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 <
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
det 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 discrete data sets and the
lin
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
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.