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.
install.packages("surbayes")# install.packages("devtools")
devtools::install_github("ethan-alt/surbayes")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 modelRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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