pcalg (version 2.2-0)

fciPlus: Estimate a PAG, using the FCI+ algorithm

Description

Estimate a Partial Ancestral Graph (PAG) from observational data, using the FCI+ Algorithm.

Usage

fciPlus(suffStat, indepTest, alpha, labels, p)

Arguments

suffStat

Sufficient statistics: List containing all necessary elements for the conditional independence decisions in the function indepTest.

indepTest

Predefined function for testing conditional independence. The function is internally called as indepTest(x,y,S,suffStat), and tests conditional independence of x and y given S. Here, x<

alpha

Significance level for the individual conditional independence tests.

labels

Character vector of variable (or node) names. Typically preferred to specifying p.

p

(optional) number of variables (or nodes). May be specified if labels are not, in which case labels is set to 1:p.

Value

• An object of class fciAlgo (see fciAlgo) containing the estimated graph (in the form of an adjacency matrix with various possible edge marks), the conditioning sets that lead to edge removals (sepset) and several other parameters.

Details

A variation of FCI. For details, please see the references.

References

T. Claassen, J. Mooij, and T. Heskes (2013). Learning Sparse Causal Models is not NP-hard. In UAI 2013, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence

See Also

fci for estimating a PAG using the FCI algorithm.

Examples

##################################################
## Example without latent variables
##################################################

set.seed(42)
p <- 7
## generate and draw random DAG :
myDAG <- randomDAG(p, prob = 0.4)

## find skeleton and PAG using the FCI algorithm
suffStat <- list(C = cov2cor(trueCov(myDAG)), n = 10^9)
res <- fciPlus(suffStat, indepTest=gaussCItest,
           alpha = 0.9999, p=p)