bma: Compute posterior model probabilities

In bmabasket: Bayesian Model Averaging for Basket Trials

Compute posterior model probabilities

Description

Given data and hyperparameters, computes posterior model probabilities

Usage

bma(pi0, y, n, P = NULL, mu0 = 0.5, phi0 = 1, priorModelProbs = NULL, pmp0 = 1)

Arguments

pi0	scalar or vector whose elements are between 0 and 1 giving threshold for the hypothesis test. If a scalar is provided, assumes same threshold for each basket
y	vector of responses
n	vector of sample sizes
P	integer giving maximum number of distinct parameters; default is all possible models
mu0	prior mean for beta prior
phi0	prior dispersion for beta prior
priorModelProbs	(optional) vector giving prior for models. Default is proportional to exp(pmp0 * D), where D is the number of distinct parameters in the model
pmp0	nonnegative scalar. Value of 0 corresponds to uniform prior across model space. Ignored if priorModelProbs is specified

Value

a list with the following structure:

bmaProbs

model-averaged probabilities that each basket is larger than pi0

bmaMeans

model-averaged posterior mean for each basket

Examples

## Simulate data with 3 baskets
probs <- c(0.5, 0.25, 0.25)
n <- rep(100, length(probs))
y <- rbinom(length(probs), size = n, prob = probs)
bma(0.5, y, n)

bmabasket documentation built on March 18, 2022, 7:05 p.m.