init_susiF_obj: Initialize a susiF object using regression coefficients

In stephenslab/susiF.alpha: Sum of Single Functions

Initialize a susiF object using regression coefficients

Description

Initialize a susiF object using regression coefficients

Usage

init_susiF_obj(
  L_max,
  G_prior,
  Y,
  X,
  L_start,
  greedy,
  backfit,
  tol_null_prior = 0.001,
  cov_lev = 0.95,
  ...
)

Arguments

L_max	upper bound on the number of non zero coefficients An L-vector containing the indices of the
G_prior	prior object defined by init_prior function
Y	Matrix of outcomes
X	matrix of covariatess
L_start	number of effect to start with
greedy	logical, if TRUE allow greedy search
backfit	logical, if TRUE allow backfitting
tol_null_prior	threshold to consider prior to be null. If the estimated weight on the point mass at zero is larger than 1-tol_null_prior then set prior weight on point mass to be 1. In the mixture normal this corresponds to removing the effect. In the mixutre per scale prior this corresponds to setting the prior of a given scale to at point mass at 0.
cov_lev	numeric between 0 and 1, corresponding to the expected level of coverage of the CS if not specified, set to 0.95
...	Other arguments.

Details

Initialize a susiF object using regression coefficients

stephenslab/susiF.alpha documentation built on March 1, 2025, 4:28 p.m.