INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

## Export: inla.collect.results

##! \name{inla.collect.results}
##! \alias{inla.collect.results}
##! \alias{collect.results}
##! \title{Collect results from a inla-call}
##! \description{\code{inla.collect.results} collect results  from a inla-call}
##! \usage{
##! inla.collect.results(
##!              results.dir,
##!              control.results = inla.set.control.results.default(),
##!              debug=FALSE,
##!              only.hyperparam=FALSE,
##!              file.log = NULL, 
##!              file.log2 = NULL)
##!}
##! \arguments{

`inla.collect.results` =
    function(
        ##! \item{results.dir}{The directory where the results of the inla run are stored}
        results.dir, 

        ##! \item{control.results}{a list of parameters controlling the
        ##! output of the function; see \code{?control.results}}
        control.results = inla.set.control.results.default(),

        ##! \item{debug}{Logical. If \code{TRUE} some debugging information are printed}
        debug=FALSE,

        ##! \item{only.hyperparam}{Binary variable indicating wheather only the
        ##! results for the hyperparameters should be collected}
        only.hyperparam=FALSE, 

        ##! \item{file.log}{Character. The filename, if any, of the logfile for
        ##! the internal calculations}
        file.log = NULL, 

        ##! \item{file.log2}{Character. The filename, if any, of the logfile2 for
        ##! the internal calculations}
        file.log2 = NULL)
{
    ##! }
    ##! \value{ The function returns an object of class \code{"inla"}, see the
    ##! help file for \code{inla} for details.}
    ##! 
    ##! \details{This function is mainly used inside \code{inla} 
    ##!  to collect results after running the inla
    ##!  function. It can also be used to collect results into R after having
    ##!  runned a inla section outside R.  }

    if (is.na(file.info(results.dir)$isdir) || 
        !file.info(results.dir)$isdir) {
        stop(paste("This is not a directory: ", results.dir, "\n"))
    }

    filename = paste(results.dir,  "/.ok",  sep="")
    res.ok = file.exists(filename)
    if (!res.ok) {
        ## try this one instead
        results.dir.new = paste(results.dir,  "/results.files", sep="")
        filename = paste(results.dir.new,  "/.ok",  sep="")
        res.ok = file.exists(filename)
        if (res.ok) {
            if (debug) {
                cat(paste("inla.collect.results: retry with directory", results.dir.new, "\n"))
            }
            return (inla.collect.results(results.dir.new, 
                                         control.results = control.results, 
                                         debug = debug, 
                                         only.hyperparam = only.hyperparam, 
                                         file.log = file.log,
                                         file.log2 = file.log2))
        } else {
            ## neither directories contain the file /.ok, then we
            ## assume the inla-program has crashed
            inla.inlaprogram.has.crashed()
        }
    }
 
    if (!only.hyperparam) {
        res.fixed = inla.collect.fixed(results.dir, debug)
        res.lincomb = inla.collect.lincomb(results.dir, debug, derived=FALSE)
        res.lincomb.derived = inla.collect.lincomb(results.dir, debug, derived = TRUE)
        res.dic = inla.collect.dic(results.dir, debug)
        res.cpo.pit = inla.collect.cpo(results.dir, debug)
        res.po = inla.collect.po(results.dir, debug)
        res.waic = inla.collect.waic(results.dir, debug)
        res.random = inla.collect.random(results.dir, control.results$return.marginals.random, debug)
        res.predictor = inla.collect.predictor(results.dir, control.results$return.marginals.predictor, debug)
        res.spde2.blc = inla.collect.spde2.blc(results.dir, control.results$return.marginals.random, debug)
        res.spde3.blc = inla.collect.spde3.blc(results.dir, control.results$return.marginals.random, debug)

        file=paste(results.dir,.Platform$file.sep,"neffp",.Platform$file.sep,"neffp.dat", sep="")
        neffp = matrix(inla.read.binary.file(file), 3, 1)
        rownames(neffp) = inla.trim(c("Expectected  number of parameters",
                        "Stdev of the number of parameters",
                        "Number of equivalent replicates"))
    } else {
        res.fixed=NULL
        res.lincomb = NULL
        res.lincomb.derived = NULL
        res.dic=NULL
        res.cpo.pit =NULL
        res.po = NULL
        res.waic = NULL
        res.random=NULL
        res.predictor =NULL
        res.spde2.blc = NULL
        res.spde3.blc = NULL
        neffp =NULL
    }
    res.mlik = inla.collect.mlik(results.dir, debug)
    res.q = inla.collect.q(results.dir, debug)
    res.graph = inla.collect.graph(results.dir, debug)
    res.offset = inla.collect.offset.linear.predictor(results.dir, debug)

    ##get the hyperparameters
    theta.mode = inla.read.binary.file(paste(results.dir,.Platform$file.sep,".theta_mode", sep=""))[-1]
    x.mode = inla.read.binary.file(paste(results.dir,.Platform$file.sep,".x_mode", sep=""))[-1]
    hgid = readLines(paste(results.dir,.Platform$file.sep,".hgid", sep=""))
    
    lfn.fnm = paste(results.dir,.Platform$file.sep,"linkfunctions.names", sep="")
    if (file.exists(lfn.fnm)) {
        linkfunctions.names = readLines(lfn.fnm)
        fp = file(paste(results.dir,.Platform$file.sep,"linkfunctions.link", sep=""), "rb")
        n = readBin(fp, integer(), 1)
        idx = readBin(fp, double(), n)
        ok = which(!is.nan(idx))
        idx[ok] = idx[ok] + 1
        close(fp)
        linkfunctions = list(names = linkfunctions.names, link = as.integer(idx))
    } else {
        linkfunctions = NULL
    }

    if (length(theta.mode)>0) {
        res.hyper = inla.collect.hyperpar(results.dir, debug)

        ##get the joint (if printed)
        alldir = dir(results.dir)
        if (length(grep("joint.dat", alldir))==1) {
            if (debug) {
                print("inla.collect.joint hyperpar")
            }
            fnm = paste(results.dir,"/joint.dat", sep="")
            if (file.info(fnm)$size > 0) {
                joint.hyper = read.table(fnm)
            } else {
                joint.hyper = NULL
            }
        } else {
            joint.hyper = NULL
        }
    } else {
        res.hyper = NULL
        joint.hyper = NULL
    }

    logfile = list(logfile = c(inla.collect.logfile(file.log, debug)$logfile, 
                               "", paste(rep("*",72),sep="",collapse=""), "", 
                               inla.collect.logfile(file.log2, debug)$logfile))
    misc = inla.collect.misc(results.dir, debug)
    theta.tags = NULL
    mode.status = NA
    if (!is.null(misc)) {
        ## put also theta.mode in here
        misc$theta.mode = theta.mode
        ## we need theta.tags for later usage
        if (!is.null(misc$theta.tags)) {
            theta.tags = misc$theta.tags
        }
        mode.status = misc$mode.status

        if (!is.null(misc$lincomb.derived.correlation.matrix)) {
            if (!is.null(res.lincomb.derived)) {
                id = res.lincomb.derived$summary.lincomb.derived$ID
                tag = rownames(res.lincomb.derived$summary.lincomb.derived)
                R = misc$lincomb.derived.correlation.matrix
                rownames(R) = colnames(R) = tag[id]
                misc$lincomb.derived.correlation.matrix = R
            } else {
                misc$lincomb.derived.correlation.matrix = NULL
            }
        }
        if (!is.null(misc$lincomb.derived.covariance.matrix)) {
            if (!is.null(res.lincomb.derived)) {
                id = res.lincomb.derived$summary.lincomb.derived$ID
                tag = rownames(res.lincomb.derived$summary.lincomb.derived)
                R = misc$lincomb.derived.covariance.matrix
                rownames(R) = colnames(R) = tag[id]
                misc$lincomb.derived.covariance.matrix = R
            } else {
                misc$lincomb.derived.covariance.matrix = NULL
            }
        }

        ## also put the linkfunctions here
        misc$linkfunctions = linkfunctions
        if (!is.null(linkfunctions)) {
            ## a better name
            misc$family = linkfunctions$link
        }
    }

    ## add the names of the theta's here, as they are available.
    if (!is.null(misc) && !is.null(joint.hyper)) {
        colnames(joint.hyper) = c(misc$theta.tags, "Log posterior density")
    }

    names(theta.mode) = theta.tags
    res = c(res.fixed, res.lincomb, res.lincomb.derived, res.mlik,
            list(cpo=res.cpo.pit), list(po = res.po), list(waic = res.waic), 
            res.random, res.predictor, res.hyper,
            res.offset, res.spde2.blc, res.spde3.blc, logfile, 
            list(misc = misc,
                 dic=res.dic, mode = list(theta = theta.mode, x = x.mode,
                                      theta.tags = theta.tags, mode.status = mode.status,
                                      log.posterior.mode = misc$log.posterior.mode),
                 neffp=neffp,
                 joint.hyper=joint.hyper, nhyper=length(theta.mode),
                 version = list(inla.call = hgid, inla.call.builtin = hgid, R.INLA=inla.version("hgid"))),
            list(Q=res.q),
            res.graph, ok = res.ok)
    class(res) = "inla"

    if (inla.getOption("internal.experimental.mode")) {
        if (debug)
            print("...Fix marginals")
        
        ## set the inla.marginal class to all the marginals, and add tag
        ## used for plotting.  all these have two levels:
        idxs = grep("marginals[.](fixed|linear[.]predictor|lincomb[.]derived|lincomb|hyperpar|fitted[.]values)", names(res))
        if (length(idxs) > 0) {
            for(idx in idxs) {
                if (!is.null(res[[idx]])) {
                    name.1 = names(res)[idx]
                    attr(res[[idx]], "inla.tag") = name.1
                    class(res[[idx]]) = "inla.marginals"

                    if (length(res[[idx]])>0) {
                        for(i in 1:length(res[[idx]])) {
                            name.2 = names(res[[idx]])[i]
                            if (!is.null(res[[idx]][[i]])) {
                                attr(res[[idx]][[i]], "inla.tag") = paste(name.1, name.2)
                                class(res[[idx]][[i]]) = "inla.marginal"
                            }
                        }
                    }
                }
            }
        }

        if (debug)
            print("...Fix marginals 1")

        ## all these have three levels:
        idxs = grep("marginals[.]random", names(res))
        if (length(idxs) > 0) {
            for(idx in idxs) {
                if (!is.null(res[[idx]])) {
                    name.1 = names(res)[idx]
                    name.2 = names(res[[idx]])

                    if (length(res[[idx]])>0) {
                        for(i in 1:length(res[[idx]])) {
                            name.3 = name.2[i]
                            name.4 = names(res[[idx]][[i]])

                            attr(res[[idx]][[i]], "inla.tag") = paste(name.1, name.3)
                            class(res[[idx]][[i]]) = "inla.marginals"

                            if (length(res[[idx]][[i]]) > 0) {
                                for(j in 1:length(res[[idx]][[i]])) {
                                    name.5 = name.4[j]
                                    if (!is.null(res[[idx]][[i]][[j]])) {
                                        attr(res[[idx]][[i]][[j]], "inla.tag") = paste(name.1, name.3, name.5)
                                        class(res[[idx]][[i]][[j]]) = "inla.marginal"
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }

        if (debug)
            print("...Fix marginals done.")
    }

    return(res)
}


## disable this for the moment
inla.internal.experimental.mode = FALSE

`inla.collect.misc` = function(dir, debug = FALSE)
{
    d = paste(dir,"/misc", sep="")
    d.info = file.info(d)$isdir

    if (debug)
        print(paste("collect misc from", d))

    if (is.na(d.info) || (d.info == FALSE))
        return (NULL)
    
    fnm = paste(d, "/theta-tags", sep="")
    if (file.exists(fnm)) {
        tags = readLines(fnm)
    } else {
        tags = NULL
    }

    fnm = paste(d, "/theta-from", sep="")
    if (file.exists(fnm)) {
        theta.from = readLines(fnm)
        ## evaluate these as functions
        theta.from = lapply(theta.from, inla.source2function)
        if (!is.null(tags)) {
            names(theta.from) = tags
        }
    } else {
        theta.from = NULL
    }

    fnm = paste(d, "/theta-to", sep="")
    if (file.exists(fnm)) {
        theta.to = readLines(fnm)
        ## evaluate these as functions
        theta.to = lapply(theta.to, inla.source2function)
        if (!is.null(tags)) {
            names(theta.to) = tags
        }
    } else {
        theta.to = NULL
    }
    
    fnm = paste(d, "/covmat-hyper-internal.dat", sep="")
    if (file.exists(fnm)) {
        siz = inla.read.binary.file(fnm)
        n = siz[1L]
        stopifnot(length(siz) == n^2L + 1L)

        cov.intern = matrix(siz[-1L], n, n)
        dd = diag(cov.intern)
        s = matrix(0.0, n, n)
        diag(s) = 1.0/sqrt(dd)
        cor.intern = s %*% cov.intern %*% s
        diag(cor.intern) = 1.0
    } else {
        cov.intern = NULL
        cor.intern = NULL
    }

    fnm = paste(d, "/covmat-eigenvectors.dat", sep="")
    if (file.exists(fnm)) {
        siz = inla.read.binary.file(fnm)
        n = siz[1L]
        stopifnot(length(siz) == n^2L + 1L)
        cov.intern.eigenvectors = matrix(siz[-1L], n, n)
    } else {
        cov.intern.eigenvectors = NULL
    }

    fnm = paste(d, "/covmat-eigenvalues.dat", sep="")
    if (file.exists(fnm)) {
        siz = inla.read.binary.file(fnm)
        n = siz[1L]
        stopifnot(length(siz) == n + 1L)
        cov.intern.eigenvalues = siz[-1L]
    } else {
        cov.intern.eigenvalues = NULL
    }

    fnm = paste(d, "/reordering.dat", sep="")
    if (file.exists(fnm)) {
        r = as.integer(inla.read.binary.file(fnm))
    } else {
        r = NULL
    }

    fnm = paste(d, "/stdev_corr_pos.dat", sep="")
    if (file.exists(fnm)) {
        stdev.corr.positive = as.numeric(inla.read.fmesher.file(fnm))
    } else {
        stdev.corr.positive = NULL
    }

    fnm = paste(d, "/stdev_corr_neg.dat", sep="")
    if (file.exists(fnm)) {
        stdev.corr.negative = as.numeric(inla.read.fmesher.file(fnm))
    } else {
        stdev.corr.negative = NULL
    }
    
    fnm = paste(d, "/lincomb_derived_correlation_matrix.dat",  sep="")
    if (file.exists(fnm)) {
        lincomb.derived.correlation.matrix = inla.read.fmesher.file(fnm)
    } else {
        lincomb.derived.correlation.matrix = NULL
    }

    fnm = paste(d, "/lincomb_derived_covariance_matrix.dat",  sep="")
    if (file.exists(fnm)) {
        lincomb.derived.covariance.matrix = inla.read.fmesher.file(fnm)
    } else {
        lincomb.derived.covariance.matrix = NULL
    }
    
    fnm = paste(d, "/mode-status.dat", sep="")
    if (file.exists(fnm)) {
        mode.status = scan(fnm, quiet=TRUE)
    } else {
        mode.status = NA
    }

    fnm = paste(d, "/nfunc.dat", sep="")
    if (file.exists(fnm)) {
        nfunc = as.numeric(scan(fnm, quiet=TRUE))
    } else {
        nfunc = NA
    }

    fnm = paste(d, "/log-posterior-mode.dat", sep="")
    if (file.exists(fnm)) {
        lpm = scan(fnm, quiet=TRUE)
    } else {
        lpm = NA
    }

    fnm = paste(d, "/config/configs.dat", sep="")
    if (file.exists(fnm)) {
        fp = file(fnm, "rb")
        iarr = readBin(fp, integer(), 3)
        configs = list(
            n = iarr[1], 
            nz = iarr[2], 
            ntheta = iarr[3])
        configs.i = readBin(fp, integer(), configs$nz) ## 0-based
        configs.j = readBin(fp, integer(), configs$nz) ## 0-based
        configs$nconfig = readBin(fp, integer(), 1)

        nc = readBin(fp, integer(), 1)
        if (nc > 0) {
            A = readBin(fp, numeric(), configs$n * nc)
            e = readBin(fp, numeric(), nc)
            configs$constr = list(
                nc = nc,
                A = matrix(A, nc, configs$n),
                e = e)
        } else {
            configs$constr = NULL
        }

        theta.tag = readLines(paste(d, "/config/theta-tag.dat", sep=""))
        configs$contents = list(
            tag = readLines(paste(d, "/config/tag.dat", sep="")),
            start = as.integer(readLines(paste(d, "/config/start.dat", sep=""))) + 1L,
            length = as.integer(readLines(paste(d, "/config/n.dat", sep=""))))

        if (configs$nconfig > 0L) {
            configs$config[[configs$nconfig]] = list()
            for(k in 1L:configs$nconfig) {
                log.post = readBin(fp, numeric(), 1)
                log.post.orig = readBin(fp, numeric(), 1)
                if (configs$ntheta > 0L) {
                    theta = readBin(fp, numeric(), configs$ntheta)
                    names(theta) = theta.tag
                } else {
                    theta = NULL
                }
                mean = readBin(fp, numeric(), configs$n)
                improved.mean = readBin(fp, numeric(), configs$n)
                skewness = readBin(fp, numeric(), configs$n)
                ## read and add the offsets here
                offsets = readBin(fp, numeric(), configs$n)
                mean = mean + offsets
                improved.mean = improved.mean + offsets

                Q = readBin(fp, numeric(), configs$nz)
                Qinv = readBin(fp, numeric(), configs$nz)
                dif = which(configs$i != configs$j)
                if (length(dif) > 0L) {
                    iadd = configs.j[dif] ## yes, its the transpose part
                    jadd = configs.i[dif] ## yes, its the transpose part
                    Qadd = Q[dif]
                    Qinvadd = Qinv[dif]
                } else {
                    iadd = c()
                    jadd = c()
                    Qadd = c()
                    Qinvadd = c()
                }
                configs$config[[k]] = list(
                    theta = theta, 
                    log.posterior = log.post, 
                    log.posterior.orig = log.post.orig, 
                    mean = mean,
                    improved.mean = improved.mean,
                    skewness = skewness, 
                    Q = sparseMatrix(
                        i = c(configs.i, iadd),
                        j = c(configs.j, jadd),
                        x = c(Q, Qadd),
                        dims = c(configs$n, configs$n),
                        index1 = FALSE,
                        giveCsparse = TRUE), 
                    Qinv = sparseMatrix(
                        i = c(configs.i, iadd),
                        j = c(configs.j, jadd),
                        x = c(Qinv, Qinvadd),
                        dims = c(configs$n, configs$n),
                        index1 = FALSE,
                        giveCsparse = TRUE))
            }

            ## rescale the log.posteriors
            configs$max.log.posterior = max(sapply(configs$config, function(x) x$log.posterior.orig))
            for(k in 1L:configs$nconfig) {
                configs$config[[k]]$log.posterior = configs$config[[k]]$log.posterior - configs$max.log.posterior
                configs$config[[k]]$log.posterior.orig = configs$config[[k]]$log.posterior.orig - configs$max.log.posterior
            }
        } else {
            configs$config = NULL
        }
        close(fp)
    } else {
        configs = NULL
    }

    if (debug)
        print(paste("collect misc from", d, "...done"))

    return (list(cov.intern = cov.intern, cor.intern = cor.intern,
                 cov.intern.eigenvalues = cov.intern.eigenvalues, cov.intern.eigenvectors = cov.intern.eigenvectors, 
                 reordering = r, theta.tags = tags, log.posterior.mode = lpm, 
                 stdev.corr.negative = stdev.corr.negative, stdev.corr.positive = stdev.corr.positive,
                 to.theta = theta.to, from.theta = theta.from, mode.status = mode.status,
                 lincomb.derived.correlation.matrix = lincomb.derived.correlation.matrix,
                 lincomb.derived.covariance.matrix = lincomb.derived.covariance.matrix,
                 configs = configs, nfunc = nfunc))
}

`inla.collect.logfile` = function(file.log = NULL, debug = FALSE)
{
    if (is.null(file.log)) {
        return (list(logfile = NULL))
    }
    if (debug) {
        print(paste("Read logfile", file.log))
    }
    if (file.exists(file.log)) {
        ## replace tab with spaces.........12345678....
        return (list(logfile = gsub("\t", "        ", readLines(file.log))))
    } else {
        return (list(logfile = NULL))
    }
}

`inla.collect.size` = function(dir, debug = FALSE)
{
    fnm = paste(dir, "/size.dat", sep="")
    siz = inla.read.binary.file(fnm)
    if (length(siz) != 5L) {
        return(rep(0L, 5))
        ##stop(paste("length of siz is not 5L: fnm=", fnm))
    }
    if (is.na(siz[1L]) || siz[1L] < 0L) stop("siz[1L] = NA")
    if (is.na(siz[2L]) || siz[2L] <= 0L) siz[2L] = siz[1L]
    if (is.na(siz[3L]) || siz[3L] <= 0L) siz[3L] = siz[2L]
    if (is.na(siz[4L]) || siz[4L] <= 0L) siz[4L] = 1L
    if (is.na(siz[5L]) || siz[5L] <= 0L) siz[5L] = 1L

    return (list(n=siz[1L], N = siz[2L], Ntotal = siz[3L], ngroup = siz[4L], nrep=siz[5L]))
}

`inla.collect.hyperid` = function(dir, debug = FALSE)
{
    fnm = paste(dir, "/hyperid.dat", sep="")
    id = readLines(fnm)
    return (id)
}

`inla.collect.fixed` = function(results.dir, debug = FALSE)
{
    alldir=dir(results.dir)
    if (debug)
        print("collect fixed effects")
    
    ## read FIXED EFFECTS
    fix = alldir[grep("^fixed.effect", alldir)]
    fix = c(fix, alldir[grep("^intercept$", alldir)])
    n.fix = length(fix)

    ##read the names of the fixed effects
    if (n.fix > 0L) {
        names.fixed = inla.trim(character(n.fix))
        for(i in 1L:n.fix) {
            tag = paste(results.dir, .Platform$file.sep, fix[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag))
                names.fixed[i] = "missing NAME"
            else
                names.fixed[i] = readLines(tag, n=1L)
        }
        ##read summary the fixed effects
        if (debug)
            print(names.fixed)
        
        summary.fixed = numeric()
        marginals.fixed = list()
        marginals.fixed[[n.fix]] = NA
        
        for(i in 1L:n.fix) {
            first.time = (i == 1L)
            file =  paste(results.dir, .Platform$file.sep, fix[i], sep="")
            dir.fix = dir(file)
            if (length(dir.fix) > 3L) {
                summ = inla.read.binary.file(paste(file, .Platform$file.sep,"summary.dat", sep=""))[-1L]
                if (first.time)
                    col.nam = c("mean","sd")
            
                ##read quantiles if existing
                if (length(grep("^quantiles.dat$", dir.fix))>0L) {
                    qq = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep, "quantiles.dat", sep="")),
                            debug=debug)
                    summ = c(summ, qq[, 2L])
                    if (first.time)
                        col.nam = c(col.nam, paste(as.character(qq[, 1L]),"quant", sep=""))
                }

                ##read mode if existing
                if (length(grep("^mode.dat$", dir.fix))>0L) {
                    mm = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep, "mode.dat", sep="")),
                            debug=debug)
                    summ = c(summ, mm[, 2L])
                    if (first.time)
                        col.nam = c(col.nam, "mode")
                }

                if (length(grep("^cdf.dat$", dir.fix))>0L) {
                    qq = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep, "cdf.dat", sep="")),
                            debug=debug)
                    summ = c(summ, qq[, 2L])
                    if (first.time)
                        col.nam = c(col.nam, paste(as.character(qq[, 1L]),"cdf", sep=""))
                }
            
                ##read also kld distance
                kld.fixed = inla.read.binary.file(paste(file, .Platform$file.sep,"symmetric-kld.dat", sep=""))[-1L]
                summ = c(summ, kld.fixed)
                if (first.time)
                    col.nam = c(col.nam, "kld")
                summary.fixed = rbind(summary.fixed, summ)

                ##read the marginals
                xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"marginal-densities.dat", sep="")),
                        debug=debug)
                if (is.null(xx))
                    xx = cbind(c(NA, NA, NA), c(NA, NA, NA))
                colnames(xx) = c("x", "y")
                marginals.fixed[[i]] = xx

                if (inla.internal.experimental.mode) {
                    class(marginals.fixed[[i]]) = "inla.marginal"
                    attr(marginals.fixed[[i]], "inla.tag") = paste("marginal fixed", names.fixed[i])
                }
            } else {
                if (first.time)
                    col.nam = c("mean", "sd", "kld")
                summary.fixed = rbind(summary.fixed, c(NA, NA, NA))
                xx = cbind(c(NA, NA, NA), c(NA, NA, NA))
                colnames(xx) = c("x", "y")
                marginals.fixed[[i]] = xx

                if (inla.internal.experimental.mode) {
                    class(marginals.fixed[[i]]) = "inla.marginal"
                    attr(marginals.fixed[[i]], "inla.tag") = paste("marginal fixed", names.fixed[i])
                }
            }
        }    
        rownames(summary.fixed) = names.fixed
        colnames(summary.fixed) = col.nam
        if (length(marginals.fixed) > 0L) {
            names(marginals.fixed) = names.fixed
        }
    }
    else {
        if (debug)
            print("No fixed effects")
        names.fixed=NULL
        summary.fixed=NULL
        marginals.fixed=NULL
    }
    
    if (inla.internal.experimental.mode) {
        class(marginals.fixed) = "inla.marginals"
        attr(marginals.fixed,  "inla.tag", "marginals fixed")
    }

    ret = list(names.fixed=names.fixed,
            summary.fixed= as.data.frame(summary.fixed),
            marginals.fixed=marginals.fixed)
    return(ret)
}

`inla.collect.lincomb` =
    function(results.dir,
             debug = FALSE,
             derived = TRUE)
{
    ## rewrite from collect.random
    alldir = dir(results.dir)
    if (derived) {
        lincomb = alldir[grep("^lincomb.*derived[.]all", alldir)]
    } else {
        lincomb1 = alldir[grep("^lincomb.*derived[.]all", alldir)]
        lincomb2 = alldir[grep("^lincomb", alldir)]
        lincomb = setdiff(lincomb2, lincomb1)
        if (debug)
            print(paste("lincomb",  lincomb))
    }        
    n.lincomb = length(lincomb)
    if (debug)
        print("collect lincombs")

    ##read the names and model of the lincomb effects
    if (n.lincomb > 0L) {
        names.lincomb = character(n.lincomb)
        model.lincomb = inla.trim(character(n.lincomb))

        summary.lincomb = list()
        summary.lincomb[[n.lincomb]] = NA
        marginals.lincomb = list()
        marginals.lincomb[[n.lincomb]] = NA
        size.lincomb = list()
        size.lincomb[[n.lincomb]] = NA
 
        for(i in 1L:n.lincomb) {
            if (debug)
                print(paste("read lincomb ", i , " of ", n.lincomb))

            ##read the summary
            file= paste(results.dir, .Platform$file.sep, lincomb[i], sep="")
            dir.lincomb = dir(file)

            if (debug)
                print(paste("read from dir ",  file))

            if (length(dir.lincomb) > 4L) {
                dd = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"summary.dat", sep="")),
                        ncol=3L, byrow=TRUE)
                col.nam = c("ID","mean","sd")
            
                ##read quantiles if existing
                if (debug)
                    cat("...quantiles.dat if any\n")
                if (length(grep("^quantiles.dat$", dir.lincomb))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,
                            "quantiles.dat", sep="")), debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
                    dd = cbind(dd, t(qq))
                }

                ##read mode if existing
                if (length(grep("^mode.dat$", dir.lincomb))>0L) {
                    mm = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep, "mode.dat", sep="")),
                            debug=debug)
                    len = dim(mm)[2L]
                    qq = mm[, seq(2L, len, by=2L), drop=FALSE]
                    dd = cbind(dd, t(qq))
                    col.nam = c(col.nam, "mode")
                }

                ##read cdf if existing
                if (debug)
                    cat("...cdf.dat if any\n")
                if (length(grep("^cdf.dat$", dir.lincomb))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"cdf.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
                    dd = cbind(dd, t(qq))
                }

                if (debug)
                    cat("...NAMES if any\n")
                if (length(grep("^NAMES$", dir.lincomb))==1L) {
                    row.names = readLines(paste(file, .Platform$file.sep,"NAMES", sep=""))
                    ## remove the prefix 'lincomb.' as we do not need it in the names.
                    row.names = sapply(row.names, function(x) gsub("^lincomb[.]", "", x))
                    names(row.names) = NULL
                } else {
                    row.names = NULL
                }
                
                ##read kld
                if (debug)
                    cat("...kld\n")
                kld1 = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"symmetric-kld.dat", sep="")),
                        ncol=2L, byrow=TRUE)
                qq = kld1[, 2L, drop=FALSE]
                dd = cbind(dd, qq)
                if (debug)
                    cat("...kld done\n")
            
                col.nam = c(col.nam, "kld")
                colnames(dd) = col.nam
                summary.lincomb[[i]] = as.data.frame(dd)
                if (!is.null(row.names)) {
                    rownames(summary.lincomb[[i]]) = row.names
                }

                xx = inla.read.binary.file(paste(file, .Platform$file.sep,"marginal-densities.dat", sep=""))
                rr = inla.interpret.vector.list(xx, debug=debug)
                rm(xx)
                if (!is.null(rr)) {
                    nd = length(rr)
                    names(rr) = paste("index.", as.character(1L:nd), sep="")
                    for(j in 1L:nd) {
                        colnames(rr[[j]]) = c("x", "y")
                        if (inla.internal.experimental.mode) {
                            class(rr[[j]]) = "inla.marginal"
                            if (derived) {
                                attr(rr[[j]], "inla.tag") = paste("marginal lincomb derived", names(rr)[j])
                            } else {
                                attr(rr[[j]], "inla.tag") = paste("marginal lincomb", names(rr)[j])
                            }
                        }
                    }
                }
                marginals.lincomb[[i]] = rr
                
                if (!is.null(row.names) && (length(marginals.lincomb)>0L)) {
                    names(marginals.lincomb[[i]]) = row.names
                }
            } else {
                N.file = paste(file, .Platform$file.sep,"N", sep="")
                if (!file.exists(N.file)) {
                    N = 0L
                } else {
                    N = scan(file=N.file, what = numeric(0L), quiet=TRUE)
                }
                summary.lincomb[[i]] = data.frame("mean" = rep(NA, N), "sd" = rep(NA, N), "kld" = rep(NA, N))
                marginals.lincomb = NULL
            }
            size.lincomb[[i]] = inla.collect.size(file)

            if (inla.internal.experimental.mode) {
                if (!is.null(marginals.lincomb)) {
                    class(marginals.lincomb[[i]]) = "inla.marginals"
                    if (derived) {
                        attr(marginals.lincomb[[i]], "inla.tag") = "marginal lincomb derived"
                    } else {
                        attr(marginals.lincomb[[i]], "inla.tag") = "marginal lincomb"
                    }                    
                }
            }
        }
        names(summary.lincomb) = names.lincomb

        ## could be that marginals.lincomb is a list of lists of NULL
        if (!is.null(marginals.lincomb)) {
            if (all(sapply(marginals.lincomb, is.null)))
                marginals.lincomb = NULL
        }

        if (!is.null(marginals.lincomb) && (length(marginals.lincomb) > 0L))
            names(marginals.lincomb) = names.lincomb
    } else {
        if (debug)
            cat("No lincomb effets\n")
        summary.lincomb=NULL
        marginals.lincomb=NULL
        size.lincomb = NULL
    }

    if (derived) {
        res = list(
                summary.lincomb.derived = as.data.frame(summary.lincomb[[1L]]), 
                marginals.lincomb.derived = inla.ifelse(length(marginals.lincomb) > 0L, marginals.lincomb[[1L]], NULL), 
                size.lincomb.derived = size.lincomb[[1L]])
    } else {
        res = list(
                summary.lincomb = as.data.frame(summary.lincomb[[1L]]),
                marginals.lincomb = inla.ifelse(length(marginals.lincomb)>0L, marginals.lincomb[[1L]], NULL), 
                size.lincomb = size.lincomb[[1L]])
    }
    return(res)
}

`inla.collect.cpo` =
    function(results.dir,
             debug = FALSE)
{
    alldir = dir(results.dir)
    if (length(grep("^cpo$", alldir))==1L) {
        if (debug)
            cat(paste("collect cpo\n", sep=""))
      
        xx = inla.read.binary.file(file=paste(results.dir, .Platform$file.sep,"cpo", .Platform$file.sep,"cpo.dat", sep=""))
        n = xx[1L]
        xx = xx[-1L]
        len = length(xx)
        cpo.res=numeric(n)
        cpo.res[1L:n] = NA
        cpo.res[xx[seq(1L, len, by=2L)] +1L] = xx[seq(2L, len, by=2L)]
        
        xx = inla.read.binary.file(file=paste(results.dir, .Platform$file.sep,"cpo", .Platform$file.sep,"pit.dat", sep=""))
        n = xx[1L]
        xx = xx[-1L]
        len = length(xx)
        pit.res = numeric(n)
        pit.res[1L:n] = NA
        pit.res[xx[seq(1L, len, by=2L)] +1L] = xx[seq(2L, len, by=2L)]

        fnm=paste(results.dir, .Platform$file.sep,"cpo", .Platform$file.sep,"failure.dat", sep="")
        if (file.exists(fnm)) {
            xx = inla.read.binary.file(fnm)
            n = xx[1L]
            xx = xx[-1L]
            len = length(xx)
            failure.res = numeric(n)
            failure.res[1L:n] = NA
            failure.res[xx[seq(1L, len, by=2L)] +1L] = xx[seq(2L, len, by=2L)]
        }
        else
            failure.res = NULL
        rm(xx)
    } else {
        cpo.res = NULL
        pit.res = NULL
        failure.res = NULL
    }

    ## want NA not NaN
    cpo.res[is.nan(cpo.res)] = NA
    pit.res[is.nan(pit.res)] = NA
    failure.res[is.nan(failure.res)] = NA
    
    return(list(cpo=cpo.res, pit=pit.res, failure=failure.res))
}

`inla.collect.po` =
    function(results.dir,
             debug = FALSE)
{
    alldir = dir(results.dir)
    if (length(grep("^po$", alldir))==1L) {
        if (debug)
            cat(paste("collect po\n", sep=""))
      
        xx = inla.read.binary.file(file=paste(results.dir, .Platform$file.sep,"po", .Platform$file.sep,"po.dat", sep=""))
        n = xx[1L]
        xx = xx[-1L]
        xx = xx[-seq(3, length(xx), by = 3L)] ## skip entry 3, 6, 9, ...
        len = length(xx)
        po.res=numeric(n)
        po.res[1L:n] = NA
        po.res[xx[seq(1L, len, by=2L)] +1L] = xx[seq(2L, len, by=2L)]
    } else {
        po.res = NULL
    }

    ## want NA not NaN
    po.res[is.nan(po.res)] = NA

    return(list(po=po.res))
}

`inla.collect.waic` =
    function(results.dir,
             debug = FALSE)
{
    ## yes, here we use the po-results!!!!
    alldir = dir(results.dir)
    if (length(grep("^po$", alldir))==1L) {
        if (debug)
            cat(paste("collect waic from po-results\n", sep=""))
      
        xx = inla.read.binary.file(file=paste(results.dir, .Platform$file.sep,"po", .Platform$file.sep,"po.dat", sep=""))
        n = xx[1L]
        xx = xx[-1L]
        len = length(xx)
        po.res=numeric(n)
        po2.res=numeric(n)
        po.res[1L:n] = NA
        po.res[xx[seq(1L, len, by=3L)] +1L] = xx[seq(2L, len, by=3L)]
        po2.res[1L:n] = NA
        po2.res[xx[seq(1L, len, by=3L)] +1L] = xx[seq(3L, len, by=3L)]

        ## want NA not NaN
        po.res[is.nan(po.res)] = NA
        po2.res[is.nan(po2.res)] = NA
        
        ## compute waic
        return (list(
            waic = -2*(sum(log(po.res), na.rm=TRUE) - sum(po2.res, na.rm=TRUE)),
            p.eff = sum(po2.res, na.rm=TRUE), 
            local.waic=-2*(log(po.res)-po2.res), 
            local.p.eff=po2.res))
    } else {
        return (NULL)
    }
}

`inla.collect.dic` =
    function(results.dir,
             debug = FALSE)
{
    alldir = dir(results.dir)
    ## get dic (if exists)
    if (length(grep("^dic$", alldir))==1L) {
        if (debug)
            cat(paste("collect dic\n", sep=""))
        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"dic.dat", sep="")
        dic.values = inla.read.binary.file(file)

        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"deviance_e.dat", sep="")
        if (inla.is.fmesher.file(file)) {
            dev.e = c(inla.read.fmesher.file(file))
            dev.e[is.nan(dev.e)] = NA
        } else {
            dev.e = NULL
        }

        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"deviance_e_sat.dat", sep="")
        if (inla.is.fmesher.file(file)) {
            dev.e.sat = c(inla.read.fmesher.file(file))
            dev.e.sat[is.nan(dev.e.sat)] = NA
        } else {
            dev.e.sat = NULL
        }

        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"e_deviance.dat", sep="")
        if (inla.is.fmesher.file(file)) {
            e.dev = c(inla.read.fmesher.file(file))
            e.dev[is.nan(e.dev)] = NA
        } else {
            e.dev = NULL
        }

        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"e_deviance_sat.dat", sep="")
        if (inla.is.fmesher.file(file)) {
            e.dev.sat = c(inla.read.fmesher.file(file))
            e.dev.sat[is.nan(e.dev.sat)] = NA
        } else {
            e.dev.sat = NULL
        }

        f.idx = NULL
        file=paste(results.dir, .Platform$file.sep,"dic", .Platform$file.sep,"family_idx.dat", sep="")
        if (inla.is.fmesher.file(file)) {
            f.idx = c(inla.read.fmesher.file(file)) + 1L  ## convert to R-indexing
            f.idx[is.nan(f.idx)] = NA
        }

        ## if there there is no data at all, then all dic'values are
        ## NA. the returned values are 0, so we override them here.
        if (!is.null(f.idx) && all(is.na(f.idx))) {
            dic.values[] = NA
        }

        local.dic = 2.0*e.dev - dev.e
        local.dic.sat = 2.0*e.dev.sat - dev.e.sat
        local.p.eff = e.dev - dev.e
        fam.dic = dic.values[4L]
        fam.p.eff = dic.values[3L]
        
        if (!is.null(f.idx) && !all(is.na(f.idx))) {
            n.fam = max(f.idx, na.rm = TRUE)
            fam.dic = numeric(n.fam)
            fam.dic.sat = numeric(n.fam)
            fam.p.eff = numeric(n.fam)
            for(i in 1:n.fam) {
                idx = which(f.idx == i)
                fam.dic[i] = sum(local.dic[idx])
                fam.dic.sat[i] = sum(local.dic.sat[idx])
                fam.p.eff[i] = sum(local.p.eff[idx])
            }
        }

        dic = list(
                "dic" = dic.values[4L],
                "p.eff"= dic.values[3L],
                "mean.deviance" = dic.values[1L],
                "deviance.mean" = dic.values[2L], 
                "dic.sat" = dic.values[4L+4L],
                "mean.deviance.sat" = dic.values[4L + 1L],
                "deviance.mean.sat" = dic.values[4L + 2L], 
                "family.dic" = fam.dic, 
                "family.dic.sat" = fam.dic.sat, 
                "family.p.eff" = fam.p.eff, 
                "family" = f.idx, 
                "local.dic" = local.dic, 
                "local.dic.sat" = local.dic.sat, 
                "local.p.eff" = local.p.eff)
    } else {
        dic = NULL
    }
    return(dic)     
}

`inla.collect.q` =
    function(results.dir,
             debug = FALSE)
{
    my.read.pnm = function(...) {
        args = list(...)
        filename = args[[1]]
        if (file.exists(filename) && inla.require("pixmap")) {
            ## disable warnings
            warn = getOption("warn")
            options(warn=-1L) ## disable...
            ret = pixmap::read.pnm(...)
            do.call("options", args = list(warn = warn))
        } else {
            if (file.exists(filename)) {
                warning("You need to install 'pixmap' to read bitmap files.")
            }
            ret = NULL
        }
        return (ret)
    }

    alldir = dir(results.dir)
    if (length(grep("^Q$", alldir))==1L) {
        if (debug)
            cat(paste("collect q\n", sep=""))
        
        file=paste(results.dir, .Platform$file.sep,"Q/precision-matrix.pbm", sep="")
        Q.matrix = my.read.pnm(file)

        file=paste(results.dir, .Platform$file.sep,"Q/precision-matrix-reordered.pbm", sep="")
        Q.matrix.reorder = my.read.pnm(file)

        file=paste(results.dir, .Platform$file.sep,"Q/precision-matrix_L.pbm", sep="")
        L = my.read.pnm(file)

        if (is.null(Q.matrix) && is.null(Q.matrix.reorder) && is.null(L)) {
            q = NULL
        } else {
            q = list(Q = Q.matrix, Q.reorder = Q.matrix.reorder, L = L)
        }
    } else {
        q = NULL
    }
    
    return(q)     
}

`inla.collect.graph` =
    function(results.dir,
             debug = FALSE)
{
    alldir = dir(results.dir)
    if (length(grep("^graph.dat$", alldir))==1L) {

        if (debug) {
            cat(paste("collect graph\n", sep=""))
        }
        file=paste(results.dir, .Platform$file.sep, "graph.dat", sep="")
        g = inla.read.graph(file)
    } else {
        g = NULL
    }

    return (list(graph = g))
}

`inla.collect.hyperpar` =
    function(results.dir,
             debug=FALSE)
{
    alldir = dir(results.dir)
    all.hyper = alldir[grep("^hyperparameter", alldir)]
    hyper = all.hyper[grep("user-scale$", all.hyper)]
    n.hyper = length(hyper)
    if (n.hyper > 0L) {
        ## get names for hyperpar
        names.hyper = character(n.hyper)
        for(i in 1L:n.hyper) {
            tag = paste(results.dir, .Platform$file.sep, hyper[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag)) {
                names.hyper[i] = "missing NAME"
            } else {
                names.hyper[i] = readLines(tag, n=1L)
            }
        }

        ## get summary and marginals
        summary.hyper = numeric()
        marginal.hyper = list()
        marginal.hyper[[n.hyper]] = NA
        
        for(i in 1L:n.hyper) {
            first.time = (i == 1L)
            dir.hyper =  paste(results.dir, .Platform$file.sep, hyper[i], sep="")
            file = paste(dir.hyper, .Platform$file.sep,"summary.dat", sep="")
            hyperid = inla.collect.hyperid(dir.hyper)
            dd = inla.read.binary.file(file)[-1L]
            summ = dd
            if (first.time)
                col.nam = c("mean","sd")
            if (length(grep("^quantiles.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "quantiles.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, paste(as.character(qq[, 1L]),"quant", sep=""))
            }
            if (length(grep("^mode.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "mode.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, "mode")
            }

            if (length(grep("^cdf.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "cdf.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, paste(as.character(qq[, 1L]),"cdf", sep=""))
            }
            summary.hyper = rbind(summary.hyper, summ)
            file =paste(results.dir, .Platform$file.sep, hyper[i], .Platform$file.sep,"marginal-densities.dat", sep="")
            xx = inla.read.binary.file(file)
            marg1 = inla.interpret.vector(xx, debug=debug)
            attr(marg1, "hyperid") = hyperid
            rm(xx)
            if (!is.null(marg1)) {
                colnames(marg1) = c("x","y")
            }

            if (inla.internal.experimental.mode) {
                class(marg1) = "inla.marginal"
                attr(marg1, "inla.tag") = paste("marginal hyper", names.hyper[i])
            }
            
            marginal.hyper[[i]] = marg1
        }
        names(marginal.hyper) = names.hyper
        rownames(summary.hyper) = names.hyper
        colnames(summary.hyper) = col.nam
    } else {
        marginal.hyper=NULL
        summary.hyper=NULL
    }

    if (inla.internal.experimental.mode) {
        if (!is.null(marginal.hyper)) {
            class(marginal.hyper) = "inla.marginals"
            attr(marginal.hyper, "inla.tag") = "marginal hyper"
        }
    }
    
    ## collect also the hyperparameters in the internal scale
    all.hyper = alldir[grep("^hyperparameter", alldir)]
    hyper = all.hyper[-grep("user-scale$", all.hyper)]
    n.hyper = length(hyper)
    if (n.hyper > 0L) {
        ## get names for hyperpar
        names.hyper = character(n.hyper)
        for(i in 1L:n.hyper) {
            tag = paste(results.dir, .Platform$file.sep, hyper[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag))
                names.hyper[i] = "missing NAME"
            else
                names.hyper[i] = readLines(tag, n=1L)
        }

        ## get summary and marginals
        internal.summary.hyper = numeric()
        internal.marginal.hyper = list()
        internal.marginal.hyper[[n.hyper]] = NA
        for(i in 1L:n.hyper) {
            first.time = (i == 1L)
            dir.hyper =  paste(results.dir, .Platform$file.sep, hyper[i], sep="")
            file = paste(dir.hyper, .Platform$file.sep,"summary.dat", sep="")
            hyperid = inla.collect.hyperid(dir.hyper)
            dd = inla.read.binary.file(file)[-1L]
            summ = dd
            if (first.time)
                col.nam = c("mean","sd")
            if (length(grep("^quantiles.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "quantiles.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, paste(as.character(qq[, 1L]),"quant", sep=""))
            }
            if (length(grep("^mode.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "mode.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, "mode")
            }
            if (length(grep("^cdf.dat$", dir(dir.hyper)))>0L) {
                qq = inla.interpret.vector(inla.read.binary.file(paste(dir.hyper, .Platform$file.sep, "cdf.dat", sep="")),
                        debug=debug)
                summ = c(summ, qq[, 2L])
                if (first.time)
                    col.nam = c(col.nam, paste(as.character(qq[, 1L]),"cdf", sep=""))
            }
            if (first.time) {
                internal.summary.hyper = matrix(NA, n.hyper, length(summ))
            }
            internal.summary.hyper[i, ] = summ
            file =paste(results.dir, .Platform$file.sep, hyper[i], .Platform$file.sep,"marginal-densities.dat", sep="")
            xx = inla.read.binary.file(file)
            marg1 = inla.interpret.vector(xx, debug=debug)
            attr(marg1, "hyperid") = hyperid
            rm(xx)
            if (!is.null(marg1))
                colnames(marg1) = c("x","y")

            if (inla.internal.experimental.mode) {
                class(marg1) = "inla.marginal"
                attr(marg1, "inla.tag") = paste("marginal hyper internal", names.hyper[i])
            }
            
            internal.marginal.hyper[[i]] = marg1
        }
        names(internal.marginal.hyper) = names.hyper
        rownames(internal.summary.hyper) = names.hyper
        colnames(internal.summary.hyper) = col.nam
    } else {
        internal.summary.hyper=NULL
        internal.marginal.hyper=NULL
    }
    
    if (inla.internal.experimental.mode) {
        if (!is.null(internal.marginal.hyper)) {
            class(internal.marginal.hyper) = "inla.marginals"
            attr(internal.marginal.hyper, "inla.tag") = "marginal hyper internal"
        }
    }
    
    ret=list(summary.hyperpar= as.data.frame(summary.hyper),
        marginals.hyperpar=marginal.hyper,
        internal.summary.hyperpar = as.data.frame(internal.summary.hyper),
        internal.marginals.hyperpar = internal.marginal.hyper)
    return(ret)
}

`inla.collect.mlik` =
    function(results.dir,
             debug = FALSE)
{
    alldir = dir(results.dir)
    if (length(grep("^marginal-likelihood$", alldir))==1L) {
        if (debug)
            cat(paste("collect mlik\n", sep=""))
        file=paste(results.dir, .Platform$file.sep,"marginal-likelihood",
            .Platform$file.sep,"marginal-likelihood.dat", sep="")
        mlik.res = matrix(inla.read.binary.file(file), 2L, 1L)
        rownames(mlik.res) = c("log marginal-likelihood (integration)",
                               "log marginal-likelihood (Gaussian)")
    }
    else
        mlik.res = NULL

    return(list(mlik=mlik.res))
}

`inla.collect.predictor` =
    function(results.dir,
             return.marginals.predictor = TRUE,
             debug = FALSE)
{
    alldir = dir(results.dir)

    ##FIRST: get the linear predictor
    subdir=paste(results.dir, .Platform$file.sep,"predictor", sep="")

    if (length(dir(subdir))>3L) {
        if (debug)
            cat(paste("collect linear predictor\n", sep=""))
        
        if (debug)
            cat("...read summary.dat\n")
        file=paste(subdir, .Platform$file.sep,"summary.dat", sep="")
        dd = matrix(inla.read.binary.file(file=file), ncol=3L, byrow=TRUE)[,-1L, drop=FALSE]
        col.nam = c("mean","sd")

        ## info about size
        size.info = inla.collect.size(subdir)
        if (!is.null(size.info)) {
            A = (size.info$nrep == 2)
            n = size.info$n
            nA = size.info$Ntotal - size.info$n
        } else {
            ## should not happen
            stop("This should not happen")
        }

        ## get quantiles if computed
        if (length(grep("^quantiles.dat$", dir(subdir)))==1L) {
            if (debug)
                cat("...read quantiles.dat\n")
            file=paste(subdir, .Platform$file.sep,"quantiles.dat", sep="")
            xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
            len = dim(xx)[2L]
            qq = xx[, seq(2L, len, by=2L), drop=FALSE]
            col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
            dd = cbind(dd, t(qq))
            rm(xx)
        }

        if (length(grep("^mode.dat$", dir(subdir)))==1L) {
            if (debug)
                cat("...read mode.dat\n")
            file=paste(subdir, .Platform$file.sep,"mode.dat", sep="")
            xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
            len = dim(xx)[2L]
            qq = xx[, seq(2L, len, by=2L)]
            col.nam = c(col.nam, "mode")
            dd = cbind(dd, qq)
            rm(xx)
        }

        ## get cdf if computed
        if (length(grep("^cdf.dat$", dir(subdir)))==1L) {
            if (debug)
                cat("...read cdf.dat\n")
            file=paste(subdir, .Platform$file.sep,"cdf.dat", sep="")
            xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
            len = dim(xx)[2L]
            qq = xx[, seq(2L, len, by=2L), drop=FALSE]
            col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
            dd = cbind(dd, t(qq))
            rm(xx)
        } else {
            if (debug)
                cat("... no cdf.dat\n")
        }
        
        ## get kld
        if (debug)
            cat("...read kld\n")
        kld =  matrix(inla.read.binary.file(file=paste(subdir, .Platform$file.sep,"symmetric-kld.dat", sep="")),
                ncol=2L, byrow=TRUE)
        dd = cbind(dd, kld[, 2L, drop=FALSE])
        col.nam = c(col.nam, "kld")
        colnames(dd) = col.nam
        summary.linear.predictor = as.data.frame(dd)

        if (A) {
            rownames(summary.linear.predictor) = c(paste("APredictor.", inla.num(1L:nA), sep=""),
                                                   paste("Predictor.", inla.num(1:n), sep=""))
        } else {
            rownames(summary.linear.predictor) = paste("Predictor.", inla.num(1L:size.info$Ntotal), sep="")
        }
        
        if (return.marginals.predictor) {
            if (debug)
                cat("...read marginal-densities.dat\n")
            file=paste(subdir, .Platform$file.sep,"marginal-densities.dat", sep="")
            xx = inla.read.binary.file(file)
            rr = inla.interpret.vector.list(xx, debug=debug)
            rm(xx)
            if (!is.null(rr)) {
                if (A) {
                    names(rr) = c(paste("APredictor.", inla.num(1L:nA), sep=""),
                                  paste("Predictor.", inla.num(1L:n), sep=""))
                } else {
                    names(rr) = paste("Predictor.", as.character(1L:length(rr)), sep="")
                }
                names.rr = names(rr)
                for(i in 1L:length(rr)) {
                    colnames(rr[[i]]) = c("x", "y")

                    if (inla.internal.experimental.mode) {
                        class(rr[[i]]) = "inla.marginal"
                        attr(rr[[i]], "inla.tag") = paste("marginal linear predictor", names.rr[i])
                    }
                }
            }

            if (inla.internal.experimental.mode) {
                class(rr) = "inla.marginals"
                attr(rr, "inla.tag") = "marginals linear predictor"
            }
            marginals.linear.predictor = rr
        } else {
            marginals.linear.predictor = NULL
        }

    } else {
        summary.linear.predictor = NULL
        marginals.linear.predictor = NULL
        size.info = NULL
    }

    ##SECOND: get the inverse linear predictor(if computed)
    if (length(grep("^predictor-user-scale$", alldir))==1L) {
        subdir=paste(results.dir, .Platform$file.sep,"predictor-user-scale", sep="")
        if (length(dir(subdir))>3L) {
            if (debug)
                cat(paste("collect fitted values\n", sep=""))
            
            file=paste(subdir, .Platform$file.sep,"summary.dat", sep="")
            dd = matrix(inla.read.binary.file(file=file), ncol=3L, byrow=TRUE)[,-1L, drop=FALSE]
            col.nam = c("mean","sd")

            ## get quantiles if computed
            if (length(grep("^quantiles.dat$", dir(subdir)))==1L) {
                file=paste(subdir, .Platform$file.sep,"quantiles.dat", sep="")
                xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
                len = dim(xx)[2L]
                qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
                dd = cbind(dd, t(qq))
                rm(xx)
            }
            if (length(grep("^mode.dat$", dir(subdir)))==1L) {
                file=paste(subdir, .Platform$file.sep,"mode.dat", sep="")
                xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
                len = dim(xx)[2L]
                qq = xx[, seq(2L, len, by=2L)]
                col.nam = c(col.nam, "mode")
                dd = cbind(dd, qq)
                rm(xx)
            }

            ## get cdf if computed
            if (length(grep("^cdf.dat$", dir(subdir)))==1L) {
                file=paste(subdir, .Platform$file.sep,"cdf.dat", sep="")
                xx = inla.interpret.vector(inla.read.binary.file(file), debug=debug)
                len = dim(xx)[2L]
                qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
                dd = cbind(dd, t(qq))
                rm(xx)
            }
       
            colnames(dd) = col.nam
            if (A) {
                rownames(dd) = c(paste("fitted.APredictor.", inla.num(1L:nA), sep=""),
                                 paste("fitted.Predictor.", inla.num(1L:n), sep=""))
            } else {
                rownames(dd) = paste("fitted.Predictor.", inla.num(1L:n), sep="")
            }
            summary.fitted.values = as.data.frame(dd)

            if (return.marginals.predictor) {
                file=paste(subdir, .Platform$file.sep,"marginal-densities.dat", sep="")
                xx = inla.read.binary.file(file)
                rr = inla.interpret.vector.list(xx, debug=debug)
                rm(xx)
                if (!is.null(rr)) {
                    if (A) {
                        names(rr) = c(paste("fitted.APredictor.", inla.num(1L:nA), sep=""),
                                      paste("fitted.Predictor.", inla.num(1:n), sep=""))
                    } else {
                        names(rr) = paste("fitted.Predictor.", inla.num(1L:length(rr)), sep="")
                    }
                    names.rr = names(rr)
                    for(i in 1L:length(rr)) {
                        colnames(rr[[i]]) = c("x", "y")
                        if (inla.internal.experimental.mode) {
                            class(rr[[i]]) = "inla.marginal"
                            attr(rr[[i]], "inla.tag") = paste("marginal fitted values", names.rr[i])
                        }
                    }
                }

                if (inla.internal.experimental.mode) {
                    class(rr) = "inla.marginals"
                    attr(rr, "inla.tag") = "marginals fitted values"
                }
                marginals.fitted.values = rr
            } else {
                marginals.fitted.values = NULL
            }
        } else {
            summary.fitted.values = NULL
            marginals.fitted.values = NULL
        }
    } else {
        summary.fitted.values = NULL
        marginals.fitted.values = NULL
    }


    res = list(summary.linear.predictor= as.data.frame(summary.linear.predictor),
            marginals.linear.predictor=marginals.linear.predictor,
            summary.fitted.values=as.data.frame(summary.fitted.values),
            marginals.fitted.values=marginals.fitted.values,
            size.linear.predictor = size.info)

    return(res)
}

`inla.collect.random` =
    function(results.dir,
             return.marginals.random,
             debug = FALSE)
{
    alldir = dir(results.dir)
    random = alldir[grep("^random.effect", alldir)]
    n.random = length(random)
    if (debug)
        print("collect random effects")

    ##read the names and model of the random effects
    if (n.random > 0L) {
        names.random = character(n.random)
        model.random = inla.trim(character(n.random))
        for(i in 1L:n.random) {
            tag = paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag))
                names.random[i] = "missing NAME"
            else
                names.random[i] = readLines(tag, n=1L)
            modelname = inla.trim(paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"MODEL", sep=""))
            if (!file.exists(modelname))
                model.random[i] = "NoModelName"
            else
                model.random[i] = inla.trim(readLines(modelname, n=1L))
        }

        summary.random = list()
        summary.random[[n.random]] = NA
        size.random = list()
        size.random[[n.random]] = NA

        if (return.marginals.random) {
            marginals.random = list()
            marginals.random[[n.random]] = NA
        } else {
            marginals.random = NULL
        }
        
        for(i in 1L:n.random) {
            if (debug)
                print(paste("read random ", i , " of ", n.random))
            ##read the summary
            file= paste(results.dir, .Platform$file.sep, random[i], sep="")
            dir.random = dir(file)

            if (length(dir.random) > 5L) {
                dd = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"summary.dat", sep="")), ncol=3L, byrow=TRUE)
                col.nam = c("ID","mean","sd")
                ##read quantiles if existing
                if (debug)
                    cat("...quantiles.dat if any\n")
                if (length(grep("^quantiles.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"quantiles.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
                    dd = cbind(dd, t(qq))
                }
                if (length(grep("^mode.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"mode.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L)]
                    col.nam = c(col.nam, "mode")
                    dd = cbind(dd, qq)
                }

                ##read cdf if existing
                if (debug)
                    cat("...cdf.dat if any\n")
                if (length(grep("^cdf.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"cdf.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
                    dd = cbind(dd, t(qq))
                }

                ##read kld
                if (debug)
                    cat("...kld\n")
                kld1 = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"symmetric-kld.dat", sep="")),
                    ncol=2L, byrow=TRUE)
                qq = kld1[, 2L, drop=FALSE]
                dd = cbind(dd, qq)
                if (debug)
                    cat("...kld done\n")

            
                col.nam = c(col.nam, "kld")
                colnames(dd) = col.nam
                summary.random[[i]] = as.data.frame(dd)

                if (return.marginals.random) {
                    xx = inla.read.binary.file(paste(file, .Platform$file.sep,"marginal-densities.dat", sep=""))
                    rr = inla.interpret.vector.list(xx, debug=debug)
                    rm(xx)
                    if (!is.null(rr)) {
                        nd = length(rr)
                        names(rr) = paste("index.", as.character(1L:nd), sep="")
                        names.rr = names(rr)
                        for(j in 1L:nd) {
                            colnames(rr[[j]]) = c("x", "y")
                            if (inla.internal.experimental.mode) {
                                class(rr[[j]]) = "inla.marginal"
                                attr(rr[[j]], "inla.tag") = paste("marginal random", names.random[i], names.rr[j])
                            }
                        }
                    }

                    if (inla.internal.experimental.mode) {
                        class(rr) = "inla.marginals"
                        attr(rr, "inla.tag") = paste("marginals random",  names.random[i])
                    }
                    marginals.random[[i]] = if (is.null(rr)) NA else rr
                } else {
                    stopifnot(is.null(marginals.random))
                }

                ## if id.names are present,  override the default names
                id.names = inla.readLines(paste(file, .Platform$file.sep,"id-names.dat", sep=""))
                if (!is.null(id.names)) {
                    len.id.names = length(id.names)
                    summary.random[[i]]$ID[1L:len.id.names] = id.names
                    if (length(marginals.random) >= i && !is.na(marginals.random[[i]])) {
                        names(marginals.random[[i]][1L:len.id.names]) = id.names
                    }
                }
                
            } else {
                N.file = paste(file, .Platform$file.sep,"N", sep="")
                if (!file.exists(N.file)) {
                    N = 0L
                } else {
                    N = scan(file=N.file, what = numeric(0L), quiet=TRUE)
                }
                summary.random[[i]] = data.frame("mean" = rep(NA, N), "sd" = rep(NA, N), "kld" = rep(NA, N))
                marginals.random = NULL
            }

            size.random[[i]] = inla.collect.size(file)
        }
        names(summary.random) = names.random

        ## could be that marginals.random is a list of lists of NULL or NA
        if (!is.null(marginals.random)) {
            if (all(sapply(marginals.random, function(x) (is.null(x) || is.na(x)))))
                marginals.random = NULL
        }

        if (!is.null(marginals.random) && (length(marginals.random) > 0L)) {
            names(marginals.random) = names.random
        }
    } else {
        if (debug)
            cat("No random effets\n")
        model.random=NULL
        summary.random=NULL
        marginals.random=NULL
        size.random = NULL
    }
    
   res = list(model.random=model.random,
            summary.random= lapply(summary.random, as.data.frame), 
            marginals.random=marginals.random,
            size.random = size.random)
    return(res)
}

`inla.collect.spde2.blc` =
    function(results.dir,
             return.marginals.random,
             debug = FALSE)
{
    ## a copy from collect.random
    alldir = dir(results.dir)
    random = alldir[grep("^spde2.blc", alldir)]
    n.random = length(random)
    if (debug)
        print("collect random effects")

    ##read the names and model of the random effects
    if (n.random > 0L) {
        names.random = character(n.random)
        model.random = inla.trim(character(n.random))
        for(i in 1L:n.random) {
            tag = paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag))
                names.random[i] = "missing NAME"
            else
                names.random[i] = readLines(tag, n=1L)
            modelname = inla.trim(paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"MODEL", sep=""))
            if (!file.exists(modelname))
                model.random[i] = "NoModelName"
            else
                model.random[i] = inla.trim(readLines(modelname, n=1L))
        }
        

        summary.random = list()
        summary.random[[n.random]] = NA
        size.random = list()
        size.random[[n.random]] = NA

        if (return.marginals.random) {
            marginals.random = list()
            marginals.random[[n.random]] = NA
        } else {
            marginals.random = NULL
        }
        
        for(i in 1L:n.random) {
            if (debug)
                print(paste("read random ", i , " of ", n.random))
            ##read the summary
            file= paste(results.dir, .Platform$file.sep, random[i], sep="")
            dir.random = dir(file)

            if (length(dir.random) > 4L) {
                dd = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"summary.dat", sep="")), ncol=3L, byrow=TRUE)
                col.nam = c("ID","mean","sd")
                ##read quantiles if existing
                if (debug)
                    cat("...quantiles.dat if any\n")
                if (length(grep("^quantiles.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"quantiles.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
                    dd = cbind(dd, t(qq))
                }
                if (length(grep("^mode.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"mode.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, "mode")
                    dd = cbind(dd, t(qq))
                }

                ##read cdf if existing
                if (debug)
                    cat("...cdf.dat if any\n")
                if (length(grep("^cdf.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"cdf.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
                    dd = cbind(dd, t(qq))
                }

                ##read kld
                if (debug)
                    cat("...kld\n")
                kld1 = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"symmetric-kld.dat", sep="")),
                    ncol=2L, byrow=TRUE)
                qq = kld1[, 2L, drop=FALSE]
                dd = cbind(dd, qq)
                if (debug)
                    cat("...kld done\n")

            
                col.nam = c(col.nam, "kld")
                colnames(dd) = col.nam
                summary.random[[i]] = as.data.frame(dd)

                if (return.marginals.random) {
                    xx = inla.read.binary.file(paste(file, .Platform$file.sep,"marginal-densities.dat", sep=""))
                    rr = inla.interpret.vector.list(xx, debug=debug)
                    rm(xx)
                    if (!is.null(rr)) {
                        nd = length(rr)
                        names(rr) = paste("index.", as.character(1L:nd), sep="")
                        names.rr = names(rr)
                        for(j in 1L:nd) {
                            colnames(rr[[j]]) = c("x", "y")
                            if (inla.internal.experimental.mode) {
                                class(rr[[j]]) = "inla.marginal"
                                attr(rr[[j]], "inla.tag") = paste("marginal random", names.random[i], names.rr[j])
                            }
                        }
                    }

                    if (inla.internal.experimental.mode) {
                        class(rr) = "inla.marginals"
                        attr(rr, "inla.tag") = paste("marginals random",  names.random[i])
                    }
                    marginals.random[[i]] = rr
                } else {
                    stopifnot(is.null(marginals.random))
                }
            } else {
                N.file = paste(file, .Platform$file.sep,"N", sep="")
                if (!file.exists(N.file)) {
                    N = 0L
                } else {
                    N = scan(file=N.file, what = numeric(0L), quiet=TRUE)
                }
                summary.random[[i]] = data.frame("mean" = rep(NA, N), "sd" = rep(NA, N), "kld" = rep(NA, N))
                marginals.random = NULL
            }

            size.random[[i]] = inla.collect.size(file)
        }
        names(summary.random) = names.random

        ## could be that marginals.random is a list of lists of NULL or NA
        if (!is.null(marginals.random)) {
            if (all(sapply(marginals.random, function(x) (is.null(x) || is.na(x)))))
                marginals.random = NULL
        }

        if (!is.null(marginals.random) && (length(marginals.random) > 0L)) {
            names(marginals.random) = names.random
        }
    } else {
        if (debug)
            cat("No random effets\n")
        model.random=NULL
        summary.random=NULL
        marginals.random=NULL
        size.random = NULL
    }
    
    res = list(model.spde2.blc=model.random,
            summary.spde2.blc= lapply(summary.random, as.data.frame), 
            marginals.spde2.blc=marginals.random,
            size.spde2.blc = size.random)
    return(res)
}

`inla.collect.spde3.blc` =
    function(results.dir,
             return.marginals.random,
             debug = FALSE)
{
    ## a copy from collect.random
    alldir = dir(results.dir)
    random = alldir[grep("^spde3.blc", alldir)]
    n.random = length(random)
    if (debug)
        print("collect random effects")

    ##read the names and model of the random effects
    if (n.random > 0L) {
        names.random = character(n.random)
        model.random = inla.trim(character(n.random))
        for(i in 1L:n.random) {
            tag = paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"TAG", sep="")
            if (!file.exists(tag))
                names.random[i] = "missing NAME"
            else
                names.random[i] = readLines(tag, n=1L)
            modelname = inla.trim(paste(results.dir, .Platform$file.sep, random[i], .Platform$file.sep,"MODEL", sep=""))
            if (!file.exists(modelname))
                model.random[i] = "NoModelName"
            else
                model.random[i] = inla.trim(readLines(modelname, n=1L))
        }
        

        summary.random = list()
        summary.random[[n.random]] = NA
        size.random = list()
        size.random[[n.random]] = NA

        if (return.marginals.random) {
            marginals.random = list()
            marginals.random[[n.random]] = NA
        } else {
            marginals.random = NULL
        }
        
        for(i in 1L:n.random) {
            if (debug)
                print(paste("read random ", i , " of ", n.random))
            ##read the summary
            file= paste(results.dir, .Platform$file.sep, random[i], sep="")
            dir.random = dir(file)

            if (length(dir.random) > 4L) {
                dd = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"summary.dat", sep="")), ncol=3L, byrow=TRUE)
                col.nam = c("ID","mean","sd")
                ##read quantiles if existing
                if (debug)
                    cat("...quantiles.dat if any\n")
                if (length(grep("^quantiles.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"quantiles.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L]),"quant", sep=""))
                    dd = cbind(dd, t(qq))
                }
                if (length(grep("^mode.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"mode.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, "mode")
                    dd = cbind(dd, t(qq))
                }

                ##read cdf if existing
                if (debug)
                    cat("...cdf.dat if any\n")
                if (length(grep("^cdf.dat$", dir.random))==1L) {
                    xx = inla.interpret.vector(inla.read.binary.file(paste(file, .Platform$file.sep,"cdf.dat", sep="")),
                            debug=debug)
                    len = dim(xx)[2L]
                    qq = xx[, seq(2L, len, by=2L), drop=FALSE]
                    col.nam = c(col.nam, paste(as.character(xx[, 1L])," cdf", sep=""))
                    dd = cbind(dd, t(qq))
                }

                ##read kld
                if (debug)
                    cat("...kld\n")
                kld1 = matrix(inla.read.binary.file(file=paste(file, .Platform$file.sep,"symmetric-kld.dat", sep="")),
                    ncol=2L, byrow=TRUE)
                qq = kld1[, 2L, drop=FALSE]
                dd = cbind(dd, qq)
                if (debug)
                    cat("...kld done\n")

            
                col.nam = c(col.nam, "kld")
                colnames(dd) = col.nam
                summary.random[[i]] = as.data.frame(dd)

                if (return.marginals.random) {
                    xx = inla.read.binary.file(paste(file, .Platform$file.sep,"marginal-densities.dat", sep=""))
                    rr = inla.interpret.vector.list(xx, debug=debug)
                    rm(xx)
                    if (!is.null(rr)) {
                        nd = length(rr)
                        names(rr) = paste("index.", as.character(1L:nd), sep="")
                        names.rr = names(rr)
                        for(j in 1L:nd) {
                            colnames(rr[[j]]) = c("x", "y")
                            if (inla.internal.experimental.mode) {
                                class(rr[[j]]) = "inla.marginal"
                                attr(rr[[j]], "inla.tag") = paste("marginal random", names.random[i], names.rr[j])
                            }
                        }
                    }

                    if (inla.internal.experimental.mode) {
                        class(rr) = "inla.marginals"
                        attr(rr, "inla.tag") = paste("marginals random",  names.random[i])
                    }
                    marginals.random[[i]] = rr
                } else {
                    stopifnot(is.null(marginals.random))
                }
            } else {
                N.file = paste(file, .Platform$file.sep,"N", sep="")
                if (!file.exists(N.file)) {
                    N = 0L
                } else {
                    N = scan(file=N.file, what = numeric(0L), quiet=TRUE)
                }
                summary.random[[i]] = data.frame("mean" = rep(NA, N), "sd" = rep(NA, N), "kld" = rep(NA, N))
                marginals.random = NULL
            }

            size.random[[i]] = inla.collect.size(file)
        }
        names(summary.random) = names.random

        ## could be that marginals.random is a list of lists of NULL or NA
        if (!is.null(marginals.random)) {
            if (all(sapply(marginals.random, function(x) (is.null(x) || is.na(x)))))
                marginals.random = NULL
        }

        if (!is.null(marginals.random) && (length(marginals.random) > 0L)) {
            names(marginals.random) = names.random
        }
    } else {
        if (debug)
            cat("No random effets\n")
        model.random=NULL
        summary.random=NULL
        marginals.random=NULL
        size.random = NULL
    }
    
    res = list(model.spde3.blc=model.random,
            summary.spde3.blc= lapply(summary.random, as.data.frame), 
            marginals.spde3.blc=marginals.random,
            size.spde3.blc = size.random)
    return(res)
}

`inla.image.reduce` = function(im, image.dim=256)
{
    ## reduce image IM to image.dim IMAGE.DIM and return the image as a matrix.
    ## order the indices so the output can be plotted by image()
    
    if ((class(im) != "pixmapGrey") || (im@size[1L] != im@size[2L])) {
        return (im)
    } else {
        return (im@grey)
    }

    ## do not need this anymore as we do this in GMRFLib.
    if (FALSE) {
        if (image.dim >= im@size[1L]) {
            n = as.integer(im@size[1L])
            x  = matrix(NA, n, n)
            for(j in 1L:n)
                x[j, n-(1L:n)+1L] = im@grey[1L:n, j]
            return (x)
        }
        block = ceiling(im@size[1L]/image.dim)
        n = floor(im@size[1L]/block)
        ii = jj = 0L
        x = matrix(NA, n, n)
        for(i in seq(1L, im@size[1L]-block+1L, by=block)) {
            ii = ii + 1L
            jj = 0L
            for(j in seq(1L, im@size[1L]-block+1L, by=block)) {
                jj = jj + 1L
                x[jj, n-ii+1L] = min(im@grey[i:(i+block-1L), j:(j+block-1L)])
            }
        }
        return (x)
    }
}

`inla.collect.offset.linear.predictor` = function(results.dir, debug = FALSE)
{
    filename = paste(results.dir, "/totaloffset/totaloffset.dat", sep="")
    stopifnot(file.exists(filename))

    xx = inla.read.binary.file(filename)
    return (list(offset.linear.predictor = xx))
}
inbo/INLA documentation built on Dec. 6, 2019, 9:51 a.m.
rdrr.io home R language documentation Run R code online
CRAN packages Bioconductor packages R-Forge packages GitHub packages
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
inbo/INLA
Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

R/collect.results.R
In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

R Package Documentation

Browse R Packages

We want your feedback!

inbo/INLA Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

R/collect.results.R In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

R Package Documentation

Browse R Packages

We want your feedback!

inbo/INLA
Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

R/collect.results.R
In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations
