[R-sig-ME] likelihood-ratio tests in conflict with coefficiants in maximal random effect model
Emilia Ellsiepen
emilia.ellsiepen at gmail.com
Tue Mar 4 11:10:27 CET 2014
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?
*** 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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