Calculate EBGM scores
ebgm calculates the Empirical Bayes Geometric Mean (EBGM),
which is ‘the geometric mean of the empirical Bayes posterior
distribution of the “true” RR’ (DuMouchel 1999, see Eq.11). The
EBGM is essentially a version of the relative reporting ratio
(RR) that uses Bayesian shrinkage.
ebgm(theta_hat, N, E, qn, digits = 2)
theta_hat |
A numeric vector of hyperparameter estimates (likely from
|
N |
A whole number vector of actual counts from
|
E |
A numeric vector of expected counts from |
qn |
A numeric vector of posterior probabilities that λ came
from the first component of the mixture, given N = n (i.e., the
mixture fraction). See function |
digits |
A scalar whole number that determines the number of decimal places used when rounding the results. |
The hyperparameter estimates (theta_hat) are:
α_1, β_1: Parameter estimates of the first component of the prior distribution
α_2, β_2: Parameter estimates of the second component
P: Mixture fraction estimate of the prior distribution
A numeric vector of EBGM scores.
DuMouchel W (1999). "Bayesian Data Mining in Large Frequency Tables, With an Application to the FDA Spontaneous Reporting System." The American Statistician, 53(3), 177-190.
autoHyper, exploreHypers,
negLLsquash, negLL,
negLLzero, and negLLzeroSquash for
hyperparameter estimation.
processRaw for finding counts.
Qn for finding mixture fractions.
Other posterior distribution functions:
Qn(),
quantBisect()
theta_init <- data.frame( alpha1 = c(0.2, 0.1), beta1 = c(0.1, 0.1), alpha2 = c(2, 10), beta2 = c(4, 10), p = c(1/3, 0.2) ) data(caers) proc <- processRaw(caers) squashed <- squashData(proc, bin_size = 100, keep_pts = 100) squashed <- squashData(squashed, count = 2, bin_size = 10, keep_pts = 20) suppressWarnings( theta_hat <- autoHyper(data = squashed, theta_init = theta_init)$estimates ) qn <- Qn(theta_hat, N = proc$N, E = proc$E) proc$EBGM <- ebgm(theta_hat, N = proc$N, E = proc$E, qn = qn) head(proc)
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