Grouping: Prepare variables for group-based inference.

In jmleach-bst/ssnet: Fit Spike-and-Slab Elastic Net GLM With Spatial Priors on Parameters

Prepare variables for group-based inference.

Description

This function uses the user-defined groups to properly prepare the variables or group-specific estimation/inference.

Usage

Grouping(all.var, group)

Arguments

all.var

A vector containing the names of the variables in the design matrix.

group

A numeric vector, or an integer, or a list indicating the groups of predictors. If group = NULL, all the predictors form a single group. If group = K, the predictors are evenly divided into groups each with K predictors. If group is a numeric vector, it defines groups as follows: Group 1: (group[1]+1):group[2], Group 2: (group[2]+1):group[3], Group 3: (group[3]+1):group[4], ... If group is a list of variable names, group[[k]] includes variables in the k-th group. The mixture double-exponential prior is only used for grouped predictors. For ungrouped predictors, the prior is double-exponential with scale ss[2] and mean 0.

Value

A list.

Note

This function is a modified version of the function Grouping from the R package BhGLM.

jmleach-bst/ssnet documentation built on March 4, 2024, 5:04 p.m.