sur_sample_gibbs_cpp: Power Prior Gibbs sampling

In surbayes: Bayesian Analysis of Seemingly Unrelated Regression Models

Description

This is a c++ implementation of Gibbs sampling SUR model with power prior

Usage

sur_sample_gibbs_cpp(
  Sigma,
  M,
  X,
  X0,
  XtX,
  X0tX0,
  Y,
  Y0,
  y,
  y0,
  a0,
  pvec,
  burnin,
  thin
)

Arguments

Sigma	initial value for covariance matrix
M	number of samples
X	design matrix for current data
X0	design matrix for historical data
XtX	matrix that is crossprod(cbind(X1, ..., XJ))
X0tX0	matrix that is crossprod(cbind(X01, ..., X0J))
Y	future response as matrix (Y1, ..., YJ)
Y0	historical response as matrix (Y01, ..., Y0J)
y	future response as vector
y0	historical response as vector
a0	power prior parameter
pvec	vector giving number of covariates per endpoint
burnin	Burn-in parameter
thin	Thin parameter

Value

sampled covariance matrix

surbayes documentation built on Aug. 26, 2020, 5:16 p.m.