
The sensitivity (or hit rate) of a decision process or diagnostic procedure.
Source:R/init_prob.R
sens.Rdsens defines a decision's sensitivity (or hit rate) value:
The conditional probability of the decision being positive
if the condition is TRUE.
Details
Understanding or obtaining the sensitivity sens
(or hit rate HR):
Definition:
sensis the conditional probability for a (correct) positive decision given that the condition isTRUE:sens = p(decision = positive | condition = TRUE)or the probability of correctly detecting true cases (
condition = TRUE).Perspective:
sensfurther classifies the subset ofcond_trueindividuals by decision (sens = hi/cond_true).Alternative names: true positive rate (
TPR), hit rate (HR), probability of detection,power = 1 - beta,recallRelationships:
a.
sensis the complement of the miss ratemirt(aka. false negative rateFNRor the rate of Type-II errors):sens = (1 - miss rate) = (1 - FNR)b.
sensis the opposite conditional probability – but not the complement – of the positive predictive valuePPV:PPV = p(condition = TRUE | decision = positive)In terms of frequencies,
sensis the ratio ofhidivided bycond_true(i.e.,hi + mi):sens = hi/cond_true = hi/(hi + mi)Dependencies:
sensis a feature of a decision process or diagnostic procedure and a measure of correct decisions (true positives).Due to being a conditional probability, the value of
sensis not intrinsic to the decision process, but also depends on the condition's prevalence valueprev.
References
Consult Wikipedia for additional information.
See also
comp_sens computes sens as the complement of mirt;
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,
spec
Examples
sens <- .85 # sets a sensitivity value of 85%
sens <- 85/100 # (decision = positive) for 85 out of 100 people with (condition = TRUE)
is_prob(sens) # TRUE
#> [1] TRUE