lmer L.ratio
On 4/14/2011 10:22 PM, Sujal Phadke wrote:
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.
That's correct syntax for two crossed factors, one of which is random.
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?
The syntax is different in lme. The following should work: random=~1|DrugPair/MatingPair
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.
You mean "approximately" normally distributed? By definition a count cannot be normally distributed.
Perhaps you should be considering generalized linear mixed models.
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.
Estimates of variance components with only three levels of the factor are very unreliable.
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
Model 1 beats model 3 according to the LR test. Change the order to test model 2 vs 1.
Models 2 and 3 are not comparable with a test (but according to AIC/BIC 2 is best).
Since you used "REML=FALSE" when fitting you can also test your model 4.
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