bcov: bcov: Bayesian Estimation of the Variance Covariance Matrix

In brxx: Bayesian Test Reliability Estimation

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)

brxx documentation built on Jan. 26, 2021, 5:06 p.m.