sLEDpermute: sLED permutation

In lingxuez/sLED: A Sparse Leading Eigenvalue Driven (sLED) Test for High-dimensional Matrices

Description

Get the sLED test statistic on permuted samples.

Usage

sLEDpermute(Z, n1, n2, adj.beta = -1, rho = 1000, sumabs.seq = 0.2,
  npermute = 100, useMC = FALSE, mc.cores = 1, seeds = NULL,
  verbose = TRUE, niter = 20, trace = FALSE)

Arguments

Z	(n1+n2)-by-p matrix, containing the samples from two populations with p features
n1	the first n1 rows in Z represent the first population
n2	the (n1+1):(n1+n2) rows in Z represent the second population
adj.beta	a positive number representing the power to transform correlation matrices to weighted adjacency matrices by A_{ij} = |r_ij|^adj.beta, where r_ij represents the Pearson correlation. When adj.beta=0, the correlation marix is used. When adj.beta<0, the covariance matrix is used. The default value is adj.beta=-1.
rho	a large positive constant such that A(X)-A(Y)+diag(rep(rho, p)) is positive definite.
sumabs.seq	a numeric vector specifing the sequence of sparsity parameters to use, each between 1/sqrt(p) and 1.
npermute	number of permutations to use, default is 100
useMC	logical, whether to use multi-core version
mc.cores	a number indicating how many cores to use in parallelization
seeds	a numeric vector with the length equals to npermute, where seeds[i] specifies the seeding for the i-th permutation. Set to NULL if do not want to specify.
verbose	whether to print the progress during permutation tests
niter	the number of iterations to use in the PMD algorithm (see symmPMD())
trace	whether to trace the progress of PMD algorithm (see symmPMD())

Value

A list containing the following components:

Tn.perm	a numeric vector with length npermute, the test statistic in permutations.
Tn.perm.sign	a vector of characters with length npermute, the sign in permutations: "pos" if the permuted test statistic is given by sEig(D), and "neg" if is given by sEig(-D), where sEig denotes the sparse leading eigenvalue.

References

Zhu, Lei, Devlin and Roeder (2016), "Testing High Dimensional Covariance Matrices, with Application to Detecting Schizophrenia Risk Genes", arXiv:1606.00252.

See Also

sLED().

lingxuez/sLED documentation built on May 7, 2019, 2:55 a.m.