[R-lang] Re: lmer: Significant fixed effect only when random slopeisincluded

Wed May 11 06:04:05 PDT 2011

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 <mailto:IFinlayson@qmu.ac.uk>  

Sent: Wednesday, May 11, 2011 12:36 PM

To: Jorrig Vogels <mailto:j.vogels@uvt.nl>  ;
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

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