SimulateData: Simulate p-values and the auxiliary covariate under various...

In jchen1981/OrderShapeEM: Optimal false discovery rate control using auxiliary (order) information based on a shape-constrained EM algorithm

Description

The function simulates p-values and the auxiliary covariate under different signal structures (density and strength) and covariate informativeness.

Usage

SimulateData(
  prior.strength = c("Weak", "Moderate", "Strong"),
  feature.no = 10000,
  sig.dist = c("Normal", "Gamma"),
  sig.density = c("None", "Lower", "Low", "Medium", "High"),
  sig.strength = c("Weak", "Moderate", "Strong")
)

Arguments

prior.strength	a character string from 'Weak', 'Moderate', 'Strong' indicating the covariate informativeness.
feature.no	an integer, the number of features to be simulated.
sig.dist	a character string from 'Normal', 'Gamma' indicating the distribution of the z-value under the alternative.
sig.density	a character string from 'None', 'Lower', 'Low', 'Medium', 'High' indicating the level of the signal density.
sig.strength	a character string from 'Weak', 'Moderate', 'Strong' indicating the level of the signal strength.

Value

A list with the elements

pvalue	a numeric vector of p-values.
prior	a vector of covariate values reflecting the order of the prior null probabilities.
truth	a vector of logical values indicating H0 (=0) or H1 (=1).

Author(s)

Jun Chen

References

Hongyuan Cao, Jun Chen, Xianyang Zhang. Optimal false discovery rate control for large-scale multiple testing with auxiliary information. Submitted.

jchen1981/OrderShapeEM documentation built on March 9, 2021, 12:19 a.m.