DS.Finite.Bayes {BayesGOF} | R Documentation |
A function that generates the finite Bayes prior and posterior distribution, along with the Bayesian credible interval for the posterior mean.
DS.Finite.Bayes(DS.GF.obj, y.0, n.0 = NULL, cred.interval = 0.9, iters = 25)
DS.GF.obj |
Object from |
y.0 |
For Binomial family, number of success y_i for new study. In the Poisson family, it is the number of counts. Represents the study mean for the Normal family. |
n.0 |
For the Binomial family, the total number of trials for the new study. In the Normal family, |
cred.interval |
The desired probability for the credible interval of the posterior mean; the default is 0.90 ( |
iters |
Integer value of total number of iterations. |
prior.fit |
Fitted values for the estimated parametric, DS, and finite Bayes prior distributions. |
post.fit |
Dataframe with θ, π_G(θ | y_0), and π_{LP}(θ | y_0). |
interval |
The |
post.vec |
Vector containing the PEB posterior mean ( |
Doug Fletcher, Subhadeep Mukhopadhyay
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Efron, B., 2018. "Bayes, Oracle Bayes, and Empirical Bayes," Technical Report.
## Not run: ### Finite Bayes: Rat with theta_71 (y_71 = 4, n_71 = 14) data(rat) rat.start <- gMLE.bb(rat$y, rat$n)$estimate rat.ds <- DS.prior(rat, max.m = 4, rat.start. family = "Binomial") rat.FB <- DS.FiniteBayes(rat.ds, y.0 = 4, n.0 = 14) plot(rat.FB) ## End(Not run)