R: Return Cross-Validated Predictions

xpred.rpart {rpart} | R Documentation

## Return Cross-Validated Predictions

### Description

Gives the predicted values for an `rpart` fit, under cross validation, for a set of complexity parameter values.

### Usage

    xpred.rpart(fit, xval = 10, cp, return.all = FALSE)

### Arguments

`fit` | a object of class `"rpart"`.
`xval` | number of cross-validation groups. This may also be an explicit list of integers that define the cross-validation groups.
`cp` | the desired list of complexity values. By default it is taken from the `cptable` component of the fit.
`return.all` | if FALSE return only the first element of the prediction

### Details

Complexity penalties are actually ranges, not values. If the `cp` values found in the table were .36, .28, and .13, for instance, this means that the first row of the table holds for all complexity penalties in the range [.36, 1], the second row for `cp` in the range [.28, .36) and the third row for [.13,.28). By default, the geometric mean of each interval is used for cross validation.

### Value

A matrix with one row for each observation and one column for each complexity value. If `return.all` is TRUE and the prediction for each node is a vector, then the result will be an array containing all of the predictions. When the response is categorical, for instance, the result contains the predicted class followed by the class probabilities of the selected terminal node; `result[1,,]` will be the matrix of predicted classes, `result[2,,]` the matrix of class 1 probabilities, etc.

### See Also

`rpart`

### Examples

    fit <- rpart(Mileage ~ Weight, car.test.frame)
    xmat <- xpred.rpart(fit)
    xerr <- (xmat - car.test.frame$Mileage)^2
    apply(xerr, 2, sum)   # cross-validated error estimate

    # approx same result as rel. error from printcp(fit)
    apply(xerr, 2, sum)/var(car.test.frame$Mileage)
    printcp(fit)

[Package rpart version 4.1-8 Index]