Find the best numeric split by concentration-index gain

Evaluate all admissible cutpoints of a numeric predictor and return the split with the largest weighted concentration-index gain.

Usage

best_numeric_split(
  x,
  y,
  wt,
  varid,
  ctrl,
  ci_fun,
  return = c("split", "candidate")
)

Arguments

x

A numeric predictor vector.

y

A two-column numeric matrix whose first column is the socioeconomic ranking variable and whose second column is the health outcome.

wt

A numeric vector of case weights with the same length as x.

varid

Integer identifier of the splitting variable, passed to partykit::partysplit().

ctrl

A list-like control object containing minbucket and minprob.

ci_fun

A concentration-index scoring function, typically created by ci_factory().

return

Either "split" to return only a partykit::partysplit() object, or "candidate" to also return the gain and left-child routing needed by the greedy tree builder.

Value

A partysplit object describing the best admissible binary split, or NULL if no split is available because all x values are tied or all candidate splits violate the control constraints.

Details

Candidate cutpoints are formed at midpoints between distinct sorted values of x. Each candidate is filtered using ctrl$minbucket and ctrl$minprob before being scored with weighted_ci_gain().

Examples

x <- c(2, 4, 6, 8, 10)
y <- cbind(
  ses = c(10, 20, 30, 40, 50),
  health = c(1, 0, 1, 0, 1)
)
wt <- rep(1, 5)
ctrl <- list(minbucket = 1, minprob = 0)

split <- best_numeric_split(
  x = x,
  y = y,
  wt = wt,
  varid = 1,
  ctrl = ctrl,
  ci_fun = ci_factory("CI")
)
split$breaks
#> [1] 3