`mirt`

defines a decision's miss rate value:
The conditional probability of the decision being negative
if the condition is `TRUE`

.

`mirt`

An object of class `numeric`

of length 1.

Understanding or obtaining the miss rate `mirt`

:

Definition:

`sens`

is the conditional probability for an incorrect negative decision given that the condition is`TRUE`

:`mirt = p(decision = negative | condition = TRUE)`

or the probability of failing to detect true cases (

`condition = TRUE`

).Perspective:

`mirt`

further classifies the subset of`cond_true`

individuals by decision (`mirt = mi/cond_true`

).Alternative names: false negative rate (

`FNR`

), rate of type-II errors (`beta`

)Relationships:

a.

`mirt`

is the complement of the sensitivity`sens`

(aka. hit rate`HR`

):`mirt = (1 - sens) = (1 - HR)`

b.

`mirt`

is the _opposite_ conditional probability -- but not the complement -- of the false omission rate`FOR`

:`FOR = p(condition = TRUE | decision = negative)`

In terms of frequencies,

`mirt`

is the ratio of`mi`

divided by`cond_true`

(i.e.,`hi + mi`

):`mirt = mi/cond_true = mi/(hi + mi)`

Dependencies:

`mirt`

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

`mirt`

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

.

Consult Wikipedia for additional information.

`comp_mirt`

computes `mirt`

as the complement of `sens`

;
`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`

,
`ppod`

, `prev`

,
`sens`

, `spec`

mirt <- .15 # => sets a miss rate of 15% mirt <- 15/100 # => (decision = negative) for 15 out of 100 people with (condition = TRUE)