rbn {bnlearn} | R Documentation |
Generate random data from a given Bayesian network.
rbn(x, n, data, debug = FALSE)
x |
an object of class bn . |
n |
non-negative integer giving the number of observations to generate. |
data |
a data frame, containing the data the Bayesian network was learned from. |
debug |
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent. |
A data frame with the same structure (column names and data types)
of the data
parameter.
Only discrete Bayesian networks are supported; the simulation uses an implementation of the Logic Sampling (LS) algorithm (Korb and Nicholson, 2004).
The execution time scales linearly with the number of generated data.
Node ordering uses a recursive approach; if there are lots of nodes
in the network, you will need to increase R's recursion limit. See
the expression
argument to the options
command for
details on how to do this.
Marco Scutari
K. Korb and A. Nicholson. Bayesian artificial intelligence. Chapman and Hall, 2004.
## Not run: library(bnlearn) 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)