"predict"(object, newdata = list(), type = c("vector", "tree", "class", "where"), split = FALSE, nwts, eps = 1e-3, ...)
tree
. This is assumed to be the result
of some function that produces an object with the same named
components as that returned by the tree
function.
formula(object)
must be present by name in newdata
.
If missing, fitted values are returned.
split = TRUE
cases with missing attributes are
split into fractional cases and dropped down each side of the split.
The predicted values are averaged over the fractions to give the
prediction.
newdata
cases, used when predicting a tree.
newdata
when predicting a tree.
type = "vector"
:
vector of predicted responses or, if the response is a factor, matrix
of predicted class probabilities. This new object is obtained by
dropping newdata
down object
. For factor predictors, if an
observation contains a level not used to grow the tree, it is left at
the deepest possible node and frame$yval
or frame$yprob
at that
node is the prediction.If type = "tree"
:
an object of class "tree"
is returned with new values
for frame$n
and frame$dev
. If
newdata
does not contain a column for the response in the formula
the value of frame$dev
will be NA
, and if some values in the
response are missing, the some of the deviances will be NA
.If type = "class"
:
for a classification tree, a factor of the predicted classes (that
with highest posterior probability, with ties split randomly).If type = "where"
:
the nodes the cases reach.
predict()
for class tree
.
It can be invoked by calling predict(x)
for an
object x
of the appropriate class, or directly by
calling predict.tree(x)
regardless of the
class of the object.
predict
, tree
.
data(shuttle, package="MASS")
shuttle.tr <- tree(use ~ ., shuttle, subset=1:253,
mindev=1e-6, minsize=2)
shuttle.tr
shuttle1 <- shuttle[254:256, ] # 3 missing cases
predict(shuttle.tr, shuttle1)
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