> That seems like an overkill, don't you think? PRESS can be expressed as:
>
> sum { e_i / (1 - h_i) }^2
>
> so all you need are residuals() and hat().
>
for "model=lm" yes, but what about "model=randomForest"? :-)
best,
Torsten
> Andy
>
> > -----Original Message-----
> > From: Torsten Hothorn [mailto:[EMAIL PROTECTED]
> > Sent: Thursday, February 27, 2003 10:26 AM
> > To: Jacob van Wyk
> > Cc: [EMAIL PROTECTED]
> > Subject: Re: [R] PRESS again
> >
> >
> >
> >
> > > Sorry for the repeat.
> > > The PRESS statistic is defined as
> > > sum(y-yhat(i))^2, where yhat(i) denotes the ith predicted
> > value using
> > > all the data except the ith case (as used typically in
> > linear models).
> > > Thanks again
> > > Jacob
> > >
> >
> >
> > library(ipred)
> > errorest(y ~., data=mydata, model=lm, estimator="cv",
> > est.para=control.errorest(k=nrow(mydata)))
> >
> > does the job, given that your data is organized in a
> > data.frame "mydata"
> > with numeric response "y".
> >
> > Torsten
> >
> >
> >
> >
> > >
> > >
> > > Jacob L van Wyk
> > > Department of Mathematics and Statistics
> > > Rand Afrikaans University
> > > P O Box 524
> > > Auckland Park 2006
> > > South Africa
> > > Tel: +27-11-489-3080
> > > Fax: +27-11-489-2832
> > >
> > >
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