FFTrees (version 1.7.0)

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

Description

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

Usage

classtable(
  prediction_v = NULL,
  criterion_v,
  sens.w = 0.5,
  cost.v = NULL,
  correction = 0.25,
  cost.outcomes = list(hi = 0, fa = 1, mi = 1, cr = 0),
  na_prediction_action = "ignore"
)

Arguments

prediction_v

logical. A logical vector of predictions.

criterion_v

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

sens.w

numeric. Sensitivity weight parameter (from 0 to 1, for computing wacc). Default: sens.w = .50.

cost.v

list. An optional list of additional costs to be added to each case.

correction

numeric. Correction added to all counts for calculating dprime.

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.

na_prediction_action

not sure.