htlr_prior: Generate Prior Configuration

In longhaiSK/HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors

Generate Prior Configuration

Description

Configure prior hyper-parameters for HTLR model fitting

Usage

htlr_prior(
  ptype = c("t", "ghs", "neg"),
  df = 1,
  logw = -(1/df) * 10,
  eta = ifelse(df > 1, 3, 0),
  sigmab0 = 2000
)

Arguments

ptype	The prior to be applied to the model. Either "t" (student-t, default), "ghs" (horseshoe), or "neg" (normal-exponential-gamma).
df	The degree freedom (aka alpha) of t/ghs/neg prior for coefficients.
logw	The log scale of priors for coefficients.
eta	The sd of the normal prior for logw. When it is set to 0, logw is fixed. Otherwise, logw is assigned with a normal prior and it will be updated during sampling.
sigmab0	The sd of the normal prior for the intercept.

Details

The output is a configuration list which is to be passed to prior argument of htlr. For naive users, you only need to specify the prior type and degree freedom, then the other hyper-parameters will be chosen automatically. For advanced users, you can supply each prior hyper-parameters by yourself. For suggestion of picking hyper-parameters, see references.

Value

A configuration list containing ptype, alpha, logw, eta, and sigmab0.

References

Longhai Li and Weixin Yao. (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. Journal of Statistical Computation and Simulation 2018, 88:14, 2827-2851.

longhaiSK/HTLR documentation built on Nov. 15, 2024, 10:16 a.m.