pcalg (version 2.0-3)

GaussParDAG-class: Class "GaussParDAG"

Description

The "GaussParDAG" class represents a Gaussian causal model.

encoding

UTF-8

concept

• Gaussian causal model
• structural equation model

Extends

Class "ParDAG", directly.

All reference classes extend and inherit methods from "envRefClass".

Details

The class "GaussParDAG" is used to simulate observational and/or interventional data from Gaussian causal models as well as for parameter estimation (maximum-likelihood estimation) for a given DAG structure in the presence of a data set with jointly observational and interventional data. A Gaussian causal model can be represented as a set of $p$ linear structural equations with Gaussian noise variables. Those equations are fully specified by indicating the regression parameters, the intercept and the variance of the noise or error terms. More details can be found e.g. in Kalisch and Bühlmann (2007) or Hauser and Bühlmann (2012).

References

A. Hauser and P. Bühlmann (2012). Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research 13, 2409--2464. M. Kalisch and P. Buehlmann (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research 8, 613--636. K.B. Korb, L.R. Hope, A.E. Nicholson, and K. Axnick (2004). Varieties of causal intervention. Proc. of the Pacific Rim International Conference on Artificial Intelligence (PRICAI 2004), 322--331

See Also

ParDAG

Examples

set.seed(307)
myDAG <- r.gauss.pardag(p = 5, prob = 0.4)
myDAG$weight.mat()
myDAG$err.var()
myDAG$intercept()
myDAG$set.intercept(runif(5, 3, 4))
myDAG$intercept()
if (require(Rgraphviz)) plot(myDAG)