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gRain integration {bnlearn} | R Documentation |
Import and export networks from the gRain package
Description
Convert bn.fit
objects to grain
objects and vice versa.
Usage
## 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, ...)
Arguments
x |
an object of class |
including.evidence |
a boolean value. If |
... |
extra arguments from the generic method (currently ignored). |
Value
An object of class grain
(for as.grain
), bn.fit
(for
as.bn.fit
) or bn
(for as.bn
).
Note
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 $m$ grain
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.
Author(s)
Marco Scutari
Examples
## 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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