# spec: The specificity of a decision process or diagnostic... In riskyr: Rendering Risk Literacy more Transparent

## Description

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

## Usage

 `1` ```spec ```

## Format

An object of class `numeric` of length 1.

## 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`.

## References

`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`

Other essential parameters: `cr`, `fa`, `hi`, `mi`, `prev`, `sens`

## Examples

 ```1 2 3``` ```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 ```

riskyr documentation built on Jan. 3, 2019, 1:06 a.m.