NPV defines some decision's negative predictive value (NPV):
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 negative predictive value
NPV is the conditional probability
for the condition being
given a negative decision:
NPV = p(condition = FALSE | decision = negative)
or the probability of a negative decision being correct.
NPV further classifies
the subset of
by condition (
NPV = cr/dec_neg = cr/(mi + cr)).
Alternative names: true omission rate
NPV is the complement of the
false omission rate
NPV = 1 - FOR
NPV is the opposite conditional probability
-- but not the complement --
of the specificity
spec = p(decision = negative | condition = FALSE)
NPV = cr/dec_neg = cr/(cr + mi)
NPV is a feature of a decision process
or diagnostic procedure and
-- similar to the specificity
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
Consult Wikipedia for additional information.
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.
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#>  TRUE