Perform a grid search over factor and return accuracy statistics for a given factor cue
fftrees_threshold_factor_grid(
thresholds = NULL,
cue_v = NULL,
criterion_v = NULL,
directions = "=",
goal.threshold = NULL,
sens.w = NULL,
my.goal = NULL,
my.goal.fun = NULL,
cost.each = NULL,
cost.outcomes = NULL
)A data frame containing accuracy statistics for factor thresholds.
numeric. A vector of factor thresholds to consider.
numeric. Feature/cue values.
logical. A logical vector of (TRUE) criterion values.
character. Character vector of threshold directions to consider.
A character string indicating the criterion to maximize when optimizing cue thresholds:
"acc" = overall accuracy, "bacc" = balanced accuracy, "wacc" = weighted accuracy,
"dprime" = discriminability, "cost" = costs (based only on cost.outcomes, as cost.cues are constant per cue).
Default: goal.threshold = "bacc".
numeric. Sensitivity weight parameter (from 0 to 1, for computing wacc).
Default: sens.w = .50.
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.
numeric. A constant cost value to add to each value (e.g., the cost of the cue).
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, in some common currency.
For instance, cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0) means that
a false alarm and miss cost 10 and 20 units, respectively, while correct decisions have no cost.
fftrees_threshold_numeric_grid for numeric cues.