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

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...

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Bibliographic Details
Published inJournal of statistical software Vol. 15; no. 6
Main Author Marchetti, Giovanni M.
Format Journal Article
LanguageEnglish
Published Foundation for Open Access Statistics 01.02.2006
Subjects
chain graphs
concentration graphs
conditional independence
covariance graphs
directed acyclic graphs
latent variables
multivariate regression graphs
selection effects
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Summary: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.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v015.i06