bnlearn (version 1.1)

model string tools: 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)
  model2network(string, debug = FALSE)
  ## S3 method for class 'bn':
as.character(x, ...)
  ## S3 method for class 'character':
as.bn(x, debug = FALSE)

Arguments

x

an object of class bn.

string

a character string describing the Bayesian network.

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).

Value

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

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).

Examples

data(learning.test)
res = set.arc(gs(learning.test), "A", "B")
res
# 
#   Bayesian network learned via Conditional Independence 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:  41 
#
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