why not use lda{MASS} and it has cv=T option; it does "loo", though.

Or use randomForest.

if you have to use lrm, then the following code might help:

n.fold <- 5 # 5-fold cv

n.sample <- 50 # assumed 50 samples

s <- sample(1:n.fold, size=n.sample, replace=T)

for (i in 1:n.fold){

# create your training data and validation data for each fold

trn <- YOURWHOLEDATAFRAME[s!=i,]

val <- YOURWHOLEDATAFRAME[s==i,]

# now do your own modeling using lrm

# todo

}

HTH,

weiwei

On 1/21/07, nitin jindal <

[hidden email]> wrote:

> 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 <

[hidden email]> 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

> >

>

> [[alternative HTML version deleted]]

>

> ______________________________________________

>

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>

https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide

http://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.

>

--

Weiwei Shi, Ph.D

Research Scientist

GeneGO, Inc.

"Did you always know?"

"No, I did not. But I believed..."

---Matrix III

______________________________________________

[hidden email] mailing list

https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide

http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.