hybrid algorithms {bnlearn} | R Documentation |
Learn the structure of a Bayesian network with the Max-Min Hill Climbing (MMHC) and the more general Restricted Hill Climbing (RSHC) hybrid algorithms.
rshc(x, whitelist = NULL, blacklist = NULL, restrict, maximize = "hc", test = NULL, score = NULL, alpha = 0.05, B = NULL, ..., restart = 0, perturb = 1, max.iter = Inf, 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)
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. The only possible value
is hc . 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 discrete data sets and the
linear correlation for continuous ones. 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. |
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. |
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.
mmhc
is simply rshc
with restrict
set to
mmpc
and maximize
set to hc
.
Marco Scutari
Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm". Machine Learning, 65(1), 31-78.
local discovery algorithms
,
score-based algorithms
, constraint-based algorithms
.