Balancing Central and Marginal Rejection of Pooled p-Values
Generate realizations of beta alternative distributions
Identify a region of plausible alternative hypotheses in the proportio...
Compute the Kullback-Leibler divergence between the beta and uniform d...
Chi-squared kappa for a given centrality quotient
Chi-squared central rejection level
Chi-squared p-value pooling
Chi-squared marginal rejection level
Chi-squared centrality quotient
Convert p-value correlation to chi-squared covariance
Compute the central rejection level
Compute the marginal rejection level
Compute the centrality quotient
Estimate parameter for a given beta KL divergence and UMP test
Empirical UMP beta central rejection level
Empirical UMP beta pooled p-value
Empirical UMP beta marginal rejection level
Empirical UMP beta centrality quotient
UMP beta p-value pooled statistic
Compute the Kullback-Leibler divergence
Heatmap with marginal histograms
Satterthwaite p-values
Pool p-values using the Satterthwaite approximation
When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.