gRain integration {bnlearn}

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 grain(code) (for as.bn.fit) or bn.fit() (for as.grain).

including.evidence

a boolean value. If FALSE, the grain object is converted without considering any evidence that has been set into it. If TRUE, any hard evidence is carried over into the bn.fit object as a zero-one probability distribution.

...

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)