`comp_FDR`

computes the false detection rate `FDR`

from 3 essential probabilities
`prev`

, `sens`

, and `spec`

.

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` ). |

## Value

The false detection rate `FDR`

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

## Details

`comp_FDR`

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

## 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

# (1) Ways to work:
comp_FDR(.50, .500, .500) # => FDR = 0.5 = (1 - PPV)

#> [1] 0.5

comp_FDR(.50, .333, .666) # => FDR = 0.5007 = (1 - PPV)

#> [1] 0.5007496