R/init_prob.R
FOR.RdFOR defines a decision's false omission rate (FOR):
The conditional probability of the condition being TRUE
provided that the decision is negative.
FORAn object of class numeric of length 1.
Understanding or obtaining the false omission rate FOR:
Definition:
FOR is the so-called false omission rate:
The conditional probability for the condition being TRUE
given a negative decision:
FOR = p(condition = TRUE | decision = negative)
Perspective:
FOR further classifies
the subset of dec_neg individuals
by condition (FOR = mi/dec_neg = mi/(mi + cr)).
Alternative names: none?
Relationships:
a. FOR is the complement of the
negative predictive value NPV:
FOR = 1 - NPV
b. FOR is the opposite conditional probability
-- but not the complement --
of the miss rate mirt
(aka. false negative rate FDR):
mirt = p(decision = negative | condition = TRUE)
In terms of frequencies,
FOR is the ratio of
mi divided by dec_neg
(i.e., mi + cr):
NPV = mi/dec_neg = mi/(mi + cr)
Dependencies:
FOR is a feature of a decision process
or diagnostic procedure and a measure of incorrect
decisions (negative decisions that are actually FALSE).
However, due to being a conditional probability,
the value of FOR is not intrinsic to
the decision process, but also depends on the
condition's prevalence value prev.
Consult Wikipedia for additional information.
comp_FOR computes FOR as the complement of NPV;
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,
NPV,
PPV,
acc,
err,
fart,
mirt,
ppod,
prev,
sens,
spec
FOR <- .05 # sets a false omission rate of 5%
FOR <- 5/100 # (condition = TRUE) for 5 out of 100 people with (decision = negative)
is_prob(FOR) # TRUE
#> [1] TRUE