thanks for your help. maybe I have poor statistics level, I can not well understand your means.

wishes
kevin<br><br>在2010-05-11，"Bert Gunter" <gunter.berton@gene.com> 写道：
>(Near) non-identifiability (especially in nonlinear models, which include
>linear mixed effects models, Bayesian hierarchical models, etc.) is
>typically a strong clue; usually indicated by software complaints (e.g.
>convergence failures, running up against iteration limits, etc.). 
>
>However this is sufficient-ish, not necessary: "over-fitting" frequently
>occurs even without such overt complaints. It should also be said that,
>except for identifiability,  "over-fitting" is not a well-defined
>statistical term: it depends on the scientific context.
>
>
>Bert Gunter
>Genentech Nonclinical Biostatistics
> 
> -----Original Message-----
>From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org] On
>Behalf Of Steve Lianoglou
>Sent: Sunday, May 09, 2010 6:13 PM
>To: David Winsemius
>Cc: r-help@r-project.org; bbslover
>Subject: Re: [R] How to estimate whether overfitting?
>
>On Sun, May 9, 2010 at 11:53 AM, David Winsemius <dwinsemius@comcast.net>
>wrote:
>>
>> On May 9, 2010, at 9:20 AM, bbslover wrote:
>>
>>>
>>> 1. is there some criterion to estimate overfitting?  e.g. R2 and Q2 in
>the
>>> training set, as well as R2 in the test set, when means overfitting.  
>for
>>> example,  in my data, I have R2=0.94 for the training set and  for the
>>> test
>>> set R2=0.70, is overfitting?
>>> 2. in this scatter, can one say this overfitting?
>>>
>>> 3. my result is obtained by svm, and the sample are 156 and 52 for the
>>> training and test sets, and predictors are 96,   In this case, can svm be
>>> employed to perform prediction?   whether the number of the predictors
>are
>>> too many ?
>>>
>>
>> I think you need to buy a copy of Hastie, Tibshirani, and Friedman and do
>> some self-study of chapters 7 and 12.
>
>And you don't even have to buy it before you can start studying since
>the PDF is available here:
>http://www-stat.stanford.edu/~tibs/ElemStatLearn/
>
>Having a hard cover is always handy, tho ..
>-steve
>
>-- 
>Steve Lianoglou
>Graduate Student: Computational Systems Biology
> | Memorial Sloan-Kettering Cancer Center
> | Weill Medical College of Cornell University
>Contact Info: http://cbio.mskcc.org/~lianos/contact
>
>______________________________________________
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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.
>

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