spec defines a decision's specificity value (or correct rejection rate):
The conditional probability of the decision being negative
if the condition is FALSE.
specAn object of class numeric of length 1.
Understanding or obtaining the specificity value spec:
Definition:
spec is the conditional probability
for a (correct) negative decision given that
the condition is FALSE:
spec = p(decision = negative | condition = FALSE)
or the probability of correctly detecting false cases
(condition = FALSE).
Perspective:
spec further classifies
the subset of cond_false individuals
by decision (spec = cr/cond_false).
Alternative names:
true negative rate (TNR),
correct rejection rate,
1 - alpha
Relationships:
a. spec is the complement of the
false alarm rate fart:
spec = 1 - fart
b. spec is the opposite conditional probability
-- but not the complement --
of the negative predictive value NPV:
NPV = p(condition = FALSE | decision = negative)
In terms of frequencies,
spec is the ratio of
cr divided by cond_false
(i.e., fa + cr):
spec = cr/cond_false = cr/(fa + cr)
Dependencies:
spec is a feature of a decision process
or diagnostic procedure and a measure of
correct decisions (true negatives).
However, due to being a conditional probability,
the value of spec is not intrinsic to
the decision process, but also depends on the
condition's prevalence value prev.
Consult Wikipedia for additional information.
comp_spec computes spec as the complement of fart;
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,
NPV,
PPV,
acc,
err,
fart,
mirt,
ppod,
prev,
sens
spec <- .75 # sets a specificity value of 75%
spec <- 75/100 # (decision = negative) for 75 out of 100 people with (condition = FALSE)
is_prob(spec) # TRUE
#> [1] TRUE