[R-sig-ME] aov p-value on nested models and issue with missing variables in lmer

[Divya Mehta]

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)
anova(lr2,1r2)#

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 :)

Thanks in advance for your comments/suggestions.
Kind regards

MDM