GeoCovmatrix() R function from [GeoModels]

Spatial and Spatio-temporal Covariance Matrix of (non) Gaussian random fields

The function computes the covariance matrix associated to a spatial or spatio(-temporal) or a bivariate spatial Gaussian or non Gaussian randomm field with given underlying covariance model and a set of spatial location sites (and temporal instants). UTF-8


GeoCovmatrix(estobj=NULL,coordx, coordy=NULL,coordz=NULL, coordt=NULL,coordx_dyn=NULL, 
            corrmodel,distance="Eucl", grid=FALSE, maxdist=NULL, maxtime=NULL,
          model="Gaussian", n=1, param, anisopars=NULL, radius=6371, sparse=FALSE,
          taper=NULL, tapsep=NULL, type="Standard",copula=NULL,X=NULL,spobj=NULL)

Arguments

estobj: An object of class Geofit that includes information about data, model and estimates.
coordx: A numeric ( $d x 2$ )-matrix or ( $d x 3$ )-matrix Coordinates on a sphere for a fixed radius radius

are passed in lon/lat format expressed in decimal degrees.
coordy: A numeric vector giving 1-dimension of spatial coordinates; Optional argument, the default is NULL.
coordz: A numeric vector giving 1-dimension of spatial coordinates; Optional argument, the default is NULL.
coordt: A numeric vector giving 1-dimension of temporal coordinates. At the moment implemented only for the Gaussian case. Optional argument, the default is NULL

then a spatial random field is expected.
coordx_dyn: A list of $T$ numeric ( $d x 2$ )-matrices containing dynamical (in time) coordinates. Optional argument, the default is NULL
corrmodel: String; the name of a correlation model, for the description see the Section Details .
distance: String; the name of the spatial distance. The default is Eucl, the euclidean distance. See GeoFit.
grid: Logical; if FALSE (the default) the data are interpreted as spatial or spatial-temporal realisations on a set of non-equispaced spatial sites (irregular grid). See GeoFit.
maxdist: Numeric; an optional positive value indicating the marginal spatial compact support in the case of tapered covariance matrix. See GeoFit.
maxtime: Numeric; an optional positive value indicating the marginal temporal compact support in the case of spacetime tapered covariance matrix. See GeoFit.
n: Numeric; the number of trials in a binomial random fields. Default is $1$ .
model: String; the type of RF. See GeoFit.
param: A list of parameter values required for the covariance model.
anisopars: A list of two elements "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
radius: Numeric; a value indicating the radius of the sphere when using covariance models valid using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
sparse: Logical; if TRUE the function return an object of class spam. This option should be used when a parametric compactly supporte covariance is used. Default is FALSE.
taper: String; the name of the taper correlation function if type is Tapering, see the Section Details .
tapsep: Numeric; an optional value indicating the separabe parameter in the space-time non separable taper or the colocated correlation parameter in a bivariate spatial taper (see Details ).
type: String; the type of covariance matrix Standard (the default) or Tapering for tapered covariance matrix.
copula: String; the type of copula. It can be "Clayton" or "Gaussian"
X: Numeric; Matrix of space-time covariates.
spobj: An object of class sp or spacetime

Returns

Returns an object of class GeoCovmatrix. An object of class GeoCovmatrix is a list containing at most the following components: - bivariate: Logical:TRUE if the Gaussian random field is bivariaete otherwise FALSE;

coordx: A $d$ -dimensional vector of spatial coordinates;
coordy: A $d$ -dimensional vector of spatial coordinates;
coordt: A $t$ -dimensional vector of temporal coordinates;
coordx_dyn: A list of $t$ matrices of spatial coordinates;
covmatrix: The covariance matrix if type isStandard. An object of class spam if type is Tapering or Standard and sparse is TRUE.
corrmodel: String: the correlation model;
distance: String: the type of spatial distance;
grid: Logical:TRUE if the spatial data are in a regular grid, otherwise FALSE;
nozero: In the case of tapered matrix the percentage of non zero values in the covariance matrix. Otherwise is NULL.
maxdist: Numeric: the marginal spatial compact support if type is Tapering;
maxtime: Numeric: the marginal temporal compact support if type is Tapering;
n: The number of trial for Binomial RFs
namescorr: String: The names of the correlation parameters;
numcoord: Numeric: the number of spatial coordinates;
numtime: Numeric: the number the temporal coordinates;
model: The type of RF, see GeoFit.
param: Numeric: The covariance parameters;
tapmod: String: the taper model if type is Tapering. Otherwise is NULL.
spacetime: TRUE if spatio-temporal and FALSE if spatial covariance model;
sparse: Logical: is the returned object of class spam? ;

Details

In the spatial case, the covariance matrix of the random vector

[Z(s_1),\ldots,Z(s_n,)]^T

with a specific spatial covariance model is computed. Here $n$ is the number of the spatial location sites.

In the space-time case, the covariance matrix of the random vector

[Z(s_1,t_1),Z(s_2,t_1),\ldots,Z(s_n,t_1),\ldots,Z(s_n,t_m)]^T

with a specific space time covariance model is computed. Here $m$ is the number of temporal instants.

In the bivariate case, the covariance matrix of the random vector

[Z_1(s_1),Z_2(s_1),\ldots,Z_1(s_n),Z_2(s_n)]^T

with a specific spatial bivariate covariance model is computed.

The location site $s_i$ can be a point in the $d$ -dimensional euclidean space with $d=2$

or a point (given in lon/lat degree format) on a sphere of arbitrary radius.

A list with all the implemented space and space-time and bivariate correlation models is given below. The argument param is a list including all the parameters of a given correlation model specified by the argument corrmodel. For each correlation model one can check the associated parameters' names using CorrParam. In what follows $\kappa>0$ , $\beta>0$ , $\alpha, \alpha_s, \alpha_t \in (0,2]$ , and $\gamma \in [0,1]$ . The associated parameters in the argument param are smooth, power2, power, power_s, power_t and sep respectively. Moreover let $1(A)=1$ when $A$ is true and $0$ otherwise.

Spatial correlation models:
1. $GenCauchy$ (generalised $Cauchy$ in Gneiting and Schlater (2004)) defined as:

R(h) = ( 1+h^{\alpha} )^{-\beta / \alpha}

  If $h$ is the geodesic distance then $\alpha \in (0,1]$.

2. $Matern$ defined as:

R(h) = 2^{1-\kappa} \Gamma(\kappa)^{-1} h^\kappa K_\kappa(h)2^{1-\kappa} \Gamma(\kappa)^{-1} h^\kappa K_\kappa(h)

  If $h$ is the geodesic distance then $\kappa \in (0,0.5]$

3. $Kummer$ (Kummer hypergeometric in Ma and Bhadra (2022)) defined as:

R(h) = \Gamma(\kappa+\alpha) U(\alpha,1-\kappa,0.5 h^2 ) / \Gamma(\kappa+\alpha)

   $U(.,.,.)$ is the Kummer hypergeometric function. If $h$ is the geodesic distance then $\kappa \in (0,0.5]$

4. $Kummer{\_}Matern$ It is a rescaled version of the $Kummer$ model that is $h$ must be divided by $(2*(1+\alpha))^{0.5}$ . When $\alpha$ goes to infinity it is the Matern model. 5. $Wave$ defined as:

R(h)=sin(h)/h

  This model is valid only for dimensions less than or equal to 3.

6. $GenWend$ (Generalized Wendland in Bevilacqua et al.(2019)) defined as:

R(h) = A (1-h^2)^{\beta+\kappa} F(\beta/2,(\beta+1)/2,2\beta+\kappa+1,1-h^2) 1(h \in [0,1])

  where $\mu \ge 0.5(d+1)+\kappa$
  
  and $A=(\Gamma(\kappa)\Gamma(2\kappa+\beta+1))/(\Gamma(2\kappa)\Gamma(\beta+1-\kappa)2^{\beta+1})$ and $F(.,.,.)$ is the Gaussian hypergeometric function. The cases $\kappa=0,1,2$ correspond to the $Wend0$, $Wend1$ and $Wend2$ models respectively.

7. $GenWend_{\_}Matern$ (Generalized Wendland Matern in Bevilacqua et al. (2022)). It is defined as a rescaled version of the Generalized Wendland that is $h$ must be divided by $(\Gamma(\beta+2\kappa+1)/\Gamma(\beta))^{1/(1+2\kappa)}$ . When $\beta$ goes to infinity it is the Matern model. 8. $GenWend_{\_}Matern2$ (Generalized Wendland Matern second parametrization. It is defined as a rescaled version of the Generalized Wendland that is $h$ must be multiplied by $\beta$ and the smoothness parameter is $\kappa-0.5$ . When $\beta$ goes to infinity it is the Matern model. 9. $Hypergeometric2$ (Hypegeometric model in Alegria and Emery (2022)). 10. $Hypergeometric$ (Optimal Hypegeometric model defined as ....). 11. $Multiquadric$ defined as:

R(h) = (1-\alpha0.5)^{2\beta}/(1+(\alpha0.5)^{2}-\alpha cos(h))^{\beta}, \quad h \in [0,\pi]

  This model is valid on the unit sphere and $h$ is the geodesic distance.

12. $Sinpower$ defined as:

R(h) = 1-(sin(h/2))^{\alpha},\quad h \in [0,\pi]

  This model is valid on the unit sphere and $h$ is the geodesic distance.

13. $Smoke$ (F family in Alegria et al. (2021)) defined as:

R(h) = K*F(1/{\alpha},1/{\alpha}+0.5,2/{\alpha}+0.5+{\kappa}),\quad h \in [0,\pi]

  where $K =(\Gamma(a)\Gamma(i))/\Gamma(i)\Gamma(o)) $. This model is valid on the unit sphere and $h$ is the geodesic distance.

Spatio-temporal correlation models.
- Non-separable models:
  1. $Gneiting$ defined as:

R(h, u) = e^{ -h^{\alpha_s}/((1+u^{\alpha_t})^{0.5 \gamma \alpha_s })}/(1+u^{\alpha_t})

  2. $Gneiting$`_`$GC$

R(h, u) = e^{ -u^{\alpha_t} /((1+h^{\alpha_s})^{0.5 \gamma \alpha_t}) }/( 1+h^{\alpha_s})

     where $h$ can be both the euclidean and the geodesic distance
  3. $Iacocesare$

R(h, u) = (1+h^{\alpha_s}+u^\alpha_t)^{-\beta}

  4. $Porcu$

R(h, u) = (0.5 (1+h^{\alpha_s})^\gamma +0.5 (1+u^{\alpha_t})^\gamma)^{-\gamma^{-1}}

  5. $Porcu1$

R(h, u) =(e^{ -h^{\alpha_s} ( 1+u^{\alpha_t})^{0.5 \gamma \alpha_s}}) / ((1+u^{\alpha_t})^{1.5})

  6. $Stein$

R(h, u) = (h^{\psi(u)}K_{\psi(u)}(h))/(2^{\psi(u)}\Gamma(\psi(u)+1))

     where $\psi(u)=\nu+u^{0.5\alpha_t} $
  7. $Gneiting$`_`$mat$`_`$S$, defined as:

R(h, u) =\phi(u)^{\tau_t}Mat(h\phi(u)^{-\beta},\nu_s)

     where $\phi(u)=(1+u^{0.5\alpha_t})$, $\tau_t \ge  3.5+\nu_s$, $\beta \in [0,1]$
  8. $Gneiting$`_`$mat$`_`$T$, defined interchanging h with u in $Gneiting$`_`$mat$`_`$S$
  9. $Gneiting$`_`$wen$`_`$S$, defined as:

R(h, u) =\phi(u)^{\tau_t}GenWend(h \phi(u)^{\beta},\nu_s,\mu_s)

     where $\phi(u)=(1+u^{0.5\alpha_t})$, $\tau_t \geq 2.5+2\nu_s$, $\beta \in [0,1]$
  10. $Gneiting$`_`$wen$`_`$T$, defined interchanging h with u in $Gneiting$`_`$wen$`_`$S$
  11. $Multiquadric$`_`$st$ defined as:

R(h, u)= ((1-0.5\alpha_s)^2/(1+(0.5\alpha_s)^2-\alpha_s \psi(u) cos(h)))^{a_s} , \quad h \in [0,\pi]

     where $\psi(u)=(1+(u/a_t)^{\alpha_t})^{-1}$. This model is valid on the unit sphere and $h$ is the geodesic distance.
  12. $Sinpower$`_`$st$ defined as:

R(h, u)=(e^{\alpha_s cos(h) \psi(u)/a_s} (1+\alpha_s cos(h) \psi(u) /a_s))/k

     where $\psi(u)=(1+(u/a_t)^{\alpha_t})^{-1} $ and $k=(1+\alpha_s/a_s) exp(\alpha_s/a_s), \quad h \in [0,\pi]$
     
     This model is valid on the unit sphere and $h$ is the geodesic distance.
* Separable models.
  
  Space-time separable correlation models are easly obtained as the product of a spatial and a temporal correlation model, that is

R(h,u)=R(h) R(u)

  Several combinations are possible:
  
  1. $Exp$`_`$Exp$ defined as:

R(h, u) =Exp(h)Exp(u)

  2. $Matern$`_`$Matern$ defined as:

R(h, u) =Matern(h;\kappa_s)Matern(u;\kappa_t)

  3. $GenWend$`_`$GenWend$ defined as

R(h, u) = GenWend(h;\kappa_s,\mu_s) GenWend(u;\kappa_t,\mu_t)

. 4. $Stable$ _ $Stable$ defined as:

R(h, u) =Stable(h;\alpha_s)Stable(u;\alpha_t)

  Note that some models are nested. (The $Exp$`_`$Exp$ with $Matern$`_`$Matern$ for instance.)

Spatial bivariate correlation models (see below):
1. $Bi$ _ $Matern$ (Bivariate full Matern model)
2. $Bi$ _ $Matern$ _ $contr$ (Bivariate Matern model with contrainsts)
3. $Bi$ _ $Matern$ _ $sep$ (Bivariate separable Matern model )
4. $Bi$ _ $LMC$ (Bivariate linear model of coregionalization)
5. $Bi$ _ $LMC$ _ $contr$ (Bivariate linear model of coregionalization with constraints )
6. $Bi$ _ $Wendx$ (Bivariate full Wendland model)
7. $Bi$ _ $Wendx$ _ $contr$ (Bivariate Wendland model with contrainsts)
8. $Bi$ _ $Wendx$ _ $sep$ (Bivariate separable Wendland model)
9. $Bi$ _ $Smoke$ (Bivariate full Smoke model on the unit sphere)
Spatial taper.

For spatial covariance tapering the taper functions are:
1. $Bohman$ defined as:

T(h)=(1-h)(sin(2\pi h)/(2 \pi h))+(1-cos(2\pi h))/(2\pi^{2}h) 1_{[0,1]}(h)

$Wendlandx, \quad x=0,1,2$ defined as:

T(h)=Wendx(h;x+2), x=0,1,2

Spatio-temporal tapers.

For spacetime covariance tapering the taper functions are:
1. $Wendlandx$ _ $Wendlandy$ (Separable tapers) $x,y=0,1,2$ defined as:

T(h,u)=Wendx(h;x+2) Wendy(h;y+2), x,y=0,1,2.

$Wendlandx$ _ $time$ (Non separable temporal taper) $x=0,1,2$ defined as: $Wenx$ _ $time$ , $x=0,1,2$ assuming $\alpha_t=2$ , $\mu_s=3.5+x$

and $\gamma \in [0,1]$ to be fixed using tapsep.
$Wendlandx$ _ $space$ (Non separable spatial taper) $x=0,1,2$ defined as: $Wenx$ _ $space$ , $x=0,1,2$ assuming $\alpha_s=2$ , $\mu_t=3.5+x$

and $\gamma \in [0,1]$ to be fixed using tapsep.

Spatial bivariate taper (see below).
1. $Bi$ _ $Wendlandx, \quad x=0,1,2$

Remarks :

In what follows we assume c(" $\\sigma^2,\\sigma_1^2,\\sigma_2^2,\\tau^2,\\tau_1^2,\\tau_2^2,\n$ ", " $a,a_s,a_t,a_{11},a_{22},a_{12},\\kappa_{11},\\kappa_{22},\\kappa_{12},f_{11},f_{12},f_{21},f_{22}$ ") positive.

The associated names of the parameters in param are sill, sill_1,sill_2, nugget, nugget_1,nugget_2, scale,scale_s,scale_t, scale_1,scale_2,scale_12, smooth_1,smooth_2,smooth_12, a_1,a_12,a_21,a_2

respectively.

Let $R(h)$ be a spatial correlation model given in standard notation. Then the covariance model applied with arbitrary variance, nugget and scale equals to $\sigma^2$ if $h=0$ and

C(h)=\sigma^2(1-\tau^2 ) R( h/a,,...), \quad h > 0

with nugget parameter $\tau^2$ between 0 and 1. Similarly if $R(h,u)$ is a spatio-temporal correlation model given in standard notation, then the covariance model is $\sigma^2$ if $h=0$ and $u=0$ and

C(h,u)=\sigma^2(1-\tau^2 )R(h/a_s ,u/a_t,...) \quad h>0, u>0

Here ... stands for additional parameters.

The bivariate models implemented are the following :

$Bi$ _ $Matern$ defined as:

C_{ij}(h)=\rho_{ij} (\sigma_i \sigma_j+\tau_i^2 1(i=j,h=0)) Matern(h/a_{ij},\kappa_{ij}) \quad i,j=1,2.\quad h\ge 0

where $\rho=\rho_{12}=\rho_{21}$ is the correlation colocated parameter and $\rho_{ii}=1$ . The model $Bi$ _ $Matern$ _ $sep$ (separable matern) is a special case when $a=a_{11}=a_{12}=a_{22}$ and $\kappa=\kappa_{11}=\kappa_{12}=\kappa_{22}$ . The model $Bi$ _ $Matern$ _ $contr$ (constrained matern) is a special case when $a_{12}=0.5 (a_{11} + a_{22})$ and $\kappa_{12}=0.5 (\kappa_{11} + \kappa_{22})$ 2. $Bi$ _ $GenWend$ defined as:

C_{ij}(h)=\rho_{ij} (\sigma_i \sigma_j+\tau_i^2 1(i=j,h=0)) GenWend(h/a_{ij},\nu_{ij},\kappa_ij) \quad i,j=1,2.\quad h\ge 0

where $\rho=\rho_{12}=\rho_{21}$ is the correlation colocated parameter and $\rho_{ii}=1$ . The model $Bi$ _ $GenWend$ _ $sep$ (separable Genwendland) is a special case when $a=a_{11}=a_{12}=a_{22}$ and $\mu=\mu_{11}=\mu_{12}=\mu_{22}$ . The model $Bi$ _ $GenWend$ _ $contr$ (constrained Genwendland) is a special case when $a_{12}=0.5 (a_{11} + a_{22})$ and $\mu_{12}=0.5 (\mu_{11} + \mu_{22})$ 3. $Bi$ _ $LMC$ defined as:

C_{ij}(h)=\sum_{k=1}^{2} (f_{ik}f_{jk}+\tau_i^2 1(i=j,h=0)) R(h/a_{k})

where $R(h)$ is a correlation model. The model $Bi$ _ $LMC$ _ $contr$

is a special case when $f=f_{12}=f_{21}$ . Bivariate LMC models, in the current version of the package, is obtained with $R(h)$ equal to the exponential correlation model.

References

Alegria, A.,Cuevas-Pacheco, F.,Diggle, P, Porcu E. (2021) The F-family of covariance functions: A Matérn analogue for modeling random fields on spheres. Spatial Statistics 43, 100512

Bevilacqua, M., Faouzi, T., Furrer, R., and Porcu, E. (2019). Estimation and prediction using generalized Wendland functions under fixed domain asymptotics. Annals of Statistics, 47(2), 828--856.

Bevilacqua, M., Caamano-Carrillo, C., and Porcu, E. (2022). Unifying compactly supported and Matérn covariance functions in spatial statistics. Journal of Multivariate Analysis, 189, 104949.

Daley J. D., Porcu E., Bevilacqua M. (2015) Classes of compactly supported covariance functions for multivariate random fields. Stochastic Environmental Research and Risk Assessment. 29 (4), 1249--1263.

Emery, X. and Alegria, A. (2022). The gauss hypergeometric covariance kernel for modeling second-order stationary random fields in euclidean spaces: its compact support, properties and spectral representation. Stochastic Environmental Research and Risk Assessment. 36 2819--2834.

Gneiting, T. (2002). Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association, 97, 590--600.

Gneiting T, Kleiber W., Schlather M. 2010. Matern cross-covariance functions for multivariate random fields. Journal of the American Statistical Association, 105, 1167--1177.

Ma, P., Bhadra, A. (2022). Beyond Matérn: on a class of interpretable confluent hypergeometric covariance functions. Journal of the American Statistical Association,1--14.

Porcu, E.,Bevilacqua, M. and Genton M. (2015) Spatio-Temporal Covariance and Cross-Covariance Functions of the Great Circle Distance on a Sphere. Journal of the American Statistical Association. DOI: 10.1080/01621459.2015.1072541

Gneiting, T. and Schlater M. (2004) Stochastic models that separate fractal dimension and the Hurst effect. SSIAM Rev 46, 269--282.

Author(s)

Moreno Bevilacqua, moreno.bevilacqua89@gmail.com ,https://sites.google.com/view/moreno-bevilacqua/home, Víctor Morales Oñate, victor.morales@uv.cl , https://sites.google.com/site/moralesonatevictor/, Christian", Caamaño-Carrillo, chcaaman@ubiobio.cl ,https://www.researchgate.net/profile/Christian-Caamano

Examples


library(GeoModels)
################################################################
###
### Example 1.Estimated  spatial covariance matrix associated to
### a WCL estimates using the Matern correlation model
###
###############################################################

set.seed(3)
N=300  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
# Set the covariance model's parameters:
corrmodel <- "Matern"
mean=0.5; 
sill <- 1
nugget <- 0
scale <- 0.2/3
smooth=0.5

param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)
data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param)$data
fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,scale=scale,sill=sill)

fit0 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, 
                    neighb=3,likelihood="Conditional",optimizer="BFGS",
                    type="Pairwise", start=start,fixed=fixed)
print(fit0)

#estimated covariance matrix using Geofit object
mm=GeoCovmatrix(fit0)$covmatrix
#estimated covariance matrix
mm1 = GeoCovmatrix(coordx=coords,corrmodel=corrmodel,
           param=c(fit0$param,fit0$fixed))$covmatrix
sum(mm-mm1)

################################################################
###
### Example 2. Spatial covariance matrix associated to
### the GeneralizedWendland-Matern correlation model
###
###############################################################

# Correlation Parameters for Gen Wendland model 
CorrParam("GenWend_Matern")
# Gen Wendland Parameters 
param=list(sill=1,scale=0.04,nugget=0,smooth=0,power2=1.5)

matrix2 = GeoCovmatrix(coordx=coords, corrmodel="GenWend_Matern", param=param,sparse=TRUE)

# Percentage of no zero values 
matrix2$nozero

################################################################
###
### Example 3. Spatial covariance matrix associated to
### the Kummer correlation model
###
###############################################################

# Correlation Parameters for kummer model 
CorrParam("Kummer")
param=list(sill=1,scale=0.2,nugget=0,smooth=0.5,power2=1)

matrix3 = GeoCovmatrix(coordx=coords, corrmodel="Kummer", param=param)

matrix3$covmatrix[1:4,1:4]

################################################################
###
### Example 4. Covariance matrix associated to
### the space-time double Matern correlation model
###
###############################################################

# Define the temporal-coordinates:
times = seq(1, 4, 1)

# Correlation Parameters for double Matern model
CorrParam("Matern_Matern")

# Define covariance parameters
param=list(scale_s=0.3,scale_t=0.5,sill=1,smooth_s=0.5,smooth_t=0.5)

# Simulation of a spatial Gaussian random field:
matrix4 = GeoCovmatrix(coordx=coords, coordt=times, corrmodel="Matern_Matern",
                     param=param)

dim(matrix4$covmatrix)

################################################################
###
### Example 5. Spatial Covariance matrix associated to
### a  skew gaussian RF with Matern correlation model
###
###############################################################

param=list(sill=1,scale=0.3/3,nugget=0,skew=4,smooth=0.5)
# Simulation of a spatial Gaussian random field:
matrix5 = GeoCovmatrix(coordx=coords,  corrmodel="Matern", param=param, 
                     model="SkewGaussian")

# covariance matrix
matrix5$covmatrix[1:4,1:4]

################################################################
###
### Example 6. Spatial Covariance matrix associated to
### a  Weibull RF with Genwend correlation model
###
###############################################################

param=list(scale=0.3,nugget=0,shape=4,mean=0,smooth=1,power2=5)
# Simulation of a spatial Gaussian random field:
matrix6 = GeoCovmatrix(coordx=coords,  corrmodel="GenWend", param=param, 
                     sparse=TRUE,model="Weibull")

# Percentage of no zero values 
matrix6$nozero

################################################################
###
### Example 7. Spatial Covariance matrix associated to
### a  binomial gaussian RF with Generalized Wendland correlation model
###
###############################################################

param=list(mean=0.2,scale=0.2,nugget=0,power2=4,smooth=0)
# Simulation of a spatial Gaussian random field:
matrix7 = GeoCovmatrix(coordx=coords,  corrmodel="GenWend", param=param,n=5, 
                     sparse=TRUE,
                     model="Binomial")

as.matrix(matrix7$covmatrix)[1:4,1:4]

################################################################
###
### Example 8.  Covariance matrix associated to
### a bivariate Matern exponential correlation model
###
###############################################################

set.seed(8)
# Define the spatial-coordinates of the points:
x = runif(4, 0, 1)
y = runif(4, 0, 1)
coords = cbind(x,y)

# Parameters 
param=list(mean_1=0,mean_2=0,sill_1=1,sill_2=2,
           scale_1=0.1,  scale_2=0.1,  scale_12=0.1,
           smooth_1=0.5, smooth_2=0.5, smooth_12=0.5,
            nugget_1=0,nugget_2=0,pcol=-0.25)

# Covariance matrix 
matrix8 = GeoCovmatrix(coordx=coords, corrmodel="Bi_matern", param=param)$covmatrix

matrix8

GeoModels package Read PDF manual

Maintainer: Moreno Bevilacqua
License: GPL (>= 3)
Last published: 2025-01-14

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GeoCovmatrix function