mcmcpoisson.r

使用R语言的马尔科夫链蒙特卡洛模拟（MCMC）源代码程序。

### sample from the posterior distribution of a Poisson regression
### model in R using linked C++ code in Scythe
###
### ADM 1/24/2003
## KQ 3/17/2003 [bug fix]
## Modified to meet new developer specification 7/15/2004 KQ
## Modified for new Scythe and rngs 7/26/2004 KQ
## Modified to handle marginal likelihood calculation 1/27/2006 KQ

"MCMCpoisson" <-
  function(formula, data = parent.frame(), burnin = 1000, mcmc = 10000,
           thin=1, tune=1.1, verbose = 0, seed = NA, beta.start = NA,
           b0 = 0, B0 = 0,
           marginal.likelihood = c("none", "Laplace"),...) {
    
    ## checks
    check.offset(list(...))
    check.mcmc.parameters(burnin, mcmc, thin)

    cl <- match.call()
    
    
    ## seeds
    seeds <- form.seeds(seed) 
    lecuyer <- seeds[[1]]
    seed.array <- seeds[[2]]
    lecuyer.stream <- seeds[[3]]

    ## form response and model matrices
    holder <- parse.formula(formula, data)
    Y <- holder[[1]]
    X <- holder[[2]]
    xnames <- holder[[3]]    
    K <- ncol(X)  # number of covariates
        
    ## starting values and priors
    beta.start <- coef.start(beta.start, K, formula, family=poisson, data)
    mvn.prior <- form.mvn.prior(b0, B0, K)
    b0 <- mvn.prior[[1]]
    B0 <- mvn.prior[[2]]


    ## get marginal likelihood argument
    marginal.likelihood  <- match.arg(marginal.likelihood)
    B0.eigenvalues <- eigen(B0)$values
    if (min(B0.eigenvalues) < 0){
      stop("B0 is not positive semi-definite.\nPlease respecify and call again.\n")
    }
    if (isTRUE(all.equal(min(B0.eigenvalues), 0))){
      if (marginal.likelihood != "none"){
        warning("Cannot calculate marginal likelihood with improper prior\n")
        marginal.likelihood <- "none"
      }
    }
    logmarglike <- NULL

    
    ## form the tuning parameter
    tune <- vector.tune(tune, K)
    V <- vcov(glm(formula=formula, data=data, family=poisson))
    
    ## test y non-negative
    if (sum(Y < 0) > 0) {
      cat("\n Elements of Y negative. ")
      stop("\n Check data and call MCMCpoisson() again. \n") 
    }
   
    ## define holder for posterior sample
    sample <- matrix(data=0, mcmc/thin, dim(X)[2] )


    
    ## marginal likelihood calculation if Laplace
    if (marginal.likelihood == "Laplace"){
      theta.start <- beta.start
      optim.out <- optim(theta.start, logpost.poisson, method="BFGS",
                         control=list(fnscale=-1),
                         hessian=TRUE, y=Y, X=X, b0=b0, B0=B0)
      
      theta.tilde <- optim.out$par
      beta.tilde <- theta.tilde[1:K]
      
      Sigma.tilde <- solve(-1*optim.out$hessian)
      
      logmarglike <- (length(theta.tilde)/2)*log(2*pi) +
        log(sqrt(det(Sigma.tilde))) + 
          logpost.poisson(theta.tilde, Y, X, b0, B0)
      
    }
    posterior <- NULL
    
    ## call C++ code to draw sample
    auto.Scythe.call(output.object="posterior", cc.fun.name="MCMCpoisson",
                     sample.nonconst=sample, Y=Y, X=X,
                     burnin=as.integer(burnin),
                     mcmc=as.integer(mcmc), thin=as.integer(thin),
                     tune=tune, lecuyer=as.integer(lecuyer),
                     seedarray=as.integer(seed.array),
                     lecuyerstream=as.integer(lecuyer.stream),
                     verbose=as.integer(verbose), betastart=beta.start,
                     b0=b0, B0=B0, V=V) 
    
    ## put together matrix and build MCMC object to return
    output <- form.mcmc.object(posterior, names=xnames,
                               title="MCMCpoisson Posterior Sample",
                               y=Y, call=cl, logmarglike=logmarglike)
    return(output)
    
  }