R/init_prob.R
PPV.RdPPV defines some decision's positive predictive value (PPV):
The conditional probability of the condition being TRUE
provided that the decision is positive.
PPVAn object of class numeric of length 1.
Understanding or obtaining the positive predictive value PPV:
Definition:
PPV is the conditional probability
for the condition being TRUE
given a positive decision:
PPV = p(condition = TRUE | decision = positive)
or the probability of a positive decision being correct.
Perspective:
PPV further classifies
the subset of dec_pos individuals
by condition (PPV = hi/dec_pos = hi/(hi + fa)).
Alternative names:
precision
Relationships:
a. PPV is the complement of the
false discovery or false detection rate FDR:
PPV = 1 - FDR
b. PPV is the opposite conditional probability
-- but not the complement --
of the sensitivity sens:
sens = p(decision = positive | condition = TRUE)
In terms of frequencies,
PPV is the ratio of
hi divided by dec_pos
(i.e., hi + fa):
PPV = hi/dec_pos = hi/(hi + fa)
Dependencies:
PPV is a feature of a decision process
or diagnostic procedure and
-- similar to the sensitivity sens --
a measure of correct decisions (positive decisions
that are actually TRUE).
However, due to being a conditional probability,
the value of PPV is not intrinsic to
the decision process, but also depends on the
condition's prevalence value prev.
Consult Wikipedia for additional information.
comp_PPV computes PPV;
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,
acc,
err,
fart,
mirt,
ppod,
prev,
sens,
spec
PPV <- .55 # sets a positive predictive value of 55%
PPV <- 55/100 # (condition = TRUE) for 55 out of 100 people with (decision = positive)
is_prob(PPV) # TRUE
#> [1] TRUE