`R/init_prob.R`

`fart.Rd`

`fart`

defines a decision's false alarm rate
(or the rate of false positives): The conditional probability
of the decision being positive if the condition is FALSE.

`fart`

An object of class `numeric`

of length 1.

Understanding or obtaining the false alarm rate `fart`

:

Definition:

`fart`

is the conditional probability for an incorrect positive decision given that the condition is`FALSE`

:`fart = p(decision = positive | condition = FALSE)`

or the probability of a false alarm.

Perspective:

`fart`

further classifies the subset of`cond_false`

individuals by decision (`fart = fa/cond_false`

).Alternative names: false positive rate (

`FPR`

), rate of type-I errors (`alpha`

), statistical significance level,`fallout`

Relationships:

a.

`fart`

is the complement of the specificity`spec`

:`fart = 1 - spec`

b.

`fart`

is the opposite conditional probability -- but not the complement -- of the false discovery rate or false detection rate`FDR`

:`FDR = p(condition = FALSE | decision = positive)`

In terms of frequencies,

`fart`

is the ratio of`fa`

divided by`cond_false`

(i.e.,`fa + cr`

):`fart = fa/cond_false = fa/(fa + cr)`

Dependencies:

`fart`

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

`fart`

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

.

Consult Wikipedia for additional information.

`comp_fart`

computes `fart`

as the complement of `spec`

`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`

, `mirt`

,
`ppod`

, `prev`

,
`sens`

, `spec`

fart <- .25 # sets a false alarm rate of 25% fart <- 25/100 # (decision = positive) for 25 out of 100 people with (condition = FALSE) is_prob(fart) # TRUE#> [1] TRUE