predict.rpart {rpart} | R Documentation

## 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 = list(),
           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 the predicted class, the class counts at that node in the fitted tree, and the class probabilities.

If `type="class"`:
(for a classification tree) a factor of classifications based on the responses.

### See Also

`predict`, `rpart.object`

### 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"])

[Package rpart version 3.1-53 Index]