fftrees_threshold_numeric_grid: Perform a grid search over thresholds and return accuracy statistics for a given numeric cue

Description

Perform a grid search over thresholds and return accuracy statistics for a given numeric cue

fftrees_threshold_numeric_grid(
  thresholds,
  cue_v,
  criterion_v,
  directions = c(">", "

A data frame containing accuracy statistics for numeric thresholds.

thresholds: numeric. A vector of thresholds to consider.
cue_v: numeric. Feature values.
criterion_v: logical. A logical vector of (TRUE) criterion values.
directions: character. Possible directions to consider.
goal.threshold: A character string indicating the criterion to maximize when optimizing cue thresholds: "acc" = overall accuracy, "bacc" = balanced accuracy, "wacc" = weighted accuracy, "dprime" = discriminability, "cost" = costs (based only on cost.outcomes, as cost.cues are constant per cue). Default: goal.threshold = "bacc".
sens.w: numeric. Sensitivity weight parameter (from 0 to 1, for computing wacc). Default: sens.w = .50.
my.goal: Name of an optional, user-defined goal (as character string). Default: my.goal = NULL.
my.goal.fun: User-defined goal function (with 4 arguments hi fa mi cr). Default: my.goal.fun = NULL.
cost.each: numeric. A constant cost value to add to each value (e.g., the cost of the cue).
cost.outcomes: list. A list of length 4 with names 'hi', 'fa', 'mi', and 'cr' specifying the costs of a hit, false alarm, miss, and correct rejection, respectively, in some common currency. For instance, cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0) means that a false alarm and miss cost 10 and 20 units, respectively, while correct decisions have no cost.