From: Ben Bolker <bbolker_at_gmail.com>

Date: Thu, 14 Apr 2011 22:14:05 +0000

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 - 01:12:59 GMT

Date: Thu, 14 Apr 2011 22:14:05 +0000

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 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1
**> â â 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]]
**>
*

>

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