spec defines a decision's specificity value (or correct rejection rate):
The conditional probability of the decision being negative
if the condition is FALSE.
An object of class
numeric of length 1.
Understanding or obtaining the specificity value
spec is the conditional probability
for a (correct) negative decision given that
the condition is
spec = p(decision = negative | condition = FALSE)
or the probability of correctly detecting false cases
condition = FALSE).
spec further classifies
the subset of
by decision (
spec = cr/cond_false).
true negative rate (
correct rejection rate,
1 - alpha
spec is the complement of the
false alarm rate
spec = 1 - fart
spec is the opposite conditional probability
-- but not the complement --
of the negative predictive value
NPV = p(condition = FALSE | decision = negative)
spec = cr/cond_false = cr/(fa + cr)
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
Consult Wikipedia for additional information.
spec as the complement of
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.
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#>  TRUE