# comp_ppod: Compute the proportion of positive decisions (ppod) from... In riskyr: Rendering Risk Literacy more Transparent

## Description

`comp_ppod` computes the proportion of positive decisions `ppod` from 3 essential probabilities `prev`, `sens`, and `spec`.

## Usage

 `1` ```comp_ppod(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_ppod` uses probabilities (not frequencies) as inputs and returns a proportion (probability) without rounding.

Definition: `ppod` is proportion (or probability) of positive decisions:

`ppod = dec_pos/N = (hi + fa)/(hi + mi + fa + cr)`

Values range from 0 (only negative decisions) to 1 (only positive decisions).

Importantly, positive decisions `dec_pos` are not necessarily correct decisions `dec_cor`.

## Value

The proportion of positive decisions `ppod` as a probability. A warning is provided for NaN values.

`comp_sens` and `comp_NPV` 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_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_prob_freq`, `comp_prob`, `comp_sens`, `comp_spec`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```# (1) ways to work: comp_ppod(.10, .200, .300) # => ppod = 0.65 comp_ppod(.50, .333, .666) # => ppod = 0.3335 # (2) watch out for vectors: prev <- seq(0, 1, .1) comp_ppod(prev, .8, .5) # => 0.50 0.53 0.56 0.59 0.62 0.65 0.68 0.71 0.74 0.77 0.80 comp_ppod(prev, 0, 1) # => 0 0 0 0 0 0 0 0 0 0 0 # (3) watch out for extreme values: comp_ppod(1, 1, 1) # => 1 comp_ppod(1, 1, 0) # => 1 comp_ppod(1, 0, 1) # => 0 comp_ppod(1, 0, 0) # => 0 comp_ppod(0, 1, 1) # => 0 comp_ppod(0, 1, 0) # => 1 comp_ppod(0, 0, 1) # => 0 comp_ppod(0, 0, 0) # => 1 ```