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...sidering four covariates > d.df=as.data.frame(read.table("c:/tina/phd/thesis/data/modified_data1.1.txt",header=T,sep=",")) > y=d.df[,ncol(d.df)] > x=d.df[,1:4] > c.df=cbind(y,x) > #x=cbind(1,x) > p <- ncol(c.df) > > # marginal log-prior of beta[] > logpriorfun <- function(beta, mu, gshape, grate) + { + logprior = -p*log(2) + log(gamma(p+gshape)) - log(gamma(gshape)) + + gshape*log(grate) - (p+gshape)* log(grate+sum(abs(beta))) + return(logprior) + } > require(MCMCpack) Loading required package: MCMCpack Loading required package: coda Loading req...

...,'%\n') sims } # Use the binomial likelihood logitll=function(beta,y,X) { X<- cbind(1,DF$nsaid,DF$diuretic,DF$diuretic*DF$nsaid) y<- DF$Var3 lF1=plogis(X%*%as.vector(beta),log.p=TRUE) lF2=plogis(-X%*%as.vector(beta),log.p=TRUE) sum(y*lF1+(1-y)*lF2) } # Use a uniform prior for p logprior <- function(beta,y,X) 0 # The log posterior is the sum. It's the target of our MCMC run logposterior <- function(beta,y,X) logprior(beta,y,X)+logitll(beta,y,X) start <- c(0,0,0,0) sims <- Metropolis(logposterior, start, 10000, sd=1) se_sims <- apply(sims, 2, sd) sims <- M...