Dear Marie Devaine,
1) The way you account for the order effects is not the way I would go, I
can see various options:
- The effect of Order on Scores is not changing the relationships between
your fixed effects part and the Scores, and each individuals is "learning"
the task differently I would then use a nested random part:
Scores~Condition*Specie+(1|Subject/Order), you would then get an
estimation of much variation there is in the Scores between subject and
also how much variation there is within subject between Order levels.
- Order is changing the relationship between your fixed effect part and
the Score, ie the Condition effect on the Scores is different whether a
primate is in its first trials or in its fourth one. You would then need
random slopes, and then one way to go would be: Scores~Condition*Species+(1+Condition|Subject/Order),
you would then get the same estimate as in the previous options plus how
much the Condition slope vary between the Subject and within the Subject,
between the Order. Seeing your number of levels I guess that the estimation
will be rather tricky ...
You can see the wiki for more infomation on this:
http://glmm.wikidot.com/faq#modelspec
I guess that your are misinterpreting the random slope part, you can see
it as an interaction term between one fixed effect term and one random
term, for example if you were to measure the weights of your primates and
made the hypothesis that the weights affect the scores but that this effect
(direction+strength ie slope) might vary between your subject then you
would have a random slope of weight depending on the subject
(weight|subject).
2-3) The first method identify if the interaction term explain a big
enough portion of the total sum of square, it is a measure of how important
is this term at explaining the variation in your data. The second method
compare the likelihood (ie the probability to find this dataset with this
particular set of parameter) between the model with and the model without
the interaction term, if the removal of the interaction term leads to a big
decline in the likelihood of the model then the p-value should score
significant and you should keep the full model, in the other case the
parcimony approach would lead you to choose the reduced model. So the
difference come from the fact that the two methods are computing a
different thing. As to which one is better this is a tricky question, the
way I would go would be to compute confidence intervals around the main
effect plus interaction term using bootMer for example and then
interpreting them. You may have a look at ?pvalues for more
options/suggestions.
As I am not familiar with lmerTest package I will not comment on your last
question.
Hoping that I clarified some points,
Lionel
On 27/11/2014 16:03, marie devaine wrote:
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
species
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,
Marie Devaine
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