The goal of surbayes is to provide tools for Bayesian analysis of the seemingly unrelated regression (SUR) model. In particular, we implement the direct Monte Carlo (DMC) approach of Zellner and Ando (2010). We also implement a Gibbs sampler to sample from a power prior on the SUR model.
You can install the released version of surbayes from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("ethan-alt/surbayes")
This is a basic example which shows you how to sample from the posterior
library(surbayes) ## Taken from bayesm package M = 10 ## number of samples set.seed(66) ## simulate data from SUR beta1 = c(1,2) beta2 = c(1,-1,-2) nobs = 100 nreg = 2 iota = c(rep(1, nobs)) X1 = cbind(iota, runif(nobs)) X2 = cbind(iota, runif(nobs), runif(nobs)) Sigma = matrix(c(0.5, 0.2, 0.2, 0.5), ncol = 2) U = chol(Sigma) E = matrix( rnorm( 2 * nobs ), ncol = 2) %*% U y1 = X1 %*% beta1 + E[,1] y2 = X2 %*% beta2 + E[,2] X1 = X1[, -1] X2 = X2[, -1] data = data.frame(y1, y2, X1, X2) names(data) = c( paste0( 'y', 1:2 ), paste0('x', 1:(ncol(data) - 2) )) ## run DMC sampler formula.list = list(y1 ~ x1, y2 ~ x2 + x3) ## Fit models out_dmc = sur_sample( formula.list, data, M = M ) ## DMC used #> Direct Monte Carlo sampling used out_powerprior = sur_sample( formula.list, data, M, data ) ## Gibbs used #> Gibbs sampling used for power prior model
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.