Return Cross-Validated Predictions

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)

Arguments

fit

a rpart object.

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.

Value

a matrix with one row for each observation and one column for each complexity value.

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

rpart

Examples

data(car.test.frame)
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
#> 0.79767456 0.28300396 0.09664299 0.04893534 0.02525439 0.01704869 0.01253756 
#>  1382.9856   846.2579   583.7998   478.4855   524.4801   541.8674   544.5723 

# approx same result as rel. error from printcp(fit)
apply(xerr, 2, sum)/var(car.test.frame$Mileage) 
#> 0.79767456 0.28300396 0.09664299 0.04893534 0.02525439 0.01704869 0.01253756 
#>   60.23708   36.85946   25.42789   20.84083   22.84416   23.60148   23.71930 
printcp(fit)
#> 
#> 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 1.02206 0.174870
#> 2 0.134528      1   0.40465 0.53486 0.100522
#> 3 0.069427      2   0.27012 0.39369 0.077349
#> 4 0.034492      3   0.20070 0.39239 0.077192
#> 5 0.018491      4   0.16620 0.47390 0.102950
#> 6 0.015719      5   0.14771 0.48335 0.110221
#> 7 0.010000      7   0.11627 0.46711 0.111425