constraint-based algorithms | R Documentation |
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.
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)
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 |
An object of class bn
.
See bn-class
for details.
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.
Marco Scutari
independence tests
, local discovery algorithms,
score-based algorithms, hybrid algorithms, cextend.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.