xpred.rpart: Return Cross-Validated Predictions

Description Usage Arguments Details Value See Also Examples

View source: R/xpred.rpart.R

Description

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

Usage

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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

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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)

Example output

0.79767456 0.28300396 0.04154257 0.01132626 
 1427.9444   749.0734   552.4648   547.0883 
0.79767456 0.28300396 0.04154257 0.01132626 
  62.19530   32.62651   24.06306   23.82888 

Regression tree:
rpart(formula = Mileage ~ Weight, data = car.test.frame)

Variables actually used in tree construction:
[1] Weight

Root node error: 1354.6/60 = 22.576

n= 60 

        CP nsplit rel error  xerror     xstd
1 0.595349      0   1.00000 1.04140 0.178724
2 0.134528      1   0.40465 0.51297 0.079626
3 0.012828      2   0.27012 0.35773 0.064566
4 0.010000      3   0.25729 0.35815 0.064450

rpart documentation built on May 29, 2017, 3:33 p.m.