balKfold.xvspec: generate a partition function for cross-validation, where the...

In MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

Description

generate a partition function for cross-validation, where the partitions are approximately balanced with respect to the distribution of a response variable

Usage

balKfold.xvspec(K)

Arguments

K	number of partitions to be computed

Details

This function returns a closure. The symbol K is bound in the environment of the returned function.

Value

A closure consisting of a function that can be used as a partitionFunc for passage in xvalSpec.

Author(s)

VJ Carey <[e-mail]>

Examples

## The function is currently defined as
function (K) 
function(data, clab, iternum) {
    clabs <- data[[clab]]
    narr <- nrow(data)
    cnames <- unique(clabs)
    ilist <- list()
    for (i in 1:length(cnames)) ilist[[cnames[i]]] <- which(clabs == 
        cnames[i])
    clens <- lapply(ilist, length)
    nrep <- lapply(clens, function(x) ceiling(x/K))
    grpinds <- list()
    for (i in 1:length(nrep)) grpinds[[i]] <- rep(1:K, nrep[[i]])[1:clens[[i]]]
    (1:narr)[-which(unlist(grpinds) == iternum)]
  }
# try it out
library("MASS")
data(crabs)
p1c = balKfold.xvspec(5)
inds = p1c( crabs, "sp", 3 )
table(crabs$sp[inds] )
inds2 = p1c( crabs, "sp", 4 )
table(crabs$sp[inds2] )
allc = 1:200
# are test sets disjoint?
intersect(setdiff(allc,inds), setdiff(allc,inds2))

MLInterfaces documentation built on Nov. 8, 2020, 8:14 p.m.