Dimitris Rizopoulos wrote:
you could something like this (based on V&R's S Programming, pp. 175):
dat <- data.frame(matrix(rnorm(100*6), 100, 6))
#####
n <- nrow(dat)
V <- 10 # number of folds
samps <- sample(rep(1:V, length=n), n, replace=FALSE)
#####
# Using the first fold:
train <- dat[samps!=1,] # fit the model
test <- dat[samps==1,] # predict
Or see ?errorest in the "ipred" package.
Uwe Ligges
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat
http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: "kolluru ramesh" <[EMAIL PROTECTED]>
To: "Rpackage help" <r-help@stat.math.ethz.ch>
Sent: Friday, January 21, 2005 11:19 AM
Subject: [R] cross validation
How to select training data set and test data set from the original
data for performing cross-validation
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