blackboost_train: Boosted trees via mboost

In censored: 'parsnip' Engines for Survival Models

Boosted trees via mboost

Description

blackboost_train() is a wrapper for the blackboost() function in the mboost package that fits tree-based models where all of the model arguments are in the main function.

Usage

blackboost_train(
  formula,
  data,
  family,
  weights = NULL,
  teststat = "quad",
  testtype = "Teststatistic",
  mincriterion = 0,
  minsplit = 10,
  minbucket = 4,
  maxdepth = 2,
  saveinfo = FALSE,
  ...
)

Arguments

teststat	a character specifying the type of the test statistic to be applied for variable selection.
testtype	a character specifying how to compute the distribution of the test statistic. The first three options refer to p-values as criterion, Teststatistic uses the raw statistic as criterion. Bonferroni and Univariate relate to p-values from the asymptotic distribution (adjusted or unadjusted). Bonferroni-adjusted Monte-Carlo p-values are computed when both Bonferroni and MonteCarlo are given.
mincriterion	the value of the test statistic or 1 - p-value that must be exceeded in order to implement a split.
minsplit	the minimum sum of weights in a node in order to be considered for splitting.
minbucket	the minimum sum of weights in a terminal node.
maxdepth	maximum depth of the tree. The default maxdepth = Inf means that no restrictions are applied to tree sizes.
saveinfo	logical. Store information about variable selection procedure in info slot of each partynode.
...	Other arguments to pass.
x	A data frame or matrix of predictors.
y	A factor vector with 2 or more levels

Value

A fitted blackboost model.

Examples

blackboost_train(Surv(time, status) ~ age + ph.ecog,
  data = lung[-14, ], family = mboost::CoxPH()
)

censored documentation built on April 14, 2023, 12:30 a.m.