estimateQ: Compute the centrality quotient

In PoolBal: Balancing Central and Marginal Rejection of Pooled p-Values

Compute the centrality quotient

Description

Estimates the centrality quotient for an arbitrary pooled p-value function.

Usage

estimateQ(
  poolFun,
  alpha = 0.05,
  M = 2,
  interval = c(0, 1),
  poolArgs = list(),
  ...
)

Arguments

poolFun	function accepting a vector of p-values
alpha	numeric between 0 and 1
M	integer, how many p-values are there?
interval	two numerics giving the bounds of root-searching
poolArgs	(optional) additional named arguments for poolFun
...	additional arguments to uniroot

Details

The centrality quotient communicates the tendency for a test to favour evidence shared among all tests over strong evidence in a single test.

This function uses the individual estimation functions for central and marginal rejection levels to compute the centrality quotient for an arbitrary pooled p-value function. The option to specify b for marginal rejection is included in case the pooled p -value has strange behaviour when p-values are equal to 1.

Value

The uniroot output.

Author(s)

Chris Salahub

Examples

estimateQ(chiPool, alpha = 0.05, M = 10, poolArgs = list(kappa = 10))

PoolBal documentation built on Nov. 22, 2023, 5:07 p.m.