R: Produce lm objects from Bayesian networks

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 `bn`, `bn.fit` or `bn.fit.gnode`.
`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 4.4.1 Index]