predict.rpart
Predictions from a Fitted Rpart Object
Description
Returns a vector of predicted responses from a fitted rpart
object.
Usage
## S3 method for class 'rpart' predict(object, newdata, type = c("vector", "prob", "class", "matrix"), na.action = na.pass, ...)
Arguments
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 the type of predicted value returned. If the |
na.action | a function to determine what should be done with missing values in |
... | 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
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"])
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Licensed under the GNU General Public License.