Dear Mailing List,

I am trying to set up custom contrasts for a mixed effect model fit by 
glmer. My design has two factors: "phrase length" (two levels: ENC and 
DISC) and "missing verb" (again two levels: Complete and Missing). The 
dependent variable is binary, indicating whether or not the subject 
rejected a sentence.

I expect an interaction of the two fixed effects, where the effect of 
"phrase length" depends on the level of "missing verb". That is, for  
"missing verb"==Complete I expect ENC sentences to have a lower 
probability of rejections, but for MissingVerb==Missing the factor level 
PhraseLength==ENC sentences should have lower rejection rates. A barplot 
looks encouraging, although the effect is attenuated for 
MissingVerb==Missing. Is it possible to test for an interaction like 
this in a mixed model?

The built-in interaction term tests [/phrase length/ ENC & /missing 
verb/ Complete] against [/phrase length/ DISC & /missing verb /Missing], 
which does not make much sense from a theoretical point of view. I 
attached the output of my model at the end of this message.

Any help would be greatly appreciated! I searched the mailing list and 
the resources on the web, but I could not find anything relating to my 
particular problem.
Best,
Esther


Generalized linear mixed model fit by the Laplace approximation
Formula: missJ ~ phraseL * missVP + (1 | subject) + (1 | sentence)
    Data: jt
    AIC   BIC logLik deviance
  269.2 292.9 -128.6    257.2
Random effects:
  Groups   Name        Variance Std.Dev.
  subject  (Intercept) 0.34768  0.58965
  sentence (Intercept) 0.94494  0.97208
Number of obs: 384, groups: subject, 24; sentence, 16

Fixed effects:
                           Estimate Std. Error z value Pr(>|z|)
(Intercept)                -2.6130     0.4647  -5.623 1.88e-08 ***

phraseLDISC                 0.9552     0.4574   2.088   0.0368 *
missVPMissing               6.0500     0.6648   9.100  < 2e-16 ***

phraseLDISC:missVPMissing  -1.9058     0.7777  -2.450   0.0143 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


(Intercept)                -2.6130     0.4647  -5.623 1.88e-08 ***
prosodyDISC                 0.9552     0.4574   2.088   0.0368 *
missVPMissing               6.0500     0.6648   9.100  < 2e-16 ***
prosodyDISC:missVPMissing  -1.9058     0.7777  -2.450   0.0143 *



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