Two-Sample Test modified from Biswas and Ghosh (2014)

Given \(M\) observations \(X_1, X_2, \ldots, X_M \in \mathcal{M}\) and \(N\) observations \(Y_1, Y_2, \ldots, Y_N \in \mathcal{M}\), perform the permutation test of equal distribution $$H_0~:~\mathcal{P}_X = \mathcal{P}_Y$$ by the method from Biswas and Ghosh (2014). The method, originally proposed for Euclidean-valued data, is adapted to the general Riemannian manifold with intrinsic/extrinsic distance.

Usage

riem.test2bg14(riemobj1, riemobj2, geometry = c("intrinsic", "extrinsic"), ...)

Arguments

riemobj1

a S3 "riemdata" class for \(M\) manifold-valued data.

riemobj2

a S3 "riemdata" class for \(N\) manifold-valued data.

geometry

(case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") geometry.

...

extra parameters including

nperm

the number of permutations (default: 999).

Value

a (list) object of S3 class htest containing:

statistic

a test statistic.

p.value

\(p\)-value under \(H_0\).

alternative

alternative hypothesis.

method

name of the test.

data.name

name(s) of provided sample data.

References

Biswas M, Ghosh AK (2014). “A nonparametric two-sample test applicable to high dimensional data.” Journal of Multivariate Analysis, 123, 160--171. ISSN 0047259X.

You K, Park H (2020). “Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity.” NeuroImage, 117464. ISSN 10538119.

Examples

#-------------------------------------------------------------------
#          Example on Sphere : a dataset with two types
#
# class 1 : 20 perturbed data points near (1,0,0) on S^2 in R^3
# class 2 : 30 perturbed data points near (0,1,0) on S^2 in R^3
#-------------------------------------------------------------------
## GENERATE DATA
mydata1 = list()
mydata2 = list()
for (i in 1:20){
  tgt = c(1, stats::rnorm(2, sd=0.1))
  mydata1[[i]] = tgt/sqrt(sum(tgt^2))
}
for (i in 1:20){
  tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1))
  mydata2[[i]] = tgt/sqrt(sum(tgt^2))
}
myriem1 = wrap.sphere(mydata1)
myriem2 = wrap.sphere(mydata2)

## PERFORM PERMUTATION TEST
#  it is expected to return a very small number.
# \donttest{
riem.test2bg14(myriem1, myriem2, nperm=999)
#> 
#> 	Two-Sample Test on Sphere as of Biswas and Ghosh (2014)
#> 
#> data:  'myriem1' and 'myriem2'
#> Tmn = 3.6793, p-value = 0.001
#> alternative hypothesis: two distributions are not equal.
#> 
# }

if (FALSE) {
## CHECK WITH EMPIRICAL TYPE-1 ERROR
set.seed(777)
ntest = 1000
pvals = rep(0,ntest)

for (i in 1:ntest){
  X = cbind(matrix(rnorm(30*2, sd=0.1),ncol=2), rep(1,30))
  Y = cbind(matrix(rnorm(30*2, sd=0.1),ncol=2), rep(1,30))
  Xnorm = X/sqrt(rowSums(X^2))
  Ynorm = Y/sqrt(rowSums(Y^2))
  
  Xriem = wrap.sphere(Xnorm)
  Yriem = wrap.sphere(Ynorm)
  pvals[i] = riem.test2bg14(Xriem, Yriem, nperm=999)$p.value
}

emperr = round(sum((pvals <= 0.05))/ntest, 5)
print(paste0("* EMPIRICAL TYPE-1 ERROR=", emperr))
}