# comp_ppod

##### Compute the proportion of positive decisions (ppod) from probabilities.

`comp_ppod`

computes the proportion of positive decisions `ppod`

from 3 essential probabilities
`prev`

, `sens`

, and `spec`

.

##### Usage

`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.

##### See Also

`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`

##### Examples

```
# NOT RUN {
# (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
# }
```

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