Configure prior hyper-parameters for HTLR model fitting
htlr_prior(
ptype = c("t", "ghs", "neg"),
df = 1,
logw = -(1/df) * 10,
eta = ifelse(df > 1, 3, 0),
sigmab0 = 2000
)A configuration list containing ptype, alpha, logw, eta, and sigmab0.
The prior to be applied to the model. Either "t" (student-t, default), "ghs" (horseshoe), or "neg" (normal-exponential-gamma).
The degree freedom (aka alpha) of t/ghs/neg prior for coefficients.
The log scale of priors for coefficients.
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.
The sd of the normal prior for the intercept.
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.
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.