Recursive Partytioning
A class for representing decision trees and corresponding accessor functions.
party(node, data, fitted = NULL, terms = NULL, names = NULL,
info = NULL)
## S3 method for class 'party'
names(x)
## S3 replacement method for class 'party'
names(x) <- value
data_party(party, id = 1L)
## Default S3 method:
data_party(party, id = 1L)
node_party(party)
is.constparty(party)
is.simpleparty(party)node |
an object of class |
data |
a (potentially empty) |
fitted |
an optional |
terms |
an optional |
names |
an optional vector of names to be assigned to each node of |
info |
additional information. |
x |
an object of class |
party |
an object of class |
value |
a character vector of up to the same length as |
id |
a node identifier. |
Objects of class party basically consist of a partynode
object representing the tree structure in a recursive way and
data. The data argument takes a data.frame which, however,
might have zero columns. Optionally, a data.frame with at least one
variable (fitted) containing the terminal node numbers of
data used for fitting the tree may be specified along with a
terms object or any additional (currently unstructured)
information as info. Argument names defines names
for all nodes in node.
Method names can be used to extract or alter names for nodes.
Function node_party returns the node element of a
party object. Further methods for party objects
are documented in party-methods and
party-predict. Trees of various flavors can be coerced
to party, see party-coercion.
Two classes inherit from class party and impose additional
assumptions on the structure of this object:
Class constparty requires that the fitted slot
contains a partitioning of the learning sample as a factor ("fitted")
and the response values of all observations in the learning sample
as ("response"). This structure is most flexible and
allows for graphical display of the response values in terminal
nodes as well as for computing predictions based on
arbitrary summary statistics.
Class simpleparty assumes that certain pre-computed information
about the distribution of the response variable is contained
in the info slot nodes. At the moment, no formal
class is used to describe this information.
The constructor returns an object of class party:
node |
an object of class |
data |
a (potentially empty) |
fitted |
an optional |
terms |
an optional |
names |
an optional vector of names to be assigned to each node of |
info |
additional information. |
names can be used to set and retrieve names of nodes and
node_party returns an object of class partynode.
data_party returns a data frame with observations contained in node
id.
Hothorn T, Zeileis A (2015). partykit: A Modular Toolkit for Recursive Partytioning in R. Journal of Machine Learning Research, 16, 3905–3909.
### data ###
## artificial WeatherPlay data
data("WeatherPlay", package = "partykit")
str(WeatherPlay)
### splits ###
## split in overcast, humidity, and windy
sp_o <- partysplit(1L, index = 1:3)
sp_h <- partysplit(3L, breaks = 75)
sp_w <- partysplit(4L, index = 1:2)
## query labels
character_split(sp_o)
### nodes ###
## set up partynode structure
pn <- partynode(1L, split = sp_o, kids = list(
partynode(2L, split = sp_h, kids = list(
partynode(3L, info = "yes"),
partynode(4L, info = "no"))),
partynode(5L, info = "yes"),
partynode(6L, split = sp_w, kids = list(
partynode(7L, info = "yes"),
partynode(8L, info = "no")))))
pn
### tree ###
## party: associate recursive partynode structure with data
py <- party(pn, WeatherPlay)
py
plot(py)
### variations ###
## tree stump
n1 <- partynode(id = 1L, split = sp_o, kids = lapply(2L:4L, partynode))
print(n1, data = WeatherPlay)
## query fitted nodes and kids ids
fitted_node(n1, data = WeatherPlay)
kidids_node(n1, data = WeatherPlay)
## tree with full data sets
t1 <- party(n1, data = WeatherPlay)
## tree with empty data set
party(n1, data = WeatherPlay[0, ])
## constant-fit tree
t2 <- party(n1,
data = WeatherPlay,
fitted = data.frame(
"(fitted)" = fitted_node(n1, data = WeatherPlay),
"(response)" = WeatherPlay$play,
check.names = FALSE),
terms = terms(play ~ ., data = WeatherPlay),
)
t2 <- as.constparty(t2)
t2
plot(t2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.