[R-sig-ME] lmertest F-test anova(fullm) and anova(fullm,reducedm)
marie.devaine at gmail.com
Thu Nov 27 16:03:04 CET 2014
Dear mixed-model list,
I am sorry if my questions sound trivial: I am all new to R and mixed model.
My data set is the following : I try to model scores of primates from
different species in different conditions of a task. Each individual
repeats each condition a certain number of time ( most of the time 4 times
but with some exceptions).
I have only few individuals by specie (from 4 to 7), 3 conditions and 7
As dependent variables, I am mostly interested in the condition and the
Specie, but I want to correct for learning effect at the individual level
(parametric effect on repetition -'Order').
I wrote the following model (letting Subject be a random effect and 'Order'
a random slope) :
fullm = lmer(Scores ~ Condition*Specie+(1+Order|Subject))
1) Is it a sensible way to model my data?
Then, I want to test for the interaction between Species and condition. I
found two ways to do so with the lmerTest :
*computing the p-value of the F-test corresponding to Specie:Condition as
given by anova(fullm).
*constructing the reduced model without the interaction
reducedm= lmer(Scores ~ Condition+Specie+(1+Order|Subject))
and performing the Likelihood ratio test : anova(reducedm,fullm).
2) What is the conceptual difference between the two methods?
3) The numerical results are different in my case (pvalues around .05,
below in the reduced model manner, above in the F-test manner), why is it
the case? Is one better than the other one?
4) This point is not directly related to my title, but on the same data and
still on the lmerTest pasckage : the Species for now are categorical, but I
could instead take a numerical value such as the encephalization quotient
for each specie. In this case how could I evaluate the significance of the
parametric effect? lsmeans seems to care only about categorical factors.
It is very likely that I miss here very simple points, and would be very
thankful if you could help me with it.
Thank you in advance,
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