Causal Inference: The R package pcalg

16 Causal Graphical Models: Package pcalg
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Figure 5: True underlying DAG (left) and estimated PAG (right), when applying the FCI
and RFCI algorithms to the data set gmL. The output of FCI and RFCI is identical. Variable
V 1 of the true underlying DAG is latent.
D-SEP sets and thus does not make tests conditioning on them. This makes rfci() much
faster than fci(). The orientation rule for v-structures and the orientation rule for so-called
discriminating paths (rule 4) were modified in order to produce a PAG which, in the oracle
version, is guaranteed to have correct ancestral relationships.
The function can be called in the following way:
rfci(suffStat, indepTest, p, alpha, verbose = FALSE, fixedGaps = NULL,
fixedEdges = NULL, NAdelete = TRUE, m.max = Inf)
where the arguments suffStat, indepTest, p, alpha, fixedGaps, fixedEdges, NAdelete
and m.max are identical to those in skeleton().
As an example, we re-run the example from Section 3.3 and show the PAG estimated with
rfci() in Figure 5. The PAG estimated with fci() and the PAG estimated with rfci() are
the same.
R> data("gmL")
R> suffStat1 pag.est