spec: The specificity of a decision process or diagnostic procedure.
Description
spec defines a decision's specificity value (or correct rejection rate):
The conditional probability of the decision being negative
if the condition is FALSE.
Usage
spec
Arguments
Format
An object of class numeric of length 1.
Details
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).
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.
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.
# NOT RUN {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# }