[R] FW: logistic regression

[Frank E Harrell Jr]

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.

Cheers
Frank

> 
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
> Behalf Of Ted Harding
> Sent: Saturday, September 27, 2008 4:30 PM
> To: r-help at stat.math.ethz.ch
> Subject: Re: [R] FW: logistic regression
> 
> 
> 
> On 27-Sep-08 21:45:23, Dieter Menne wrote:
>> Frank E Harrell Jr <f.harrell <at> vanderbilt.edu> writes:
>>
>>> Estimates from this model (and especially standard errors and
>>> P-values)
>>> will be invalid because they do not take into account the stepwise 
>>> procedure above that was used to torture the data until they 
>>> confessed.
>>>
>>> Frank
>> Please book this as a fortune.
>>
>> Dieter
> 
> Seconded!
> Ted.
> 
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> E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
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> Date: 27-Sep-08                                       Time: 23:30:19
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University