# FDR

##### The false detection rate of a decision process or diagnostic procedure.

`FDR`

defines a decision's false detection (or false discovery)
rate (`FDR`

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

provided that the decision is positive.

- Keywords
- datasets

##### Usage

`FDR`

##### Details

Understanding or obtaining the false detection fate
or false discovery rate (`FDR`

):

Definition:

`FDR`

is the conditional probability for the condition being`FALSE`

given a positive decision:`FDR = p(condition = FALSE | decision = positive)`

Perspective:

`FDR`

further classifies the subset of`dec_pos`

individuals by condition (`FDR = fa/dec_pos = fa/(hi + fa)`

).Alternative names: false discovery rate

Relationships:

a.

`FDR`

is the complement of the positive predictive value`PPV`

:`FDR = 1 - PPV`

b.

`FDR`

is the opposite conditional probability -- but not the complement -- of the false alarm rate`fart`

:`fart = p(decision = positive | condition = FALSE)`

In terms of frequencies,

`FDR`

is the ratio of`fa`

divided by`dec_pos`

(i.e.,`hi + fa`

):`FDR = fa/dec_pos = fa/(hi + fa)`

Dependencies:

`FDR`

is a feature of a decision process or diagnostic procedure and a measure of incorrect decisions (positive decisions that are actually`FALSE`

).However, due to being a conditional probability, the value of

`FDR`

is not intrinsic to the decision process, but also depends on the condition's prevalence value`prev`

.

##### Format

An object of class `numeric`

of length 1.

##### References

Consult Wikipedia for additional information.

##### See Also

`prob`

contains current probability information;
`comp_prob`

computes current probability information;
`num`

contains basic numeric parameters;
`init_num`

initializes basic numeric parameters;
`freq`

contains current frequency information;
`comp_freq`

computes current frequency information;
`is_prob`

verifies probabilities.

Other probabilities: `FOR`

, `NPV`

,
`PPV`

, `acc`

, `err`

,
`fart`

, `mirt`

,
`ppod`

, `prev`

,
`sens`

, `spec`

##### Examples

```
# NOT RUN {
FDR <- .45 # sets a false detection rate (FDR) of 45%
FDR <- 45/100 # (condition = FALSE) for 45 out of 100 people with (decision = positive)
is_prob(FDR) # TRUE
# }
```

*Documentation reproduced from package riskyr, version 0.2.0, License: GPL-2 | GPL-3*