plogf

df1, df2

non-centrality parameter. If omitted the central F is assumed.

logical; if TRUE (default), probabilities are P[X <= x],="" otherwise,="" p[x=""> x].</=>

lower.tail

logical; if TRUE, probabilities p are given as log(p).

log.p


Distribution function for the log-F distribution with <code>df1</code> and <code>df2</code>
 degrees of freedom (and optional non-centrality parameter <code>ncp</code>).


The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users.

Peter Carbonetto

ashr

Methods for Adaptive Shrinkage, using Empirical Bayes

plogf function

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

plogf: The log-F distribution

Description

Usage

Arguments

Value