gaussian_copula_density

Provides functions for the joint analysis of Q sets of
p-values obtained for the same list of items. This joint analysis is
performed by querying a composite hypothesis, i.e. an arbitrary
complex combination of simple hypotheses, as described in Mary-Huard
et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
al.(2025) <doi: 10.1093/nargab/lqaf118>. In this approach, the Q-uplet
of p-values associated with each item is distributed as a multivariate
mixture, where each of the 2^Q components corresponds to a specific
combination of simple hypotheses. The dependence between the p-value
series is considered using a Gaussian copula function. A p-value for
the composite hypothesis test is derived from the posterior
probabilities.

Tristan Mary-Huard

Query Composite Hypotheses

Annaig De Walsche

Franck Gauthier

gaussian_copula_density function

<dl><dt>zeta</dt>
<dd>the matrix of probit-transformed observations.</dd>
<dt>R</dt>
<dd>the correlation matrix.</dd>
<dt>Rinv</dt>
<dd>the inverse correlation matrix.</dd></dl>

Arguments

Gaussian copula density — gaussian_copula_density

<dl>

<dt>zeta</dt>
<dd>the matrix of probit-transformed observations.</dd>


<dt>R</dt>
<dd>the correlation matrix.</dd>


<dt>Rinv</dt>
<dd>the inverse correlation matrix.</dd>

</dl>

gaussian_copula_density: Gaussian copula density

Description

Usage

Value

Arguments