centralityQuantile: centralityQuantile

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

centralityQuantile

Description

Computes quantile of distanceToCenter of data in a dataset, 0 is the median, 1 is the farthest point

Usage

centralityQuantile(x, ...)

## Default S3 method:
centralityQuantile(
  x,
  ...,
  d = distanceToCenter(x, ..., distType = distType),
  distType = c("meandistance", "median", "spatialdepth"),
  ties.method = "first"
)

centralityRank(x, ...)

## Default S3 method:
centralityRank(
  x,
  ...,
  d = distanceToCenter(x, ..., distType = distType),
  distType = c("meandistance", "median", "spatialdepth"),
  ties.method = "first"
)

Arguments

x	the dataset
...	further parameters to [distanceToCenter]
d	the distances to median
distType	"meandistance" for mean distance to datapoints, "median" for distance to median, "spatialdepth" for the spatial depth.
ties.method	see [rank]: the method for breaking ties in the ranks

Value

A numeric vector in [0,1], 0 for the most central point and 1 for the most peripheral.

Methods (by class)

• centralityQuantile(default): default method

Functions

• centralityRank(): returns the rank rather than the quantile: an integer vector of ranks, 1 for the most central point up to the number of observations for the most peripheral.

• centralityRank(default): default method returning ranks

See Also

Other caseStatistics: caseE(), caseLogRatios(), caseMeans(), caseQuantiles(), caseSds(), caseVars(), distanceToCenter(), median.BData(), spatialDepth()

Examples

x = rnorm(100)
plot(x,centralityQuantile(x))
plot(x,centralityRank(x))

DistributionalDDA documentation built on Oct. 20, 2026, 5:06 p.m.