Usage
rsmax2(x, whitelist = NULL, blacklist = NULL, restrict, maximize = "hc",
test = NULL, score = NULL, alpha = 0.05, B = NULL, ...,
maximize.args = list(), optimized = TRUE, strict = FALSE, debug = FALSE)
mmhc(x, whitelist = NULL, blacklist = NULL, test = NULL, score = NULL,
alpha = 0.05, B = NULL, ..., restart = 0, perturb = 1, max.iter = Inf,
optimized = TRUE, strict = FALSE, debug = FALSE)
Arguments
x
a data frame containing the variables in the model.
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.
restrict
a character string, the constraint-based algorithm to be used
in the “restrict” phase. Possible values are gs, iamb,
fast.iamb, inter.iamb and mmpc. See
bnlearn-package and the documentation of each algorithm for
details. maximize
a character string, the score-based algorithm to be used in
the “maximize” phase. Possible values are hc and tabu.
See bnlearn-package for details. test
a character string, the label of the conditional independence test
to be used by the constraint-based algorithm. If none is specified, the
default test statistic is the mutual information for categorical
variables, the Jonckheere-Terpstra test for ordered factors and the
linear correlation for continuous variables. See
bnlearn-package for details. score
a character string, the label of the network score to be used in
the score-based 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. alpha
a numeric value, the target nominal type I error rate of the
conditional independence test.
B
a positive integer, the number of permutations considered for each
permutation test. It will be ignored with a warning if the conditional
independence test specified by the test argument is not a permutation
test.
…
additional tuning parameters for the network score used by the
score-based algorithm. See score for details. maximize.args
a list of arguments to be passed to the score-based
algorithm specified by maximize, such as restart for
hill-climbing or tabu for tabu search.
restart
an integer, the number of random restarts for the score-based
algorithm.
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 for the
score-based algorithm.
debug
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
strict
a boolean value. If TRUE conflicting results in the
learning process generate an error; otherwise they result in a warning.