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

[Michal Figurski]

Tim,

If I understand correctly, you are saying that one can't improve on 
estimating a mean by doing bootstrap and summarizing means of many such 
steps. As far as I understand (again), you're saying that this way one 
can only add bias without any improvement...

Well, this is in contradiction to some guides to bootstrap, that I found 
on the web (I did my homework), for example to this one:
http://people.revoledu.com/kardi/tutorial/Bootstrap/Lyra/Bootstrap 
Statistic Mean.htm

It is all confusing, guys... Once somebody said, that there are as many 
opinions on a topic, as there are statisticians...

Also, translating your statements into the example of hammer and rock, 
you are saying that one cannot use hammer to break rocks because it was 
created to drive nails.

With all respect, despite my limited knowledge, I do not agree.
The big point is that the mean, or standard error, or confidence 
intervals of the data itself are *meaningless* in the pharmacokinetic 
dataset. These data are time series of a highly variable quantity, that 
is known to display a peak (or two in the case of Pawinski's paper). It 
is as if you tried to calculate a mean of a chromatogram (example for 
chemists, sorry).

Nevertheless, I thank all of you, experts, for your insight and advice. 
In the end, I learned a lot, though I keep my initial view. Summarizing 
your criticism of the procedure described in Pawinski's paper:
  - Some of you say that this isn't bootstrap at all. In terms of 
terminology I totally submit to that, because I know too little. Would 
anyone suggest a name?
  - Most of you say that this procedure is not the best one, that there 
are better ways. I will definitely do my homework on penalized 
regression, though no one of you has actually discredited this 
methodology. Therefore, though possibly not optimal, it remains valid.
  - The criticism on "predictive performance" is that one has to take 
into account also other important quantities, like bias, variance, etc. 
Fortunately I did that in my work: using RMSE and log residuals from the 
validation process. I just observed that models with relatively small 
RMSE and log residuals (compared to other models) usually possess good 
predictive performance. And vice versa.
Predictive performance has also a great advantage over RMSE or variance 
or anything else suggested here - it is easily understood by 
non-statisticians. I don't think it is /too simple/ in Einstein's terms, 
it's just simple.

Kind regards,

--
Michal J. Figurski


Tim Hesterberg wrote:
> I'll address the question of whether you can use the bootstrap to
> improve estimates, and whether you can use the bootstrap to "virtually
> increase the size of the sample".
> 
> Short answer - no, with some exceptions (bumping / Random Forests).
> 
> Longer answer:
> Suppose you have data (x1, ..., xn) and a statistic ThetaHat,
> that you take a number of bootstrap samples (all of size n) and
> let ThetaHatBar be the average of those bootstrap statistics from
> those samples.
> 
> Is ThetaHatBar better than ThetaHat?  Usually not.  Usually it
> is worse.  You have not collected any new data, you are just using the
> existing data in a different way, that is usually harmful:
> * If the statistic is the sample mean, all this does is to add
>   some noise to the estimate
> * If the statistic is nonlinear, this gives an estimate that
>   has roughly double the bias, without improving the variance.
> 
> What are the exceptions?  The prime example is tree models (random
> forests) - taking bootstrap averages helps smooth out the
> discontinuities in tree models.  For a simple example, suppose that a
> simple linear regression model really holds:
> 	y = beta x + epsilon
> but that you fit a tree model; the tree model predictions are
> a step function.  If you bootstrap the data, the boundaries of
> the step function will differ from one sample to another, so
> the average of the bootstrap samples smears out the steps, getting
> closer to the smooth linear relationship.
> 
> Aside from such exceptions, the bootstrap is used for inference
> (bias, standard error, confidence intervals), not improving on
> ThetaHat.
> 
> Tim Hesterberg
> 
>> Hi Doran,
>>
>> Maybe I am wrong, but I think bootstrap is a general resampling method which
>> can be used for different purposes...Usually it works well when you do not
>> have a presentative sample set (maybe with limited number of samples).
>> Therefore, I am positive with Michal...
>>
>> P.S., overfitting, in my opinion, is used to depict when you got a model
>> which is quite specific for the training dataset but cannot be generalized
>> with new samples......
>>
>> Thanks,
>>
>> --Jerry
>> 2008/7/21 Doran, Harold <HDoran at air.org>:
>>
>>>> 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, not really. The bootstrap is a resampling method for variance
>>> estimation. It is often used when there is not an easy way, or a closed
>>> form expression, for estimating the sampling variance of a statistic.
>