Predictions from a Fitted Tree Object
Returns a vector of predicted responses from a fitted tree object.
## S3 method for class 'tree'
predict(object, newdata = list(),
type = c("vector", "tree", "class", "where"),
split = FALSE, nwts, eps = 1e-3, ...)object |
fitted model object of class |
newdata |
data frame containing the values at which predictions are required.
The predictors referred to in the right side
of |
type |
character string denoting whether the predictions are returned as a vector (default) or as a tree object. |
split |
governs the handling of missing values. If false, cases with missing
values are dropped down the tree until a leaf is reached or a node
for which the attribute is missing, and that node is used for
prediction. If |
nwts |
weights for the |
eps |
a lower bound for the probabilities, used if events of predicted
probability zero occur in |
... |
further arguments passed to or from other methods. |
This function is a method for the generic function
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.
If 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.
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Chapter 7.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.