bart.step: Full-service variable selection

In cjcarlson/embarcadero: Species distribution models with BART

Full-service variable selection

Description

A wrapper for a few core functions, including a few diagnostic plots of variable importance, and the automated stepwise variable set reduction algorithm.

Usage

bart.step(
  x.data,
  y.data,
  ri.data = NULL,
  iter.step = 100,
  tree.step = 10,
  iter.plot = 100,
  full = FALSE,
  quiet = FALSE
)

Arguments

x.data	A data frame of covariates
y.data	A vector of outcomes (1/0)
iter.step	How many BART models to run for each iteration of the stepwise reduction
tree.step	How many trees to use in the variable set reduction.Should be a SMALL number (10 or 20 trees) in order to create the maximum disparity in variable importance between informative and uninformative predictors (recommendations taken from Chipman et al. 2010).
iter.plot	How many iterations to use in the first diagnostic plot
full	If this is set to FALSE (by default), this runs a stepwise variable set reduction and returns a model with the optimal variable step - much like gbm::gbm.step() or similar functions. In running varimp.step() it generates a single plot of RMSE against variables dropped. If this is set to TRUE, it also runs summary() on the model, and two additional plots are generated: the initial variable importance diagnostic generated by varimp.diag() (this is SLOW), and a final variable importance bar chart for the final model.

Value

Returns a model object run with the optimal, reduced variable set.

cjcarlson/embarcadero documentation built on Sept. 9, 2023, 10:47 p.m.