hs: Horseshoe regression Gibbs-sampler

In horserule: Flexible Non-Linear Regression with the HorseRule Algorithm

Description

Generates posterior samples using the horseshoe prior. The Gibbs sampling method from \insertRefsimplehorserule is used to generate the posterior samples.

Usage

hs(X, y, niter = 1000, hsplus = F, prior = NULL, thin = 1,
  restricted = 0)

Arguments

X	A matrix containing the predictor variables to be used.
y	The vector of numeric responses.
niter	Number of posterior samples.
hsplus	If "hsplus=T" the horseshoe+ extension will be used.
prior	Prior for the individual predictors. If all 1 a standard horseshoe model is fit.
thin	If > 1 thinning is performed to reduce autocorrelation.
restricted	Threshold for restricted Gibbs sampling. In each iteration only coefficients with scale > restricted are updated. Set restricted = 0 for unrestricted Gibbs sampling.

Value

A list containing the posterior samples of the following parameters:

beta	Matrix containing the posterior samples for the regression coefficients.
sigma	Vector contraining the Posterior samples of the error variance.
tau	Vector contraining the Posterior samples of the overall shrinkage.
lambda	Matrix containing the posterior samples for the individual shrinkage parameter.

Examples

x = matrix(rnorm(1000), ncol=10)
y = apply(x,1,function(x)sum(x[1:5])+rnorm(1))
hsmod = hs(X=x, y=y, niter=100)

Example output

horserule documentation built on May 2, 2019, 10:04 a.m.