bis: Bayesian Iterated Screening (ultra-high, high or low...

In Run-Wang/bsvs: Bayesian Screening and Variable Selection

Description

Perform Bayesian iterated screening in Gaussian regression models

Usage

bis(X, y, lam = nrow(X)/ncol(X)^2, w = sqrt(nrow(X))/ncol(X),
  criteria = "PP")

Arguments

X	An n x p matrix. Sparse matrices are supported and every care is taken not to make copies of this (typically) giant matrix. No need to center or scale.
y	The response vector of length n.
lam	The slab precision parameter. Default: n/p^2.
w	The prior inclusion probability of each variable. Default: sqrt(n)/p as suggested by the theory of Wang et al. (2019).
criteria	The stopping criteria. Could be "PP" for posterior probability stopping rule, or "eBIC" for extended BIC stopping rule, or "both" (default). Note that for "eBIC" the value of w is not used.

Value

A list with components

model.pp	An integer vector of screened model under posterior probability stopping rule. This will be null if only "eBIC" stopping criterion was used.
mdoel.ebic	An integer vector of screened model under eBIC criterion. This will be NULL if only "PP" stopping criterion was used.
postprobs	The sequence of posterior probabilities until the last included variable. This will be null if only "eBIC" stopping criterion was used. Here the last included variable is the last one included by either "PP" or "eBIC" if criteria="both" was selected
ebics	The sequence of eBIC values until the last included variable. This will be null if only "PP" stopping criterion was used. Here the last included variable is the last one included by either "PP" or "eBIC" if criteria="both" was selected

Run-Wang/bsvs documentation built on Aug. 18, 2021, 9:42 p.m.