Nothing

```
# sanitize the extra arguments passed to Bayesian classifiers.
check.classifier.args = function(method, data, training, explanatory,
extra.args) {
if (method == "tree.bayes") {
# check the label of the mutual information estimator.
extra.args$estimator = check.mi.estimator(extra.args$estimator, data)
# check the node to use the root of the tree (if not specified pick the first
# explanatory variable assuming natural ordering).
if (!is.null(extra.args$root))
check.nodes(extra.args$root, graph = explanatory, max.nodes = 1)
else
extra.args$root = explanatory[1]
}#THEN
return(extra.args)
}#CHECK.CLASSIFIER.ARGS
# check a prior distribution against the observed variable.
check.classifier.prior = function(prior, training) {
if (missing(prior) || is.null(prior)) {
# use the empirical probabilities in the fitted network, or a flat prior
# as a last resort.
if (is(training, c("bn.fit.dnode", "bn.fit.onode")))
prior = training$prob
else
prior = rep(1, nlevels(training))
}#THEN
else {
if (is(training, c("bn.fit.dnode", "bn.fit.onode")))
nlvls = dim(training$prob)[1]
else
nlvls = nlevels(training)
if (length(prior) != nlvls)
stop("the prior distribution and the training variable have a different number of levels.")
if (!is.nonnegative.vector(prior))
stop("the prior distribution must be expressed as a probability vector.")
# make sure the prior probabilities sum to one.
prior = prior / sum(prior)
}#ELSE
return(prior)
}#CHECK.CLASSIFIER.PRIOR
```

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