R/init_prob.R
NPV.RdNPV defines some decision's negative predictive value (NPV):
The conditional probability of the condition being FALSE
provided that the decision is negative.
NPVAn object of class numeric of length 1.
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.
Consult Wikipedia for additional information.
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
#> [1] TRUE