Hola!

If the original questioner wants a guide as to how variables to measure
IN THE FUTURE, when using his model in practice, thwen I think he will be
unhappy with any advice which forces him to measure each of the 44 variables
when probably a small subset will do!   What is wrong with first using, let
us say, penalized likelihood, maybe with CV to choose degree of smoothing,
and SECONDLY using stepwise (maybee stepAIC from MASS) with
the predicted values from the first step model to get a good few-vatiables
approximation which can be used in practice? If my memory is'nt too bad,
that
idea is from harrel's book.

Kjetil

On Mon, Sep 29, 2008 at 9:50 PM, Frank E Harrell Jr <
f.harrell@vanderbilt.edu> wrote:

> Greg Snow wrote:
>
>> -----Original Message-----
>>> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
>>> project.org] On Behalf Of Frank E Harrell Jr
>>> Sent: Saturday, September 27, 2008 7:15 PM
>>> To: Darin Brooks
>>> Cc: dieter.menne@menne-biomed.de; r-help@stat.math.ethz.ch;
>>> ted.harding@manchester.ac.uk
>>> Subject: Re: [R] FW: logistic regression
>>>
>>> Darin Brooks wrote:
>>>
>>>> Glad you were amused.
>>>>
>>>> I assume that "booking this as a fortune" means that this was an
>>>>
>>> idiotic way
>>>
>>>> to model the data?
>>>>
>>> Dieter was nominating this for the "fortunes" package in R.  (Thanks
>>> Dieter)
>>>
>>>  MARS?  Boosted Regression Trees?  Any of these a better choice to
>>>>
>>> extract
>>>
>>>> significant predictors (from a list of about 44) for a measured
>>>>
>>> dependent
>>>
>>>> variable?
>>>>
>>> Or use a data reduction method (principal components, variable
>>> clustering, etc.) or redundancy analysis (to remove individual
>>> predictors before examining associations with Y), or fit the full model
>>> using penalized maximum likelihood estimation.  lasso and lasso-like
>>> methods are also worth pursuing.
>>>
>>
>> Frank (and any others who want to share an opinion):
>>
>> What are your thoughts on model averaging as part of the above list?
>>
>
> Model averaging has good performance but no advantage over fitting a single
> complex model using penalized maximum likelihood estimation.
>
> Frank
>
>
>>
>> --
>> Gregory (Greg) L. Snow Ph.D.
>> Statistical Data Center
>> Intermountain Healthcare
>> greg.snow@imail.org
>> 801.408.8111
>>
>>
>>
>>
>
> --
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                     Department of Biostatistics   Vanderbilt University
>
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