Nothing

```
# check bn metadata against the data it's used with.
check.bn.vs.data = function(bn, data) {
# check which type of data we are dealing with.
type = data.type(data)
# the number of variables must be the same
if (length(names(bn$nodes)) != ncol(data))
stop("the network and the data have different numbers of variables.")
# the variables must be the same.
if (length(setdiff(names(bn$nodes), names(data))) != 0)
stop("the variables in the data and in the network do not match.")
# data type versus network structure.
if (type == "mixed-cg")
check.arcs.against.assumptions(bn$arcs, data, "mi-cg")
}#CHECK.BN.VS.DATA
# check bn.fit metadata against the data it's used with.
check.fit.vs.data = function(fitted, data, subset) {
fitted.names = names(fitted)
# check which type of data we are dealing with.
dtype = data.type(data)
if (missing(subset)) {
# the number of variables must be the same.
if (length(fitted.names) != ncol(data))
stop("the network and the data have different numbers of variables.")
# the variables must be the same.
if (length(setdiff(fitted.names , names(data))) != 0)
stop("the variables in the data and in the network do not match.")
subset = fitted.names
}#THEN
else {
# the number of variables must not exceed that of the network.
if (length(subset) > length(fitted.names))
stop("the data have more variables than the network.")
# all the variables in the subset must be present in the data.
absent = (subset %!in% names(data))
if (any(absent))
stop("required variables '", paste(subset[absent], collapse = " "),
"' are not present in the data.")
# all the variables in the subset must also be present in the network.
absent = (subset %!in% fitted.names)
if (any(absent))
stop("required variables '", paste(subset[absent], collapse = " "),
"' are not present in the network.")
}#ELSE
.Call(call_fitted_vs_data,
fitted = fitted,
data = data,
subset = subset)
}#CHECK.FIT.VS.DATA
# check bn.fit.{d,g}node metadata against the data it's used with.
check.fit.node.vs.data = function(fitted, data) {
relevant = c(fitted$node, fitted$parents)
# check which type of data we are dealing with.
type = data.type(data)
# check whether all relevant nodes are in the data.
if (any(relevant %!in% names(data)))
stop("not all required nodes are present in the data.")
# data type versus network type.
if (is(fitted, "bn.fit.dnode") && (type == "continuous"))
stop("continuous data and discrete network.")
if (is(fitted, "bn.fit.gnode") &&
(type %in% discrete.data.types))
stop("discrete data and continuous network.")
# double-check the levels of the variables against those of the nodes.
if (is(fitted, "bn.fit.dnode")) {
for (node in relevant) {
data.levels = levels(data[, node])
if (length(relevant) == 1)
node.levels = dimnames(fitted$prob)[[1]]
else
node.levels = dimnames(fitted$prob)[[node]]
if (!identical(data.levels, node.levels))
stop("the levels of node '", node, "' do not match the levels of the ",
"corresponding variable in the data.")
}#FOR
}#THEN
}#CHECK.FIT.NODE.VS.DATA
```

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