plotDAG

An <code><a rd-options="" href="/link/edgeList?package=sparsebn&version=0.1.2" data-mini-rdoc="sparsebn::edgeList">edgeList</a></code>, <code><a rd-options="" href="/link/sparsebnFit?package=sparsebn&version=0.1.2" data-mini-rdoc="sparsebn::sparsebnFit">sparsebnFit</a></code>, or <code><a rd-options="" href="/link/sparsebnPath?package=sparsebn&version=0.1.2" data-mini-rdoc="sparsebn::sparsebnPath">sparsebnPath</a></code> object.

Additional arguments to <code><a rd-options="" href="/link/plot?package=sparsebn&version=0.1.2" data-mini-rdoc="sparsebn::plot">plot</a></code>.

Using some sensible defaults for large graphs, plot a DAG object. Uses <code><a rd-options="igraph" href="/link/igraph?package=sparsebn&version=0.1.2&to=igraph" data-mini-rdoc="igraph::igraph">igraph</a></code>
package by default.

Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Bryon Aragam

sparsebn

Learning Sparse Bayesian Networks from High-Dimensional Data

Jiaying Gu

Dacheng Zhang

Qing Zhou

plotDAG function

An <code><a rd-options='' href='edgeList'>edgeList</a></code>, <code><a rd-options='' href='sparsebnFit'>sparsebnFit</a></code>, or <code><a rd-options='' href='sparsebnPath'>sparsebnPath</a></code> object.

Additional arguments to <code><a rd-options='' href='plot'>plot</a></code>.

Using some sensible defaults for large graphs, plot a DAG object. Uses <code><a rd-options='igraph' href='igraph'>igraph</a></code>
package by default.

plotDAG: Plot a DAG

Description

Usage

Arguments

Details