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constraint-based algorithms {bnlearn} | R Documentation |
Constraint-based structure learning algorithms
Description
Learn the equivalence class of a directed acyclic graph (DAG) from data using the PC, Grow-Shrink (GS), Incremental Association (IAMB), Fast Incremental Association (Fast-IAMB), Interleaved Incremental Association (Inter-IAMB), Incremental Association with FDR (IAMB-FDR), Max-Min Parents and Children (MMPC), Semi-Interleaved HITON-PC or Hybrid Parents and Children (HPC) constraint-based algorithms.
Usage
pc.stable(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
gs(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
fast.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
inter.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb.fdr(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = FALSE)
mmpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = TRUE)
si.hiton.pc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = TRUE)
hpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, ..., max.sx = NULL, debug = FALSE, undirected = TRUE)
Arguments
x |
a data frame containing the variables in the model. |
cluster |
an optional cluster object from package parallel. |
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. |
test |
a character string, the label of the conditional independence test to be used in the 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 |
alpha |
a numeric value, the target nominal type I error rate. |
... |
optional arguments to be passed to the test specified by |
max.sx |
a positive integer, the maximum allowed size of the conditioning sets used in conditional independence tests. The default is that there is no limit on size. |
debug |
a boolean value. If |
undirected |
a boolean value. If |
Value
An object of class bn
. See bn-class
for
details.
Note
Note that even when undirected
is set to FALSE
there is no guarantee that all
arcs in the returned network will be directed; some arc directions are impossible to learn just from data
due to score equivalence. cextend()
provides a consistent extension of partially directed
networks into directed acyclic graphs, which can then be used (for instance) for parameter learning.
See structure learning
for a complete list of
structure learning algorithms with the respective references. All algorithms accept incomplete data, which
they handle by computing individual conditional independence tests on locally complete observations.
Author(s)
Marco Scutari
See Also
independence tests
, local discovery algorithms, score-based algorithms, hybrid
algorithms, cextend.
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