihoods reported in the results of anova(). I notice that you’re not using REML=F as an argument in your call to lmer(). Could you perhaps rerun all four model-fitting functions and the two anova() calls with REML=F, and show us the complete results?
Best & thanks again,
Roger
> *** with dummy coding:
>
>> m1 = lmer(score ~ Order*Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,rawdata)
> Warning message:
> In mer_finalize(ans) : singular convergence (7)
>> summary(m1)
> Linear mixed model fit by REML
> Formula: score ~ Order * Voice + (Order * Voice | subject) + (Order *
> Voice | sentence)
> Data: rawdata
> AIC BIC logLik deviance REMLdev
> 1626 1740 -787.9 1562 1576
> Random effects:
> Groups Name Variance Std.Dev. Corr
> subject (Intercept) 0.097201 0.31177
> OrderDat second 0.055057 0.23464 -0.690
> VoicePassive 0.161333 0.40166 -0.952 0.878
> OrderDat second:VoicePassive 0.182879 0.42764 0.732 -0.986 -0.900
> sentence (Intercept) 0.015671 0.12518
> OrderDat second 0.068464 0.26166 -0.663
> VoicePassive 0.025271 0.15897 -0.237 -0.031
> OrderDat second:VoicePassive 0.053708 0.23175 0.031 -0.589 -0.425
> Residual 0.465541 0.68231
> Number of obs: 720, groups: subject, 36; sentence, 20
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 0.16341 0.07791 2.097
> OrderDat second -0.01835 0.10062 -0.182
> VoicePassive -0.05604 0.10449 -0.536
> OrderDat second:VoicePassive 0.54250 0.13458 4.031
>
> Correlation of Fixed Effects:
> (Intr) OrdrDs VcPssv
> OrderDtscnd -0.647
> VoicePassiv -0.754 0.459
> OrdrDscn:VP 0.509 -0.717 -0.729
>
>> mi = lmer(score ~ Order+Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,rawdata)
>> summary(mi)
> Linear mixed model fit by REML
> Formula: score ~ Order + Voice + (Order * Voice | subject) + (Order *
> Voice | sentence)
> Data: rawdata
> AIC BIC logLik deviance REMLdev
> 1634 1744 -793.2 1574 1586
> Random effects:
> Groups Name Variance Std.Dev. Corr
> subject (Intercept) 0.115077 0.33923
> OrderDat second 0.094406 0.30726 -0.738
> VoicePassive 0.211833 0.46025 -0.951 0.893
> OrderDat second:VoicePassive 0.330228 0.57465 0.761 -0.999 -0.902
> sentence (Intercept) 0.019567 0.13988
> OrderDat second 0.111327 0.33366 -0.814
> VoicePassive 0.048545 0.22033 -0.451 0.420
> OrderDat second:VoicePassive 0.184456 0.42948 0.478 -0.771 -0.793
> Residual 0.463180 0.68057
> Number of obs: 720, groups: subject, 36; sentence, 20
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 0.009676 0.066665 0.145
> OrderDat second 0.276029 0.069910 3.948
> VoicePassive 0.237470 0.071133 3.338
>
> Correlation of Fixed Effects:
> (Intr) OrdrDs
> OrderDtscnd -0.472
> VoicePassiv -0.631 -0.160
>
>> anova(m1,mi)
> Data: rawdata
> Models:
> mi: score ~ Order + Voice + (Order * Voice | subject) + (Order *
> mi: Voice | sentence)
> m1: score ~ Order * Voice + (Order * Voice | subject) + (Order *
> m1: Voice | sentence)
> Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
> mi 24 1622.5 1732.4 -787.24
> m1 25 1611.7 1726.2 -780.86 12.772 1 0.0003518 ***
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> *** with sum-coding
>
>> contrasts(rawdata$Order) = contr.sum(2)
>> contrasts(rawdata$Voice) = contr.sum(2)
>> m1 = lmer(score ~ Order*Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,rawdata)
>> summary(m1)
> Linear mixed model fit by REML
> Formula: score ~ Order * Voice + (Order * Voice | subject) + (Order *
> Voice | sentence)
> Data: rawdata
> AIC BIC logLik deviance REMLdev
> 1631 1746 -790.7 1562 1581
> Random effects:
> Groups Name Variance Std.Dev. Corr
> subject (Intercept) 1.9949e-02 0.1412424
> Order1 1.4195e-05 0.0037676 -1.000
> Voice1 1.3413e-02 0.1158136 0.833 -0.833
> Order1:Voice1 1.1566e-02 0.1075434 0.286 -0.286 0.615
> sentence (Intercept) 2.9850e-03 0.0546352
> Order1 1.1405e-02 0.1067934 0.257
> Voice1 5.8322e-03 0.0763687 0.297 -0.438
> Order1:Voice1 3.4893e-03 0.0590699 -0.884 0.182 -0.328
> Residual 4.6543e-01 0.6822234
> Number of obs: 720, groups: subject, 36; sentence, 20
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 0.26184 0.03674 7.127
> Order1 -0.12645 0.03489 -3.625
> Voice1 -0.10761 0.03620 -2.972
> Order1:Voice1 0.13562 0.03380 4.013
>
> Correlation of Fixed Effects:
> (Intr) Order1 Voice1
> Order1 0.047
> Voice1 0.331 -0.150
> Order1:Voc1 -0.018 0.046 0.113
>> mi = lmer(score ~ Order+Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,rawdata)
>> summary(mi)
> Linear mixed model fit by REML
> Formula: score ~ Order + Voice + (Order * Voice | subject) + (Order *
> Voice | sentence)
> Data: rawdata
> AIC BIC logLik deviance REMLdev
> 1637 1747 -794.6 1575 1589
> Random effects:
> Groups Name Variance Std.Dev. Corr
> subject (Intercept) 2.0497e-02 0.1431662
> Order1 4.1101e-06 0.0020273 -1.000
> Voice1 1.3959e-02 0.1181500 0.808 -0.808
> Order1:Voice1 2.0579e-02 0.1434552 0.290 -0.290 0.540
> sentence (Intercept) 2.6765e-03 0.0517352
> Order1 1.1696e-02 0.1081500 0.320
> Voice1 5.0552e-03 0.0710997 0.275 -0.531
> Order1:Voice1 1.1587e-02 0.1076410 -0.805 0.200 -0.293
> Residual 4.6331e-01 0.6806653
> Number of obs: 720, groups: subject, 36; sentence, 20
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 0.26626 0.03667 7.261
> Order1 -0.13508 0.03495 -3.865
> Voice1 -0.11835 0.03568 -3.317
>
> Correlation of Fixed Effects:
> (Intr) Order1
> Order1 0.066
> Voice1 0.332 -0.175
>> anova(m1,mi)
> Data: rawdata
> Models:
> mi: score ~ Order + Voice + (Order * Voice | subject) + (Order *
> mi: Voice | sentence)
> m1: score ~ Order * Voice + (Order * Voice | subject) + (Order *
> m1: Voice | sentence)
> Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
> mi 24 1622.6 1732.5 -787.29
> m1 25 1611.8 1726.3 -780.90 12.78 1 0.0003504 ***
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
>
> 2014-03-03 18:22 GMT+01:00 Levy, Roger <rlevy at ucsd.edu>:
>> (cc-ing the list, which I forgot to do on the last response...)
>>
>>
>> On Mar 3, 2014, at 8:41 AM, Emilia Ellsiepen <emilia.ellsiepen at gmail.com> wrote:
>>
>>>
>>>
>>> 2014-03-03 16:23 GMT+01:00 Levy, Roger <rlevy at ucsd.edu>:
>>>>
>>>>> On Mar 3, 2014, at 3:09, "Emilia Ellsiepen" <emilia.ellsiepen at gmail.com> wrote:
>>>>>
>>>>> Thank you for your feedback and this interesting discussion.
>>>>>
>>>>>
>>>>>>> First off, it is not clear that Emilia's specific problem is being caused
>>>>>>> by over-parameterization. Emilia, could you perhaps give more information
>>>>>>> about the nature of the dataset that you're analyzing? Is it a 2x2
>>>>>>> within-subjects, within-sentence balanced design without a great deal of
>>>>>>> missing data? In my experience with the last few pre-1.0 versions, lme4 is
>>>>>>> generally very good at converging to an optimum for these kinds of datasets
>>>>>>> with the number of observations and groups your fitted model reports. Have
>>>>>>> you tried fitting the model with the nlminb optimizer, either by including
>>>>>>>
>>>>>>> optimizer="optimx",optCtrl=list(method="nlminb")
>>>>>>>
>>>>>>> in the list of arguments to lmerControl, or by using the last pre-1.0
>>>>>>> version of lme4 (available as lme4.0 on R-Forge)? Do you still get similar
>>>>>>> problems with the nlminb optimizer? (You should definitely not get the
>>>>>>> result that the simpler model has a higher log-likelihood.)
>>>>>
>>>>> The design was a balanced 2x2 with-in subjects and with-in sentences
>>>>> design without any missing data from a magnitude estimation
>>>>> experiment.
>>>>> When I use the nlminb optimizer (by installing the lme4.0 version), I
>>>>> do get the interaction using the likelihood-ratio test, but I also get
>>>>> the following warning message:
>>>>>
>>>>> Warning message:
>>>>> In mer_finalize(ans) : singular convergence (7)
>>>>
>>>> Thanks for this follow-up, Emilia! Question: do you get this warning when you fit the more complex model (with the fixed-effects interaction), or the null-hypothesis model (without the fixed-effects interaction)?
>>
>>> That was for the more complex model with interaction. The one without
>>> interaction converged without problems.
>>
>> Thanks Emilia. Could you please give us a bit more information -- show us the fitted models (both the null and alternative-hypothesis models), and also the results of the call to anova()?
>>
>> Best
>>
>> Roger
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