[R-sig-ME] likelihood-ratio tests in conflict with coefficiants in maximal random effect model
Emilia Ellsiepen
emilia.ellsiepen at gmail.com
Wed Mar 5 11:15:35 CET 2014
2014-03-04 18:39 GMT+01:00 Levy, Roger <rlevy at ucsd.edu>:
>
>
>
> 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
>
Rerunning with REML=F led to a singular convergence in the
null-hypothesis model with dummy coding. The sum coded variant still
converges without warnings. While the two LRTs give in general the
same result for both coding schemes, as I would have expected, there
are minimal differences in the logLik, AIC and BIC values.
Here are the results in detail:
**** dummy coding:
> m1 = lmer(score ~ Order*Voice + (Order*Voice|subject)+(Order*Voice|sentence), REML=F ,rawdata)
> summary(m1)
Linear mixed model fit by maximum likelihood
Formula: score ~ Order * Voice + (Order * Voice | subject) + (Order *
Voice | sentence)
Data: rawdata
AIC BIC logLik deviance REMLdev
1612 1726 -780.8 1562 1576
Random effects:
Groups Name Variance Std.Dev. Corr
subject (Intercept) 0.092788 0.30461
OrderDat second 0.051950 0.22793 -0.689
VoicePassive 0.152335 0.39030 -0.954 0.875
OrderDat second:VoicePassive 0.171303 0.41389 0.736 -0.991 -0.903
sentence (Intercept) 0.013700 0.11705
OrderDat second 0.060511 0.24599 -0.652
VoicePassive 0.021227 0.14570 -0.215 -0.067
OrderDat second:VoicePassive 0.045796 0.21400 0.016 -0.587 -0.404
Residual 0.466165 0.68276
Number of obs: 720, groups: subject, 36; sentence, 20
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.16341 0.07650 2.136
OrderDat second -0.01835 0.09823 -0.187
VoicePassive -0.05604 0.10234 -0.548
OrderDat second:VoicePassive 0.54250 0.13194 4.112
Correlation of Fixed Effects:
(Intr) OrdrDs VcPssv
OrderDtscnd -0.646
VoicePassiv -0.757 0.461
OrdrDscn:VP 0.514 -0.719 -0.730
> mi = lmer(score ~ Order+Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,REML=F,rawdata)
Warning message:
In mer_finalize(ans) : singular convergence (7)
> summary(mi)
Linear mixed model fit by maximum likelihood
Formula: score ~ Order + Voice + (Order * Voice | subject) + (Order *
Voice | sentence)
Data: rawdata
AIC BIC logLik deviance REMLdev
1622 1732 -787.2 1574 1586
Random effects:
Groups Name Variance Std.Dev. Corr
subject (Intercept) 0.112099 0.33481
OrderDat second 0.095834 0.30957 -0.743
VoicePassive 0.206993 0.45497 -0.953 0.895
OrderDat second:VoicePassive 0.333823 0.57777 0.765 -0.999 -0.906
sentence (Intercept) 0.017953 0.13399
OrderDat second 0.105527 0.32485 -0.832
VoicePassive 0.046129 0.21478 -0.473 0.454
OrderDat second:VoicePassive 0.183020 0.42781 0.526 -0.786 -0.819
Residual 0.463528 0.68083
Number of obs: 720, groups: subject, 36; sentence, 20
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.008662 0.065187 0.133
OrderDat second 0.275830 0.068133 4.048
VoicePassive 0.237267 0.069355 3.421
Correlation of Fixed Effects:
(Intr) OrdrDs
OrderDtscnd -0.468
VoicePassiv -0.632 -0.161
> 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 1622.4 1732.3 -787.21
m1 25 1611.6 1726.1 -780.82 12.788 1 0.0003489 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
**** 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), REML=F ,rawdata)
> summary(m1)
Linear mixed model fit by maximum likelihood
Formula: score ~ Order * Voice + (Order * Voice | subject) + (Order *
Voice | sentence)
Data: rawdata
AIC BIC logLik deviance REMLdev
1612 1726 -780.9 1562 1581
Random effects:
Groups Name Variance Std.Dev. Corr
subject (Intercept) 1.8907e-02 0.1375028
Order1 1.2567e-05 0.0035449 -1.000
Voice1 1.2530e-02 0.1119373 0.866 -0.866
Order1:Voice1 1.0808e-02 0.1039636 0.292 -0.292 0.630
sentence (Intercept) 2.7251e-03 0.0522022
Order1 1.0143e-02 0.1007109 0.250
Voice1 5.1207e-03 0.0715590 0.333 -0.465
Order1:Voice1 2.9324e-03 0.0541514 -0.877 0.186 -0.353
Residual 4.6608e-01 0.6826980
Number of obs: 720, groups: subject, 36; sentence, 20
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.26184 0.03618 7.238
Order1 -0.12645 0.03398 -3.721
Voice1 -0.10761 0.03538 -3.042
Order1:Voice1 0.13562 0.03308 4.100
Correlation of Fixed Effects:
(Intr) Order1 Voice1
Order1 0.042
Voice1 0.338 -0.147
Order1:Voc1 -0.007 0.043 0.116
> mi = lmer(score ~ Order+Voice + (Order*Voice|subject)+(Order*Voice|sentence) ,REML=F,rawdata)
> summary(mi)
Linear mixed model fit by maximum likelihood
Formula: score ~ Order + Voice + (Order * Voice | subject) + (Order *
Voice | sentence)
Data: rawdata
AIC BIC logLik deviance REMLdev
1623 1732 -787.3 1575 1589
Random effects:
Groups Name Variance Std.Dev. Corr
subject (Intercept) 1.9424e-02 0.1393698
Order1 2.6892e-06 0.0016399 -1.000
Voice1 1.3041e-02 0.1141955 0.838 -0.838
Order1:Voice1 2.0839e-02 0.1443579 0.290 -0.290 0.541
sentence (Intercept) 2.4074e-03 0.0490650
Order1 1.0471e-02 0.1023269 0.323
Voice1 4.3300e-03 0.0658028 0.280 -0.571
Order1:Voice1 1.1412e-02 0.1068262 -0.784 0.204 -0.300
Residual 4.6362e-01 0.6808933
Number of obs: 720, groups: subject, 36; sentence, 20
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.26513 0.03609 7.346
Order1 -0.13474 0.03407 -3.955
Voice1 -0.11837 0.03480 -3.401
Correlation of Fixed Effects:
(Intr) Order1
Order1 0.063
Voice1 0.334 -0.173
> 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 -787.26
m1 25 1611.7 1726.2 -780.86 12.796 1 0.0003473 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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