From c6149a3e7b54ac0b870a21eba2a4f745569a546e Mon Sep 17 00:00:00 2001
From: Solveig Pospiech <s.pospiech@hzdr.de>
Date: Sun, 28 Nov 2021 12:11:15 +0100
Subject: [PATCH] setCgram: small improvements in the help-page
---
R/variograms.R | 412 +++++++++++++++++++++++++------------------------
1 file changed, 207 insertions(+), 205 deletions(-)
diff --git a/R/variograms.R b/R/variograms.R
index 1f68ea5..256bf6f 100644
--- a/R/variograms.R
+++ b/R/variograms.R
@@ -1,34 +1,36 @@
#### theoretical variogram ---------------------
-#' gmGeostats Variogram models
-#' set up a D-variate variogram models
+#' Generate D-variate variogram models
#'
-#' @param type constant indicating the model of correlation function
-#' @param nugget (DxD)-matrix for the nugget effect
+#' @description Function to set up D-variate variogram models based on model type, the variogram parameters sill and nugget and a matrix describing the anisotropy of the range.
+#'
+#' @param type model of correlation function. The function expects a constant, e.g. the internal constants 'vg.Gau' for Gaussian model or 'vg.Exp'. for exponential models. See examples for usage.
+#' @param nugget (DxD)-matrix for the nugget effect. Default is a muted nugget (0).
#' @param sill (DxD)-matrix for the partial sills of the correlation function
#' @param anisRanges 2x2 or 3x3 matrix of ranges (see details)
#' @param extraPar for certain correlation functions, extra parameters (smoothness, period, etc)
#'
#' @return an object of class "gmCgram" containing the linear model of corregionalization
-#' of the nugget and the structure given.
-#' @details The argument `type` must be an integer indicating the model to be used.
-#' Some constants are available to make reading code more understandable. That is, you can
-#' either write `1`, `vg.Sph` or `vg.Spherical`, they will all work and produce
-#' a spherical model. The same applies for the following models: `vg.Gauss=0`;
-#' `vg.Exp=vg.Exponential=2`. These constants are available after calling
-#' `data("variogramModels")`.
-#' No other model is currently available, but this data object will be
+#' of the nugget and the structure given.
+#' @details The argument `type` must be an integer indicating the model to be used.
+#' Some constants are available to make reading code more understandable. That is, you can
+#' either write `1`, `vg.sph`, `vg.Sph` or `vg.Spherical`, they will all work and produce
+#' a spherical model. The same applies for the following models:
+#' `vg.Gauss = vg.Gau = vg.gau = 0`;
+#' `vg.Exponential = vg.Exp = vg. exp = 2`.
+#' These constants are available after calling `data("variogramModels")`.
+#' No other model is currently available, but this data object will be
#' regularly updated.
#' The constant vector `gsi.validModels` contains all currently valid models.
-#'
-#' Argument `anisRange` expects a matrix $M$ such that
+#'
+#' Argument `anisRange` expects a matrix $M$ such that
#' \deqn{
#' h^2 = (\mathbf{x}_i-\mathbf{x}_j)\cdot M^{-1}\cdot (\mathbf{x}_i-\mathbf{x}_j)^t
#' }
#' is the (square of) the lag distance to be fed into the correlation function.
-#' @family gmCgram
+#' @family gmCgram
#' @export
-#' @aliases vg.Exp vg.exp vg.Exponential vg.Gau vg.gauss
+#' @aliases vg.Exp vg.exp vg.Exponential vg.Gau vg.gauss
#' vg.Gauss vg.Sph vg.sph vg.Spherical gsi.validModels
#'
#' @examples
@@ -39,7 +41,7 @@
#' plot(vm)
setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
utils::data("variogramModels")
- stopifnot(all(dim(sill)==dim(nugget)),
+ stopifnot(all(dim(sill)==dim(nugget)),
ncol(sill)==nrow(sill),
type %in% gsi.validModels)
dim(sill) = c(1,dim(sill))
@@ -57,18 +59,18 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
#' Subsetting of gmCgram variogram structures
-#'
+#'
#' Extraction or combination of nested structures of a gmCgram object
#'
#' @param x `gmCgram` variogram object
#' @param i indices of the structures that are desired to be kept (0=nugget) or removed (see details)
#' @param ... extra arguments for generic functionality
#'
-#' @return a `gmCgram` variogram object with the desired structures only.
-#' @details This function can be used to: extract the nugget (i=0), extract some
+#' @return a `gmCgram` variogram object with the desired structures only.
+#' @details This function can be used to: extract the nugget (i=0), extract some
#' structures (i=indices of the structures, possibly including 0 for the nugget),
-#' or filter some structures out (i=negative indices of the structures to remove;
-#' nugget will always removed in this case). If you want to extract "slots" or
+#' or filter some structures out (i=negative indices of the structures to remove;
+#' nugget will always removed in this case). If you want to extract "slots" or
#' "elements" of the variogram, use the $-notation. If you want to extract variables of the
#' variogram matrices, use the `[`-notation. The contrary operation (adding structures together)
#' is obtained by summing (+) two `gmCgram` objects.
@@ -87,24 +89,24 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
nSo = length(i)
nSi = dim(x$M)[1]
if(i==0){# case only nugget wanted
- out = with(x, list(type=type[i],
- data=data[i],
- nugget=nugget,
- sill=structure(0*sill[1,,], dim=c(nSo, nD, nD)),
+ out = with(x, list(type=type[i],
+ data=data[i],
+ nugget=nugget,
+ sill=structure(0*sill[1,,], dim=c(nSo, nD, nD)),
M=structure(M[1,,], dim=c(nSo, nG, nG))))
}else if( (0 %in% i) & !any(i<0)){
j = i[i>0] # case some structures wanted, including nugget
- out = with(x, list(type=type[j],
- data=data[j],
- nugget=nugget,
- sill=structure(sill[j,,], dim=c(nSo-1, nD, nD)),
+ out = with(x, list(type=type[j],
+ data=data[j],
+ nugget=nugget,
+ sill=structure(sill[j,,], dim=c(nSo-1, nD, nD)),
M=structure(M[j,,], dim=c(nSo-1, nG, nG))))
}else if(all(i>0) | all(i<0)){# case some structured (un)wanted, nugget surely not wanted
if(all(i<0)) nSo = nSi-nSo
- out = with(x, list(type=type[i],
- data=data[i],
- nugget=nugget*0,
- sill=structure(sill[i,,], dim=c(nSo, nD, nD)),
+ out = with(x, list(type=type[i],
+ data=data[i],
+ nugget=nugget*0,
+ sill=structure(sill[i,,], dim=c(nSo, nD, nD)),
M=structure(M[i,,], dim=c(nSo, nG, nG))))
}else{# unsolvable case
stop("index set i cannot merge 0 and negative numbers")
@@ -115,7 +117,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
#' Combination of gmCgram variogram structures
-#'
+#'
#' combination of nested structures of a gmCgram object
#'
#' @param x `gmCgram` variogram object
@@ -130,7 +132,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
#' vm = v1+v2
"+.gmCgram" <- function(x,y) {
y = as.gmCgram(y)
- stopifnot(class(y)=="gmCgram",
+ stopifnot(class(y)=="gmCgram",
dim(x$sill)[-1]==dim(y$sill)[-1],
dim(x$M)[-1]==dim(y$M)[-1])
myfun = function(A,B){
@@ -155,7 +157,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
#' Subsetting of gmCgram variogram structures
-#'
+#'
#' Extraction of some variables of a gmCgram object
#'
#' @param x \code{gmCgram} variogram object
@@ -164,15 +166,15 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
#' is specified, \code{j} will be taken as equal to \code{i}!)
#' @param ... extra arguments for generic functionality
#'
-#' @return a \code{gmCgram} variogram object with the desired variables only.
-#' @details This function can be used to extract the model for a a subset of variables.
-#' If only \code{i} is specified, \code{j} will be taken as equal to \code{i}.
+#' @return a \code{gmCgram} variogram object with the desired variables only.
+#' @details This function can be used to extract the model for a a subset of variables.
+#' If only \code{i} is specified, \code{j} will be taken as equal to \code{i}.
#' If you want to select all \code{i}'s for certain \code{j}'s or vice versa, give
-#' \code{i=1:dim(x$nugget)[1]} and \code{j=} your desired indices, respectively
+#' \code{i=1:dim(x$nugget)[1]} and \code{j=} your desired indices, respectively
#' \code{j=1:dim(x$nugget)[2]} and \code{i=} your desired indices; replace \code{x} by the
-#' object you are giving. If \code{i!=j}, the output will be a \code{c("gmXCgram","gmCgram")}
+#' object you are giving. If \code{i!=j}, the output will be a \code{c("gmXCgram","gmCgram")}
#' object, otherwise it will be a regular class \code{"gmCgram"} object.
-#' If you want to extract "slots" or
+#' If you want to extract "slots" or
#' "elements" of the variogram, use the $-notation. If you want to extract variables of the
#' variogram matrices, use the `[`-notation.
#' @export
@@ -188,10 +190,10 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
nDi = length(i)
nDj = length(j)
nS = dim(x$M)[1]
- out = with(x, list(type=type,
- data=data,
- nugget=structure(nugget[i,j, drop=F], dim=c(nDi, nDj)),
- sill=structure(sill[,i,j, drop=F], dim=c(nS, nDi, nDj)),
+ out = with(x, list(type=type,
+ data=data,
+ nugget=structure(nugget[i,j, drop=F], dim=c(nDi, nDj)),
+ sill=structure(sill[,i,j, drop=F], dim=c(nS, nDi, nDj)),
M=M))
class(out) = class(x)
if(!all(i==j)) class(out) = unique(c("gmXCgram", class(out)))
@@ -199,8 +201,8 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
}
#' Length, and number of columns or rows
-#'
-#' Provide number of structures, and nr of variables of an LMC of class gmCgram
+#'
+#' Provide number of structures, and nr of variables of an LMC of class gmCgram
#'
#' @param x gmCgram object
#'
@@ -234,14 +236,14 @@ nrow.gmCgram = function(x) nrow(x$nugget)
#' Convert a gmCgram object to an (evaluable) function
-#'
+#'
#' Evaluate a gmCgram on some h values, or convert the gmCgram object into an evaluable function
#'
#' @param x a gmCgram object
#' @param ... extra arguments for generic functionality
#'
#' @return a \code{function} that can be evaluated normally, with an argument \code{X}
-#' and possibly another argument \code{Y}; both must have the same number of columns
+#' and possibly another argument \code{Y}; both must have the same number of columns
#' than the geographic dimension of the variogram (i.e. \code{dim(x$M)[3]}).
#' @export
#' @method as.function gmCgram
@@ -280,55 +282,55 @@ predict.gmCgram = function(object, newdata, ...){
}
-#' Convert theoretical structural functions to gmCgram format
-#'
-#' Convert covariance function or variogram models to the format gmCgram
+#' Convert theoretical structural functions to gmCgram format
+#'
+#' Convert covariance function or variogram models to the format gmCgram
#' of package gmGeostats
-#'
+#'
#' @param m model to be converted
#' @param ... further parameters
#'
-#' @return the covariance/variogram model, recasted to class \code{gmCgram}.
+#' @return the covariance/variogram model, recasted to class \code{gmCgram}.
#' This is a generic function. Methods exist for objects of class
#' \code{LMCAnisCompo} (for compositional data) and \code{variogramModelList}
-#' (as provided by package \code{gstat}).
+#' (as provided by package \code{gstat}).
#' @export
#' @family gmCgram functions
-as.gmCgram <- function(m, ...) UseMethod("as.gmCgram",m)
+as.gmCgram <- function(m, ...) UseMethod("as.gmCgram",m)
-#' @describeIn as.gmCgram Convert theoretical structural functions to gmCgram format
+#' @describeIn as.gmCgram Convert theoretical structural functions to gmCgram format
#' @method as.gmCgram default
#' @export
-as.gmCgram.default <- function(m,...) m
+as.gmCgram.default <- function(m,...) m
#' Draw cuves for covariance/variogram models
-#'
+#'
#' Represent a gmCgram object as a matrix of lines in several plots
#'
#' @param x object to draw, of class gmCgram // curently only valid for symmetric functions
#' @param xlim.up range of lag values to use in plots of the upper triangle
#' @param xlim.lo range of lag values to use in plots of the lower triangle
-#' @param vdir.up geograohic directions to represent in the upper triangle
+#' @param vdir.up geograohic directions to represent in the upper triangle
#' @param vdir.lo geograohic directions to represent in the lower triangle
#' @param xlength number of discretization points to use for the curves (defaults to 200)
#' @param varnames string vector, variable names to use in the labelling of axes
#' @param add logical, should a new plot be created or stuff be added to an existing one?
#' @param commonAxis logical, is a common Y axis for all plots in a row desired?
-#' @param cov logical, should the covariance function (=TRUE) or the variogram (=FALSE) be plotted?
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
+#' @param cov logical, should the covariance function (=TRUE) or the variogram (=FALSE) be plotted?
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
#' defaults to TRUE
#' @param ... further graphical parameters for the plotting function
#'
#' @return This function is called for its side effect of producing a plot: the plot will be
#' open to further changes if you provide `closeplot=FALSE`. Additionally, the function
-#' invisibly returns the graphical parameters that were active before starting the plot. Hence,
+#' invisibly returns the graphical parameters that were active before starting the plot. Hence,
#' if you want to freeze a plot and not add anymore to it, you can do \code{par(plot(x, closeplot=FALSE, ...))},
#' or \code{plot(x, closeplot=TRUE, ...)}.
#' If you want to further add stuff to it, better just call \code{plot(x, closeplot=FALSE,...)}. The difference
-#' is only relevant when working with the screen graphical device.
-#' @export
+#' is only relevant when working with the screen graphical device.
+#' @export
#' @method plot gmCgram
#' @family gmCgram functions
#' @examples
@@ -338,7 +340,7 @@ as.gmCgram.default <- function(m,...) m
#' vm = v1+v2
#' plot(vm)
#' plot(vm, cov=FALSE)
-plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL, xlength=200, varnames = colnames(x$nugget),
+plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL, xlength=200, varnames = colnames(x$nugget),
add=FALSE, commonAxis=TRUE, cov =TRUE, closeplot=TRUE, ...){
Dg = dim(x$M)[2]
Ns = dim(x$M)[1]
@@ -386,12 +388,12 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
if(!is.null(xlim.lo)){
xseq.lo = seq(from=xlim.lo[1], to=xlim.lo[2], length.out=xlength)
}else{ xseq.lo=NULL}
-
+
opar = par()
opar = par_remove_readonly(opar)
-
+
if(closeplot) on.exit(par(opar))
-
+
getVdens = function(vdir, xseq){
if(is.null(vdir)|is.null(xseq)) return(NULL)
Vdens = sapply(1:nrow(vdir), function(k){
@@ -407,16 +409,16 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
dim(C0) = c(Dv,Dv)
Vdens = sweep(-Vdens, c(1,3), C0, "+") ## this must be corrected when we allow non-symmetric covariances
}
- return(Vdens)
+ return(Vdens)
}
Vdens.up = getVdens(vdir.up, xseq.up)
Vdens.lo = getVdens(vdir.lo, xseq.lo)
-
+
myplot = function(...) matplot(type="l",ylab="", xlab="",xaxt="n", ...)
if(add) myplot = function(...) matlines(...)
if(!add){
par(mfrow=c(Dv+1,Dv+1), mar=c(2,3,0,0), oma=c(1,4,1,1), xpd=NA)
- myplot(c(0,0), c(0,0), pch="", ann=FALSE, bty="n", yaxt="n")
+ myplot(c(0,0), c(0,0), pch="", ann=FALSE, bty="n", yaxt="n")
}
for(i in 1:Dv){
for(j in 1:Dv){
@@ -441,8 +443,8 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
mtext(text=varnames[i], side = 1, line=3)
mtext(text=varnames[i], side = 2, line=3)
}
- }
- }}
+ }
+ }}
mtext(text="lag distance", side=1, outer = TRUE, line=0)
mtext(text=c("semivariogram","covariance")[cov+1], side=2, outer = TRUE, line=2)
invisible(opar)
@@ -452,11 +454,11 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
#' Check for anisotropy of a theoretical variogram
-#'
+#'
#' Checks for anisotropy of a theoretical variogram or covariance function model
#' @param x variogram or covariance model object
-#' @param tol tolerance for
+#' @param tol tolerance for
#' @param ... extra arguments for generic functionality
#'
#' @return Generic function. Returns of boolean answering the question of the name,
@@ -511,7 +513,7 @@ is.isotropic.LMCAnisCompo = function(x, tol=1e-10, ...){
#' Variogram method for gmSpatialModel objects
-#'
+#'
#' Compute the empirical variogram of the conditioning data contained in a [gmSpatialModel-class] object
#'
#' @param object a gmSpatialModel object containing spatial data.
@@ -519,7 +521,7 @@ is.isotropic.LMCAnisCompo = function(x, tol=1e-10, ...){
#' @param ... further parameters to [gstat::variogram()]
#'
#' @return Currently the function is just a convenience wrapper on
-#' the variogram calculation functionalities of package "gstat",
+#' the variogram calculation functionalities of package "gstat",
#' and returns objects of class "\code{gstatVariogram}". Check the
#' help of \code{gstat::variogram} for further information.
#' In the near future, methods will be created, which will depend on
@@ -534,15 +536,15 @@ variogram_gmSpatialModel <- function(object, methodPars=NULL, ...){
# Variogram calculations
-#
+#
# Compute empricial variograms out of a spatial data object
-#
+#
# @param object spatial data container
# @param ... further parameters for variogram calculation
-#
-# @return depending on the input data, different kinds of empirical variograms
+#
+# @return depending on the input data, different kinds of empirical variograms
# will be produced. See appropriate method descriptions.
-#
+#
# @importFrom sp variogram
# @export
##variogram <- function(object, ...) UseMethod("variogram", object)
@@ -558,25 +560,25 @@ variogram_gmSpatialModel <- function(object, methodPars=NULL, ...){
#' Empirical variogram or covariance function in 2D
-#'
-#' compute the empirical variogram or covariance function in a 2D case study
-#'
+#'
+#' compute the empirical variogram or covariance function in a 2D case study
+#'
#' @param X matrix of Nx2 columns with the geographic coordinates
#' @param Z matrix or data.frame of data with dimension (N,Dv)
-#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N (can be a N-vector of 1s);
+#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N (can be a N-vector of 1s);
#' for covariance function, a (N,Dv)-matrix or a Dv-vector giving the mean values
#' @param maxdist maximum lag distance to consider
#' @param lagNr number of lags to consider
-#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
-#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
+#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
+#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
#' @param azimuthNr number of azimuths to consider
-#' @param azimuths if azimuthNr is not specified, either: (a) a matrix of 2 columns giving
+#' @param azimuths if azimuthNr is not specified, either: (a) a matrix of 2 columns giving
#' minimal and maximal azimuth defining the azimuth classes to consider, or (b) a vector of azimuth breaks
#' @param maxbreadth maximal breadth (in lag units) orthogonal to the lag direction
#' @param minpairs minimal number of pairs falling in each class to consider the calculation sufficient; defaults to 10
#' @param cov boolean, is covariance (TRUE) or variogram (FALSE) desired? defaults to variogram
#'
-#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
+#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
#' represent either internal functions, or preliminary, not fully-tested functions. Use \code{\link{variogram}} instead.
#' @export
#' @family gmEVario functions
@@ -594,7 +596,7 @@ variogram_gmSpatialModel <- function(object, methodPars=NULL, ...){
#' vge["npairs",1]
#' vge["lags",1]
gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
- maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
+ maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
lagNr = 15, lags = seq(from=0, to=maxdist, length.out=lagNr+1),
azimuthNr=4, azimuths = seq(from=0, to=180, length.out=azimuthNr+1)[1:azimuthNr],
maxbreadth=Inf, minpairs=10, cov=FALSE){
@@ -611,7 +613,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
}
# expand the information given into a set of columns stating conditions
if(length(dim(lags))==0){
- lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
+ lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
if(maxbreadth!=Inf) lags[,"maxbreadth"]=maxbreadth
}else if(dim(lags)==2){
lags = data.frame(lags)
@@ -621,21 +623,21 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
if(length(dim(azimuths))==0){
tol = (azimuths[2]-azimuths[1])/2
if(is.na(tol)) tol=180
- azimuths = data.frame(minaz=azimuths-tol, maxaz=azimuths+tol)
+ azimuths = data.frame(minaz=azimuths-tol, maxaz=azimuths+tol)
}else if(dim(azimuths)==2){
azimuths = data.frame(azimuths)
colnames(azimuths) = c("minaz","maxaz")
}else stop("azimuths can be either a vector of lags or a data.frame, see ?gsi.EVario2D")
-
+
# compute pairs of locations
ij = expand.grid(1:nrow(X), 1:nrow(X))# indices
XX = X[ij[,1],]-X[ij[,2],] # locations
- XXabs = gmApply(XX, 1, function(x) sqrt(sum(x^2)))
+ XXabs = gmApply(XX, 1, function(x) sqrt(sum(x^2)))
XXaz = gmApply(XX, 1, function(x) pi/2-atan2(x[2],x[1])) +2*pi
XXaz = XXaz %% pi # residual to 180??
-
+
# compute residuals and pairs of variables appropriate to the structural function
- if(cov){
+ if(cov){
if(all(dim(Z)==dim(Ff))){
Z = as.matrix(Z-Ff)
}else if(Dv==length(c(unlist(Ff)))){
@@ -648,8 +650,8 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
Z = lm(as.matrix(Z)~as.matrix(Ff)+0)$residuals ## ideally this should be a GLS fit
ZZ = Z[ij[,1],]-Z[ij[,2],]
}
-
-
+
+
# output
## ATTENTION: needs to be changed to return a structure (3,Na)-matrix of objects,
# like logratioVariogramAnisotropy
@@ -667,7 +669,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
if(ncol(lags)>2){
tk_b = outer(xxabs * abs(sin((xxaz-(azs[i,2]-azs[i,1])))), lags[,3], "<=")
tk_h = tk_h & tk_b
- }
+ }
n[,i] = colSums(tk_h)
for(j in 1:Nh){
if(!is.na(n[j,i]) && n[j,i]>minpairs){
@@ -675,7 +677,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
vg[j,,,i] = gmApply((ZZ[tk_a,,][tk_h[,j],,]), c(2,3),"sum")/(n[j,i])
}else{ # semi-variogram
aux = rowSums(
- gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
+ gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
)/(2*n[j,i])
vg[j,,,i] = aux
}
@@ -685,7 +687,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
}
return(list(gamma=vg[,,,i], lags=gsi.lagClass(lags), npairs =n[,i]))
})
-
+
# output
attr(res, "directions") = gsi.azimuthInterval(azimuths)
# attr(res, "lags") = gsi.lagClass(lags)
@@ -695,34 +697,34 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
}
#' Empirical variogram or covariance function in 3D
-#'
-#' compute the empirical variogram or covariance function in a 3D case study
-#'
+#'
+#' compute the empirical variogram or covariance function in a 3D case study
+#'
#' @param X matrix of Nx3 columns with the geographic coordinates
#' @param Z matrix or data.frame of data with dimension (N,Dv)
-#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N
-#' (can be a N-vector of 1s; should include the vector of 1s!!);
+#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N
+#' (can be a N-vector of 1s; should include the vector of 1s!!);
#' for covariance function, a (N,Dv)-matrix or a Dv-vector giving the mean values
#' @param maxdist maximum lag distance to consider
#' @param lagNr number of lags to consider
-#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
-#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
-#' @param dirvecs matrix which rows are the director vectors along which variograms will be computed (these will be normalized!)
-#' @param angtol scalar, angular tolerance applied (in degrees; defaults to 90??, ie. isotropic)
+#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
+#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
+#' @param dirvecs matrix which rows are the director vectors along which variograms will be computed (these will be normalized!)
+#' @param angtol scalar, angular tolerance applied (in degrees; defaults to 90??, ie. isotropic)
#' @param maxbreadth maximal breadth (in lag units) orthogonal to the lag direction (defaults to `Inf`, ie. not used)
#' @param minpairs minimal number of pairs falling in each class to consider the calculation sufficient; defaults to 10
#' @param cov boolean, is covariance (TRUE) or variogram (FALSE) desired? defaults to variogram
#'
-#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
+#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
#' represent either internal functions, or preliminary, not fully-tested functions. Use \code{\link{variogram}} instead.
-#'
-#'
+#'
+#'
#' @export
#' @family gmEVario functions
#'
# @examples
# dt <- readr::read_csv("~/Geochem_sum_imp2.csv")
-#
+#
# X = as.matrix(dt[,c("X","Y","Z")])
# Z = as.matrix(compositions::clr(dt[,c("Cu","Zn","Pb", "As", "Cd", "In", "Other")]))
# dirvecs = rbind( c(1,0,0), c(1,1,0), c(0,1,0), c(-1,1,0), c(0,0,1))
@@ -736,11 +738,11 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
# vge["lags",1]
# plot.gmEVario(vge, varnames = colnames(Z), commonAxis=FALSE,
# vdir.up = 1:4, vdir.lo=5, xlim.up=c(0,50), xlim.lo=c(0,15)
-# )
+# )
## ie. the 4 planar directions on the upper triangle,
## the downhole direction in the lower triangle
gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
- maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
+ maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
lagNr = 15, lags = seq(from=0, to=maxdist, length.out=lagNr+1),
dirvecs=t(c(1,0,0)), angtol=90,
maxbreadth=Inf, minpairs=10, cov=FALSE){
@@ -757,15 +759,15 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
## expand the information given into a set of columns stating conditions
# prepare lags
if(length(dim(lags))==0){
- lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
- # if(maxbreadth!=Inf)
+ lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
+ # if(maxbreadth!=Inf)
lags[,"maxbreadth"] = maxbreadth
}else if(dim(lags)==2){
lags = data.frame(lags)
colnames(lags) = c("minlag","maxlag","maxbreadth")[1:ncol(lags)]
}else stop("lags can be either a vector of lags or a data.frame, see ?gsi.EVario3D")
lagsSq = lags^2
-
+
# prepare directions with tolerance
if(length(dim(dirvecs))==0){
if(length(dirvecs)!=3) stop("dirvecs must be a matrix with 3 columns!")
@@ -775,15 +777,15 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
if(length(angtol) %in% c(1, nrow(dirvecs))){
dirvecs = cbind(dirvecs, tol=cos(angtol*pi/180) )
}else stop("angtol can be either a scalar or a vector of length=nrow(dirvecs), see ?gsi.EVario3D")
-
+
# compute pairs of locations
ij = expand.grid(1:nrow(X), 1:nrow(X))# indices
XX = X[ij[,1],]-X[ij[,2],] # locations
XXmodSq = gmApply(XX, 1, function(x) sum(x^2))
XXmod = sqrt(XXmodSq)
-
+
# compute residuals and pairs of variables appropriate to the structural function
- if(cov){
+ if(cov){
if(all(dim(Z)==dim(Ff))){
Z = as.matrix(Z-Ff)
}else if(Dv==length(c(unlist(Ff)))){
@@ -796,7 +798,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
Z = lm(as.matrix(Z)~as.matrix(Ff)+0)$residuals ## ideally this should be a GLS fit
ZZ = Z[ij[,1],]-Z[ij[,2],]
}
-
+
# output
## ATTENTION: needs to be changed to return a structure (3,Na)-matrix of objects,
# like logratioVariogramAnisotropy
@@ -816,7 +818,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
residualsSq = XXmodSq[tk_a]-projections[tk_a]^2
tk_b = outer(residualsSq, lagsSq[,3], "<=")
tk_h = tk_h & tk_b
- }
+ }
n[,i] = colSums(tk_h)
for(j in 1:Nh){
if(!is.na(n[j,i]) && n[j,i]>minpairs){
@@ -824,7 +826,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
vg[j,,,i] = gmApply((ZZ[tk_a,,][tk_h[,j],,]), c(2,3),"sum")/(n[j,i])
}else{ # semi-variogram
aux = rowSums(
- gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
+ gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
)/(2*n[j,i])
vg[j,,,i] = aux
}
@@ -834,7 +836,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
}
return(list(gamma=vg[,,,i], lags=gmGeostats:::gsi.lagClass(lags), npairs =n[,i]))
})
-
+
# output
attr(res, "directions") = gmGeostats:::gsi.directorVector(dirvecs[,1:3])
# attr(res, "lags") = gsi.lagClass(lags)
@@ -848,13 +850,13 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
#' Plot empirical variograms
-#'
+#'
#' Flexible plot of an empirical variogram of class gmEVario
-#'
+#'
#' @param x object to print, of class gmEVario
#' @param xlim.up range of X values to be used for the diagrams of the upper triangle
#' @param xlim.lo range of X values to be used for the diagrams of the lower triangle
-#' @param vdir.up in case of anisotropic variograms, indices of the directions to be plotted
+#' @param vdir.up in case of anisotropic variograms, indices of the directions to be plotted
#' on the upper triangle
#' @param vdir.lo ..., indices of the directions to be plotted on the lower triangle
#' @param varnames variable names to be used
@@ -862,16 +864,16 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
#' @param add boolean, add stuff to an existing diagram?
#' @param commonAxis boolean, should vertical axes be shared by all plots in a row?
#' @param cov boolean, is this a covariance? (if FALSE, it is a variogram)
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
#' defaults to TRUE
#' @param ... further parameters to \code{\link{matplot}}
#'
-#' @return invisibly, the graphical parameters active before calling the function.
+#' @return invisibly, the graphical parameters active before calling the function.
#' This is useful for freezing the plot if you provided `closeplot=FALSE`.
-#'
-#' How to use arguments `vdir.lo` and `vdir.up`? Each empirical variogram \code{x} has been
+#'
+#' How to use arguments `vdir.lo` and `vdir.up`? Each empirical variogram \code{x} has been
#' computed along certain distances, recorded in its attributes and retrievable with command
-#' \code{\link{ndirections}}.
+#' \code{\link{ndirections}}.
#' @export
#' @family gmEVario functions
#' @method plot gmEVario
@@ -884,8 +886,8 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
#' vge = gsi.EVario2D(X,Z)
#' plot(vge)
#' plot(vge, pch=22, lty=1, bg="grey")
-plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL,
- varnames = dimnames(x$gamma)[[2]], type="o",
+plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL,
+ varnames = dimnames(x$gamma)[[2]], type="o",
add=FALSE, commonAxis=TRUE, cov =attr(x,"type")=="covariance",
closeplot=TRUE, ...){
Dv = dim(x[1,1][[1]])[2]
@@ -912,12 +914,12 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
xlim.lo = c(0, maxdist)
}
}
-
+
opar = par()
opar = par_remove_readonly(opar)
-
+
if(closeplot) on.exit(par(opar))
-
+
myplot = function(...) matplot(type=type, ylab="", xlab="",xaxt="n", ...)
if(add) myplot = function(...) matpoints(type=type, ...)
if(!add){
@@ -931,7 +933,7 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
ylim = range(sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,i,j]), na.rm = TRUE)
if(commonAxis) ylim=range(sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,,j]), na.rm=TRUE)
myplot(
- sapply(vdir.lo, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
+ sapply(vdir.lo, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,i,j]), ylim=ylim, ...)
if(i==j){
axis(side = 3)
@@ -944,15 +946,15 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
ylim = range(sapply(vdir.up, function(kk) x["gamma",kk][[1]][,i,j]), na.rm = TRUE)
if(commonAxis) ylim=range(sapply(vdir.up, function(kk) x["gamma",kk][[1]][,,j]), na.rm=TRUE)
myplot(
- sapply(vdir.up, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
+ sapply(vdir.up, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
sapply(vdir.up, function(kk) x["gamma",kk][[1]][,i,j]), ylim=ylim, ...)
if(i==j){
axis(side = 1)
mtext(text=varnames[i], side = 1, line=3)
mtext(text=varnames[i], side = 2, line=3)
}
- }
- }}
+ }
+ }}
mtext(text="lag distance", side=1, outer = TRUE, line=0)
mtext(text=c("semivariogram","covariance")[cov+1], side=2, outer = TRUE, line=2)
attr(opar, "vdir.up")=vdir.up
@@ -965,19 +967,19 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
-#' Convert empirical structural function to gmEVario format
-#'
-#' Convert empirical covariance functions or variograms to the format gmEVario
+#' Convert empirical structural function to gmEVario format
+#'
+#' Convert empirical covariance functions or variograms to the format gmEVario
#' of package gmGeostats
-#'
+#'
#' @param vgemp variogram/covariance function to be converted
#' @param ... further parameters
#'
-#' @return the empirical covariance function or variogram, recasted to class
-#' \code{gmEVario}. This is a generic function. Methods exist for objects of
-#' class \code{logratioVariogram}\code{logratioVariogramAnisotropy}
+#' @return the empirical covariance function or variogram, recasted to class
+#' \code{gmEVario}. This is a generic function. Methods exist for objects of
+#' class \code{logratioVariogram}\code{logratioVariogramAnisotropy}
#' (for compositional data) and \code{gstatVariogram}
-#' (from package \code{gstat}).
+#' (from package \code{gstat}).
#' @export
#' @aliases as.gmEVario.gstatVariogram as.gmEVario.logratioVariogram
#' as.gmEVario.logratioVariogramAnisotropy
@@ -995,26 +997,26 @@ variogramModelPlot <- function(vg, ...) UseMethod("variogramModelPlot", vg)
#' Quick plotting of empirical and theoretical variograms
-#' Quick and dirty plotting of empirical variograms/covariances with or without their models
+#' Quick and dirty plotting of empirical variograms/covariances with or without their models
#' @param vg empirical variogram or covariance function
#' @param model optional, theoretical variogram or covariance function
#' @param col colors to use for the several directional variograms
#' @param commonAxis boolean, should all plots in a row share the same vertical axis?
-#' @param newfig boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
+#' @param newfig boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
#' defaults to TRUE
#' @param ... further parameters to underlying plot or matplot functions
#'
-#' @return The function is primarily called for producing a plot. However, it
-#' invisibly returns the graphical parameters active before the call
-#' occurred. This is useful for constructing complex diagrams, by adding layers
+#' @return The function is primarily called for producing a plot. However, it
+#' invisibly returns the graphical parameters active before the call
+#' occurred. This is useful for constructing complex diagrams, by adding layers
#' of info. If you want to "freeze" your plot, embed your call in another
#' call to \code{\link{par}}, e.g. \code{par(variogramModelPlot(...))}; if you
#' want to leave the plot open for further changes give the extra argument `closeplot=FALSE`.
#' @export
-#' @family variogramModelPlot
-#' @family gmEVario functions
-#' @family gmCgram functions
+#' @family variogramModelPlot
+#' @family gmEVario functions
+#' @family gmCgram functions
#' @seealso [logratioVariogram()]
#' @method variogramModelPlot gmEVario
#'
@@ -1030,15 +1032,15 @@ variogramModelPlot <- function(vg, ...) UseMethod("variogramModelPlot", vg)
#' Z = as.matrix(jura.pred[,c("Zn","Cd","Pb")])
#' vge = gsi.EVario2D(X,Z)
#' variogramModelPlot(vge, vm)
-#'
-#'
+#'
+#'
variogramModelPlot.gmEVario <- function(vg, model = NULL, # gstat or variogramModelList object containing a variogram model fitted to vg
col = rev(rainbow(ndirections(vg))),
commonAxis = FALSE,
newfig = TRUE, closeplot=TRUE, ...){
opar = plot(vg, commonAxis=commonAxis, add=!newfig, col=col, closeplot=is.null(model), ...)
opar = par_remove_readonly(opar)
-
+
if(closeplot) on.exit(par(opar))
vdir.lo = attr(opar, "vdir.lo")
vdir.up = attr(opar, "vdir.up")
@@ -1046,8 +1048,8 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL, # gstat or variogra
return(invisible(opar))
# OTHERWISE: add the curves for the model
aux = as.directorVector(attr(vg, "directions"))
- if(!is.null(vdir.lo)) vdir.lo = aux[vdir.lo,]
- if(!is.null(vdir.up)) vdir.up = aux[vdir.up,]
+ if(!is.null(vdir.lo)) vdir.lo = aux[vdir.lo,]
+ if(!is.null(vdir.up)) vdir.up = aux[vdir.up,]
opar = plot(as.gmCgram(model), vdir.up= vdir.up, vdir.lo= vdir.lo, add=TRUE, cov =FALSE, ...)
#f = as.function(as.gmCgram(gg))
#Dv = dim(vg$gamma)[2]
@@ -1062,7 +1064,7 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL, # gstat or variogra
# hd = seq(from=xlim[1], to=xlim[2], length.out = 200)
# Y = outer(hd, 1:nrow(dirs.lo), function(h,k){
# f(rep(0,Dg), dirs.lo[k,]*h)[i,j]
- # })
+ # })
# matlines(hd, Y, col=col[vdir.lo], ...)
# }
# if((i<=j)&(!is.null(vdir.up))){
@@ -1072,26 +1074,26 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL, # gstat or variogra
# hd = seq(from=xlim[1], to=xlim[2], length.out = 200)
# Y = outer(hd, 1:nrow(dirs.up), function(h,k){
# f(rep(0,Dg), dirs.up[k,]*h)[i,j]
- # })
+ # })
# matlines(hd, Y, col=col[vdir.up], ...)
- # }
- # }}
+ # }
+ # }}
invisible(opar)
-}
+}
#' Number of directions of an empirical variogram
-#'
+#'
#' Returns the number of directions at which an empirical variogram was computed
-#'
+#'
#' @param x empirical variogram object
#'
-#' @return Generic function. It provides the
+#' @return Generic function. It provides the
#' number of directions at which an empirical variogram was computed
#' @export
-#' @family gmEVario functions
-#' @family gmCgram functions
+#' @family gmEVario functions
+#' @family gmCgram functions
#' @seealso [logratioVariogram()], [gstat::variogram()]
ndirections <- function(x){ UseMethod("ndirections", x) }
@@ -1107,7 +1109,7 @@ ndirections.azimuth = function(x) length(x)
#' @method ndirections azimuthInterval
#' @export
ndirections.azimuthInterval = function(x) length(x[[1]])
-#' @describeIn ndirections method for empirical logratio variograms with anisotropy
+#' @describeIn ndirections method for empirical logratio variograms with anisotropy
#' @method ndirections logratioVariogramAnisotropy
#' @export
ndirections.logratioVariogramAnisotropy = function(x) ndirections(attr(x,"directions"))
@@ -1124,7 +1126,7 @@ ndirections.gmEVario = function(x) ndirections(attr(x,"directions"))
#' @export
ndirections.gstatVariogram = function(x){
length(unique(paste(x$dir.hor, x$dir.ver)))
-}
+}
@@ -1135,12 +1137,12 @@ ndirections.gstatVariogram = function(x){
gsi.azimuth = function(x){
class(x) = c("azimuth","directionClass")
return(x)
-}
+}
gsi.azimuthInterval = function(x){
class(x) = c("azimuthInterval","directionClass")
return(x)
-}
+}
@@ -1155,14 +1157,14 @@ print.directionClass = function(x, complete=TRUE, ...){
if(complete){
print(unclass(x), ...)
}
-}
+}
as.directorVector <- function(x){ UseMethod("as.directorVector",x) }
-#' @method as.directorVector default
+#' @method as.directorVector default
as.directorVector.default = function(x,...) x
#' @method as.directorVector azimuth
@@ -1185,7 +1187,7 @@ as.directorVector.azimuthInterval = function(x, D=2){
gsi.lagdists = function(x){
class(x) = c("lagdist","lagClass")
return(x)
-}
+}
gsi.lagClass = function(x){
if(length(dim(x))!=2) stop("provided lag classes are not matrix-like!")
@@ -1209,7 +1211,7 @@ gsi.midValues.default = function(x) x
#' @method gsi.midValues lagClass
gsi.midValues.lagClass <- function(x){ (x[[1]]+x[[2]])/2 }
-
+
#' @method gsi.midValues azimuthIntervals
gsi.midValues.azimuthInterval <- function(x){ (x[[1]]+x[[2]])/2 }
@@ -1218,49 +1220,49 @@ gsi.midValues.azimuthInterval <- function(x){ (x[[1]]+x[[2]])/2 }
## theoretical structural functions
-# S3 -> S4 classes
+# S3 -> S4 classes
# cat("creating variogram model classes\n")
# abstract classes
#' @title Structural function model specification
-#' @description Abstract class, containing any specification of a variogram (or covariance) model.
+#' @description Abstract class, containing any specification of a variogram (or covariance) model.
#' Members must implement a coercion method to
-#' class "gmCgram" (see [setCgram()] for an example), and (possibly) coercion to
+#' class "gmCgram" (see [setCgram()] for an example), and (possibly) coercion to
#' class "variogramModel" or "variogramModelList" (see [gstat::vgm()])
#' @export
#' @include compositionsCompatibility.R
#' @include gstatCompatibility.R
#' @include preparations.R
-setClassUnion(name="ModelStructuralFunctionSpecification",
+setClassUnion(name="ModelStructuralFunctionSpecification",
members=c("NULL","gmCgram", "LMCAnisCompo", "variogramModelList", "variogramModel"))
-#
+#
# #### container class --------------
# # An S4 class to represent a Gaussian random field specification
# #
-# # @slot structure ModelStructuralFunctionSpecification. Variogram or
+# # @slot structure ModelStructuralFunctionSpecification. Variogram or
# # (generalised) covariance function specification, typically an object
# # obtained from a call to functions such as \code{\link{setCgram}},
# # \code{\link{LMCAnisCompo}} or \code{gstat::vgm}.
# # @slot formula formula specifying the structure
-# # of dependence of the mean of the random field w.r.to spatial coordinates
-# # and/or covariables; typically it will have no left-hand-side term;
+# # of dependence of the mean of the random field w.r.to spatial coordinates
+# # and/or covariables; typically it will have no left-hand-side term;
# # @slot beta numeric, a vector with as many coefficients as terms the formula
# # above requires for a full specification of the trend; if unknown, these can
-# # be NAs, as many as needed.
+# # be NAs, as many as needed.
# #
# # @return A object with the slots populated as given
# # @export
# # @seealso [gmSpatialModel-class], and the `make.gm*` functions referenced there
-# setClass("gmGaussianModel",
+# setClass("gmGaussianModel",
# slots = list(structure = "ModelStructuralFunctionSpecification",
# formula="formula",
# beta = "structure")
# )
-#
+#
# #setMethod("initialize", signature="gmGaussianModel",
# # def=function(.Object, structure, formula, beta){
# # .Object@formula = formula
@@ -1274,21 +1276,21 @@ setClassUnion(name="ModelStructuralFunctionSpecification",
#### container class --------------
# An S4 class to represent a Gaussian random field specification
#
-# @slot structure ModelStructuralFunctionSpecification. Variogram or
+# @slot structure ModelStructuralFunctionSpecification. Variogram or
# (generalised) covariance function specification, typically an object
# obtained from a call to functions such as \code{\link{setCgram}},
# \code{\link{LMCAnisCompo}} or \code{gstat::vgm}.
# @slot formula formula specifying the structure
-# of dependence of the mean of the random field w.r.to spatial coordinates
-# and/or covariables; typically it will have no left-hand-side term;
+# of dependence of the mean of the random field w.r.to spatial coordinates
+# and/or covariables; typically it will have no left-hand-side term;
# @slot beta numeric, a vector with as many coefficients as terms the formula
# above requires for a full specification of the trend; if unknown, these can
-# be NAs, as many as needed.
+# be NAs, as many as needed.
#
# @return A object with the slots populated as given
# @export
# @seealso [gmSpatialModel-class], and the `make.gm*` functions referenced there
-setClass("gmGaussianModel",
+setClass("gmGaussianModel",
slots = list(structure = "ModelStructuralFunctionSpecification",
formula="formula",
beta = "numeric")
@@ -1302,9 +1304,9 @@ setClass("gmGaussianModel",
# abstract classes
#' @title Empirical structural function specification
-#' @description Abstract class, containing any specification of an empirical variogram
+#' @description Abstract class, containing any specification of an empirical variogram
#' (or covariance function, or variations). Members must implement a coercion method to
-#' class "gmEVario" (see [gsi.EVario2D()] for an example), and (possibly) coercion to
+#' class "gmEVario" (see [gsi.EVario2D()] for an example), and (possibly) coercion to
#' class "gstatVariogram" (see [gstat::variogram()])
#' @export
#' @include compositionsCompatibility.R
--
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