What you describe is not cross-validation, so I am afraid we do not
know what you mean. And cv.glm does 'prediction for the hold-out
data' for you: you can read the code to see how it does so.
I suspect you mean you want to do validation on a test set, but that
is not what you actually claim. There are lots of examples of this
sort of thing in MASS (the book, scripts in the package).
On Wed, 24 Aug 2011, Andra Isan wrote:
Hi All,
I have a fitted model called glm.fit which I used glm and data dat
is my data frame
pred= predict(glm.fit, data = dat, type="response")
to predict how it predicts on my whole data but obviously I have to
do cross-validation to train the model on one part of my data and
predict on the other part. So, I searched for it and I found a
function cv.glm which is in package boot. So, I tired to use it as:
cv.glm = (cv.glm(dat, glm.fit, cost, K=nrow(dat))$delta)
but I am not sure how to do the prediction for the hold-out data. Is
there any better way for cross-validation to learn a model on
training data and test it on test data in R?
Thanks,
Andra
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Brian D. Ripley, rip...@stats.ox.ac.uk
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