R: Simulate random data from a given Bayesian network

rbn {bnlearn}

R Documentation

Simulate random data from a given Bayesian network

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 argument (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.

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)

[Package bnlearn version 4.3 Index]