FOR {riskyr} | R Documentation |
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
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 beingTRUE
given a negative decision:FOR = p(condition = TRUE | decision = negative)
Perspective:
FOR
further classifies the subset ofdec_neg
individuals by condition (FOR = mi/dec_neg = mi/(mi + cr)
).Alternative names: none?
Relationships:
a.
FOR
is the complement of the negative predictive valueNPV
:FOR = 1 - NPV
b.
FOR
is the opposite conditional probability – but not the complement – of the miss ratemirt
(aka. false negative rateFDR
):mirt = p(decision = negative | condition = TRUE)
In terms of frequencies,
FOR
is the ratio ofmi
divided bydec_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 actuallyFALSE
).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 valueprev
.
References
Consult Wikipedia for additional information.
See Also
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
Examples
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