[R] Bayesian stepwise

[Kjetil Brinchmann Halvorsen]

Spencer Graves wrote:

>      Does anyone know of research fully Bayesian stepwise procedures 
> assuming that models not considered by the stepwise would essentially 
> have zero posterior probability?
>      I need to analyze the results of ad hoc experiments run in 
> manufacturing with crazy confounding and possible supersaturation 
> (i.e., more potentially explanatory variables than runs), when each 
> run is very expensive in both time and money.   There have to be ways 
> to summarize concisely and intelligently what the data can tell us and 
> what remains uncertain,

What about chapter 8 section 5 page 363 in Wu/Hamada "Experiments --- 
Planning , analysis, and Parameter
 design Optimization", titled:
A Bayesian variable selection Strategy for Designs with complex Aliasing

Kjetil


> including the level of partial confounding between alternative 
> explanations.  I think I've gotten reasonable results with my own 
> modification of Venables & Ripley's stepAIC to compute an approximate 
> posterior over tested models using the AICc criterion described, e.g., 
> by Burnham and Anderson (2002) Model Selection and Multi-Model 
> Inference (Springer).  Preliminary simulations showed that when I used 
> the naive prior (that all models are equally likely, including the 
> null model), the null model is usually rejected when true.  What a 
> surprise!  I think I can fix that using a more intelligent prior.  I 
> also think I can evaluate the partial confounding between alternative 
> models by studying the correlation matrix between the predictions of 
> alternative models.
>      Comments?
>      Thanks,
>      Spencer Graves
>
> Frank E Harrell Jr wrote:
>
>> Smit, Robin wrote:
>>
>>> I am hoping to get some advise on the following:
>>>  
>>> I am looking for an automatic variable selection procedure to reduce 
>>> the
>>> number of potential predictor variables (~ 50) in a multiple regression
>>> model.
>>>  
>>> I would be interested to use the forward stepwise regression using the
>>> partial F test. I have looked into possible R-functions but could 
>>> not find this
>>> particular approach.  There is a function (stepAIC) that uses the 
>>> Akaike criterion or Mallow's
>>> Cp criterion. In addition, the drop1 and add1 functions came closest 
>>> to what I want
>>> but with them I cannot perform the required procedure. Do you have 
>>> any ideas?  Kind regards,
>>> Robin Smit
>>> --------------------------------------------
>>> Business Unit TNO Automotive
>>> Environmental Studies & Testing
>>> PO Box 6033, 2600 JA Delft
>>> THE NETHERLANDS
>>
>>
>>
>> Robin,
>>
>> If you are looking for a method that does not offer the best 
>> predictive accuracy and that violates every aspect of statistical 
>> inference, you are on the right track.  See 
>> http://www.stata.com/support/faqs/stat/stepwise.html for details.
>>
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
>
>
>


-- 

Kjetil Halvorsen.

Peace is the most effective weapon of mass construction.
               --  Mahdi Elmandjra




-- 
No virus found in this outgoing message.
Checked by AVG Anti-Virus.