Description Usage Arguments Value See Also Examples
Crossvalidate a glmnet PIM
1 2 3 |
pimob |
Object of class |
method |
|
type.measure, nfolds, foldid, weights |
See |
include.extrainfo |
If |
verbosity |
The higher this value, the more levels of progress and debug information is displayed (note: in R for Windows, turn off buffered output) |
... |
Passed on to |
An object of class cv.pim
, of class cv.glmnet
, and depending on the type
of linkfunction (see cv.glmnet
) some more classes.
It holds all the items of the original pimob
and all necessary items for a
cv.glmnet
object (so the relevant S3 methods like print
and plot
will
work for them.)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | set.seed(1)
pen.N<-100
pen.noisep<-50
pen.noisemat<-matrix(rnorm(pen.N*pen.noisep), nrow=pen.noisep)
pendta<-data.frame(y=rnorm(pen.N), x=factor(sample(2, pen.N, replace=TRUE)), pen.noisemat)
pendta$y[pendta$x=="2"]<-pendta$y[pendta$x=="2"]+1
colnames(pendta)[(1:pen.noisep)+2]<-paste("X", 1:pen.noisep, "X", sep="")
pen.formula<-paste("y~", paste(c("F(x)", setdiff(colnames(pendta), c("x", "y"))), collapse="+"), sep="")
penpim<-pim(as.formula(pen.formula), data=pendta, link="identity", poset=noselfposet,
estimator=estimator.glmnet(), varianceestimator=NULL, keep.data=TRUE, verbosity=0,
interpretation="regular")
cv.penpim.naive<-crossvalidate.pim(penpim, method="naive")
cv.penpim<-crossvalidate.pim(penpim, method="fullsplit")
cv.penpim.ps<-crossvalidate.pim(penpim, method="semisplit")
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