Return Cross-Validated Predictions

DESCRIPTION:

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

USAGE:

xpred.arbor(fit, xval=10, cp)

REQUIRED ARGUMENTS:

fit
an arbor object.

OPTIONAL ARGUMENTS:

xval
integer number of cross-validation groups. This may also be an explicit list of integers that define the cross-validation groups.
cp
vector of arbitrary length giving the desired set of complexity parameter values. By default it is taken from the cptable component of the fit.

VALUE:

If the data is longitudinal a matrix of predicted values is returned for each cp. For all other data a matrix with one row for each observation and one column for each complexity value is returned.

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.

SEE ALSO:

EXAMPLES:

fit <- arbor(Mileage ~ Weight, car.test.frame)
xmat <- xpred.arbor(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)