FOR: The false omission rate (FOR) of a decision process or diagnostic procedure.
Description
FOR defines a decision's false omission rate (FOR):
The conditional probability of the condition being TRUE
provided that the decision is negative.
Usage
FOR
Arguments
Format
An object of class numeric of length 1.
Details
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.
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.