Gives the predicted values for an
rpart fit, under
cross validation, for a set of complexity parameter values.
xpred.rpart(fit, xval = 10, cp, return.all = FALSE)
a object of class
number of cross-validation groups. This may also be an explicit list of integers that define the cross-validation groups.
the desired list of complexity values. By default it is taken from the
if FALSE return only the first element of the prediction
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.
A matrix with one row for each observation and one column for each complexity
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.
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)
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