Summarize a Fitted Rpart Object

Returns a detailed listing of a fitted rpart object.

Usage

# S3 method for class 'rpart'
summary(object, cp=0, digits=getOption("digits"), file, ...)

Arguments

object

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.

digits

Number of significant digits to be used in the result.

cp

trim nodes with a complexity of less than cp from the listing.

file

write the output to a given file name. (Full listings of a tree are often quite long).

...

arguments to be passed to or from other methods.

Details

This function is a method for the generic function summary for class "rpart". It can be invoked by calling summary for an object of the appropriate class, or directly by calling summary.rpart regardless of the class of the object.

See also

summary, rpart.object, printcp.

Examples

data(car.test.frame)
z.auto <- rpart(Mileage ~ Weight, car.test.frame)
summary(z.auto)
#> Call:
#> rpart(formula = Mileage ~ Weight, data = car.test.frame)
#>   n= 60 
#> 
#>           CP nsplit rel error    xerror       xstd
#> 1 0.59534912      0 1.0000000 1.0341282 0.17766961
#> 2 0.13452819      1 0.4046509 0.5907329 0.10524049
#> 3 0.06942684      2 0.2701227 0.4388141 0.07724889
#> 4 0.03449195      3 0.2006958 0.3910305 0.07021715
#> 5 0.01849081      4 0.1662039 0.3898483 0.07308486
#> 6 0.01571904      5 0.1477131 0.3831385 0.07292204
#> 7 0.01000000      7 0.1162750 0.3643134 0.08431422
#> 
#> Node number 1: 60 observations,    complexity param=0.5953491
#>   mean=24.58333, MSE=22.57639 
#>   left son=2 (45 obs) right son=3 (15 obs)
#>   Primary splits:
#>       Weight < 2567.5 to the right, improve=0.5953491, (0 missing)
#> 
#> Node number 2: 45 observations,    complexity param=0.1345282
#>   mean=22.46667, MSE=8.026667 
#>   left son=4 (22 obs) right son=5 (23 obs)
#>   Primary splits:
#>       Weight < 3087.5 to the right, improve=0.5045118, (0 missing)
#> 
#> Node number 3: 15 observations,    complexity param=0.06942684
#>   mean=30.93333, MSE=12.46222 
#>   left son=6 (9 obs) right son=7 (6 obs)
#>   Primary splits:
#>       Weight < 2280   to the right, improve=0.5030908, (0 missing)
#> 
#> Node number 4: 22 observations,    complexity param=0.01849081
#>   mean=20.40909, MSE=2.78719 
#>   left son=8 (6 obs) right son=9 (16 obs)
#>   Primary splits:
#>       Weight < 3637.5 to the right, improve=0.4084816, (0 missing)
#> 
#> Node number 5: 23 observations,    complexity param=0.01571904
#>   mean=24.43478, MSE=5.115312 
#>   left son=10 (15 obs) right son=11 (8 obs)
#>   Primary splits:
#>       Weight < 2747.5 to the right, improve=0.1476996, (0 missing)
#> 
#> Node number 6: 9 observations,    complexity param=0.03449195
#>   mean=28.88889, MSE=8.54321 
#>   left son=12 (3 obs) right son=13 (6 obs)
#>   Primary splits:
#>       Weight < 2337.5 to the left,  improve=0.607659, (0 missing)
#> 
#> Node number 7: 6 observations
#>   mean=34, MSE=2.666667 
#> 
#> Node number 8: 6 observations
#>   mean=18.66667, MSE=0.5555556 
#> 
#> Node number 9: 16 observations
#>   mean=21.0625, MSE=2.058594 
#> 
#> Node number 10: 15 observations
#>   mean=23.8, MSE=4.026667 
#> 
#> Node number 11: 8 observations,    complexity param=0.01571904
#>   mean=25.625, MSE=4.984375 
#>   left son=22 (3 obs) right son=23 (5 obs)
#>   Primary splits:
#>       Weight < 2650   to the left,  improve=0.6321839, (0 missing)
#> 
#> Node number 12: 3 observations
#>   mean=25.66667, MSE=0.2222222 
#> 
#> Node number 13: 6 observations
#>   mean=30.5, MSE=4.916667 
#> 
#> Node number 22: 3 observations
#>   mean=23.33333, MSE=0.2222222 
#> 
#> Node number 23: 5 observations
#>   mean=27, MSE=2.8 
#>