NPV: The negative predictive value of a decision process or diagnostic procedure.
Description
NPV defines some decision's negative predictive value (NPV):
The conditional probability of the condition being FALSE
provided that the decision is negative.
Usage
NPV
Arguments
Format
An object of class numeric of length 1.
Details
Understanding or obtaining the negative predictive value NPV:
Definition:
NPV is the conditional probability
for the condition being FALSE
given a negative decision:
NPV = p(condition = FALSE | decision = negative)
or the probability of a negative decision being correct.
Perspective:
NPV further classifies
the subset of dec_neg individuals
by condition (NPV = cr/dec_neg = cr/(mi + cr)).
Alternative names:
true omission rate
Relationships:
a. NPV is the complement of the
false omission rate FOR:
NPV = 1 - FOR
b. NPV is the opposite conditional probability
-- but not the complement --
of the specificity spec:
spec = p(decision = negative | condition = FALSE)
In terms of frequencies,
NPV is the ratio of
cr divided by dec_neg
(i.e., cr + mi):
NPV = cr/dec_neg = cr/(cr + mi)
Dependencies:
NPV is a feature of a decision process
or diagnostic procedure and
-- similar to the specificity spec --
a measure of correct decisions (negative decisions
that are actually FALSE).
However, due to being a conditional probability,
the value of NPV is not intrinsic to
the decision process, but also depends on the
condition's prevalence value prev.
comp_NPV computes 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,
FOR,
PPV,
acc,
err,
fart,
mirt,
ppod,
prev,
sens,
spec
NPV <- .95 # sets a negative predictive value of 95%NPV <- 95/100# (condition = FALSE) for 95 out of 100 people with (decision = negative)is_prob(NPV) # TRUE