calc_var_nonzero_mu

Internal function to calculate variance of S(t) when the Z_1,...,Z_d have nonzero mean. 
See GBJ paper for more details. Vectorized in t.

internal

Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented
in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
The GBJ has been shown to outperform other tests in genetic association studies when signals
are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin
(2017) <arXiv:1710.02469> for more details.

Ryan Sun

Generalized Berk-Jones Test for Set-Based Inference in Genetic
Association Studies

calc_var_nonzero_mu function

<dl><dt>d</dt>
<dd>The number of test statistics in the set.</dd>
<dt>t</dt>
<dd>The threshold which determines the properties of S(t).</dd>
<dt>mu</dt>
<dd>The common mean of the test statistics Z_1,...,Z_d</dd>
<dt>pairwise_cors</dt>
<dd>A vector of all d(d-1)/2 pairwise correlations between the test 
statistics, where d is total number of test statistics in the set.</dd></dl>

Arguments

calc_var_mu_nonzero.R — calc_var_nonzero_mu

<dl>

<dt>d</dt>
<dd>The number of test statistics in the set.</dd>


<dt>t</dt>
<dd>The threshold which determines the properties of S(t).</dd>


<dt>mu</dt>
<dd>The common mean of the test statistics Z_1,...,Z_d</dd>


<dt>pairwise_cors</dt>
<dd>A vector of all d(d-1)/2 pairwise correlations between the test 
statistics, where d is total number of test statistics in the set.</dd>

</dl>

calc_var_nonzero_mu: calc_var_mu_nonzero.R

Description

Usage

Value

Arguments

Examples