FOR defines a decision's false omission rate (
The conditional probability of the condition being
provided that the decision is negative.
An object of class
numeric of length 1.
Understanding or obtaining the false omission rate
FOR is the so-called false omission rate:
The conditional probability for the condition being
given a negative decision:
FOR = p(condition = TRUE | decision = negative)
FOR further classifies
the subset of
by condition (
FOR = mi/dec_neg = mi/(mi + cr)).
Alternative names: none?
FOR is the complement of the
negative predictive value
FOR = 1 - NPV
FOR is the opposite conditional probability
-- but not the complement --
of the miss rate
(aka. false negative rate
mirt = p(decision = negative | condition = TRUE)
NPV = mi/dec_neg = mi/(mi + cr)
FOR is a feature of a decision process
or diagnostic procedure and a measure of incorrect
decisions (negative decisions that are actually
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
Consult Wikipedia for additional information.
FOR as the complement of
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.
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#>  TRUE