Plots the Approximate R-Square for the Different Splits

Produces 2 plots. The first plots the r-square (apparent and apparent - from cross-validation) versus the number of splits. The second plots the Relative Error(cross-validation) +/- 1-SE from cross-validation versus the number of splits.

Usage

rsq.rpart(x)

Arguments

x

fitted model object of class rpart. This is assumed to be the result of some function that produces an object with the same named components as that returned by the rpart function.

Side Effects

Two plots are produced.

Note

The labels are only appropriate for the "anova" method.

Examples

data(car.test.frame)
z.auto <- rpart(Mileage ~ Weight, car.test.frame)
rsq.rpart(z.auto)
#> 
#> 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 1.04411 0.178078
#> 2 0.134528      1   0.40465 0.47927 0.070607
#> 3 0.069427      2   0.27012 0.37681 0.061414
#> 4 0.034492      3   0.20070 0.28985 0.046051
#> 5 0.018491      4   0.16620 0.34143 0.067161
#> 6 0.015719      5   0.14771 0.33838 0.067030
#> 7 0.010000      7   0.11627 0.27161 0.061983