PPV: The positive predictive value of a decision process or diagnostic procedure.
Description
PPV defines some decision's positive predictive value (PPV):
The conditional probability of the condition being TRUE
provided that the decision is positive.
Usage
PPV
Arguments
Format
An object of class numeric of length 1.
Details
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.
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