| Title: | Symmetrized Data Aggregation |
|---|---|
| Description: | We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>. |
| Authors: | Lilun Du [aut, cre], Xu Guo [ctb], Wenguang Sun [ctb], Changliang Zou [ctb] |
| Maintainer: | Lilun Du <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2024-09-06 06:52:48 UTC |
| Source: | CRAN |
This is the core function for the paper posted in arXiv preprint arXiv:2002.11992
SDA_M(dat, alpha, Omega, nonsparse = FALSE, stable = TRUE)SDA_M(dat, alpha, Omega, nonsparse = FALSE, stable = TRUE)
dat |
a n by p data matrix |
alpha |
the FDR level |
Omega |
the inverse covariance matrix; if missing, it will be estimated by the glasso package |
nonsparse |
if TRUE, the covariance matrix will be estimated by the POET package |
stable |
if TRUE, the sample will be randomly splitted B=10 times for stability performance; otherwise, only single sample splitting is used. |
the indices of the hypotheses rejected
n = 50 p = 100 dat = matrix(rnorm(n*p), nrow=n) mu = rep(0, p) mu[1:as.integer(0.1*p)]=0.3 dat = dat+rep(1, n)%*%t(mu) alpha = 0.2 out = SDA_M(dat, alpha, diag(p)) print(out)n = 50 p = 100 dat = matrix(rnorm(n*p), nrow=n) mu = rep(0, p) mu[1:as.integer(0.1*p)]=0.3 dat = dat+rep(1, n)%*%t(mu) alpha = 0.2 out = SDA_M(dat, alpha, diag(p)) print(out)