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

  • Giovanni M. Marchetti
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    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.

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    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 15 ()
    Issue (Month): i06 ()
    Pages:

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    Handle: RePEc:jss:jstsof:15:i06
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    1. Steen A. Andersson, 2001. "Alternative Markov Properties for Chain Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 33-85.
    2. Claus Dethlefsen & Søren Højsgaard, . "A Common Platform for Graphical Models in R: The gRbase Package," Journal of Statistical Software, American Statistical Association, vol. 14(i17).
    3. Nanny Wermuth & D. R. Cox, 2004. "Joint response graphs and separation induced by triangular systems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 687-717.
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