minNodePruningCompRisks: Minimal Node Size Pruning in the Presence of Competing Risks

In discSurv: Discrete Time Survival Analysis

Minimal Node Size Pruning in the Presence of Competing Risks

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

minNodePruningCompRisks(
  formula,
  data,
  treetype = "rpart",
  splitruleranger = "gini",
  sizes,
  indexList,
  timeColumn,
  eventColumns,
  lambda = 1,
  logOut = FALSE
)

Arguments

formula	Model formula for tree fitting("class formula")
data	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. Possible values are "rpart" or "ranger" ("character vector"). 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 ("character vector"). Possible values are either "gini" or "extratrees". Default is "gini".
sizes	Vector of different node sizes to try ("integer vector"). Values need to be non-negative.
indexList	List of data partitioning indices for cross-validation ("class list"). Each element represents the test indices of one fold ("integer vector").
timeColumn	Character giving the column name of the observed times in the "data"-argument("character vector").
eventColumns	Character vector giving the column names of the event indicators (excluding censoring column) in the "data"-argument("character vector").
lambda	Parameter for laplace-smoothing. A value of 0 corresponds to no laplace-smoothing ("numeric vector").
logOut	Logical value("logical vector"). If True, 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

• Optimal minimal node size - Node size with lowest out-of-sample log-likelihood

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

Examples

# Example unemployment data
library(Ecdat)
library(caret)
data(UnempDur)
# Select training and testing subsample
subUnempDur <- UnempDur[which(UnempDur$spell < 10),]
subUnempDur <- subUnempDur[1:250,]
#creating status variable for data partitioning
subUnempDur$status <- ifelse(subUnempDur$censor1, 1, 
ifelse(subUnempDur$censor2, 2, ifelse(
subUnempDur$censor3, 3, ifelse(subUnempDur$censor4, 4, 0))))
indexList <- createFolds(subUnempDur$status*max(subUnempDur$spell) + subUnempDur$spell, k = 5)
# performing minimal node size pruning
formula <- responses ~ timeInt + age + logwage
sizes <- 1:10
timeColumn <- "spell"
eventColumns <- c("censor1", "censor2", "censor3","censor4")
optiTree <- minNodePruningCompRisks(formula, subUnempDur, treetype = "rpart", sizes = sizes, 
indexList = indexList, timeColumn = timeColumn, eventColumns = eventColumns, lambda = 1, 
logOut = TRUE)

discSurv documentation built on March 18, 2022, 7:12 p.m.