Definition:
FDR is the conditional probability
for the condition being FALSE
given a positive decision:
FDR = p(condition = FALSE | decision = positive)
Perspective:
FDR further classifies
the subset of dec.pos individuals
by condition (FDR = fa/dec.pos = fa/(hi + fa)).
Alternative names:
false discovery rate
Relationships:
a. FDR is the complement of the
positive predictive value PPV:
FDR = 1 - PPV
b. FDR is the opposite conditional probability
-- but not the complement --
of the false alarm rate fart:
fart = p(decision = positive | condition = FALSE)
In terms of frequencies,
FDR is the ratio of
fa divided by dec.pos
(i.e., hi + fa):
FDR = fa/dec.pos = fa/(hi + fa)
Dependencies:
FDR is a feature of a decision process
or diagnostic procedure and
a measure of incorrect decisions (positive decisions
that are actually FALSE).
However, due to being a conditional probability,
the value of FDR is not intrinsic to
the decision process, but also depends on the
condition's prevalence value prev.