OptimalCutPoint: Cut point search via cross-validation

In sagade/STEPPup:

Description

Searches an optimal cut-point for a continuous variable 'var' in interaction with treatmen 'treat' for a binary response. The cut point is optimized via cross-validated AUC

Usage

OptimalCutPoint(response, var, treat, covars = NULL, data, num.cuts = 10000,
  K = 10, repeats = 5, cores = 1, seed = 123)

Arguments

response	the name of the binary response variable
var	the name of continuous variable
treat	the name of the treatment variable
covars	optional vector with covariate names
data	the data frame with the data
num.cuts	the number of cuts to be tested. If the number of cuts is larger than the number of unique values...
K	the number of folds of the cross-validation
repeats	the nu
cores
seed	the seed to be used for cross-validation

Value

matrix with cut points, median AUC and AUCs for every repeat

sagade/STEPPup documentation built on Dec. 31, 2020, 3:15 a.m.