`spec`

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

`spec`

An object of class `numeric`

of length 1.

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`

.

Consult Wikipedia for additional information.

`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`

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#> [1] TRUE