Thanks a lot. I have changed the calculation method by using optimism defined
by Efron. The results from using boot and rms packages are quite close now.
Jim
--- On Tue, 6/3/12, Frank Harrell wrote:
> From: Frank Harrell
> Subject: Re: [R] 632 estimator using boot package
> To:
aybe 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.harrell@> wrote:
>
>> From: Frank Harrell <f.harrell@>
>> Subject: Re: [R] 632 estimator usi
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 wrote:
> From: Frank Harrell
> Subject: Re: [R] 632 estimator using boot package
> To:
Thanks a lot,
I will check that.
Jim
--- On Mon, 5/3/12, Angelo Canty wrote:
> From: Angelo Canty
> Subject: Re: [R] 632 estimator using boot package
> To: r-help@r-project.org
> Date: Monday, 5 March, 2012, 18:19
> There is an example of calculating
> the 0.632 prediction
Sorry, the final sentence should say sim="parametric"
Angelo Canty wrote:
There is an example of calculating the 0.632 prediction error
estimator in Chapter 6 of Davison & Hinkley (Practical 6.5)
I'm not sure what you mean by leave-one-out bootstrapping. If you
actually mean the jackknife the
There is an example of calculating the 0.632 prediction error estimator
in Chapter 6 of Davison & Hinkley (Practical 6.5)
I'm not sure what you mean by leave-one-out bootstrapping. If you
actually mean the jackknife then look at the empinf function. If you
mean subsampling, this can be impleme
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 (proporti
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