mmpc {bnlearn} | R Documentation |
Estimate the underlying structure of a directed acyclic graph (DAG) from data using the Max-Min Parents and Children (MMPC) constraint-based algorithm.
mmpc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, debug = FALSE, optimized = TRUE, strict = FALSE)
x |
a data frame, containing the variables in the model. |
cluster |
an optional cluster object from package snow.
See snow integration for details and a simple
example. |
whitelist |
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph. |
blacklist |
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph. |
test |
a character string, the label of the conditional
independence test to be used in the algorithm. If none is
specified, the default test statistic is the mutual information
for discrete data sets and the linear correlation for
continuous ones. See bnlearn-package for details. |
alpha |
a numeric value, the target nominal type I error rate. |
debug |
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent. |
optimized |
a boolean value. See bnlearn-package
for details. |
strict |
a boolean value. If TRUE conflicting results in
the learning process generate an error; otherwise they result in
a warning. |
An object of class bn
.
See bn-class
for details.
Marco Scutari
I. Tsamardinos, C. F. Aliferis, A. Statnikov. Time and sample efficient discovery of Markov blankets and direct causal relations. Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining KDD, pages 673-8, 2003.
I. Tsamardinos, L. E. Brown, C. Aliferis. The max-min hill-climbing Bayesian network learning algorithm. Machine Learning, 65(1), pages 31-78. Kluwer Academic Publishers, 2006.
gs
, fast.iamb
, iamb
,
inter.iamb
, hc
.