Internal helper that computes the sensitivity and specificity values for a
set of user-supplied cutoff points (or computed via OptimalCutpoints methods)
across categories defined by group_by and/or facet_by columns. Used by
ROCCurveAtomic() to annotate the ROC curve with cutoff markers and labels.
Usage
get_cutoffs_data(
data,
truth_by,
score_by,
cat_by,
cutoffs_at = NULL,
cutoffs_labels = NULL,
cutoffs_accuracy = 0.001,
n_cuts = 0,
increasing = TRUE
)Arguments
- data
A data frame with the truth and score columns.
- truth_by
A character string of the column name that contains the true class labels (binary, 0/1 or TRUE/FALSE).
- score_by
A character string of the column name that contains the predicted scores.
- cat_by
A character string of the column name to categorise/group the data. When specified, cutoffs are calculated separately for each category level, enabling per-group or per-facet cutoff annotation.
- cutoffs_at
A vector of user-supplied cutoff values to plot as points. When non-NULL, overrides
n_cuts. Supports both raw numeric values and method names fromoptimal.cutpoints.- cutoffs_labels
A character vector of user-supplied labels for the cutoffs. Must be the same length as
cutoffs_at. When NULL, labels are generated automatically.- cutoffs_accuracy
A numeric value specifying the rounding precision for automatically generated cutoff labels. Default:
0.001.- n_cuts
An integer specifying the number of evenly-spaced quantile-based cutoff points. Ignored when
cutoffs_atis non-NULL. Default:0(no quantile cutoffs).- increasing
A logical value. If TRUE (default), higher scores indicate the positive class; if FALSE, lower scores indicate the positive class.