R/init_prob.R
FDR.RdFDR 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.
FDRAn object of class numeric of length 1.
Understanding or obtaining the false detection fate
or false discovery rate (FDR):
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.
Consult Wikipedia for additional information.
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
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