classtable: Compute classification statistics for binary prediction and...

In FFTrees: Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Compute classification statistics for binary prediction and criterion (e.g.; truth) vectors

Description

The main input are 2 logical vectors of prediction and criterion values.

Usage

classtable(
  prediction_v = NULL,
  criterion_v = NULL,
  correction = 0.25,
  sens.w = NULL,
  cost.outcomes = NULL,
  cost_v = NULL,
  my.goal = NULL,
  my.goal.fun = NULL,
  quiet_mis = FALSE,
  na_prediction_action = "ignore"
)

Arguments

prediction_v	logical. A logical vector of predictions.
criterion_v	logical. A logical vector of (TRUE) criterion values.
correction	numeric. Correction added to all counts for calculating dprime. Default: correction = .25.
sens.w	numeric. Sensitivity weight parameter (from 0 to 1, for computing wacc). Default: sens.w = NULL (to ensure that values are passed by calling function).
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. For instance, cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0) means that a false alarm and miss cost 10 and 20, respectively, while correct decisions have no cost. Default: cost.outcomes = NULL (to ensure that values are passed by calling function).
cost_v	numeric. Additional cost value of each decision (as an optional vector of numeric values). Typically used to include the cue cost of each decision (as a constant for the current level of an FFT). Default: cost_v = 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.
quiet_mis	A logical value passed to hide/show NA user feedback (usually x$params$quiet$mis of the calling function). Default: quiet_mis = FALSE (i.e., show user feedback).
na_prediction_action	What happens when no prediction is possible? (Experimental and currently unused.)

Details

The primary confusion matrix is computed by confusionMatrix of the caret package.

FFTrees documentation built on June 7, 2023, 5:56 p.m.