--- On Thu, 7/24/08, Michal Figurski <[EMAIL PROTECTED]> wrote:
> From: Michal Figurski <[EMAIL PROTECTED]>
> Subject: Re: [R] Coefficients of Logistic Regression from bootstrap - how to
> get them?
> To:
> Cc: "r-help@r-project.org" <r-help@r-project.org>
> Received: Thursday, July 24, 2008, 11:02 AM
> 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?
I think you need to look at Greg Snow's comment again. I am not a statistician
but ....
Greg says:
"But to get the model with maximum reliability and accuracy you need to account
for bias and minimize variability."
As I read it, your predictive validity is partly a function of how well you
account for bia and minimize variablility.
Prediction may be the desired outcome but you don't get the best possible
outcome unless you manage to account for these issues.
> 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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