prune function evaluates and prunes a survival tree that has
been fitted by
DStree. Different criteria can be used for evaluation
(e.g. Brier Score or AIC). The result of the
is the optimal subtree (of class
DStree) with regard to the chosen
criterium, as well as various performance measures that were obtained from the subtrees
during analysis. The returned performance measures are the Brier Score, the deviance,
and an information criterion defined by
fitted model of class
optional data frame that is used to evaluate
the fit of the tree. The
predictors referred to in
optional positive integer value that determines
the user defined information criterion. Setting
An optional string that determines which performance criteria
should be computed from the subtrees. One of
further arguments passed to or from other methods.
The subtrees are the cost-minimzing subtrees in terms of deviance for given complexity parameters of the fitted tree. See Therneau et al (2013) p.12-13.
prune returns one
DStree object and four vectors of length equal to the
number of subtrees:
nsplit number of splits for every subtree
CRIT value of the user defined information criterion (underlying
formula: CRIT = deviance + gamma * |terminal leaves| * |time
Integrated Brier Score, see Hothorn et al. (2004)
pruned.fit optimal subtree regarding the choosen
criterium specified in
Hothorn T., Lausen B., Benner A. and Radespiel-Troeger M. (2004), Bagging Survival Trees. Statistics in medicine 23 (1), 77-91.
Therneau T. and Atkinson E., An introduction to recursive partitioning using the RPART routines, Technical Report 61, Section of Biostatistics, Mayo Clinic, Rochester.
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data(cost) ## Discretize observed days to years d.cost <- dis.cost(cost) ##Build tree tree <- DStree(time~prevStroke+age+sex+alcohol+smoke,status="status",data=d.cost) # Determine subtree with minimum AIC prunedtree <- prune(tree,d.cost,which="CRIT") prunedtree$prunedfit # Visualize AIC/Deviance of subtrees plot(prunedtree$nsplit,prunedtree$CRIT) plot(prunedtree$nsplit,prunedtree$DEV)
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