Description Usage Arguments Details Value Author(s) Examples
Can be used to downscale discrete data to local scales by means of Bayesian Networks.
1 2 3 4 5 6 7 8 9 10 | buildCBN(data, structure.learning.algorithm = "tabu",
structure.learning.args.list = list(),
param.learning.method = "bayes", forbid.GG = FALSE,
forbid.DD = FALSE, forbid.DtoG = FALSE, force.closest.GD = NULL,
closest.GD.direction = NULL, forbid.GD = FALSE,
structure.learning.steps = 1, fix.intermediate = TRUE,
structure.learning.algorithm2 = NULL,
structure.learning.args.list2 = list(), return.intermediate = FALSE,
compile.junction = TRUE, parallelize = FALSE, n.cores = NULL,
cluster.type = "FORK")
|
data |
Expects output from |
structure.learning.algorithm |
Algorithm used to perform structure learning, with name
as text. Supports all the score-based, constraint-based and hybrid bayesian network structure
learning algorithms from |
structure.learning.args.list |
List of arguments passed to structure.learning.algorithm,
in particular distance argument if local learning is used. Note that other arguments, e.g.
|
param.learning.method |
Either "bayes" or "mle", passed to learn the parameters for the
Conditional Probability Tables for the built DAG |
forbid.GG |
If set to TRUE, arcs between grid or G nodes will be forbidden. |
forbid.DD |
If set to TRUE, arcs between local, i.e. station or D nodes, will be forbidden. |
forbid.DtoG |
If set to TRUE, arcs from D nodes to G nodes will be forbidden. |
force.closest.GD |
Expects a positive integer or |
closest.GD.direction |
Either |
forbid.GD |
If set to TRUE, arcs between G and D nodes will be forbidden. See
|
structure.learning.steps |
It is used to perform structure learning in
two steps.
Refer to
Note that only first two options are valid when |
fix.intermediate |
Set to TRUE to forbid the creation of new arcs in the next steps
for already built DAGs. See |
structure.learning.algorithm2 |
Same as structure.learning.algorithm for the second
step if |
structure.learning.args.list2 |
Same as structure.learning.args.list for the second
step if |
return.intermediate |
Add the intermediate DAGs to the output, as $intermediateDBN1 and
$intermediateDBN2 (if any) if |
compile.junction |
Compile the junction from BN.fit to compute probabilities. Can be set
to FALSE, in which case it can still be computed if needed at the training stage, i.e. through
|
parallelize |
Set to |
n.cores |
When |
cluster.type |
Either "PSOCK" or "FORK". Use the former under Windows systems,
refer to |
Structure Learning Algorithms
Use structure.learning.algorithm
to specify the algorithm for the structure (DAG) learning process.
Currently it DOES NOT support local discovery algorithms, expect malfuncion if used.
List of supported algorithms:
"hc"
, "tabu"
(score-based), "gs"
, "iamb"
, "fast.iamb"
, "inter.iamb"
(constraint-based),
"mmhc"
, "rsmax2"
(hybrid).
Check their corresponding parameters in bnlearn
, arguments may be passed to the algorithm through
the parameter structure.learning.args.list. Do not forget to set the distance argument in structure.learning.args.list
for
local learning.
Two or Three Step Learning
structure.learning.steps
allows to build separate DAGs for each set of nodes. Note that by employing the three
structure.learning.algorithm
, structure.learning.algorithm2
, structure.learning.algorithm3
arguments and their
corresponding structure.learning.args.list*
counterparts, many different configurations can be used for the structure learning
process, e.g. by using grow-shrink for D nodes with distance set to 1, then injecting the left nodes using hill-climbing without distance
restriction.
fix.intermediate
, if set to TRUE
, will forbid the creation of new arcs between nodes that were present in the previous
learning step. E.g. if structure.learning.steps = c("local", "global\-past")
, no new arcs between D nodes will be created in the
second step, as the first DAG will be considered finished. If set to FALSE
, the previous step DAG will be kept, but the next
learning algorithm could create new arcs between D nodes over the first one.
Forbidding or Forcing Arcs
For non dynamic Bayesian Networks, i.e. when dynamic = FALSE
(default),
forbid.GG
, forbid.DD
, forbid.DtoG
, force.closest.GD
,
forbid.GD
, fix.intermediate
, structure.learning.steps
allow
introducing constraints to the structure learning algorithm. The user might also combine them
with structure.learning.args.list$whitelist
and
structure.learning.args.list$blacklist
. As whitelist
has priority over
blacklist
, i.e. an arc placed in both will always be present in the DAG, they
provide maximum flexibility. Bearing the priority of the whitelist
,
force.closest.GD = TRUE
and forbid.GD = TRUE
will, for example, forbid
the placement of aditional arcs beyond those specified as the closest G-D.
When manually specifying a whitelist or blacklist through
structure.learning.args.list
, beware of the naming convention. It overrides
the names and marks them as either "D.X" or "G.X", preditand and predictor nodes,
respectivelly. It is best to plot a dummy network using plotDBN() first.
Aditional details
Parameters output.marginals
and compile.junction
are useful to save time
if the user only intends to visualize the DAG.
An object of type DBN which contains the learnt Bayesian Network.
Mikel N Legasa
1 2 | # Not yet
# Loading predictors
|
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