Recursive Partitioning and Regression Trees Object
These are objects representing fitted
data frame with one row for each node in the tree.
frame contain the (unique) node numbers that
follow a binary ordering indexed by node depth.
var, a factor giving the names of the variables used in the
split at each node (leaf nodes are denoted by the level
n, the number of observations reaching the node,
wt, the sum of case weights for observations reaching the node,
dev, the deviance of the node,
yval, the fitted value of the response at the node,
splits, a two column matrix of left and right split labels
for each node. Also included in the frame are
complexity parameter at which this split will collapse,
the number of competitor splits recorded, and
number of surrogate splits recorded.
Extra response information which may be present is in
which contains the number of events at the node (poisson tree), or a
matrix containing the fitted class, the class counts for each node,
the class probabilities and the ‘node probability’ (classification trees).
an integer vector of the same length as the number of observations in the
root node, containing the row number of
frame corresponding to
the leaf node that each observation falls into.
an image of the call that produced the object, but with the arguments
all named and with the actual formula included as the formula argument.
To re-evaluate the call, say
an object of class
c("terms", "formula") (see
terms.object) summarizing the formula. Used by various
methods, but typically not of direct relevance to users.
a numeric matrix describing the splits: only present if there are any.
The row label is the name of
the split variable, and columns are
count, the number of
observations (which are not missing and are of positive weight) sent
left or right by the split (for competitor splits this is the number
that would have been sent left or right had this split been used, for
surrogate splits it is the number missing the primary split variable
which were decided using this surrogate),
ncat, the number of
categories or levels for the variable (
+/-1 for a continuous
improve, which is the improvement in deviance given
by this split, or, for surrogates, the concordance of the surrogate
with the primary, and
index, the numeric split point. The last
adj gives the adjusted concordance for surrogate splits.
For a factor, the
index column contains the row number of the
csplit matrix. For a continuous variable, the sign of
determines whether the subset
x < cutpoint or
cutpoint is sent to the left.
an integer matrix. (Only present only if at least one of the split
variables is a factor or ordered factor.) There is a row for
each such split, and the number of columns is the largest number of
levels in the factors. Which row is given by the
splits matrix. The columns record
1 if that
level of the factor goes to the left,
3 if it goes to the
2 if that level is not present at this node
of the tree (or not defined for the factor).
character string: the method used to grow the tree. One of
"user" (if splitting functions were supplied).
a matrix of information on the optimal prunings based on a complexity parameter.
a named numeric vector giving the importance of each variable. (Only
present if there are any splits.) When printed by
summary.rpart these are rescaled to add to 100.
integer number of responses; the number of levels for a factor response.
a record of the arguments supplied, which defaults filled in.
text functions for method used.
a named logical vector recording for each variable if it was an ordered factor.
(where relevant) information returned by
the special handling of
NAs derived from the
There may be attributes "xlevels" and "levels" recording the levels of any factor splitting variables and of a factor response respectively.
Optional components include the model frame (model), the matrix of predictors (x) and the response variable (y) used to construct the rpart object.
The following components must be included in a legitimate