hi all i would like to use r and winbugs in order to undertake seemingly unrelated regression. i am using the R2WINBUGS library. i just simulated a simple example (sample size is 25) in order to get the correct code.
i suspect that the problem is in my definition of the prior. it wants a multivariate node. the example could be extended by including more than 1 X variable in the system of equations. how can one specify the following prior: the sum of the estimated betas (including the constant) is normal "a" and variance "b" say? my code is given below: library(R2WinBUGS) set.seed(1) x1=rnorm(25) x2=rnorm(25) n=25 #i know that the systems are not related but this will be extended later. y1=2+5*x1+rnorm(25)*2 y2=25-7*x2+rnorm(25)*2 X1=cbind(1,x1) X2=cbind(1,x2) Y=cbind(y1,y2) I=diag(2) J=diag(2)*0.001 m=matrix(0,nrow=2,ncol=0) init<-list(b=matrix(0,nrow=2,ncol=2),tau=1) inits<-list(init,init,init) data<-c("n","Y","X1","X2","I","J","m") parameters<-c("b","tau") a<-bugs(data=data, inits=inits, parameters, model.file="c:/try/sur.txt", n.chains = 3, n.iter = 1000, bugs.directory = "c:/Program Files/WinBUGS14/", working.directory = "c:/try", clearWD = FALSE,codaPkg = FALSE,debug=T) model { for (i in 1:n) { Y[i,1:2] ~ dmnorm(mu[i,],P[1:2,1:2]) # means in separate time series mu[i,1] <- inprod(X1[i,],b[,1]) mu[i,2] <- inprod(X2[i,],b[,2]) P[1:2,1:2]<-tau*I[1:2,1:2] } # priors on regression coefficients for (i in 1:2) { for (j in 1:2) { b[i,j]<-dnorm(0,0.001) } } tau~dgamma(0.001,0.001) } thanking you in advance / allan ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.