dist.apply.samples.ranked: Create a Distance Vector based on Normalized Ranking of...

In thomasWeise/distanceR: An R Package for Creating and Dealing with \code{\link[stats]{dist}} Objects

Description

Create a distance matrix/vector where the actual distances from samples are replaced by their mean ranks. This should allow for scale-independent, robust distance matrices.

Usage

dist.apply.samples.ranked(X, FUN = distance.euclidean, sampler = identity,
  rank.all = rank.dist, rank.fromSingle = identity, aggregate = mean,
  FUN.VALUE = +Inf, cores = 1L, logging = FALSE)

Arguments

X	the list or vector of elements to be compared
FUN	the distance function, a function accepting two samples and returning one distance value
sampler	the sampling function, returning a vector or list of samples for an element of X
rank.all	the ranking to be applied to all distances
rank.fromSingle	the ranking to be applied to all the distances from one specific sample to the other samples
aggregate	the aggregation function which will join distances computed with FUN of all combinations of the samples from two elements of X into a single value
FUN.VALUE	the value to be used for situations where an element of X contains no samples
cores	the number of cores to be used for parallel computation
logging	the logging setup, see makeLogger

Value

a vector of values that can be used to produce a distance matrix

thomasWeise/distanceR documentation built on May 14, 2019, 7:35 a.m.