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 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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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).
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:15:i06. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.