SEL.caution.parameter: Based on a Decision-Theoretic Approach, Performs a Multiple...

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

Description

Assuming a squared error loss function, it provides three caution-type actions using estimated LFDRs computed based on both separate and combined reference classes.

Usage

SEL.caution.parameter(x1,x2)

Arguments

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

Value

Much like caution.parameter.actions, this function returns three vectors of equal size as seen below:

CGM1	Squared error loss value for the Conditional Gamma Minimax (CGMinimax).
CGM0	Squared error loss value for the Conditional Gamma Minimin (CGMinimin).
CGM0.5	Squared error loss value for the Action/Decision estimate (a balance between CGMinimax and CGMinimin.

For each index of the vectors, the squared error loss values are given.

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

#Similar to caution.parameter actions we have the following classes

#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 <- SEL.caution.parameter(LFDR.Separate, LFDR.Combined)

# Three caution cases with SEL values.
output

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