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Specificity indexes

specificity_indexes.Rd

Specificity indexes provide different ways of calculating area under ROC curve in a specific FPR region. Two different approaches to calculate this area are available:

  • tp_auc() applies tighter partial area under curve index (SpAUC). This one calculates area under curve adjusting to points defined by the curve in the selected region.

  • sp_auc() applies standardized partial area under curve index (TpAUC), which calculates area under curve over the whole specified region.

Usage

sp_auc(
  data = NULL,
  response,
  predictor,
  lower_fpr,
  upper_fpr,
  .condition = NULL,
  .invalid = FALSE
)

tp_auc(
  data = NULL,
  response,
  predictor,
  lower_fpr,
  upper_fpr,
  .condition = NULL
)

Arguments

data

A data.frame or extension (e.g. a tibble) containing values for predictors and response variables.

response

A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard).

If the variable presents more than two possible outcomes, classes or categories:

  • The outcome of interest (the one to be predicted) will remain distinct.

  • All other categories will be combined into a single category.

New combined category represents the "absence" of the condition to predict. See .condition for more information.

predictor

A data variable which must be numeric, representing values of a classifier or predictor for each observation.

lower_fpr, upper_fpr

Two numbers between 0 and 1, inclusive. These numbers represent lower and upper values of FPR region where to calculate partial area under curve.

.condition

A value from response that represents class, category or condition of interest which wants to be predicted.

If NULL, condition of interest will be selected automatically depending on response type.

Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted.

See vignette("selecting-condition") for further information on how automatic selection is performed and details on selecting the condition of interest.

.invalid

If FALSE, the default, sp_auc() will return NA when ROC curve does not fit theoretical bounds and index cannot be applied. If TRUE, function will force the calculation and return a value despite probably being incorrect.

Value

A numeric value representing the index score for the partial area under ROC curve.

References

McClish D. K. Analyzing a Portion of the ROC Curve. Medical Decision Making 9, 190-195 (1989).

Vivo J.-M., Franco M. y Vicari D. Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range. Advances in Data Analysis and Classification 12, 683-704 (2018).

Examples

# Calculate sp_auc of Sepal.Width as a classifier of setosa species
# in FPR = (0.9, 1)
sp_auc(
 iris,
 response = Species,
 predictor = Sepal.Width,
 lower_fpr = 0,
 upper_fpr = 0.1
)
#> ℹ Lower 0 and upper 0.1 thresholds already included in points
#> • Skipping lower and upper threshold interpolation
#> [1] 0.7242105
# Calculate tp_auc of Sepal.Width as a classifier of setosa species
 # in FPR = (0.9, 1)
tp_auc(
 iris,
 response = Species,
 predictor = Sepal.Width,
 lower_fpr = 0,
 upper_fpr = 0.1
)
#> ℹ Lower 0 and upper 0.1 thresholds already included in points
#> • Skipping lower and upper threshold interpolation
#> [1] 0.7212121