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

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tr.beta.binomial(alpha=1.0, beta=1.0, 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 distribution on the number of included predictos 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 hyerparameters.

Author(s)

Merlise Clyde

See Also

bas.lm, Bernoulli,uniform

Examples

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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")

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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