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bn.fit utilities {bnlearn} | R Documentation |
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'
logLik(object, data, nodes, by.sample = FALSE, na.rm = FALSE, debug = FALSE, ...)
## S3 method for class 'bn.fit'
AIC(object, data, ..., k = 1)
## S3 method for class 'bn.fit'
BIC(object, data, ...)
## non-method functions for "bn.fit"
identifiable(x, by.node = FALSE)
singular(x, by.node = FALSE)
## methods available for "bn.fit.dnode"
## S3 method for class 'bn.fit.dnode'
coef(object, for.parents, ...)
## methods available for "bn.fit.onode"
## S3 method for class 'bn.fit.onode'
coef(object, for.parents, ...)
## 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, for.parents, ...)
## S3 method for class 'bn.fit.cgnode'
residuals(object, ...)
## S3 method for class 'bn.fit.cgnode'
sigma(object, for.parents, ...)
Arguments
object |
an object of class |
x |
an object of class |
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. |
... |
additional arguments, currently ignored. |
k |
a numeric value, the penalty coefficient to be used; the default |
by.sample |
a boolean value. If |
by.node |
a boolean value. if |
na.rm |
a boolean value, whether missing values should be used in computing the log-likelihood. See below
for details. The default value is |
debug |
a boolean value. If |
for.parents |
a named list in which each element contains a set of values for the discrete parents of the nodes.
|
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.
logLik()
returns the log-likelihood for the observations in data
. If
na.rm
is set to TRUE
, the log-likelihood will be NA
if the data
contain missing values. If na.rm
is set to FALSE
, missing values will be dropped
and the log-likelihood will be computed using only locally-complete observations (effectively returning the
node-average log-likelihood times the sample size). Note that the log-likelihood may be NA
even if na.rm = TRUE
if the network contains NA
parameters or is singular.
The for.parents
argument in the methods for coef()
and sigma()
can be used to have both functions return the parameters associated with a specific configuration of the
discrete parents of a node. If for.parents
is not specified, all relevant parameters are
returned.
Value
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
).
Author(s)
Marco Scutari
See Also
Examples
data(gaussian.test)
dag = hc(gaussian.test)
fitted = bn.fit(dag, gaussian.test)
coefficients(fitted)
coefficients(fitted$C)
str(residuals(fitted))
data(learning.test)
dag2 = hc(learning.test)
fitted2 = bn.fit(dag2, learning.test)
coefficients(fitted2$E)
coefficients(fitted2$E, for.parents = list(F = "a", B = "b"))
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