classtable {FFTrees}

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.