[R-lang] Re: lmer: Significant fixed effect only when random slopeisincluded
Levy, Roger
rlevy@ucsd.edu
Wed May 11 11:17:02 PDT 2011
Just as a brief follow-up: since this is categorical data, given the magnitudes of some of the coefficients in question I would indeed worry a bit. It looks like some condition means may be close to 100%, but that different subjects may have really dramatically different behavior and that this factor may dominate everything else. Also, getting close to 100% can mess with the Z statistic. What do your condition-mean and subject-by-condition mean tables look like? And what do likelihood-ratio tests for your fixed effects tell you in these models? You may be fine and the random-slope model you included in your first message may indeed be a good one for interpreting your data, but eyeballing these tables would be useful as well.
Roger
On May 11, 2011, at 6:04 AM, Finlayson, Ian wrote:
> What was the random effect structure of this first converging model? As I said earlier the significant interaction seems fine (as does your explanation), but I’m just curious about how you carried out backward stepwise elimination when the full model didn’t converge.
>
> Ian
>
>
> From: ling-r-lang-l-bounces+ifinlayson=qmu.ac.uk@mailman.ucsd.edu [mailto:ling-r-lang-l-bounces+ifinlayson=qmu.ac.uk@mailman.ucsd.edu] On Behalf Of Jorrig Vogels
> Sent: 11 May 2011 11:56
> To: ling-r-lang-l@mailman.ucsd.edu
> Subject: [R-lang] Re: lmer: Significant fixed effect only when random slopeisincluded
>
> Hello Ian,
>
> In the full model, I included random slopes for cAGTOP and cAGVIS and their interaction for both subjects and items. However, this did not converge. The first model that converged showed a significant interaction between cAGTOP1 and cAGVIS, but not between cAGTOP2 and AGVIS.
>
> Jorrig
>
>
> From: Finlayson, Ian
> Sent: Wednesday, May 11, 2011 12:36 PM
> To: Jorrig Vogels ; ling-r-lang-l@mailman.ucsd.edu
> Subject: RE: [R-lang] lmer: Significant fixed effect only when random slope isincluded
>
> Hello,
>
> I assume that you only have one random slope because the removal of the other two (cAGTOP, and its interaction with cAGVIS) didn’t significantly harm fit. Were the fixed effects for the interaction significant in the full model?
>
> FWIW, I have seen this happen before and it seems perfectly reasonable to me that an effect may only become significant after controlling for some of the noise.
>
> Ian
>
> From: ling-r-lang-l-bounces@mailman.ucsd.edu [mailto:ling-r-lang-l-bounces@mailman.ucsd.edu] On Behalf Of Jorrig Vogels
> Sent: 11 May 2011 10:50
> To: ling-r-lang-l@mailman.ucsd.edu
> Subject: [R-lang] lmer: Significant fixed effect only when random slope isincluded
>
> Dear R users,
>
> I have a logit mixed model with two categorical predictors (two types of salience measures) and a categorical dependent variable (pronoun used Y/N). One predictor has 2 levels, and the other has 3. I centered the 2-level predictor, and transformed the 3-level predictor into two binary predictors using contrast (sum) coding. I determined the random-effects structure by starting from a full model, and eliminating step by step all terms without a significant contribution to the model.
>
> In the final model, I end up with random intercepts for subjects and items, and a by-subject random slope for my 2-level predictor. In this model, I get significant interactions between the fixed factors, which I had not expected to be significant by just looking at the data. Removing the random slope from the model completely eliminates these interactions, but model comparison suggests the random slope should be included. I have attached the two model summaries below.
>
> Now my question is: is it normal to find such a large influence of random effects on the fixed effects structure? How do I know the interaction effects are not spurious? And what exactly do these findings mean? Participants varied greatly in their reaction to predictor B, but when this variation is accounted for, predictor B affects pronoun use, but differently for each level of predictor A?
>
>
> Jorrig Vogels
> PhD candidate
> Tilburg Univ., Netherlands
>
> ================================================================
>
> Model with random slope:
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: PRO ~ cAGTOP * cAGVIS + (1 + cAGVIS | SUBJ) + (1 | ITEM)
> Data: vislingag
> AIC BIC logLik deviance
> 318.4 361.4 -149.2 298.4
> Random effects:
> Groups Name Variance Std.Dev. Corr
> SUBJ (Intercept) 49.6457 7.0460
> cAGVIS 21.8342 4.6727 0.663
> ITEM (Intercept) 1.3205 1.1491
> Number of obs: 544, groups: SUBJ, 48; ITEM, 12
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -2.578 1.217 -2.117 0.03422 *
> cAGTOP1 -6.627 0.913 -7.259 3.90e-13 ***
> cAGTOP2 9.868 1.502 6.569 5.05e-11 ***
> cAGVIS -1.699 1.008 -1.685 0.09207 .
> cAGTOP1:cAGVIS -3.223 1.170 -2.755 0.00587 **
> cAGTOP2:cAGVIS 3.120 1.371 2.275 0.02289 *
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) cAGTOP1 cAGTOP2 cAGVIS cAGTOP1:
> cAGTOP1 0.075
> cAGTOP2 -0.041 -0.867
> cAGVIS 0.535 0.108 -0.059
> cAGTOP1:AGV 0.074 0.562 -0.346 0.128
> cAGTOP2:AGV -0.049 -0.480 0.528 -0.054 -0.668
>
>
> Model without random slope:
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: PRO ~ cAGTOP * cAGVIS + (1 | SUBJ) + (1 | ITEM)
> Data: vislingag
> AIC BIC logLik deviance
> 324.3 358.7 -154.2 308.3
> Random effects:
> Groups Name Variance Std.Dev.
> SUBJ (Intercept) 21.63217 4.65104
> ITEM (Intercept) 0.61539 0.78447
> Number of obs: 544, groups: SUBJ, 48; ITEM, 12
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.41142 0.77639 -1.818 0.0691 .
> cAGTOP1 -4.59707 0.52139 -8.817 <2e-16 ***
> cAGTOP2 7.13115 0.84489 8.440 <2e-16 ***
> cAGVIS -0.35538 0.40416 -0.879 0.3792
> cAGTOP1:cAGVIS -0.59940 0.58255 -1.029 0.3035
> cAGTOP2:cAGVIS -0.08268 0.56682 -0.146 0.8840
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) cAGTOP1 cAGTOP2 cAGVIS cAGTOP1:
> cAGTOP1 0.070
> cAGTOP2 -0.040 -0.867
> cAGVIS 0.000 0.061 -0.036
> cAGTOP1:AGV 0.038 0.082 -0.012 0.102
> cAGTOP2:AGV -0.020 0.008 -0.037 0.037 -0.575
--
Roger Levy Email: rlevy@ucsd.edu
Assistant Professor Phone: 858-534-7219
Department of Linguistics Fax: 858-534-4789
UC San Diego Web: http://idiom.ucsd.edu/~rlevy
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