add_stats {FFTrees}

Add decision statistics to data (based on frequency counts of a 2x2 classification outcomes)

Description

add_stats assumes the input of the 4 essential classification outcomes (as frequency counts in a data frame "data" with variable names "hi", "fa", "mi", and "cr") and uses them to compute various decision accuracy measures.

Usage

add_stats(
  data,
  correction = 0.25,
  sens.w = NULL,
  my.goal = NULL,
  my.goal.fun = NULL,
  cost.outcomes = NULL,
  cost.each = NULL
)

Arguments

data

A data frame with 4 frequency counts (as integer values, named "hi", "fa", "mi", and "cr").

correction

numeric. Correction added to all counts for calculating dprime. Default: correction = .25.

sens.w

numeric. Sensitivity weight (for computing weighted accuracy, wacc). Default: sens.w = NULL (to ensure that values are passed by calling function).

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.outcomes

list. A list of length 4 named "hi", "fa", "mi", "cr", and specifying the costs of a hit, false alarm, miss, and correct rejection, respectively. E.g.; 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 incur no costs. Default: cost.outcomes = NULL (to ensure that values are passed by calling function).

cost.each

numeric. An optional fixed cost added to all outputs (e.g., the cost of using the cue). Default: cost.each = NULL (to ensure that values are passed by calling function).

Details

Providing numeric values for cost.each (as a vector) and cost.outcomes (as a named list) allows computing cost information for the counts of corresponding classification decisions.

Value

A data frame with variables of computed accuracy and cost measures (but dropping inputs).