model string tools {bnlearn} | R Documentation |
Build a model string from a Bayesian network and vice versa.
modelstring(x) model2network(string, debug = FALSE) ## S3 method for class 'bn': as.character(x, ...) ## S3 method for class 'character': as.bn(x, debug = FALSE)
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). |
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).
model2network
and as.bn.character
return an object of
class bn
; modelstring
and as.character.bn
return
a character string.
Marco Scutari
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