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Sensitivity indexes

sensitivity_indexes.Rd

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

  • fp_auc() applies fitted partial area under curve index (FpAUC). This one calculates area under curve adjusting to points defined by the curve in the selected region.

  • np_auc() applies normalized partial area under curve index (NpAUC), which calculates area under curve over the whole specified region.

Usage

fp_auc(data = NULL, response, predictor, lower_tpr, .condition = NULL)

np_auc(data, response, predictor, lower_tpr, .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_tpr

A numeric value between 0 and 1, inclusive, which represents lower value of TPR for the region where to calculate the partial area under curve.

Because of definition of sensitivity indexes, upper bound of the region will be established as 1.

.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.

Value

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

References

Franco M. y Vivo J.-M. Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity. Mathematics 9, 2826 (2021).

Jiang Y., Metz C. E. y Nishikawa R. M. A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201, 745-750 (1996).

Examples

# Calculate fp_auc of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
fp_auc(iris, response = Species, predictor = Sepal.Width, lower_tpr = 0.9)
#> ℹ Upper threshold 1 already included in points.
#> • Skipping upper threshold interpolation
#> [1] 0.8516667
# Calculate np_auc of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
np_auc(iris, response = Species, predictor = Sepal.Width, lower_tpr = 0.9)
#> ℹ Upper threshold 1 already included in points.
#> • Skipping upper threshold interpolation
#> [1] 0.472