
The false detection rate of a decision process or diagnostic procedure.
Source:R/init_prob.R
FDR.Rd
FDR
defines a decision's false detection (or false discovery)
rate (FDR
): The conditional probability of the condition
being FALSE
provided that the decision is positive.
Details
Understanding or obtaining the false detection fate
or false discovery rate (FDR
):
Definition:
FDR
is the conditional probability for the condition beingFALSE
given a positive decision:FDR = p(condition = FALSE | decision = positive)
Perspective:
FDR
further classifies the subset ofdec_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 valuePPV
:FDR = 1 - PPV
b.
FDR
is the opposite conditional probability – but not the complement – of the false alarm ratefart
:fart = p(decision = positive | condition = FALSE)
In terms of frequencies,
FDR
is the ratio offa
divided bydec_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 actuallyFALSE
).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 valueprev
.
References
Consult Wikipedia for additional information.
See also
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:
FOR
,
NPV
,
PPV
,
acc
,
err
,
fart
,
mirt
,
ppod
,
prev
,
sens
,
spec
Examples
FDR <- .45 # sets a false detection rate (FDR) of 45%
FDR <- 45/100 # (condition = FALSE) for 45 out of 100 people with (decision = positive)
is_prob(FDR) # TRUE
#> [1] TRUE