dlprior: Function to simulate the dirichlet laplace shrinkage prior

In xylimeng/BayesianVariableSelection: Use Dirichlet Laplace Prior to Solve Linear Regression Problem and Do Variable Selection

Description

This function generates random deviates from dirichlet laplace shrinkage prior and can plot the distribution function.

Usage

dlprior(hyper=1/2,p=10000000,plt=TRUE,min=-5,max=5,sigma=1)

Arguments

hyper	important hyperparameter that related to posterior shrinkage scales and prior distribution.
p	number of observations
plt	whether to plot the dirichlet laplace prior. default TRUE means plot the distribution.
min	left point of the plot graph.
max	right point of the plot graph.
sigma	the value equals to normal noises' standard deviations

Value

beta	A p*1 vector. p observations from the distribution

Examples

## Not run: 
theta=dlprior(hyper=1/2,p=10000000,plt=TRUE,min=-5,max=5,sigma=1)
## End(Not run)

xylimeng/BayesianVariableSelection documentation built on May 4, 2019, 3:19 p.m.