distcor: Calculates the distance correlation...

View source: R/distcov_function.R

distcorR Documentation

Calculates the distance correlation \insertCiteszekely2007,szekely2009browniandcortools.

Description

Calculates the distance correlation \insertCiteszekely2007,szekely2009browniandcortools.

Usage

distcor(
  X,
  Y,
  affine = FALSE,
  standardize = FALSE,
  bias.corr = FALSE,
  type.X = "sample",
  type.Y = "sample",
  metr.X = "euclidean",
  metr.Y = "euclidean",
  use = "all",
  algorithm = "auto"
)

Arguments

X

contains either the first sample or its corresponding distance matrix.

In the first case, X can be provided either as a vector (if one-dimensional), a matrix or a data.frame (if two-dimensional or higher).

In the second case, the input must be a distance matrix corresponding to the sample of interest.

If X is a sample, type.X must be specified as "sample". If X is a distance matrix, type.X must be specified as "distance".

Y

see X.

affine

logical; specifies if the affinely invariant distance correlation \insertCitedueck2014affinelydcortools should be calculated or not.

standardize

logical; specifies if X and Y should be standardized dividing each component by its standard deviations. No effect when affine = TRUE.

bias.corr

logical; specifies if the bias corrected version of the sample distance correlation \insertCitehuo2016fastdcortools should be calculated.

type.X

For "distance", X is interpreted as a distance matrix. For "sample", X is interpreted as a sample.

type.Y

see type.X.

metr.X

specifies the metric which should be used to compute the distance matrix for X (ignored when type.X = "distance").

Options are "euclidean", "discrete", "alpha", "minkowski", "gaussian", "gaussauto", "boundsq" or user-specified metrics (see examples).

For "alpha", "minkowski", "gaussian", "gaussauto" and "boundsq", the corresponding parameters are specified via "c(metric, parameter)", e.g. c("gaussian", 3) for a Gaussian metric with bandwidth parameter 3; the default parameter is 2 for "minkowski" and "1" for all other metrics.

See \insertCitelyons2013distance,sejdinovic2013equivalence,bottcher2017detecting;textualdcortools for details.

metr.Y

see metr.X.

use

specifies how to treat missing values. "complete.obs" excludes observations containing NAs, "all" uses all observations.

algorithm

specifies the algorithm used for calculating the distance correlation.

"fast" uses an O(n log n) algorithm if the observations are one-dimensional and metr.X and metr.Y are either "euclidean" or "discrete", see also \insertCitehuo2016fast;textualdcortools.

"memsave" uses a memory saving version of the standard algorithm with computational complexity O(n^2) but requiring only O(n) memory.

"standard" uses the classical algorithm. User-specified metrics always use the classical algorithm.

"auto" chooses the best algorithm for the specific setting using a rule of thumb.

Value

numeric; the distance correlation between samples X and Y.

References

\insertRef

bottcher2017detectingdcortools

\insertRef

dueck2014affinelydcortools

\insertRef

huo2016fastdcortools

\insertRef

lyons2013distancedcortools

\insertRef

sejdinovic2013equivalencedcortools

\insertRef

szekely2007dcortools

\insertRef

szekely2009browniandcortools

Examples

X <- rnorm(200)
Y <- rnorm(200)
Z <- X + rnorm(200)
dim(X) <- dim(Y) <- dim(Z) <- c(20, 10)

#Demonstration that biased-corrected distance correlation is
#often more meaningful than without using bias-correction

distcor(X, Y) 
distcor(X, Z) 
distcor(X, Y, bias.corr = TRUE)
distcor(X, Z, bias.corr = TRUE)

#For more examples of the different options,
#see the documentation of distcov.

dcortools documentation built on Dec. 8, 2022, 1:11 a.m.