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tr.beta.binomial

Truncated Beta-Binomial Prior Distribution for Models


Description

Creates an object representing the prior distribution on models for BAS using a truncated Beta-Binomial Distribution on the Model Size

Usage

tr.beta.binomial(alpha = 1, beta = 1, trunc)

Arguments

alpha

parameter in the beta prior distribution

beta

parameter in the beta prior distribution

trunc

parameter that determines truncation in the distribution i.e. P(M; alpha, beta, trunc) = 0 if M > trunc.

Details

The beta-binomial distribution on model size is obtained by assigning each variable inclusion indicator independent Bernoulli distributions with probability w, and then giving w a beta(alpha,beta) distribution. Marginalizing over w leads to the number of included predictors having a beta-binomial distribution. The default hyperparameters lead to a uniform distribution over model size. The Truncated version assigns zero probability to all models of size > trunc.

Value

returns an object of class "prior", with the family and hyperparameters.

Author(s)

Merlise Clyde

See Also

Other priors modelpriors: Bernoulli.heredity(), Bernoulli(), beta.binomial(), tr.poisson(), tr.power.prior(), uniform()

Examples

tr.beta.binomial(1, 10, 5)
library(MASS)
data(UScrime)
UScrime[, -2] <- log(UScrime[, -2])
crime.bic <- bas.lm(y ~ .,
  data = UScrime, n.models = 2^15, prior = "BIC",
  modelprior = tr.beta.binomial(1, 1, 8),
  initprobs = "eplogp"
)

BAS

Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

v1.5.5
GPL (>= 3)
Authors
Merlise Clyde [aut, cre, cph] (ORCID=0000-0002-3595-1872), Michael Littman [ctb], Quanli Wang [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb], Don van de Bergh [ctb]
Initial release
2020-1-24

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