calc_vlogLR

Return the vector of log-likelihood ratios of the data
 betahat, with standard errors betahatsd, for a given g() prior
 on beta, or an ash object containing that, vs the null that g()
 is point mass on 0

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

calc_vlogLR function

<dl><dt>g</dt>
<dd>the fitted g, or an ash object containing g</dd>
<dt>data</dt>
<dd>a data object, see set_data</dd></dl>

Arguments

Compute vector of loglikelihood ratio for data from ash fit — calc_vlogLR

<dl>

<dt>g</dt>
<dd>the fitted g, or an ash object containing g</dd>


<dt>data</dt>
<dd>a data object, see set_data</dd>

</dl>

Compute vector of loglikelihood ratio for data from ash fit

calc_vlogLR: Compute vector of loglikelihood ratio for data from ash fit

Description

Usage

Arguments