# mirt

0th

Percentile

##### The miss rate of a decision process or diagnostic procedure.

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

Keywords
datasets
##### Usage
mirt
##### Details

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.

##### Format

An object of class numeric of length 1.

##### References

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
• FNR
• beta
##### Examples
# NOT RUN {
mirt <- .15     # => sets a miss rate of 15%
mirt <- 15/100  # => (decision = negative) for 15 out of 100 people with (condition = TRUE)
# }

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

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