KMD (version 0.1.0)

KMD_test: Testing based on KMD

Description

Testing based on the kernel measure of multi-sample dissimilarity (KMD). Both permutation test and asymptotic test are available. The tests are consistent against all alternatives where at least two samples have different distributions.

Usage

KMD_test(
  X,
  Y,
  M = length(unique(Y)),
  Knn = ceiling(length(Y)/10),
  Kernel = "discrete",
  Permutation = TRUE,
  B = 500
)

Value

If Permutation == TRUE, permutation test is performed and the algorithm returns a p-value for testing H0: the M distributions are equal against H1: not all the distributions are equal. If Permutation == FALSE, asymptotic test is performed and a 1 by 2 matrix: (z value, p-value) is returned.

Arguments

X

the data matrix (n by dx) or the distance/similarity matrix (n by n)

Y

a vector of length n, indicating the labels (from 1 to M) of the data

M

the number of possible labels

Knn

the number of nearest neighbors to use, or "MST"

Kernel

an M by M kernel matrix with row i and column j being the kernel value \(k(i, j)\); or "discrete" which indicates using the discrete kernel.

Permutation

TRUE or FALSE; whether to perform permutation test or the asymptotic test.

B

the number of permutations to perform, only used for permutation test.

Details

The kernel measure of multi-sample dissimilarity (KMD) measures the dissimilarity between multiple samples using geometric graphs such as K-nearest neighbor (K-NN) graph and minimum spanning tree (MST), based on the observations from them. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The test rejects the null hypothesis that all samples are from the same distribution for large value of sample KMD. The permutation test returns the p-value given by (sum(KMD_i >= KMD_0) + 1)/(B + 1), where KMD_i is the KMD computed after a random permutation on the Y labels, and B is the total number of permutations that have been performed. The asymptotic test first normalizes the KMD by the square root of the permutation variance, and then returns the p-value given by: P(N(0,1) > normalized KMD).

If X is an n by n matrix, it will be interpreted as a distance/similarity matrix. In such case, MST requires it to be symmetric (an undirected graph). K-NN graph does not require it to be symmetric, with the nearest neighbors of point i computed based on the i-th row, and ties broken at random. The diagonal terms (self-distances) will be ignored. If X is an n by dx data matrix, Euclidean distance will be used for computing the K-NN graph (ties broken at random) and the MST.

See Also

KMD

Examples

d = 2
set.seed(1)
X1 = matrix(rnorm(100*d), nrow = 100, ncol = d)
X2 = matrix(rnorm(100*d,sd=sqrt(1.5)), nrow = 100, ncol = d)
X3 = matrix(rnorm(100*d,sd=sqrt(2)), nrow = 100, ncol = d)
X = rbind(X1,X2,X3)
Y = c(rep(1,100),rep(2,100),rep(3,100))
print(KMD_test(X, Y, M = 3, Knn = 1, Kernel = "discrete"))
# A small p-value since the three distributions are not the same.
print(KMD_test(X, Y, M = 3, Knn = 1, Kernel = "discrete", Permutation = FALSE))
# p-value of the asymptotic test is similar to that of the permutation test
print(KMD_test(X, Y, M = 3, Knn = 1, Kernel = diag(c(10,1,1))))
# p-value is improved by using a different kernel
print(KMD_test(X, Y, M = 3, Knn = 30, Kernel = "discrete"))
# The suggested choice Knn = 0.1n yields a very small p-value.
print(KMD_test(X, Y, M = 3, Knn = "MST", Kernel = "discrete"))
# One can also use the MST.
print(KMD_test(X, Y, M = 3, Knn = 2, Kernel = "discrete"))
# MST has similar performance as 2-NN, which is between 1-NN and 30-NN

# Check null distribution of the z values
ni = 100
n = 3*ni
d = 2
Null_KMD = function(id){
  set.seed(id)
  X = matrix(rnorm(n*d), nrow = n, ncol = d)
  Y = c(rep(1,ni),rep(2,ni),rep(3,ni))
  return(KMD_test(X, Y, M = 3, Knn = "MST", Kernel = "discrete", Permutation = FALSE)[1,1])
}
hist(sapply(1:500, Null_KMD), breaks = c(-Inf,seq(-5,5,length=50),Inf), freq = FALSE,
  xlim = c(-4,4), ylim = c(0,0.5), main = expression(paste(n[i]," = 100")),
  xlab = expression(paste("normalized ",hat(eta))))
lines(seq(-5,5,length=1000),dnorm(seq(-5,5,length=1000)),col="red")
# The histogram of the normalized KMD is close to that of a standard normal distribution.