rbn: Simulate random data from a given Bayesian network

In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Description

Simulate random data from a given Bayesian network.

Usage

## S3 method for class 'bn'
rbn(x, n = 1, data, fit = "mle", ..., debug = FALSE)
## S3 method for class 'bn.fit'
rbn(x, n = 1, ..., debug = FALSE)

Arguments

x	an object of class bn or bn.fit.
n	a positive integer giving the number of observations to generate.
data	a data frame containing the data the Bayesian network was learned from.
fit	a character string, the label of the method used to fit the parameters of the newtork. See bn.fit for details.
...	additional arguments for the parameter estimation prcoedure, see again bn.fit for details..
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Value

A data frame with the same structure (column names and data types) of the data parameter (if x is an object of class bn) or with the same structure as the data originally used to to fit the parameters of the Bayesian network (if x is an object of class bn.fit).

Author(s)

Marco Scutari

References

Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.

See Also

bn.boot, bn.cv.

Examples

## Not run: 
data(learning.test)
res = gs(learning.test)
res = set.arc(res, "A", "B")
par(mfrow = c(1,2))
plot(res)
sim = rbn(res, 500, learning.test)
plot(gs(sim))
## End(Not run)

jtrecenti/bnlearn documentation built on May 20, 2019, 3:16 a.m.