# spec

##### The specificity of a decision process or diagnostic procedure.

`spec`

defines a decision's specificity value (or correct rejection rate):
The conditional probability of the decision being negative
if the condition is FALSE.

- Keywords
- datasets

##### Usage

`spec`

##### Details

Understanding or obtaining the specificity value `spec`

:

Definition:

`spec`

is the conditional probability for a (correct) negative decision given that the condition is`FALSE`

:`spec = p(decision = negative | condition = FALSE)`

or the probability of correctly detecting false cases (

`condition = FALSE`

).Perspective:

`spec`

further classifies the subset of`cond_false`

individuals by decision (`spec = cr/cond_false`

).Alternative names: true negative rate (

`TNR`

), correct rejection rate,`1 - alpha`

Relationships:

a.

`spec`

is the complement of the false alarm rate`fart`

:`spec = 1 - fart`

b.

`spec`

is the opposite conditional probability -- but not the complement -- of the negative predictive value`NPV`

:`NPV = p(condition = FALSE | decision = negative)`

In terms of frequencies,

`spec`

is the ratio of`cr`

divided by`cond_false`

(i.e.,`fa + cr`

):`spec = cr/cond_false = cr/(fa + cr)`

Dependencies:

`spec`

is a feature of a decision process or diagnostic procedure and a measure of correct decisions (true negatives).However, due to being a conditional probability, the value of

`spec`

is not intrinsic to the decision process, but also depends on the condition's prevalence value`prev`

.

##### Format

An object of class `numeric`

of length 1.

##### References

Consult Wikipedia for additional information.

##### See Also

`comp_spec`

computes `spec`

as the complement of `fart`

;
`prob`

contains current probability information;
`comp_prob`

computes current probability information;
`num`

contains basic numeric parameters;
`init_num`

initializes basic numeric parameters;
`comp_freq`

computes current frequency information;
`is_prob`

verifies probabilities.

Other probabilities: `FDR`

, `FOR`

,
`NPV`

, `PPV`

, `acc`

,
`err`

, `fart`

,
`mirt`

, `ppod`

,
`prev`

, `sens`

##### Examples

```
# NOT RUN {
spec <- .75 # sets a specificity value of 75%
spec <- 75/100 # (decision = negative) for 75 out of 100 people with (condition = FALSE)
is_prob(spec) # TRUE
# }
```

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