oc_youden_normal: Determine an optimal cutpoint for the Youden-Index assuming...

In cutpointr: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

Determine an optimal cutpoint for the Youden-Index assuming normal distributions

Description

An optimal cutpoint maximizing the Youden- or J-Index (sensitivity + specificity - 1) is calculated parametrically assuming normal distributions per class.

Usage

oc_youden_normal(
  data,
  x,
  class,
  pos_class = NULL,
  neg_class = NULL,
  direction,
  ...
)

Arguments

data	A data frame or tibble in which the columns that are given in x and class can be found.
x	(character) The variable name to be used for classification, e.g. predictions or test values.
class	(character) The variable name indicating class membership.
pos_class	The value of class that indicates the positive class.
neg_class	The value of class that indicates the negative class.
direction	(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.
...	To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.

See Also

Other method functions: maximize_boot_metric(), maximize_gam_metric(), maximize_loess_metric(), maximize_metric(), maximize_spline_metric(), oc_manual(), oc_mean(), oc_median(), oc_youden_kernel()

Examples

data(suicide)
oc_youden_normal(suicide, "dsi", "suicide",
  pos_class = "yes", neg_class = "no", direction = ">=")
cutpointr(suicide, dsi, suicide, method = oc_youden_normal)

cutpointr documentation built on April 14, 2022, 1:06 a.m.