R/MCMClogit.R

In MCMCpack: Markov Chain Monte Carlo (MCMC) Package

##########################################################################
## sample from the posterior distribution of a logistic regression
## model in R using linked C++ code in Scythe
##
## KQ 1/23/2003
##
## This software is distributed under the terms of the GNU GENERAL
## PUBLIC LICENSE Version 2, June 1991.  See the package LICENSE
## file for more information.
##
## Modified to meet new developer specification 7/15/2004 KQ
## Modified for new Scythe and rngs 7/25/2004 KQ
## note: B0 is now a precision
## Modified to allow user-specified prior density 8/17/2005 KQ
## Modified to handle marginal likelihood calculation 1/27/2006 KQ
##
## Copyright (C) 2003-2007 Andrew D. Martin and Kevin M. Quinn
## Copyright (C) 2007-present Andrew D. Martin, Kevin M. Quinn,
##    and Jong Hee Park
##########################################################################

#' Markov Chain Monte Carlo for Logistic Regression
#'
#' This function generates a sample from the posterior distribution of a
#' logistic regression model using a random walk Metropolis algorithm. The user
#' supplies data and priors, and a sample from the posterior distribution is
#' returned as an mcmc object, which can be subsequently analyzed with
#' functions provided in the coda package.
#'
#' \code{MCMClogit} simulates from the posterior distribution of a logistic
#' regression model using a random walk Metropolis algorithm. The simulation
#' proper is done in compiled C++ code to maximize efficiency.  Please consult
#' the coda documentation for a comprehensive list of functions that can be
#' used to analyze the posterior sample.
#'
#' The model takes the following form:
#'
#' \deqn{y_i \sim \mathcal{B}ernoulli(\pi_i)}
#'
#' Where the inverse link function:
#'
#' \deqn{\pi_i = \frac{\exp(x_i'\beta)}{1 + \exp(x_i'\beta)}}
#'
#' By default, we assume a multivariate Normal prior on \eqn{\beta}:
#'
#' \deqn{\beta \sim \mathcal{N}(b_0,B_0^{-1})}
#'
#' Additionally, arbitrary user-defined priors can be specified with
#' the \code{user.prior.density} argument.
#'
#' If the default multivariate normal prior is used, the Metropolis proposal
#' distribution is centered at the current value of \eqn{\beta} and has
#' variance-covariance \eqn{V = T (B_0 + C^{-1})^{-1} T }, where \eqn{T} is a the
#' diagonal positive definite matrix formed from the \code{tune}, \eqn{B_0}
#' is the prior precision, and \eqn{C} is the large sample
#' variance-covariance matrix of the MLEs. This last calculation is done via an
#' initial call to \code{glm}.
#'
#' If a user-defined prior is used, the Metropolis proposal distribution is
#' centered at the current value of \eqn{\beta} and has
#' variance-covariance \eqn{V = T C T}, where
#' \eqn{T} is a the diagonal positive definite matrix formed from the
#' \code{tune} and \eqn{C} is the large sample variance-covariance matrix of
#' the MLEs. This last calculation is done via an initial call to \code{glm}.
#'
#' @param formula Model formula.
#'
#' @param data Data frame.
#'
#' @param burnin The number of burn-in iterations for the sampler.
#'
#' @param mcmc The number of Metropolis iterations for the sampler.
#'
#' @param thin The thinning interval used in the simulation.  The number of
#' mcmc iterations must be divisible by this value.
#'
#' @param tune Metropolis tuning parameter. Can be either a positive scalar or
#' a \eqn{k}-vector, where \eqn{k} is the length of
#' \eqn{\beta}.Make sure that the acceptance rate is satisfactory
#' (typically between 0.20 and 0.5) before using the posterior sample for
#' inference.
#'
#' @param verbose A switch which determines whether or not the progress of the
#' sampler is printed to the screen.  If \code{verbose} is greater than 0 the
#' iteration number, the current beta vector, and the Metropolis acceptance
#' rate are printed to the screen every \code{verbose}th iteration.
#'
#' @param seed The seed for the random number generator.  If NA, the Mersenne
#' Twister generator is used with default seed 12345; if an integer is passed
#' it is used to seed the Mersenne twister.  The user can also pass a list of
#' length two to use the L'Ecuyer random number generator, which is suitable
#' for parallel computation.  The first element of the list is the L'Ecuyer
#' seed, which is a vector of length six or NA (if NA a default seed of
#' \code{rep(12345,6)} is used).  The second element of list is a positive
#' substream number. See the MCMCpack specification for more details.
#'
#' @param beta.start The starting value for the \eqn{\beta} vector.  This
#' can either be a scalar or a column vector with dimension equal to the number
#' of betas. If this takes a scalar value, then that value will serve as the
#' starting value for all of the betas.  The default value of NA will use the
#' maximum likelihood estimate of \eqn{\beta} as the starting value.
#'
#' @param b0 If \code{user.prior.density==NULL} \code{b0} is the prior mean of
#' \eqn{\beta} under a multivariate normal prior.  This can either be a
#' scalar or a column vector with dimension equal to the number of betas. If
#' this takes a scalar value, then that value will serve as the prior mean for
#' all of the betas.
#'
#' @param B0 If \code{user.prior.density==NULL} \code{B0} is the prior
#' precision of \eqn{\beta} under a multivariate normal prior.  This can
#' either be a scalar or a square matrix with dimensions equal to the number of
#' betas.  If this takes a scalar value, then that value times an identity
#' matrix serves as the prior precision of \eqn{\beta}. Default value of
#' 0 is equivalent to an improper uniform prior for beta.
#'
#' @param user.prior.density If non-NULL, the prior (log)density up to a
#' constant of proportionality. This must be a function defined in R whose
#' first argument is a continuous (possibly vector) variable. This first
#' argument is the point in the state space at which the prior (log)density is
#' to be evaluated. Additional arguments can be passed to
#' \code{user.prior.density()} by inserting them in the call to
#' \code{MCMClogit()}. See the Details section and the examples below for more
#' information.
#'
#' @param logfun Logical indicating whether \code{use.prior.density()} returns
#' the natural log of a density function (TRUE) or a density (FALSE).
#'
#' @param marginal.likelihood How should the marginal likelihood be calculated?
#' Options are: \code{none} in which case the marginal likelihood will not be
#' calculated or \code{Laplace} in which case the Laplace approximation (see
#' Kass and Raftery, 1995) is used.
#'
#' @param ... further arguments to be passed
#'
#' @return An mcmc object that contains the posterior sample.  This object can
#' be summarized by functions provided by the coda package.
#'
#' @export
#'
#' @seealso \code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}},
#' \code{\link[stats]{glm}}
#'
#' @references Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2011.
#' ``MCMCpack: Markov Chain Monte Carlo in R.'', \emph{Journal of Statistical
#' Software}. 42(9): 1-21.  \doi{10.18637/jss.v042.i09}.
#'
#' Daniel Pemstein, Kevin M. Quinn, and Andrew D. Martin.  2007.  \emph{Scythe
#' Statistical Library 1.0.} \url{http://scythe.wustl.edu.s3-website-us-east-1.amazonaws.com/}.
#'
#' Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2006.  ``Output
#' Analysis and Diagnostics for MCMC (CODA)'', \emph{R News}. 6(1): 7-11.
#' \url{https://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf}.
#'
#' @keywords models
#'
#' @examples
#'
#'    \dontrun{
#'    ## default improper uniform prior
#'    data(birthwt)
#'    posterior <- MCMClogit(low~age+as.factor(race)+smoke, data=birthwt)
#'    plot(posterior)
#'    summary(posterior)
#'
#'
#'    ## multivariate normal prior
#'    data(birthwt)
#'    posterior <- MCMClogit(low~age+as.factor(race)+smoke, b0=0, B0=.001,
#'                           data=birthwt)
#'    plot(posterior)
#'    summary(posterior)
#'
#'
#'    ## user-defined independent Cauchy prior
#'    logpriorfun <- function(beta){
#'      sum(dcauchy(beta, log=TRUE))
#'    }
#'
#'    posterior <- MCMClogit(low~age+as.factor(race)+smoke,
#'                           data=birthwt, user.prior.density=logpriorfun,
#'                           logfun=TRUE)
#'    plot(posterior)
#'    summary(posterior)
#'
#'
#'    ## user-defined independent Cauchy prior with additional args
#'    logpriorfun <- function(beta, location, scale){
#'      sum(dcauchy(beta, location, scale, log=TRUE))
#'    }
#'
#'    posterior <- MCMClogit(low~age+as.factor(race)+smoke,
#'                           data=birthwt, user.prior.density=logpriorfun,
#'                           logfun=TRUE, location=0, scale=10)
#'    plot(posterior)
#'    summary(posterior)
#'
#'
#'    }
#'
"MCMClogit" <-
  function(formula, data=NULL, burnin = 1000, mcmc = 10000,
           thin=1, tune=1.1, verbose = 0, seed = NA, beta.start = NA,
           b0 = 0, B0 = 0, user.prior.density=NULL, logfun=TRUE,
           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=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=binomial, 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


    ## setup the environment so that fun can see the things passed as ...
    userfun <- function(ttt) user.prior.density(ttt, ...)
    my.env <- environment(fun = userfun)



    ## check to make sure user.prior.density returns a numeric scalar and
    ## starting values have positive prior mass
    if (is.function(user.prior.density)){
      funval <- userfun(beta.start)
      if (length(funval) != 1){
        cat("user.prior.density does not return a scalar.\n")
        stop("Respecify and call MCMClogit() again. \n")
      }
      if (!is.numeric(funval)){
        cat("user.prior.density does not return a numeric value.\n")
        stop("Respecify and call MCMClogit() again. \n")
      }

      if (identical(funval,  Inf)){
        cat("user.prior.density(beta.start) == Inf.\n")
        stop("Respecify and call MCMClogit() again. \n")
      }

      if (logfun){
        if (identical(funval, -Inf)){
          cat("user.prior.density(beta.start) == -Inf.\n")
          stop("Respecify and call MCMClogit() again. \n")
        }
      }
      else{
        if (funval <= 0){
          cat("user.prior.density(beta.start) <= 0.\n")
          stop("Respecify and call MCMClogit() again. \n")
        }
      }
    }
    else if (!is.null(user.prior.density)){
      cat("user.prior.density is neither a NULL nor a function.\n")
      stop("Respecify and call MCMClogit() again. \n")
    }



    ## form the tuning parameter
    tune <- vector.tune(tune, K)
    V <- vcov(glm(formula=formula, data=data, family=binomial))

    ## y \in {0, 1} error checking
    if (sum(Y!=0 & Y!=1) > 0) {
      cat("Elements of Y equal to something other than 0 or 1.\n")
      stop("Check data and call MCMClogit() again. \n")
    }



    propvar <- tune %*% V %*% tune

    posterior <- NULL
    ## call C++ code to draw sample
    if (is.null(user.prior.density)){
      ## define holder for posterior density sample
      sample <- matrix(data=0, mcmc/thin, dim(X)[2] )

      auto.Scythe.call(output.object="posterior", cc.fun.name="cMCMClogit",
                       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)

      ## marginal likelihood calculation if Laplace
      if (marginal.likelihood == "Laplace"){
        theta.start <- beta.start
        optim.out <- optim(theta.start, logpost.logit, 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.logit(theta.tilde, Y, X, b0, B0)

      }


      ## put together matrix and build MCMC object to return
      output <- form.mcmc.object(posterior, names=xnames,
                                 title="MCMClogit Posterior Sample",
                                 y=Y, call=cl, logmarglike=logmarglike)

    }
    else {
      sample <- .Call("MCMClogituserprior_cc",
                      userfun, as.integer(Y), as.matrix(X),
                      as.double(beta.start),
                      my.env, as.integer(burnin), as.integer(mcmc),
                      as.integer(thin),
                      as.integer(verbose),
                      lecuyer=as.integer(lecuyer),
                      seedarray=as.integer(seed.array),
                      lecuyerstream=as.integer(lecuyer.stream),
                      as.logical(logfun),
                      as.matrix(propvar),
                      PACKAGE="MCMCpack")


      ## marginal likelihood calculation if Laplace
      if (marginal.likelihood == "Laplace"){
        theta.start <- beta.start
        optim.out <- optim(theta.start, logpost.logit.userprior, method="BFGS",
                           control=list(fnscale=-1),
                           hessian=TRUE, y=Y, X=X, userfun=userfun,
                           logfun=logfun, my.env=my.env)

        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.logit.userprior(theta.tilde, Y, X, userfun=userfun,
                                    logfun=logfun, my.env=my.env)

      }


      output <- mcmc(data=sample, start=burnin+1,
                     end=burnin+mcmc, thin=thin)
      varnames(output) <- as.list(xnames)
      attr(output, "title") <- "MCMClogit Posterior Sample"
      attr(output, "y") <- Y
      attr(output, "call") <- cl
      attr(output, "logmarglike") <- logmarglike
    }

    return(output)
  }

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