Abstract
In this chapter, we present the two case studies that are used as running examples throughout the book.
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Notes
- 1.
It should be noted that the estimation of the “effective number of parameters” \(p_D\) is controversial. The definition reported in [12] and in [3], which is also the one adopted in BUGS, should be preferred instead of the one reported by R2jags [13]. This statistic is calculated by R2jags as:
$$ p_D=\text {Var}[\bar{D}_\mathrm{{ {model}}}]/2 $$while both [12] and in [3] report that the preferred definition is:
$$ p_D=\bar{D}_\mathrm{{ {model}}}-D(\widehat{\theta }) $$where \(\bar{D}_\mathrm{{ {model}}}\) is the posterior deviance of the model and \(D(\widehat{\theta })\) is the deviance in correspondence of the estimated posterior mean of the vector of parameters \(\theta \). It should be noted that the definition of \(p_D\) has a direct impact on the deviance information criteria (DIC), which is an index commonly used for model comparisons, defined as \(\text {DIC}=\bar{D}_\mathrm{{ {model}}}+p_D=D(\widehat{\theta })+2p_D\).
- 2.
When using OpenBUGS and R2OpenBUGS, the object can be attached to the R workspace using the command attach.bugs(object) or attach.jags(object), respectively.
- 3.
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Baio, G., Berardi, A., Heath, A. (2017). Case Studies. In: Bayesian Cost-Effectiveness Analysis with the R package BCEA. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-55718-2_2
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