`R/init_prob.R`

`PPV.Rd`

`PPV`

defines some decision's positive predictive value (PPV):
The conditional probability of the condition being `TRUE`

provided that the decision is positive.

`PPV`

An object of class `numeric`

of length 1.

Understanding or obtaining the positive predictive value `PPV`

:

Definition:

`PPV`

is the conditional probability for the condition being`TRUE`

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

or the probability of a positive decision being correct.

Perspective:

`PPV`

further classifies the subset of`dec_pos`

individuals by condition (`PPV = hi/dec_pos = hi/(hi + fa)`

).Alternative names:

`precision`

Relationships:

a.

`PPV`

is the complement of the false discovery or false detection rate`FDR`

:`PPV = 1 - FDR`

b.

`PPV`

is the opposite conditional probability -- but not the complement -- of the sensitivity`sens`

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

In terms of frequencies,

`PPV`

is the ratio of`hi`

divided by`dec_pos`

(i.e.,`hi + fa`

):`PPV = hi/dec_pos = hi/(hi + fa)`

Dependencies:

`PPV`

is a feature of a decision process or diagnostic procedure and -- similar to the sensitivity`sens`

-- a measure of correct decisions (positive decisions that are actually TRUE).However, due to being a conditional probability, the value of

`PPV`

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

.

Consult Wikipedia for additional information.

`comp_PPV`

computes `PPV`

;
`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`

, `acc`

, `err`

,
`fart`

, `mirt`

,
`ppod`

, `prev`

,
`sens`

, `spec`

PPV <- .55 # sets a positive predictive value of 55% PPV <- 55/100 # (condition = TRUE) for 55 out of 100 people with (decision = positive) is_prob(PPV) # TRUE#> [1] TRUE