bn.fit utilities  R Documentation 
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
.
## 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, ...)
## 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, ...)
object 
an object of class 
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. 
... 
additional arguments, currently ignored. 
k 
a numeric value, the penalty coefficient to be used; the default

by.sample 
a boolean value. If 
na.rm 
a boolean value, whether missing values should be used in
computing the loglikelihood. 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. 
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 loglikelihood for the observations in
data
. If na.rm
is set to TRUE
, the loglikelihood will be
NA
if the data contain missing values. If na.rm
is set to
FALSE
, missing values will be dropped and the loglikelihood will be
computed using only locallycomplete observations (effectively returning the
nodeaverage loglikelihood times the sample size). Note that the
loglikelihood 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.
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
).
Marco Scutari
bn.fit
, bn.fitclass
.
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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