[R-sig-ME] p-values vs likelihood ratios

Rubén Roa rroa at azti.es
Mon Feb 21 09:46:51 CET 2011


This is a very interesting topic, though a bit offtopic for the R list. I hope nobody minds too much.

I fully agree with you on the use of the raw likelihood ratio for statistical inference. It tells us directly how much likely a hypothesis is vis.a.vis another, two times more likely, five times more likely, one hundred times more likely, whatever. The problem is that currently the raw likelihood ratio has less intuitive appeal than a probability value because the former is not a standardized quantity and people like simple binary answers, like in the reject-don't reject dichotomy. Have you read Richard Royall's Statistical Evidence. A Likelihood Paradigm? He proposes a canonical experiment that helps with this by appealing to intuition. The experiment suggests that a likelihood ratio of 8 or more is enough evidence to support one hypothesis over another (kind of a p-value of 0.05 or less is a threshold for rejecting a null). The same argument with the canonical experiment can be used to define a raw likelihood interval (not to be confused with a likelihood-ratio test confidence interval).

Regarding your AIC versus BIC question, I have a hard time understanding why I have to include the sample size when I am comparing models on the same data. It seems redundant to me as the effect of the sample size goes into the value of the log-likelihood of all models being compared. The BIC results when applying an asymptotic reasoning, producing a result that is more consistent than the AIC, but the likelihood approach to inference privileges conditional inference, conditional to the sample at hand, rather than statistical consistency. So I still prefer to use the AIC.


Dr. Rubén Roa-Ureta
AZTI - Tecnalia / Marine Research Unit
Txatxarramendi Ugartea z/g
48395 Sukarrieta (Bizkaia)

> -----Mensaje original-----
> De: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] En nombre 
> de Mike Lawrence
> Enviado el: lunes, 21 de febrero de 2011 6:09
> Para: r-sig-mixed-models at r-project.org
> Asunto: [R-sig-ME] p-values vs likelihood ratios
> Hi folks,
> I've noticed numerous posts here that discuss the 
> appropriateness of p-values obtained by one method or another 
> in the context of mixed effects modelling. Following these 
> discussions, I have an observation
> (mini-rant) then a question.
> First the observation:
> I am not well versed in the underlying mathematical mechanics 
> of mixed effects modelling, but I would like to suggest that 
> the apparent difficulty of determining appropriate p-values 
> itself may be a sign that something is wrong with the whole 
> idea of using mixed effects modelling as a means of 
> implementing a null-hypothesis testing approach to data 
> analysis. That is, despite the tradition-based fetish for 
> p-values generally encountered in the peer-review process, 
> null hypothesis significance testing itself is inappropriate 
> for most cases of data analysis. p-values are for 
> politicians; they help inform one-off decisions by fixing the 
> rate at which one specific type of decision error will occur 
> (notably ignoring other types of decision errors). Science on 
> the other hand is a cumulative process that is harmed by 
> dichotmized and incomplete representation of the data as 
> null-rejected/fail-to-reject-the-null. Data analysis in 
> science should be about quantifying and comparing evidence 
> between models of the process that generated the data. My 
> impression is that the likelihood ratio (n.b. not likelihood 
> ratio *test*) is an easily computed quantity that facilitates 
> quantitative representation of such comparison of evidence.
> Now the question:
> Am I being naive in thinking that there are no nuances to the 
> computation of likelihood ratios and appropriateness of their 
> interpretation in the mixed effects modelling context? To 
> provide fodder for criticism, here are a few ways in which I 
> imagine computing then interpreting likelihood ratios:
> Evaluation of evidence for or against a fixed effect:
> m0 = lmer( dv ~ (1|rand) + 1 )
> m1 = lmer( dv ~ (1|rand) + iv )
> AIC(m0)-AIC(m1)
> Evaluation of evidence for or against an interaction between 
> two fixed effects:
> m0 = lmer( dv ~ (1|rand) + iv1 + iv2 )
> m1 = lmer( dv ~ (1|rand) + iv1 + iv2 + iv1:iv2 )
> AIC(m0)-AIC(m1)
> Evaluation of evidence for or against a random effect:
> m0 = lmer( dv ~ (1|rand1) + 1 )
> m1 = lmer( dv ~ (1|rand1) + (1|rand2) + 1 )
> AIC(m0)-AIC(m1)
> Evaluation of evidence for or against correlation between the 
> intercept and slope of a fixed effect that is allowed to vary 
> within levels of the random effect:
> m0 = lmer( dv ~ (1+iv|rand) + iv )
> m1 = lmer( dv ~ (1|rand) + (0+iv|rand) + iv )
> AIC(m0)-AIC(m1)
> Certainly I've already encountered uncertainty in this 
> approach in that I'm unsure whether AIC() or BIC() is more 
> appropriate for correcting the likelihood estimates to 
> account for the differential complexity of the models 
> involved in these types of comparisons. I get the impression 
> that both corrections were developed in the context of 
> exploratory research where model selection involves many 
> models involving multiple usually observed variables (vs 
> manipulated), so I don't have a good understanding of how 
> their different derivations/intentions apply to this simpler 
> context of comparing two nested models to determine evidence 
> for a specific effect of interest.
> I would greatly appreciate any thoughts on this AIC/BIC 
> issue, or any other complexities that I've overlooked in my 
> proscription to abandon p-values in favor of the likelihood 
> ratio (at least, for all non-decision-making scientific 
> applications of data analysis).
> Cheers,
> Mike
> --
> Mike Lawrence
> Graduate Student
> Department of Psychology
> Dalhousie University
> Looking to arrange a meeting? Check my public calendar:
> http://tr.im/mikes_public_calendar
> ~ Certainty is folly... I think. ~
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