PvalSets_cor

<p>PvalSets_cor is a data.frame with 10,000 rows and 3 columns. Each row corresponds to an item,
columns <code>Pval1</code> and <code>Pval2</code>  each correspond to a test serie over the items, and column 'Class'
provides the truth, i.e. if item \(i\) belongs to class 1 then the \(H_0\) hypothesis is true for the 2 tests,
if item \(i\) belongs to class 2 (resp. 3) then the \(H_0\) hypothesis is true for the first (resp. second)
test only, and if item \(i\) belongs to class 4 then both H0 hypotheses are false (for the first
and the second test). The correlation between the two pvalues series within each class is 0.3.</p>

datasets

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

PvalSets_cor function

Format

Synthetic example to illustrate the main qch functions using Gaussian copula — PvalSets_cor

Synthetic example to illustrate the main qch functions using Gaussian copula

PvalSets_cor: Synthetic example to illustrate the main qch functions using Gaussian copula

Description

Usage

Arguments

Format