gtestsmulti {gTestsMulti}

New graph-based multi-sample tests

Description

This function provides graph-based multi-sample tests.

Usage

gtestsmulti(E, data_list, perm=0)

Arguments

E

The edge matrix for the similarity graph. Each row contains the node indices of an edge.

data_list

The list of multivariate matrices corresponding to the K different classes. The length of the list is K. Each element of the list is a matrix containing observations as the rows and features as the columns.

perm

The number of permutations performed to calculate the p-value of the test. The default value is 0, which means the permutation is not performed and only approximated p-value based on the asymptotic theory is provided. Doing permutation could be time consuming, so be cautious if you want to set this value to be larger than 10,000.

Value

Returns a list teststat with each test statistic value and a list pval with p-values of the tests. See below for more details.

S

The value of the test statistic S.

S_A

The value of the test statistic S^{A}.

S_appr

The approximated p-value of S based on asymptotic theory with a Bonferroni procedure.

S_A_appr

The approximated p-value of S^{A} based on asymptotic theory.

S_perm

The permutation p-value of S when argument ‘perm’ is positive.

S_A_perm

The permutation p-value of S^{A} when argument ‘perm’ is positive.

See Also

gTestsMulti-package

Examples

## Mean difference in Gaussian distribution.
d = 50
mu = 0.2
sam = 50

set.seed(500)
X1 = matrix(rnorm(d*sam), sam)
X2 = matrix(rnorm(d*sam,mu), sam)
X3 = matrix(rnorm(d*sam,2*mu), sam)

data_list = list(X1, X2, X3)

# We use 'mstree' in 'ade4' package to construct the minimum spanning tree.
require(ade4)
x = rbind(X1, X2, X3)
E = mstree(dist(x))


a = gtestsmulti(E, data_list, perm = 1000)
# output results based on the permutation and the asymptotic results
# the test statistic values can be found in a$teststat
# p-values can be found in a$pval