[R-sig-ME] aov p-value on nested models and issue with missing variables in lmer
bates at stat.wisc.edu
Tue Mar 22 16:52:42 CET 2011
On Mon, Mar 21, 2011 at 10:10 AM, Divya Mehta <mehta at mpipsykl.mpg.de> wrote:
> Greetings to you all,
> I have read the long discussions regarding the lmer and why one is not
> provided with p-values but rather the t-values.
> Also, the discussion regarding the comparison of two nested lmer models
> using anova function.
> Since i am not a statistician, I would be very grateful for some expert
> advice regarding a problem that I am currently facing.
> I have compared two groups using a paired t-test and for the top hits I
> wanted to check if the p-values from the anova on two nested lmer models is
> comparable and I saw that there were not comparable at all (of course these
> are two different statistics!).
> Perhaps I did not frame the model correctly, here is what I did.
> lr1<- lmer(Y ~ timepoint +(1|person), REML=F, data)
> lr2<- lmer(Y ~ (1|person), REML=F, data)
> I am interested in the effects of the fixed "timepoint" variable and
> "person" is the random factor since for each person i have two repeated
> measures in this case for the "Y".
> I removed all NAs and took only 45 paired samples so I assumed this would be
> relatively similar to the paired t-test.
> Also when I ran the analysis with one individual who did not have two
> repeated measures like all others, i got different values for the anova
> outputs and the power seemed lower than when i removed the one individual -
> this again surprised me a bit since i thought the whole idea of mixed models
> was the flexibility of having some missing data points?
> I am just wondering if I have done something silly :)
I don't think so but there is not really enough information to go on
here. Can you provide a reproducible example, even if it is
simulated data, so that we can see exactly what you did and what the
My inclination is to suggest that if the data have a structure for
which a paired t-test would be appropriate then I would use that,
simply because it is easier to explain.
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