Description Usage Arguments Details Value See Also Examples

Computes the optimal Bayes p-value weights for multiple testing.
Given estimated means `mu`

and standard errors `sigma`

of test statistics `T`

,
the weighting scheme optimizes the expected number of discoveries
using `T`

as test statistics in multiple testing
at some specific level q.

1 | ```
bayes_weights(mu, sigma, q)
``` |

`mu` |
the estimated means, a vector of length J. |

`sigma` |
the standard errors, a positive vector of length J |

`q` |
level at which tests will be performed |

Specifically, it is assumed that `T`

are Gaussian with mean
`eta`

. One-sided tests of `eta>=0`

against `eta<0`

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 `eta`

in `mu,sigma`

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;

Other p.value.weighting: `exp_weights`

;
`iGWAS`

; `spjotvoll_weights`

1 2 3 4 5 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.