R/LML.R

In LaplacesDemon: Complete Environment for Bayesian Inference

###########################################################################
# LML (Logarithm of the Marginal Likelihood)                              #
#                                                                         #
# This function provides a few different methods of approximating the     #
# logarithm of the marginal likelihood (LML), and usually also            #
# approximates the Hessian matrix (returning a variance-covariance        #
# matrix, its negative inverse).                                          #
###########################################################################

LML <- function(Model=NULL, Data=NULL, Modes=NULL, theta=NULL, LL=NULL,
     Covar=NULL, method="NSIS")
     {
     LML.out <- list(LML=NA, VarCov=NA)
     if(!is.null(Modes) & !is.null(theta) & !is.null(LL) &
          !is.null(Covar) & method == "GD") {
          log.g.theta <- dmvn(theta, Modes, Covar, log=TRUE)
          LML <- log(1 / mean(exp(log.g.theta - LL)))
          ### Output
          LML.out <- list(LML=LML, VarCov=NA)
          }
     if(!is.null(LL) & method == "HME") {
          med <- median(LL)
          LML <- med - log(mean(exp(-LL + med)))
          ### Output
          LML.out <- list(LML=LML, VarCov=NA)
          }
     if(!is.null(Model) & !is.null(Data) & !is.null(Modes) &
          (method == "LME")) {
          Interval <- 1.0e-6
          parm.len <- length(Modes)
          if(is.null(Covar)) {
               eps <- Interval * Modes
               Approx.Hessian <- Hessian(Model, Modes, Data)
               VarCov <- try(-as.inverse(Approx.Hessian), silent=TRUE)
               if(!inherits(VarCov, "try-error"))
                    diag(VarCov)[which(diag(VarCov) <= 0)] <- .Machine$double.eps
               else {
                    cat("\nWARNING: Failure to solve matrix inversion of ",
                         "Approx. Hessian in LML.\n", sep="")
                    cat("NOTE: Identity matrix is supplied instead.\n")
                    VarCov <- diag(parm.len)}
               }
          else {
               VarCov <- Covar
               rm(Covar)}
          ### Logarithm of the Marginal Likelihood
          LML <- NA
          options(warn=-1)
          LML.test <- try(parm.len/2 * log(2*pi) + 0.5*logdet(VarCov) +
               as.vector(Model(Modes, Data)[["LP"]]), silent=TRUE)
          if(!inherits(LML.test, "try-error")) LML <- LML.test[1]
          options(warn=0)
          ### Output
          LML.out <- list(LML=LML, VarCov=VarCov)
          }
     if(!is.null(theta) & !is.null(LL) & (method =="NSIS")) {
          if(!is.matrix(theta)) stop("theta must be a matrix.")
          thetacol <- ncol(theta)
          LL <- as.vector(LL)
          LLlen <- length(LL)
          if(nrow(theta) != LLlen)
               stop("The number of rows in theta differs from the ",
                    "length of LL.")
          if(LLlen < 301) {
               cat("\nWARNING: At least 301 samples are required for NSIS in LML.\n")
               return(list(LML=NA, VarCov=NA))}
          if(thetacol > round(LLlen / 2)) {
               cat("\nWARNING: The number of parameters,", thetacol,
                    ",\n exceeds half the number of stationary samples,",
                    round(LLlen / 2), ",\n required for NSIS.\n")
               return(list(LML=NA, VarCov=NA))}
          cov.prob <- 0.5
          bounds <- matrix(c(-Inf, Inf), 2, ncol(theta))
          .GetID <- function(point, center, width)
               {return(paste(floor((point - center) / width),
                    collapse=" "))}
          .PopulateHist <- function(points, heights, width)
               {
               max.point <- points[heights == max(heights), ,
                    drop=FALSE][1, ]
               center <- max.point - width / 2
               hist.env <- new.env(hash=TRUE, parent=emptyenv())
               for (i in seq(along=heights)) {
                    id <- .GetID(points[i, ], center, width)
                    if(exists(id, envir=hist.env))
                         cur.tuple <- get(id, envir=hist.env)
                    else cur.tuple <- c(Inf, 0)
                    assign(id, c(min(heights[i], cur.tuple[1]),
                         cur.tuple[2] + 1), envir=hist.env)
                    }
               return(list(env=hist.env, center=center, width=width,
                    dim=length(center)))
               }
          .Profile <- function(hist)
               {
               ids <- ls(hist$env)
               counts <- sapply(ids, function(id) get(id,
                    envir=hist$env)[2])
               names(counts) <- ids
               return(sort(counts, decreasing=TRUE))
               }
          .Lookup <- function(point, hist)
               {
               id <- .GetID(point, hist$center, hist$width)
               return(ifelse(exists(id, envir=hist$env),
                    get(id, envir=hist$env)[1], -Inf))
               }
          .SetHistNorm <- function(hist)
               {
               ids <- ls(hist$env)
               heights <- sapply(ids, function(id) get(id,
                    envir=hist$env)[1])
               max.height <- max(heights)
               hist$norm <- (log(hist$width ^ hist$dim *
                    sum(exp(heights - max.height))) + max.height)
               return(hist)
               }
          .Coverage <- function(points, hist)
               {
               heights <- apply(points, 1, function(point) .Lookup(point,
                    hist))
               return(length(heights[heights > -Inf]) / length(heights))
               }
          .Dist <- function(p1, p2)
               {
               return(max(abs(p1 - p2)))
               }
          .GetWidth <- function(theta.hist, theta.width, LL.hist,
               opt.prob=0.5)
               {
               low.point <- theta.hist[which(LL.hist == min(LL.hist))[1], ,
                    drop=FALSE]
               high.point <- theta.hist[which(LL.hist == max(LL.hist))[1], ,
                    drop=FALSE]
               minLL.dists <- apply(theta.hist, 1, function(theta) {
                    .Dist(theta, low.point)})
               maxLL.dists <- apply(theta.hist, 1, function(theta) {
                    .Dist(theta, high.point)})
               small.dist <- 0.5 * min(maxLL.dists[maxLL.dists > 0])
               big.dist <- 2 * max(minLL.dists)
               F <- function(width) {
                    hist <- .PopulateHist(theta.hist, LL.hist, width)
                    return(.Coverage(theta.width, hist) - opt.prob)
                    }
               options(warn=-1)
               solve.test <- try(uniroot(F, lower=small.dist,
                    upper=big.dist, tol=min(.05, 2/nrow(theta.width))),
                    silent=TRUE)
               options(warn=0)
               if(!inherits(solve.test, "try-error")) return(solve.test$root)
               else return(1)
               }
          .GetLimits <- function(hist)
               {
               split.ids <- strsplit(ls(hist$env), " ")
               coords <- matrix(NA, ncol=hist$dim, nrow=length(split.ids))
               for (i in seq(along=split.ids))
                    coords[i, ] <- as.integer(split.ids[[i]])
               max.coords <- apply(coords, 2, max)
               min.coords <- apply(coords, 2, min)
               result <- rbind(apply(coords, 2, min) * hist$width +
                    hist$center, (apply(coords, 2, max) + 1) * hist$width +
                    hist$center)
               rownames(result) <- c("min", "max")
               return(result)
               }
          .MargLL <- function(hist, theta.imp, LL.imp)
               {
               n <- length(LL.imp)
               samples <- rep(0, n)
               for (i in seq(length=n))
                    samples[i] <- exp(.Lookup(theta.imp[i, ],
                         hist) - LL.imp[i])
               return(list(samples=samples, mll=-log(mean(samples))))
               }
          .SplitComponents <- function(theta, LL)
               {
               tot.count <- length(LL)
               hist.count <- min(0.2 * tot.count,
                    round(sqrt(tot.count) * 2))
               width.count <- 40
               imp.count <- tot.count - hist.count - width.count
               return(list(theta.hist=theta[1:hist.count, , drop=FALSE],
                    theta.bw=theta[(hist.count + 1):(hist.count +
                         width.count), , drop=FALSE],
                    theta.imp=theta[(tot.count - imp.count + 1):tot.count, ,
                         drop=FALSE],
                    LL.hist=LL[1:hist.count],
                    LL.imp=LL[(tot.count - imp.count + 1):tot.count]))
               }
          .CheckBounds <- function(hist, bounds)
               {
               limits <- hist$limits <- .GetLimits(hist)
               if(!all((bounds[1, ] == -Inf) |
                    (bounds[1, ] < limits[1, ])))
                    stop(paste("Bounds error: histogram lower limits (",
                         paste(limits[1, ], collapse=" "),
                         ") lower than specified bounds (",
                         paste(bounds[1, ], collapse=" "), ").", sep=""))
               if(!all((bounds[2, ] == Inf) | (bounds[2, ] > limits[2, ])))
                    stop(paste("Bounds error: histogram upper limits (",
                         paste(limits[2, ], collapse=" "),
                         ") greater than specified bounds (",
                         paste(bounds[2, ], collapse=" "), ").", sep=""))
               return(hist)
               }
          options(warn=-1)
          comps <- .SplitComponents(theta, LL)
          width <- .GetWidth(comps$theta.hist, comps$theta.bw,
               comps$LL.hist, cov.prob)
          hist <- .PopulateHist(comps$theta.hist, comps$LL.hist, width)
          hist <- .SetHistNorm(hist)
          hist <- .CheckBounds(hist, bounds)
          ml.out <- .MargLL(hist, comps$theta.imp, comps$LL.imp)
          ml.out$samples <- ml.out$samples[is.finite(ml.out$samples)]
          sd.samples <- sd(ml.out$samples) / sqrt(length(ml.out$samples))
          conf.interval <- qnorm(c(0.975, 0.025)) * sd.samples +
               mean(ml.out$samples)
          conf.interval[which(conf.interval < .Machine$double.eps)] <- .Machine$double.eps
          conf.interval <- -log(conf.interval)
          options(warn=0)
          LML <- ml.out$mll + hist$norm
          LML[which(!is.finite(LML))] <- NA
          ### Output
          LML.out <- list(LML=LML, VarCov=NA)
          }
     return(LML.out)
     }

#End