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```
# estimate the predicted values for a particular node.
predict.bn.fit = function(object, node, data, method = "parents", ...,
prob = FALSE, debug = FALSE) {
# check the data are there.
check.data(data, allow.levels = TRUE)
# a valid node is needed.
check.nodes(nodes = node, graph = object, max.nodes = 1)
# check the prediction method.
check.prediction.method(method, data)
# check debug and prob.
check.logical(debug)
check.logical(prob)
if (prob && !is(object, c("bn.fit.dnet", "bn.fit.onet", "bn.fit.donet")))
stop("prediction probabilities are only available for discrete networks.")
# warn about unused arguments.
extra.args = list(...)
check.unused.args(extra.args, prediction.extra.args[[method]])
if (method == "parents") {
# check the fitted model (parents are the only nodes that are actually
# needed).
check.fit.vs.data(fitted = object, data = data,
subset = object[[node]]$parents)
if (is(object, c("bn.fit.dnet", "bn.fit.onet", "bn.fit.donet")))
discrete.prediction(node = node, fitted = object, data = data,
prob = prob, debug = debug)
else if (is(object, "bn.fit.gnet"))
gaussian.prediction(node = node, fitted = object, data = data,
debug = debug)
else if (is(object, "bn.fit.cgnet"))
mixedcg.prediction(node = node, fitted = object, data = data,
debug = debug)
}#THEN
else if (method == "bayes-lw") {
# check the variables to predict from using the network as a reference.
if (is.null(extra.args$from))
extra.args$from = intersect(setdiff(names(data), node), names(object))
else
check.nodes(nodes = extra.args$from, graph = object, min.nodes = 1)
# check that they do not include the node to predict.
if (node %in% extra.args$from)
stop("node ", node, " is both a predictor and being predicted.")
# check the fitted model and the conditioning variables.
check.fit.vs.data(fitted = object, data = data, subset = extra.args$from)
# check the number of particles to be used for each prediction.
if (is.null(extra.args$n))
extra.args$n = 500
else if (!is.positive.integer(extra.args$n))
stop("the number of observations to be sampled must be a positive integer number.")
map.prediction(node = node, fitted = object, data = data, n = extra.args$n,
from = extra.args$from, prob = prob, debug = debug)
}#THEN
}#PREDICT.BN.FIT
# estimate the predicted values for a naive Bayes classfier.
predict.bn.naive = function(object, data, prior, ..., prob = FALSE, debug = FALSE) {
# check the data are there.
check.data(data, allowed.types = discrete.data.types, allow.levels = TRUE)
# check the bn.{naive,tan} object.
if (is(object, "bn.naive"))
check.bn.naive(object)
else
check.bn.tan(object)
# check debug and prob.
check.logical(debug)
check.logical(prob)
# fit the network if needed.
if (is(object, "bn"))
fitted = bn.fit(object, data)
else
fitted = object
# get the response variable.
training = attr(fitted, "training")
# check the fitted model.
check.fit.vs.data(fitted = fitted, data = data,
subset = setdiff(names(fitted), training))
# warn about unused arguments.
check.unused.args(list(...), character(0))
# check the prior distribution.
prior = check.classifier.prior(prior, fitted[[training]])
# compute the predicted values.
naive.classifier(training = training, fitted = fitted, data = data,
prior = prior, prob = prob, debug = debug)
}#PREDICT.BN.NAIVE
# estimate the predicted values for a TAN classfier.
predict.bn.tan = predict.bn.naive
```

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