[R-sig-ME] singularity issue in lmer
Catalina Ratala
c@r@t@|@ @end|ng |rom donder@@ru@n|
Mon Apr 22 12:29:14 CEST 2019
Dear Thierry,
Thank you very much for your reply!
Indeed, i tried to simplify the model in different ways - the ones that run without errors are the ones where f_Order is not modelled as a random slope. So, the one you suggested:
> lmer(Rate ~ f_Order*f_Valence + f_Attribute + (1 | Subject), data = data)
works, without any errors.
However, won’t the p-values I get for this model be inflated, since I don’t have random effects for any of the fixed factors in which I am interested in (f_Order and f_Valence)?
Are these p-values reliable?
best,
Catalina
> On 17 Apr 2019, at 21:04, Thierry Onkelinx <thierry.onkelinx using inbo.be> wrote:
>
> Dear Catalina,
>
> Your model is too complex for the data. The NaN values in the output are a hint. I see two solutions. 1) collect more data. 2) simplify your model.
>
> Ttest if lmer(Rate ~ f_Order*f_Valence + f_Attribute + (1 | Subject), data = data) works.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be <mailto:thierry.onkelinx using inbo.be>
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be <http://www.inbo.be/>
>
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>
> Op wo 17 apr. 2019 om 18:13 schreef Catalina Ratala <c.ratala using donders.ru.nl <mailto:c.ratala using donders.ru.nl>>:
> Dear List,
>
> I’m trying to make the following model converge without warnings, using the lme4 package:
>
> Variables: Order (3 levels), Valence (2 levels), and Attribute (2 levels) are all factors, Rate is the DV, continuous, 1-10 range.
>
> model_lme4 <- lmer(Rate ~ f_Order*f_Valence + f_Attribute + (1 + f_Order*f_Valence | Subject), data = data)
>
> summary(model_lme4)
>
> Linear mixed model fit by REML ['lmerMod']
> Formula: Rate ~ f_Order * f_Valence + f_Attribute + (1 + f_Order * f_Valence |
> Subject)
> Data: data
>
> REML criterion at convergence: 12167.1
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -5.0966 -0.5745 -0.0331 0.5851 5.1733
>
> Random effects:
> Groups Name Variance Std.Dev. Corr
> Subject (Intercept) 0.135654 0.36831
> f_Order1 0.002681 0.05178 -0.66
> f_Order2 0.003578 0.05982 0.31 -0.92
> f_Valence1 0.343519 0.58611 0.35 -0.01 -0.17
> f_Order1:f_Valence1 0.009916 0.09958 -0.09 -0.49 0.66 0.36
> f_Order2:f_Valence1 0.001073 0.03276 0.01 0.02 -0.02 -0.88 -0.70
> Residual 0.974439 0.98714
> Number of obs: 4237, groups: Subject, 29
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 4.8820728 0.0700895 69.655
> f_Order1 -0.0005486 0.0235150 -0.023
> f_Order2 0.0220543 0.0241765 0.912
> f_Valence1 -1.3284410 0.1099083 -12.087
> f_Attribute1 0.0401858 0.0221600 1.813
> f_Attribute2 -0.1078952 0.0217247 -4.966
> f_Order1:f_Valence1 -0.0157678 0.0283515 -0.556
> f_Order2:f_Valence1 0.0552990 0.0223037 2.479
>
> Correlation of Fixed Effects:
> (Intr) f_Ord1 f_Ord2 f_Vln1 f_Att1 f_Att2 f_O1:_
> f_Order1 -0.263
> f_Order2 0.138 -0.578
> f_Valence1 0.340 -0.005 -0.077
> f_Attribut1 0.014 -0.014 -0.024 -0.001
> f_Attribut2 0.000 -0.007 0.023 0.007 -0.531
> f_Ordr1:_V1 -0.057 -0.131 0.200 0.234 0.016 -0.008
> f_Ordr2:_V1 0.001 0.001 -0.002 -0.237 -0.019 0.027 -0.488
> convergence code: 0
> boundary (singular) fit: see ?isSingular
>
>
>
> My problem is that I get this warning message saying that the model is singular:
>
> boundary (singular) fit: see ?isSingular
>
>
>
> I used the function isSingular() (package lme4) to test whether actually the warning is valid. It returned TRUE as an outcome, meaning the parameters are on the boundary of the feasible parameter space, and variances of one or more linear combinations of effects are (close to) zero.
>
> I also used allfits() function (afex package) to check whether different optimizers give the same error, which was the case for all the optimizers with which the model converged.
>
> Following different guidelines presented in the literature I tried:
>
> a) to remove the covariance between random effects, by running the following model:
>
> model_nocov <- lmer(Rate ~ f_Order*f_Valence + f_Attribute + (0 + f_Order*f_Valence | Subject) + (1 | Subject), data = data);
>
> Linear mixed model fit by REML ['lmerMod']
> Formula: Rate ~ f_Order * f_Valence + f_Attribute + (0 + f_Order * f_Valence |
> Subject) + (1 | Subject)
> Data: data
>
> REML criterion at convergence: 11707
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -5.1014 -0.5736 -0.0361 0.5815 5.1984
>
> Random effects:
> Groups Name Variance Std.Dev. Corr
> Subject f_Orderfirst 0.000000 0.00000
> f_Ordersecond 0.009510 0.09752 NaN
> f_Orderthird 0.004304 0.06561 NaN 0.66
> f_Valence1 0.105713 0.32514 NaN 0.74 0.68
> f_Order1:f_Valence1 0.009706 0.09852 NaN 0.83 0.13 0.49
> f_Order2:f_Valence1 0.001249 0.03534 NaN -0.52 -0.14 -0.82 -0.60
> Subject.1 (Intercept) 0.127141 0.35657
> Residual 0.973996 0.98691
> Number of obs: 4087, groups: Subject, 28
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 4.873808 0.069807 69.818
> f_Order1 -0.005478 0.023784 -0.230
> f_Order2 0.028555 0.024044 1.188
> f_Valence1 -1.420611 0.063388 -22.411
> f_Attribute1 0.048127 0.022588 2.131
> f_Attribute2 -0.120467 0.022118 -5.447
> f_Order1:f_Valence1 -0.016950 0.028729 -0.590
> f_Order2:f_Valence1 0.057331 0.022845 2.510
>
> Correlation of Fixed Effects:
> (Intr) f_Ord1 f_Ord2 f_Vln1 f_Att1 f_Att2 f_O1:_
> f_Order1 -0.054
> f_Order2 0.045 -0.548
> f_Valence1 0.103 -0.301 0.255
> f_Attribut1 0.015 -0.015 -0.025 -0.001
> f_Attribut2 0.000 -0.004 0.019 0.010 -0.533
> f_Ordr1:_V1 0.053 -0.154 0.260 0.306 0.018 -0.009
> f_Ordr2:_V1 -0.016 0.046 -0.070 -0.233 -0.018 0.028 -0.477
> convergence code: 0
> boundary (singular) fit: see ?isSingular
>
>
>
>
> b) to remove outlier cases;
>
> However, I still got the same warning and the is.singular() indicated that the warnings were to be considered (TRUE).
>
> Moreover, I realized that in this case, what causes the problem are the correlations between the levels of the factor ORDER, which is the one related to my main hypothesis and therefore, I cannot exclude its random slope from the model, as I am interested in its statistical significance and, thus, getting a p-value for it.
>
> I would like to ask for advice on what I can do to make the model converge without warnings. Is there anything that could be done with the levels of the factor (Order), that seem to be causing the problem? Any other suggestion is, of course, welcomed.
>
>
>
> Thank you in advance for your time and help!
>
>
>
> Best,
>
> Catalina
>
>
>
>
>
>
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>
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Cătălina E Rățală MSc
PhD candidate
ROTTERDAM SCHOOL OF MANAGEMENT
ERASMUS UNIVERSITY
DEPARTMENT OF MARKETING MANAGEMENT
postal address: P.O. Box 1738; 3000 DR Rotterdam
email: cratala using rsm.nl <mailto:touburg using rsm.nl>
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DONDERS INSTITUTE FOR BRAIN, COGNITION & BEHAVIOUR
DONDERS CENTRE FOR COGNITIVE NEUROIMAGING
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