leukemia

<p>Expression levels recorded for 3,571 genes in 72 patients with leukemia
(Golub et al, 1999). The binary outcome encodes the disease subtype:
acute lymphobastic leukemia (ALL) or acute myeloid leukemia (AML).</p>

datasets

Fast algorithms for fitting Bayesian variable selection
models and computing Bayes factors, in which the outcome (or
response variable) is modeled using a linear regression or a
logistic regression. The algorithms are based on the variational
approximations described in "Scalable variational inference for
Bayesian variable selection in regression, and its accuracy in
genetic association studies" (P. Carbonetto & M. Stephens, 2012,
<DOI:10.1214/12-BA703>). This software has been applied to large
data sets with over a million variables and thousands of samples.

Peter Carbonetto

varbvs

Large-Scale Bayesian Variable Selection Using Variational
Methods

Matthew Stephens

David Gerard

leukemia function

<p>Data are represented as a 72 x 3,571 matrix <code>x</code> of gene expression
values, and a vector <code>y</code> of 72 binary disease outcomes.</p>

leukemia: Expression levels recorded in leukemia patients.

Description

Usage

Arguments

Format

References

Examples