PRGM.action: Based on the Robust Bayes Approach, Performs a Multiple...

In LFDREmpiricalBayes: Estimating Local False Discovery Rates Using Empirical Bayes Methods

Description

Assuming a squared error loss function, it provides Robust Bayes estimates of the LFDR estimates giving credit to both separate and combined reference classes.

Usage

PRGM.action(x1,x2)

Arguments

x1	Input numeric vector of LFDR estimates of the separate reference class.
x2	Input numeric vector of LFDR estimated of the combined reference class.

Value

The output is a vector of the LFDR estimates based on the two reference classes.

Author(s)

Code: Ali Karimnezhad.
Documentation: Johnary Kim and Anna Akpawu.

References

Karimnezhad, A. and Bickel, D. R. (2016). Incorporating prior knowledge about genetic variants into the analysis of genetic association data: An empirical Bayes approach. Working paper. Retrieved from http://hdl.handle.net/10393/34889

Examples

#LFDR reference class values generated

#First reference class
LFDR.Separate <- c(0.14, 0.8, 0.16, 0.30)
#Second reference class
LFDR.Combined <- c(0.21, 0.61, 0.12, 0.10)

output <- PRGM.action(LFDR.Separate, LFDR.Combined)

# Vector of the LFDR estimates
output

LFDREmpiricalBayes documentation built on May 2, 2019, 6:38 a.m.