Usage
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)
Arguments
x
a data frame containing the variables in the model.
start
an object of class bn, the preseeded directed acyclic
graph used to initialize the algorithm. If none is specified, an empty one
(i.e. without any arc) is used.
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
bnlearn-package for details. …
additional tuning parameters for the network score. See
score for details. debug
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
restart
an integer, the number of random restarts.
tabu
a positive integer number, the length of the tabu list used in the
tabu function.
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 for a node. The default value is
Inf.