[R-sig-ME] ML or REML for LR tests

Ken Beath ken at kjbeath.com.au
Fri Aug 29 09:53:37 CEST 2008

On 29/08/2008, at 2:47 PM, Austin Frank wrote:

> On Thu, Aug 28 2008, Doran, Harold wrote:
>>> The likelihood-ratio test approach directly compares these two.
>> Since these models differ in their fixed effects, you need REML=FALSE
>> for the LRT to be meaningful.
> This is a standard operating procedure that I picked up and accepted  
> on
> faith when I first started using lmer, before I really knew what I was
> doing.  It occurs to me that this is the case for much of my
> understanding of model comparison, so I'd like to check my  
> understanding
> of the use of LR tests with lmer.  If this is a case of RTFM, please
> provide a pointer to the relevant Friendly Manual ;)
> 1) Can anyone offer a reference where the case is made for doing LR
> tests on models fit by ML (as opposed to REML)?

Any decent mixed models text. Verbeke and Molenberghs "Linear Mixed  
Models for Longitudinal Data" p63 or Pinheiro and Bates "Mixed-Effects  
Models in S and S-Plus" p76.

> 2) Can non-nested ML models with the same number of fixed effects be
> meaningfully compared with an LR test?  Something like:

No. General principle is for LR test models must be nested.

> --8<---------------cut here---------------start------------->8---
> data(sleepstudy)
> set.seed(535353)
> sleepstudy$Fake <- rnorm(nrow(sleepstudy))
> m1 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy, REML=FALSE)
> m2 <- lmer(Reaction ~ Fake + (1 | Subject), sleepstudy, REML=FALSE)
> anova(m1, m2)                          # Is this test meaningful...
> ## When possible, test against superset model
> m12 <- lmer(Reaction ~ Days + Fake (1 | Subject),
>            sleepstudy, REML=FALSE)
> anova(m1, m2, m12)                     # ... or only this one?
> --8<---------------cut here---------------end--------------->8---
> 3) Is it the case that LR tests between REML models with different
> random effects are meaningful?  Does this apply to both nested and
> non-nested models?

Maybe, but only for nested (see Q2). Supposedly it works better than  
ML. The significance tests wont be correct but if there is a huge  
significance level then there is probably a random effect. Simulation  
seems a better idea.


> Thanks for the help,
> /au
> -- 
> Austin Frank
> http://aufrank.net
> GPG Public Key (D7398C2F): http://aufrank.net/personal.asc
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