require(rjags)
n=nrow(mtcars1)
#covariates and response
y=mtcars1$mpg; drat=mtcars1$drat; wt=mtcars1$wt; qsec=mtcars1$qsec
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x=cbind(rep(1,n),drat,wt,qsec)
model_string <- "model{ # Likelihood for(i in 1:n){ y[i]~dnorm(mu[i],tau) mu[i]=inprod(beta[],x[i,]) # Prior for beta for(j in 1:4){ beta[j]~dnorm(mu0,tau0) tau0=1/sigma02 # Prior for the inverse variance tau~dgamma(a, b) # Compute the variance sigma2=1/tau mu0=0; sigma02=1000; a=0.1; b=0.1 data=list(y=y,x=x,n=n,mu0=mu0,sigma02=sigma02,a=a,b=b) model=jags.model(textConnection(model_string),n.chains=1,data=data) update(model,100000,progress.bar="none") res=coda.samples(model,variable.names=c("beta","sigma2"),n.iter=500000,thin=50,progress.bar="none") summary(res) 程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com