[R-sig-ME] Model comparison between a full model and a random effect reduced model

[lei.he at uzh.ch]

Dear all,



I’m studying speaker idiosyncratic intensity variability in the speech signal. My dataset looks like this:



-nPVIm: numeric variable, quantifying intensity variability

-tempo: factor with 5 levels, indicating five levels of speech rates (normal, slow, even slower, fast, fastest possible)

-sentence: factor with 7 levels, i.e. seven different sentences

-speaker: factor 12 levels, i.e. twelve speakers



I first I fitted speaker as a random effect with the rationale that we cannot exhaust all the possible speakers:

Full1 = lmer(nPVIm ~ tempo + (1|speaker) + (1|sentence), data=dat, REML=F)



Then I fitted speaker as a fixed effect. The rationale is that if we apply “nPVIm” in a close-set speaker identification or verification system, the speakers are fixed:

Full2 = lmer(nPVIm~tempo + speaker + (1|sentence), data=dat, REML=F)



Next, I fitted a reduced model without speaker effect:

Reduced = lmer(nPVIm~tempo + (1|sentence), data=dat, REML=F)



Finally, I used the anova () function to test whether Full1 and Full2 are significantly different from Reduced. 



Results showed that speaker as both random and fixed effects are significant, and the AICs of both Full1 and Full2 are lower than that of Reduced.



Now we have received the reviewer’s comments. The reviewer wasn’t certain if it allows the models to be compared like this, especially anova(Full1, Reduced). So I would like to ask if our way of model fitting and comparisons is free from problems.



Thank you very much in advance!



Kindest regards,


Lei

---------------------------
Lei He

Phonetics Laboratory
Department of Comparative Linguistics
University of Zurich

Plattenstrasse 54
CH8032
Zurich
Switzerland

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