| breg {bayesm} | R Documentation |
breg makes one draw from the posterior of a univariate regression
(scalar dependent variable) given the error variance = 1.0.
A natural conjugate, normal prior is used.
breg(y, X, betabar, A)
y |
vector of values of dep variable. |
X |
n (length(y)) x k Design matrix. |
betabar |
k x 1 vector. Prior mean of regression coefficients. |
A |
Prior precision matrix. |
model: y=x'β + e. e ~ N(0,1).
prior: β ~ N(betabar,A^{-1}).
k x 1 vector containing a draw from the posterior distribution.
This routine is a utility routine that does not check the input arguments for proper dimensions and type.
In particular, X must be a matrix. If you have a vector for X, coerce it into a matrix with one column
Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.
For further discussion, see Bayesian Statistics and Marketing
by Rossi,Allenby and McCulloch.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
##
if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10}
## simulate data
set.seed(66)
n=100
X=cbind(rep(1,n),runif(n)); beta=c(1,2)
y=X%*%beta+rnorm(n)
##
## set prior
A=diag(c(.05,.05)); betabar=c(0,0)
##
## make draws from posterior
betadraw=matrix(double(R*2),ncol=2)
for (rep in 1:R) {betadraw[rep,]=breg(y,X,betabar,A)}
##
## summarize draws
mat=apply(betadraw,2,quantile,probs=c(.01,.05,.5,.95,.99))
mat=rbind(beta,mat); rownames(mat)[1]="beta"; print(mat)