logp {mederrRank}

Negative Log-Posterior Function of the Bayesian Hierarchical Model for Identifying the Most Harmful Medication Errors

Description

This function computes the negative log-posterior distribution of the Bayesian hierarchical model described in Myers et al (2011). It is a helper function and not meant to be used on its own.

Usage

logp(theta, deltaj, sigma2, i, k, eta, dat)

Arguments

theta

value of the error profile random effect at which the log.posterior distribution is calculated.

deltaj

vector of hospital random effect values.

sigma2

scale parameter (> 0).

i

error profile index for which the calculate of the log.posterior distribution is needed.

k

degrees of freedom (> 0, maybe non-integer). df = Inf is allowed.

eta

skewness parameter (> 0).

dat

an object of class "mederrData".

Details

For further details see Myers et al. (2011).

Value

logp returns a vector of log-posterior values.

Author(s)

Sergio Venturini [e-mail],

Jessica A. Myers [e-mail]

References

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.

See Also

bhm.constr.resamp, bhm.mcmc, bhm.resample.