This class of objects is returned by the DStree function
to represent a fitted decision tree for right-censored survival data.
Value
frame
data frame with one row for each node in the tree.
The row.names of frame contain the (unique) node numbers that
follow a binary ordering indexed by node depth.
Columns of frame include
var, a factor giving the names of the variables used in the
split at each node (leaf nodes are denoted by the level "<leaf>"),
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 estimated value of the median survival time at the node,
and splits, a two-column matrix of left and right split labels
for each node. Also included in the frame are complexity, the
complexity parameter at which this split will collapse, ncompete,
the number of competitor splits recorded, and nsurrogate, the
number of surrogate splits recorded. yval2 contains in its first columns the fitted hazard probabilities and in last the fitted survival probabilities (see surv).
where
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.
call
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 update(tree).
terms
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.
splits
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
variable), 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
column 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 ncat
determines whether the subset x < cutpoint or x >
cutpoint is sent to the left.
csplit
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 index column
of the splits matrix. The columns record 1 if that
level of the factor goes to the left, 3 if it goes to the
right, and 2 if that level is not present at this node
of the tree (or not defined for the factor).
method
character string: the method used to grow the tree. Since DStree is based on a user-defined split function of rpart the method is always
"user".
cptable
a matrix of information on the optimal prunings based on a
complexity parameter.
variable.importance
a named numeric vector giving the importance of each variable. (Only
present if there are any splits.) When printed by
summary.DStree these are rescaled to add to 100.
numresp
integer number of responses; the number of levels for a factor response.
parms, control
a record of the arguments supplied, which defaults filled in.
functions
the summary and split function used.
ordered
a named logical vector recording for each variable if it was an
ordered factor.
na.action
(where relevant) information returned by model.frame on
the special handling of NAs derived from the na.action
argument.
names
a string vector of size two, which denotes the column names of the observed time points and of the status variable.
wt
a numeric vector of equal length as the number of rows of the dataset, which denotes the optional case weigths, defined in the weights argument.
Optional components include the model frame (model), the matrix
of predictors (x) and the response variable (y) used to
construct the DStree object.
Structure
The above components must be included in a legitimate DStree object.