From: seatales <ssphadke_at_uh.edu>

Date: Thu, 14 Apr 2011 18:57:44 -0700 (PDT)

R-help_at_r-project.org mailing list

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Date: Thu, 14 Apr 2011 18:57:44 -0700 (PDT)

- The three levels of the vector DrugPair actually represent three genotypes, which are some randomly chosen genotypes from a population of many genotypes. That's why I thought it was justified as random effect. Does estimating them as random make sense then?
- Also could you please elaborate on your suggestion "=ran"?
- Wouldn't (MatingPair|DrugPair) represent nesting rather than the interaction as a random effect? I got (1|DrugPair:MatingPair) from the following post: https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001966.html

*>
*

> 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.
**>
*

*>
*

> 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.

*>
*

> 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.

> 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]]
**>
**>
*

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-- View this message in context: http://r.789695.n4.nabble.com/mixed-model-random-interaction-term-log-likelihood-ratio-test-tp3448718p3451092.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Fri 15 Apr 2011 - 06:13:25 GMT

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