decide_variable_type_iterative: Decide Variable Type (Iterative)

In SplitWise: 'SplitWise': Hybrid Stepwise Regression with Single-Split Dummy Encoding

Decide Variable Type (Iterative)

Description

A stepwise variable-selection method that iteratively chooses each variable's best form: "linear", single-split "dummy", or double-split ("middle=1") dummy, based on AIC/BIC improvement. Supports "forward", "backward", or "both" strategies.

Usage

decide_variable_type_iterative(
  X,
  Y,
  minsplit = 5,
  direction = c("backward", "forward", "both"),
  criterion = c("AIC", "BIC"),
  exclude_vars = NULL,
  verbose = FALSE,
  ...
)

Arguments

X	A data frame of predictors (no response).
Y	A numeric vector (the response).
minsplit	Minimum number of observations in a node to consider splitting. Default = 5.
direction	Stepwise strategy: "forward", "backward", or "both". Default = "backward".
criterion	A character string: either "AIC" or "BIC". Default = "AIC".
exclude_vars	A character vector of variable names to exclude from dummy transformations. These variables will always be treated as linear. Default = NULL.
verbose	Logical; if TRUE, prints messages for debugging. Default = FALSE.
...	Additional arguments (currently unused).

Details

Dummy forms come from a shallow (maxdepth = 2) rpart tree fit to the partial residuals of the current model. We extract up to two splits:

• Single cutoff dummy (e.g., x >= c)

• Double cutoff dummy (e.g., c1 < x < c2)

The function then picks the form (linear, single-split dummy, or double-split dummy) that yields the lowest AIC/BIC. Variables listed in exclude_vars will be forced to remain linear (dummy transformations are never attempted).

Value

A named list of decisions, where each element is a list with:

type

Either "linear" or "dummy".

cutoff

A numeric vector of length 1 or 2 (the chosen split points).

SplitWise documentation built on June 8, 2025, 10:59 a.m.