Recursive Partitioning and Regression Trees

Fit a rpart model

Usage

rpart(formula, data=NULL, weights, subset, na.action=na.rpart, method,
      dissim, model=FALSE, x=FALSE, y=TRUE, parms, control, cost, ...)

Arguments

formula

a formula, as in the lm function.

data

an optional data frame in which to interpret the variables named in the formula

weights

optional case weights.

subset

optional expression saying that only a subset of the rows of the data should be used in the fit.

na.action

The default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.

method

one of "anova", "poisson", "class", "mrt", "dist", or "exp". If method is missing then the routine tries to make an intellegent guess. If y is a survival object, then method="exp" is assumed, if y is a matrix then method="mrt" is assumed, if y is a factor then method="class" is assumed, otherwise method="anova" is assumed. It is wisest to specifiy the method directly, especially as more criteria are added to the function.

For method="dist" the response must be a square symmetric distance matrix; e.g. returned by gdist or xdiss. Weights and cross-validation are currently not implemented for method="dist".

Alternatively, method can be a list of functions named init, split and eval.

dissim

used when method="anova" or method="mrt". Dissimilarity types are either "euc" for Euclidean (sums of squares about the mean) or "man" for Manhattan (sums of absolute deviations about the mean. The latter is experimental and has proved useful for ecological data.

model

keep a copy of the model frame in the result. If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.

x

keep a copy of the x matrix in the result.

y

keep a copy of the dependent variable in the result.

parms

optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagnoal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.

control

options that control details of the rpart algorithm.

cost

a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.

...

arguments to rpart.control may also be specified in the call to rpart. They are checked against the list of valid arguments.

Value

an object of class rpart, a superset of class tree.

Details

This differs from the tree function mainly in its handling of surrogate variables. In most details it follows Breiman et. al. quite closely.

References

Breiman, Friedman, Olshen, and Stone. (1984) Classification and Regression Trees. Wadsworth.

De'ath G. (2002) Multivariate Regression Trees : A New Technique for Constrained Classification Analysis. Ecology 83(4):1103-1117.

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 1.0296258 0.17651975
#> 2 0.13452819      1 0.4046509 0.5877509 0.10867329
#> 3 0.06942684      2 0.2701227 0.4707942 0.08525448
#> 4 0.03449195      3 0.2006958 0.3362592 0.06371445
#> 5 0.01849081      4 0.1662039 0.3826438 0.07645957
#> 6 0.01571904      5 0.1477131 0.3503326 0.07593372
#> 7 0.01000000      7 0.1162750 0.3417547 0.08797556
#> 
#> 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 
#> 
plot(z.auto); text(z.auto)


data(spider)
fit1 <- rpart(data.matrix(spider[,1:12])~water+twigs+reft+herbs+
moss+sand,spider,method="mrt")
plot(fit1); text(fit1)

fit2 <- rpart(data.matrix(spider[,1:12])~water+twigs+reft+herbs+
moss+sand,spider,method="mrt",dissim="man")
plot(fit2); text(fit2)

fit3 <- rpart(gdist(spider[,1:12],meth="bray",full=TRUE,sq=TRUE)
~water+twigs+reft+herbs+moss+sand,spider,method="dist")
plot(fit3); text(fit3)