Hello,
I am using the following model

model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair),
data=MateChoice, REML=F)

1. After reading around through the R help, I have learned that the above
code is the right way to analyze a mixed model with the MatingPair as the
fixed effect, DrugPair as the random effect and the interaction between
these two as the random effect as well. Please confirm if that seems
correct.

2. Assuming the above code is correct, I have model2 in which I remove the
interaction term, model3 in which I remove the DrugPair term and model4 in
which I only keep the fixed effect of MatingPair.

3. I want to perform the log likelihood ratio test to compare these models
and that's why I have REML=F. However the code anova(model1, model2, model3,
model4) gives me a chisq estimate and a p-value, not the LRT values. How do
I get LRT (L.Ratio) while using lmer?

4. I am under the impression after reading a few posts that LRT is not
usually obtained with lmer but it is given if I use lme (the old model).

5. I could not find how to input the random interaction term while using
lme? Is it the following way? Would someone please guide me to some already
existing posts or help here?

lme(PairFrequency~MatingPair, random=~(1|DrugPair)+(1|DrugPair:MatingPair),
data=MateChoice, method="ML")...is this the right way? would lme give me
loglikelihood ratio test values (L.ratio)?

Thanks a lot. I hope someone can help. Most posts I have found deal with
nesting but there is absolutely no nesting in my data. 

Sujal P.
p.s: If it matters how data is arranged, then I have one vector called
MatingPair which has 3 levels and another vector DrugPair which also has 3
levels. The PairFrequency data is a count data and is normally distributed.
The data are huge, hence I am not able to post it here.

Also, here is what I mean by getting chisq value rather than L.Ratio:
Data: MateChoice
Models:
model2: PairFrequency ~ MatingPair + (1 | DrugPair)
model3: PairFrequency ~ MatingPair + (1 | DrugPair:MatingPair)
model1: PairFrequency ~ MatingPair + (1 | DrugPair) + (1 |
DrugPair:MatingPair)

       Df    AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)   
model2  5 274.90 282.82 -132.45                            
model3  5 282.44 290.36 -136.22 0.0000      0    1.00000   
model1  6 276.90 286.40 -132.45 7.5443      1    0.00602 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 


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