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]