Calculates several classification statistics from binary prediction and criterion (e.g.; truth) vectors
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"
)logical. A logical vector of predictions
logical A logical vector of criterion (true) values
numeric. Weight given to sensitivity, must range from 0 to 1.
list. An optional list of additional costs to be added to each case.
numeric. Correction added to all counts for calculating dprime
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 rspectively. E.g.; 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.
not sure.