# comp_FDR

##### Compute a decision's false detection rate (FDR) from probabilities.

`comp_FDR`

computes the false detection rate `FDR`

from 3 essential probabilities
`prev`

, `sens`

, and `spec`

.

##### Usage

`comp_FDR(prev, sens, spec)`

##### Arguments

- prev
The condition's prevalence

`prev`

(i.e., the probability of condition being`TRUE`

).- sens
The decision's sensitivity

`sens`

(i.e., the conditional probability of a positive decision provided that the condition is`TRUE`

).- spec
The decision's specificity value

`spec`

(i.e., the conditional probability of a negative decision provided that the condition is`FALSE`

).

##### Details

`comp_FDR`

uses probabilities (not frequencies)
and does not round results.

##### Value

The false detection rate `FDR`

as a probability.
A warning is provided for NaN values.

##### See Also

`comp_sens`

and `comp_PPV`

compute related probabilities;
`is_extreme_prob_set`

verifies extreme cases;
`comp_complement`

computes a probability's complement;
`is_complement`

verifies probability complements;
`comp_prob`

computes current probability information;
`prob`

contains current probability information;
`is_prob`

verifies probabilities.

Other functions computing probabilities: `comp_FOR`

,
`comp_NPV`

, `comp_PPV`

,
`comp_accu_freq`

,
`comp_accu_prob`

, `comp_acc`

,
`comp_comp_pair`

,
`comp_complement`

,
`comp_complete_prob_set`

,
`comp_err`

, `comp_fart`

,
`comp_mirt`

, `comp_ppod`

,
`comp_prob_freq`

, `comp_prob`

,
`comp_sens`

, `comp_spec`

##### Examples

```
# NOT RUN {
# (1) Ways to work:
comp_FDR(.50, .500, .500) # => FDR = 0.5 = (1 - PPV)
comp_FDR(.50, .333, .666) # => FDR = 0.5007 = (1 - PPV)
# }
```

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