bn.fit.methods: Utilities to manipulate fitted Bayesian networks
In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Description Usage Arguments Details Value Note Author(s) See Also Examples

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

## 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'
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, ...)
## S3 method for class 'bn.fit.dnode'
predict(object, data, ..., debug = FALSE)

## methods available for "bn.fit.onode"
## S3 method for class 'bn.fit.onode'
coef(object, ...)
## S3 method for class 'bn.fit.onode'
predict(object, data, ..., debug = FALSE)

## 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'
predict(object, data, ..., debug = FALSE)

`object`	an object of class `bn.fit`, `bn.fit.dnode` or `bn.fit.gnode`.
`node`	a character string, the label of a node.
`nodes`	a vector of character strings, the label of a nodes whose loglikelihood components are to be computed.
`data`	a data frame containing the variables in the model.
`method`	a character sting, 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 sample.
`debug`	a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

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

residuals (and its alias resid) extracts model residuals and fitted (and its alias fitted.values) extracts fitted values from fitted Gaussian networks. If the bn.fit object does not include the residuals or the fitted values (for the nodes of interest, in the case of bn.fit.gnode objects), both functions return NULL.

predict returns the predicted values for node given the data specified by data. 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 of node in the local probability distribution of node extracted from fitted.
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 the n optional argument; the default is 500. 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.

Both methods are available for bn.fit objects; predictions for bn.fit.dnode, bn.fit.onode and bn.fit.gnode objects can only be estimated with method = "parents". That is the default method for bn.fit objects as well.

predict returns a numeric vector (for Gaussian networks) or a factor (for discrete networks).

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 (if object has class bn.fit.dnode or bn.fit.gnode).

Ties in prediction are broken using Bayesian tie breaking, i.e. sampling at random from the tied values. Therefore, setting the random seed is required to get reproducible results.

predict accepts either a bn or a bn.fit object as its first argument. For the former, the parameters of the network are fitted on data, that is, the observations whose class labels the function is trying to predict.

Marco Scutari

bn.fit, bn.fit-class.

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)