Calculates v-fold or leave-one-out cross-validation without selecting a new set of features with each fold. See Details.
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. The argument
fold specifies the number of v-folds to use during cross-validation.
Set fold = 0 to perform leave-one-out cross-validation. Cross-validation
accuracy is defined as the average accuracy from calcStats.
This type of cross-validation is most appropriate if the data
has not undergone any prior feature selection. However, it can also serve
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 model performance with calcStats, this
function forces aucSkip = TRUE and plotSkip = TRUE.