
The positive predictive value of a decision process or diagnostic procedure.
Source:R/init_prob.R
PPV.RdPPV defines some decision's positive predictive value (PPV):
The conditional probability of the condition being TRUE
provided that the decision is positive.
Details
Understanding or obtaining the positive predictive value PPV:
Definition:
PPVis the conditional probability for the condition beingTRUEgiven a positive decision:PPV = p(condition = TRUE | decision = positive)or the probability of a positive decision being correct.
Perspective:
PPVfurther classifies the subset ofdec_posindividuals by condition (PPV = hi/dec_pos = hi/(hi + fa)).Alternative names:
precisionRelationships:
a.
PPVis the complement of the false discovery or false detection rateFDR:PPV = 1 - FDRb.
PPVis the opposite conditional probability – but not the complement – of the sensitivitysens:sens = p(decision = positive | condition = TRUE)In terms of frequencies,
PPVis the ratio ofhidivided bydec_pos(i.e.,hi + fa):PPV = hi/dec_pos = hi/(hi + fa)Dependencies:
PPVis a feature of a decision process or diagnostic procedure and – similar to the sensitivitysens– a measure of correct decisions (positive decisions that are actually TRUE).However, due to being a conditional probability, the value of
PPVis not intrinsic to the decision process, but also depends on the condition's prevalence valueprev.
References
Consult Wikipedia for additional information.
See also
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
Examples
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