sens defines a decision's sensitivity (or hit rate) value: The conditional probability of the decision being positive if the condition is TRUE.

sens

Format

An object of class numeric of length 1.

Details

Understanding or obtaining the sensitivity sens (or hit rate HR):

  • Definition: sens is the conditional probability for a (correct) positive decision given that the condition is TRUE:

    sens = p(decision = positive | condition = TRUE)

    or the probability of correctly detecting true cases (condition = TRUE).

  • Perspective: sens further classifies the subset of cond_true individuals by decision (sens = hi/cond_true).

  • Alternative names: true positive rate (TPR), hit rate (HR), probability of detection, power = 1 - beta, recall

  • Relationships:

    a. sens is the complement of the miss rate mirt (aka. false negative rate FNR or the rate of Type-II errors):

    sens = (1 - miss rate) = (1 - FNR)

    b. sens is the opposite conditional probability -- but not the complement -- of the positive predictive value PPV:

    PPV = p(condition = TRUE | decision = positive)

  • In terms of frequencies, sens is the ratio of hi divided by cond_true (i.e., hi + mi):

    sens = hi/cond_true = hi/(hi + mi)

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

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

References

Consult Wikipedia for additional information.

See also

comp_sens computes sens as the complement of mirt; 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, spec

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

Examples

sens <- .85 # sets a sensitivity value of 85% sens <- 85/100 # (decision = positive) for 85 out of 100 people with (condition = TRUE) is_prob(sens) # TRUE
#> [1] TRUE