[R] Coefficients of Logistic Regression from bootstrap - how to get them?

[Michal Figurski]

What are the arguments against fidelity of this concept to scientific 
validity?

The concept of predictive performance was devised by one of you, 
biostatisticians - not me! I accept the authoritative view of the person 
that did it, especially because I do understand it.

When I think of it, excuse my ignorance, it looks to me that this 
measure summarizes effects of bias, variance, etc, and all the 
analytical and other errors. Please correct me if I am wrong, but spare 
me your sarcasm.

--
Michal J. Figurski

Bert Gunter wrote:
> To quote (or as nearly so as I can) Einstein's famous remark:
> 
> "Make everything as simple as possible ... but no simpler" 
> 
> Moreover, "as possible" here means "maintaining fidelity to scientific
> validity," not "simple enough for me to understand." So I don't think a
> physicist can explain relativistic cosmology to me (or an organic chemist,
> how to synthesize ketones) so that I can understand it without compromising
> scientific validity. The onus is then on me to either learn what I need to
> know to understand it, or accept the authoritative view of the physicist (or
> chemist). I cannot claim ignorance and reject the cosmology because it is
> beyond me. That's the "flat earth" philosophy of science, and it is a
> terrible obstacle to scientific progress and human enlightenment, in
> general.
> 
> Cheers,
> Bert Gunter
> 
> 
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
> Behalf Of Michal Figurski
> Sent: Thursday, July 24, 2008 8:03 AM
> Cc: r-help at r-project.org
> Subject: Re: [R] Coefficients of Logistic Regression from bootstrap - how to
> get them?
> 
> Greg and all,
> 
> Just another thought on bias and variability. As I tried to explain, I 
> perceive this problem as a very practical problem.
> 
> The equation, that is the goal of this work, is supposed to serve the 
> clinicians to estimate a pharmacokinetic parameter. It therefore must be 
> simple and also presented in simple language, so that an average 
> spreadsheet user can make use of it.
> 
> Therefore, in the end, isn't the *predictive performance* an ultimate 
> measure of it all? Doesn't it account for bias and all the other stuff? 
> It does tell you in how many cases you may expect to have the predicted 
> value within 15% of the true value.
> I apologize for my naive questions again, but aren't then the 
> calculations of bias and variance, etc, just a waste of time, while you 
> have it all summarized in the predictive performance?
> 
> --
> Michal J. Figurski
> 
> Greg Snow wrote:
>>> -----Original Message-----
>>> From: r-help-bounces at r-project.org
>>> [mailto:r-help-bounces at r-project.org] On Behalf Of Michal Figurski
>>> Sent: Wednesday, July 23, 2008 10:22 AM
>>> To: r-help at r-project.org
>>> Subject: Re: [R] Coefficients of Logistic Regression from
>>> bootstrap - how to get them?
>>>
>>> Thank you all for your words of wisdom.
>>>
>>> I start getting into what you mean by bootstrap. Not
>>> surprisingly, it seems to be something else than I do. The
>>> bootstrap is a tool, and I would rather compare it to a
>>> hammer than to a gun. People say that hammer is for driving
>>> nails. This situation is as if I planned to use it to break rocks.
>> The bootstrap is more like a whole toolbox than just a single tool.  I
> think part of the confusion in this discussion is because you kept asking
> for a hammer and Frank and others kept looking at their toolbox full of
> hammers and asking you which one you wanted.  Yes you can break a rock with
> a hammer designed to drive nails, but why not use the hammer designed to
> break rocks when it is easily available.
>>> The key point is that I don't really care about the bias or
>>> variance of the mean in the model. These things are useful
>>> for statisticians; regular people (like me, also a chemist)
>>> do not understand them and have no use for them (well, now I
>>> somewhat understand). My goal is very
>>> practical: I need an equation that can predict patient's
>>> outcome, based on some data, with maximum reliability and accuracy.
>> But to get the model with maximum reliability and accuracy you need to
> account for bias and minimize variability.  You may not care what those
> numbers are directly, but you do care indirectly about their influence on
> your final model.  Another instance where both sides were talking past each
> other.
>> --
>> Gregory (Greg) L. Snow Ph.D.
>> Statistical Data Center
>> Intermountain Healthcare
>> greg.snow at imail.org
>> (801) 408-8111
> 
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