R Under development (unstable) (2019-04-05 r76323) -- "Unsuffered Consequences"
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> pkgname <- "rpart"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('rpart')
>
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("car.test.frame")
> ### * car.test.frame
>
> flush(stderr()); flush(stdout())
>
> ### Name: car.test.frame
> ### Title: Automobile Data from 'Consumer Reports' 1990
> ### Aliases: car.test.frame
> ### Keywords: datasets
>
> ### ** Examples
>
> 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 1.0337818 0.18046532
2 0.13452819 1 0.4046509 0.5836606 0.10900973
3 0.01282843 2 0.2701227 0.4409221 0.08652804
4 0.01000000 3 0.2572943 0.4415805 0.08663003
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
>
>
>
> cleanEx()
> nameEx("car90")
> ### * car90
>
> flush(stderr()); flush(stdout())
>
> ### Name: car90
> ### Title: Automobile Data from 'Consumer Reports' 1990
> ### Aliases: car90
> ### Keywords: datasets
>
> ### ** Examples
>
> data(car90)
> plot(car90$Price/1000, car90$Weight,
+ xlab = "Price (thousands)", ylab = "Weight (lbs)")
> mlowess <- function(x, y, ...) {
+ keep <- !(is.na(x) | is.na(y))
+ lowess(x[keep], y[keep], ...)
+ }
> with(car90, lines(mlowess(Price/1000, Weight, f = 0.5)))
>
>
>
> cleanEx()
> nameEx("cu.summary")
> ### * cu.summary
>
> flush(stderr()); flush(stdout())
>
> ### Name: cu.summary
> ### Title: Automobile Data from 'Consumer Reports' 1990
> ### Aliases: cu.summary
> ### Keywords: datasets
>
> ### ** Examples
>
> fit <- rpart(Price ~ Mileage + Type + Country, cu.summary)
> par(xpd = TRUE)
> plot(fit, compress = TRUE)
> text(fit, use.n = TRUE)
>
>
>
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("kyphosis")
> ### * kyphosis
>
> flush(stderr()); flush(stdout())
>
> ### Name: kyphosis
> ### Title: Data on Children who have had Corrective Spinal Surgery
> ### Aliases: kyphosis
> ### Keywords: datasets
>
> ### ** Examples
>
> fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
> fit2 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
+ parms = list(prior = c(0.65, 0.35), split = "information"))
> fit3 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis,
+ control = rpart.control(cp = 0.05))
> par(mfrow = c(1,2), xpd = TRUE)
> plot(fit)
> text(fit, use.n = TRUE)
> plot(fit2)
> text(fit2, use.n = TRUE)
>
>
>
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("meanvar.rpart")
> ### * meanvar.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: meanvar.rpart
> ### Title: Mean-Variance Plot for an Rpart Object
> ### Aliases: meanvar meanvar.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> meanvar(z.auto, log = 'xy')
>
>
>
> cleanEx()
> nameEx("path.rpart")
> ### * path.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: path.rpart
> ### Title: Follow Paths to Selected Nodes of an Rpart Object
> ### Aliases: path.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
> print(fit)
n= 81
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 81 17 absent (0.79012346 0.20987654)
2) Start>=8.5 62 6 absent (0.90322581 0.09677419)
4) Start>=14.5 29 0 absent (1.00000000 0.00000000) *
5) Start< 14.5 33 6 absent (0.81818182 0.18181818)
10) Age< 55 12 0 absent (1.00000000 0.00000000) *
11) Age>=55 21 6 absent (0.71428571 0.28571429)
22) Age>=111 14 2 absent (0.85714286 0.14285714) *
23) Age< 111 7 3 present (0.42857143 0.57142857) *
3) Start< 8.5 19 8 present (0.42105263 0.57894737) *
> path.rpart(fit, nodes = c(11, 22))
node number: 11
root
Start>=8.5
Start< 14.5
Age>=55
node number: 22
root
Start>=8.5
Start< 14.5
Age>=55
Age>=111
>
>
>
> cleanEx()
> nameEx("plot.rpart")
> ### * plot.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: plot.rpart
> ### Title: Plot an Rpart Object
> ### Aliases: plot.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> fit <- rpart(Price ~ Mileage + Type + Country, cu.summary)
> par(xpd = TRUE)
> plot(fit, compress = TRUE)
> text(fit, use.n = TRUE)
>
>
>
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("post.rpart")
> ### * post.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: post.rpart
> ### Title: PostScript Presentation Plot of an Rpart Object
> ### Aliases: post.rpart post
> ### Keywords: tree
>
> ### ** Examples
>
> ## Not run:
> ##D z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> ##D post(z.auto, file = "") # display tree on active device
> ##D # now construct postscript version on file "pretty.ps"
> ##D # with no title
> ##D post(z.auto, file = "pretty.ps", title = " ")
> ##D z.hp <- rpart(Mileage ~ Weight + HP, car.test.frame)
> ##D post(z.hp)
> ## End(Not run)
>
>
>
> cleanEx()
> nameEx("predict.rpart")
> ### * predict.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: predict.rpart
> ### Title: Predictions from a Fitted Rpart Object
> ### Aliases: predict.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> predict(z.auto)
Eagle Summit 4 Ford Escort 4
30.93333 30.93333
Ford Festiva 4 Honda Civic 4
30.93333 30.93333
Mazda Protege 4 Mercury Tracer 4
30.93333 30.93333
Nissan Sentra 4 Pontiac LeMans 4
30.93333 30.93333
Subaru Loyale 4 Subaru Justy 3
30.93333 30.93333
Toyota Corolla 4 Toyota Tercel 4
30.93333 30.93333
Volkswagen Jetta 4 Chevrolet Camaro V8
30.93333 20.40909
Dodge Daytona Ford Mustang V8
23.80000 20.40909
Ford Probe Honda Civic CRX Si 4
25.62500 30.93333
Honda Prelude Si 4WS 4 Nissan 240SX 4
25.62500 23.80000
Plymouth Laser Subaru XT 4
23.80000 30.93333
Audi 80 4 Buick Skylark 4
25.62500 25.62500
Chevrolet Beretta 4 Chrysler Le Baron V6
25.62500 23.80000
Ford Tempo 4 Honda Accord 4
23.80000 23.80000
Mazda 626 4 Mitsubishi Galant 4
23.80000 25.62500
Mitsubishi Sigma V6 Nissan Stanza 4
20.40909 23.80000
Oldsmobile Calais 4 Peugeot 405 4
25.62500 25.62500
Subaru Legacy 4 Toyota Camry 4
23.80000 23.80000
Volvo 240 4 Acura Legend V6
23.80000 20.40909
Buick Century 4 Chrysler Le Baron Coupe
23.80000 23.80000
Chrysler New Yorker V6 Eagle Premier V6
20.40909 20.40909
Ford Taurus V6 Ford Thunderbird V6
20.40909 20.40909
Hyundai Sonata 4 Mazda 929 V6
23.80000 20.40909
Nissan Maxima V6 Oldsmobile Cutlass Ciera 4
20.40909 23.80000
Oldsmobile Cutlass Supreme V6 Toyota Cressida 6
20.40909 20.40909
Buick Le Sabre V6 Chevrolet Caprice V8
20.40909 20.40909
Ford LTD Crown Victoria V8 Chevrolet Lumina APV V6
20.40909 20.40909
Dodge Grand Caravan V6 Ford Aerostar V6
20.40909 20.40909
Mazda MPV V6 Mitsubishi Wagon 4
20.40909 20.40909
Nissan Axxess 4 Nissan Van 4
20.40909 20.40909
>
> fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
> predict(fit, type = "prob") # class probabilities (default)
absent present
1 0.4210526 0.5789474
2 0.8571429 0.1428571
3 0.4210526 0.5789474
4 0.4210526 0.5789474
5 1.0000000 0.0000000
6 1.0000000 0.0000000
7 1.0000000 0.0000000
8 1.0000000 0.0000000
9 1.0000000 0.0000000
10 0.4285714 0.5714286
11 0.4285714 0.5714286
12 1.0000000 0.0000000
13 0.4210526 0.5789474
14 1.0000000 0.0000000
15 1.0000000 0.0000000
16 1.0000000 0.0000000
17 1.0000000 0.0000000
18 0.8571429 0.1428571
19 1.0000000 0.0000000
20 1.0000000 0.0000000
21 1.0000000 0.0000000
22 0.4210526 0.5789474
23 0.4285714 0.5714286
24 0.4210526 0.5789474
25 0.4210526 0.5789474
26 1.0000000 0.0000000
27 0.4210526 0.5789474
28 0.4285714 0.5714286
29 1.0000000 0.0000000
30 1.0000000 0.0000000
31 1.0000000 0.0000000
32 0.8571429 0.1428571
33 0.8571429 0.1428571
34 1.0000000 0.0000000
35 0.8571429 0.1428571
36 1.0000000 0.0000000
37 1.0000000 0.0000000
38 0.4210526 0.5789474
39 1.0000000 0.0000000
40 0.4285714 0.5714286
41 0.4210526 0.5789474
42 1.0000000 0.0000000
43 0.4210526 0.5789474
44 0.4210526 0.5789474
45 1.0000000 0.0000000
46 0.8571429 0.1428571
47 1.0000000 0.0000000
48 0.8571429 0.1428571
49 0.4210526 0.5789474
50 0.8571429 0.1428571
51 0.4285714 0.5714286
52 1.0000000 0.0000000
53 0.4210526 0.5789474
54 1.0000000 0.0000000
55 1.0000000 0.0000000
56 1.0000000 0.0000000
57 1.0000000 0.0000000
58 0.4210526 0.5789474
59 1.0000000 0.0000000
60 0.4285714 0.5714286
61 0.4210526 0.5789474
62 0.4210526 0.5789474
63 0.4210526 0.5789474
64 1.0000000 0.0000000
65 1.0000000 0.0000000
66 1.0000000 0.0000000
67 1.0000000 0.0000000
68 0.8571429 0.1428571
69 1.0000000 0.0000000
70 1.0000000 0.0000000
71 0.8571429 0.1428571
72 0.8571429 0.1428571
73 1.0000000 0.0000000
74 0.8571429 0.1428571
75 1.0000000 0.0000000
76 1.0000000 0.0000000
77 0.8571429 0.1428571
78 1.0000000 0.0000000
79 0.8571429 0.1428571
80 0.4210526 0.5789474
81 1.0000000 0.0000000
> predict(fit, type = "vector") # level numbers
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
2 1 2 2 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 2 2 2 2 1
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
2 2 1 1 1 1 1 1 1 1 1 2 1 2 2 1 2 2 1 1 1 1 2 1 2 1
53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
2 1 1 1 1 2 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
79 80 81
1 2 1
> predict(fit, type = "class") # factor
1 2 3 4 5 6 7 8 9 10
present absent present present absent absent absent absent absent present
11 12 13 14 15 16 17 18 19 20
present absent present absent absent absent absent absent absent absent
21 22 23 24 25 26 27 28 29 30
absent present present present present absent present present absent absent
31 32 33 34 35 36 37 38 39 40
absent absent absent absent absent absent absent present absent present
41 42 43 44 45 46 47 48 49 50
present absent present present absent absent absent absent present absent
51 52 53 54 55 56 57 58 59 60
present absent present absent absent absent absent present absent present
61 62 63 64 65 66 67 68 69 70
present present present absent absent absent absent absent absent absent
71 72 73 74 75 76 77 78 79 80
absent absent absent absent absent absent absent absent absent present
81
absent
Levels: absent present
> predict(fit, type = "matrix") # level number, class frequencies, probabilities
[,1] [,2] [,3] [,4] [,5] [,6]
1 2 8 11 0.4210526 0.5789474 0.23456790
2 1 12 2 0.8571429 0.1428571 0.17283951
3 2 8 11 0.4210526 0.5789474 0.23456790
4 2 8 11 0.4210526 0.5789474 0.23456790
5 1 29 0 1.0000000 0.0000000 0.35802469
6 1 29 0 1.0000000 0.0000000 0.35802469
7 1 29 0 1.0000000 0.0000000 0.35802469
8 1 29 0 1.0000000 0.0000000 0.35802469
9 1 29 0 1.0000000 0.0000000 0.35802469
10 2 3 4 0.4285714 0.5714286 0.08641975
11 2 3 4 0.4285714 0.5714286 0.08641975
12 1 29 0 1.0000000 0.0000000 0.35802469
13 2 8 11 0.4210526 0.5789474 0.23456790
14 1 12 0 1.0000000 0.0000000 0.14814815
15 1 29 0 1.0000000 0.0000000 0.35802469
16 1 29 0 1.0000000 0.0000000 0.35802469
17 1 29 0 1.0000000 0.0000000 0.35802469
18 1 12 2 0.8571429 0.1428571 0.17283951
19 1 29 0 1.0000000 0.0000000 0.35802469
20 1 12 0 1.0000000 0.0000000 0.14814815
21 1 29 0 1.0000000 0.0000000 0.35802469
22 2 8 11 0.4210526 0.5789474 0.23456790
23 2 3 4 0.4285714 0.5714286 0.08641975
24 2 8 11 0.4210526 0.5789474 0.23456790
25 2 8 11 0.4210526 0.5789474 0.23456790
26 1 12 0 1.0000000 0.0000000 0.14814815
27 2 8 11 0.4210526 0.5789474 0.23456790
28 2 3 4 0.4285714 0.5714286 0.08641975
29 1 29 0 1.0000000 0.0000000 0.35802469
30 1 29 0 1.0000000 0.0000000 0.35802469
31 1 29 0 1.0000000 0.0000000 0.35802469
32 1 12 2 0.8571429 0.1428571 0.17283951
33 1 12 2 0.8571429 0.1428571 0.17283951
34 1 29 0 1.0000000 0.0000000 0.35802469
35 1 12 2 0.8571429 0.1428571 0.17283951
36 1 29 0 1.0000000 0.0000000 0.35802469
37 1 12 0 1.0000000 0.0000000 0.14814815
38 2 8 11 0.4210526 0.5789474 0.23456790
39 1 12 0 1.0000000 0.0000000 0.14814815
40 2 3 4 0.4285714 0.5714286 0.08641975
41 2 8 11 0.4210526 0.5789474 0.23456790
42 1 12 0 1.0000000 0.0000000 0.14814815
43 2 8 11 0.4210526 0.5789474 0.23456790
44 2 8 11 0.4210526 0.5789474 0.23456790
45 1 29 0 1.0000000 0.0000000 0.35802469
46 1 12 2 0.8571429 0.1428571 0.17283951
47 1 29 0 1.0000000 0.0000000 0.35802469
48 1 12 2 0.8571429 0.1428571 0.17283951
49 2 8 11 0.4210526 0.5789474 0.23456790
50 1 12 2 0.8571429 0.1428571 0.17283951
51 2 3 4 0.4285714 0.5714286 0.08641975
52 1 29 0 1.0000000 0.0000000 0.35802469
53 2 8 11 0.4210526 0.5789474 0.23456790
54 1 29 0 1.0000000 0.0000000 0.35802469
55 1 29 0 1.0000000 0.0000000 0.35802469
56 1 29 0 1.0000000 0.0000000 0.35802469
57 1 12 0 1.0000000 0.0000000 0.14814815
58 2 8 11 0.4210526 0.5789474 0.23456790
59 1 12 0 1.0000000 0.0000000 0.14814815
60 2 3 4 0.4285714 0.5714286 0.08641975
61 2 8 11 0.4210526 0.5789474 0.23456790
62 2 8 11 0.4210526 0.5789474 0.23456790
63 2 8 11 0.4210526 0.5789474 0.23456790
64 1 29 0 1.0000000 0.0000000 0.35802469
65 1 29 0 1.0000000 0.0000000 0.35802469
66 1 12 0 1.0000000 0.0000000 0.14814815
67 1 29 0 1.0000000 0.0000000 0.35802469
68 1 12 2 0.8571429 0.1428571 0.17283951
69 1 12 0 1.0000000 0.0000000 0.14814815
70 1 29 0 1.0000000 0.0000000 0.35802469
71 1 12 2 0.8571429 0.1428571 0.17283951
72 1 12 2 0.8571429 0.1428571 0.17283951
73 1 29 0 1.0000000 0.0000000 0.35802469
74 1 12 2 0.8571429 0.1428571 0.17283951
75 1 29 0 1.0000000 0.0000000 0.35802469
76 1 29 0 1.0000000 0.0000000 0.35802469
77 1 12 2 0.8571429 0.1428571 0.17283951
78 1 12 0 1.0000000 0.0000000 0.14814815
79 1 12 2 0.8571429 0.1428571 0.17283951
80 2 8 11 0.4210526 0.5789474 0.23456790
81 1 12 0 1.0000000 0.0000000 0.14814815
>
> sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
> fit <- rpart(Species ~ ., data = iris, subset = sub)
> fit
n= 75
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 75 50 setosa (0.33333333 0.33333333 0.33333333)
2) Petal.Length< 2.5 25 0 setosa (1.00000000 0.00000000 0.00000000) *
3) Petal.Length>=2.5 50 25 versicolor (0.00000000 0.50000000 0.50000000)
6) Petal.Length< 4.85 26 2 versicolor (0.00000000 0.92307692 0.07692308) *
7) Petal.Length>=4.85 24 1 virginica (0.00000000 0.04166667 0.95833333) *
> table(predict(fit, iris[-sub,], type = "class"), iris[-sub, "Species"])
setosa versicolor virginica
setosa 25 0 0
versicolor 0 22 1
virginica 0 3 24
>
>
>
> cleanEx()
> nameEx("print.rpart")
> ### * print.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: print.rpart
> ### Title: Print an Rpart Object
> ### Aliases: print.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> z.auto
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 *
> ## Not run:
> ##D node), split, n, deviance, yval
> ##D * denotes terminal node
> ##D
> ##D 1) root 60 1354.58300 24.58333
> ##D 2) Weight>=2567.5 45 361.20000 22.46667
> ##D 4) Weight>=3087.5 22 61.31818 20.40909 *
> ##D 5) Weight<3087.5 23 117.65220 24.43478
> ##D 10) Weight>=2747.5 15 60.40000 23.80000 *
> ##D 11) Weight<2747.5 8 39.87500 25.62500 *
> ##D 3) Weight<2567.5 15 186.93330 30.93333 *
> ## End(Not run)
>
>
> cleanEx()
> nameEx("printcp")
> ### * printcp
>
> flush(stderr()); flush(stdout())
>
> ### Name: printcp
> ### Title: Displays CP table for Fitted Rpart Object
> ### Aliases: printcp
> ### Keywords: tree
>
> ### ** Examples
>
> z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> printcp(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 1.03378 0.180465
2 0.134528 1 0.40465 0.58366 0.109010
3 0.012828 2 0.27012 0.44092 0.086528
4 0.010000 3 0.25729 0.44158 0.086630
> ## Not run:
> ##D Regression tree:
> ##D rpart(formula = Mileage ~ Weight, data = car.test.frame)
> ##D
> ##D Variables actually used in tree construction:
> ##D [1] Weight
> ##D
> ##D Root node error: 1354.6/60 = 22.576
> ##D
> ##D CP nsplit rel error xerror xstd
> ##D 1 0.595349 0 1.00000 1.03436 0.178526
> ##D 2 0.134528 1 0.40465 0.60508 0.105217
> ##D 3 0.012828 2 0.27012 0.45153 0.083330
> ##D 4 0.010000 3 0.25729 0.44826 0.076998
> ## End(Not run)
>
>
> cleanEx()
> nameEx("prune.rpart")
> ### * prune.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: prune.rpart
> ### Title: Cost-complexity Pruning of an Rpart Object
> ### Aliases: prune.rpart prune
> ### Keywords: tree
>
> ### ** Examples
>
> z.auto <- rpart(Mileage ~ Weight, car.test.frame)
> zp <- prune(z.auto, cp = 0.1)
> plot(zp) #plot smaller rpart object
>
>
>
> cleanEx()
> nameEx("residuals.rpart")
> ### * residuals.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: residuals.rpart
> ### Title: Residuals From a Fitted Rpart Object
> ### Aliases: residuals.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> fit <- rpart(skips ~ Opening + Solder + Mask + PadType + Panel,
+ data = solder.balance, method = "anova")
> summary(residuals(fit))
Min. 1st Qu. Median Mean 3rd Qu. Max.
-13.8000 -1.0361 -0.6833 0.0000 0.9639 16.2000
> plot(predict(fit),residuals(fit))
>
>
>
> cleanEx()
> nameEx("rpart")
> ### * rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: rpart
> ### Title: Recursive Partitioning and Regression Trees
> ### Aliases: rpart
> ### Keywords: tree
>
> ### ** Examples
>
> fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
> fit2 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
+ parms = list(prior = c(.65,.35), split = "information"))
> fit3 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
+ control = rpart.control(cp = 0.05))
> par(mfrow = c(1,2), xpd = NA) # otherwise on some devices the text is clipped
> plot(fit)
> text(fit, use.n = TRUE)
> plot(fit2)
> text(fit2, use.n = TRUE)
>
>
>
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("rsq.rpart")
> ### * rsq.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: rsq.rpart
> ### Title: Plots the Approximate R-Square for the Different Splits
> ### Aliases: rsq.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> 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 1.03378 0.180465
2 0.134528 1 0.40465 0.58366 0.109010
3 0.012828 2 0.27012 0.44092 0.086528
4 0.010000 3 0.25729 0.44158 0.086630
>
>
>
> cleanEx()
> nameEx("snip.rpart")
> ### * snip.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: snip.rpart
> ### Title: Snip Subtrees of an Rpart Object
> ### Aliases: snip.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> ## dataset not in R
> ## Not run:
> ##D z.survey <- rpart(market.survey) # grow the rpart object
> ##D plot(z.survey) # plot the tree
> ##D z.survey2 <- snip.rpart(z.survey, toss = 2) # trim subtree at node 2
> ##D plot(z.survey2) # plot new tree
> ##D
> ##D # can also interactively select the node using the mouse in the
> ##D # graphics window
> ## End(Not run)
>
>
> cleanEx()
> nameEx("solder.balance")
> ### * solder.balance
>
> flush(stderr()); flush(stdout())
>
> ### Name: solder.balance
> ### Title: Soldering of Components on Printed-Circuit Boards
> ### Aliases: solder.balance solder
> ### Keywords: datasets
>
> ### ** Examples
>
> fit <- rpart(skips ~ Opening + Solder + Mask + PadType + Panel,
+ data = solder.balance, method = "anova")
> summary(residuals(fit))
Min. 1st Qu. Median Mean 3rd Qu. Max.
-13.8000 -1.0361 -0.6833 0.0000 0.9639 16.2000
> plot(predict(fit), residuals(fit))
>
>
>
> cleanEx()
> nameEx("stagec")
> ### * stagec
>
> flush(stderr()); flush(stdout())
>
> ### Name: stagec
> ### Title: Stage C Prostate Cancer
> ### Aliases: stagec
> ### Keywords: datasets
>
> ### ** Examples
>
> require(survival)
Loading required package: survival
> rpart(Surv(pgtime, pgstat) ~ ., stagec)
n= 146
node), split, n, deviance, yval
* denotes terminal node
1) root 146 192.111100 1.0000000
2) grade< 2.5 61 44.799010 0.3634439
4) g2< 11.36 33 9.117405 0.1229835 *
5) g2>=11.36 28 27.602190 0.7345610
10) gleason< 5.5 20 14.297110 0.5304115 *
11) gleason>=5.5 8 11.094650 1.3069940 *
3) grade>=2.5 85 122.441500 1.6148600
6) age>=56.5 75 103.062900 1.4255040
12) gleason< 7.5 50 66.119800 1.1407320
24) g2< 13.475 24 27.197170 0.8007306 *
25) g2>=13.475 26 36.790960 1.4570210
50) g2>=17.915 15 20.332740 0.9789825 *
51) g2< 17.915 11 13.459010 2.1714480 *
13) gleason>=7.5 25 33.487250 2.0307290
26) g2>=15.29 10 11.588480 1.2156230 *
27) g2< 15.29 15 18.939150 2.7053610 *
7) age< 56.5 10 13.769010 3.1822320 *
>
>
>
> cleanEx()
detaching ‘package:survival’
> nameEx("summary.rpart")
> ### * summary.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: summary.rpart
> ### Title: Summarize a Fitted Rpart Object
> ### Aliases: summary.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> ## a regression tree
> 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 1.0337818 0.18046532
2 0.13452819 1 0.4046509 0.5836606 0.10900973
3 0.01282843 2 0.2701227 0.4409221 0.08652804
4 0.01000000 3 0.2572943 0.4415805 0.08663003
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
>
> ## a classification tree with multiple variables and surrogate splits.
> summary(rpart(Kyphosis ~ Age + Number + Start, data = kyphosis))
Call:
rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis)
n= 81
CP nsplit rel error xerror xstd
1 0.17647059 0 1.0000000 1.000000 0.2155872
2 0.01960784 1 0.8235294 1.058824 0.2200975
3 0.01000000 4 0.7647059 1.058824 0.2200975
Variable importance
Start Age Number
64 24 12
Node number 1: 81 observations, complexity param=0.1764706
predicted class=absent expected loss=0.2098765 P(node) =1
class counts: 64 17
probabilities: 0.790 0.210
left son=2 (62 obs) right son=3 (19 obs)
Primary splits:
Start < 8.5 to the right, improve=6.762330, (0 missing)
Number < 5.5 to the left, improve=2.866795, (0 missing)
Age < 39.5 to the left, improve=2.250212, (0 missing)
Surrogate splits:
Number < 6.5 to the left, agree=0.802, adj=0.158, (0 split)
Node number 2: 62 observations, complexity param=0.01960784
predicted class=absent expected loss=0.09677419 P(node) =0.7654321
class counts: 56 6
probabilities: 0.903 0.097
left son=4 (29 obs) right son=5 (33 obs)
Primary splits:
Start < 14.5 to the right, improve=1.0205280, (0 missing)
Age < 55 to the left, improve=0.6848635, (0 missing)
Number < 4.5 to the left, improve=0.2975332, (0 missing)
Surrogate splits:
Number < 3.5 to the left, agree=0.645, adj=0.241, (0 split)
Age < 16 to the left, agree=0.597, adj=0.138, (0 split)
Node number 3: 19 observations
predicted class=present expected loss=0.4210526 P(node) =0.2345679
class counts: 8 11
probabilities: 0.421 0.579
Node number 4: 29 observations
predicted class=absent expected loss=0 P(node) =0.3580247
class counts: 29 0
probabilities: 1.000 0.000
Node number 5: 33 observations, complexity param=0.01960784
predicted class=absent expected loss=0.1818182 P(node) =0.4074074
class counts: 27 6
probabilities: 0.818 0.182
left son=10 (12 obs) right son=11 (21 obs)
Primary splits:
Age < 55 to the left, improve=1.2467530, (0 missing)
Start < 12.5 to the right, improve=0.2887701, (0 missing)
Number < 3.5 to the right, improve=0.1753247, (0 missing)
Surrogate splits:
Start < 9.5 to the left, agree=0.758, adj=0.333, (0 split)
Number < 5.5 to the right, agree=0.697, adj=0.167, (0 split)
Node number 10: 12 observations
predicted class=absent expected loss=0 P(node) =0.1481481
class counts: 12 0
probabilities: 1.000 0.000
Node number 11: 21 observations, complexity param=0.01960784
predicted class=absent expected loss=0.2857143 P(node) =0.2592593
class counts: 15 6
probabilities: 0.714 0.286
left son=22 (14 obs) right son=23 (7 obs)
Primary splits:
Age < 111 to the right, improve=1.71428600, (0 missing)
Start < 12.5 to the right, improve=0.79365080, (0 missing)
Number < 3.5 to the right, improve=0.07142857, (0 missing)
Node number 22: 14 observations
predicted class=absent expected loss=0.1428571 P(node) =0.1728395
class counts: 12 2
probabilities: 0.857 0.143
Node number 23: 7 observations
predicted class=present expected loss=0.4285714 P(node) =0.08641975
class counts: 3 4
probabilities: 0.429 0.571
>
>
>
> cleanEx()
> nameEx("text.rpart")
> ### * text.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: text.rpart
> ### Title: Place Text on a Dendrogram Plot
> ### Aliases: text.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> freen.tr <- rpart(y ~ ., freeny)
> par(xpd = TRUE)
> plot(freen.tr)
> text(freen.tr, use.n = TRUE, all = TRUE)
>
>
>
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("xpred.rpart")
> ### * xpred.rpart
>
> flush(stderr()); flush(stdout())
>
> ### Name: xpred.rpart
> ### Title: Return Cross-Validated Predictions
> ### Aliases: xpred.rpart
> ### Keywords: tree
>
> ### ** Examples
>
> fit <- rpart(Mileage ~ Weight, car.test.frame)
> xmat <- xpred.rpart(fit)
> xerr <- (xmat - car.test.frame$Mileage)^2
> apply(xerr, 2, sum) # cross-validated error estimate
0.79767456 0.28300396 0.04154257 0.01132626
1396.6687 773.1546 577.8990 594.1341
>
> # approx same result as rel. error from printcp(fit)
> apply(xerr, 2, sum)/var(car.test.frame$Mileage)
0.79767456 0.28300396 0.04154257 0.01132626
60.83306 33.67539 25.17087 25.87800
> 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 1.03378 0.180465
2 0.134528 1 0.40465 0.58366 0.109010
3 0.012828 2 0.27012 0.44092 0.086528
4 0.010000 3 0.25729 0.44158 0.086630
>
>
>
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed: 0.777 0.048 0.825 0 0
> grDevices::dev.off()
null device
1
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')