prev defines a condition's prevalence value
(or baseline probability):
The probability of the condition being TRUE.
Details
Understanding or obtaining the prevalence value prev:
Definition:
previs the (non-conditional) probability:prev = p(condition = TRUE)or the base rate (or baseline probability) of the condition's occurrence or truth.
In terms of frequencies,
previs the ratio ofcond_true(i.e.,hi + mi) divided byN(i.e.,hi + mi+fa + cr):prev = cond_true/N = (hi + mi)/(hi + mi + fa + cr)Perspective:
prevclassifies a population ofNindividuals by condition (prev = cond_true/N).previs the "by condition" counterpart toppod(when adopting a "by decision" perspective) and toacc(when adopting a "by accuracy" perspective).Alternative names: base rate of condition, proportion affected, rate of condition
= TRUEcases.previs often distinguished from the incidence rate (i.e., the rate of new cases within a certain time period).Dependencies:
previs a feature of the population and of the condition, but independent of the decision process or diagnostic procedure.While the value of
prevdoes not depend on features of the decision process or diagnostic procedure,prevmust be taken into account when computing the conditional probabilitiessens,mirt,spec,fart,PPV, andNPV(as they depend onprev).
References
Consult Wikipedia for additional information.
See also
prob contains current probability information;
num contains basic numeric variables;
init_num initializes basic numeric variables;
comp_prob computes derived probabilities;
comp_freq computes natural frequencies from probabilities;
is_prob verifies probabilities.
Other probabilities:
FDR,
FOR,
NPV,
PPV,
acc,
err,
fart,
mirt,
ppod,
sens,
spec
Examples
prev <- .10 # sets a prevalence value of 10%
prev <- 10/100 # (condition = TRUE) for 10 out of 100 individuals
is_prob(prev) # TRUE
#> [1] TRUE