Hello Frank,

Thanks for your advice. 

I also used the validation using rms package. The results from rms indicate 
that the linear model used in my project is overfitted around 0.1 in terms of 
R2. But, the results from using ordinary boot method in boot package suggest 
the mean of R2 is even higher than the original one.  Then I guess maybe 
direclty using Boot package may not be a good idea for assessing the 
overfitting in the regression. 

Jim


--- On Mon, 5/3/12, Frank Harrell <f.harr...@vanderbilt.edu> wrote:

> From: Frank Harrell <f.harr...@vanderbilt.edu>
> Subject: Re: [R] 632 estimator using boot package
> To: r-help@r-project.org
> Date: Monday, 5 March, 2012, 17:06
> Bootstrapping does not leave one
> out.  As for .632 this is implemented in the
> rms package's validate and calibrate functions.  Note
> however that any
> claimed advantages of .632 over the ordinary optimism
> bootstrap seem to be a
> result only of the use of a discontinuous improper scoring
> role (proportion
> classified correctly).  The advantage may vanish when
> better scoring rules
> are used.  Simulations showing this may be found at
> http://biostat.mc.vanderbilt.edu/rms
> 
> Frank
> 
> Jin Minming wrote
> > 
> > Dear All,
> > 
> > Anyone has some idea how to implement 632 estimator and
> leave-one out
> > bootstraping method by using boot package.  I know
> the bootstrap package
> > has this function, but it sounds not very flexible for
> my project. 
> > 
> > Thanks,
> > 
> > Jim
> > 
> > ______________________________________________
> > R-help@ 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.
> > 
> 
> 
> -----
> Frank Harrell
> Department of Biostatistics, Vanderbilt University
> --
> View this message in context: 
> http://r.789695.n4.nabble.com/632-estimator-using-boot-package-tp4446720p4446736.html
> Sent from the R help mailing list archive at Nabble.com.
> 
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