powered by
This is the core function for the paper posted in arXiv preprint arXiv:2002.11992
SDA_M(dat, alpha, Omega, nonsparse = FALSE, stable = TRUE)
a n by p data matrix
the FDR level
the inverse covariance matrix; if missing, it will be estimated by the glasso package
if TRUE, the covariance matrix will be estimated by the POET package
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
# NOT RUN { 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) # }
Run the code above in your browser using DataLab