| Index | Topics |
| lm integration {bnlearn} | R Documentation |
Produce lm objects from Bayesian networks
Description
Take a bn object or bn.fit object encoding a Gaussian network and refit all
the local distributions using lm(). This makes it possible to use all the functions provided
by R for lm objects (summary, anova, etc.) to investigate the
network.
Usage
## S3 method for class 'bn'
as.lm(x, data, ...)
## S3 method for class 'bn.fit'
as.lm(x, data, ...)
## S3 method for class 'bn.fit.gnode'
as.lm(x, data, ...)
Arguments
x |
an object of class |
data |
a data frame containing the variables in the model. |
... |
additional arguments, currently ignored. |
Value
If x is an object of class bn or bn.fit, as.lm()
returns a list of lm objects, one for each node in x. If x is an
object of class bn or bn.fit.gnode, as.lm() returns a single
lm object.
Author(s)
Marco
Examples
dag = hc(gaussian.test)
fitted = bn.fit(dag, gaussian.test)
as.lm(dag, gaussian.test)
as.lm(fitted, gaussian.test)
as.lm(fitted$F, gaussian.test)
[Package bnlearn version 5.1-20250224 Index]
| Index | Topics |
