Description Usage Format Details References See Also Examples

`FOR`

defines a decision's false omission rate (`FOR`

):
The conditional probability of the condition being `TRUE`

provided that the decision is negative.

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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.

`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`

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