# predict.rpart

##### Predictions from a Fitted Rpart Object

Returns a vector of predicted responses from a fitted `rpart`

object.

- Keywords
- tree

##### Usage

```
# S3 method for rpart
predict(object, newdata,
type = c("vector", "prob", "class", "matrix"),
na.action = na.pass, …)
```

##### Arguments

- object
fitted model object of class

`"rpart"`

. This is assumed to be the result of some function that produces an object with the same named components as that returned by the`rpart`

function.- newdata
data frame containing the values at which predictions are required. The predictors referred to in the right side of

`formula(object)`

must be present by name in`newdata`

. If missing, the fitted values are returned.- type
character string denoting the type of predicted value returned. If the

`rpart`

object is a classification tree, then the default is to return`prob`

predictions, a matrix whose columns are the probability of the first, second, etc. class. (This agrees with the default behavior of`tree`

). Otherwise, a vector result is returned.- na.action
a function to determine what should be done with missing values in

`newdata`

. The default is to pass them down the tree using surrogates in the way selected when the model was built. Other possibilities are`na.omit`

and`na.fail`

.- …
further arguments passed to or from other methods.

##### Details

This function is a method for the generic function predict for class
`"rpart"`

. It can be invoked by calling `predict`

for an object
of the appropriate class, or directly by calling `predict.rpart`

regardless of the class of the object.

##### Value

A new object is obtained by
dropping `newdata`

down the 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`

at the node is the
prediction.

If `type = "vector"`

:
vector of predicted responses.
For regression trees this is the mean response at the node, for Poisson
trees it is the estimated response rate, and for classification trees
it is the predicted class (as a number).

If `type = "prob"`

:
(for a classification tree) a matrix of class probabilities.

If `type = "matrix"`

: a matrix of the full responses
(`frame$yval2`

if this exists, otherwise `frame$yval`

). For
regression trees, this is the mean response, for Poisson trees it is
the response rate and the number of events at that node in the fitted
tree, and for classification trees it is the concatenation of at least
the predicted class, the class counts at that node in the fitted tree,
and the class probabilities (some versions of rpart may contain
further columns).

If `type = "class"`

:
(for a classification tree) a factor of classifications based on the
responses.

##### See Also

##### Examples

```
# NOT RUN {
z.auto <- rpart(Mileage ~ Weight, car.test.frame)
predict(z.auto)
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
predict(fit, type = "prob") # class probabilities (default)
predict(fit, type = "vector") # level numbers
predict(fit, type = "class") # factor
predict(fit, type = "matrix") # level number, class frequencies, probabilities
sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
fit <- rpart(Species ~ ., data = iris, subset = sub)
fit
table(predict(fit, iris[-sub,], type = "class"), iris[-sub, "Species"])
# }
```

*Documentation reproduced from package rpart, version 4.1-15, License: GPL-2 | GPL-3*