R.MLE.memory

Gaussian copula correlation matrix Maximum Likelihood estimator (memory handling)

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.(2023) <doi:10.1101/2024.03.17.585412>. 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

R.MLE.memory function

<dl><dt>Hconfig</dt>
<dd>A list of all possible combination of \(H_0\) and \(H_1\) hypotheses generated by the <code>GetHconfig()</code> function.</dd>
<dt>fHconfig_sum</dt>
<dd>a vector containing \(\sum_c(w_c*psi_c)\) for each items.</dd>
<dt>OldPrior</dt>
<dd>a vector containing the prior probabilities for each of the H-configurations.</dd>
<dt>Logf0Mat</dt>
<dd>a matrix containing \(\log(f_0Mat)\), each column corresponding to a p-value serie.</dd>
<dt>Logf1Mat</dt>
<dd>a matrix containing \(\log(f_1Mat)\), each column corresponding to a p-value serie.</dd>
<dt>zeta0</dt>
<dd>a matrix containing <code>qnorm(F0Mat)</code>, each column corresponding to a p-value serie.</dd>
<dt>zeta1</dt>
<dd>a matrix containing <code>qnorm(F1Mat)</code>, each column corresponding to a p-value serie.</dd>
<dt>OldR</dt>
<dd>the copula correlation matrix.</dd>
<dt>OldRinv</dt>
<dd>the inverse of copula correlation matrix.</dd></dl>

Arguments

Gaussian copula correlation matrix Maximum Likelihood estimator (memory handling) — R.MLE.memory

<dl>

<dt>Hconfig</dt>
<dd>A list of all possible combination of \(H_0\) and \(H_1\) hypotheses generated by the <code>GetHconfig()</code> function.</dd>


<dt>fHconfig_sum</dt>
<dd>a vector containing \(\sum_c(w_c*psi_c)\) for each items.</dd>


<dt>OldPrior</dt>
<dd>a vector containing the prior probabilities for each of the H-configurations.</dd>


<dt>Logf0Mat</dt>
<dd>a matrix containing \(\log(f_0Mat)\), each column corresponding to a p-value serie.</dd>


<dt>Logf1Mat</dt>
<dd>a matrix containing \(\log(f_1Mat)\), each column corresponding to a p-value serie.</dd>


<dt>zeta0</dt>
<dd>a matrix containing <code>qnorm(F0Mat)</code>, each column corresponding to a p-value serie.</dd>


<dt>zeta1</dt>
<dd>a matrix containing <code>qnorm(F1Mat)</code>, each column corresponding to a p-value serie.</dd>


<dt>OldR</dt>
<dd>the copula correlation matrix.</dd>


<dt>OldRinv</dt>
<dd>the inverse of copula correlation matrix.</dd>

</dl>

R.MLE.memory: Gaussian copula correlation matrix Maximum Likelihood estimator (memory handling)

Description

Usage

Value

Arguments