Cross-posting here because it was suggested so.

I am using the following model

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

1. After reading https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001966.html, 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 would like to know how would the same model(s), especially the interaction term, be written in lme rather than lmer.  (MatingPair|DrugPair) was suggested but I am under the impression that this represents nesting. Please confirm. *Most posts I have found deal with nesting but there is absolutely no nesting in my data* 

or do I have to use pdBlocked and pdIdent (Pinheiro-Bates 2000, P.163-167)...I still could not figure it out? or will the following do?

lme(PairFrequency~MatingPair, random=~(1|DrugPair)+(1|DrugPair:MatingPair),
data=MateChoice, method="ML")...is this a right way for lme?

Sujal. P.
p.s: If it matters how data are 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. Levels of DrugPair represent three randomly chosen genotypes from a population of many.

lmer gives me the following output which I guess suggests that model1 gives best fit?

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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