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

FOR

Format

An object of class numeric of length 1.

Details

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.

References

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

Examples

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