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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