bcov {brxx} | R Documentation |
bcov: Bayesian Estimation of the Variance Covariance Matrix
Description
This function estimates the variance covariance matrix for a
Usage
bcov(data, iter, burn, seed, CI, S0, nu0, mu0)
Arguments
data |
N by P data matrix. |
iter |
Number of iterations for the Gibbs sampler. |
burn |
Number of samples to burn in. |
seed |
Seed for the Gibbs sampler |
CI |
Credible interval quantile, as a decimal (ie, for 95 percent, 0.95). |
S0 |
Prior variance covariance matrix. |
nu0 |
Prior degrees of freedom for inverse Wishart prior distribution. |
mu0 |
Prior means for each column. |
Value
Returns median posterior estimates of the variance covariance matrix.
Examples
## Not run:
set.seed(999)
your_data=mvrnorm(n=15,mu=c(0,0),Sigma=matrix(c(4,3,3,9),nrow=2,ncol=2))
Mu0=c(0,0)
Sigma0=matrix(c(1,0.6,0.6,4),nrow=2,ncol=2)
Nu0=3-1
bcov(data=your_data,iter=5000,burn=2500,seed=999,CI=0.95,
mu0=Mu0,S0=Sigma0,nu0=Nu0)
## End(Not run)
[Package brxx version 0.1.2 Index]