BlendedLFDR: Blended Estimator of Local False Discovery Rate (LFDR)
In CorrectedFDR: Correcting False Discovery Rates

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/BlendedLFDR.R

BlendedLFDR is a function used to compute the blended estimator based on a benchmark estimator, usually the nonlocal false discovery rate (NFDR), and a set of estimators of local false discovery rates (LFDR).

1	BlendedLFDR(Benchmark, EstLFDR)

`Benchmark`	Input numeric vector for benchmark estimator (often NFDR).
`EstLFDR`	Input a matrix containing two or more sets of LFDR estimators.

Benchmark is an estimator of the FDR. This is usually the nonlocal false discovery rate (NFDR).
EstLFDR is a matrix of several LFDR estimators such as corrected FDR (CFDR), re-ranked FDR (RFDR), MLE (Maximum Likelihood Estimator), BBE1(Binomial Based Estimator), etc.
The output returns a single numeric vector containing the blended estimator of the LFDR.

The value of the blended estimator is an estimator of the LFDR.

The number of rows for the Benchmark and EstLFDR must have equal lengths.

Code: Abbas Rahal.
Documentation: Anna Akpawu, Justin Chitpin and Abbas Rahal.
Maintainer: Abbas Rahal <Abbas.Rahal13@gmail.com>

Bickel, D. R. (2015). Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing. Statistical Methods and Applications, 24(4), pp. 523-546.

#The data used to compute the LFDR estimators (CFDR, RFDR, MLE, and BBE1)
#comes from the ER/PR breast cancer data from the "ProData" package.
#To read more about the data, visit the website: https://www.bioconductor.org/
#Test statistics were first obtain, then the estimators for the FDR and LFDR were estimated.
#Benchmark vector
NFDR<-c(0.5661106448, 0.6897735492, 0.0000288516, 0.1549745113, 0.1305508970, 0.2421032979,
0.1482335568, 1, 1, 1, 0.6602562820, 0.7034682859, 0.7036332234, 0.0071192090,
0.8204536037, 0.9757716498, 0.7379329991, 1, 0.6333245479, 0.9904389701)
#Estimators of LFDR
CFDR<- c(1, 1, 0.0000288516, 0.2841199373, 0.2980912149, 0.5931530799, 0.3088199101,
1, 1, 1, 1, 1, 1, 0.0106788135, 1, 1, 1, 1, 1, 1)

RFDR<- c(0.689773549, 1, 0.007119209, 0.130550897, 0.703633223, 0.660256282, 0.242103298,
1, 1, 1, 0.820453604, 1, 0.703468286, 0.154974511, 1, 1, 1, 1, 0.975771650,1)

MLE<- c(0.9865479126, 0.9969935995, 0.0002372158, 0.6531633437, 0.7611453549, 0.9187425383,
0.7359259207, 0.9996548155, 0.9997310453, 0.9997437131, 0.9944712582, 0.9981685029,
0.9937604664, 0.0215892618, 0.9990504315, 0.9997493086, 0.9967673540, 0.9997016985,
0.9970142319, 0.9997625673)

BBE1<- c(1,1, 0.0003169812, 0.1138333734, 1, 1, 1, 1, 1, 1, 0.3279109564, 1, 0.0504755806,
0.0091823115, 0.0182614994, 0.0165386682, 1, 0.6964403713, 0.1001337298, 0.8415641198 )

#Matrix of LFDR Estimators
Est.LFDR<- matrix(c(CFDR,RFDR,MLE,BBE1), ncol=4)
output<-BlendedLFDR(Benchmark = NFDR, EstLFDR = Est.LFDR)
output$Blended