Fit, assign or replace the parameters of a Bayesian network conditional on its structure.
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an object of class
a data frame containing the variables in the model.
an optional cluster object from package parallel.
a named list, with element for each node of
a character string, either
additional arguments for the parameter estimation procedure, see below.
a vector of character strings, the labels of the discrete
nodes which should be saved as ordinal random variables
a boolean value. If
a boolean value. If
bn.fit() fits the parameters of a Bayesian network given its structure
and a data set;
bn.net returns the structure underlying a fitted
bn.fit() accepts data with missing values encoded as
NA, and it
uses locally complete observations to fit the parameters of each local
Additional arguments for the
iss: a numeric value, the imaginary sample size used by the
bayes method to estimate the conditional probability tables
associated with discrete nodes (see
score for details).
replace.unidentifiable: a boolean value. If
mle, unidentifiable parameters are replaced by
zeroes (in the case of regression coefficients and standard errors in
Gaussian and conditional Gaussian nodes) or by uniform conditional
probabilities (in discrete nodes).
FALSE (the default), the conditional probabilities in the local
distributions of discrete nodes have a mximum likelihood estimate of
NaN for all parents configurations that are not observed in
data. Similarly, regression coefficients are set to
if the linear regressions correspoding to the local distributions of
continuous nodes are singular. Such missing values propagate to the
results of functions such as
An in-place replacement method is available to change the parameters of each
node in a
bn.fit object; see the examples for discrete, continuous and
hybrid networks below. For a discrete node (class
bn.fit.onode), the new parameters must be in a
For a Gaussian node (class
bn.fit.gnode), the new parameters can be
defined either by an
pensim object (the
latter is from the
penalized package) or in a list with elements named
sd and optionally
a conditional Gaussian node (class
bn.fit.cgnode), the new parameters
can be defined by a list with elements named
configs. In both cases
coef should contain the new regression coefficients,
standard deviation of the residuals,
fitted the fitted values and
resid the residuals.
configs should contain the configurations
if the discrete parents of the conditional Gaussian node, stored as a factor.
custom.fit() takes a set of user-specified distributions and their
parameters and uses them to build a
bn.fit object. Its purpose is to
specify a Bayesian network (complete with the parameters, not only the
structure) using knowledge from experts in the field instead of learning it
from a data set. The distributions must be passed to the function in a list,
with elements named after the nodes of the network structure
element of the list must be in one of the formats described above for
custom.fit()returns an object of class
bn.net() an object of class
bn class and
bn.fit class for details.
Due to the way Bayesian networks are defined it is possible to estimate their
parameters only if the network structure is completely directed (i.e. there
are no undirected arcs). See
for two ways of manually setting the direction of one or more arcs.
mle methods in
bn.fit() handle missing
data by estimating the parameters of each local distribution
using the observations that are complete for the variables involved (the node
and its parents).
method is set to
parameter estimates for discrete and conditional Gaussian nodes when there
are (discrete) parents configurations that are not observed in
To avoid this either set
TRUE or, in
the case of discrete networks, use
method = "bayes".
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data(learning.test) # learn the network structure. res = pc.stable(learning.test) # set the direction of the only undirected arc, A - B. res = set.arc(res, "A", "B") # estimate the parameters of the Bayesian network. fitted = bn.fit(res, learning.test) # replace the parameters of the node B. new.cpt = matrix(c(0.1, 0.2, 0.3, 0.2, 0.5, 0.6, 0.7, 0.3, 0.1), byrow = TRUE, ncol = 3, dimnames = list(B = c("a", "b", "c"), A = c("a", "b", "c"))) fitted$B = as.table(new.cpt) # the network structure is still the same. all.equal(res, bn.net(fitted)) # learn the network structure. res = hc(gaussian.test) # estimate the parameters of the Bayesian network. fitted = bn.fit(res, gaussian.test) # replace the parameters of the node F. fitted$F = list(coef = c(1, 2, 3, 4, 5), sd = 3) # set again the original parameters fitted$F = lm(F ~ A + D + E + G, data = gaussian.test) # discrete Bayesian network from expert knowledge. net = model2network("[A][B][C|A:B]") cptA = matrix(c(0.4, 0.6), ncol = 2, dimnames = list(NULL, c("LOW", "HIGH"))) cptB = matrix(c(0.8, 0.2), ncol = 2, dimnames = list(NULL, c("GOOD", "BAD"))) cptC = c(0.5, 0.5, 0.4, 0.6, 0.3, 0.7, 0.2, 0.8) dim(cptC) = c(2, 2, 2) dimnames(cptC) = list("C" = c("TRUE", "FALSE"), "A" = c("LOW", "HIGH"), "B" = c("GOOD", "BAD")) cfit = custom.fit(net, dist = list(A = cptA, B = cptB, C = cptC)) # for ordinal nodes it is nearly the same. cfit = custom.fit(net, dist = list(A = cptA, B = cptB, C = cptC), ordinal = c("A", "B")) # Gaussian Bayesian network from expert knowledge. distA = list(coef = c("(Intercept)" = 2), sd = 1) distB = list(coef = c("(Intercept)" = 1), sd = 1.5) distC = list(coef = c("(Intercept)" = 0.5, "A" = 0.75, "B" = 1.32), sd = 0.4) cfit = custom.fit(net, dist = list(A = distA, B = distB, C = distC)) # conditional Gaussian Bayesian network from expert knowledge. cptA = matrix(c(0.4, 0.6), ncol = 2, dimnames = list(NULL, c("LOW", "HIGH"))) distB = list(coef = c("(Intercept)" = 1), sd = 1.5) distC = list(coef = matrix(c(1.2, 2.3, 3.4, 4.5), ncol = 2, dimnames = list(c("(Intercept)", "B"), NULL)), sd = c(0.3, 0.6)) cgfit = custom.fit(net, dist = list(A = cptA, B = distB, C = distC))
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