Re: [R] Logistic regression problem
The only solution I can see is fitting all possib le 2 factor models enabling interactions and then assessing if interaction term is significant... any more ideas? Milicic B. Marko wrote: > > 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 > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Logistic-regression-problem-tp19704948p19746846.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
Milicic B. Marko wrote: The only solution I can see is fitting all possib le 2 factor models enabling interactions and then assessing if interaction term is significant... any more ideas? Please don't suggest such a thing unless you do simulations to back up its predictive performance, type I error properties, and the impact of collinearities. You'll find this approach works as well as the U.S. economy. Frank Harrell Milicic B. Marko wrote: 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 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
Em Sáb, 2008-09-27 às 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
--
Bernardo Rangel Tura, M.D,MPH,Ph.D
National Institute of Cardiology
Brazil
__
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Re: [R] Logistic regression problem
So...I wouldn't suggest the trying all possible logistic models approach either and I'm not sure exactly what your goals are in modeling. However, I've been fiddling around with the variable importance (varimp) functions that come with the randomForest and party packages. The idea is to get an idea of which independent variables are likely to be useful and then to focus on those variables (identified as being of high importance) with more attention than you could spend on the whole set. A general advantage of the recursive partitioning approach is that it deals fairly nicely with interactions and collinearity. Theoretically, the recursive partitioning approaches should be able to deal with missing values (often a problem with large datasets), but I have been unable to apply this with the variable importance functions. Let me know if you require more details. You can check out http://www.biomedcentral.com/1471-2105/9/307 for a couple examples of variable importance. Jason Jones, PhD Medical Informatics [EMAIL PROTECTED] 801.707.6898 -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Frank E Harrell Jr Sent: Tuesday, September 30, 2008 2:54 PM To: Milicic B. Marko Cc: r-help@r-project.org Subject: Re: [R] Logistic regression problem Milicic B. Marko wrote: > The only solution I can see is fitting all possib le 2 factor models enabling > interactions and then assessing if interaction term is significant... > > > any more ideas? Please don't suggest such a thing unless you do simulations to back up its predictive performance, type I error properties, and the impact of collinearities. You'll find this approach works as well as the U.S. economy. Frank Harrell > > > > > Milicic B. Marko wrote: >> 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 >> >> __ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
Bernardo Rangel Tura wrote:
Em Sáb, 2008-09-27 às 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.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
Em Ter, 2008-09-30 às 18:56 -0500, Frank E Harrell Jr escreveu:
> Bernardo Rangel Tura wrote:
> > Em Sáb, 2008-09-27 às 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
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
Bernardo Rangel Tura wrote:
Em Ter, 2008-09-30 às 18:56 -0500, Frank E Harrell Jr escreveu:
Bernardo Rangel Tura wrote:
Em Sáb, 2008-09-27 às 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?
Hi Bernardo,
If there is a large number of potential predictors and no previous
knowledge to guide the modeling, principal components (PC) is often an
excellent way to proceed. The first few PCs can be put into the model.
The result is not always very interpretable, but you can "decode" the
PCs by using stepwise regression or recursive partitioning (which are
safer in this context because the stepwise methods are not exposed to
the Y variable). You can also add PCs in a stepwise fashion in the
pre-specified order of variance explained.
There are many variations on this theme including nonlinear principal
components (e.g., the transcan function in the Hmisc package) which may
explain more variance of the predictors.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic regression problem
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 Sáb, 2008-09-27 às 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
http://www.R-project.org/posting-guide.html and
provide commented, minimal, self-contained, reproducible code.
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED]
Least Cost Formulations, Ltd.URL: http://lcfltd.com/
824 Timberlake Drive Tel: 757-467-0954
Virginia Beach, VA 23464-3239Fax: 757-467-2947
"Vere scire est per causas scire"
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Re: [R] Logistic regression problem
From: Frank E Harrell Jr
>
> Bernardo Rangel Tura wrote:
> > Em Ter, 2008-09-30 às 18:56 -0500, Frank E Harrell Jr escreveu:
> >> Bernardo Rangel Tura wrote:
> >>> Em Sáb, 2008-09-27 às 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?
>
>
> Hi Bernardo,
>
> If there is a large number of potential predictors and no previous
> knowledge to guide the modeling, principal components (PC) is
> often an
> excellent way to proceed. The first few PCs can be put into
> the model.
> The result is not always very interpretable, but you can
> "decode" the
> PCs by using stepwise regression or recursive partitioning (which are
> safer in this context because the stepwise methods are not exposed to
> the Y variable). You can also add PCs in a stepwise fashion in the
> pre-specified order of variance explained.
>
> There are many variations on this theme including nonlinear principal
> components (e.g., the transcan function in the Hmisc package)
> which may
> explain more variance of the predictors.
While I agree with much of what Frank had said, I'd like to add some points.
Variable selection is a treacherous business whether one is interested in
prediction or inference. If the goal is inference, Frank's book is a
must read, IMHO. (It's great for predictive model building, too.)
If interaction is of high interest, principal components are not going
to give you that.
Regarding cutpoint selection: The machine learners have found that
the `optimal' split point for a continuous predictor in tree algorithms
are extremely variable, that interpreting them would be risky at best.
Breiman essentially gave up on interpretation of a single tree when he
went to random forests.
Best,
Andy
> Frank
> --
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics
> Vanderbilt University
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
Notice: This e-mail message, together with any attachme...{{dropped:12}}
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Re: [R] Logistic regression problem
Hi Bernardo,
Do you have to use logistic regression? If not, try Random Forests... It has
worked for me in past situations when I have to analyze huge datasets.
Some want to understand the DGP with a simple linear equation; others want high
generalization power. It is your call... See, e.g.,
www.cis.upenn.edu/group/datamining/ReadingGroup/papers/breiman2001.pdf.
Maybe you are also interested in AD-HOC, an algorithm for feature selection,
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.9130
Regards,
Pedro
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Liaw, Andy
Sent: Wednesday, October 01, 2008 12:01 PM
To: Frank E Harrell Jr; [EMAIL PROTECTED]
Cc: r-help@r-project.org
Subject: Re: [R] Logistic regression problem
From: Frank E Harrell Jr
>
> Bernardo Rangel Tura wrote:
> > Em Ter, 2008-09-30 às 18:56 -0500, Frank E Harrell Jr escreveu:
> >> Bernardo Rangel Tura wrote:
> >>> Em Sáb, 2008-09-27 às 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?
>
>
> Hi Bernardo,
>
> If there is a large number of potential predictors and no previous
> knowledge to guide the modeling, principal components (PC) is
> often an
> excellent way to proceed. The first few PCs can be put into
> the model.
> The result is not always very interpretable, but you can
> "decode" the
> PCs by using stepwise regression or recursive partitioning (which are
> safer in this context because the stepwise methods are not exposed to
> the Y variable). You can also add PCs in a stepwise fashion in the
> pre-specified order of variance explained.
>
> There are many variations on this theme including nonlinear principal
> components (e.g., the transcan function in the Hmisc package)
> which may
> explain more variance of the predictors.
While I agree with much of what Frank had said, I'd like to add some points.
Variable selection is a treacherous business whether one is interested in
prediction or inference. If the goal is inference, Frank's book is a
must read, IMHO. (It's great for predictive model building, too.)
If int
