[R] Coefficients of Logistic Regression from bootstrap - how to get them?

[Bert Gunter]

The bootstrap **can** be used for bias correction. However, it may not be
such a good thing to do. I quote from Efron and Tibshirani's AN INTRODUCTION
TO THE BOOTSTRAP (p.138):

"... bias estimation is usually interesting and worthwhile, but the exact
use of a bias estimate is often problematic. Biases are harder to estimate
than than standard errors... The straightforward bias correxction can be
dangerous to use in practice, due to high variability in bias.  Correcting
the bias may cause a large increase in the standard error, which in turn
results in a larger rms... "

Proceed at your own risk...

Cheers,
Bert Gunter
Genentech

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Michal Figurski
Sent: Tuesday, July 22, 2008 7:44 AM
To: Doran, Harold; r-help at r-project.org
Subject: Re: [R] Coefficients of Logistic Regression from bootstrap - how to
get them?

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: r-help-bounces at r-project.org 
>> [mailto:r-help-bounces at r-project.org] On Behalf Of Michal Figurski
>> Sent: Tuesday, July 22, 2008 9:52 AM
>> To: r-help at 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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