ind_fun_pemom {BayesS5}R Documentation

the log-marginal likelhood function based on peMoM priors and inverse gamma prior (0.01,0.01)

Description

a log-marginal likelhood value of a model, based on the peMoM prior on the regression coefficients and inverse gamma prior (0.01,0.01) on the variance.

Usage

ind_fun_pemom(X.ind,y,n,p,tuning)

Arguments

X.ind

the subset of covariates in a model

y

the response variable

n

the sample size

p

the total number of covariates

tuning

a value of the tuning parameter

References

Shin, M., Bhattacharya, A., Johnson V. E. (2018) A Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings, Statistica Sinica.

Rossell, D., Telesca, D., and Johnson, V. E. (2013) High-dimensional Bayesian classifiers using non-local priors, Statistical Models for Data Analysis, 305-313.

See Also

ind_fun_g, ind_fun_pimom


[Package BayesS5 version 1.41 Index]