Import and export networks from the gRain package
Convert bn.fit objects to grain objects and vice versa.
## S3 method for class 'grain' as.bn.fit(x, including.evidence = FALSE, ...) ## S3 method for class 'bn.fit' as.grain(x) ## S3 method for class 'grain' as.bn(x, ...)
x |
an object of class |
including.evidence |
a boolean value. If |
... |
extra arguments from the generic method (currently ignored). |
An object of class grain (for as.grain), bn.fit (for
as.bn.fit) or bn (for as.bn).
Conditional probability tables in grain objects must be completely
specified; on the other hand, bn.fit allows NaN values for
unobserved parents' configurations. Such bn.fit objects will be
converted to $m$ codegrain objects by replacing the missing conditional
probability distributions with uniform distributions.
Another solution to this problem is to fit another bn.fit with
method = "bayes" and a low iss value, using the same data
and network structure.
Ordinal nodes will be treated as categorical by as.grain,
disregarding the ordering of the levels.
Marco Scutari
## Not run: library(gRain) a = bn.fit(hc(learning.test), learning.test) b = as.grain(a) c = as.bn.fit(b) ## End(Not run)
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