specificity function

Specificity | Selectivity | True Negative Rate

specificity estimates the specificity (a.k.a. selectivity, or true negative rate -TNR-) for a nominal/categorical predicted-observed dataset.

selectivity alternative to specificity().

TNR alternative to specificity().

FPR estimates the false positive rate (a.k.a fall-out or false alarm) for a nominal/categorical predicted-observed dataset.

specificity(
  data = NULL,
  obs,
  pred,
  atom = FALSE,
  pos_level = 2,
  tidy = FALSE,
  na.rm = TRUE
)

selectivity(
  data = NULL,
  obs,
  pred,
  atom = FALSE,
  pos_level = 2,
  tidy = FALSE,
  na.rm = TRUE
)

TNR(
  data = NULL,
  obs,
  pred,
  atom = FALSE,
  pos_level = 2,
  tidy = FALSE,
  na.rm = TRUE
)

FPR(
  data = NULL,
  obs,
  pred,
  atom = FALSE,
  pos_level = 2,
  tidy = FALSE,
  na.rm = TRUE
)

Arguments

• data: (Optional) argument to call an existing data frame containing the data.
• obs: Vector with observed values (character | factor).
• pred: Vector with predicted values (character | factor).
• atom: Logical operator (TRUE/FALSE) to decide if the estimate is made for each class (atom = TRUE) or at a global level (atom = FALSE); Default : FALSE. When dataset is "binomial" atom does not apply.
• pos_level: Integer, for binary cases, indicating the order (1|2) of the level corresponding to the positive. Generally, the positive level is the second (2) since following an alpha-numeric order, the most common pairs are (Negative | Positive), (0 | 1), (FALSE | TRUE). Default : 2.
• tidy: Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.
• na.rm: Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

Returns

an object of class numeric within a list (if tidy = FALSE) or within a data frame (if tidy = TRUE).

Details

The specificity (or selectivity, or true negative rate-TNR-) is a non-normalized coefficient that represents the ratio between the correctly negative predicted cases (or true negative -TN- for binary cases) to the total number of actual observations not belonging to a given class (actual negatives -N- for binary cases).

For binomial cases, $specificity = \frac{TN}{N} = \frac{TN}{TN+FP}$

The specificity metric is bounded between 0 and 1. The closer to 1 the better. Values towards zero indicate low performance. For multinomial cases, it can be either estimated for each particular class or at a global level.

Metrica offers 3 identical alternative functions that do the same job: i) specificity, ii) selectivity, and iii) TNR. However, consider when using metrics_summary, only the specificity alternative is used.

The false positive rate (or false alarm, or fall-out) is the complement of the specificity, representing the ratio between the number of false positives (FP) to the actual number of negatives (N). The FPR formula is:

$FPR = 1 - specificity = 1 - TNR = \frac{FP}{N}$

The FPR is bounded between 0 and 1. The closer to 0 the better. Low performance is indicated with FPR > 0.5.

For the formula and more details, see online-documentation

Examples

set.seed(123)
# Two-class
binomial_case <- data.frame(labels = sample(c("True","False"), 100, 
replace = TRUE), predictions = sample(c("True","False"), 100, replace = TRUE))
# Multi-class
multinomial_case <- data.frame(labels = sample(c("Red","Blue", "Green"), 100, 
replace = TRUE), predictions = sample(c("Red","Blue", "Green"), 100, replace = TRUE)    )

# Get specificity and FPR estimates for two-class case
specificity(data = binomial_case, obs = labels, pred = predictions, tidy = TRUE)
FPR(data = binomial_case, obs = labels, pred = predictions, tidy = TRUE)

# Get specificity estimate for each class for the multi-class case
specificity(data = multinomial_case, obs = labels, pred = predictions, tidy = TRUE)

# Get specificity estimate for the multi-class case at a global level
specificity(data = multinomial_case, obs = labels, pred = predictions, tidy = TRUE)

References

Ting K.M. (2017) Sensitivity and Specificity. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning and Data Mining.

Springer, Boston, MA. tools:::Rd_expr_doi("10.1007/978-0-387-30164-8_752")

Trevethan, R. (2017). Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls

_ in Research and Practice. Front. Public Health 5:307_ tools:::Rd_expr_doi("10.3389/fpubh.2017.00307")