divideUp: Partition data into mutiple nearly equal subsets

In hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

Partition data into mutiple nearly equal subsets

Description

Randomly partition data into nearly equal subsets. If balanced=TRUE the requirement is imposed that the subsets should as far as possible be balanced with respect to a classifying factor. The multiple sets are suitable for use for determining the folds in a cross-validation.

Usage

divideUp(cl, nset = 2, seed = NULL, balanced=TRUE)

Arguments

cl	classifying factor
nset	number of subsets into which to partition data
seed	set the seed, if required, in order to obtain reproducible results
balanced	logical: should subsets be as far as possible balanced with respect to the classifying factor?

Value

a set of indices that identify the nset subsets

Author(s)

John Maindonald

Examples

foldid <- divideUp(cl=rep(1:3, c(17,14,8)), nset=10)
table(rep(1:3, c(17,14,8)), foldid)
foldid <- divideUp(cl=rep(1:3, c(17,14,8)), nset=10,
       	    balanced=FALSE)
table(rep(1:3, c(17,14,8)), foldid)


## The function is currently defined as
function(cl = rep(1:3, c(7, 4, 8)), nset=2, seed=NULL, balanced=TRUE){
    if(!is.null(seed))set.seed(seed)
    if(balanced){
      ord <- order(cl)
      ordcl <- cl[ord]
      gp0 <- rep(sample(1:nset), length.out=length(cl))
      gp <- unlist(split(gp0,ordcl), function(x)sample(x))
      gp[ord] <- gp
    } else
    gp <- sample(rep(1:nset, length.out=length(cl)))
    as.vector(gp)
  }

hddplot documentation built on Sept. 14, 2023, 5:07 p.m.