`R/init_prob.R`

`sens.Rd`

`sens`

defines a decision's sensitivity (or hit rate) value:
The conditional probability of the decision being positive
if the condition is `TRUE`

.

`sens`

An object of class `numeric`

of length 1.

Understanding or obtaining the sensitivity `sens`

(or hit rate `HR`

):

Definition:

`sens`

is the conditional probability for a (correct) positive decision given that the condition is`TRUE`

:`sens = p(decision = positive | condition = TRUE)`

or the probability of correctly detecting true cases (

`condition = TRUE`

).Perspective:

`sens`

further classifies the subset of`cond_true`

individuals by decision (`sens = hi/cond_true`

).Alternative names: true positive rate (

`TPR`

), hit rate (`HR`

), probability of detection,`power = 1 - beta`

,`recall`

Relationships:

a.

`sens`

is the complement of the miss rate`mirt`

(aka. false negative rate`FNR`

or the rate of Type-II errors):`sens = (1 - miss rate) = (1 - FNR)`

b.

`sens`

is the opposite conditional probability -- but not the complement -- of the positive predictive value`PPV`

:`PPV = p(condition = TRUE | decision = positive)`

In terms of frequencies,

`sens`

is the ratio of`hi`

divided by`cond_true`

(i.e.,`hi + mi`

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

Dependencies:

`sens`

is a feature of a decision process or diagnostic procedure and a measure of correct decisions (true positives).Due to being a conditional probability, the value of

`sens`

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

.

Consult Wikipedia for additional information.

`comp_sens`

computes `sens`

as the complement of `mirt`

;
`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`

, `spec`

sens <- .85 # sets a sensitivity value of 85% sens <- 85/100 # (decision = positive) for 85 out of 100 people with (condition = TRUE) is_prob(sens) # TRUE#> [1] TRUE