bayes.rank | R Documentation |
This function estimates the ranks of the log odds of harm of the various medication error profiles as described in Myers et al. (2011).
bayes.rank(model)
model |
a mederrFit object. |
Using the posterior samples of the \theta_i
, the function estimates the ranks of the log odds of harm of the various error profiles. Optimal Bayesian ranking gives estimates of rank for profile i
as
\hat{R}_i = \sum_{k=1}^{n}{\hat{P}(\theta_k \leq \theta_i | \boldsymbol{y}, \boldsymbol{N})},
where \hat{P}(\theta_k \leq \theta_i | \boldsymbol{y}, \boldsymbol{N})
is the posterior probability that \theta_k \leq \theta_i
.
bayes.rank
returns the numerical vector of Optimal Bayesian ranks for the chosen mederrFit model (see the references for the details).
Sergio Venturini sergio.venturini@unicatt.it,
Jessica A. Myers jmyers6@partners.org
Myers, J. A., Venturini, S., Dominici, F. and Morlock, L. (2011), "Random Effects Models for Identifying the Most Harmful Medication Errors in a Large, Voluntary Reporting Database". Technical Report.
bhm.constr.resamp
,
bhm.mcmc
.
## Not run:
data("simdata", package = "mederrRank")
summary(simdata)
fit <- bhm.mcmc(simdata, nsim = 1000, burnin = 500, scale.factor = 1.1)
ranks <- bayes.rank(fit)
summary(ranks)
## End(Not run)
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