Description Usage Arguments Value References See Also
Calculate the sLED test statistic given a differential matrix D. A differential matrix is the difference between two symmetric relationship matrices. For any symmetric differential matrix D, sLED test statistic is defined as
max{ sEig(D), sEig(-D) }
where sEig()
is the sparse leading eigenvalue, defined as
max_{v} t(v)*A*v
subject to ||v||_2 <= 1, ||v||_1 <= s.
1 2 | sLEDTestStat(Dmat, rho = 1000, sumabs.seq = 0.2, niter = 20,
trace = FALSE)
|
Dmat |
p-by-p numeric matrix, the differential matrix |
rho |
a large positive constant such that D+diag(rep(rho, p)) and -D+diag(rep(rho, p)) are positive definite. |
sumabs.seq |
a numeric vector specifing the sequence of sparsity parameters, each between 1/sqrt(p) and 1. Each sumabs*sqrt(p) is the upperbound of the L_1 norm of leading sparse eigenvector v. |
niter |
the number of iterations to use in the PMD algorithm (see |
trace |
whether to trace the progress of PMD algorithm (see |
A list containing the following components:
sumabs.seq |
the sequence of sparsity parameters |
rho |
a positive constant to augment the diagonal of the differential matrix such that D + rho*I becomes positive definite. |
stats |
a numeric vector of test statistics when using different sparsity parameters
(corresponding to |
sign |
a vector of signs when using different sparsity parameters (corresponding to |
v |
the sequence of sparse leading eigenvectors, each row corresponds to one sparsity
parameter given by |
leverage |
the leverage score for genes (defined as v^2 element-wise) using
different sparsity parameters. Each row corresponds to one sparsity
parameter given by |
Zhu, Lei, Devlin and Roeder (2016), "Testing High Dimensional Covariance Matrices, with Application to Detecting Schizophrenia Risk Genes", arXiv:1606.00252.
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