NPV defines some decision's negative predictive value (NPV): The conditional probability of the condition being FALSE provided that the decision is negative.



An object of class numeric of length 1.


Understanding or obtaining the negative predictive value NPV:

  • Definition: NPV is the conditional probability for the condition being FALSE given a negative decision:

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

    or the probability of a negative decision being correct.

  • Perspective: NPV further classifies the subset of dec_neg individuals by condition (NPV = cr/dec_neg = cr/(mi + cr)).

  • Alternative names: true omission rate

  • Relationships:

    a. NPV is the complement of the false omission rate FOR:

    NPV = 1 - FOR

    b. NPV is the opposite conditional probability -- but not the complement -- of the specificity spec:

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

  • In terms of frequencies, NPV is the ratio of cr divided by dec_neg (i.e., cr + mi):

    NPV = cr/dec_neg = cr/(cr + mi)

  • Dependencies: NPV is a feature of a decision process or diagnostic procedure and -- similar to the specificity spec -- a measure of correct decisions (negative decisions that are actually FALSE).

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


NPV <- .95 # sets a negative predictive value of 95% NPV <- 95/100 # (condition = FALSE) for 95 out of 100 people with (decision = negative) is_prob(NPV) # TRUE
#> [1] TRUE