# comp_NPV: Compute a decision's negative predictive value (NPV) from... In riskyr: Rendering Risk Literacy more Transparent

## Description

`comp_NPV` computes the negative predictive value `NPV` from 3 essential probabilities `prev`, `sens`, and `spec`.

## Usage

 `1` ```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`).

## Details

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

## Value

The negative predictive value `NPV` as a probability. A warning is provided for NaN values.

`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`, `comp_acc`, `comp_comp_pair`, `comp_complement`, `comp_complete_prob_set`, `comp_fart`, `comp_mirt`, `comp_ppod`, `comp_prob_freq`, `comp_prob`, `comp_sens`, `comp_spec`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# (1) Ways to work: comp_NPV(.50, .500, .500) # => NPV = 0.5 comp_NPV(.50, .333, .666) # => NPV = 0.4996 # (2) Watch out for vectors: prev <- seq(0, 1, .1) comp_NPV(prev, .5, .5) # => without NaN values comp_NPV(prev, 1, 0) # => with NaN values # (3) Watch out for extreme values: comp_NPV(1, 1, 1) # => NaN, as cr = 0 and mi = 0: 0/0 comp_NPV(1, 1, 0) # => NaN, as cr = 0 and mi = 0: 0/0 comp_NPV(.5, sens = 1, spec = 0) # => NaN, no dec.neg cases: NPV = 0/0 = NaN is_extreme_prob_set(.5, sens = 1, spec = 0) # => verifies extreme cases ```