Description Usage Arguments Details Author(s) References See Also Examples
This function computes crossvalidated deviance or prediction errors
for step.plr.
The parameters that can be crossvalidated are
lambda
and cp
.
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x 
matrix of features 
y 
binary response 
weights 
optional vector of weights for observations 
nfold 
number of folds to be used in crossvalidation. Default is

folds 
list of crossvalidation folds. Its length must be 
lambda 
vector of the candidate values for 
cp 
vector of the candidate values for 
cv.type 
If 
trace 
If 
... 
other options for 
This function computes crossvalidated deviance or prediction errors
for step.plr.
The parameters that can be crossvalidated are
lambda
and cp
. If both are input as vectors (of length
greater than 1), then a twodimensional crossvalidation is done. If
either one is input as a single value, then the crossvalidation is
done only on the parameter with multiple inputs.
Mee Young Park and Trevor Hastie
Mee Young Park and Trevor Hastie (2008) Penalized Logistic Regression for Detecting Gene Interactions
step.plr
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