spec defines a decision's specificity value (or correct rejection rate): The conditional probability of the decision being negative if the condition is FALSE.



An object of class numeric of length 1.


Understanding or obtaining the specificity value spec:

  • Definition: spec is the conditional probability for a (correct) negative decision given that the condition is FALSE:

    spec = p(decision = negative | condition = FALSE)

    or the probability of correctly detecting false cases (condition = FALSE).

  • Perspective: spec further classifies the subset of cond_false individuals by decision (spec = cr/cond_false).

  • Alternative names: true negative rate (TNR), correct rejection rate, 1 - alpha

  • Relationships:

    a. spec is the complement of the false alarm rate fart:

    spec = 1 - fart

    b. spec is the opposite conditional probability -- but not the complement -- of the negative predictive value NPV:

    NPV = p(condition = FALSE | decision = negative)

  • In terms of frequencies, spec is the ratio of cr divided by cond_false (i.e., fa + cr):

    spec = cr/cond_false = cr/(fa + cr)

  • Dependencies: spec is a feature of a decision process or diagnostic procedure and a measure of correct decisions (true negatives).

    However, due to being a conditional probability, the value of spec is not intrinsic to the decision process, but also depends on the condition's prevalence value prev.


Consult Wikipedia for additional information.

See also

comp_spec computes spec as the complement of fart; prob contains current probability information; comp_prob computes current probability information; num contains basic numeric parameters; init_num initializes basic numeric parameters; comp_freq computes current frequency information; is_prob verifies probabilities.

Other probabilities: FDR, FOR, NPV, PPV, acc, err, fart, mirt, ppod, prev, sens

Other essential parameters: cr, fa, hi, mi, prev, sens


spec <- .75 # sets a specificity value of 75% spec <- 75/100 # (decision = negative) for 75 out of 100 people with (condition = FALSE) is_prob(spec) # TRUE
#> [1] TRUE