Computes the optimal Bayes p-value weights for multiple testing.
Given estimated means
mu and standard errors
sigma of test statistics
the weighting scheme optimizes the expected number of discoveries
T as test statistics in multiple testing
at some specific level q.
the estimated means, a vector of length J.
the standard errors, a positive vector of length J
level at which tests will be performed
Specifically, it is assumed that
T are Gaussian with mean
eta. One-sided tests of
are conducted using the test statistics
T. To optimize power,
different levels are allocated to different tests. This is based on the
prior information about
For more details, see the paper "Optimal Multiple Testing Under a Gaussian Prior on the Effect Sizes", by Dobriban, Fortney, Kim and Owen, http://arxiv.org/abs/1504.02935
A list containing:
w - the optimal Bayes weights;
lambda - the dual certificate, normalizing constant produced by solving the optimization problem;
q_star - true value of q solved for;
q_threshold - maximal value of q for which problem can be solve exactly;
1 2 3 4 5
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.