
Compute classification statistics for binary prediction and criterion (e.g.; truth) vectors
Source:R/util_stats.R
classtable.Rd
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 (usuallyx$params$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
.