[R-sig-ME] test significance of single random effect

[Matthias Gralle]

I had basically the same problem a short time ago, and resorted to lme 
instead of lmer, because one can directly compare lme and lm objects 
using anova(). Is that OK, or is this feature of lme depreciated ?

Ben Bolker wrote:
>   Have you tried the RLRsim package??
>
> Tom Van Dooren wrote:
>   
>> I tried to find an easy way to test whether the random effect would be 
>> significant in a (generalized) mixed model with a single random effect.
>> It annoyed me that log-likelihoods of lm or glm and lmer are not 
>> necesarily directly comparable -> trouble with calculating likelihood 
>> ratios.
>> What do members of this list think of the following simulation approach?
>> It basically amounts to simulating a distribution for the log 
>> likelihood, given the null hypothesis that there is no random effect 
>> variance and that the fixed effect model is correct.
>>
>>
>> library(lme4)
>> mm1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy)
>> lm1<- lm(Reaction ~ Days, sleepstudy)
>>
>>
>> LL<-numeric(500)
>> for(i in 1:500){
>> resp<-simulate(lm1)
>> LL[i]<-logLik(lmer(resp[,1] ~ Days + (1|Subject), sleepstudy))
>> }
>>
>> hist(LL)
>> logLik(mm1)
>> mean(LL>logLik(mm1))
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>     
>
>
>   


-- 
Matthias Gralle, PhD
Dept. Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
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Tel +49 341 3550 519
Fax +49 341 3550 555