Berger.Normal {ClinicalRobustPriors} | R Documentation |
Compute the distributions (prior, likelihood and posterior) and moments for the Normal/Normal conjugate model and Berger/Normal robust model. The plots are processed in Log-Odds and OR Scale.
Berger.Normal(mu,ym,tau,sigma,n0,n,min.value=NULL,max.value=NULL,OR.xlim=NULL)
mu |
the location parameter in Log-Odds Scale for Normal and Berger's Priors. |
ym |
the location parameter in Log-Odds Scale for Normal Likelihood. |
tau |
the scale parameter in Log-Odds for Normal and Berger's Priors. |
sigma |
the scale parameter in Log-Odds Normal Likelihood. |
n0 |
number of prior observations. |
n |
sample size. |
min.value |
minimum value in Log-Odds scale for the plots. The default min.value is 5. |
max.value |
maximum value in Log-Odds scale for the plots. The default max.value is -5. |
OR.xlim |
maximum value for the OR scale in the plot of the Berger/Normal model. The default OR.xlim is c(0,2). |
Jairo A. Fuquene P. <jairo.a.fuquene@uprrp.edu>
Berger, J. O. (1985), Statistical Decision Theory and Bayesian Analysis, second edn, Springer-Verlag.
Fuquene, J. A., Cook, J. D. & Pericchi, L. R. (2008), A Case for Robust Bayesian priors with Applications to Binary Clinical Trials. UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series. Working Paper 44. 2008. http://www.bepress.com/mdandersonbiostat/paper44.
Spiegelhalter, D. J., Abrams, K. R. & Myles, J. P. (2004), Bayesian Approaches to Clinical Trials and Health-Care Evaluation, Wiley, London.
####################### # Example 1: ####################### Berger.Normal(-1.97,-0.73,0.31,0.15,406,170) ####################### # Example 2 ####################### Berger.Normal(-1.97,-0.73,0.31,0.15,406,170,min.value=-3.5,max.value=0,OR.xlim=c(0,1)) ####################### # Example 3 ####################### Berger.Normal(-1.97,-0.73,0.15,0.31,406,170)