View source: R/frontend-learning.R
score-based algorithms | R Documentation |
Learn the structure of a Bayesian network using a hill-climbing (HC) or a Tabu search (TABU) greedy search.
hc(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, restart = 0, perturb = 1, max.iter = Inf, maxp = Inf, optimized = TRUE)
tabu(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, tabu = 10, max.tabu = tabu, max.iter = Inf, maxp = Inf, optimized = TRUE)
x |
a data frame containing the variables in the model. |
start |
an object of class |
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. |
score |
a character string, the label of the network score to be used in
the algorithm. If none is specified, the default score is the Bayesian
Information Criterion for both discrete and continuous data sets. See
|
... |
additional tuning parameters for the network score. See
|
debug |
a boolean value. If |
restart |
an integer, the number of random restarts. |
tabu |
a positive integer number, the length of the tabu list used in the
|
max.tabu |
a positive integer number, the iterations tabu search can perform without improving the best network score. |
perturb |
an integer, the number of attempts to randomly insert/remove/reverse an arc on every random restart. |
max.iter |
an integer, the maximum number of iterations. |
maxp |
the maximum number of parents allowed for a node in any network
that is considered in the search, including that that is returned. The
default value is |
optimized |
a boolean value. If |
An object of class bn
. See bn-class
for details.
See structure learning
for a complete list of structure learning
algorithms with the respective references.
Marco Scutari
network scores
, constraint-based algorithms,
hybrid algorithms, local discovery algorithms,
alpha.star.
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