FOR: The false omission rate (FOR) of a decision process or... In riskyr: Rendering Risk Literacy more Transparent

Description

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

Usage

 `1` ```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 probability inputs.
Other probabilities: `FDR`, `NPV`, `PPV`, `fart`, `mirt`, `ppod`, `prev`, `sens`, `spec`
 ```1 2 3``` ```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 (as FOR is a probability) ```