Defines functions check.classifier.prior check.classifier.args

# 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)
      extra.args$root = explanatory[1]




# 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
      prior = rep(1, nlevels(training))

  else {

    if (is(training, c("bn.fit.dnode", "bn.fit.onode")))
      nlvls = dim(training$prob)[1]
      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)




Try the bnlearn package in your browser

Any scripts or data that you put into this service are public.

bnlearn documentation built on Sept. 7, 2021, 1:07 a.m.