PPV defines some decision's positive predictive value (PPV): The conditional probability of the condition being TRUE provided that the decision is positive.



An object of class numeric of length 1.


Understanding or obtaining the positive predictive value PPV:

  • Definition: PPV is the conditional probability for the condition being TRUE given a positive decision:

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

    or the probability of a positive decision being correct.

  • Perspective: PPV further classifies the subset of dec_pos individuals by condition (PPV = hi/dec_pos = hi/(hi + fa)).

  • Alternative names: precision

  • Relationships:

    a. PPV is the complement of the false discovery or false detection rate FDR:

    PPV = 1 - FDR

    b. PPV is the opposite conditional probability -- but not the complement -- of the sensitivity sens:

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

  • In terms of frequencies, PPV is the ratio of hi divided by dec_pos (i.e., hi + fa):

    PPV = hi/dec_pos = hi/(hi + fa)

  • Dependencies: PPV is a feature of a decision process or diagnostic procedure and -- similar to the sensitivity sens -- a measure of correct decisions (positive decisions that are actually TRUE).

    However, due to being a conditional probability, the value of PPV 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_PPV computes PPV; 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, acc, err, fart, mirt, ppod, prev, sens, spec


PPV <- .55 # sets a positive predictive value of 55% PPV <- 55/100 # (condition = TRUE) for 55 out of 100 people with (decision = positive) is_prob(PPV) # TRUE
#> [1] TRUE