The estimated s gives information about the consistency between the z scores and LD matrix. A larger \(s\) means there is a strong inconsistency between z scores and LD matrix. The “null-mle” method obtains mle of \(s\) under \(z | R ~ N(0,(1-s)R + s I)\), \(0 < s < 1\). The “null-partialmle” method obtains mle of \(s\) under \(U^T z | R ~ N(0,s I)\), in which \(U\) is a matrix containing the of eigenvectors that span the null space of R; that is, the eigenvectors corresponding to zero eigenvalues of R. The estimated \(s\) from “null-partialmle” could be greater than 1. The “null-pseudomle” method obtains mle of \(s\) under pseudolikelihood \(L(s) = \prod_{j=1}^{p} p(z_j | z_{-j}, s, R)\), \(0 < s < 1\).
estimate_s_rss(z, R, n, r_tol = 1e-08, method = "null-mle")A number between 0 and 1.