FOR defines a decision's false omission rate (FOR): The conditional probability of the condition being TRUE provided that the decision is negative.



An object of class numeric of length 1.


Understanding or obtaining the false omission rate FOR:

  • Definition: FOR is the so-called false omission rate: The conditional probability for the condition being TRUE given a negative decision:

    FOR = p(condition = TRUE | decision = negative)

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

  • Alternative names: none?

  • Relationships:

    a. FOR is the complement of the negative predictive value NPV:

    FOR = 1 - NPV

    b. FOR is the opposite conditional probability -- but not the complement -- of the miss rate mirt (aka. false negative rate FDR):

    mirt = p(decision = negative | condition = TRUE)

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

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

  • Dependencies: FOR is a feature of a decision process or diagnostic procedure and a measure of incorrect decisions (negative decisions that are actually FALSE).

    However, due to being a conditional probability, the value of FOR 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_FOR computes FOR as the complement of 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, NPV, PPV, acc, err, fart, mirt, ppod, prev, sens, spec


FOR <- .05 # sets a false omission rate of 5% FOR <- 5/100 # (condition = TRUE) for 5 out of 100 people with (decision = negative) is_prob(FOR) # TRUE
#> [1] TRUE