R version 4.3.0 (2023-04-21) -- "Already Tomorrow"
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> # Small example to illustrate rpart() on the car.test.frame data
> 
> 
> library("rpart")
> 
> set.seed(784)
> car.test.frame[sample(nrow(car.test.frame), 4), ]
                           Price Country Reliability Mileage    Type Weight
Mercury Tracer 4            8672  Mexico           4      26   Small   2285
Nissan Stanza 4            11650   Japan           5      21 Compact   2920
Buick Century 4            13150     USA           3      21  Medium   2880
Oldsmobile Cutlass Ciera 4 13150     USA           2      21  Medium   2765
                           Disp.  HP
Mercury Tracer 4              97  82
Nissan Stanza 4              146 138
Buick Century 4              151 110
Oldsmobile Cutlass Ciera 4   151 110
> 
> car.tree <- rpart(Mileage ~ Weight, car.test.frame, method = "anova")
> 
> car.tree
n= 60 

node), split, n, deviance, yval
      * denotes terminal node

 1) root 60 1354.58300 24.58333  
   2) Weight>=2567.5 45  361.20000 22.46667  
     4) Weight>=3087.5 22   61.31818 20.40909 *
     5) Weight< 3087.5 23  117.65220 24.43478  
      10) Weight>=2747.5 15   60.40000 23.80000 *
      11) Weight< 2747.5 8   39.87500 25.62500 *
   3) Weight< 2567.5 15  186.93330 30.93333 *
> printcp(car.tree)

Regression tree:
rpart(formula = Mileage ~ Weight, data = car.test.frame, method = "anova")

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 1.04683 0.181646
2 0.134528      1   0.40465 0.61696 0.112918
3 0.012828      2   0.27012 0.46574 0.091113
4 0.010000      3   0.25729 0.46802 0.090865
> summary(car.tree)
Call:
rpart(formula = Mileage ~ Weight, data = car.test.frame, method = "anova")
  n= 60 

          CP nsplit rel error    xerror       xstd
1 0.59534912      0 1.0000000 1.0468270 0.18164614
2 0.13452819      1 0.4046509 0.6169648 0.11291752
3 0.01282843      2 0.2701227 0.4657376 0.09111300
4 0.01000000      3 0.2572943 0.4680214 0.09086511

Variable importance
Weight 
   100 

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
  mean=30.93333, MSE=12.46222 

Node number 4: 22 observations
  mean=20.40909, MSE=2.78719 

Node number 5: 23 observations,    complexity param=0.01282843
  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 10: 15 observations
  mean=23.8, MSE=4.026667 

Node number 11: 8 observations
  mean=25.625, MSE=4.984375 

> names(car.tree) 
 [1] "frame"               "where"               "call"               
 [4] "terms"               "cptable"             "method"             
 [7] "parms"               "control"             "functions"          
[10] "numresp"             "splits"              "variable.importance"
[13] "y"                   "ordered"            
> 
> 
> 
> if (FALSE) {
+   postscript("cartree.eps",
+              horiz = FALSE, onefile = FALSE, print.it = FALSE,
+              width = 5.0, height = 3.0)
+ } else {
+   pdf("cartree.pdf",
+       onefile = FALSE,
+       width = 5.0, height = 3.0)
+ }
> 
> 
> # partition(car.tree)
> 
> # par(mar = c(5, 4, 2, 2) + 0.8, mfrow = c(1, 1), xpd = TRUE)
> par(xpd = TRUE, cex = 1.0)
> plot(car.tree)
> text(car.tree)
> dev.off() 
null device 
          1 
> 
> 
> 
> proc.time()
   user  system elapsed 
  0.169   0.032   0.228