Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm

Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm

Show other versions (1)

We describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables. The package has a few basic functions that find the essential graph, the...

Full description

Bibliographic Details
Main Author: Giovanni M. Marchetti
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2006-02-01
Series:Journal of Statistical Software
Online Access:http://www.jstatsoft.org/index.php/jss/article/view/1481
id doaj-a197e2e9a2cd4563bff49bce6f955384
record_format Article
spelling doaj-a197e2e9a2cd4563bff49bce6f9553842020-11-24T23:10:05ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602006-02-0115111510.18637/jss.v015.i0685Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggmGiovanni M. MarchettiWe describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables. The package has a few basic functions that find the essential graph, the induced concentration and covariance graphs, and several types of chain graphs implied by the directed acyclic graph (DAG) after grouping and reordering the variables. These functions can be useful to explore the impact of latent variables or of selection effects on a chosen data generating model.http://www.jstatsoft.org/index.php/jss/article/view/1481
collection DOAJ
language English
format Article
sources DOAJ
author Giovanni M. Marchetti
spellingShingle Giovanni M. Marchetti
Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
Journal of Statistical Software
author_facet Giovanni M. Marchetti
author_sort Giovanni M. Marchetti
title Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
title_short Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
title_full Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
title_fullStr Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
title_full_unstemmed Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm
title_sort independencies induced from a graphical markov model after marginalization and conditioning: the r package ggm
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2006-02-01
description We describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables. The package has a few basic functions that find the essential graph, the induced concentration and covariance graphs, and several types of chain graphs implied by the directed acyclic graph (DAG) after grouping and reordering the variables. These functions can be useful to explore the impact of latent variables or of selection effects on a chosen data generating model.
url http://www.jstatsoft.org/index.php/jss/article/view/1481
work_keys_str_mv AT giovannimmarchetti independenciesinducedfromagraphicalmarkovmodelaftermarginalizationandconditioningtherpackageggm
_version_ 1725608152712347648

Similar Items