bnlearn (version 3.9)

bn.fit utilities: Utilities to manipulate fitted Bayesian networks

Description

Assign, extract or compute various quantities of interest from an object of class bn.fit, bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode or bn.fit.onode.

Usage

## methods available for "bn.fit"
## S3 method for class 'bn.fit':
fitted(object, ...)
## S3 method for class 'bn.fit':
coef(object, ...)
## S3 method for class 'bn.fit':
residuals(object, ...)
## S3 method for class 'bn.fit':
sigma(object, ...)
## S3 method for class 'bn.fit':
predict(object, node, data, method = "parents", ..., debug = FALSE)
## S3 method for class 'bn.fit':
logLik(object, data, nodes, by.sample = FALSE, ...)
## S3 method for class 'bn.fit':
AIC(object, data, ..., k = 1)
## S3 method for class 'bn.fit':
BIC(object, data, ...)
## methods available for "bn.fit.dnode"
## S3 method for class 'bn.fit.dnode':
coef(object, ...)
## methods available for "bn.fit.onode"
## S3 method for class 'bn.fit.onode':
coef(object, ...)
## methods available for "bn.fit.gnode"
## S3 method for class 'bn.fit.gnode':
fitted(object, ...)
## S3 method for class 'bn.fit.gnode':
coef(object, ...)
## S3 method for class 'bn.fit.gnode':
residuals(object, ...)
## S3 method for class 'bn.fit.gnode':
sigma(object, ...)
## methods available for "bn.fit.cgnode"
## S3 method for class 'bn.fit.cgnode':
fitted(object, ...)
## S3 method for class 'bn.fit.cgnode':
coef(object, ...)
## S3 method for class 'bn.fit.cgnode':
residuals(object, ...)
## S3 method for class 'bn.fit.cgnode':
sigma(object, ...)

Arguments

object

an object of class bn.fit, bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode or bn.fit.onode.

node

a character string, the label of a node.

nodes

a vector of character strings, the label of a nodes whose log-likelihood components are to be computed.

data

a data frame containing the variables in the model.

method

a character string, the method used to estimate predictions. See below.

...

additional arguments. See below.

k

a numeric value, the penalty per parameter to be used; the default k = 1 gives the expression used to compute AIC.

by.sample

a boolean value. If TRUE, logLik returns a vector containing the the log-likelihood of each observations in the sample. If FALSE, logLik returns a single value, the likelihood of the whole s

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Value

• predict returns a numeric vector (for Gaussian and conditional Gaussian nodes), a factor (for categorical nodes) or an ordered factor (for ordinal nodes).

  logLik returns a numeric vector or a single numeric value, depending on the value of by.sample. AIC and BIC always return a single numeric value.

  All the other functions return a list with an element for each node in the network (if object has class bn.fit) or a numeric vector or matrix (if object has class bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode or bn.fit.onode).

Details

coef (and its alias coefficients) extracts model coefficients (which are conditional probabilities for discrete nodes and linear regression coefficients for Gaussian and conditional Gaussian nodes).

residuals (and its alias resid) extracts model residuals and fitted (and its alias fitted.values) extracts fitted values from Gaussian and conditional Gaussian nodes. If the bn.fit object does not include the residuals or the fitted values for the node of interest both functions return NULL.

sigma extracts the standard deviations of the residuals from Gaussian and conditional Gaussian networks and nodes.

predict returns the predicted values for node given the data specified by data and the fitted network. Depending on the value of method, the predicted values are computed as follows.

• parents: the predicted values are computed by plugging in the new values for the parents ofnodein the local probability distribution ofnodeextracted fromfitted.
• bayes-lw: the predicted values are computed by averaging likelihood weighting simulations performed using all the available nodes as evidence (obviously, with the exception of the node whose values we are predicting). The number of random samples which are averaged for each new observation is controlled by thenoptional argument; the default is500. If the variable being predicted is discrete, the predicted level is that with the highest conditional probability. If the variable is continuous, the predicted value is the expected value of the conditional distribution.

See Also

bn.fit, bn.fit-class.

Examples

data(gaussian.test)
res = hc(gaussian.test)
fitted = bn.fit(res, gaussian.test)
coefficients(fitted)
coefficients(fitted$C)
str(residuals(fitted))

data(learning.test)
res2 = hc(learning.test)
fitted2 = bn.fit(res2, learning.test)
coefficients(fitted2$E)