mgc.ksample: MGC K Sample Testing

In neurodata/r-mgc: Multiscale Graph Correlation

Description

MGC K Sample Testing provides a wrapper for MGC Sample testing under the constraint that the Ys here are categorical labels with K possible sample ids. This function uses a 0-1 loss for the Ys (one-hot-encoding)).

Usage

mgc.ksample(X, Y, mgc.opts = list(), ...)

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	[n] the labels of the samples with K unique labels.
mgc.opts	Arguments to pass to MGC, as a named list. See mgc.test for details. Do not pass arguments for is.dist.Y, dist.xfm.Y, dist.params.Y, nor dist.return.Y, as they will be ignored.
...	trailing args.

Value

A list containing the following:

p.value	P-value of MGC
stat	is the sample MGC statistic within [-1,1]
pLocalCorr	P-value of the local correlations by double matrix index
localCorr	the local correlations
optimalScale	the optimal scale identified by MGC

Author(s)

Eric Bridgeford

References

Youjin Lee, et al. "Network Dependence Testing via Diffusion Maps and Distance-Based Correlations." ArXiv (2019).

Examples

## Not run: 
library(mgc)
library(MASS)

n = 100; d = 2
# simulate 100 samples, where first 50 have mean [0,0] and second 50 have mean [1,1]
Y <- c(replicate(n/2, 0), replicate(n/2, 1))
X <- do.call(rbind, lapply(Y, function(y) {
    return(rnorm(d) + y)
}))
# p value is small
mgc.ksample(X, Y, mgc.opts=list(nperm=100))$p.value

## End(Not run)

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