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model string utilities {bnlearn} R Documentation

Build a model string from a Bayesian network and vice versa

Description

Build a model string from a Bayesian network and vice versa.

Usage

modelstring(x)
modelstring(x, debug = FALSE) <- value

model2network(string, debug = FALSE)

## S3 method for class 'bn':
as.character(x, ...)
## S3 method for class 'character':
as.bn(string, ...)

Arguments

x an object of class bn. modelstring (but not its replacement form) accepts also objects of class bn.fit.
string a character string describing the Bayesian network.
value a character string, the same as the string.
debug a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.
... extra arguments from the generic method (currently ignored).

Details

The strings returned by modelstring have the same format as the ones returned by the modelstring function in package deal; network structures may be easily exported to and imported from that package (via the model2network function).

Value

model2network and as.bn return an object of class bn; modelstring and as.character.bn return a character string.

Author(s)

Marco Scutari

Examples

data(learning.test)
res = set.arc(gs(learning.test), "A", "B")
res
#
#   Bayesian network learned via Constraint-based methods
#
#   model:
#     [A][C][F][B|A][D|A:C][E|B:F]
#   nodes:                                 6
#   arcs:                                  5
#     undirected arcs:                     0
#     directed arcs:                       5
#   average markov blanket size:           2.33
#   average neighbourhood size:            1.67
#   average branching factor:              0.83
#
#   learning algorithm:                    Grow-Shrink
#   conditional independence test:         Mutual Information (discrete)
#   alpha threshold:                       0.05
#   tests used in the learning procedure:  43
#
modelstring(res)
# [1] "[A][C][F][B|A][D|A:C][E|B:F]"
res2 = model2network(modelstring(res))
res2
#
#   Randomly generated Bayesian network
#
#   model:
#     [A][C][F][B|A][D|A:C][E|B:F]
#   nodes:                                 6
#   arcs:                                  5
#     undirected arcs:                     0
#     directed arcs:                       5
#   average markov blanket size:           2.33
#   average neighbourhood size:            1.67
#   average branching factor:              0.83
#
#   generation algorithm:                  Empty
#
compare(res, res2)
# [1] TRUE
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