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: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Michal Figurski
Sent: Wednesday, July 23, 2008 10:22 AM
To: r-help@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
[EMAIL PROTECTED]
(801) 408-8111
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