predict.tree | R Documentation |

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.

`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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