[R] Logistic regression problem
Robert A LaBudde
ral at lcfltd.com
Wed Oct 1 17:51:06 CEST 2008
It would not be possible to answer your original
question until you specify your goal.
Is it to develop a model with external validity
that will generalize to new data? (You are not
likely to succeed, if you are starting with a
"boil the ocean" approach with 44,000+ covariates
and millions of records.) This is the point Prof. Harrell is making.
Or is it to reduce a large dataset to a tractable
predictor formula that only interpolates your dataset?
If the former, you will need external modeling
information to select the "wheat from the chaff"
in your excessive predictor set.
Assuming it is the latter, then almost any
approach that ends up with a tractable model
(that has no meaning other than interpolation of
this specific dataset) will be useful. For this,
regression trees or even stepwise regression
would work. The algorithm must be very simple and
computer efficient. This is the area of data mining approaches.
I would suggest you start by looking at covariate
patterns to find out where the scarcity lies.
These will end up high leverage data.
Another place to start is common sense: Thousands
of covariates cannot all contain independent
information of value. Try to cluster them and
pick the best representative from each cluster
based on expert knowledge. You may solve your problem quickly that way.
At 05:34 AM 10/1/2008, Bernardo Rangel Tura wrote:
>Em Ter, 2008-09-30 Ã s 18:56 -0500, Frank E
>Harrell Jr escreveu: > Bernardo Rangel Tura
>wrote: > > Em SÃ¡b, 2008-09-27 Ã s 10:51 -0700,
>milicic.marko escreveu: > >> I have a huge data
>set with thousands of variable and one
>binary > >> variable. I know that most of the
>variables are correlated and are not > >> good
>predictors... but... > >> > >> It is very hard
>to start modeling with such a huge dataset. What
>would > >> be your suggestion. How to make a
>first cut... how to eliminate most > >> of the
>variables but not to ignore potential
>interactions... for > >> example, maybe variable
>A is not good predictor and variable B is
>not > >> good predictor either, but maybe A and
>B together are good > >> predictor... > >> > >>
>Any suggestion is welcomed > > > > > >
>milicic.marko > > > > I think do you start with
>a rpart("binary variable"~.) > > This show you a
>set of variables to start a model and the start
>set to > > curoff for continous variables > > I
>cannot imagine a worse way to formulate a
>regression model. Reasons > include > > 1.
>Results of recursive partitioning are not
>trustworthy unless the > sample size exceeds
>50,000 or the signal to noise ratio is extremely
>high. > > 2. The type I error of tests from the
>final regression model will be > extraordinarily
>inflated. > > 3. False interactions will appear
>in the model. > > 4. The cutoffs so chosen will
>not replicate and in effect assume that >
>covariate effects are discontinuous and
>piecewise flat. The use of > cutoffs results in
>a huge loss of information and power and makes
>the > analysis arbitrary and impossible to
>interpret (e.g., a high covariate > value:low
>covariate value odds ratio or mean difference is
>a complex > function of all the covariate values
>in the sample). > > 5. The model will not
>validate in new data. Professor Frank, Thank you
>for your explain. Well, if my first idea is
>wrong what is your opinion on the following
>approach? 1- Make PCA with data excluding the
>binary variable 2- Put de principal components
>in logistic model 3- After revert principal
>componentes in variable (only if is interesting
>for milicic.marko) If this approach is wrong too
>what is your approach? -- Bernardo Rangel Tura,
>M.D,MPH,Ph.D National Institute of Cardiology
>Brazil
>______________________________________________
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>PLEASE do read the posting guide
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Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
824 Timberlake Drive Tel: 757-467-0954
Virginia Beach, VA 23464-3239 Fax: 757-467-2947
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