DStree.object: DStree Object

Description Value Structure See Also


This class of objects is returned by the DStree function to represent a fitted decision tree for right-censored survival data.



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).


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 update(tree).


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 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.


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).


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".


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.DStree these are rescaled to add to 100.


integer number of responses; the number of levels for a factor response.

parms, control

a record of the arguments supplied, which defaults filled in.


the summary and split function used.


a named logical vector recording for each variable if it was an ordered factor.


(where relevant) information returned by model.frame on the special handling of NAs derived from the na.action argument.


a string vector of size two, which denotes the column names of the observed time points and of the status variable.


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.


The above components must be included in a legitimate DStree object.

See Also


DStree documentation built on May 2, 2019, 3:37 p.m.