dlanalysis: Function to analyse dirichlet laplace posterior by using...

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

Description

This is a function that analyse the MCMC sampling result by computing the posterior mean, median and credible intervals.

Usage

dlanalysis(dlresult,alpha=0.05)

Arguments

dlresult	Posterior samples of beta. A large matrix (nmc/thin)*p
alpha	Level for the credible intervals. For example,the default is alpha = 0.05 means 95% credible intervals.

Value

betamean	Posterior mean of beta, a p*1 vector.
LeftCI	The left bounds of the credible intervals.
RightCI	The right bounds of the credible intervals.
betamedian	Posterior median of Beta, a p*1 vector.

Examples

## Not run: 
  rho=0.5
  p=1000
  n=100
  #set up correlation matrix
  m<-matrix(NA,p,p)
  for(i in 1:p){
    for(j in i:p)
      m[i,j]=rho^(j-i)}
  m[lower.tri(m)]<-t(m)[lower.tri(m)]
  #generate x
  library("mvtnorm")
  x=rmvnorm(n,mean=rep(0,p),sigma=m)
  #generate beta
  beta=c(rep(0,10),runif(n=5,min=-1,max=1),rep(0,20),runif(n=5,min=-1,max=1),rep(0,p-40))
  #generate y
  y=x%*%beta+rnorm(n)
  hyper=dlhyper(x,y)
  dlresult=dl(x,y,hyper=hyper)
  da=dlanalysis(dlresult,alpha=0.05)
  da$betamean
  da$betamedian
  da$LeftCI
  da$RightCI
## End(Not run)

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