[R-lang] Simple effects in mixed logit models

[Roger van-Gompel]

Hi there,
 
I have a 2 (language: Swe vs. Eng) x 2 (prime: pp vs. do) design and
was wondering how I do simple effects in mixed logit models.  I could
delete one level of a variable and then analyse the other level of a
variable (e.g., Swe pp vs. Swe do), but I suspect that that’s not the
appropriate way of doing it.
 
I have attached my analyses and output. Note that my variables are
effect coded.  I don’t have an interaction and so strictly speaking, I
shouldn’t analyse simple effects, but reviewers have asked us to.
 
Thanks a lot for any help!
 
Roger van Gompel
 
 
> library(lme4)
> L2prim = read.table("forLME.txt", header=TRUE)
> L2prim$langcent <- scale(as.numeric(L2prim$language)) 
> L2prim$primecent <- scale(as.numeric(L2prim$prime))
> L2prim$PID=as.factor(L2prim$PID)
> L2prim$ITEM=as.factor(L2prim$ITEM)
> head(L2prim)
  LIST PID ITEM language prime score  langcent  primecent
1    1   2    4      swe    do     1  1.008740 -0.9742244
2    1   2    7      swe    do     0  1.008740 -0.9742244
3    1   2    8      swe    pp     0  1.008740  1.0248334
4    1   2   20      swe    pp     0  1.008740  1.0248334
5    1   2   22      eng    pp     0 -0.989767  1.0248334
6    1   2   23      swe    pp     0  1.008740  1.0248334
> primanal=lmer(score ~langcent*primecent + (1|PID) + (1|ITEM),
data=L2prim, family = "binomial")
> summary(primanal)
Generalized linear mixed model fit using PQL 
Formula: score ~ langcent * primecent + (1 | PID) + (1 | ITEM) 
   Data: L2prim 
 Family: binomial(logit link)
      AIC      BIC    logLik deviance
 559.0337 590.1759 -272.5168 545.0337
Random effects:
 Groups Name        Variance Std.Dev.
 ITEM   (Intercept) 2.2551   1.5017  
 PID    (Intercept) 2.5343   1.5919  
# of obs: 632, groups: ITEM, 40; PID, 32
 
Estimated scale (compare to 1)  0.880811 
 
Fixed effects:
                    Estimate Std. Error  z value Pr(>|z|)   
(Intercept)        -0.946211   0.393646 -2.40371 0.016230 * 
langcent            0.012060   0.108898  0.11075 0.911818   
primecent           0.337744   0.110732  3.05010 0.002288 **
langcent:primecent -0.099484   0.109631 -0.90744 0.364172   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
 
Correlation of Fixed Effects:
            (Intr) lngcnt prmcnt
langcent    -0.001              
primecent   -0.025 -0.033       
lngcnt:prmc  0.007 -0.048 -0.005
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