minNodePruning: Minimal Node Size Pruning

In discSurv: Discrete Time Survival Analysis

Minimal Node Size Pruning

Description

Computes optimal minimal node size of a discrete survival tree from a given vector of possible node sizes by cross-validation. Laplace-smoothing can be applied to the estimated hazards.

Usage

## S3 method for class 'discSurvMinNodeSizePrune'
plot(x, ...)

minNodePruning(
  formulaVariable,
  dataShort,
  treetype = "rpart",
  splitruleranger = "hellinger",
  sizes,
  indexList,
  timeColumn,
  eventColumn,
  alpha = 1,
  logOut = FALSE,
  ...
)

Arguments

x	Object of class "discSurvMinNodeSizePrune"
...	Additional arguments to the estimation function. It is either "rpart" or "ranger" (see argument treetype).
formulaVariable	Model formula for tree fitting (class "formula") of the form "~ x1 + x2 + ..." without response.
dataShort	Discrete survival data in short format for which a survival tree is to be fitted (class "data.frame").
treetype	Type of tree to be fitted (class "character"). Possible values are "rpart" or "ranger". The default is to fit an rpart tree; when "ranger" is chosen, a ranger forest with a single tree is fitted.
splitruleranger	String specifying the splitting rule of the ranger tree (class "character"). Possible values are either "gini", "extratrees" or "hellinger". Default is "hellinger".
sizes	Vector of different node sizes to try (class "integer"). Values should be non-negative.
indexList	List of data partitioning indices for cross-validation (class "list"). Each element represents the test indices of one fold (class "integer").
timeColumn	Character giving the column name of the observed times in the data argument (class "character").
eventColumn	Character giving the column name of the event indicator in the data argument (class "character").
alpha	Parameter for laplace-smoothing. A value of 0 corresponds to no laplace-smoothing (class "numeric").
logOut	Logical value (class "logical"). If the argument is set to TRUE, then computation progress will be written to console.

Details

Computes the out-of-sample log likelihood for all data partitionings for each node size in sizes and returns the node size for which the log likelihood was minimal. Also returns an rpart tree with the optimal minimal node size using the entire data set.

Value

A list containing the two items

• OptimNodeSize - Node size with lowest out-of-sample log-likelihood

• OptimTree - A tree object with type corresponding to treetype argument with the optimal minimal node size

Note

Note that depending on argument treetype some arguments are fixed and can not be changed:

• treetype="rpart": formula, data, method, minbucket

• treetype="ranger": formula, data, num.trees, mtry, classification, splitrule, replace, sample.fraction, min.node.size

Examples

library(pec)
library(caret)
data(cost)

# Take subsample and convert time to years
cost$time <- ceiling(cost$time / 365)
costSub <- cost[1:50, ]

# Specify column names for data augmentation
timeColumn <- "time"
eventColumn <- "status"

# Create cross validation sets
# Stratified by event and time distribution
indexList <- createFolds(factor(paste(costSub$status, 
costSub$time, sep="_")), k = 5)

# Perform minimal node size pruning
formula1 <- ~ timeInt + prevStroke + age + sex
sizes <- 1:10
optiTree <- minNodePruning(formula1, costSub, treetype = "rpart", sizes = sizes, 
indexList = indexList, timeColumn =  timeColumn, eventColumn = eventColumn, 
alpha = 1, logOut = TRUE)
plot(optiTree)

discSurv documentation built on April 29, 2026, 9:07 a.m.