# spec

0th

Percentile

##### 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

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

• spec
• TNR
##### 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

### Community examples

Looks like there are no examples yet.