Cauchy.Binomial {ClinicalRobustPriors} | R Documentation |
Compute the distributions (prior, likelihood, posterior predictive and posterior) and moments for the Beta/Binomial conjugate model and Cauchy/Binomial robust model. The plots are processed in Log-Odds and Theta Scale.
Cauchy.Binomial(n,x,a,b,m,min.value=NULL,max.value=NULL,iter=NULL)
n |
sample size or number of observed patients. |
x |
number of positive responses in n trials. |
a |
the usual parameter of Beta prior and the number of positive responses in the prior information. |
b |
the usual parameter of Beta prior and the number of negative responses in the prior information. |
m |
number of additional patients for predictions |
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. |
iter |
number of iterations in rejection sampling for the moments for the Cauchy/Binomial model. The default iter is 10000. |
Jairo A. Fuquene P. <jairo.a.fuquene@uprrp.edu>
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: sample and prior are in conflict ############################################## Cauchy.Binomial(20,16,3,12,40) ############################################## # Example 2: sample and prior are consistent ############################################## Cauchy.Binomial(20,16,12,3,50,min.value=-5,max.value=5,iter=5000)