`comp_NPV`

computes the negative predictive value `NPV`

from 3 essential probabilities
`prev`

, `sens`

, and `spec`

.

comp_NPV(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 negative predictive value `NPV`

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

## Details

`comp_NPV`

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

## See also

`comp_spec`

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_FDR`

,
`comp_FOR`

, `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_NPV(.50, .500, .500) # => NPV = 0.5

#> [1] 0.5

comp_NPV(.50, .333, .666) # => NPV = 0.4996

#> [1] 0.4996249

#> [1] 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

comp_NPV(prev, 1, 0) # => with NaN values

#> Warning: NPV is NaN.

#> [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

# (3) Watch out for extreme values:
comp_NPV(1, 1, 1) # => NaN, as cr = 0 and mi = 0: 0/0

#> Warning: NPV is NaN.

#> [1] NaN

comp_NPV(1, 1, 0) # => NaN, as cr = 0 and mi = 0: 0/0

#> Warning: NPV is NaN.

#> [1] NaN

comp_NPV(.5, sens = 1, spec = 0) # => NaN, no dec_neg cases: NPV = 0/0 = NaN

#> Warning: NPV is NaN.

#> [1] NaN

#> Warning: Extreme case (sens = 1 & spec = 0):
#> 0 mi (FN) and 0 cr (TN) cases; 0 dec_neg cases; NPV = NaN.

#> [1] TRUE