`R/init_prob.R`

`NPV.Rd`

`NPV`

defines some decision's negative predictive value (NPV):
The conditional probability of the condition being `FALSE`

provided that the decision is negative.

`NPV`

An object of class `numeric`

of length 1.

Understanding or obtaining the negative predictive value `NPV`

:

Definition:

`NPV`

is the conditional probability for the condition being`FALSE`

given a negative decision:`NPV = p(condition = FALSE | decision = negative)`

or the probability of a negative decision being correct.

Perspective:

`NPV`

further classifies the subset of`dec_neg`

individuals by condition (`NPV = cr/dec_neg = cr/(mi + cr)`

).Alternative names: true omission rate

Relationships:

a.

`NPV`

is the complement of the false omission rate`FOR`

:`NPV = 1 - FOR`

b.

`NPV`

is the opposite conditional probability -- but not the complement -- of the specificity`spec`

:`spec = p(decision = negative | condition = FALSE)`

In terms of frequencies,

`NPV`

is the ratio of`cr`

divided by`dec_neg`

(i.e.,`cr + mi`

):`NPV = cr/dec_neg = cr/(cr + mi)`

Dependencies:

`NPV`

is a feature of a decision process or diagnostic procedure and -- similar to the specificity`spec`

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

`NPV`

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

.

Consult Wikipedia for additional information.

`comp_NPV`

computes `NPV`

;
`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`

,
`PPV`

, `acc`

, `err`

,
`fart`

, `mirt`

,
`ppod`

, `prev`

,
`sens`

, `spec`

NPV <- .95 # sets a negative predictive value of 95% NPV <- 95/100 # (condition = FALSE) for 95 out of 100 people with (decision = negative) is_prob(NPV) # TRUE#> [1] TRUE