DS.posterior.reduce: Posterior Expectation and Modes of DS object

In BayesGOF: Bayesian Modeling via Frequentist Goodness-of-Fit

Description

A function that determines the posterior expectations E(θ_0 | y_0) and posterior modes for a set of observed data.

Usage

DS.posterior.reduce(DS.GF.obj, exposure)

Arguments

DS.GF.obj	Object resulting from running DS.prior function on a data set.
exposure	In the case of the Poisson family with exposure, represents the exposure values for the count data.

Value

Returns k \times 4 matrix with the columns indicating PEB mean, DS mean, PEB mode, and DS modes for k observations in the data set.

Author(s)

Doug Fletcher

References

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.

Examples

data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start, family = "Binomial")
DS.posterior.reduce(rat.ds)

BayesGOF documentation built on May 2, 2019, 8:57 a.m.