xval-methods {MLInterfaces}R Documentation

support for cross-validatory machine learning with exprSets

Description

support for cross-validatory machine learning with exprSets

Usage

balKfold(K)

Arguments

K number of balanced cross-validation partitions

Methods

data = "exprSet", classLab = "character", proc = "nonstandardGeneric", xvalMethod = "character", group = "integer"
classLab is the name of a component of the phenoData of the exprSet passed as data. proc is an actual MLInterfaces method (not the name of a method). xvalMethod may have value "LOO" for leave-one-out or "LOG" for leave-group-out. The latter makes use of the group parameter. samples sharing a value of group are left out in one iteration of the cross-validation procedure, and predictions are made for them together on the basis of the fit from which they were excluded.

Examples

library(golubEsets)
data(golubMerge)
smallG <- golubMerge[200:250,]
lk1 <- xval(smallG, "ALL.AML", knnB, xvalMethod="LOO", group=as.integer(0))
table(lk1,smallG$ALL.AML)
lk2 <- xval(smallG, "ALL.AML", knnB, xvalMethod="LOG", group=as.integer(
 rep(1:8,each=9)))
table(lk2,smallG$ALL.AML)

[Package MLInterfaces version 1.0.10 Index]