mixed model random interaction term log likelihood ratio test
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 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. -- 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.