Calculates v-fold or leave-one-out cross-validation without selecting a new set of features with each fold.
plCV(array, top, how, fold, ...)
Specifies the ExprsArray
object to undergo cross-validation.
A numeric scalar or character vector. A numeric scalar indicates
the number of top features that should undergo feature selection. A character vector
indicates specifically which features by name should undergo feature selection.
Set top = 0
to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.
A character string. Specifies the build
method to iterate.
A numeric scalar. Specifies the number of folds for cross-validation.
Set fold = 0
to perform leave-one-out cross-validation.
Arguments passed to the how
method.
A numeric scalar. The cross-validation accuracy.
plCV
performs v-fold or leave-one-out cross-validation for an
ExprsArray
object. The argument fold
specifies the number
of v-folds to use during cross-validation. Set fold = 0
to perform
leave-one-out cross-validation. This approach to cross-validation
will work for ExprsBinary
and ExprsMulti
objects alike. The
peformance metric used to measure cross-validation accuracy is the
acc
slot returned by calcStats
.
This type of cross-validation is most appropriate if the ExprsArray
has not undergone any prior feature selection. However, it may also have a role
as an unbiased guide to parameter selection when embedded in
plGrid
. If using cross-validation in lieu of an independent test
set in the setting of one or more feature selection methods, consider using
a more "sophisticated" form of cross-validation as implemented in
plMonteCarlo
or plNested
.
When calculating classifier performance with calcStats
, this
function forces aucSkip = TRUE
and plotSkip = TRUE
.
fs
build
doMulti
exprso-predict
plCV
plGrid
plGridMulti
plMonteCarlo
plNested