`ppod`

defines the proportion (baseline probability or rate) of
a decision being `positive`

(but not necessarily accurate/correct).

`ppod`

An object of class `numeric`

of length 1.

Understanding or obtaining the proportion of positive decisions `ppod`

:

Definition:

`ppod`

is the (non-conditional) probability:`ppod = p(decision = positive)`

or the base rate (or baseline probability) of a decision being positive (but not necessarily accurate/correct).

Perspective:

`ppod`

classifies a population of`N`

individuals by decision (`ppod = dec_pos/N`

).`ppod`

is the "by decision" counterpart to`prev`

(which adopts a "by condition" perspective).Alternative names: base rate of positive decisions (

`PR`

), proportion predicted or diagnosed, rate of decision`= positive`

casesIn terms of frequencies,

`ppod`

is the ratio of`dec_pos`

(i.e.,`hi + fa`

) divided by`N`

(i.e.,`hi + mi`

+`fa + cr`

):`ppod = dec_pos/N = (hi + fa)/(hi + mi + fa + cr)`

Dependencies:

`ppod`

is a feature of the decision process or diagnostic procedure.However, the conditional probabilities

`sens`

,`mirt`

,`spec`

,`fart`

,`PPV`

, and`NPV`

also depend on the condition's prevalence`prev`

.

Consult Wikipedia for additional information.

`prob`

contains current probability information;
`comp_prob`

computes current probability information;
`num`

contains basic numeric parameters;
`init_num`

initializes basic numeric parameters;
`freq`

contains current frequency information;
`comp_freq`

computes current frequency information;
`is_prob`

verifies probabilities.

Other probabilities: `FDR`

, `FOR`

,
`NPV`

, `PPV`

, `acc`

,
`err`

, `fart`

,
`mirt`

, `prev`

,
`sens`

, `spec`

ppod <- .50 # sets a rate of positive decisions of 50% ppod <- 50/100 # (decision = TRUE) for 50 out of 100 individuals is_prob(ppod) # TRUE#> [1] TRUE