dlogf

Density 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. The
ash() and ash.workhorse() also provides a flexible modeling
interface that can accommodate a variety of likelihoods (e.g.,
normal, Poisson) and mixture priors (e.g., uniform, normal).

Peter Carbonetto

ashr

Methods for Adaptive Shrinkage, using Empirical Bayes

Matthew Stephens

Chaoxing Dai

David Gerard

Mengyin Lu

Lei Sun

Jason Willwerscheid

Nan Xiao

Mazon Zeng

dlogf function

<dl><dt>x</dt>
<dd>vector of quantiles</dd>
<dt>df1</dt>
<dd>degrees of freedom</dd>
<dt>df2</dt>
<dd>degrees of freedom</dd>
<dt>ncp</dt>
<dd>non-centrality parameter. If omitted the central F is assumed.</dd>
<dt>log</dt>
<dd>logical; if TRUE, probabilities p are given as log(p).</dd></dl>

Arguments

The log-F distribution — dlogf

<dl>

<dt>x</dt>
<dd>vector of quantiles</dd>


<dt>df1</dt>
<dd>degrees of freedom</dd>


<dt>df2</dt>
<dd>degrees of freedom</dd>


<dt>ncp</dt>
<dd>non-centrality parameter. If omitted the central F is assumed.</dd>


<dt>log</dt>
<dd>logical; if TRUE, probabilities p are given as log(p).</dd>

</dl>

The log-F distribution

dlogf: The log-F distribution

Description

Usage

Value

Arguments