ridgeParamEst(dat, X, weights = rep(1,times=nRows), refs,
tol=1.0e-010, only.ridge=FALSE, doPlot=FALSE,
col="blue",type="l", ...)
refs
using a method that is a function of the sample size N
:
if N<=20< code="">, leave-one-out is used refs=1:N
, if N<=40< code="">,
10-fold Cross Validation is used where group membership is chosen randomly
but with equal size groups, otherwise 5-fold CV with random group memberships.
=40<>
=20<>
only.ridge=FALSE
the returned list additionally contains the element:
manylm
data(spider)
spiddat <- mvabund(spider$abund)
X <- spider$x
ridgeParamEst(dat = spiddat, X = model.matrix(spiddat~X))
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