Nodes and Splits in an rpart Tree

The rpart result object has a complex and compact design. This can make practical use tedious for occasional users as it is difficult to figure out how to access some specific information. The function Node() is designed as accessor to the most important properties of a node, being a 'split' or a 'leaf' (aka. 'endnode'). It also serves as base for further convenience functions as e.g. LeafRates().

Usage

Node(x, node = NULL, type = c("all", "split", "leaf"), digits = 3)

Arguments

x: fitted model object of class rpart.
node: integer vector, defining the nodes whose details are required.
type: one out of "all" (default), "split", "leaf", where the latter two restrict the result set to splits or end nodes only. Can be abbreviated.
digits: the number of digits for numeric values

Details

Node() returns detailed information for a single node in the tree. It reports all the data in the summary of a node, but with the option to provide a nodelist. The structure of the result is organised as a list.

Value

A list containing:

id: int, id of the node
vname: character, one out of 'leaf' or 'split'
isleaf: logical, TRUE for leaves FALSE else
nobs: integer, number of observation in the node
group: character, the predicted class for the node
ycount: numeric, the number of observation per class in the node
yprob: numeric, the relative frequencies for the each class
nodeprob: the global probability for an observation to fall in the node
complexity: numeric, the complexity parameter for the node
tprint: character, the text to be printed

Author

Andri Signorell <andri@signorell.net>

Examples

r.rpart <- FitMod(Species ~ ., data=iris, fitfn="rpart")
# return Node nr. 3
Node(r.rpart, node=3)
#> 
#> Node number 3: 100 observations,    complexity param=0.44
#>   predicted class=versicolor  expected loss=0.5  P(node) =0.667
#>     class counts:     0    50    50
#>    probabilities: 0.000 0.500 0.500 
#>   left son=6 (54 obs) right son=7 (46 obs)
#>   Primary splits:
#>       Petal.Width < 1.75 to the left,  improve=39, (0 missing)
#>       Petal.Length < 4.75 to the left,  improve=37.4, (0 missing)
#>       Sepal.Length < 6.15 to the left,  improve=10.7, (0 missing)
#>       Sepal.Width < 2.45 to the left,  improve=3.56, (0 missing)
#>   Surrogate splits:
#>       Petal.Length < 4.75 to the left,  agree=0.91, adj=0.804, (0 split)
#>       Sepal.Length < 6.15 to the left,  agree=0.73, adj=0.413, (0 split)
#>       Sepal.Width < 2.95 to the left,  agree=0.67, adj=0.283, (0 split)
#> 

r.rp <- FitMod(Type ~ ., data = d.glass, fitfn="rpart")
# return all the splits
Node(r.rpart, type="split")
#> 
#> Node number 1: 150 observations,    complexity param=0.5
#>   predicted class=setosa      expected loss=0.667  P(node) =1
#>     class counts:    50    50    50
#>    probabilities: 0.333 0.333 0.333 
#>   left son=2 (50 obs) right son=3 (100 obs)
#>   Primary splits:
#>       Petal.Length < 2.45 to the left,  improve=50, (0 missing)
#>       Petal.Width < 0.8  to the left,  improve=50, (0 missing)
#>       Sepal.Length < 5.45 to the left,  improve=34.2, (0 missing)
#>       Sepal.Width < 3.35 to the right, improve=19, (0 missing)
#>   Surrogate splits:
#>       Petal.Width < 0.8  to the left,  agree=1, adj=1, (0 split)
#>       Sepal.Length < 5.45 to the left,  agree=0.92, adj=0.76, (0 split)
#>       Sepal.Width < 3.35 to the right, agree=0.833, adj=0.5, (0 split)
#> 
#> 
#> Node number 3: 100 observations,    complexity param=0.44
#>   predicted class=versicolor  expected loss=0.5  P(node) =0.667
#>     class counts:     0    50    50
#>    probabilities: 0.000 0.500 0.500 
#>   left son=6 (54 obs) right son=7 (46 obs)
#>   Primary splits:
#>       Petal.Width < 1.75 to the left,  improve=39, (0 missing)
#>       Petal.Length < 4.75 to the left,  improve=37.4, (0 missing)
#>       Sepal.Length < 6.15 to the left,  improve=10.7, (0 missing)
#>       Sepal.Width < 2.45 to the left,  improve=3.56, (0 missing)
#>   Surrogate splits:
#>       Petal.Length < 4.75 to the left,  agree=0.91, adj=0.804, (0 split)
#>       Sepal.Length < 6.15 to the left,  agree=0.73, adj=0.413, (0 split)
#>       Sepal.Width < 2.95 to the left,  agree=0.67, adj=0.283, (0 split)
#>