mgc.stat: MGC Test

In neurodata/r-mgc: Multiscale Graph Correlation

Description

The main function that computes the MGC measure between two datasets: It first computes all local correlations, then use the maximal statistic among all local correlations based on thresholding.

Usage

mgc.stat(
  X,
  Y,
  is.dist.X = FALSE,
  dist.xfm.X = mgc.distance,
  dist.params.X = list(method = "euclidean"),
  dist.return.X = NULL,
  is.dist.Y = FALSE,
  dist.xfm.Y = mgc.distance,
  dist.params.Y = list(method = "euclidean"),
  dist.return.Y = NULL,
  option = "mgc"
)

Arguments

X	is interpreted as: a [n x d] data matrix X is a data matrix with n samples in d dimensions, if flag is.dist.X=FALSE. a [n x n] distance matrix X is a distance matrix. Use flag is.dist.X=TRUE.
Y	is interpreted as: a [n x d] data matrix Y is a data matrix with n samples in d dimensions, if flag is.dist.Y=FALSE. a [n x n] distance matrix Y is a distance matrix. Use flag is.dist.Y=TRUE.
is.dist.X	a boolean indicating whether your X input is a distance matrix or not. Defaults to FALSE.
dist.xfm.X	if is.dist == FALSE, a distance function to transform X. If a distance function is passed, it should accept an [n x d] matrix of n samples in d dimensions and return a [n x n] distance matrix as the $D return argument. See mgc.distance for details.
dist.params.X	a list of trailing arguments to pass to the distance function specified in dist.xfm.X. Defaults to list(method='euclidean').
dist.return.X	the return argument for the specified dist.xfm.X containing the distance matrix. Defaults to FALSE. is.null(dist.return) use the return argument directly from dist.xfm as the distance matrix. Should be a [n x n] matrix. is.character(dist.return) | is.integer(dist.return) use dist.xfm.X[[dist.return]] as the distance matrix. Should be a [n x n] matrix.
is.dist.Y	a boolean indicating whether your Y input is a distance matrix or not. Defaults to FALSE.
dist.xfm.Y	if is.dist == FALSE, a distance function to transform Y. If a distance function is passed, it should accept an [n x d] matrix of n samples in d dimensions and return a [n x n] distance matrix as the dist.return.Y return argument. See mgc.distance for details.
dist.params.Y	a list of trailing arguments to pass to the distance function specified in dist.xfm.Y. Defaults to list(method='euclidean').
dist.return.Y	the return argument for the specified dist.xfm.Y containing the distance matrix. Defaults to FALSE. is.null(dist.return) use the return argument directly from dist.xfm.Y(Y) as the distance matrix. Should be a [n x n] matrix. is.character(dist.return) | is.integer(dist.return) use dist.xfm.Y(Y)[[dist.return]] as the distance matrix. Should be a [n x n] matrix.
option	is a string that specifies which global correlation to build up-on. Defaults to 'mgc'. 'mgc' use the MGC global correlation. 'dcor' use the dcor global correlation. 'mantel' use the mantel global correlation. 'rank' use the rank global correlation.

Value

A list containing the following:

stat	is the sample MGC statistic within [-1,1]
localCorr	the local correlations
optimalScale	the optimal scale identified by MGC
option	specifies which global correlation was used

Author(s)

C. Shen and Eric Bridgeford

References

Joshua T. Vogelstein, et al. "Discovering and deciphering relationships across disparate data modalities." eLife (2019).

Examples

library(mgc)

n=200; d=2
data <- mgc.sims.linear(n, d)
mgc.stat.res <- mgc.stat(data$X, data$Y)

neurodata/r-mgc documentation built on March 12, 2021, 9:45 a.m.