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

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%