R help - Apr 2011 - mixed model random interaction term log likelihood ratio test

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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seatales <ssphadke <at> uh.edu> writes:


> 
> 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.

  You should probably send this sort of question to the 
r-sig-mixed-models mailing list ...

  You probably want (MatingPair|DrugPair) rather than
(1|DrugPair:MatingPair).   
Whether REML=FALSE or REML=TRUE depends what you want
to do next.
> 
> 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?
  The chi-squared values are the differences in deviance (-2 log likelihood)
between the respective papers of models, which under the null hypothesis
of the LRT will be chi-squared distributed.  In other words, these
*are* the LRT test statistics.
> 
> 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).
   I don't know what you mean by this.
   The main difference between lmer and lme in the testing/inference
context is that lme is willing to guess at "denominator degrees of
freedom"
to perform conditional F-tests.
> 
> 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?
  = ran
> 
> 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)?
> 
  See above.
> 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.
  It is probably unwise to estimate DrugPair as a random effect if
it only has three levels.

> 
> Also, here is what I mean by getting chisq value rather than L.Ratio:
  See above.
> 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 
> 
> --
> View this message in context:
http://r.789695.n4.nabble.com/mixed-model-random-interaction-term-log-likelihood-ratio-test-tp3448718p3448718.html
> Sent from the R help mailing list archive at Nabble.com.
> 	[[alternative HTML version deleted]]
> 
>