lm integration

as.lm

as.lm.bn

as.lm.bn.fit

as.lm.bn.fit.gnode

Take a <code>bn</code> object or <code>bn.fit</code> object encoding a Gaussian network
 and refit all the local distributions using <code><a href="/link/lm()?package=bnlearn&version=5.0.1" data-mini-rdoc="bnlearn::lm()">lm()</a></code>. This makes it
 possible to use all the functions provided by R for <code>lm</code> objects 
 (<code>summary</code>, <code>anova</code>, etc.) to investigate the network.

interfaces to other packages

Bayesian network structure learning, parameter learning and inference.
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
Gaussian and conditional Gaussian networks, along with many score functions and
conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
Some utility functions (model comparison and manipulation, random data generation, arc
orientation testing, simple and advanced plots) are included, as well as support for
parameter estimation (maximum likelihood and Bayesian) and inference, conditional
probability queries, cross-validation, bootstrap and model averaging.
Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

Tomi Silander

lm integration function

<dl> <dt>x</dt>
<dd>an object of class <code>bn</code>, <code>bn.fit</code> or <code>bn.fit.gnode</code>.</dd> <dt>data</dt>
<dd>a data frame containing the variables in the model.</dd> <dt>...</dt>
<dd>additional arguments, currently ignored.</dd></dl>

Arguments

Author

Take a <code>bn</code> object or <code>bn.fit</code> object encoding a Gaussian network
 and refit all the local distributions using <code><a href='https://rdrr.io/r/stats/lm.html'>lm()</a></code>. This makes it
 possible to use all the functions provided by R for <code>lm</code> objects 
 (<code>summary</code>, <code>anova</code>, etc.) to investigate the network.

Produce lm objects from Bayesian networks — lm integration

<dl>

 <dt>x</dt>
<dd>an object of class <code>bn</code>, <code>bn.fit</code> or <code>bn.fit.gnode</code>.</dd>

 <dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>

 <dt>...</dt>
<dd>additional arguments, currently ignored.</dd>

</dl>

lm integration: Produce lm objects from Bayesian networks

Description

Usage

Value

Arguments

Author

Examples