What about evidence ratios (based on Akaike's weights) as described in:


Burnham, K. P., and D. R. Anderson.  2002. Model Selection and 
Multimodel Inference: A Practical Information-Theoretic Approach. _2nd 
edition_ Springer-Verlag, New York.


Anderson, D. R.  (2008).  Model based inference in the life sciences:  a 
primer on evidence.  Springer, New York, NY.


I still don't understand what are the benefits of p-values instead, for 
example, of effect size.  Many well known statisticians were very 
critical on p-values.

See this sites against null hypothesis significant testinga and 
p-values: http://warnercnr.colostate.edu/~anderson/nester.html

What is the use of a p-value on observational studies?

Best,

Manuel Spínola


On 21/02/2011 08:45 a.m., Mike Lawrence wrote:
> On Mon, Feb 21, 2011 at 9:24 AM, Ben Bolker<bbolker@gmail.com>  wrote:
>>   I don't see why you're using AIC differences here.
> My understanding it that taking the difference of the values resulting
> from AIC() is equivalent to computing the likelihood ratio then
> applying the AIC correction to account for the different number of
> parameters in each model (then log-transforming at the end).
>
> My original exposure to likelihood ratios (and the AIC/BIC correction
> thereof) comes from Glover&  Dixon (2004,
> http://www.psych.ualberta.ca/~pdixon/Home/Preprints/EasyLRms.pdf), who
> describe the raw likelihood ratio as inappropriately favoring the
> model with more parameters because more complex models have the
> ability to fit noise more precisely than less complex models. Hence
> application of some form of correction to account for the differential
> complexity of the models being compared.
>
> I wonder, however, whether cross validation might be a less
> controversial approach to achieving fair comparison of two models that
> differ in parameter number. That is, fit the models to a subset of the
> data, then compute the likelihoods on another subset of the data. I'll
> play around with this idea and report back any interesting findings...
>
>>    If one is really trying to test for "evidence of an effect" I see
>> nothing wrong with a p-value stated on the basis of the null
>> distribution of deviance differences between a full and a reduced model
>> - -- it's figuring out that distribution that is the hard part. If I were
>> doing this in a Bayesian framework I would look at the credible interval
>> of the parameters (although doing this for multi-parameter effects is
>> harder, which is why some MCMC-based "p values" have been concocted on
>> this list and elsewhere).
> We'll possibly have to simply disagree on the general utility of
> p-values for cumulative science (as opposed to one-off decision
> making). I do, however, agree that Bayesian credible intervals have a
> role in cumulative science insofar as they permit a means of relative
> evaluation of models that differ not in the presence of an effect but
> in the specific magnitude of the effect, as may be encountered in more
> advanced/fleshed-out areas of inquiry. Otherwise, in the context of
> areas where the simple existence of an effect is of theoretical
> interest, computing credible intervals on effects seems like overkill
> and have (from my anti-p perspective) a dangerously easy connection to
> null-hypothesis significance testing.
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>


-- 
*Manuel Spínola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspinola@una.ac.cr
mspinola10@gmail.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
Personal website: Lobito de río 
<https://sites.google.com/site/lobitoderio/>
Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>

	[[alternative HTML version deleted]]


