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

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

Usage

fftrees_threshold_factor_grid(
  thresholds = NULL,
  cue_v = NULL,
  criterion_v = NULL,
  directions = "=",
  goal.threshold = NULL,
  sens.w = NULL,
  my.goal = NULL,
  my.goal.fun = NULL,
  cost.each = NULL,
  cost.outcomes = NULL
)

Arguments

thresholds

numeric. A vector of factor thresholds to consider.

cue_v

numeric. Feature/cue values.

criterion_v

logical. A logical vector of (TRUE) criterion values.

directions

character. Character vector of threshold 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.

Value

A data frame containing accuracy statistics for factor thresholds.

See also

fftrees_threshold_numeric_grid for numeric cues.