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Generate Prior Configuration

Source: R/util.r
htlr_prior.Rd

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.

Value

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

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.

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.