1. 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? 2. Also could you please elaborate on your suggestion "=ran"? 3. 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]] > > ______________________________________________ R-help@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. -- 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@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.
