blfdr: Boca-Leek False Discovery Rate
In pkimes/otherfdr: Other FDR Methods

Description Usage Arguments Value Author(s) References See Also Examples

A simple wrapper function to the false discovery rate controlling procedure of Boca and Leek (2018) which returns adjusted p-values. The heavy lifting of modeling the relationship between covariates and the proportion of null hypotheses is handled by the lm_pi0 function in the swfdr package.

1	blfdr(p, x, ...)

`p`	numeric vector of unadjusted p-values.
`x`	matrix of covariates with rows corresponding to p-values `p`; alternatively, numeric vector for a single covariate of same length as `p`.
`...`	additional parameters to pass to `lm_pi0`.

Numeric vector of adjusted p-values of equal length and order as the to input vector of p-values.

Patrick Kimes

Reference for the false discovery rate controlling procedure (underlying theory):

Boca, S.M., Leek, J.T. (2018) A direct approach to estimating false discovery rates conditional on covariates. bioRxiv. https://doi.org/10.1101/035675

Reference for the primary null proportion estimating function (underlying code):

Leek, J.T., Jager, L., Boca, S.M. (2018) swfdr: Science-wise false discovery rate and proportion of true null hypotheses estimation. R package version 1.6.0. https://doi.org/doi:10.18129/B9.bioc.swfdr

lm_pi0

## generate example p-values and covariate values
pv <- runif(100)
x <- sample(100)

## adjust p-values, grouping by covariate bin
adjpv <- blfdr(p = pv, x = x)