R/schlather.R

##This file contains all the function to fit the max-stable
##characterisation of Schlather

##This functions fits the model without any spatial structure for the
##GEV parameters. Thus, each GEV parameters are estimated at each
##location. However, if fit.marge = FALSE, observation are supposed to
##be unit Frechet and only the covariance function parameters are
##estimated.
schlatherfull <- function(data, coord, start, cov.mod = "whitmat", ...,
                          fit.marge = FALSE, warn = TRUE, method = "BFGS",
                          control = list(), corr = FALSE,
                          weights = NULL, check.grad = FALSE){
    ##data is a matrix with each column corresponds to one location
    ##locations is a matrix giving the coordinates (1 row = 1 station)
    n.site <- ncol(data)
    n.obs <- nrow(data)
    dist.dim <- ncol(coord)
    n.pairs <- n.site * (n.site - 1) / 2

    dist <- distance(coord)
    weighted <- !is.null(weights)

    if (!weighted)
        ##Set the weights to 0 as it won't be used anyway
        weights <- 0

    if (!(cov.mod %in% c("whitmat","cauchy","powexp","bessel","caugen")))
        stop("''cov.mod'' must be one of 'whitmat', 'cauchy', 'powexp', 'bessel', 'caugen'")

    if (cov.mod == "whitmat")
        cov.mod.num <- 1
    if (cov.mod == "cauchy")
        cov.mod.num <- 2
    if (cov.mod == "powexp")
        cov.mod.num <- 3
    if (cov.mod == "bessel")
        cov.mod.num <- 4
    if (cov.mod == "caugen")
        cov.mod.num <- 5

    param <- c("nugget", "range", "smooth")

    if (cov.mod == "caugen")
        param <- c(param, "smooth2")

    else
        ##Fix it to 0 as it won't be used anyway
        smooth2 <- 0

    if (fit.marge){
        loc.names <- paste("loc", 1:n.site, sep="")
        scale.names <- paste("scale", 1:n.site, sep="")
        shape.names <- paste("shape", 1:n.site, sep="")
        param <- c(param, loc.names, scale.names, shape.names)
    }

    else
        loc.names <- scale.names <- shape.names <- rep(1, n.site)

    ##First create a "void" function
    nplk <- function(x) x

    ##And define the "body" of the function as the number of parameters
    ##to estimate depends on n.site
    body(nplk) <- parse(text = paste("-.C(C_schlatherfull, as.integer(cov.mod.num), as.double(data), as.double(dist), as.integer(n.site), as.integer(n.obs), as.integer(dist.dim), as.integer(weighted), as.double(weights),",
                        paste("as.double(c(", paste(loc.names, collapse = ","), ")), "),
                        paste("as.double(c(", paste(scale.names, collapse = ","), ")), "),
                        paste("as.double(c(", paste(shape.names, collapse = ","), ")), "),
                        "as.double(nugget), as.double(range), as.double(smooth), as.double(smooth2), fit.marge, dns = double(1), NAOK = TRUE)$dns"))

    fixed.param <- list(...)[names(list(...)) %in% param]

    ##Define the formal arguments of the function
    form.nplk <- NULL
    for (i in 1:length(param))
        form.nplk <- c(form.nplk, alist(a=))

    names(form.nplk) <- param
    formals(nplk) <- form.nplk

    if (missing(start)) {

        start <- list()
        if (fit.marge){
            locs <- scales <- rep(NA, n.site)
            shapes <- rep(0, n.site)

            for (i in 1:n.site){
                marg.param <- gevmle(data[,i])
                locs[i] <- marg.param["loc"]
                scales[i] <- marg.param["scale"]
                shapes[i] <- marg.param["shape"]
            }

            start <- as.list(unlist(list(loc = locs, scale = scales, shape = shapes)))
        }

        if (length(fixed.param) > 0){
            args <- c(list(data = data, coord = coord, cov.mod = cov.mod, marge = "emp"), fixed.param)
            cov.start <- do.call("fitcovariance", args)$param
        }

        else
            cov.start <- fitcovariance(data, coord, cov.mod, marge = "emp")$param

        start <- c(as.list(cov.start), start)
        start <- start[!(param %in% names(list(...)))]
    }


    if (!is.list(start))
        stop("'start' must be a named list")

    if (!length(start))
        stop("there are no parameters left to maximize over")

    nm <- names(start)
    l <- length(nm)
    f <- formals(nplk)
    names(f) <- param
    m <- match(nm, param)

    if(any(is.na(m)))
        stop("'start' specifies unknown arguments")

    formals(nplk) <- c(f[m], f[-m])
    nllh <- function(p, ...) nplk(p, ...)

    if(l > 1)
        body(nllh) <- parse(text = paste("nplk(", paste("p[",1:l,
                            "]", collapse = ", "), ", ...)"))

    if(any(!(param %in% c(nm,names(fixed.param)))))
        stop("unspecified parameters")

    start.arg <- c(list(p = unlist(start)), fixed.param)

    init.lik <- do.call("nllh", start.arg)
    if (warn && (init.lik >= 1.0e15))
        warning("negative log-likelihood is infinite at starting values")

    if (method == "nlminb"){
        start <- as.numeric(start)
        opt <- nlminb(start, nllh, ..., control = control)
        opt$counts <- opt$evaluations
        opt$value <- opt$objective
        names(opt$par) <- nm

        if ((opt$convergence != 0) || (opt$value >= 1.0e15)) {
            if (warn)
                warning("optimization may not have succeeded")
        }

        if (opt$convergence == 0)
            opt$convergence <- "successful"
    }

    if (method == "nlm"){
        start <- as.numeric(start)
        opt <- nlm(nllh, start, ...)
        opt$counts <- opt$iterations
        names(opt$counts) <- "function"
        opt$value <- opt$minimum
        opt$par <- opt$estimate
        names(opt$par) <- nm

        if (opt$code <= 2)
            opt$convergence <- "sucessful"

        if (opt$code == 3)
            opt$convergence <- "local minimum or 'steptol' is too small"

        if (opt$code == 4)
            opt$convergence <- "iteration limit reached"

        if (opt$code == 5)
            opt$convergence <- "optimization failed"
    }

    if (!(method %in% c("nlm", "nlminb"))){
        opt <- optim(start, nllh, ..., method = method, control = control)

        if ((opt$convergence != 0) || (opt$value >= 1.0e15)) {

            if (warn)
                warning("optimization may not have succeeded")

            if (opt$convergence == 1)
                opt$convergence <- "iteration limit reached"
        }

        else opt$convergence <- "successful"
    }

    if (opt$value == init.lik){
        if (warn)
            warning("optimization stayed at the starting values.")

        opt$convergence <- "Stayed at start. val."
    }

    param.names <- param
    param <- c(opt$par, unlist(fixed.param))
    param <- param[param.names]

    ##Reset the weights to their original values
    if ((length(weights) == 1) && (weights == 0))
        weights <- NULL


    std.err <- .schlatherstderr(param, data, dist, cov.mod.num, as.double(0),
                                as.double(0), as.double(0), as.double(0), as.double(0),
                                as.double(0), rep(FALSE, 3), fit.marge = fit.marge,
                                fixed.param = names(fixed.param),
                                param.names = param.names, weights = weights)


    if (check.grad)
        print(round(rbind(numerical = -opt$grad, analytical = std.err$grad), 3))

    opt$hessian <- std.err$hessian
    var.score <- std.err$var.score
    ihessian <- try(solve(opt$hessian), silent = TRUE)

    if(!is.matrix(ihessian) || any(is.na(var.score))){
        if (warn)
            warning("Observed information matrix is singular. No standard error will be computed.")

        std.err.type <- "none"
    }

    else{
        std.err.type <- "yes"
        var.cov <- ihessian %*% var.score %*% ihessian
        std.err <- diag(var.cov)

        std.idx <- which(std.err <= 0)
        if(length(std.idx) > 0){
            if (warn)
                warning("Some (observed) standard errors are negative;\n passing them to NA")

            std.err[std.idx] <- NA
        }

        std.err <- sqrt(std.err)

        if(corr) {
            .mat <- diag(1/std.err, nrow = length(std.err))
            corr.mat <- structure(.mat %*% var.cov %*% .mat, dimnames = list(nm,nm))
            diag(corr.mat) <- rep(1, length(std.err))
        }

        else
            corr.mat <- NULL

        colnames(var.cov) <- rownames(var.cov) <- colnames(ihessian) <-
            rownames(ihessian) <- names(std.err) <- nm
    }

    if (std.err.type == "none"){
        std.err <- std.err.type <- corr.mat <- NULL
        var.cov <- ihessian <- var.score <- NULL
    }

    if (cov.mod == "caugen")
        cov.fun <-  covariance(nugget = param["nugget"], sill = 1 - param["nugget"], range = param["range"],
                               smooth = param["smooth"], smooth2 = param["smooth2"],
                               cov.mod = cov.mod, plot = FALSE)

    else
        cov.fun <-  covariance(nugget = param["nugget"], sill = 1 - param["nugget"], range = param["range"],
                               smooth = param["smooth"], cov.mod = cov.mod,
                               plot = FALSE)

    ext.coeff <- function(h)
        1 + sqrt(0.5 - 0.5 * cov.fun(h))

    conc.prob <- function(h){
        n.sim <- 20000
        n.site <- length(h)
        rho <- cov.fun(h)
        rho <- matrix(rho, 2 * n.sim, n.site, byrow = TRUE)

        Y <- sqrt(2 * pi) * rgp(n.sim, h, cov.mod, nugget = param["nugget"], sill = 1 - param["nugget"],
                                range = param["range"], smooth = param["smooth"])
        Y <- rbind(pmax(Y, 0), pmax(-Y, 0))##antithetic

        dummy <- 1 / (0.5 * (1/Y[,1] + 1 / Y) * (1 + sqrt(1 - 2 * (1 + rho) *
                                                              Y[,1] * Y / (Y[,1] + Y)^2)))

        dummy <- replace(dummy, is.na(dummy), 0)
        colMeans(dummy)
    }

    fitted <- list(fitted.values = opt$par, std.err = std.err,
                   var.cov = var.cov, param = param, cov.fun = cov.fun, fixed = unlist(fixed.param),
                   deviance = 2*opt$value, corr = corr.mat, convergence = opt$convergence,
                   counts = opt$counts, message = opt$message, est = "MPLE", data = data,
                   logLik = -opt$value, opt.value = opt$value, model = "Schlather", iso = TRUE,
                   cov.mod = cov.mod, fit.marge = fit.marge, ext.coeff = ext.coeff,
                   hessian = opt$hessian, lik.fun = nllh, coord = coord, ihessian = ihessian,
                   var.score = var.score, marg.cov = NULL, nllh = nllh, weighted = weighted,
                   conc.prob = conc.prob)

    class(fitted) <- c(fitted$model, "maxstab")
    return(fitted)
}

##This functions fits the model from a generic R formula
##i.e. classical linear models as well as smoothing splines with
##radial basis functions may be used to model spatially the GEV
##parameters
schlatherform <- function(data, coord, cov.mod, loc.form, scale.form, shape.form,
                          start, fit.marge = TRUE, marg.cov = NULL, ...,
                          warn = TRUE, method = "BFGS", control = list(),
                          corr = FALSE, weights = NULL,
                          temp.cov = NULL, temp.form.loc = NULL, temp.form.scale = NULL,
                          temp.form.shape = NULL, check.grad = FALSE){
    ##data is a matrix with each column corresponds to one location
    ##coord is a matrix giving the coordinates (1 row = 1 station)
    n.site <- ncol(data)
    n.obs <- nrow(data)
    dist.dim <- ncol(coord)
    n.pair <- n.site * (n.site - 1) / 2

    dist <- distance(coord)
    weighted <- !is.null(weights)

    if (!weighted)
        ##Set the weights to 0 as it won't be used anyway
        weights <- 0

    use.temp.cov <- c(!is.null(temp.form.loc), !is.null(temp.form.scale), !is.null(temp.form.shape))

    if (any(use.temp.cov) && (n.obs != nrow(temp.cov)))
        stop("'data' and 'temp.cov' doesn't match")

    if (any(use.temp.cov) && is.null(temp.cov))
        stop("'temp.cov' must be supplied if at least one temporal formula is given")

    if (!(cov.mod %in% c("whitmat","cauchy","powexp","bessel","caugen")))
        stop("''cov.mod'' must be one of 'whitmat', 'cauchy', 'powexp', 'bessel', 'caugen'")

    if (cov.mod == "whitmat")
        cov.mod.num <- 1
    if (cov.mod == "cauchy")
        cov.mod.num <- 2
    if (cov.mod == "powexp")
        cov.mod.num <- 3
    if (cov.mod == "bessel")
        cov.mod.num <- 4
    if (cov.mod == "caugen")
        cov.mod.num <- 5

    ##With our notation, formula must be of the form y ~ xxxx
    loc.form <- update(loc.form, y ~ .)
    scale.form <- update(scale.form, y ~ .)
    shape.form <- update(shape.form, y ~ .)

    if (use.temp.cov[1])
        temp.form.loc <- update(temp.form.loc, y ~. + 0)

    if (use.temp.cov[2])
        temp.form.scale <- update(temp.form.scale, y ~. + 0)

    if (use.temp.cov[3])
        temp.form.shape <- update(temp.form.shape, y ~. + 0)

    if (is.null(marg.cov))
        covariables <- data.frame(coord)

    else
        covariables <- data.frame(coord, marg.cov)

    loc.model <- modeldef(covariables, loc.form)
    scale.model <- modeldef(covariables, scale.form)
    shape.model <- modeldef(covariables, shape.form)

    loc.dsgn.mat <- loc.model$dsgn.mat
    scale.dsgn.mat <- scale.model$dsgn.mat
    shape.dsgn.mat <- shape.model$dsgn.mat

    loc.pen.mat <- loc.model$pen.mat
    scale.pen.mat <- scale.model$pen.mat
    shape.pen.mat <- shape.model$pen.mat

    loc.penalty <- loc.model$penalty.tot
    scale.penalty <- scale.model$penalty.tot
    shape.penalty <- shape.model$penalty.tot

    loc.type <- loc.model$type
    scale.type <- scale.model$type
    shape.type <- shape.model$type

    ##The total number of parameters to be estimated for each GEV
    ##parameter
    n.loccoeff <- ncol(loc.dsgn.mat)
    n.scalecoeff <- ncol(scale.dsgn.mat)
    n.shapecoeff <- ncol(shape.dsgn.mat)

    ##The number of ``purely parametric'' parameters to estimate i.e. we
    ##do not consider the weigths given to each basis function
    n.pparloc <- loc.model$n.ppar
    n.pparscale <- scale.model$n.ppar
    n.pparshape <- shape.model$n.ppar

    loc.names <- paste("locCoeff", 1:n.loccoeff, sep="")
    scale.names <- paste("scaleCoeff", 1:n.scalecoeff, sep="")
    shape.names <- paste("shapeCoeff", 1:n.shapecoeff, sep="")

    param <- c("nugget", "range", "smooth")

    if (cov.mod == "caugen")
        param <- c(param, "smooth2")

    else
        smooth2 <- 0

    ##Do the same for the temporal regression coefficients
    if (use.temp.cov[1]){
        temp.model.loc <- modeldef(temp.cov, temp.form.loc)
        temp.dsgn.mat.loc <- temp.model.loc$dsgn.mat
        temp.pen.mat.loc <- temp.model.loc$pen.mat
        temp.penalty.loc <- temp.model.loc$penalty.tot
        n.tempcoeff.loc <- ncol(temp.dsgn.mat.loc)
        n.ppartemp.loc <- temp.model.loc$n.ppar
        temp.names.loc <- paste("tempCoeffLoc", 1:n.tempcoeff.loc, sep="")
    }

    else {
        temp.model.loc <- temp.dsgn.mat.loc <- temp.pen.mat.loc <- temp.names.loc <- NULL
        n.tempcoeff.loc <- n.ppartemp.loc <- temp.penalty.loc <- 0
    }

    if (use.temp.cov[2]){
        temp.model.scale <- modeldef(temp.cov, temp.form.scale)
        temp.dsgn.mat.scale <- temp.model.scale$dsgn.mat
        temp.pen.mat.scale <- temp.model.scale$pen.mat
        temp.penalty.scale <- temp.model.scale$penalty.tot
        n.tempcoeff.scale <- ncol(temp.dsgn.mat.scale)
        n.ppartemp.scale <- temp.model.scale$n.ppar
        temp.names.scale <- paste("tempCoeffScale", 1:n.tempcoeff.scale, sep="")
    }

    else {
        temp.model.scale <- temp.dsgn.mat.scale <- temp.pen.mat.scale <- temp.names.scale <- NULL
        n.tempcoeff.scale <- n.ppartemp.scale <- temp.penalty.scale <- 0
    }

    if (use.temp.cov[3]){
        temp.model.shape <- modeldef(temp.cov, temp.form.shape)
        temp.dsgn.mat.shape <- temp.model.shape$dsgn.mat
        temp.pen.mat.shape <- temp.model.shape$pen.mat
        temp.penalty.shape <- temp.model.shape$penalty.tot
        n.tempcoeff.shape <- ncol(temp.dsgn.mat.shape)
        n.ppartemp.shape <- temp.model.shape$n.ppar
        temp.names.shape <- paste("tempCoeffShape", 1:n.tempcoeff.shape, sep="")
    }

    else {
        temp.model.shape <- temp.dsgn.mat.shape <- temp.pen.mat.shape <- temp.names.shape <- NULL
        n.tempcoeff.shape <- n.ppartemp.shape <- temp.penalty.shape <- 0
    }

    param <- c(param, loc.names, scale.names, shape.names, temp.names.loc, temp.names.scale,
               temp.names.shape)

    ##First create a "void" function
    nplk <- function(x) x

    ##And define the "body" of the function as the number of parameters
    ##to estimate depends on n.site

    body(nplk) <- parse(text = paste("-.C(C_schlatherdsgnmat, as.integer(cov.mod.num),
as.double(data), as.double(dist), as.integer(n.site), as.integer(n.obs), as.integer(dist.dim),
as.integer(weighted), as.double(weights), as.double(loc.dsgn.mat), as.double(loc.pen.mat),
as.integer(n.loccoeff), as.integer(n.pparloc), as.double(loc.penalty), as.double(scale.dsgn.mat),
as.double(scale.pen.mat), as.integer(n.scalecoeff), as.integer(n.pparscale),
as.double(scale.penalty), as.double(shape.dsgn.mat), as.double(shape.pen.mat),
as.integer(n.shapecoeff), as.integer(n.pparshape), as.double(shape.penalty),
as.integer(use.temp.cov), as.double(temp.dsgn.mat.loc), as.double(temp.pen.mat.loc),
as.integer(n.tempcoeff.loc), as.integer(n.ppartemp.loc), as.double(temp.penalty.loc),
as.double(temp.dsgn.mat.scale), as.double(temp.pen.mat.scale), as.integer(n.tempcoeff.scale),
as.integer(n.ppartemp.scale), as.double(temp.penalty.scale), as.double(temp.dsgn.mat.shape),
as.double(temp.pen.mat.shape), as.integer(n.tempcoeff.shape), as.integer(n.ppartemp.shape),
as.double(temp.penalty.shape),",
                        paste("as.double(c(", paste(loc.names, collapse = ","), ")), "),
                        paste("as.double(c(", paste(scale.names, collapse = ","), ")), "),
                        paste("as.double(c(", paste(shape.names, collapse = ","), ")), "),
                        paste("as.double(c(", paste(temp.names.loc, collapse = ","), ")), "),
                        paste("as.double(c(", paste(temp.names.scale, collapse = ","), ")), "),
                        paste("as.double(c(", paste(temp.names.shape, collapse = ","), ")), "),
                        "as.double(nugget), as.double(range), as.double(smooth), as.double(smooth2),
dns = double(1), NAOK = TRUE)$dns"))

    ##Define the formal arguments of the function
    form.nplk <- NULL
    for (i in 1:length(param))
        form.nplk <- c(form.nplk, alist(a=))

    names(form.nplk) <- param
    formals(nplk) <- form.nplk

    if (missing(start)) {

        start <- .start.schlather(data, coord, covariables, cov.mod, loc.form,
                                  scale.form, shape.form, method = method, ...)

        if (use.temp.cov[1]){
            tempCoeff.loc <- rep(0, n.tempcoeff.loc)
            names(tempCoeff.loc) <- temp.names.loc
        }

        else
            tempCoeff.loc <- NULL

        if (use.temp.cov[2]){
            tempCoeff.scale <- rep(0, n.tempcoeff.scale)
            names(tempCoeff.scale) <- temp.names.scale
        }

        else
            tempCoeff.scale <- NULL

        if (use.temp.cov[3]){
            tempCoeff.shape <- rep(0, n.tempcoeff.shape)
            names(tempCoeff.shape) <- temp.names.shape
        }

        else
            tempCoeff.shape <- NULL

        start <- c(start, as.list(c(tempCoeff.loc, tempCoeff.scale, tempCoeff.shape)))
        start <- start[!(param %in% names(list(...)))]

    }

    if (!is.list(start))
        stop("'start' must be a named list")

    if (!length(start))
        stop("there are no parameters left to maximize over")

    nm <- names(start)
    l <- length(nm)
    f <- formals(nplk)
    names(f) <- param
    m <- match(nm, param)

    if(any(is.na(m)))
        stop("'start' specifies unknown arguments")

    formals(nplk) <- c(f[m], f[-m])
    nllh <- function(p, ...) nplk(p, ...)

    if(l > 1)
        body(nllh) <- parse(text = paste("nplk(", paste("p[",1:l,
                            "]", collapse = ", "), ", ...)"))

    fixed.param <- list(...)[names(list(...)) %in% param]

    if(any(!(param %in% c(nm,names(fixed.param)))))
        stop("unspecified parameters")

    start.arg <- c(list(p = unlist(start)), fixed.param)

    init.lik <- do.call("nllh", start.arg)
    if (warn && (init.lik >= 1.0e15))
        warning("negative log-likelihood is infinite at starting values")

    if (method == "nlminb"){
        start <- as.numeric(start)
        opt <- nlminb(start, nllh, ..., control = control)
        opt$counts <- opt$evaluations
        opt$value <- opt$objective
        names(opt$par) <- nm

        if ((opt$convergence != 0) || (opt$value >= 1.0e15)) {
            if (warn)
                warning("optimization may not have succeeded")
        }

        if (opt$convergence == 0)
            opt$convergence <- "successful"
    }

    if (method == "nlm"){
        start <- as.numeric(start)
        opt <- nlm(nllh, start, ...)
        opt$counts <- opt$iterations
        names(opt$counts) <- "function"
        opt$value <- opt$minimum
        opt$par <- opt$estimate
        names(opt$par) <- nm

        if (opt$code <= 2)
            opt$convergence <- "sucessful"

        if (opt$code == 3)
            opt$convergence <- "local minimum or 'steptol' is too small"

        if (opt$code == 4)
            opt$convergence <- "iteration limit reached"

        if (opt$code == 5)
            opt$convergence <- "optimization failed"

    }

    if (!(method %in% c("nlm", "nlminb"))){
        opt <- optim(start, nllh, ..., method = method,
                     control = control)

        if ((opt$convergence != 0) || (opt$value >= 1.0e15)){
            if (warn)
                warning("optimization may not have succeeded")

            if (opt$convergence != 0)
                opt$convergence <- "iteration limit reached"
        }

        else opt$convergence <- "successful"
    }

    if (opt$value == init.lik){
        if (warn)
            warning("optimization stayed at the starting values.")

        opt$convergence <- "Stayed at start. val."
    }

    param.names <- param
    param <- c(opt$par, unlist(fixed.param))
    param <- param[param.names]

    ##Reset the weights to their original values
    if ((length(weights) == 1) && (weights == 0))
        weights <- NULL

    std.err <- .schlatherstderr(param, data, dist, cov.mod.num, loc.dsgn.mat, scale.dsgn.mat,
                                shape.dsgn.mat, temp.dsgn.mat.loc, temp.dsgn.mat.scale,
                                temp.dsgn.mat.shape, use.temp.cov, fit.marge = fit.marge,
                                fixed.param = names(fixed.param),
                                param.names = param.names, weights = weights)

    if (check.grad)
        print(round(rbind(numerical = -opt$grad, analytical = std.err$grad), 3))

    opt$hessian <- std.err$hessian
    var.score <- std.err$var.score
    ihessian <- try(solve(opt$hessian), silent = TRUE)

    if(!is.matrix(ihessian) || any(is.na(var.score))){
        if (warn)
            warning("Observed information matrix is singular. No standard error will be computed.")

        std.err.type <- "none"
    }

    else{
        std.err.type <- "yes"
        var.cov <- ihessian %*% var.score %*% ihessian

        std.err <- diag(var.cov)

        std.idx <- which(std.err <= 0)
        if(length(std.idx) > 0){
            if (warn)
                warning("Some (observed) standard errors are negative;\n passing them to NA")

            std.err[std.idx] <- NA
        }


        std.err <- sqrt(std.err)

        if(corr) {
            .mat <- diag(1/std.err, nrow = length(std.err))
            corr.mat <- structure(.mat %*% var.cov %*% .mat, dimnames = list(nm,nm))
            diag(corr.mat) <- rep(1, length(std.err))
        }

        else
            corr.mat <- NULL

        colnames(var.cov) <- rownames(var.cov) <- colnames(ihessian) <-
            rownames(ihessian) <- names(std.err) <- nm
    }


    if (std.err.type == "none"){
        std.err <- std.err.type <- corr.mat <- NULL
        var.cov <- ihessian <- var.score <- NULL
    }

    if (cov.mod == "caugen")
        cov.fun <- covariance(nugget = param["nugget"], sill = 1 - param["nugget"], range = param["range"],
                              smooth = param["smooth"], smooth2 = param["smooth2"],
                              cov.mod = cov.mod, plot = FALSE)

    else
        cov.fun <- covariance(nugget = param["nugget"], sill = 1- param["nugget"], range = param["range"],
                              smooth = param["smooth"], cov.mod = cov.mod, plot = FALSE)

    ext.coeff <- function(h)
        1 + sqrt(0.5 - 0.5 * cov.fun(h))

    conc.prob <- function(h){
        n.sim <- 20000
        n.site <- length(h)
        rho <- cov.fun(h)
        rho <- matrix(rho, 2 * n.sim, n.site, byrow = TRUE)

        Y <- sqrt(2 * pi) * rgp(n.sim, h, cov.mod, nugget = param["nugget"], sill = 1 - param["nugget"],
                                range = param["range"], smooth = param["smooth"])
        Y <- rbind(pmax(Y, 0), pmax(-Y, 0))##antithetic

        dummy <- 1 / (0.5 * (1/Y[,1] + 1 / Y) * (1 + sqrt(1 - 2 * (1 + rho) *
                                                              Y[,1] * Y / (Y[,1] + Y)^2)))

        dummy <- replace(dummy, is.na(dummy), 0)
        colMeans(dummy)
    }

    fitted <- list(fitted.values = opt$par, std.err = std.err,
                   var.cov = var.cov, fixed = unlist(fixed.param), param = param, iso = TRUE,
                   deviance = 2*opt$value, corr = corr.mat, convergence = opt$convergence,
                   counts = opt$counts, message = opt$message, data = data, est = "MPLE",
                   logLik = -opt$value, opt.value = opt$value, model = "Schlather", coord = coord,
                   fit.marge = fit.marge, ext.coeff = ext.coeff, cov.mod = cov.mod, cov.fun = cov.fun,
                   loc.form = loc.form, scale.form = scale.form, shape.form = shape.form,
                   lik.fun = nllh, loc.type = loc.type, scale.type = scale.type,
                   shape.type = shape.type, ihessian = ihessian, var.score = var.score,
                   marg.cov = marg.cov, nllh = nllh, weighted = weighted, conc.prob = conc.prob)

    class(fitted) <- c(fitted$model, "maxstab")
    return(fitted)
}

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SpatialExtremes documentation built on May 2, 2019, 5:45 p.m.