# bits: Bayesian Iterated Screening (ultra-high, high or low... In dongjli/bravo: Bayesian Screening and Variable Selection

## Description

Perform Bayesian iterated screening in Gaussian regression models

## Usage

 1 bits(X, y, lam = 1, w = NULL, pp = FALSE, max.var = nrow(X)) 

## Arguments

 X An n\times 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). pp Booliean: If FALSE (default) the algorithm stops after including max.var many variables. If true, the posterior probability stopping rule is used. max.var The maximum number of variables to be included.

## 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

## References

Wang, R., Dutta, S., Roy, V.(2021) Bayesian iterative screening in ultra-high dimensional settings https://arxiv.org/abs/2107.10175

dongjli/bravo documentation built on Sept. 20, 2021, 3:33 a.m.