metropLambda: Metropolis-Hastings algorithm to sample lambda with a Beta...

In SafeBayes: Generalized and Safe-Bayesian Ridge and Lasso Regression

Description

Metropolis-Hastings algorithm to sample lambda with a Beta prior from (de los Campos et al., 2009) for the Bayesian Lasso regression model.

Usage

metropLambda(tau2, lambda, shape1 = 1.2, shape2 = 1.2, max = 200, ncp = 0)

Arguments

tau2	Latent parameter tau-squared to form the Laplace prior on the coefficients of the Lasso from a normal-mixture.
lambda	Initial value for lambda.
shape1	First shape parameter for the Beta distribution.
shape2	Second shape parameter for the Beta distribution.
max	Maximum value of lambda.
ncp	Dummy parameter.

Details

Metropolis-Hastings algorithm to sample lambda with a Beta prior from (de los Campos et al., 2009) for the Bayesian Lasso regression model.

Value

Returns a value for lambda to use in the Gibbs samplers of the functions in the SafeBayes package.

Author(s)

Copied from (de los Campos et al., 2009).

References

de los Campos G., H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K. Weigel and J. Cotes. 2009. Predicting Quantitative Traits with Regression Models for Dense Molecular Markers and Pedigree. Genetics 182: 375-385.

Examples

rm(list=ls())
library(SafeBayes)
tau2 <- 1/4
lambda <- 50

metropLambda(tau2=tau2, lambda=lambda)

Example output

[1] 2.104193

SafeBayes documentation built on May 1, 2019, 9:23 p.m.