Hmm...
It sounds like ideology to me. I was asking for technical help. I know
what I want to do, just don't know how to do it in R. I'll go back to
SAS then. Thank you.
--
Michal J. Figurski
Doran, Harold wrote:
I think the answer has been given to you. If you want to continue to
ignore that advice and use bootstrap for point estimates rather than the
properties of those estimates (which is what bootstrap is for) then you
are on your own.
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Michal Figurski
Sent: Tuesday, July 22, 2008 9:52 AM
To: r-help@r-project.org
Subject: Re: [R] Coefficients of Logistic Regression from
bootstrap - how to get them?
Dear all,
I don't want to argue with anybody about words or about what
bootstrap is suitable for - I know too little for that.
All I need is help to get the *equation coefficients*
optimized by bootstrap - either by one of the functions or by
simple median.
Please help,
--
Michal J. Figurski
HUP, Pathology & Laboratory Medicine
Xenobiotics Toxicokinetics Research Laboratory 3400 Spruce
St. 7 Maloney Philadelphia, PA 19104 tel. (215) 662-3413
Frank E Harrell Jr wrote:
Michal Figurski wrote:
Frank,
"How does bootstrap improve on that?"
I don't know, but I have an idea. Since the data in my set
are just a
small sample of a big population, then if I use my whole
dataset to
obtain max likelihood estimates, these estimates may be
best for this
dataset, but far from ideal for the whole population.
The bootstrap, being a resampling procedure from your
sample, has the
same issues about the population as MLEs.
I used bootstrap to virtually increase the size of my dataset, it
should result in estimates more close to that from the
population -
isn't it the purpose of bootstrap?
No
When I use such median coefficients on another dataset (another
sample from population), the predictions are better, than
using max
likelihood estimates. I have already tested that and it worked!
Then your testing procedure is probably not valid.
I am not a statistician and I don't feel what
"overfitting" is, but
it may be just another word for the same idea.
Nevertheless, I would still like to know how can I get the
coeffcients for the model that gives the "nearly unbiased
estimates".
I greatly appreciate your help.
More info in my book Regression Modeling Strategies.
Frank
--
Michal J. Figurski
HUP, Pathology & Laboratory Medicine
Xenobiotics Toxicokinetics Research Laboratory 3400 Spruce St. 7
Maloney Philadelphia, PA 19104 tel. (215) 662-3413
Frank E Harrell Jr wrote:
Michal Figurski wrote:
Hello all,
I am trying to optimize my logistic regression model by using
bootstrap. I was previously using SAS for this kind of
tasks, but I
am now switching to R.
My data frame consists of 5 columns and has 109 rows.
Each row is a
single record composed of the following values: Subject_name,
numeric1, numeric2, numeric3 and outcome (yes or no). All three
numerics are used to predict outcome using LR.
In SAS I have written a macro, that was splitting the dataset,
running LR on one half of data and making predictions on second
half. Then it was collecting the equation coefficients from each
iteration of bootstrap. Later I was just taking medians of these
coefficients from all iterations, and used them as an
optimal model
- it really worked well!
Why not use maximum likelihood estimation, i.e., the coefficients
from the original fit. How does the bootstrap improve on that?
Now I want to do the same in R. I tried to use the 'validate' or
'calibrate' functions from package "Design", and I also
experimented with function 'sm.binomial.bootstrap' from package
"sm". I tried also the function 'boot' from package
"boot", though
without success
- in my case it randomly selected _columns_ from my data frame,
while I wanted it to select _rows_.
validate and calibrate in Design do resampling on the rows
Resampling is mainly used to get a nearly unbiased
estimate of the
model performance, i.e., to correct for overfitting.
Frank Harrell
Though the main point here is the optimized LR equation. I would
appreciate any help on how to extract the LR equation
coefficients
from any of these bootstrap functions, in the same form
as given by
'glm' or 'lrm'.
Many thanks in advance!
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