fciPlus {pcalg} | R Documentation |
Estimate a Partial Ancestral Graph (PAG) from observational data, using the FCI+ (Fast Causal Inference) Algorithm.
fciPlus(suffStat, indepTest, alpha, labels, p, verbose=TRUE)
suffStat |
sufficient statistics: A named |
indepTest |
a |
alpha |
numeric significance level (in (0, 1)) for the individual conditional independence tests. |
labels |
(optional) |
p |
(optional) number of variables (or nodes). May be specified
if |
verbose |
logical indicating if progress of the algorithm should be printed. The default is true, which used to be hard coded previously. |
A variation of FCI (Fast Causal Inference). For details, please see
the references, and also fci
.
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.
Emilija Perkovic and Markus Kalisch (kalisch@stat.math.ethz.ch).
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
fci
for estimating a PAG using the FCI algorithm.
################################################## ## Example without latent variables ################################################## ## generate a random DAG ( p = 7 ) set.seed(42) p <- 7 myDAG <- randomDAG(p, prob = 0.4) ## find PAG using the FCI+ algorithm on "Oracle" suffStat <- list(C = cov2cor(trueCov(myDAG)), n = 10^9) m.fci <- fciPlus(suffStat, indepTest=gaussCItest, alpha = 0.9999, p=p) summary(m.fci) ## require("Rgraphviz") sfsmisc::mult.fig(2, main="True DAG // fciPlus(.) \"oracle\" estimate") plot(myDAG) plot(m.fci)