
The positive predictive value of a decision process or diagnostic procedure.
Source: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.
Details
Understanding or obtaining the positive predictive value PPV
:
Definition:
PPV
is the conditional probability for the condition beingTRUE
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 ofdec_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 rateFDR
:PPV = 1 - FDR
b.
PPV
is the opposite conditional probability – but not the complement – of the sensitivitysens
:sens = p(decision = positive | condition = TRUE)
In terms of frequencies,
PPV
is the ratio ofhi
divided bydec_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 sensitivitysens
– 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 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