The main input are 2 logical vectors of prediction and criterion values.
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"
)logical. A logical vector of predictions.
logical. A logical vector of (TRUE) criterion values.
numeric. Correction added to all counts for calculating dprime.
Default: correction = .25.
numeric. Sensitivity weight parameter (from 0 to 1, for computing wacc).
Default: sens.w = NULL (to ensure that values are passed by calling function).
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).
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).
Name of an optional, user-defined goal (as character string). Default: my.goal = NULL.
User-defined goal function (with 4 arguments hi fa mi cr). Default: my.goal.fun = NULL.
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).
What happens when no prediction is possible? (Experimental and currently unused.)
The primary confusion matrix is computed by confusionMatrix of the caret package.