horseshoe: Horseshoe method for graphical structure inference

In CARlasso: Conditional Autoregressive LASSO

Description

Horseshoe method for graphical structure inference

Usage

horseshoe(obj, Bbar = NULL, A = NULL, nu = 3, V = NULL, thr = 0.5)

Arguments

obj	The carlasso_out object from CARlasso
Bbar	Prior mean of regression coefficients, default all 0s
A	Prior precision of regression coefficients, default 1e-8
nu	Prior degree of freedom of the Wishart on precision matrix
V	prior covariance matrix of the Wishart on precision matrix
thr	threshold for horseshoe inference, default 0.5

Details

This method fits a linear regression with less informative prior on any parameters and compare the posterior mean with the LASSO result. If LASSO is comparably less than result without sparsity prior, we argue that the edge should be absent

Value

A carlasso_out object with learned binary adjacency matrix and multi-response linear regression MCMC out put

Examples

set.seed(42)
dt <- simu_AR1()
car_res <- CARlasso(y1+y2+y3+y4+y5~x1+x2+x3+x4+x5, data = dt, adaptive = TRUE)
car_res <- horseshoe(car_res)
plot(car_res)

CARlasso documentation built on Aug. 12, 2021, 1:05 a.m.