R/fit.variogram.R

Defines functions vgm_fill_na

# $Id: fit.variogram.q,v 1.10 2008-12-15 14:27:29 edzer Exp $

"fit.variogram" <-
function (object, model, fit.sills = TRUE, fit.ranges = TRUE, 
    fit.method = 7, debug.level = 1, warn.if.neg = FALSE, fit.kappa = FALSE) 
{
	cl = match.call()
    if (missing(object)) 
        stop("nothing to fit to")
	if (!inherits(object, "gstatVariogram") && !inherits(object, "variogramCloud"))
		stop("object should be of class gstatVariogram or variogramCloud")
	if (inherits(object, "variogramCloud"))
		object$np = rep(1, nrow(object))
	if (length(unique(object$id)) > 1)
		stop("to use fit.variogram, variogram object should be univariable")
    if (missing(model)) 
        stop("no model to fit")
	if (is(model, "variogramModelList")) {
		ret = lapply(model, function(x) fit.variogram(object, x, fit.sills = fit.sills, 
			fit.ranges = fit.ranges, fit.method = fit.method, debug.level = debug.level, 
			warn.if.neg = warn.if.neg, fit.kappa = fit.kappa))
		sse = sapply(ret, function(x) attr(x, "SSErr"))
		return(ret[[which.min(sse)]])
	}
    if (!inherits(model, "variogramModel"))
        stop("model should be of class variogramModel (use vgm)")
    if (fit.method == 5)
    	stop("use function fit.variogram.reml() to use REML")
    if (length(fit.sills) < length(model$model)) 
        fit.sills = rep(fit.sills, length(model$model))
    if (length(fit.ranges) < length(model$model)) 
        fit.ranges = rep(fit.ranges, length(model$model))
	if (fit.method == 7 && any(object$dist == 0))
		stop("fit.method 7 will not work with zero distance semivariances; use another fit.method value")
	if (any(is.na(model$psill)) || any(is.na(model$range)))
		model = vgm_fill_na(model, object)
    fit.ranges = fit.ranges & !(model$model %in% c("Nug", "Err")) # no ranges to fit for Nug/Err
	if (isTRUE(fit.kappa))
		fit.kappa = seq(0.3, 5, 0.1)
	if (any(model$model %in% c("Mat", "Ste")) && length(fit.kappa) > 1) {
		f = function(x, o, m) {
			m[m$model %in% c("Mat", "Ste"), "kappa"] = x
			fit.variogram(o, m, fit.kappa = FALSE, 
				fit.method = fit.method, debug.level = debug.level) # fits range
		}
		ret = lapply(fit.kappa, f, object, model)
		return(ret[[ which.min(sapply(ret, function(x) attr(x, "SSErr"))) ]])
	}
	initialRange = model$range
    .Call(gstat_init, as.integer(debug.level))
    .Call(gstat_load_ev, object$np, object$dist, object$gamma)
    load.variogram.model(model)
    ret = .Call(gstat_fit_variogram, as.integer(fit.method), 
        as.integer(fit.sills), as.integer(fit.ranges))
    .Call(gstat_exit, 0)
    model$psill = ret[[1]]
    model$range = ret[[2]]
	attr(model, "singular") = as.logical(ret[[3]]);
	attr(model, "SSErr") = ret[[4]]
	direct = attr(object, "direct")
	if (!is.null(direct)) {
		id = unique(object$id)
		if (direct[direct$id == id, "is.direct"] && any(model$psill < 0)) {
			if (warn.if.neg)
				warning("partial sill or nugget fixed at zero value")
			fit.sills = model$psill > 0
			model$psill[model$psill < 0] = 0.0
			model$range = initialRange
			return(fit.variogram(object, model, fit.sills = fit.sills, fit.ranges =
				fit.ranges, fit.method = fit.method, debug.level = debug.level,
				warn.if.neg = warn.if.neg, fit.kappa = fit.kappa))
		}
	}
	if (attr(model, "singular") && debug.level) {
		rat = mean(object$gamma) / mean(object$dist) 
		if (rat > 1e6 || rat < 1e-6)
			print("a possible solution MIGHT be to scale semivariances and/or distances")
	}
	attr(model, "call") = cl
    model
}

vgm_fill_na = function(model, obj) {
	if (any(is.na(model$range))) {
    	model[model$model %in% c("Nug", "Err"), "range"] = 0
    	model[!model$model %in% c("Nug", "Err"), "range"] = max(obj$dist) / 3
	}
	if (any(model$model %in% "Nug") && is.na(model[model$model == "Nug","psill"]))
    	model[model$model == "Nug", "psill"] = mean(head(obj$gamma, 3))
	na_sills = is.na(model[model$model != "Nug", "psill"])
	if (any(na_sills)) {
		n = length(model[model$model != "Nug",]$psill)
    	model[model$model != "Nug", "psill"] = mean(tail(obj$gamma, 5)) / n
	}
	model
}

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gstat documentation built on Oct. 19, 2022, 5:28 p.m.