If validate.lrm does not has this option, do any other function has it.
I will certainly look into your advice on cross validation. Thnx.

nitin

On 1/21/07, Frank E Harrell Jr <f.harrell@vanderbilt.edu> wrote:
>
> nitin jindal wrote:
> > Hi,
> >
> > I am trying to cross-validate a logistic regression model.
> > I am using logistic regression model (lrm) of package Design.
> >
> > f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE)
> > val <- validate.lrm(f, method="cross", B=5)
>
> val <- validate(f, ...)    # .lrm not needed
>
> >
> > My class cy has values 0 and 1.
> >
> > "val" variable will give me indicators like slope and AUC. But, I also
> need
> > the vector of predicted values of class variable "cy" for each record
> while
> > cross-validation, so that I can manually look at the results. So, is
> there
> > any way to get those probabilities assigned to each class.
> >
> > regards,
> > Nitin
>
> No, validate.lrm does not have that option.  Manually looking at the
> results will not be easy when you do enough cross-validations.  A single
> 5-fold cross-validation does not provide accurate estimates.  Either use
> the bootstrap or repeat k-fold cross-validation between 20 and 50 times.
>   k is often 10 but the optimum value may not be 10.  Code for averaging
> repeated cross-validations is in
> http://biostat.mc.vanderbilt.edu/twiki/pub/Main/RmS/logistic.val.pdf
> along with simulations of bootstrap vs. a few cross-validation methods
> for binary logistic models.
>
> Frank
> --
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                       Department of Biostatistics   Vanderbilt University
>

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