Package 'GeoModels' reference manual

Title:	Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Description:	Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.
Authors:	Moreno Bevilacqua [aut, cre] , Víctor Morales-Oñate [aut] , Christian Caamaño-Carrillo [aut]
Maintainer:	Moreno Bevilacqua <[email protected]>
License:	GPL (>= 3)
Version:	2.0.8
Built:	2024-11-19 06:56:30 UTC
Source:	CRAN

Annual precipitation anomalies in U.S.

Description

A ( $7252 x 3$ )-matrix containing lon/lat and yearly total precipitation anomalies registered at 7.352 location sites in USA.

Usage

data(anomalies)data(anomalies)

Format

A numerical matrix of dimension $7252 x 3$ .

Source

Kaufman, C.G., Schervish, M.J., Nychka, D.W. (2008) Covariance tapering for likelihood-based estimation in large spatial data sets. Journal of the American Statistical Association, Theory & Methods, 103, 1545–1555.

Maximum australian temperature

Description

A matrix containing maximum temperature in Australia in July 2011.

Usage

data(austemp)data(austemp)

Format

A ( $446 \times 4$ )-matrix containing longitude, latitude, maximum temperature, and the 'so called' geometric temperature covariate.

Source

Bevilacqua M., Caamaño C., Morales-Oñate V., Arellano-Valle R. B. (2020) Non-Gaussian Geostatistical Modeling using (skew) t Processes, Scandinavian Journal of Statistics.

Checking Bivariate covariance models

Description

The procedure control if the correlation model is bivariate.

Usage

CheckBiv(numbermodel)
CheckBiv(numbermodel)

Arguments

numbermodel

numeric; the number associated to a given correlation model.

Details

The function check if the correlation model is bivariate.

Value

Return TRUE or FALSE depending if the correlation model is bivariate or not.

Author(s)

Moreno Bevilacqua, [email protected],https://sites.google.com/view/moreno-bevilacqua/home, Víctor Morales Oñate, [email protected], https://sites.google.com/site/moralesonatevictor/, Christian", Caamaño-Carrillo, [email protected],https://www.researchgate.net/profile/Christian-Caamano

Examples


library(GeoModels)
CheckBiv(CkCorrModel("Bi_matern_sep"))

library(GeoModels)
CheckBiv(CkCorrModel("Bi_matern_sep"))

Checking Distance

Description

The procedure controls the type of distance.

Usage

CheckDistance(distance)
CheckDistance(distance)

Arguments

distance

String; the type of distance, for the description see GeoCovmatrix. Default is Eucl. Other possible values are Geod and Chor that is euclidean, geodesic and chordal distance.

Details

The function check if the type of distance is valid.

Value

Returns 0,1,2 for euclidean,geodesic, chordal distances respectively. Otherwise returns NULL.

Author(s)

Checking if a covariance is valid only on the sphere

Description

Subroutine called by InitParam. The procedure controls if a covariance model is valid only on the sphere.

Usage

CheckSph(numbermodel)CheckSph(numbermodel)

Arguments

numbermodel

Numeric; the code number for the covariance model.

Details

The function checks if a covariance is valid only on the sphere

Value

Returns TRUE or FALSE

Author(s)

Checking SpaceTime covariance models

Description

The procedure control if the correlation model is spacetime.

Usage

CheckST(numbermodel)
CheckST(numbermodel)

Arguments

numbermodel

numeric; the number associated to a given correlation model.

Details

The function check if the correlation model is spacetime.

Value

Returns TRUE or FALSE depending if the correlation model is spacetime or not.

Author(s)

Examples


library(GeoModels)
CheckST(CkCorrModel("gneiting"))

library(GeoModels)
CheckST(CkCorrModel("gneiting"))

Checking Correlation Model

Description

The procedure controls if the correlation model inserted is correct.

Usage

CkCorrModel(corrmodel)
CkCorrModel(corrmodel)

Arguments

corrmodel

String; the name of a correlation model, for the description see GeoCovmatrix.

Details

The procedure controls if the correlation model is correct

Value

Return a number associated to a given correlation model if the model is considered in the package. Otherwise return NULL.

Author(s)

Checking Input

Description

Subroutine called by the fitting procedures. The procedure controls the the validity of the input inserted by the users.

Usage

CkInput(coordx, coordy, coordt, coordx_dyn,corrmodel, data, distance, 
           fcall, fixed, grid,likelihood, maxdist, maxtime, 
            model, n,  optimizer, param, radius,
           start, taper, tapsep,  type, varest, vartype, 
           weighted,copula,X)
CkInput(coordx, coordy, coordt, coordx_dyn,corrmodel, data, distance, 
           fcall, fixed, grid,likelihood, maxdist, maxtime, 
            model, n,  optimizer, param, radius,
           start, taper, tapsep,  type, varest, vartype, 
           weighted,copula,X)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates.
`corrmodel`	String; the name of a correlation model, for the description see `GeoFit`.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details.
`fcall`	String; `Fitting` to call the fitting procedure and `simulation` to call the simulation.
`fixed`	A named list giving the values of the parameters that will be considered as known values. The listed parameters for a given correlation function will be not estimated, i.e. if `list(nugget=0)` the nugget effect is ignored.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`likelihood`	String; the configuration of the composite likelihood. `Marginal` is the default.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial distance considered in the composite-likelihood computation.
`maxtime`	Numeric; an optional positive value indicating the maximum temporal lag separation in the composite-likelihood.
`radius`	Numeric; the radius of the sphere in the case of lon-lat coordinates. The default is 6371, the radius of the earth.
`model`	String; the density associated to the likelihood objects. `Gaussian` is the default.
`n`	Numeric; the number of trials in a binomial random fields. Default is $1$ .
`optimizer`	String; the optimization algorithm (see `optim` for details). 'Nelder-Mead' is the default.
`param`	A numeric vector of parameters, needed only in simulation. See `GeoSim`.
`start`	A named list with the initial values of the parameters that are used by the numerical routines in maximization procedure. `NULL` is the default.
`taper`	String; the name of the tapered correlation function.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time quasi taper (see Details).
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.
`varest`	Logical; if `TRUE` the estimate' variances and standard errors are returned. `FALSE` is the default.
`vartype`	String; the type of estimation method for computing the estimate variances, see `GeoFit`.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted. If `FALSE` (the default) the composite likelihood is not weighted.
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`X`	Numeric; Matrix of space-time covariates in the linear mean specification.

Details

Subroutine called by the fitting procedures. The procedure controls the the validity of the input inserted by the users.

Value

A list with the type of error associated with the input parameters.

Author(s)

Checking Composite-likelihood Type

Description

Subroutine called by InitParam. The procedure controls the type of the composite-likelihood inserted by the users.

Usage

CkLikelihood(likelihood)
CkLikelihood(likelihood)

Arguments

likelihood

String; the configuration of the composite likelihood. Marginal is the default.

Details

The function controls the type of the composite-likelihood inserted by the users.

Value

The function returns a numeric positive integer, or NULL if the likelihood is invalid.

Author(s)

Checking Random Field type

Description

Subroutine called by InitParam. The procedure controls the type of random field inserted by the users.

Usage

CkModel(model)
CkModel(model)

Arguments

model

String; the density associated to the likelihood objects. Gaussian is the default.

Details

The function controls the type of random field inserted by the users.

Value

The function returns a numeric positive integer, or NULL if the model is invalid.

Author(s)

Checking Likelihood Objects

Description

Subroutine called by InitParam. The procedure controls the type of likelihood objects inserted by the users.

Usage

CkType(type)
CkType(type)

Arguments

type

String; the type of the likelihood objects. If Pairwise (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.

Details

The procedure checks the likelihood Object

Value

The function returns a numeric positive integer, or NULL if the type of likelihood is invalid.

Author(s)

Checking Variance Estimates Type

Description

Subroutine called by InitParam. The procedure controls the method used to compute the estimates' variances.

Usage

CkVarType(type)
CkVarType(type)

Arguments

type

String; the method used to compute the estimates' variances. If SubSamp the estimates' variances are computed by the sub-sampling method, see GeoFit.

Details

The procedure controls the method used to compute the estimates' variances

Value

The function returns a numeric positive integer, or NULL if the method is invalid.

Author(s)

Optimizes the Composite indipendence log-likelihood

Description

Subroutine called by GeoFit. The procedure estimates the model parameters by maximisation of the indipendence composite log-likelihood.

Usage

CompIndLik2(bivariate, coordx, coordy ,coordt,
coordx_dyn, data, flagcorr, flagnuis, fixed,grid,
              lower, model, n, namescorr, namesnuis, 
              namesparam,
              numparam, optimizer, onlyvar, parallel, 
              param, spacetime, type,
              upper, namesupper, varest, ns, X,
              sensitivity,copula,MM)
CompIndLik2(bivariate, coordx, coordy ,coordt,
coordx_dyn, data, flagcorr, flagnuis, fixed,grid,
              lower, model, n, namescorr, namesnuis, 
              namesparam,
              numparam, optimizer, onlyvar, parallel, 
              param, spacetime, type,
              upper, namesupper, varest, ns, X,
              sensitivity,copula,MM)

Arguments

`bivariate`	Logical; if `TRUE` then the data come froma a bivariate random field. Otherwise from a univariate random field.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`flagcorr`	A numeric vector of binary values denoting which paramerters of the correlation function will be estimated.
`flagnuis`	A numeric vector of binary values denoting which nuisance paramerters will be estimated.
`fixed`	A numeric vector of parameters that will be considered as known values.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`model`	Numeric; the id value of the density associated to the likelihood objects.
`n`	Numeric; number of trials in a binomial random fields.
`namescorr`	String; the names of the correlation parameters.
`namesnuis`	String; the names of the nuisance parameters.
`namesparam`	String; the names of the parameters to be maximised.
`numparam`	Numeric; the number of parameters to be maximised.
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS` `L-BFGS-B` and `nlminb`. In these last two cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`param`	A numeric vector of parameters values.
`spacetime`	Logical; if `TRUE` the random field is spatial-temporal otherwise is a spatial field.
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`namesupper`	String; the names of the upper limit of the parameters.
`varest`	Logical; if `TRUE` the estimate variances and standard errors are returned. `FALSE` is the default.
`ns`	Numeric; Number of (dynamical) temporal instants.
`X`	Numeric; Matrix of space-time covariates in the linear mean specification.
`sensitivity`	Logical; if `TRUE` then the sensitivy matrix is computed
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`MM`	Numeric;a non constant fixed mean

Value

Return a list from an optim call.

Author(s)

Optimizes the Composite log-likelihood

Description

Subroutine called by GeoFit. The procedure estimates the model parameters by maximisation of the composite log-likelihood.

Usage

CompLik(copula,bivariate, coordx, coordy ,coordt, 
coordx_dyn, corrmodel, data,distance,  flagcorr, 
flagnuis, fixed, GPU, grid,likelihood,local,lower, 
                 model, n, namescorr, namesnuis, 
                 namesparam,
               numparam, numparamcorr, optimizer, 
               onlyvar, parallel, param,
               spacetime, type,upper, varest, vartype,
               weigthed, winconst, winstp,winconst_t, 
               winstp_t, ns, X,sensitivity,MM,aniso)
CompLik(copula,bivariate, coordx, coordy ,coordt, 
coordx_dyn, corrmodel, data,distance,  flagcorr, 
flagnuis, fixed, GPU, grid,likelihood,local,lower, 
                 model, n, namescorr, namesnuis, 
                 namesparam,
               numparam, numparamcorr, optimizer, 
               onlyvar, parallel, param,
               spacetime, type,upper, varest, vartype,
               weigthed, winconst, winstp,winconst_t, 
               winstp_t, ns, X,sensitivity,MM,aniso)

Arguments

`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`bivariate`	Logical; if `TRUE` then the data come froma a bivariate random field. Otherwise from a univariate random field.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	Numeric; the id of the correlation model.
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details.
`flagcorr`	A numeric vector of binary values denoting which paramerters of the correlation function will be estimated.
`flagnuis`	A numeric vector of binary values denoting which nuisance paramerters will be estimated.
`fixed`	A numeric vector of parameters that will be considered as known values.
`GPU`	Numeric; if `NULL` (the default) no GPU computation is performed.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`likelihood`	String; the configuration of the compositelikelihood, see `GeoFit`.
`local`	Numeric; number of local work-items of the GPU
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`model`	Numeric; the id value of the density associated to the likelihood objects.
`n`	Numeric; number of trials in a binomial random fields.
`namescorr`	String; the names of the correlation parameters.
`namesnuis`	String; the names of the nuisance parameters.
`namesparam`	String; the names of the parameters to be maximised.
`numparam`	Numeric; the number of parameters to be maximised.
`numparamcorr`	Numeric; the number of correlation parameters.
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS` `L-BFGS-B` and `nlminb`. In these last two cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`param`	A numeric vector of parameters' values.
`spacetime`	Logical; if `TRUE` the random field is spatial-temporal otherwise is a spatial field.
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`varest`	Logical; if `TRUE` the estimate' variances and standard errors are returned. `FALSE` is the default.
`vartype`	String; the type of estimation method for computing the estimate variances, see `GeoFit`.
`weigthed`	Logical; if `TRUE` then decreasing weigths coming from a compactly supported correlation function with compact support `maxdist` (`maxtime`)are used.
`winconst`	Numeric; a positive value for computing the spatial sub-window in the sub-sampling procedure.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure.
`ns`	Numeric; Number of (dynamical) temporal instants.
`X`	Numeric; Matrix of space-time covariates in the linear mean specification.
`sensitivity`	Logical; if `TRUE` then the sensitivy matrix is computed
`MM`	Numeric;a non constant fixed mean
`aniso`	Logical; should anisotropy be considered?

Details

Subroutine called by GeoFit. The procedure estimates the model parameters by maximisation of the composite log-likelihood

Value

Return a list from an optim call.

Author(s)

Optimizes the Composite log-likelihood

Description

Subroutine called by GeoFit. The procedure estimates the model parameters by maximisation of the composite log-likelihood.

Usage

CompLik2(copula,bivariate, coordx, coordy ,coordt,
coordx_dyn,corrmodel, data, distance, flagcorr, flagnuis, 
         fixed, GPU,grid,likelihood, local,lower, 
         model, n, namescorr, namesnuis, namesparam,
         numparam, numparamcorr, optimizer, onlyvar,
         parallel, param, spacetime, type,
         upper, varest, vartype, weigthed, winconst, 
         winstp,winconst_t, winstp_t, ns, X,sensitivity,
         colidx,rowidx,neighb,MM,aniso)
CompLik2(copula,bivariate, coordx, coordy ,coordt,
coordx_dyn,corrmodel, data, distance, flagcorr, flagnuis, 
         fixed, GPU,grid,likelihood, local,lower, 
         model, n, namescorr, namesnuis, namesparam,
         numparam, numparamcorr, optimizer, onlyvar,
         parallel, param, spacetime, type,
         upper, varest, vartype, weigthed, winconst, 
         winstp,winconst_t, winstp_t, ns, X,sensitivity,
         colidx,rowidx,neighb,MM,aniso)

Arguments

`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`bivariate`	Logical; if `TRUE` then the data come froma a bivariate random field. Otherwise from a univariate random field.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	Numeric; the id of the correlation model.
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details.
`flagcorr`	A numeric vector of binary values denoting which paramerters of the correlation function will be estimated.
`flagnuis`	A numeric vector of binary values denoting which nuisance paramerters will be estimated.
`fixed`	A numeric vector of parameters that will be considered as known values.
`GPU`	Numeric; if `NULL` (the default) no GPU computation is performed.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`likelihood`	String; the configuration of the compositelikelihood, see `GeoFit`.
`local`	Numeric; number of local work-items of the GPU
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`model`	Numeric; the id value of the density associated to the likelihood objects.
`n`	Numeric; number of trials in a binomial random fields.
`namescorr`	String; the names of the correlation parameters.
`namesnuis`	String; the names of the nuisance parameters.
`namesparam`	String; the names of the parameters to be maximised.
`numparam`	Numeric; the number of parameters to be maximised.
`numparamcorr`	Numeric; the number of correlation parameters.
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS` `L-BFGS-B` and `nlminb`. In these last two cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`param`	A numeric vector of parameters' values.
`spacetime`	Logical; if `TRUE` the random field is spatial-temporal otherwise is a spatial field.
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`varest`	Logical; if `TRUE` the estimate' variances and standard errors are returned. `FALSE` is the default.
`vartype`	String; the type of estimation method for computing the estimate variances, see `GeoFit`.
`weigthed`	Logical; if `TRUE` then decreasing weigths coming from a compactly supported correlation function with compact support `maxdist` (`maxtime`)are used.
`winconst`	Numeric; a positive value for computing the spatial sub-window in the sub-sampling procedure.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure.
`ns`	Numeric; Number of (dynamical) temporal instants.
`X`	Numeric; Matrix of space-time covariates in the linear mean specification.
`sensitivity`	Logical; if `TRUE` then the sensitivy matrix is computed
`colidx`	Numeric; Vector of indexes for spatial distances.
`rowidx`	Numeric; Vector of indexes for spatial distances.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood location.
`MM`	Numeric;a non constant fixed mean
`aniso`	Logical; should anisotropy be considered?

Value

Return a list from an optim call.

Author(s)

Lists the Parameters of a Correlation Model

Description

Subroutine called by InitParam and other procedures. The procedure returns a list with the parameters of a given correlation model.

Usage

CorrelationPar(corrmodel)
CorrelationPar(corrmodel)

Arguments

corrmodel

Integer; an integer associated to a given correlation model.

Details

The function return a list with the Parameters of a Correlation Model

Value

Return a vector string of correlation parameters.

Author(s)

Lists the Parameters of a Correlation Model

Description

The procedure returns a list with the names of the parameters of a given correlation model.

Usage

CorrParam(corrmodel)
CorrParam(corrmodel)

Arguments

corrmodel

String: the name associated to a given correlation model.

Details

The function return a list with the Parameters of a Correlation Model

Value

Return a vector string of correlation parameters.

Author(s)

Examples

require(GeoModels)
################################################################
###
### Example 1. Parameters of the  Matern  model
###
###############################################################

CorrParam("Matern")


################################################################
###
### Example 2. Parameters of the  Generalized Wendland  model
###
###############################################################

CorrParam("GenWend")


################################################################
###
### Example 3. Parameters of the  Generalized Cauchy  model
###
###############################################################

CorrParam("GenCauchy")


################################################################
###
### Example 4. Parameters of the  space time Gneiting  model
###
###############################################################

CorrParam("Gneiting")


################################################################
###
### Example 5. Parameters of the bi-Matern separable  model.
###            Note that in the bivariate case variance paramters are
###            included
###############################################################

CorrParam("Bi_Matern_sep")
require(GeoModels)
################################################################
###
### Example 1. Parameters of the  Matern  model
###
###############################################################

CorrParam("Matern")


################################################################
###
### Example 2. Parameters of the  Generalized Wendland  model
###
###############################################################

CorrParam("GenWend")


################################################################
###
### Example 3. Parameters of the  Generalized Cauchy  model
###
###############################################################

CorrParam("GenCauchy")


################################################################
###
### Example 4. Parameters of the  space time Gneiting  model
###
###############################################################

CorrParam("Gneiting")


################################################################
###
### Example 5. Parameters of the bi-Matern separable  model.
###            Note that in the bivariate case variance paramters are
###            included
###############################################################

CorrParam("Bi_Matern_sep")

Spatial Anisotropy correction

Description

Transforms or back-transforms a set of coordinates according to the geometric anisotropy parameters.

Usage

GeoAniso(coords, anisopars=c(0,1), inverse = FALSE)GeoAniso(coords, anisopars=c(0,1), inverse = FALSE)

Arguments

`coords`	An n x 2 matrix with the coordinates to be transformed.
`anisopars`	A bivariate vector with the the anisotropy angle and the anisotropy ratio, respectively. The angle must be given in radians in [0,pi] and the anisotropy ratio must be greater or equal than 1.
`inverse`	Logical: Default to FALSE. If TRUE the reverse transformation is performed.

Details

Geometric anisotropy is defined by a linear tranformation from the anisotropic space to the isotropic space that is

$Y = X R S$

where X is a matrix with original coordinates (anisotropic space), and Y is a matrix with transformed coordinates (isotropic space). Here R is a rotation matrix with associated anisotropy angle parameter (in $[0,pi]$ ) and a $S$ is a shrinking matrix with associated anisotropy ratio parameter (greeater or equal than one). The two parameters are specified in the anisopars argument as a bivariate numeric vector. The case $(.,1)$ corresponds to the isotropic case.

Value

Returns a matrix of transformed coordinates

Author(s)

Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields

Description

The function computes the correlations of a spatial (or spatio-temporal or bivariate spatial) Gaussian or non Gaussian randomm field for a given correlation model and a set of spatial (temporal) distances.

Usage

GeoCorrFct(x,t=NULL,corrmodel, model="Gaussian",
distance="Eucl", param, radius=6371,n=1,
covariance=FALSE,variogram=FALSE)
GeoCorrFct(x,t=NULL,corrmodel, model="Gaussian",
distance="Eucl", param, radius=6371,n=1,
covariance=FALSE,variogram=FALSE)

Arguments

`x`	A set of spatial distances.
`t`	A set of (optional) temporal distances.
`corrmodel`	String; the name of a correlation model, for the description see the Section Details.
`model`	String; the type of RF. See `GeoFit`.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See `GeoFit`.
`param`	A list of parameter values required for the covariance model.
`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)
`n`	Numeric; the number of trials in a (negative) binomial random fields. Default is $1$ .
`covariance`	Logic; if TRUE then the covariance is returned. Default is FALSE
`variogram`	Logic; if FALSE then the covariance/correlation is returned. Otherwise the associated semivariogram is returned

Value

Returns correlations or covariances values associated to a given parametric spatial and temporal correlation models.

Author(s)

Examples

library(GeoModels)

################################################################
###
### Example 1. Covariance of a Gaussian random field with underlying 
### Matern correlation model with nugget
###
###############################################################
# Define the spatial distances
#x = seq(0,1,0.002)
# Correlation Parameters for Matern model 
#CorrParam("Matern")
#NuisParam("Gaussian")
# Matern Parameters 
#param=list(sill=2,smooth=0.5,scale=0.2/3,nugget=0.2,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="Matern", covariance=TRUE,
#  param=param,model="Gaussian")
#plot(cc,ylab="Corr",lwd=2,main="Matern correlation",type="l")

################################################################
###
### Example 2. Covariance of a Gaussian random field with underlying 
### Generalized Wendland-Matern correlation model
###
###############################################################
#CorrParam("GenWend_Matern")
#NuisParam("Gaussian")
# GenWend Matern Parameters 
#param=list(sill=2,smooth=1,scale=0.1,nugget=0,power2=1/4,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="GenWend_Matern", param=param,model="Gaussian",covariance=FALSE)
#plot(cc,ylab="Cov",lwd=2,,main="GenWend covariance",type="l")

################################################################
###
### Example 3. Semivariogram of a Tukeyh random field with underlying 
### Generalized Wendland correlation model
###
###############################################################
#CorrParam("GenWend")
#NuisParam("Tukeyh")
#x = seq(0,1,0.005)
#param=list(sill=1,smooth=1,scale=0.5,nugget=0,power2=5,tail=0.1,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="GenWend", param=param,model="Tukeyh",variogram=TRUE)
#plot(cc,ylab="Corr",lwd=2,main="Tukey semivariogram",type="l")

################################################################
###
### Example 4. Semi-Variogram of a LoggGaussian random field with underlying 
### Kummer correlation model
###
###############################################################
#CorrParam("Kummer")
#NuisParam("LogGaussian")
# GenWend Matern Parameters 
#param=list(smooth=1,sill=0.5,scale=0.1,nugget=0,power2=1,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="Kummer", param=param,model="LogGaussian",
#       ,covariance=TRUE,variogram=TRUE)
#plot(cc,ylab="Semivario",lwd=2,
#  main="LogGaussian semivariogram",type="l")


################################################################
###
### Example 5. Covariance of Poisson random field with underlying 
### Matern correlation model
###
###############################################################
#CorrParam("Matern")
#NuisParam("Poisson")
#x = seq(0,1,0.005)
#param=list(scale=0.6/3,nugget=0,smooth=0.5,mean=2)
#cc= GeoCorrFct(x=x, corrmodel="Matern", param=param,model="Poisson",covariance=TRUE)
#plot(cc,ylab="Cov",lwd=2,
#  main="Poisson covariance",type="l")

################################################################
###
### Example 6.  Space time  semivariogram of a Gaussian random field 
### with  separable Matern correlation model
###
###############################################################

## spatial and temporal distances 
#h<-seq(0,3,by=0.04)
#times<-seq(0,3,by=0.04)

# Correlation Parameters for the space time separable Matern model 
#CorrParam("Matern")
#NuisParam("Gaussian")
# Matern Parameters 
#param=list(sill=1,scale_s=0.6/3,scale_t=0.5,nugget=0,mean=0,smooth_s=1.5,smooth_t=0.5)
#cc= GeoCorrFct(x=h,t=times,corrmodel="Matern_Matern", param=param,
#        model="Gaussian",variogram=TRUE)
#plot(cc,lwd=2,type="l")


################################################################
###
### Example 7. Correlation of a bivariate Gaussian random field 
### with underlying  separable bivariate  Matern correlation model
###
###############################################################
# Define the spatial distances
#x = seq(0,1,0.005)
# Correlation Parameters for the bivariate sep Matern model 
#CorrParam("Bi_Matern")
# Matern Parameters 
#param=list(sill_1=1,sill_2=1,smooth_1=0.5,smooth_2=1,smooth_12=0.75,
#           scale_1=0.2/3, scale_2=0.2/3, scale_12=0.2/3,
#           mean_1=0,mean_2=0,nugget_1=0,nugget_2=0,pcol=-0.2)
#cc= GeoCorrFct(x=x, corrmodel="Bi_Matern", param=param,model="Gaussian")
#plot(cc,ylab="corr",lwd=2,type="l")
library(GeoModels)

################################################################
###
### Example 1. Covariance of a Gaussian random field with underlying 
### Matern correlation model with nugget
###
###############################################################
# Define the spatial distances
#x = seq(0,1,0.002)
# Correlation Parameters for Matern model 
#CorrParam("Matern")
#NuisParam("Gaussian")
# Matern Parameters 
#param=list(sill=2,smooth=0.5,scale=0.2/3,nugget=0.2,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="Matern", covariance=TRUE,
#  param=param,model="Gaussian")
#plot(cc,ylab="Corr",lwd=2,main="Matern correlation",type="l")

################################################################
###
### Example 2. Covariance of a Gaussian random field with underlying 
### Generalized Wendland-Matern correlation model
###
###############################################################
#CorrParam("GenWend_Matern")
#NuisParam("Gaussian")
# GenWend Matern Parameters 
#param=list(sill=2,smooth=1,scale=0.1,nugget=0,power2=1/4,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="GenWend_Matern", param=param,model="Gaussian",covariance=FALSE)
#plot(cc,ylab="Cov",lwd=2,,main="GenWend covariance",type="l")

################################################################
###
### Example 3. Semivariogram of a Tukeyh random field with underlying 
### Generalized Wendland correlation model
###
###############################################################
#CorrParam("GenWend")
#NuisParam("Tukeyh")
#x = seq(0,1,0.005)
#param=list(sill=1,smooth=1,scale=0.5,nugget=0,power2=5,tail=0.1,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="GenWend", param=param,model="Tukeyh",variogram=TRUE)
#plot(cc,ylab="Corr",lwd=2,main="Tukey semivariogram",type="l")

################################################################
###
### Example 4. Semi-Variogram of a LoggGaussian random field with underlying 
### Kummer correlation model
###
###############################################################
#CorrParam("Kummer")
#NuisParam("LogGaussian")
# GenWend Matern Parameters 
#param=list(smooth=1,sill=0.5,scale=0.1,nugget=0,power2=1,mean=0)
#cc= GeoCorrFct(x=x, corrmodel="Kummer", param=param,model="LogGaussian",
#       ,covariance=TRUE,variogram=TRUE)
#plot(cc,ylab="Semivario",lwd=2,
#  main="LogGaussian semivariogram",type="l")


################################################################
###
### Example 5. Covariance of Poisson random field with underlying 
### Matern correlation model
###
###############################################################
#CorrParam("Matern")
#NuisParam("Poisson")
#x = seq(0,1,0.005)
#param=list(scale=0.6/3,nugget=0,smooth=0.5,mean=2)
#cc= GeoCorrFct(x=x, corrmodel="Matern", param=param,model="Poisson",covariance=TRUE)
#plot(cc,ylab="Cov",lwd=2,
#  main="Poisson covariance",type="l")

################################################################
###
### Example 6.  Space time  semivariogram of a Gaussian random field 
### with  separable Matern correlation model
###
###############################################################

## spatial and temporal distances 
#h<-seq(0,3,by=0.04)
#times<-seq(0,3,by=0.04)

# Correlation Parameters for the space time separable Matern model 
#CorrParam("Matern")
#NuisParam("Gaussian")
# Matern Parameters 
#param=list(sill=1,scale_s=0.6/3,scale_t=0.5,nugget=0,mean=0,smooth_s=1.5,smooth_t=0.5)
#cc= GeoCorrFct(x=h,t=times,corrmodel="Matern_Matern", param=param,
#        model="Gaussian",variogram=TRUE)
#plot(cc,lwd=2,type="l")


################################################################
###
### Example 7. Correlation of a bivariate Gaussian random field 
### with underlying  separable bivariate  Matern correlation model
###
###############################################################
# Define the spatial distances
#x = seq(0,1,0.005)
# Correlation Parameters for the bivariate sep Matern model 
#CorrParam("Bi_Matern")
# Matern Parameters 
#param=list(sill_1=1,sill_2=1,smooth_1=0.5,smooth_2=1,smooth_12=0.75,
#           scale_1=0.2/3, scale_2=0.2/3, scale_12=0.2/3,
#           mean_1=0,mean_2=0,nugget_1=0,nugget_2=0,pcol=-0.2)
#cc= GeoCorrFct(x=x, corrmodel="Bi_Matern", param=param,model="Gaussian")
#plot(cc,ylab="corr",lwd=2,type="l")

Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields (copula models)

Description

The function computes the correlations of a spatial or spatio-temporal or a bivariate spatial Gaussian or non Gaussian copula randomm field with a given covariance model and a set of spatial (temporal) distances.

Usage

GeoCorrFct_Cop(x,t=NULL,corrmodel, 
model="Gaussian",copula="Gaussian",
distance="Eucl", param, radius=6371,
n=1,covariance=FALSE,variogram=FALSE)
GeoCorrFct_Cop(x,t=NULL,corrmodel, 
model="Gaussian",copula="Gaussian",
distance="Eucl", param, radius=6371,
n=1,covariance=FALSE,variogram=FALSE)

Arguments

`x`	A set of spatial distances.
`t`	A set of (optional) temporal distances.
`corrmodel`	String; the name of a correlation model, for the description see the Section Details.
`model`	String; the type of RF. See `GeoFit`.
`copula`	String; the type of copula. The two options are Gaussian and Clayton.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See `GeoFit`.
`param`	A list of parameter values required for the covariance model.
`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)
`n`	Numeric; the number of trials in a (negative) binomial random fields. Default is $1$ .
`covariance`	Logic; if TRUE then the covariance is returned. Default is FALSE
`variogram`	Logic; if FALSE then the covariance/coorelation is returned. Otherwise the associated semivariogram is returned

Value

Returns a vector of correlations or covariances values associated to a given parametric spatial and temporal correlation models.

Author(s)

Examples

library(GeoModels)

################################################################
###
### Example 1. Correlation of a (mean reparametrized) beta random field with underlying 
### Matern correlation model using Gaussian and Clayton copulas
###
###############################################################

# Define the spatial distances
x = seq(0,0.4,0.01)

# Correlation Parameters for Matern model 
CorrParam("Matern")
NuisParam("Beta2")
# corr Gaussian copula
param=list(smooth=0.5,sill=1,scale=0.2/3,nugget=0,mean=0,min=0,max=1,shape=0.5)
corr1= GeoCorrFct_Cop(x=x, corrmodel="Matern", param=param,copula="Gaussian",model="Beta2")

plot(corr1,ylab="corr",main="Gauss copula correlation",lwd=2)

# corr Clayton copula
param=list(smooth=0.5,sill=1,scale=0.2/3,nugget=0,mean=0,min=0,max=1,shape=0.5,nu=2)
corr2= GeoCorrFct_Cop(x=x, corrmodel="Matern", param=param,copula="Clayton",model="Beta2")
lines(x,corr2$corr,ylim=c(0,1),lty=2)

plot(corr1,ylab="corr",main="Clayton copula correlation",lwd=2)



library(GeoModels)

################################################################
###
### Example 1. Correlation of a (mean reparametrized) beta random field with underlying 
### Matern correlation model using Gaussian and Clayton copulas
###
###############################################################

# Define the spatial distances
x = seq(0,0.4,0.01)

# Correlation Parameters for Matern model 
CorrParam("Matern")
NuisParam("Beta2")
# corr Gaussian copula
param=list(smooth=0.5,sill=1,scale=0.2/3,nugget=0,mean=0,min=0,max=1,shape=0.5)
corr1= GeoCorrFct_Cop(x=x, corrmodel="Matern", param=param,copula="Gaussian",model="Beta2")

plot(corr1,ylab="corr",main="Gauss copula correlation",lwd=2)

# corr Clayton copula
param=list(smooth=0.5,sill=1,scale=0.2/3,nugget=0,mean=0,min=0,max=1,shape=0.5,nu=2)
corr2= GeoCorrFct_Cop(x=x, corrmodel="Matern", param=param,copula="Clayton",model="Beta2")
lines(x,corr2$corr,ylim=c(0,1),lty=2)

plot(corr1,ylab="corr",main="Clayton copula correlation",lwd=2)

Computes the fitted variogram model.

Description

The procedure computes and plots estimated covariance or semivariogram models of a Gaussian or a non Gaussian spatial (temporal or bivariate spatial) random field. It allows to add the empirical estimates in order to compare them with the fitted model.

Usage

GeoCovariogram(fitted, distance="Eucl",answer.cov=FALSE,
            answer.vario=FALSE, answer.range=FALSE, fix.lags=NULL, 
             fix.lagt=NULL, show.cov=FALSE, show.vario=FALSE, 
            show.range=FALSE, add.cov=FALSE, add.vario=FALSE,
            pract.range=95, vario, invisible=FALSE, ...)
GeoCovariogram(fitted, distance="Eucl",answer.cov=FALSE,
            answer.vario=FALSE, answer.range=FALSE, fix.lags=NULL, 
             fix.lagt=NULL, show.cov=FALSE, show.vario=FALSE, 
            show.range=FALSE, add.cov=FALSE, add.vario=FALSE,
            pract.range=95, vario, invisible=FALSE, ...)

Arguments

`fitted`	A fitted object obtained from the `GeoFit` or `GeoWLS` procedures.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See `GeoFit`.
`answer.cov`	Logical; if `TRUE` a vector with the estimated covariance function is returned; if `FALSE` (the default) the covariance is not returned.
`answer.vario`	Logical; if `TRUE` a vector with the estimated variogram is returned; if `FALSE` (the default) the variogram is not returned.
`answer.range`	Logical; if `TRUE` the estimated pratical range is returned; if `FALSE` (the default) the pratical range is not returned.
`fix.lags`	Integer; a positive value denoting the spatial lag to consider for the plot of the temporal profile.
`fix.lagt`	Integer; a positive value denoting the temporal lag to consider for the plot of the spatial profile.
`show.cov`	Logical; if `TRUE` the estimated covariance function is plotted; if `FALSE` (the default) the covariance function is not plotted.
`show.vario`	Logical; if `TRUE` the estimated variogram is plotted; if `FALSE` (the default) the variogram is not plotted.
`show.range`	Logical; if `TRUE` the estimated pratical range is added on the plot; if `FALSE` (the default) the pratical range is not added.
`add.cov`	Logical; if `TRUE` the vector of the estimated covariance function is added on the current plot; if `FALSE` (the default) the covariance is not added.
`add.vario`	Logical; if `TRUE` the vector with the estimated variogram is added on the current plot; if `FALSE` (the default) the correlation is not added.
`pract.range`	Numeric; the percent of the sill to be reached.
`vario`	A `Variogram` object obtained from the `GeoVariogram` procedure.
`invisible`	Logical;If TRUE then a statistic the (sum of the squared diffeence between the empirical semivariogram and the estimated semivariogram) is computed.
`...`	other optional parameters which are passed to plot functions.

Details

The function computes the fitted variogram model

Value

Produces a plot. No values are returned.

Author(s)

References

Cressie, N. A. C. (1993) Statistics for Spatial Data. New York: Wiley.

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Spring Verlang, New York.

Examples

library(GeoModels)
library(scatterplot3d)

################################################################
###
### Example 1. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Gaussian RF 
### with Matern correlation. 
###
###############################################################
set.seed(21)
# Set the coordinates of the points:
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "Matern"
model = "Gaussian"
mean = 0
sill = 1
nugget = 0
scale = 0.2/3
smooth=0.5

param=list(mean=mean,sill=sill, nugget=nugget, scale=scale, smooth=smooth)
# Simulation of the Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param)$data
I=Inf
start=list(mean=0,scale=scale,sill=sill)
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
fixed=list(nugget=nugget,smooth=smooth)
# Maximum composite-likelihood fitting of the Gaussian random field:
fit = GeoFit(data=data,coordx=coords, corrmodel=corrmodel,model=model,
            likelihood="Marginal",type='Pairwise',start=start,
            lower=lower,upper=upper,
            optimizer="nlminb", fixed=fixed,neighb=3)

# Empirical estimation of the variogram:
vario = GeoVariogram(data=data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit,show.vario=TRUE, vario=vario,pch=20)

################################################################
###
### Example 2. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Bernoulli 
###  RF with Genwend  correlation. 
###
###############################################################
set.seed(2111)

model="Binomial";n=1
# Set the coordinates of the points:
x = runif(500, 0, 1)
y = runif(500, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "GenWend"
mean = 0
nugget = 0
scale = 0.2
smooth=0
power=4
param=list(mean=mean, nugget=nugget, scale=scale,smooth=0,power2=4)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param,n=n)$data

start=list(mean=0,scale=scale)
fixed=list(nugget=nugget,power2=4,smooth=0)
# Maximum composite-likelihood fitting of the Binomial random field:
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
             likelihood="Marginal",type='Pairwise',start=start,n=n,
             optimizer="BFGS", fixed=fixed,neighb=4)

# Empirical estimation of the variogram:
vario = GeoVariogram(data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit, show.vario=TRUE, vario=vario,pch=20,ylim=c(0,0.3))


################################################################
###
### Example 3. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Weibull  RF
###   with Wend0 correlation. 
###
###############################################################
set.seed(111)

model="Weibull";shape=4
# Set the coordinates of the points:
x = runif(700, 0, 1)
y = runif(700, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "Wend0"
mean = 0
nugget = 0
scale = 0.4
power2=4


param=list(mean=mean, nugget=nugget, scale=scale,shape=shape,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param)$data

start=list(mean=0,scale=scale,shape=shape)
I=Inf
lower=list(mean=-I,scale=0,shape=0)
upper=list(mean= I,scale=I,shape=I)
fixed=list(nugget=nugget,power2=power2)

fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
             likelihood="Marginal",type='Pairwise',start=start,
             lower=lower,upper=upper,
             optimizer="nlminb", fixed=fixed,neighb=3)

# Empirical estimation of the variogram:
vario = GeoVariogram(data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit, show.vario=TRUE, vario=vario,pch=20)

################################################################
###
### Example 4. Plot of  fitted  and empirical semivariogram
### from a space time Gaussian random fields  
### with double Matern correlation. 
###
###############################################################
set.seed(92)
# Define the spatial-coordinates of the points:
x = runif(50, 0, 1)
y = runif(50, 0, 1)
coords=cbind(x,y)
# Define the temporal sequence:
time = seq(0, 10, 1)


param=list(mean=mean,nugget=nugget,
  smooth_s=0.5,smooth_t=0.5,scale_s=0.5/3,scale_t=2/2,sill=sill)
# Simulation of the spatio-temporal Gaussian random field:
data = GeoSim(coordx=coords, coordt=time, corrmodel="Matern_Matern",param=param)$data

fixed=list(nugget=0, mean=0, smooth_s=0.5,smooth_t=0.5)
start=list(scale_s=0.2, scale_t=0.5, sill=1)
# Maximum composite-likelihood fitting of the space-time Gaussian random field:
fit = GeoFit(data, coordx=coords, coordt=time, corrmodel="Matern_Matern", maxtime=1,
             neighb=3, likelihood="Marginal", type="Pairwise",fixed=fixed, start=start)

# Empirical estimation of spatio-temporal covariance:
vario = GeoVariogram(data,coordx=coords, coordt=time, maxtime=5,maxdist=0.5)

# Plot of the fitted space-time variogram
GeoCovariogram(fit,vario=vario,show.vario=TRUE)

# Plot of covariance, variogram and spatio and temporal profiles:
GeoCovariogram(fit,vario=vario,fix.lagt=1,fix.lags=1,show.vario=TRUE,pch=20)


################################################################
###
### Example 5. Plot of  fitted  and empirical semivariogram
### from a bivariate Gaussian random fields  
### with  Matern correlation. 
###
###############################################################
set.seed(92)
# Define the spatial-coordinates of the points:
x <- runif(600, 0, 2)
y <- runif(600, 0, 2)
coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a  Bivariate Matern
set.seed(12)
param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
           scale_1=0.12,scale_2=0.1,scale_12=0.15,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=-0.5)
data <- GeoSim(coordx=coords,corrmodel="Bi_matern",
              param=param)$data



# selecting fixed and estimated parameters
fixed=list(mean_1=4,mean_2=2,nugget_1=0,nugget_2=0,
      smooth_1=0.5,smooth_2=0.5,smooth_12=0.5)
start=list(sill_1=var(data[1,]),sill_2=var(data[2,]),
           scale_1=0.1,scale_2=0.1,scale_12=0.1,
           pcol=cor(data[1,],data[2,]))


# Maximum marginal pairwise likelihood
fitcl<- GeoFit(data=data, coordx=coords, corrmodel="Bi_Matern",
                     likelihood="Marginal",type="Pairwise",
                     optimizer="BFGS" , start=start,fixed=fixed,
                     neighb=4)
print(fitcl)

# Empirical estimation of spatio-temporal covariance:
vario = GeoVariogram(data,coordx=coords,maxdist=0.4,bivariate=TRUE)
GeoCovariogram(fitcl,vario=vario,show.vario=TRUE,pch=20)
library(GeoModels)
library(scatterplot3d)

################################################################
###
### Example 1. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Gaussian RF 
### with Matern correlation. 
###
###############################################################
set.seed(21)
# Set the coordinates of the points:
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "Matern"
model = "Gaussian"
mean = 0
sill = 1
nugget = 0
scale = 0.2/3
smooth=0.5

param=list(mean=mean,sill=sill, nugget=nugget, scale=scale, smooth=smooth)
# Simulation of the Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param)$data
I=Inf
start=list(mean=0,scale=scale,sill=sill)
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
fixed=list(nugget=nugget,smooth=smooth)
# Maximum composite-likelihood fitting of the Gaussian random field:
fit = GeoFit(data=data,coordx=coords, corrmodel=corrmodel,model=model,
            likelihood="Marginal",type='Pairwise',start=start,
            lower=lower,upper=upper,
            optimizer="nlminb", fixed=fixed,neighb=3)

# Empirical estimation of the variogram:
vario = GeoVariogram(data=data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit,show.vario=TRUE, vario=vario,pch=20)

################################################################
###
### Example 2. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Bernoulli 
###  RF with Genwend  correlation. 
###
###############################################################
set.seed(2111)

model="Binomial";n=1
# Set the coordinates of the points:
x = runif(500, 0, 1)
y = runif(500, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "GenWend"
mean = 0
nugget = 0
scale = 0.2
smooth=0
power=4
param=list(mean=mean, nugget=nugget, scale=scale,smooth=0,power2=4)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param,n=n)$data

start=list(mean=0,scale=scale)
fixed=list(nugget=nugget,power2=4,smooth=0)
# Maximum composite-likelihood fitting of the Binomial random field:
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
             likelihood="Marginal",type='Pairwise',start=start,n=n,
             optimizer="BFGS", fixed=fixed,neighb=4)

# Empirical estimation of the variogram:
vario = GeoVariogram(data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit, show.vario=TRUE, vario=vario,pch=20,ylim=c(0,0.3))


################################################################
###
### Example 3. Plot of fitted covariance and fitted 
### and empirical semivariogram from a  Weibull  RF
###   with Wend0 correlation. 
###
###############################################################
set.seed(111)

model="Weibull";shape=4
# Set the coordinates of the points:
x = runif(700, 0, 1)
y = runif(700, 0, 1)
coords=cbind(x,y)

# Set the model's parameters:
corrmodel = "Wend0"
mean = 0
nugget = 0
scale = 0.4
power2=4


param=list(mean=mean, nugget=nugget, scale=scale,shape=shape,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, model=model,param=param)$data

start=list(mean=0,scale=scale,shape=shape)
I=Inf
lower=list(mean=-I,scale=0,shape=0)
upper=list(mean= I,scale=I,shape=I)
fixed=list(nugget=nugget,power2=power2)

fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
             likelihood="Marginal",type='Pairwise',start=start,
             lower=lower,upper=upper,
             optimizer="nlminb", fixed=fixed,neighb=3)

# Empirical estimation of the variogram:
vario = GeoVariogram(data,coordx=coords,maxdist=0.5)

# Plot of covariance and variogram functions:
GeoCovariogram(fit, show.vario=TRUE, vario=vario,pch=20)

################################################################
###
### Example 4. Plot of  fitted  and empirical semivariogram
### from a space time Gaussian random fields  
### with double Matern correlation. 
###
###############################################################
set.seed(92)
# Define the spatial-coordinates of the points:
x = runif(50, 0, 1)
y = runif(50, 0, 1)
coords=cbind(x,y)
# Define the temporal sequence:
time = seq(0, 10, 1)


param=list(mean=mean,nugget=nugget,
  smooth_s=0.5,smooth_t=0.5,scale_s=0.5/3,scale_t=2/2,sill=sill)
# Simulation of the spatio-temporal Gaussian random field:
data = GeoSim(coordx=coords, coordt=time, corrmodel="Matern_Matern",param=param)$data

fixed=list(nugget=0, mean=0, smooth_s=0.5,smooth_t=0.5)
start=list(scale_s=0.2, scale_t=0.5, sill=1)
# Maximum composite-likelihood fitting of the space-time Gaussian random field:
fit = GeoFit(data, coordx=coords, coordt=time, corrmodel="Matern_Matern", maxtime=1,
             neighb=3, likelihood="Marginal", type="Pairwise",fixed=fixed, start=start)

# Empirical estimation of spatio-temporal covariance:
vario = GeoVariogram(data,coordx=coords, coordt=time, maxtime=5,maxdist=0.5)

# Plot of the fitted space-time variogram
GeoCovariogram(fit,vario=vario,show.vario=TRUE)

# Plot of covariance, variogram and spatio and temporal profiles:
GeoCovariogram(fit,vario=vario,fix.lagt=1,fix.lags=1,show.vario=TRUE,pch=20)


################################################################
###
### Example 5. Plot of  fitted  and empirical semivariogram
### from a bivariate Gaussian random fields  
### with  Matern correlation. 
###
###############################################################
set.seed(92)
# Define the spatial-coordinates of the points:
x <- runif(600, 0, 2)
y <- runif(600, 0, 2)
coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a  Bivariate Matern
set.seed(12)
param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
           scale_1=0.12,scale_2=0.1,scale_12=0.15,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=-0.5)
data <- GeoSim(coordx=coords,corrmodel="Bi_matern",
              param=param)$data



# selecting fixed and estimated parameters
fixed=list(mean_1=4,mean_2=2,nugget_1=0,nugget_2=0,
      smooth_1=0.5,smooth_2=0.5,smooth_12=0.5)
start=list(sill_1=var(data[1,]),sill_2=var(data[2,]),
           scale_1=0.1,scale_2=0.1,scale_12=0.1,
           pcol=cor(data[1,],data[2,]))


# Maximum marginal pairwise likelihood
fitcl<- GeoFit(data=data, coordx=coords, corrmodel="Bi_Matern",
                     likelihood="Marginal",type="Pairwise",
                     optimizer="BFGS" , start=start,fixed=fixed,
                     neighb=4)
print(fitcl)

# Empirical estimation of spatio-temporal covariance:
vario = GeoVariogram(data,coordx=coords,maxdist=0.4,bivariate=TRUE)
GeoCovariogram(fitcl,vario=vario,show.vario=TRUE,pch=20)

Image plot displaying the pattern of the sparsness of a covariance matrix.

Description

Image plot displaying the pattern of the sparsness of a covariance matrix.

Usage

GeoCovDisplay(covmatrix,limits=FALSE,pch=2)
GeoCovDisplay(covmatrix,limits=FALSE,pch=2)

Arguments

`covmatrix`	An object of class GeoCovmatrix. See the Section Details.
`limits`	Logical; If TRUE and the covariance matrix is spatiotemporal or spatial bivariate then vertical and horizontal lines are added to the image plot.
`pch`	Type of symbols to use in the image plot.

Details

For a given covariance matrix object (GeoCovmatrix) the function diplays the pattern of the sparsness of a covariance matrix where the white color represents 0 entries and black color represents non zero entries

Value

Produces a plot. No values are returned.

Author(s)

Examples


library(GeoModels)


  # Define the spatial-coordinates of the points:
x <- runif(100, 0, 2)
y <- runif(100, 0, 2)
coords=cbind(x,y)
matrix1 <- GeoCovmatrix(coordx=coords, corrmodel="GenWend", param=list(smooth=0,
                      power2=4,sill=1,scale=0.2,nugget=0))
 
GeoCovDisplay(matrix1)

library(GeoModels)


  # Define the spatial-coordinates of the points:
x <- runif(100, 0, 2)
y <- runif(100, 0, 2)
coords=cbind(x,y)
matrix1 <- GeoCovmatrix(coordx=coords, corrmodel="GenWend", param=list(smooth=0,
                      power2=4,sill=1,scale=0.2,nugget=0))
 
GeoCovDisplay(matrix1)

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

Description

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).

Usage

GeoCovmatrix(estobj=NULL,coordx, coordy=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)
GeoCovmatrix(estobj=NULL,coordx, coordy=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 ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`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 ( $d_t \times 2$ )-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

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:
  
  $R(h) = 2^{1-\kappa} \Gamma(\kappa)^{-1} h^\kappa K_\kappa(h)$
  
  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. $Hypergeometric$ (Parsimonious Hypegeometric model in Alegria and Emery (2022)) defined as the model $Hypergeometric2$ with the restriction $\beta=\alpha$ .
10. $Multiquadric$ 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.
11. $Sinpower$ 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.
12. $Smoke$ (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:
  
  $T(h)=(1-h)(sin(2\pi h)/(2 \pi h))+(1-cos(2\pi h))/(2\pi^{2}h) 1_{[0,1]}(h)$
2. $Wendlandx, \quad x=0,1,2$ 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:
  
  $T(h,u)=Wendx(h;x+2) Wendy(h;y+2), x,y=0,1,2.$
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.
3. $Wendlandx$ _ $space$ (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 $\sigma^2,\sigma_1^2,\sigma_2^2,\tau^2,\tau_1^2,\tau_2^2, a,a_s,a_t,a_{11},a_{22},a_{12},\kappa_{11},\kappa_{22},\kappa_{12},f_{11},f_{12},f_{21},f_{22}$ 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 $\kappa_{12}=0.5 (\kappa_{11} + \kappa_{22})$
$Bi$ _ $GenWend$ 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 $\mu_{12}=0.5 (\mu_{11} + \mu_{22})$
$Bi$ _ $LMC$ 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.

Value

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` is`Standard`. 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? ;

Author(s)

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.

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

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

n-fold kriging Cross-validation

Description

The procedure use the GeoKrig or GeoKrigloc function to compute n-fold kriging cross-validation using informations from a GeoFit object. The function returns some prediction scores.

Usage

GeoCV(fit, K=100, estimation=TRUE, optimizer=NULL,
     lower=NULL, upper=NULL, n.fold=0.05,local=FALSE,
    neighb=NULL, maxdist=NULL,maxtime=NULL,sparse=FALSE,
    type_krig="Simple", which=1, parallel=FALSE, ncores=NULL)GeoCV(fit, K=100, estimation=TRUE, optimizer=NULL,
     lower=NULL, upper=NULL, n.fold=0.05,local=FALSE,
    neighb=NULL, maxdist=NULL,maxtime=NULL,sparse=FALSE,
    type_krig="Simple", which=1, parallel=FALSE, ncores=NULL)

Arguments

`fit`	An object of class `GeoFit`.
`K`	The number of iterations in cross-validation.
`estimation`	Logical; if `TRUE` then an estimation is performed at each iteration and the estimates are used in the prediction. Otherwise the estimates in the object fit are used.
`optimizer`	The type of optimization algorithm if estimation is `TRUE`. See `GeoFit` for details. If NULL then the optimization algorithm of the object fit is chosen.
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize` if estimation is `TRUE`.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize` if estimation is `TRUE`.
`n.fold`	Numeric; the percentage of data to be deleted (and predicted) in the cross-validation procedure.
`local`	Logical; If local is TRUE, then local kriging is performed. The default is FALSE.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood if local kriging is performed.
`maxdist`	Numeric; an optional positive value indicating the distance in the spatial neighborhood if local kriging is performed.
`maxtime`	Numeric; an optional positive value indicating the distance in the temporal neighborhood if local kriging is performed.
`sparse`	Logical; if `TRUE` kriging and simulation are computed with sparse matrices algorithms using spam package. Default is FALSE. It should be used with compactly supported covariances.
`type_krig`	String; the type of kriging. If `Simple` (the default) then simple kriging is performed. If `Optim` then optimal kriging is performed for some non-Gaussian RFs
`which`	Numeric; In the case of bivariate cokriging it indicates which variable to predict. It can be 1 or 2
`parallel`	Logical; if `TRUE` then the estimation step is parallelized
`ncores`	Numeric; number of cores involved in parallelization.

Value

Returns an object containing the following informations:

`predicted`	A list of the predicted values in the CV procedure;
`data_to_pred`	A list of the data to predict in the CV procedure;
`mae`	The vector of mean absolute error in the CV procedure;
`mad`	The vector of median absolute error in the CV procedure;
`brie`	The vector of brie score in the CV procedure;
`rmse`	The vector of root mean squared error in the CV procedure;
`lscore`	The vector of log-score in the CV procedure;
`crps`	The vector of continuous ranked probability score in the CV procedure;

Author(s)

Examples

  
 library(GeoModels)

################################################################
########### Examples of spatial kriging ############
################################################################

model="Gaussian"
set.seed(79)
x = runif(400, 0, 1)
y = runif(400, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
                    likelihood='Marginal', type='Pairwise',neighb=3,
                    optimizer="nlminb", lower=lower,upper=upper,
                    start=start,fixed=fixed)

#a=GeoCV(fit,K=100,estimation=TRUE,parallel=TRUE) 
#a$rmse
library(GeoModels)

################################################################
########### Examples of spatial kriging ############
################################################################

model="Gaussian"
set.seed(79)
x = runif(400, 0, 1)
y = runif(400, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
                    likelihood='Marginal', type='Pairwise',neighb=3,
                    optimizer="nlminb", lower=lower,upper=upper,
                    start=start,fixed=fixed)

#a=GeoCV(fit,K=100,estimation=TRUE,parallel=TRUE) 
#a$rmse

Computation of drop-one predictive scores

Description

The function computes RMSE, MAE, LSCORE, CRPS predictive scores based on drop-one prediction for a spatial, spatiotemporal and bivariate Gaussian RFs

Usage

GeoDoScores(data,  method="cholesky", matrix)
GeoDoScores(data,  method="cholesky", matrix)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a a( $t \times d$ )-matrix (a single spatial-temporal realisation). or a a( $2 \times d$ )-matrix (a single bivariate realisation).
`method`	String; the type of matrix decomposition used in the computation of the predictive scores. Default is `cholesky`. The other possible choices is `svd`.
`matrix`	An object of class GeoCovmatrix. See the Section Details.

Details

For a given covariance matrix object (GeoCovmatrix) and a given spatial, spatiotemporal or bivariare realization from a Gaussian random field, the function computes four predictive scores based on drop-one prediction.

Value

Returns a list containing the following informations:

`RMSE`	Root-mean-square error predictive score
`MAE`	Mean absolute error predictive score
`LSCORE`	Logarithmic predictive score
`CRPS`	Continuous ranked probability predictive score

Author(s)

References

Zhang H. and Wang Y. (2010). Kriging and cross-validation for massive spatial data. Environmetrics, 21, 290–304. Gneiting T. and Raftery A. Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102

Examples


library(GeoModels)

################################################################
######### Examples of predictive score computation  ############
################################################################
set.seed(8)
  # Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords=cbind(x,y)
matrix1 <- GeoCovmatrix(coordx=coords, corrmodel="Matern", param=list(smooth=0.5,
                      sill=1,scale=0.2,nugget=0))
 
data <- GeoSim(coordx=coords, corrmodel="Matern", param=list(mean=0,smooth=0.5,
                      sill=1,scale=0.2,nugget=0))$data

Pr_scores <- GeoDoScores(data,matrix=matrix1)

Pr_scores

library(GeoModels)

################################################################
######### Examples of predictive score computation  ############
################################################################
set.seed(8)
  # Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords=cbind(x,y)
matrix1 <- GeoCovmatrix(coordx=coords, corrmodel="Matern", param=list(smooth=0.5,
                      sill=1,scale=0.2,nugget=0))
 
data <- GeoSim(coordx=coords, corrmodel="Matern", param=list(mean=0,smooth=0.5,
                      sill=1,scale=0.2,nugget=0))$data

Pr_scores <- GeoDoScores(data,matrix=matrix1)

Pr_scores

Max-Likelihood-Based Fitting of Gaussian and non Gaussian random fields.

Description

Maximum weighted composite-likelihood fitting for Gaussian and some Non-Gaussian univariate spatial, spatio-temporal and bivariate spatial random fieldss The function allows to fix any of the parameters and setting upper/lower bound in the optimization. Different methods of optimization can be used.

Usage

GeoFit(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,copula=NULL,
      corrmodel=NULL,distance="Eucl",fixed=NULL,anisopars=NULL,
      est.aniso=c(FALSE,FALSE),GPU=NULL, grid=FALSE, likelihood='Marginal',
     local=c(1,1),  lower=NULL,maxdist=Inf,neighb=NULL,
      maxtime=Inf, memdist=TRUE,method="cholesky", 
      model='Gaussian',n=1, onlyvar=FALSE ,
      optimizer='Nelder-Mead', parallel=FALSE, 
      radius=6371,  sensitivity=FALSE,sparse=FALSE, 
      start=NULL, taper=NULL, tapsep=NULL, 
      type='Pairwise', upper=NULL, varest=FALSE, 
      vartype='SubSamp', weighted=FALSE, winconst=NULL, winstp=NULL, 
      winconst_t=NULL, winstp_t=NULL,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL)
GeoFit(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,copula=NULL,
      corrmodel=NULL,distance="Eucl",fixed=NULL,anisopars=NULL,
      est.aniso=c(FALSE,FALSE),GPU=NULL, grid=FALSE, likelihood='Marginal',
     local=c(1,1),  lower=NULL,maxdist=Inf,neighb=NULL,
      maxtime=Inf, memdist=TRUE,method="cholesky", 
      model='Gaussian',n=1, onlyvar=FALSE ,
      optimizer='Nelder-Mead', parallel=FALSE, 
      radius=6371,  sensitivity=FALSE,sparse=FALSE, 
      start=NULL, taper=NULL, tapsep=NULL, 
      type='Pairwise', upper=NULL, varest=FALSE, 
      vartype='SubSamp', weighted=FALSE, winconst=NULL, winstp=NULL, 
      winconst_t=NULL, winstp_t=NULL,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid). For the description see the Section Details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random fields is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`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 the Section Details.
`fixed`	An optional named list giving the values of the parameters that will be considered as known values. The listed parameters for a given correlation function will be not estimated.
`anisopars`	A list of two elements: "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
`est.aniso`	A bivariate logical vector providing which anisotropic parameters must be estimated.
`GPU`	Numeric; if `NULL` (the default) no OpenCL computation is performed. The user can choose the device to be used. Use `DeviceInfo()` function to see available devices, only double precision devices are allowed
`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).
`likelihood`	String; the configuration of the composite likelihood. `Marginal` is the default, see the Section Details.
`local`	Numeric; number of local work-items of the OpenCL setup
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `bobyqa` or `optimize`. The names of the list must be the same of the names in the `start` list.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial distance considered in the composite or tapered likelihood computation. See the Section Details for more information.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood in the composite likelihood computation. See the Section Details for more information.
`maxtime`	Numeric; an optional positive integer indicating the order of temporal neighborhood in the composite likelihood computation.
`memdist`	Logical; if `TRUE` then all the distances useful in the composite likelihood estimation are computed before the optimization. `FALSE` is deprecated.
`method`	String; the type of matrix decomposition used in the simulation. Default is cholesky. The other possible choices is `svd`.
`model`	String; the type of random fields and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`n`	Numeric; number of trials in a binomial random fields; number of successes in a negative binomial random fields
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS`, `SANN`, `L-BFGS-B` and `nlminb` and `bobyqa`. In these last three cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`radius`	Numeric; the radius of the sphere in the case of lon-lat coordinates. The default is 6371, the radius of the earth.
`sensitivity`	Logical; if `TRUE` then the sensitivy matrix is computed
`sparse`	Logical; if `TRUE` then maximum likelihood is computed using sparse matrices algorithms (spam packake).It should be used with compactly supported covariance models.`FALSE` is the default.
`start`	An optional named list with the initial values of the parameters that are used by the numerical routines in maximization procedure. `NULL` is the default (see Details).
`taper`	String; the name of the type of covariance matrix. It can be `Standard` (the default value) or `Tapering` for taperd covariance matrix.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time adaptive taper (see Details).
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods (see Details).
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `bobyqa` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`varest`	Logical; if `TRUE` the estimates' variances and standard errors are returned. For composite likelihood estimation it is deprecated. Use sensitivity TRUE and update the object using the function `GeoVarestbootstrap` `FALSE` is the default.
`vartype`	String; (`SubSamp` the default) the type of method used for computing the estimates' variances, see the Section Details.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted, see the Section Details. If `FALSE` (the default) the composite likelihood is not weighted.
`winconst`	Numeric; a bivariate positive vector for computing the spatial sub-window in the sub-sampling procedure. See Details for more information.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure. The case $1$ correspond to no overlapping. See Details for more information.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure. See Details for more information.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure. The case $1$ correspond to no overlapping. See Details for more information.
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`nosym`	Logical; if TRUE simmetric weights are not considered. This allows a faster but less efficient CL estimation.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object

Details

GeoFit provides weighted composite likelihood estimation based on pairs and independence composite likelihood estimation for Gaussian and non Gaussian random fields. Specifically, marginal and conditional pairwise likelihood are considered for each type of random field (Gaussian and not Gaussian). The optimization method is specified using optimizer. The default method is Nelder-mead and other available methods are nlm, BFGS, SANN, L-BFGS-B, bobyqa and nlminb. In the last three cases upper and lower bounds constraints in the optimization can be specified using lower and upper parameters.

Depending on the dimension of data and on the name of the correlation model, the observations are assumed as a realization of a spatial, spatio-temporal or bivariate random field. Specifically, with data, coordx, coordy, coordt parameters:

If data is a numeric $d$ -dimensional vector, coordx and coordy are two numeric $d$ -dimensional vectors (or coordx is ( $d \times 2$ )-matrix and coordy=NULL), then the data are interpreted as a single spatial realisation observed on $d$ spatial sites;
If data is a numeric ( $t \times d$ )-matrix, coordx and coordy are two numeric $d$ -dimensional vectors (or coordx is ( $d \times 2$ )-matrix and coordy=NULL), coordt is a numeric $t$ -dimensional vector, then the data are interpreted as a single spatial-temporal realisation of a random fields observed on d spatial sites and for t times.
If data is a numeric ( $2 \times d$ )-matrix, coordx and coordy are two numeric $d$ -dimensional vectors (or coordx is ( $d \times 2$ )-matrix and coordy=NULL), then the data are interpreted as a single spatial realisation of a bivariate random fields observed on d spatial sites.
If data is a list, coordxdyn is a list and coordt is a numeric $t$ -dimensional vector, then the data are interpreted as a single spatial-temporal realisation of a random fields observed on dynamical spatial sites (different locations sites for each temporal instants) and for t times.

Is is also possible to specify a matrix of covariates using X. Specifically:

In the spatial case X must be a ( $d \times k$ ) covariates matrix associated to data a numeric $d$ -dimensional vector;
In the spatiotemporal case X must be a ( $N \times k$ ) covariates matrix associated to data a numeric ( $t \times d$ )-matrix, where $N=t\times d$ ;
In the spatiotemporal case X must be a ( $N \times k$ ) covariates matrix associated to data a numeric ( $t \times d$ )-matrix, where $N=2\times d$ ;

The corrmodel parameter allows to select a specific correlation function for the random fields. (See GeoCovmatrix ).

The distance parameter allows to consider differents kinds of spatial distances. The settings alternatives are:

Eucl, the euclidean distance (default value);
Chor, the chordal distance;
Geod, the geodesic distance;

The likelihood parameter represents the composite-likelihood configurations. The settings alternatives are:

Conditional, the composite-likelihood is formed by conditionals likelihoods;
Marginal, the composite-likelihood is formed by marginals likelihoods (default value);
Full, the composite-likelihood turns out to be the standard likelihood;

It must be coupled with the type parameter that can be fixed to

Pairwise, the composite-likelihood is based on pairs;
Independence, the composite-likelihood is based on indepedence;
Standard, this is the option for the standard likelihood;

The possible combinations are:

likelihood="Marginal" and type="Pairwise" for maximum marginal pairwise likelihood estimation (the default setting)
likelihood="Conditional" and type="Pairwise" for maximum conditional pairwise likelihood estimation
likelihood="Marginal" and type="Independence" for maximum independence composite likelihood estimation
likelihood="Full" and type="Standard" for maximum stardard likelihood estimation

The first three combinations can be used for any model. The standard likelihood can be used only for some specific model.

The model parameter indicates the type of random field considered. The available options are:

random fields with marginal symmetric distribution:

Gaussian, for a Gaussian random field.
StudentT, for a StudentT random field (see Bevilacqua M., Caamaño C., Arellano Valle R.B., Morales-Oñate V., 2020).
Tukeyh, for a Tukeyh random field.
Tukeyh2, for a Tukeyhh random field. (see Caamaño et al., 2023)
Logistic, for a Logistic random field.

random fields with positive values and right skewed marginal distribution:

Gamma for a Gamma random fields (see Bevilacqua M., Caamano C., Gaetan, 2020)
Weibull for a Weibull random fields (see Bevilacqua M., Caamano C., Gaetan, 2020)
LogGaussian for a LogGaussian random fields (see Bevilacqua M., Caamano C., Gaetan, 2020)
LogLogistic for a LogLogistic random fields.

random fields with with possibly asymmetric marginal distribution:

SkewGaussian for a skew Gaussian random field (see Alegrıa et al. (2017)).
SinhAsinh for a Sinh-arcsinh random field (see Blasi et. al 2022).
TwopieceGaussian for a Twopiece Gaussian random field (see Bevilacqua et. al 2022).
TwopieceTukeyh for a Twopiece Tukeyh random field (see Bevilacqua et. al 2022).

random fields with for directional data

Wrapped for a wrapped Gaussian random field (see Alegria A., Bevilacqua, M., Porcu, E. (2016))

random fields with marginal counts data

Poisson for a Poisson random field (see Morales-Navarrete et. al 2021).
PoissonZIP for a zero inflated Poisson random field (see Morales-Navarrete et. al 2021).
Binomial for a Binomial random field.
BinomialNeg for a negative Binomial random field.
BinomialNegZINB for a zero inflated negative Binomial random field.

random fields using Gaussian and Clayton copula (see Bevilacqua, Alvarado and Caamaño, 2023) with the following marginal distribution:

Gaussian for Gaussian random field.
Beta2 for Beta random field.

For a given model the associated parameters are given by nuisance and correlation parameters. In order to obtain the nuisance parameters associated with a specific model use NuisParam. In order to obtain the correlation parameters associated with a given correlation model use CorrParam.

All the nuisance and covariance parameters must be specified by the user using the start and the fixed parameter. Specifically:

The option start sets the starting values parameters in the optimization process for the parameters to be estimated. The option fixed parameter allows to fix some of the parameters.

Regression parameters in the linear specification must be specified as mean,mean1,..meank (see NuisParam). In this case a matrix of covariates with suitable dimension must be specified using X. In the case of a single mean then X should not be specified and it is interpreted as a vector of ones. It is also possible to fix a vector of spatial or spatio-temporal means (estimated with other methods for instance).

Two types of binary weights can be used in the weighted composite likelihood estimation based on pairs, one based on neighboords and one based on distances.

In the first case binary weights are set to 1 or 0 depending if the pairs are neighboords of a certain order (1, 2, 3, ..) specified by the parameter (neighb). This weighting scheme is effecient for large-data sets since the computation of the 'useful' pairs in based on fast nearest neighbour search (Caamaño et al., 2023).

In the second case, binary weights are set to 1 or 0 depending if the pairs have distance lower than (maxdist). This weighting scheme is less inefficient for large data. The same arguments of neighb applies for maxtime that sets the order (1, 2, 3, ..) of temporal neighboords in spatial-temporal field.

For estimation of the variance-covariance matrix of the weighted composite likelihood estimates the option sensitivity=TRUE must be specified. Then the GeoFit object must be updated using the function GeoVarestbootstrap. This allows to estimate the Godambe Information matrix (see Bevilacqua et. al. (2012) , Bevilacqua and Gaetan (2013)). Then standard error estimation, confidence intervals, pvalues and composite likelihood information critera can be obtained.

The option varest=TRUE is deprecated.

Value

Returns an object of class GeoFit. An object of class GeoFit is a list containing at most the following components:

`bivariate`	Logical:`TRUE` if the Gaussian random fields is bivariate, otherwise `FALSE`;
`clic`	The composite information criterion, if the full likelihood is considered then it coincides with the Akaike information criterion;
`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 dynamical (in time) spatial coordinates;
`conf.int`	Confidence intervals for standard maximum likelihood estimation;
`convergence`	A string that denotes if convergence is reached;
`copula`	The type of copula;
`corrmodel`	The correlation model;
`data`	The vector or matrix or array of data;
`distance`	The type of spatial distance;
`fixed`	A list of the fixed parameters;
`iterations`	The number of iteration used by the numerical routine;
`likelihood`	The configuration of the composite likelihood;
`logCompLik`	The value of the log composite-likelihood at the maximum;
`maxdist`	The maximum spatial distance used in the weigthed composite likelihood. If no spatial distance is specified then it is NULL;
`maxtime`	The order of temporal neighborhood in the composite likelihood computation.
`message`	Extra message passed from the numerical routines;
`model`	The density associated to the likelihood objects;
`missp`	True if a misspecified Gaussian model is ued in the composite likelihhod;
`n`	The number of trials in a binominal random fields;the number of successes in a negative Binomial random fieldss;
`neighb`	The order of spatial neighborhood in the composite likelihood computation.
`ns`	The number of (different) location sites in the bivariate case;
`nozero`	In the case of tapered likelihood the percentage of non zero values in the covariance matrix. Otherwise is NULL.
`numcoord`	The number of spatial coordinates;
`numtime`	The number of the temporal realisations of the random fields;
`param`	A list of the parameters' estimates;
`radius`	The radius of the sphere in the case of great circle distance;
`stderr`	The vector of standard errors for standard maximum likelihood estimation;
`sensmat`	The sensitivity matrix;
`varcov`	The matrix of the variance-covariance of the estimates;
`varimat`	The variability matrix;
`vartype`	The method used to compute the variance of the estimates;
`type`	The type of the likelihood objects.
`winconst`	The constant used to compute the window size in the spatial sub-sampling;
`winstp`	The step used for moving the window in the spatial sub-sampling;
`winconst_t`	The constant used to compute the window size in the spatio-temporal sub-sampling;
`winstp_`	The step used for moving the window in the spatio-temporal sub-sampling;
`X`	The matrix of covariates;

Author(s)

References

General Composite-likelihood:

Varin, C., Reid, N. and Firth, D. (2011). An Overview of Composite Likelihood Methods. Statistica Sinica, 21, 5–42.

Varin, C. and Vidoni, P. (2005) A Note on Composite Likelihood Inference and Model Selection. Biometrika, 92, 519–528.

Non Gaussian random fields:

Alegrıa A., Caro S., Bevilacqua M., Porcu E., Clarke J. (2017) Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere. Spatial Statistics 22 388-402

Alegria A., Bevilacqua, M., Porcu, E. (2016) Likelihood-based inference for multivariate space-time wrapped-Gaussian fields. Journal of Statistical Computation and Simulation. 86(13), 2583–2597.

Bevilacqua M., Caamano C., Gaetan C. (2020) On modeling positive continuous data with spatio-temporal dependence. Environmetrics 31(7)

Bevilacqua M., Caamaño C., Arellano Valle R.B., Morales-Oñate V. (2020) Non-Gaussian Geostatistical Modeling using (skew) t Processes. Scandinavian Journal of Statistics.

Blasi F., Caamaño C., Bevilacqua M., Furrer R. (2022) A selective view of climatological data and likelihood estimation Spatial Statistics 10.1016/j.spasta.2022.100596

Bevilacqua M., Caamaño C., Arellano-Valle R. B., Camilo Gomez C. (2022) A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers. Test 10.1007/s11749-021-00797-5

Morales-Navarrete D., Bevilacqua M., Caamaño C., Castro L.M. (2022) Modelling Point Referenced Spatial Count Data: A Poisson Process Approach TJournal of the American Statistical Association To appear

Caamaño C., Bevilacqua M., López C., Morales-Oñate V. (2023) Nearest neighbours weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation Computational Statistics and Data Analysis To appear

Bevilacqua M., Alvarado E., Caamaño C., (2023) A flexible Clayton-like spatial copula with application to bounded support data Journal of Multivariate Analysis To appear

Weighted Composite-likelihood for (non-)Gaussian random fields:

Bevilacqua, M. Gaetan, C., Mateu, J. and Porcu, E. (2012) Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach. Journal of the American Statistical Association, Theory & Methods, 107, 268–280.

Bevilacqua, M., Gaetan, C. (2015) Comparing composite likelihood methods based on pairs for spatial Gaussian random fields. Statistics and Computing, 25(5), 877-892.

Sub-sampling estimation:

Heagerty, P. J. and Lumley T. (2000) Window Subsampling of Estimating Functions with Application to Regression Models. Journal of the American Statistical Association, Theory & Methods, 95, 197–211.

Examples

library(GeoModels)

###############################################################
############ Examples of spatial Gaussian random fieldss ################
###############################################################


# Define the spatial-coordinates of the points:
set.seed(3)
N=300  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates and regression parameters
X=cbind(rep(1,N),runif(N))
mean <- 0.2
mean1 <- -0.5

# Set the covariance model's parameters:
corrmodel <- "Matern"
sill <- 1
nugget <- 0
scale <- 0.2/3
smooth=0.5


param<-list(mean=mean,mean1=mean1,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian random fields:
data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param,X=X)$data



################################################################
###
### Example 0. Maximum independence composite likelihood fitting of
### a Gaussian random fields (no dependence parameters)
### 
###############################################################
# setting starting parameters to be estimated
start<-list(mean=mean,mean1=mean1,sill=sill)

fit1 <- GeoFit(data=data,coordx=coords,likelihood="Marginal",
                    type="Independence", start=start,X=X)
print(fit1)


################################################################
###
### Example 1. Maximum conditional pairwise likelihood fitting of
### a Gaussian random fields using BFGS
### 
###############################################################
# setting fixed and starting parameters to be estimated
fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)

fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, 
                    neighb=3,likelihood="Conditional",optimizer="BFGS",
                    type="Pairwise", start=start,fixed=fixed,X=X)
print(fit1)

################################################################
###
### Example 2. Standard Maximum likelihood fitting of
### a Gaussian random fields using nlminb
###
###############################################################
# Define the spatial-coordinates of the points:
set.seed(3)
N=250  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param)$data

# setting fixed and parameters to be estimated
fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,scale=scale,sill=sill)

I=Inf
lower<-list(mean=-I,scale=0,sill=0)
upper<-list(mean=I,scale=I,sill=I)
fit2 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
                    optimizer="nlminb",upper=upper,lower=lower,
                    likelihood="Full",type="Standard", 
                    start=start,fixed=fixed)
print(fit2)


###############################################################
############ Examples of spatial non-Gaussian random fieldss #############
###############################################################


################################################################
###
### Example 3. Maximum pairwise likelihood fitting of a Weibull  random fields 
### with Generalized Wendland correlation with Nelder-Mead
### 
###############################################################
set.seed(524)
# Define the spatial-coordinates of the points:
N=300
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
X=cbind(rep(1,N),runif(N))
mean=1; mean1=2 # regression parameters
nugget=0
shape=2
scale=0.2
smooth=0

model="Weibull"
corrmodel="GenWend"
param=list(mean=mean,mean1=mean1,scale=scale,
                     shape=shape,nugget=nugget,power2=4,smooth=smooth)
# Simulation of a  non stationary weibull random fields:
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model,X=X,
           param=param)$data


fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,shape=shape)

# Maximum independence likelihood:
fit <- GeoFit(data=data, coordx=coords, X=X, 
           likelihood="Marginal",type="Independence", corrmodel=corrmodel,
         ,model=model, start=start, fixed=fixed)
print(unlist(fit$param))

## estimating  dependence parameter fixing vector mean   parameter
Xb=as.numeric(X %*% c(mean,mean1))
fixed<-list(nugget=nugget,power2=4,smooth=smooth,mean=Xb)
start<-list(scale=scale,shape=shape)

# Maximum conditional composite-likelihood fitting of the random fields:
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",
                    optimizer="Nelder-Mead",
                    start=start,fixed=fixed)
print(unlist(fit1$param))



### joint estimation  of the dependence parameter and  mean   parameters
fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,shape=shape)
fit2 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",X=X,
                    optimizer="Nelder-Mead",
                    start=start,fixed=fixed)
print(unlist(fit2$param))



################################################################
###
### Example 4. Maximum pairwise likelihood fitting of
### a Skew-Gaussian spatial  random fields with Wendland correlation
###
###############################################################
set.seed(261)
model="SkewGaussian"
# Define the spatial-coordinates of the points:
x <- runif(500, 0, 1)
y <- runif(500, 0, 1)
coords <- cbind(x,y)

corrmodel="Wend0"
mean=0;nugget=0
sill=1
skew=-4.5
power2=4
c_supp=0.2

# model parameters
param=list(power2=power2,skew=skew,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data

plot(density(data))
fixed=list(power2=power2,nugget=nugget)
start=list(scale=c_supp,skew=skew,mean=mean,sill=sill)
lower=list(scale=0,skew=-I,mean=-I,sill=0)
upper=list(scale=I,skew=I,mean=I,sill=I)
# Maximum marginal pairwise likelihood:
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Marginal",type="Pairwise",
                    optimizer="bobyqa",lower=lower,upper=upper,
                    start=start,fixed=fixed)
print(unlist(fit1$param))


################################################################
###
### Example 5. Maximum pairwise likelihood fitting of 
### a Binomial random fields with exponential correlation 
###
###############################################################

set.seed(422)
N=250
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
mean=0.1; mean1=0.8; mean2=-0.5 # regression parameters
X=cbind(rep(1,N),runif(N),runif(N)) # marix covariates
corrmodel <- "Wend0"
param=list(mean=mean,mean1=mean1,mean2=mean2,nugget=0,scale=0.2,power2=4)
# Simulation of the spatial Binomial-Gaussian random fields:
data <- GeoSim(coordx=coords, corrmodel=corrmodel, model="Binomial", n=10,X=X,
             param=param)$data


## estimating the marginal parameters using independence cl
fixed <- list(power2=4,scale=0.2,nugget=0)
start <- list(mean=mean,mean1=mean1,mean2=mean2)

# Maximum independence likelihood:
fit <- GeoFit(data=data, coordx=coords,n=10, X=X, 
           likelihood="Marginal",type="Independence",corrmodel=corrmodel,
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit)


## estimating  dependence parameter fixing vector mean   parameter
Xb=as.numeric(X %*% c(mean,mean1,mean2))
fixed <- list(nugget=0,power2=4,mean=Xb)
start <- list(scale=0.2)

# Maximum conditional pairwise likelihood:
fit1 <- GeoFit(data=data, coordx=coords, corrmodel=corrmodel,n=10, 
          likelihood="Conditional",type="Pairwise",  neighb=3
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit1)


## estimating jointly marginal   and dependnce parameters
fixed <- list(nugget=0,power2=4)
start <- list(mean=mean,mean1=mean1,mean2=mean2,scale=0.2)

# Maximum conditional pairwise likelihood:
fit2 <- GeoFit(data=data, coordx=coords, corrmodel=corrmodel,n=10, X=X, 
          likelihood="Conditional",type="Pairwise",  neighb=3
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit2)


###############################################################
######### Examples of Gaussian spatio-temporal random fieldss ###########
###############################################################
set.seed(52)
# Define the temporal sequence:
time <- seq(1, 9, 1)

# Define the spatial-coordinates of the points:
x <- runif(20, 0, 1)
y <- runif(20, 0, 1)
coords=cbind(x,y)

# Set the covariance model's parameters:
scale_s=0.2/3;scale_t=1
smooth_s=0.5;smooth_t=0.5
sill=1
nugget=0
mean=0

param<-list(mean=0,scale_s=scale_s,scale_t=scale_t,
 smooth_t=smooth_t, smooth_s=smooth_s ,sill=sill,nugget=nugget)

# Simulation of the spatial-temporal Gaussian random fields:
data <- GeoSim(coordx=coords,coordt=time,corrmodel="Matern_Matern",
              param=param)$data

################################################################
###
### Example 6. Maximum pairwise likelihood fitting of a
### space time Gaussian random fields with double-exponential correlation
###
###############################################################
# Fixed parameters
fixed<-list(nugget=nugget,smooth_s=smooth_s,smooth_t=smooth_t)
# Starting value for the estimated parameters
start<-list(mean=mean,scale_s=scale_s,scale_t=scale_t,sill=sill)

# Maximum composite-likelihood fitting of the random fields:
fit <- GeoFit(data=data,coordx=coords,coordt=time,
                    corrmodel="Matern_Matern",maxtime=1,neighb=3,
                    likelihood="Marginal",type="Pairwise",
                     start=start,fixed=fixed)
print(fit)



###############################################################
######### Examples of a bivariate Gaussian  random fields ###########
###############################################################

################################################################
### Example 7. Maximum pairwise  likelihood fitting of a
### bivariate Gaussian random fields with separable Bivariate  matern 
### (cross) correlation model 
###############################################################

# Define the spatial-coordinates of the points:
set.seed(89)
x <- runif(300, 0, 1)
y <- runif(300, 0, 1)
coords=cbind(x,y)
# parameters
param=list(mean_1=0,mean_2=0,scale=0.1,smooth=0.5,sill_1=1,sill_2=1,
           nugget_1=0,nugget_2=0,pcol=0.2)

# Simulation of a spatial bivariate Gaussian random fields:
data <- GeoSim(coordx=coords, corrmodel="Bi_Matern_sep", 
              param=param)$data

# selecting fixed and estimated parameters
fixed=list(mean_1=0,mean_2=0,nugget_1=0,nugget_2=0,smooth=0.5)
start=list(sill_1=var(data[1,]),sill_2=var(data[2,]),
           scale=0.1,pcol=cor(data[1,],data[2,]))


# Maximum marginal pairwise likelihood
fitcl<- GeoFit(data=data, coordx=coords, corrmodel="Bi_Matern_sep",
                     likelihood="Marginal",type="Pairwise",
                     optimizer="BFGS" , start=start,fixed=fixed,
                     neighb=c(4,4,4))
print(fitcl)

library(GeoModels)

###############################################################
############ Examples of spatial Gaussian random fieldss ################
###############################################################


# Define the spatial-coordinates of the points:
set.seed(3)
N=300  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates and regression parameters
X=cbind(rep(1,N),runif(N))
mean <- 0.2
mean1 <- -0.5

# Set the covariance model's parameters:
corrmodel <- "Matern"
sill <- 1
nugget <- 0
scale <- 0.2/3
smooth=0.5


param<-list(mean=mean,mean1=mean1,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian random fields:
data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param,X=X)$data



################################################################
###
### Example 0. Maximum independence composite likelihood fitting of
### a Gaussian random fields (no dependence parameters)
### 
###############################################################
# setting starting parameters to be estimated
start<-list(mean=mean,mean1=mean1,sill=sill)

fit1 <- GeoFit(data=data,coordx=coords,likelihood="Marginal",
                    type="Independence", start=start,X=X)
print(fit1)


################################################################
###
### Example 1. Maximum conditional pairwise likelihood fitting of
### a Gaussian random fields using BFGS
### 
###############################################################
# setting fixed and starting parameters to be estimated
fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)

fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, 
                    neighb=3,likelihood="Conditional",optimizer="BFGS",
                    type="Pairwise", start=start,fixed=fixed,X=X)
print(fit1)

################################################################
###
### Example 2. Standard Maximum likelihood fitting of
### a Gaussian random fields using nlminb
###
###############################################################
# Define the spatial-coordinates of the points:
set.seed(3)
N=250  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param)$data

# setting fixed and parameters to be estimated
fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,scale=scale,sill=sill)

I=Inf
lower<-list(mean=-I,scale=0,sill=0)
upper<-list(mean=I,scale=I,sill=I)
fit2 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
                    optimizer="nlminb",upper=upper,lower=lower,
                    likelihood="Full",type="Standard", 
                    start=start,fixed=fixed)
print(fit2)


###############################################################
############ Examples of spatial non-Gaussian random fieldss #############
###############################################################


################################################################
###
### Example 3. Maximum pairwise likelihood fitting of a Weibull  random fields 
### with Generalized Wendland correlation with Nelder-Mead
### 
###############################################################
set.seed(524)
# Define the spatial-coordinates of the points:
N=300
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
X=cbind(rep(1,N),runif(N))
mean=1; mean1=2 # regression parameters
nugget=0
shape=2
scale=0.2
smooth=0

model="Weibull"
corrmodel="GenWend"
param=list(mean=mean,mean1=mean1,scale=scale,
                     shape=shape,nugget=nugget,power2=4,smooth=smooth)
# Simulation of a  non stationary weibull random fields:
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model,X=X,
           param=param)$data


fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,shape=shape)

# Maximum independence likelihood:
fit <- GeoFit(data=data, coordx=coords, X=X, 
           likelihood="Marginal",type="Independence", corrmodel=corrmodel,
         ,model=model, start=start, fixed=fixed)
print(unlist(fit$param))

## estimating  dependence parameter fixing vector mean   parameter
Xb=as.numeric(X %*% c(mean,mean1))
fixed<-list(nugget=nugget,power2=4,smooth=smooth,mean=Xb)
start<-list(scale=scale,shape=shape)

# Maximum conditional composite-likelihood fitting of the random fields:
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",
                    optimizer="Nelder-Mead",
                    start=start,fixed=fixed)
print(unlist(fit1$param))



### joint estimation  of the dependence parameter and  mean   parameters
fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,shape=shape)
fit2 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",X=X,
                    optimizer="Nelder-Mead",
                    start=start,fixed=fixed)
print(unlist(fit2$param))



################################################################
###
### Example 4. Maximum pairwise likelihood fitting of
### a Skew-Gaussian spatial  random fields with Wendland correlation
###
###############################################################
set.seed(261)
model="SkewGaussian"
# Define the spatial-coordinates of the points:
x <- runif(500, 0, 1)
y <- runif(500, 0, 1)
coords <- cbind(x,y)

corrmodel="Wend0"
mean=0;nugget=0
sill=1
skew=-4.5
power2=4
c_supp=0.2

# model parameters
param=list(power2=power2,skew=skew,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data

plot(density(data))
fixed=list(power2=power2,nugget=nugget)
start=list(scale=c_supp,skew=skew,mean=mean,sill=sill)
lower=list(scale=0,skew=-I,mean=-I,sill=0)
upper=list(scale=I,skew=I,mean=I,sill=I)
# Maximum marginal pairwise likelihood:
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Marginal",type="Pairwise",
                    optimizer="bobyqa",lower=lower,upper=upper,
                    start=start,fixed=fixed)
print(unlist(fit1$param))


################################################################
###
### Example 5. Maximum pairwise likelihood fitting of 
### a Binomial random fields with exponential correlation 
###
###############################################################

set.seed(422)
N=250
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
mean=0.1; mean1=0.8; mean2=-0.5 # regression parameters
X=cbind(rep(1,N),runif(N),runif(N)) # marix covariates
corrmodel <- "Wend0"
param=list(mean=mean,mean1=mean1,mean2=mean2,nugget=0,scale=0.2,power2=4)
# Simulation of the spatial Binomial-Gaussian random fields:
data <- GeoSim(coordx=coords, corrmodel=corrmodel, model="Binomial", n=10,X=X,
             param=param)$data


## estimating the marginal parameters using independence cl
fixed <- list(power2=4,scale=0.2,nugget=0)
start <- list(mean=mean,mean1=mean1,mean2=mean2)

# Maximum independence likelihood:
fit <- GeoFit(data=data, coordx=coords,n=10, X=X, 
           likelihood="Marginal",type="Independence",corrmodel=corrmodel,
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit)


## estimating  dependence parameter fixing vector mean   parameter
Xb=as.numeric(X %*% c(mean,mean1,mean2))
fixed <- list(nugget=0,power2=4,mean=Xb)
start <- list(scale=0.2)

# Maximum conditional pairwise likelihood:
fit1 <- GeoFit(data=data, coordx=coords, corrmodel=corrmodel,n=10, 
          likelihood="Conditional",type="Pairwise",  neighb=3
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit1)


## estimating jointly marginal   and dependnce parameters
fixed <- list(nugget=0,power2=4)
start <- list(mean=mean,mean1=mean1,mean2=mean2,scale=0.2)

# Maximum conditional pairwise likelihood:
fit2 <- GeoFit(data=data, coordx=coords, corrmodel=corrmodel,n=10, X=X, 
          likelihood="Conditional",type="Pairwise",  neighb=3
         ,model="Binomial", start=start, fixed=fixed)
                  
print(fit2)


###############################################################
######### Examples of Gaussian spatio-temporal random fieldss ###########
###############################################################
set.seed(52)
# Define the temporal sequence:
time <- seq(1, 9, 1)

# Define the spatial-coordinates of the points:
x <- runif(20, 0, 1)
y <- runif(20, 0, 1)
coords=cbind(x,y)

# Set the covariance model's parameters:
scale_s=0.2/3;scale_t=1
smooth_s=0.5;smooth_t=0.5
sill=1
nugget=0
mean=0

param<-list(mean=0,scale_s=scale_s,scale_t=scale_t,
 smooth_t=smooth_t, smooth_s=smooth_s ,sill=sill,nugget=nugget)

# Simulation of the spatial-temporal Gaussian random fields:
data <- GeoSim(coordx=coords,coordt=time,corrmodel="Matern_Matern",
              param=param)$data

################################################################
###
### Example 6. Maximum pairwise likelihood fitting of a
### space time Gaussian random fields with double-exponential correlation
###
###############################################################
# Fixed parameters
fixed<-list(nugget=nugget,smooth_s=smooth_s,smooth_t=smooth_t)
# Starting value for the estimated parameters
start<-list(mean=mean,scale_s=scale_s,scale_t=scale_t,sill=sill)

# Maximum composite-likelihood fitting of the random fields:
fit <- GeoFit(data=data,coordx=coords,coordt=time,
                    corrmodel="Matern_Matern",maxtime=1,neighb=3,
                    likelihood="Marginal",type="Pairwise",
                     start=start,fixed=fixed)
print(fit)



###############################################################
######### Examples of a bivariate Gaussian  random fields ###########
###############################################################

################################################################
### Example 7. Maximum pairwise  likelihood fitting of a
### bivariate Gaussian random fields with separable Bivariate  matern 
### (cross) correlation model 
###############################################################

# Define the spatial-coordinates of the points:
set.seed(89)
x <- runif(300, 0, 1)
y <- runif(300, 0, 1)
coords=cbind(x,y)
# parameters
param=list(mean_1=0,mean_2=0,scale=0.1,smooth=0.5,sill_1=1,sill_2=1,
           nugget_1=0,nugget_2=0,pcol=0.2)

# Simulation of a spatial bivariate Gaussian random fields:
data <- GeoSim(coordx=coords, corrmodel="Bi_Matern_sep", 
              param=param)$data

# selecting fixed and estimated parameters
fixed=list(mean_1=0,mean_2=0,nugget_1=0,nugget_2=0,smooth=0.5)
start=list(sill_1=var(data[1,]),sill_2=var(data[2,]),
           scale=0.1,pcol=cor(data[1,],data[2,]))


# Maximum marginal pairwise likelihood
fitcl<- GeoFit(data=data, coordx=coords, corrmodel="Bi_Matern_sep",
                     likelihood="Marginal",type="Pairwise",
                     optimizer="BFGS" , start=start,fixed=fixed,
                     neighb=c(4,4,4))
print(fitcl)

Max-Likelihood-Based Fitting of Gaussian and non Gaussian RFs.

Description

Maximum weighted composite-likelihood fitting for Gaussian and some Non-Gaussian univariate spatial, spatio-temporal and bivariate spatial RFs. A first preliminary estimation is performed using independence composite-likelihood for the marginal parameters of the model. The estimates are then used as starting values in the second final estimation step. The function allows to fix any of the parameters and setting upper/lower bound in the optimization.

Usage

GeoFit2(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
  copula=NULL,corrmodel,distance="Eucl",fixed=NULL,
     anisopars=NULL,est.aniso=c(FALSE,FALSE),GPU=NULL, 
     grid=FALSE, likelihood='Marginal',
     local=c(1,1),  lower=NULL,maxdist=Inf,neighb=NULL,
      maxtime=Inf, memdist=TRUE,method="cholesky", 
      model='Gaussian',n=1, onlyvar=FALSE ,
      optimizer='Nelder-Mead', parallel=FALSE, 
      radius=6371,  sensitivity=FALSE,sparse=FALSE,
       start=NULL, taper=NULL, tapsep=NULL, 
      type='Pairwise', upper=NULL, varest=FALSE, 
      vartype='SubSamp', weighted=FALSE, winconst=NULL, winstp=NULL, 
      winconst_t=NULL, winstp_t=NULL,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL)
GeoFit2(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
  copula=NULL,corrmodel,distance="Eucl",fixed=NULL,
     anisopars=NULL,est.aniso=c(FALSE,FALSE),GPU=NULL, 
     grid=FALSE, likelihood='Marginal',
     local=c(1,1),  lower=NULL,maxdist=Inf,neighb=NULL,
      maxtime=Inf, memdist=TRUE,method="cholesky", 
      model='Gaussian',n=1, onlyvar=FALSE ,
      optimizer='Nelder-Mead', parallel=FALSE, 
      radius=6371,  sensitivity=FALSE,sparse=FALSE,
       start=NULL, taper=NULL, tapsep=NULL, 
      type='Pairwise', upper=NULL, varest=FALSE, 
      vartype='SubSamp', weighted=FALSE, winconst=NULL, winstp=NULL, 
      winconst_t=NULL, winstp_t=NULL,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid). For the description see the Section Details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`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 the Section Details.
`fixed`	An optional named list giving the values of the parameters that will be considered as known values. The listed parameters for a given correlation function will be not estimated.
`anisopars`	A list of two elements: "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
`est.aniso`	A bivariate logical vector providing which anisotropic parameters must be estimated.
`GPU`	Numeric; if `NULL` (the default) no OpenCL computation is performed. The user can choose the device to be used. Use `DeviceInfo()` function to see available devices, only double precision devices are allowed
`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).
`likelihood`	String; the configuration of the composite likelihood. `Marginal` is the default, see the Section Details.
`local`	Numeric; number of local work-items of the OpenCL setup
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `bobyqa` or `optimize`. The names of the list must be the same of the names in the `start` list.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial distance considered in the composite or tapered likelihood computation. See the Section Details for more information.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood in the composite likelihood computation. See the Section Details for more information.
`maxtime`	Numeric; an optional positive integer indicating the order of temporal neighborhood in the composite likelihood computation.
`memdist`	Logical; if `TRUE` then all the distances useful in the composite likelihood estimation are computed before the optimization. `FALSE` is deprecated.
`method`	String; the type of matrix decomposition used in the simulation. Default is cholesky. The other possible choices is `svd`.
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`n`	Numeric; number of trials in a binomial RF; number of successes in a negative binomial RF
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS`, `SANN`, `L-BFGS-B` and `nlminb` and `bobyqa`. In these last three cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`radius`	Numeric; the radius of the sphere in the case of lon-lat coordinates. The default is 6371, the radius of the earth.
`sensitivity`	Logical; if `TRUE` then the sensitivy matrix is computed
`sparse`	Logical; if `TRUE` then maximum likelihood is computed using sparse matrices algorithms (spam packake).It should be used with compactly supported covariance models.`FALSE` is the default.
`start`	An optional named list with the initial values of the parameters that are used by the numerical routines in maximization procedure. `NULL` is the default (see Details).
`taper`	String; the name of the type of covariance matrix. It can be `Standard` (the default value) or `Tapering` for taperd covariance matrix.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time adaptive taper (see Details).
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods (see Details).
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `nlminb` or `bobyqa` or `optimize`. The names of the list must be the same of the names in the `start` list.
`varest`	Logical; if `TRUE` the estimates' variances and standard errors are returned. For composite likelihood estimation it is deprecated. Use sensitivity TRUE and update the object using the function `GeoVarestbootstrap` `FALSE` is the default.
`vartype`	String; (`SubSamp` the default) the type of method used for computing the estimates' variances, see the Section Details.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted, see the Section Details. If `FALSE` (the default) the composite likelihood is not weighted.
`winconst`	Numeric; a bivariate positive vector for computing the spatial sub-window in the sub-sampling procedure. See Details for more information.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure. The case $1$ correspond to no overlapping. See Details for more information.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure. See Details for more information.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure. The case $1$ correspond to no overlapping. See Details for more information.
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`nosym`	Logical; if TRUE simmetric weights are not considered. This allows a faster but less efficient CL estimation.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object

Details

The function GeoFit2 is similar to the function GeoFit. However GeoFit2 performs a preliminary estimation using maximum indenpendence composite likelihood of the marginal parameters of the model and then use the obtained estimates as starting value in the global weighted composite likelihood estimation (that includes marginal and dependence parameters). This allows to obtain "good" starting values in the optimization algorithm for the marginal parameters.

Value

Returns an object of class GeoFit. An object of class GeoFit is a list containing at most the following components:

`bivariate`	Logical:`TRUE` if the Gaussian RF is bivariate, otherwise `FALSE`;
`clic`	The composite information criterion, if the full likelihood is considered then it coincides with the Akaike information criterion;
`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 dynamical (in time) spatial coordinates;
`conf.int`	Confidence intervals for standard maximum likelihood estimation;
`convergence`	A string that denotes if convergence is reached;
`copula`	The type of copula;
`corrmodel`	The correlation model;
`data`	The vector or matrix or array of data;
`distance`	The type of spatial distance;
`fixed`	A list of the fixed parameters;
`iterations`	The number of iteration used by the numerical routine;
`likelihood`	The configuration of the composite likelihood;
`logCompLik`	The value of the log composite-likelihood at the maximum;
`maxdist`	The maximum spatial distance used in the weigthed composite likelihood. If no spatial distance is specified then it is NULL;
`maxtime`	The maximum temporal distance used in the weigthed composite likelihood. If no spatial distance is specified then it is NULL;
`message`	Extra message passed from the numerical routines;
`model`	The density associated to the likelihood objects;
`missp`	True if a misspecified Gaussian model is ued in the composite likelihhod;
`n`	The number of trials in a binominal RF;the number of successes in a negative Binomial RFs;
`neighb`	The order of spatial neighborhood in the composite likelihood computation.
`ns`	The number of (different) location sites in the bivariate case;
`nozero`	In the case of tapered likelihood the percentage of non zero values in the covariance matrix. Otherwise is NULL.
`numcoord`	The number of spatial coordinates;
`numtime`	The number of the temporal realisations of the RF;
`param`	A list of the parameters' estimates;
`radius`	The radius of the sphere in the case of great circle distance;
`stderr`	The vector of standard errors for standard maximum likelihood estimation;
`sensmat`	The sensitivity matrix;
`varcov`	The matrix of the variance-covariance of the estimates;
`varimat`	The variability matrix;
`vartype`	The method used to compute the variance of the estimates;
`type`	The type of the likelihood objects.
`winconst`	The constant used to compute the window size in the spatial sub-sampling;
`winstp`	The step used for moving the window in the spatial sub-sampling;
`winconst_t`	The constant used to compute the window size in the spatio-temporal sub-sampling;
`winstp_`	The step used for moving the window in the spatio-temporal sub-sampling;
`X`	The matrix of covariates;

Author(s)

Examples

library(GeoModels)


###############################################################
############ Examples of spatial Gaussian RFs ################
###############################################################

################################################################
###
### Example 1 : Maximum pairwise conditional likelihood fitting 
###  of a Gaussian RF with Matern correlation
###
###############################################################
model="Gaussian"
# Define the spatial-coordinates of the points:
set.seed(3)
N=400  # number of location sites
x <- runif(N, 0, 1)
set.seed(6)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates
X=cbind(rep(1,N),runif(N))

# Set the covariance model's parameters:
corrmodel <- "Matern"
mean <- 0.2
mean1 <- -0.5
sill <- 1
nugget <- 0
scale <- 0.2/3
smooth=0.5
param<-list(mean=mean,mean1=mean1,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian RF:
data <- GeoSim(coordx=coords,model=model,corrmodel=corrmodel, param=param,X=X)$data

fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)

################################################################
###
### Maximum pairwise likelihood fitting of
### Gaussian RFs with exponential correlation.
### 
###############################################################
fit1 <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, 
                    optimizer="BFGS",neighb=3,likelihood="Conditional",
                    type="Pairwise", start=start,fixed=fixed,X=X)
print(fit1)




###############################################################
############ Examples of spatial non-Gaussian RFs #############
###############################################################


################################################################
###
### Example 2. Maximum pairwise likelihood fitting of 
### a LogGaussian  RF with Generalized Wendland correlation
### 
###############################################################
set.seed(524)
# Define the spatial-coordinates of the points:
N=500
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
X=cbind(rep(1,N),runif(N))
mean=1; mean1=2 # regression parameters
nugget=0
sill=0.5
scale=0.2
smooth=0

model="LogGaussian"
corrmodel="GenWend"
param=list(mean=mean,mean1=mean1,sill=sill,scale=scale,
                    nugget=nugget,power2=4,smooth=smooth)
# Simulation of a  non stationary LogGaussian RF:
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model,X=X,
           param=param)$data

fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)
I=Inf
lower<-list(mean=-I,mean1=-I,scale=0,sill=0)
upper<-list(mean= I,mean1= I,scale=I,sill=I)

# Maximum pairwise composite-likelihood fitting of the RF:
fit <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",X=X,
                    optimizer="nlminb",lower=lower,upper=upper,
                    start=start,fixed=fixed)
print(unlist(fit$param))


################################################################
###
### Example 3. Maximum pairwise likelihood fitting of
### SinhAsinh   RFs with Wendland0 correlation
###
###############################################################
set.seed(261)
model="SinhAsinh"
# Define the spatial-coordinates of the points:
x <- runif(500, 0, 1)
y <- runif(500, 0, 1)
coords <- cbind(x,y)

corrmodel="Wend0"
mean=0;nugget=0
sill=1
skew=-0.5
tail=1.5
power2=4
c_supp=0.2

# model parameters
param=list(power2=power2,skew=skew,tail=tail,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data

plot(density(data))
fixed=list(power2=power2,nugget=nugget)
start=list(scale=c_supp,skew=skew,tail=tail,mean=mean,sill=sill)
# Maximum pairwise likelihood:
fit1 <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Marginal",type="Pairwise",
                    start=start,fixed=fixed)
print(unlist(fit1$param))



library(GeoModels)


###############################################################
############ Examples of spatial Gaussian RFs ################
###############################################################

################################################################
###
### Example 1 : Maximum pairwise conditional likelihood fitting 
###  of a Gaussian RF with Matern correlation
###
###############################################################
model="Gaussian"
# Define the spatial-coordinates of the points:
set.seed(3)
N=400  # number of location sites
x <- runif(N, 0, 1)
set.seed(6)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates
X=cbind(rep(1,N),runif(N))

# Set the covariance model's parameters:
corrmodel <- "Matern"
mean <- 0.2
mean1 <- -0.5
sill <- 1
nugget <- 0
scale <- 0.2/3
smooth=0.5
param<-list(mean=mean,mean1=mean1,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian RF:
data <- GeoSim(coordx=coords,model=model,corrmodel=corrmodel, param=param,X=X)$data

fixed<-list(nugget=nugget,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)

################################################################
###
### Maximum pairwise likelihood fitting of
### Gaussian RFs with exponential correlation.
### 
###############################################################
fit1 <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, 
                    optimizer="BFGS",neighb=3,likelihood="Conditional",
                    type="Pairwise", start=start,fixed=fixed,X=X)
print(fit1)




###############################################################
############ Examples of spatial non-Gaussian RFs #############
###############################################################


################################################################
###
### Example 2. Maximum pairwise likelihood fitting of 
### a LogGaussian  RF with Generalized Wendland correlation
### 
###############################################################
set.seed(524)
# Define the spatial-coordinates of the points:
N=500
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
X=cbind(rep(1,N),runif(N))
mean=1; mean1=2 # regression parameters
nugget=0
sill=0.5
scale=0.2
smooth=0

model="LogGaussian"
corrmodel="GenWend"
param=list(mean=mean,mean1=mean1,sill=sill,scale=scale,
                    nugget=nugget,power2=4,smooth=smooth)
# Simulation of a  non stationary LogGaussian RF:
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model,X=X,
           param=param)$data

fixed<-list(nugget=nugget,power2=4,smooth=smooth)
start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)
I=Inf
lower<-list(mean=-I,mean1=-I,scale=0,sill=0)
upper<-list(mean= I,mean1= I,scale=I,sill=I)

# Maximum pairwise composite-likelihood fitting of the RF:
fit <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Conditional",type="Pairwise",X=X,
                    optimizer="nlminb",lower=lower,upper=upper,
                    start=start,fixed=fixed)
print(unlist(fit$param))


################################################################
###
### Example 3. Maximum pairwise likelihood fitting of
### SinhAsinh   RFs with Wendland0 correlation
###
###############################################################
set.seed(261)
model="SinhAsinh"
# Define the spatial-coordinates of the points:
x <- runif(500, 0, 1)
y <- runif(500, 0, 1)
coords <- cbind(x,y)

corrmodel="Wend0"
mean=0;nugget=0
sill=1
skew=-0.5
tail=1.5
power2=4
c_supp=0.2

# model parameters
param=list(power2=power2,skew=skew,tail=tail,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data

plot(density(data))
fixed=list(power2=power2,nugget=nugget)
start=list(scale=c_supp,skew=skew,tail=tail,mean=mean,sill=sill)
# Maximum pairwise likelihood:
fit1 <- GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    neighb=3,likelihood="Marginal",type="Pairwise",
                    start=start,fixed=fixed)
print(unlist(fit1$param))

Spatial (bivariate) and spatio temporal optimal linear prediction for Gaussian and non Gaussian random fields.

Description

For a given set of spatial location sites (and temporal instants), the function computes optimal linear prediction and associated mean square error for the Gaussian and non Gaussian case.

Usage

GeoKrig(estobj=NULL,data, coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL, corrmodel,distance="Eucl",
    grid=FALSE, loc, maxdist=NULL, maxtime=NULL,
    method="cholesky", model="Gaussian", n=1,nloc=NULL,mse=FALSE, 
    lin_opt=TRUE,  param, anisopars=NULL,radius=6371, sparse=FALSE,
    taper=NULL,tapsep=NULL, time=NULL, type="Standard",type_mse=NULL,
     type_krig="Simple",weigthed=TRUE,which=1,
     copula=NULL, X=NULL,Xloc=NULL,Mloc=NULL,spobj=NULL,spdata=NULL)
GeoKrig(estobj=NULL,data, coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL, corrmodel,distance="Eucl",
    grid=FALSE, loc, maxdist=NULL, maxtime=NULL,
    method="cholesky", model="Gaussian", n=1,nloc=NULL,mse=FALSE, 
    lin_opt=TRUE,  param, anisopars=NULL,radius=6371, sparse=FALSE,
    taper=NULL,tapsep=NULL, time=NULL, type="Standard",type_mse=NULL,
     type_krig="Simple",weigthed=TRUE,which=1,
     copula=NULL, X=NULL,Xloc=NULL,Mloc=NULL,spobj=NULL,spdata=NULL)

Arguments

`estobj`	An object of class Geofit that includes information about data, model and estimates.
`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid) giving the data used for prediction.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates used for prediction. $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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 used for prediction; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates used for prediction; the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial 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 the Section Details of `GeoFit`.
`grid`	Logical; if `FALSE` (the default) the data used for prediction are interpreted as spatial or spatial-temporal realisations on a set of non-equispaced spatial sites (irregular grid).
`lin_opt`	Logical;If TRUE (default) then optimal (pairwise) linear kriging is computed. Otherwise optimal (pairwise) kriging is computed in the mean square sense.
`loc`	A numeric ( $n \times 2$ )-matrix (where `n` is the number of spatial sites) giving 2-dimensions of spatial coordinates to be predicted.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial compact support in the case of covariance tapering kriging.
`maxtime`	Numeric; an optional positive value indicating the maximum temporal compact support in the case of covasriance tapering kriging.
`method`	String; the type of matrix decomposition used in the simulation. Default is `cholesky`. The other possible choices is `svd`.
`n`	Numeric; the number of trials in a binomial random fields. Default is $1$ .
`nloc`	Numeric; the number of trials of the locations sites to be predicted in a binomial random fields type II. Default is $1$ .
`mse`	Logical; if `TRUE` (the default) MSE of the kriging predictor is computed
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`param`	A list of parameter values required for the correlation model.See the Section Details.
`anisopars`	A list of two elements: "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
`radius`	Numeric: the radius of the sphere if coordinates are passed in lon/lat format;
`sparse`	Logical; if `TRUE` kriging is computed with sparse matrices algorithms using spam package. Default is FALSE. It should be used with compactly supported covariances.
`taper`	String; the name of the taper correlation function, see the Section Details.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time quasi taper (see Details).
`time`	A numeric ( $m \times 1$ ) vector (where `m` is the number of temporal instants) giving the temporal instants to be predicted; the default is `NULL` then only spatial prediction is performed.
`type`	String; if `Standard` then standard kriging is performed;if `Tapering` then kriging with covariance tapering is performed;if `Pairwise` then pairwise kriging is performed
`type_mse`	String; if `Theoretical` then theoretical MSE pairwise kriging is computed. If `SubSamp` then an estimation based on subsampling is computed.
`type_krig`	String; the type of kriging. If `Simple` (the default) then simple kriging is performed. If `Optim` then optimal kriging is performed for some non-Gaussian RFs
`weigthed`	Logical; if `TRUE` then decreasing weigths coming from a compactly supported correlation function with compact support `maxdist` (`maxtime`)are used in the pairwise kriging.
`which`	Numeric; In the case of bivariate (tapered) cokriging it indicates which variable to predict. It can be 1 or 2
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`Xloc`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification associated to predicted locations.
`Mloc`	Numeric; Vector of spatio(temporal) estimated means associated to predicted locations.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object

Details

Best linear unbiased predictor and associated mean square error is computed for Gaussian and some non Gaussian cases. Specifically, for a spatial or spatio-temporal or spatial bivariate dataset, given a set of spatial locations and temporal istants and a correlation model corrmodel with some fixed parameters and given the type of RF (model) the function computes simple kriging, for the specified spatial locations loc and temporal instants time, providing also the respective mean square error. For the choice of the spatial or spatio temporal correlation model see details in GeoCovmatrix function. The list param specifies mean and covariance parameters, see CorrParam and GeoCovmatrix for details. The type_krig parameter indicates the type of kriging. In the case of simple kriging, the known mean can be specified by the parameter mean in the list param (See examples).

Value

Returns an object of class Kg. An object of class Kg is a list containing at most the following components:

`bivariate`	`TRUE` if spatial bivariate cokriging is performed, otherwise `FALSE`;
`coordx`	A $d$ -dimensional vector of spatial coordinates used for prediction;
`coordy`	A $d$ -dimensional vector of spatial coordinates used for prediction;
`coordt`	A $t$ -dimensional vector of temporal coordinates used for prediction;
`corrmodel`	String: the correlation model;
`covmatrix`	The covariance matrix if `type` is `Standard`. An object of class spam if `type` is `Tapering`
`data`	The vector or matrix or array of data used for prediction
`distance`	String: the type of spatial distance;
`grid`	`TRUE` if the spatial data used for prediction are observed in a regular grid, otherwise `FALSE`;
`loc`	A ( $n \times 2$ )-matrix of spatial locations to be predicted.
`n`	The number of trial for Binomial RFs
`nozero`	In the case of tapered simple kriging the percentage of non zero values in the covariance matrix. Otherwise is NULL.
`numcoord`	Numeric:he number $d$ of spatial coordinates used for prediction;
`numloc`	Numeric: the number $n$ of spatial coordinates to be predicted;
`numtime`	Numeric: the number $d$ of the temporal instants used for prediction;
`numt`	Numeric: the number $m$ of the temporal instants to be predicted;
`model`	The type of RF, see `GeoFit`.
`param`	Numeric: The covariance parameters;
`pred`	A ( $m \times n$ )-matrix of spatio or spatio temporal kriging prediction;
`radius`	Numeric: the radius of the sphere if coordinates are pssed in lon/lat format;
`spacetime`	`TRUE` if spatio-temporal kriging and `FALSE` if spatial kriging;
`tapmod`	String: the taper model if `type` is `Tapering`. Otherwise is NULL.
`time`	A $m$ -dimensional vector of temporal coordinates to be predicted;
`type`	String: the type of kriging (Standard or Tapering).
`type_krig`	String: the type of kriging.
`mse`	A ( $m \times n$ )-matrix of spatio or spatio temporal mean square error kriging prediction;

Author(s)

References

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Spring Verlang, New York.

Examples


library(GeoModels)
################################################################
########### Examples of spatial kriging ############
################################################################

################################################################
###
### Example 1. Spatial  kriging of a
### Gaussian random fields with Gen wendland correlation.
###
################################################################

model="Gaussian"
set.seed(79)
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
             likelihood='Marginal', type='Pairwise',neighb=3,
             optimizer="nlminb", lower=lower,upper=upper,
             start=start,fixed=fixed)

# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))

## first option
#param=append(fit$param,fit$fixed)
#pr=GeoKrig(loc=loc_to_pred,coordx=coords,corrmodel=corrmodel,
#       model=model,param=param,data=data,mse=TRUE)

## second option using object GeoFit
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)


colour = rainbow(100)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,col=colour)
# simple kriging map prediction
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           xlab="",ylab="",
           main=" Kriging ")

# simple kriging MSE map prediction variance
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           xlab="",ylab="",main="Std error")
par(opar)

################################################################
###
### Example 2. Spatial  kriging of a Skew
### Gaussian random fields with Matern correlation.
###
################################################################
model="SkewGaussian"
set.seed(79)
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "Matern"
mean=0
sill=2
nugget=0
scale=0.1
smooth=0.5
skew=3
param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,skew=skew)

# Simulation of the spatial skew Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,
              param=param)$data

fixed=list(nugget=nugget,smooth=smooth)
start=list(mean=0,scale=scale,sill=1,skew=skew)
I=Inf
lower=list(mean=-I,scale=0,sill=0,skew=-I)
upper=list(mean= I,scale=I,sill=I,skew=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit2(data, coordx=coords, corrmodel=corrmodel,model=model,
              likelihood='Marginal', type='Pairwise',neighb=3,
              optimizer="nlminb", lower=lower,upper=upper,
              start=start,fixed=fixed)

# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))
## optimal linear kriging
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)

colour = rainbow(100)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,col=colour)
# simple kriging map prediction
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           xlab="",ylab="",
           main=" Kriging ")

# simple kriging MSE map prediction variance
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           xlab="",ylab="",main="Std error")
par(opar)

################################################################
###
### Example 3. Spatial  kriging  of a 
###   Gamma random field with mean spatial regression
###
###############################################################
set.seed(312)
model="Gamma"
corrmodel = "GenWend"  
# Define the spatial-coordinates of the points:
NN=300
coords=cbind(runif(NN),runif(NN))
## matrix covariates
a0=rep(1,NN)
a1=runif(NN,0,1)
X=cbind(a0,a1)
##Set model parameters
shape=2
## regression parameters
mean = 1;mean1= -0.2
# correlation parameters
nugget = 0;power2=4
scale = 0.3;smooth=0    

## simulation
param=list(shape=shape,nugget=nugget,mean=mean,mean1=mean1, 
           scale=scale,power2=power2,smooth=smooth)
data = GeoSim(coordx=coords,corrmodel=corrmodel, param=param,
              model=model,X=X)$data

#####starting and fixed parameters
fixed=list(nugget=nugget,power2=power2,smooth=smooth)
start=list(mean=mean,mean1=mean1, scale=scale,shape=shape)

## estimation with pairwise likelihood
fit2 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel,X=X,
              neighb=3,likelihood="Conditional",type="Pairwise",
              start=start,fixed=fixed, model = model)

# locations to predict with associated covariates
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))
NP=nrow(loc_to_pred)
a0=rep(1,NP)
a1=runif(NP,0,1)
Xloc=cbind(a0,a1)

#optimal linear  kriging 
pr=GeoKrig(fit2,loc=loc_to_pred,Xloc=Xloc,sparse=TRUE,mse=TRUE)

## map 
opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,main="Data")
map=matrix(pr$pred,ncol=length(xx))
mapmse=matrix(pr$mse,ncol=length(xx))
image.plot(xx, xx, map,
           xlab="",ylab="",main="Kriging ")

image.plot(xx, xx, mapmse,
           xlab="",ylab="",main="MSE")
par(opar)


################################################################
########### Examples of spatio temporal kriging ############
################################################################

################################################################
###
### Example 4. Spatio temporal simple kriging of n locations
### sites and m temporal instants for a Gaussian random fields
### with estimated double Wendland correlation.
###
###############################################################
model="Gaussian"
# Define the spatial-coordinates of the points:
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
times=1:4

# Define model correlation modek and associated parameters
corrmodel="Wend0_Wend0"
param=list(nugget=0,mean=0,power2_s=4,power2_t=4,
           scale_s=0.2,scale_t=2,sill=1)

# Simulation of the space time Gaussian random field:
set.seed(31)
data=GeoSim(coordx=coords,coordt=times,corrmodel=corrmodel,sparse=TRUE,
            param=param)$data

# Maximum pairwise likelihood fitting of the space time random field:
start = list(scale_s=0.15,scale_t=2,sill=1,mean=0)
fixed = list(nugget=0,power2_s=4,power2_t=4)

fit = GeoFit(data, coordx=coords, coordt=times, model=model, corrmodel=corrmodel, 
             likelihood='Conditional', type='Pairwise',start=start,fixed=fixed,
             neighb=3,maxtime=1)

# locations to predict
xx=seq(0,1,0.04)
loc_to_pred=as.matrix(expand.grid(xx,xx))
#  Define the times to predict
times_to_pred=2

pr=GeoKrig(fit,loc=loc_to_pred,time=times_to_pred,sparse=TRUE,mse=TRUE)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
zlim=c(-2.5,2.5)
colour = rainbow(100)


quilt.plot(coords,data[2,] ,col=colour,main = paste(" data  at Time 2"))   
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           main = paste(" Kriging at Time 2"),ylab="")
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           main = paste("Std err Time at time 2"),ylab="")


par(opar)


################################################################
###
### Example r. Spatial bivariate  simple cokriging of n locations
### sites  for a bivariate Gaussian random fields
### with estimated Matern correlation.
###
###############################################################
#set.seed(6)
#NN=1500 # number of spatial locations 
#x = runif(NN, 0, 1); 
#y = runif(NN, 0, 1) 
#coords=cbind(x,y) 

## setting parameters
#mean_1 = 2; mean_2= -1
#nugget_1 =0;nugget_2=0
#sill_1 =0.5; sill_2 =1; 

### correlation parameters
#CorrParam("Bi_Matern")
#scale_1=0.2/3; scale_2=0.15/3; scale_12=0.5*(scale_2+scale_1) 
#smooth_1=smooth_2=smooth_12=0.5
#pcol = -0.4
#param= list(nugget_1=nugget_1,nugget_2=nugget_2,
#          sill_1=sill_1,sill_2=sill_2,
#          mean_1=mean_1,mean_2=mean_2,
#          smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12,
#          scale_1=scale_1, scale_2=scale_2,scale_12=scale_12,
#          pcol=pcol)

## simulation
#data = GeoSim(coordx=coords, corrmodel="Bi_Matern",model=model,param=param)$data

#fixed=list(mean_1=mean_1,mean_2=mean_2, nugget_1=nugget_1,nugget_2=nugget_2, 
#       smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12)

#start=list( sill_1=sill_1,sill_2=sill_2,
#        scale_1=scale_1,scale_2=scale_2,scale_12=scale_12, pcol=pcol)

## estimation with maximum likelihood 
#fit = GeoFit(data=data,coordx=coords, corrmodel="Bi_Matern",
 #likelihood="Full",type="Standard",optimizer="BFGS",
  #likelihood="Marginal",type="Pairwise",optimizer="BFGS",neighb=5,
  #start=start,fixed=fixed)

###### co-kriging for the fist component ##############
#xx=seq(0,1,0.022)
#loc_to_pred=as.matrix(expand.grid(xx,xx))
#pr1 = GeoKrig(fit,which=1,mse=TRUE,loc=loc_to_pred)

#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#zlim=c(-2.5,2.5)
#colour = rainbow(100)
#quilt.plot(coords,data[1,] ,col=colour,main = paste(" Fist component"))   
#quilt.plot(loc_to_pred,pr1$pred,col=colour,
#           main = paste(" Kriging first component"),ylab="")
#par(opar)


library(GeoModels)
################################################################
########### Examples of spatial kriging ############
################################################################

################################################################
###
### Example 1. Spatial  kriging of a
### Gaussian random fields with Gen wendland correlation.
###
################################################################

model="Gaussian"
set.seed(79)
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
             likelihood='Marginal', type='Pairwise',neighb=3,
             optimizer="nlminb", lower=lower,upper=upper,
             start=start,fixed=fixed)

# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))

## first option
#param=append(fit$param,fit$fixed)
#pr=GeoKrig(loc=loc_to_pred,coordx=coords,corrmodel=corrmodel,
#       model=model,param=param,data=data,mse=TRUE)

## second option using object GeoFit
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)


colour = rainbow(100)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,col=colour)
# simple kriging map prediction
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           xlab="",ylab="",
           main=" Kriging ")

# simple kriging MSE map prediction variance
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           xlab="",ylab="",main="Std error")
par(opar)

################################################################
###
### Example 2. Spatial  kriging of a Skew
### Gaussian random fields with Matern correlation.
###
################################################################
model="SkewGaussian"
set.seed(79)
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "Matern"
mean=0
sill=2
nugget=0
scale=0.1
smooth=0.5
skew=3
param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,skew=skew)

# Simulation of the spatial skew Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,
              param=param)$data

fixed=list(nugget=nugget,smooth=smooth)
start=list(mean=0,scale=scale,sill=1,skew=skew)
I=Inf
lower=list(mean=-I,scale=0,sill=0,skew=-I)
upper=list(mean= I,scale=I,sill=I,skew=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit2(data, coordx=coords, corrmodel=corrmodel,model=model,
              likelihood='Marginal', type='Pairwise',neighb=3,
              optimizer="nlminb", lower=lower,upper=upper,
              start=start,fixed=fixed)

# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))
## optimal linear kriging
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)

colour = rainbow(100)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,col=colour)
# simple kriging map prediction
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           xlab="",ylab="",
           main=" Kriging ")

# simple kriging MSE map prediction variance
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           xlab="",ylab="",main="Std error")
par(opar)

################################################################
###
### Example 3. Spatial  kriging  of a 
###   Gamma random field with mean spatial regression
###
###############################################################
set.seed(312)
model="Gamma"
corrmodel = "GenWend"  
# Define the spatial-coordinates of the points:
NN=300
coords=cbind(runif(NN),runif(NN))
## matrix covariates
a0=rep(1,NN)
a1=runif(NN,0,1)
X=cbind(a0,a1)
##Set model parameters
shape=2
## regression parameters
mean = 1;mean1= -0.2
# correlation parameters
nugget = 0;power2=4
scale = 0.3;smooth=0    

## simulation
param=list(shape=shape,nugget=nugget,mean=mean,mean1=mean1, 
           scale=scale,power2=power2,smooth=smooth)
data = GeoSim(coordx=coords,corrmodel=corrmodel, param=param,
              model=model,X=X)$data

#####starting and fixed parameters
fixed=list(nugget=nugget,power2=power2,smooth=smooth)
start=list(mean=mean,mean1=mean1, scale=scale,shape=shape)

## estimation with pairwise likelihood
fit2 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel,X=X,
              neighb=3,likelihood="Conditional",type="Pairwise",
              start=start,fixed=fixed, model = model)

# locations to predict with associated covariates
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))
NP=nrow(loc_to_pred)
a0=rep(1,NP)
a1=runif(NP,0,1)
Xloc=cbind(a0,a1)

#optimal linear  kriging 
pr=GeoKrig(fit2,loc=loc_to_pred,Xloc=Xloc,sparse=TRUE,mse=TRUE)

## map 
opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
quilt.plot(coords,data,main="Data")
map=matrix(pr$pred,ncol=length(xx))
mapmse=matrix(pr$mse,ncol=length(xx))
image.plot(xx, xx, map,
           xlab="",ylab="",main="Kriging ")

image.plot(xx, xx, mapmse,
           xlab="",ylab="",main="MSE")
par(opar)


################################################################
########### Examples of spatio temporal kriging ############
################################################################

################################################################
###
### Example 4. Spatio temporal simple kriging of n locations
### sites and m temporal instants for a Gaussian random fields
### with estimated double Wendland correlation.
###
###############################################################
model="Gaussian"
# Define the spatial-coordinates of the points:
x = runif(300, 0, 1)
y = runif(300, 0, 1)
coords=cbind(x,y)
times=1:4

# Define model correlation modek and associated parameters
corrmodel="Wend0_Wend0"
param=list(nugget=0,mean=0,power2_s=4,power2_t=4,
           scale_s=0.2,scale_t=2,sill=1)

# Simulation of the space time Gaussian random field:
set.seed(31)
data=GeoSim(coordx=coords,coordt=times,corrmodel=corrmodel,sparse=TRUE,
            param=param)$data

# Maximum pairwise likelihood fitting of the space time random field:
start = list(scale_s=0.15,scale_t=2,sill=1,mean=0)
fixed = list(nugget=0,power2_s=4,power2_t=4)

fit = GeoFit(data, coordx=coords, coordt=times, model=model, corrmodel=corrmodel, 
             likelihood='Conditional', type='Pairwise',start=start,fixed=fixed,
             neighb=3,maxtime=1)

# locations to predict
xx=seq(0,1,0.04)
loc_to_pred=as.matrix(expand.grid(xx,xx))
#  Define the times to predict
times_to_pred=2

pr=GeoKrig(fit,loc=loc_to_pred,time=times_to_pred,sparse=TRUE,mse=TRUE)

opar=par(no.readonly = TRUE)
par(mfrow=c(1,3))
zlim=c(-2.5,2.5)
colour = rainbow(100)


quilt.plot(coords,data[2,] ,col=colour,main = paste(" data  at Time 2"))   
image.plot(xx, xx, matrix(pr$pred,ncol=length(xx)),col=colour,
           main = paste(" Kriging at Time 2"),ylab="")
image.plot(xx, xx, matrix(pr$mse,ncol=length(xx)),col=colour,
           main = paste("Std err Time at time 2"),ylab="")


par(opar)


################################################################
###
### Example r. Spatial bivariate  simple cokriging of n locations
### sites  for a bivariate Gaussian random fields
### with estimated Matern correlation.
###
###############################################################
#set.seed(6)
#NN=1500 # number of spatial locations 
#x = runif(NN, 0, 1); 
#y = runif(NN, 0, 1) 
#coords=cbind(x,y) 

## setting parameters
#mean_1 = 2; mean_2= -1
#nugget_1 =0;nugget_2=0
#sill_1 =0.5; sill_2 =1; 

### correlation parameters
#CorrParam("Bi_Matern")
#scale_1=0.2/3; scale_2=0.15/3; scale_12=0.5*(scale_2+scale_1) 
#smooth_1=smooth_2=smooth_12=0.5
#pcol = -0.4
#param= list(nugget_1=nugget_1,nugget_2=nugget_2,
#          sill_1=sill_1,sill_2=sill_2,
#          mean_1=mean_1,mean_2=mean_2,
#          smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12,
#          scale_1=scale_1, scale_2=scale_2,scale_12=scale_12,
#          pcol=pcol)

## simulation
#data = GeoSim(coordx=coords, corrmodel="Bi_Matern",model=model,param=param)$data

#fixed=list(mean_1=mean_1,mean_2=mean_2, nugget_1=nugget_1,nugget_2=nugget_2, 
#       smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12)

#start=list( sill_1=sill_1,sill_2=sill_2,
#        scale_1=scale_1,scale_2=scale_2,scale_12=scale_12, pcol=pcol)

## estimation with maximum likelihood 
#fit = GeoFit(data=data,coordx=coords, corrmodel="Bi_Matern",
 #likelihood="Full",type="Standard",optimizer="BFGS",
  #likelihood="Marginal",type="Pairwise",optimizer="BFGS",neighb=5,
  #start=start,fixed=fixed)

###### co-kriging for the fist component ##############
#xx=seq(0,1,0.022)
#loc_to_pred=as.matrix(expand.grid(xx,xx))
#pr1 = GeoKrig(fit,which=1,mse=TRUE,loc=loc_to_pred)

#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#zlim=c(-2.5,2.5)
#colour = rainbow(100)
#quilt.plot(coords,data[1,] ,col=colour,main = paste(" Fist component"))   
#quilt.plot(loc_to_pred,pr1$pred,col=colour,
#           main = paste(" Kriging first component"),ylab="")
#par(opar)

Spatial (bivariate) and spatio temporal optimal linear local prediction for Gaussian and non Gaussian RFs.

Description

For a given set of spatial location sites (and temporal instants), the function computes optmal local linear prediction and the associated mean squared error for the Gaussian and non Gaussian case using a spatial (temporal) neighborhood computed using the function GeoNeighborhood

Usage

GeoKrigloc(estobj=NULL,data, coordx, coordy=NULL, coordt=NULL,
    coordx_dyn=NULL, corrmodel, distance="Eucl", grid=FALSE, 
    loc, neighb=NULL, maxdist=NULL, 
    maxtime=NULL, method="cholesky",
    model="Gaussian", n=1,nloc=NULL, mse=FALSE,  
    param, anisopars=NULL,radius=6371,
    sparse=FALSE, time=NULL, type="Standard",type_mse=NULL,
    type_krig="Simple",weigthed=TRUE, 
    which=1, copula=NULL,X=NULL,Xloc=NULL,
    Mloc=NULL,spobj=NULL,spdata=NULL,parallel=FALSE,ncores=NULL)
GeoKrigloc(estobj=NULL,data, coordx, coordy=NULL, coordt=NULL,
    coordx_dyn=NULL, corrmodel, distance="Eucl", grid=FALSE, 
    loc, neighb=NULL, maxdist=NULL, 
    maxtime=NULL, method="cholesky",
    model="Gaussian", n=1,nloc=NULL, mse=FALSE,  
    param, anisopars=NULL,radius=6371,
    sparse=FALSE, time=NULL, type="Standard",type_mse=NULL,
    type_krig="Simple",weigthed=TRUE, 
    which=1, copula=NULL,X=NULL,Xloc=NULL,
    Mloc=NULL,spobj=NULL,spdata=NULL,parallel=FALSE,ncores=NULL)

Arguments

`estobj`	An object of class Geofit that includes information about data, model and estimates.
`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid) giving the data used for prediction.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates used for prediction. $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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 used for prediction; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates used for prediction; the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial 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 the Section Details of `GeoFit`.
`grid`	Logical; if `FALSE` (the default) the data used for prediction are interpreted as spatial or spatial-temporal realisations on a set of non-equispaced spatial sites (irregular grid).
`loc`	A numeric ( $n \times 2$ )-matrix (where `n` is the number of spatial sites) giving 2-dimensions of spatial coordinates to be predicted.
`neighb`	Numeric; an optional positive integer indicating the order of the neighborhood.
`maxdist`	Numeric; an optional positive value indicating the distance in the spatial neighborhood.
`maxtime`	Numeric; an optional positive integer value indicating the order of the temporal neighborhood.
`method`	String; the type of matrix decomposition used in the simulation. Default is `cholesky`. The other possible choices is `svd`.
`n`	Numeric; the number of trials in a binomial random fields. Default is $1$ .
`nloc`	Numeric; the number of trials of the locations sites to be predicted in the binomial random field. If missing then a rounded mean of n is considered.
`mse`	Logical; if `TRUE` (the default) MSE of the kriging predictor is computed
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`param`	A list of parameter values required for the correlation model.See the Section Details.
`anisopars`	A list of two elements: "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
`radius`	Numeric: the radius of the sphere if coordinates are passed in lon/lat format;
`sparse`	Logical; if `TRUE` kriging is computed with sparse matrices algorithms using spam package. Default is FALSE. It should be used with compactly supported covariances.
`time`	A numeric ( $m \times 1$ ) vector (where `m` is the number of temporal instants) giving the temporal instants to be predicted; the default is `NULL` then only spatial prediction is performed.
`type`	String; if `Standard` then standard kriging is performed;if `Tapering` then kriging with covariance tapering is performed;if `Pairwise` then pairwise kriging is performed
`type_mse`	String; if `Theoretical` then theoretical MSE pairwise kriging is computed. If `SubSamp` then an estimation based on subsampling is computed.
`type_krig`	String; the type of kriging. If `Simple` (the default) then simple kriging is performed. If `Optim` then optimal kriging is performed for some non-Gaussian RFs
`weigthed`	Logical; if `TRUE` then decreasing weigths coming from a compactly supported correlation function with compact support `maxdist` (`maxtime`)are used in the pairwise kriging.
`which`	Numeric; In the case of bivariate (tapered) cokriging it indicates which variable to predict. It can be 1 or 2
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`Xloc`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification associated to predicted locations.
`Mloc`	Numeric; Vector of spatio(temporal) estimated means associated to predicted locations.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object
`parallel`	Logical; if `TRUE` then the prediction computation is parallelized
`ncores`	Numeric; number of cores involved in parallelization.

Details

This function use the GeoKrig with a spatial or spatio-temporal neighborhood computed using the function GeoNeighborhood. The neighborhood is specified with the option maxdist and maxtime.

Value

Returns an object of class Kg. An object of class Kg is a list containing at most the following components:

`bivariate`	`TRUE` if spatial bivariate cokriging is performed, otherwise `FALSE`;
`coordx`	A $d$ -dimensional vector of spatial coordinates used for prediction;
`coordy`	A $d$ -dimensional vector of spatial coordinates used for prediction;
`coordt`	A $t$ -dimensional vector of temporal coordinates used for prediction;
`corrmodel`	String: the correlation model;
`covmatrix`	The covariance matrix if `type` is `Standard`. An object of class spam if `type` is `Tapering`
`data`	The vector or matrix or array of data used for prediction
`distance`	String: the type of spatial distance;
`grid`	`TRUE` if the spatial data used for prediction are observed in a regular grid, otherwise `FALSE`;
`loc`	A ( $n \times 2$ )-matrix of spatial locations to be predicted.
`n`	The number of trial for Binomial RFs
`nozero`	In the case of tapered simple kriging the percentage of non zero values in the covariance matrix. Otherwise is NULL.
`numcoord`	Numeric:he number $d$ of spatial coordinates used for prediction;
`numloc`	Numeric: the number $n$ of spatial coordinates to be predicted;
`numtime`	Numeric: the number $d$ of the temporal instants used for prediction;
`numt`	Numeric: the number $m$ of the temporal instants to be predicted;
`model`	The type of RF, see `GeoFit`.
`param`	Numeric: The covariance parameters;
`pred`	A ( $m \times n$ )-matrix of spatio or spatio temporal kriging prediction;
`radius`	Numeric: the radius of the sphere if coordinates are pssed in lon/lat format;
`spacetime`	`TRUE` if spatio-temporal kriging and `FALSE` if spatial kriging;
`tapmod`	String: the taper model if `type` is `Tapering`. Otherwise is NULL.
`time`	A $m$ -dimensional vector of temporal coordinates to be predicted;
`type`	String: the type of kriging (Standard or Tapering).
`type_krig`	String: the type of kriging.
`mse`	A ( $m \times n$ )-matrix of spatio or spatio temporal mean square error kriging prediction;

Author(s)

References

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Spring Verlang, New York. Furrer R., Genton, M.G. and Nychka D. (2006). Covariance Tapering for Interpolation of Large Spatial Datasets. Journal of Computational and Graphical Statistics, 15-3, 502–523.

Examples


################################################################
############### Examples of Spatial local kriging  #############
################################################################
require(GeoModels)
####
model="Gaussian"

# Define the spatial-coordinates of the points:
set.seed(759)
x = runif(1000, 0, 1)
y = runif(1000, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=1
nugget=0; scale=0.2
param=list(mean=mean,sill=sill,nugget=nugget,smooth=0,
scale=scale,power2=4)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

# Maximum pairwise likelihood fitting of the space time random field:

start=list(scale=scale,sill=sill,mean=mean)
fixed=list(power2=4,smooth=0,nugget=0)
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel, 
              start=start,fixed=fixed,
             likelihood='Conditional', type='Pairwise',
              neighb=3)

# locations to predict
loc_to_pred=matrix(runif(8),4,2)
################################################################
###
### Example 1. Comparing spatial kriging with local kriging for
### a Gaussian random field with GenWend correlation.
### 
###############################################################
param=append(fit$param,fit$fixed)
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)

pr_loc=GeoKrigloc(fit,loc=loc_to_pred,neighb=100,mse=TRUE)

pr$pred;
pr_loc$pred


############################################################
#### Example: spatio temporal  Gaussian local kriging ######
############################################################


require(GeoModels)
require(fields)
set.seed(78)
coords=cbind(runif(100),runif(100))
coordt=seq(0,5,0.25)
corrmodel="Matern_Matern"
param=list(nugget=0,mean=0,scale_s=0.2/3,scale_t=0.25/3,sill=2,
  smooth_s=0.5,smooth_t=0.5)

data = GeoSim(coordx=coords, coordt=coordt,
     corrmodel=corrmodel, param=param)$data


# Maximum pairwise likelihood fitting of the space time random field:
start = list(scale_s=0.2/3,scale_t=0.25,sill=2,mean=0)
fixed = list(smooth_s=0.5,smooth_t=0.5,nugget=0)
I=Inf
lower=list(scale_s=0,scale_t=0,sill=0,mean=-I)
upper=list(scale_s=I,scale_t=I,sill=I,mean=I)
fit = GeoFit(data, coordx=coords, coordt=coordt, model=model, corrmodel=corrmodel, 
             likelihood='Conditional', type='Pairwise',start=start,fixed=fixed,
             optimizer="nlminb",lower=lower,upper=upper,
              neighb=3,maxtime=1)

##  four location to predict
loc_to_pred=matrix(runif(8),4,2)
## three temporal instants to predict
time=c(0.5,1.5,3.5)


pr=GeoKrig(fit,loc=loc_to_pred,time=time,mse=TRUE)
pr_loc=GeoKrigloc(fit,loc=loc_to_pred,time=time,
  neigh=25,maxtime=1, mse=TRUE)

##  full and local prediction 
pr$pred
pr_loc$pred



############################################################
#### Example: spatio bivariate  Gaussian local cokriging ######
############################################################
#set.seed(6)
#NN=1500 # number of spatial locations 
#x = runif(NN, 0, 1); 
#y = runif(NN, 0, 1) 
#coords=cbind(x,y) 

## setting parameters
#mean_1 = 2; mean_2= -1
#nugget_1 =0;nugget_2=0
#sill_1 =0.5; sill_2 =1; 

### correlation parameters
#CorrParam("Bi_Matern")
#scale_1=0.2/3; scale_2=0.15/3; scale_12=0.5*(scale_2+scale_1) 
#smooth_1=smooth_2=smooth_12=0.5
#pcol = -0.4
#param= list(nugget_1=nugget_1,nugget_2=nugget_2,
#          sill_1=sill_1,sill_2=sill_2,
#          mean_1=mean_1,mean_2=mean_2,
#          smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12,
#          scale_1=scale_1, scale_2=scale_2,scale_12=scale_12,
#          pcol=pcol)

## simulation
#data = GeoSim(coordx=coords, corrmodel="Bi_Matern",model=model,param=param)$data

#fixed=list(mean_1=mean_1,mean_2=mean_2, nugget_1=nugget_1,nugget_2=nugget_2, 
#       smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12)

#start=list( sill_1=sill_1,sill_2=sill_2,
#        scale_1=scale_1,scale_2=scale_2,scale_12=scale_12, pcol=pcol)

## estimation with maximum likelihood 
#fit = GeoFit(data=data,coordx=coords, corrmodel="Bi_Matern",
#likelihood="Full",type="Standard",optimizer="BFGS",
 # likelihood="Marginal",type="Pairwise",optimizer="BFGS",neighb=5,
  #start=start,fixed=fixed)

###### co-kriging for the fist component ##############
#xx=seq(0,1,0.022)
#loc_to_pred=as.matrix(expand.grid(xx,xx))
#pr1 = GeoKrigloc(fit,which=1,mse=TRUE,loc=loc_to_pred,neighb=100)

#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#zlim=c(-2.5,2.5)
#colour = rainbow(100)
#quilt.plot(coords,data[1,] ,col=colour,main = paste(" Fist component"))   
#quilt.plot(loc_to_pred,pr1$pred,col=colour,
#          main = paste(" Kriging first component"),ylab="")
#par(opar)

################################################################
############### Examples of Spatial local kriging  #############
################################################################
require(GeoModels)
####
model="Gaussian"

# Define the spatial-coordinates of the points:
set.seed(759)
x = runif(1000, 0, 1)
y = runif(1000, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=1
nugget=0; scale=0.2
param=list(mean=mean,sill=sill,nugget=nugget,smooth=0,
scale=scale,power2=4)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data

# Maximum pairwise likelihood fitting of the space time random field:

start=list(scale=scale,sill=sill,mean=mean)
fixed=list(power2=4,smooth=0,nugget=0)
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel, 
              start=start,fixed=fixed,
             likelihood='Conditional', type='Pairwise',
              neighb=3)

# locations to predict
loc_to_pred=matrix(runif(8),4,2)
################################################################
###
### Example 1. Comparing spatial kriging with local kriging for
### a Gaussian random field with GenWend correlation.
### 
###############################################################
param=append(fit$param,fit$fixed)
pr=GeoKrig(fit,loc=loc_to_pred,mse=TRUE)

pr_loc=GeoKrigloc(fit,loc=loc_to_pred,neighb=100,mse=TRUE)

pr$pred;
pr_loc$pred


############################################################
#### Example: spatio temporal  Gaussian local kriging ######
############################################################


require(GeoModels)
require(fields)
set.seed(78)
coords=cbind(runif(100),runif(100))
coordt=seq(0,5,0.25)
corrmodel="Matern_Matern"
param=list(nugget=0,mean=0,scale_s=0.2/3,scale_t=0.25/3,sill=2,
  smooth_s=0.5,smooth_t=0.5)

data = GeoSim(coordx=coords, coordt=coordt,
     corrmodel=corrmodel, param=param)$data


# Maximum pairwise likelihood fitting of the space time random field:
start = list(scale_s=0.2/3,scale_t=0.25,sill=2,mean=0)
fixed = list(smooth_s=0.5,smooth_t=0.5,nugget=0)
I=Inf
lower=list(scale_s=0,scale_t=0,sill=0,mean=-I)
upper=list(scale_s=I,scale_t=I,sill=I,mean=I)
fit = GeoFit(data, coordx=coords, coordt=coordt, model=model, corrmodel=corrmodel, 
             likelihood='Conditional', type='Pairwise',start=start,fixed=fixed,
             optimizer="nlminb",lower=lower,upper=upper,
              neighb=3,maxtime=1)

##  four location to predict
loc_to_pred=matrix(runif(8),4,2)
## three temporal instants to predict
time=c(0.5,1.5,3.5)


pr=GeoKrig(fit,loc=loc_to_pred,time=time,mse=TRUE)
pr_loc=GeoKrigloc(fit,loc=loc_to_pred,time=time,
  neigh=25,maxtime=1, mse=TRUE)

##  full and local prediction 
pr$pred
pr_loc$pred



############################################################
#### Example: spatio bivariate  Gaussian local cokriging ######
############################################################
#set.seed(6)
#NN=1500 # number of spatial locations 
#x = runif(NN, 0, 1); 
#y = runif(NN, 0, 1) 
#coords=cbind(x,y) 

## setting parameters
#mean_1 = 2; mean_2= -1
#nugget_1 =0;nugget_2=0
#sill_1 =0.5; sill_2 =1; 

### correlation parameters
#CorrParam("Bi_Matern")
#scale_1=0.2/3; scale_2=0.15/3; scale_12=0.5*(scale_2+scale_1) 
#smooth_1=smooth_2=smooth_12=0.5
#pcol = -0.4
#param= list(nugget_1=nugget_1,nugget_2=nugget_2,
#          sill_1=sill_1,sill_2=sill_2,
#          mean_1=mean_1,mean_2=mean_2,
#          smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12,
#          scale_1=scale_1, scale_2=scale_2,scale_12=scale_12,
#          pcol=pcol)

## simulation
#data = GeoSim(coordx=coords, corrmodel="Bi_Matern",model=model,param=param)$data

#fixed=list(mean_1=mean_1,mean_2=mean_2, nugget_1=nugget_1,nugget_2=nugget_2, 
#       smooth_1=smooth_1, smooth_2=smooth_2,smooth_12=smooth_12)

#start=list( sill_1=sill_1,sill_2=sill_2,
#        scale_1=scale_1,scale_2=scale_2,scale_12=scale_12, pcol=pcol)

## estimation with maximum likelihood 
#fit = GeoFit(data=data,coordx=coords, corrmodel="Bi_Matern",
#likelihood="Full",type="Standard",optimizer="BFGS",
 # likelihood="Marginal",type="Pairwise",optimizer="BFGS",neighb=5,
  #start=start,fixed=fixed)

###### co-kriging for the fist component ##############
#xx=seq(0,1,0.022)
#loc_to_pred=as.matrix(expand.grid(xx,xx))
#pr1 = GeoKrigloc(fit,which=1,mse=TRUE,loc=loc_to_pred,neighb=100)

#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#zlim=c(-2.5,2.5)
#colour = rainbow(100)
#quilt.plot(coords,data[1,] ,col=colour,main = paste(" Fist component"))   
#quilt.plot(loc_to_pred,pr1$pred,col=colour,
#          main = paste(" Kriging first component"),ylab="")
#par(opar)

Deleting NA values (missing values) from a spatial or spatio-temporal dataset.

Description

The function deletes NA values from a spatial or spatio-temporal dataset

Usage

GeoNA(data, coordx, coordy=NULL, coordt=NULL,
coordx_dyn=NULL, grid=FALSE, X=NULL, setting="spatial")GeoNA(data, coordx, coordy=NULL, coordt=NULL,
coordx_dyn=NULL, grid=FALSE, X=NULL, setting="spatial")

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid) giving the data.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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 ; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates; the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`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).
`X`	Numeric; Matrix of spatio(temporal) covariates in the linear mean specification.
`setting`	String; are data spatial, spatio-temporal or spatial bivariate (respectively `spatial`, `spacetime`, `bivariate`)

Value

Returns a list containing the following components:

`coordx`	A $d$ -dimensional vector of spatial coordinates;
`coordy`	A $d$ -dimensional vector of spatial coordinates;
`coordt`	A $t$ -dimensional vector of temporal coordinates;
`data`	The data without NAvalues
`grid`	`TRUE` if the spatial data are observed in a regular grid, otherwise `FALSE`;
`perc`	The percentage of NA values .
`setting`	Are data of spatial or spatio-temporal or spatial bivariate type
`X`	Covariates matrix

Author(s)

Examples


library(GeoModels)

# Define the spatial-coordinates of the points:
set.seed(79)
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "Matern"
mean=0
sill=1
nugget=0
scale=0.3/3
smooth=0.5
param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data


data[1:100]=NA
# removing NA
a=GeoNA(data,coordx=coords)
a$perc # percentage of NA values 
#a$coordx# spatial coordinates without missing values
#a$data # data without missinng values


library(GeoModels)

# Define the spatial-coordinates of the points:
set.seed(79)
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords=cbind(x,y)
# Set the exponential cov parameters:
corrmodel = "Matern"
mean=0
sill=1
nugget=0
scale=0.3/3
smooth=0.5
param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data


data[1:100]=NA
# removing NA
a=GeoNA(data,coordx=coords)
a$perc # percentage of NA values 
#a$coordx# spatial coordinates without missing values
#a$data # data without missinng values

Spatio (temporal) neighborhood selection for local kriging.

Description

Given a set of spatio (temporal) locations and data, the procedure selects a spatio (temporal) neighborhood associated to some given spatio (temporal) locations. The neighborhood is computed using a fixed spatio (temporal) threshold or considering a fixed number of spatio (temporal) neighbors.

Usage

GeoNeighborhood(data=NULL, coordx, coordy=NULL,
coordt=NULL, coordx_dyn=NULL, bivariate=FALSE,
               distance="Eucl", grid=FALSE, 
               loc, neighb=NULL,maxdist=NULL,
               maxtime=NULL, radius=6371, time=NULL, 
               X=NULL,M=NULL,spobj=NULL,spdata=NULL,
               parallel=FALSE,ncores=NULL)
GeoNeighborhood(data=NULL, coordx, coordy=NULL,
coordt=NULL, coordx_dyn=NULL, bivariate=FALSE,
               distance="Eucl", grid=FALSE, 
               loc, neighb=NULL,maxdist=NULL,
               maxtime=NULL, radius=6371, time=NULL, 
               X=NULL,M=NULL,spobj=NULL,spdata=NULL,
               parallel=FALSE,ncores=NULL)

Arguments

`data`	An optional $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid).
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`bivariate`	If TRUE then data is considered as spatial bivariate data.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `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).
`loc`	A ( $1 \times 2$ )-matrix giving the spatial coordinate of the location for which a neighborhood is computed .
`neighb`	Numeric; an optional positive integer indicating the order of spatial neighborhood.
`maxdist`	Numeric; a positive value indicating the maximum spatial distance considered in the spatial neighborhood selection.
`maxtime`	Numeric; an optional positive integer indicating the order of temporal neighborhood.
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`time`	Numeric; a value giving the temporal instant for which a neighborhood is computed.
`X`	Numeric; an optional Matrix of spatio (temporal) covariates.
`M`	Numeric; an estimated spatio (temporal) mean vector.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object
`parallel`	Logical; if `TRUE` then parallelizzation is used
`ncores`	Numeric; number of cores involved in parallelization.

Value

Returns a list containing the following informations:

`coordx`	A list of the matrix coordinates of the computed spatial neighborhood ;
`coordt`	A vector of the computed temporal neighborhood;
`data`	A list of the vector of data associated with the spatio (temporal) neighborhood;
`distance`	The type of spatial distance;
`numcoord`	The vector of numbers of location sites involved the spatial neighborhood;
`numtime`	The vector of numbers of temporal insttants involved the temporal neighborhood;
`radius`	The radius of the sphere if coordinates are passed in lon/lat format;
`spacetime`	`TRUE` if spatio-temporal and `FALSE` if spatial RF;
`X`	The matrix of spatio (temporal) covariates associated with the computed spatio (temporal) neighborhood;

Author(s)

Examples

library(GeoModels)
##########################################
#### Example: spatial neighborhood  ######
##########################################
set.seed(75)
coords=cbind(runif(500),runif(500))

param=list(nugget=0,mean=0,scale=0.2,sill=1,
            power2=4,smooth=1)

data_all = GeoSim(coordx=coords, corrmodel="GenWend", 
                         param=param)$data

plot(coords)
##two locations 
loc_to_pred=matrix(c(0.3,0.5,0.7,0.2),2,2)

points(loc_to_pred,pch=20)
neigh=GeoNeighborhood(data_all, coordx=coords,  
                  loc=loc_to_pred,neighb=8)

# two Neighborhoods 
neigh$coordx
points(neigh$coordx[[1]],pch=20,col="red")
points(neigh$coordx[[2]],pch=20,col="blue")
# associated data
neigh$data


###################################################
#### Example: spatio temporal spatial neighborhood#  
###################################################

set.seed(78)
coords=matrix(runif(80),40,2)
coordt=seq(0,6,0.25)

param=list(nugget=0,mean=0,scale_s=0.2/3,scale_t=0.25/3,sill=2)

data_all = GeoSim(coordx=coords, coordt=coordt,corrmodel="Exp_Exp", 
                         param=param)$data
##  two location to predict
loc_to_pred=matrix(runif(4),2,2)
## three temporal instants to predict
time=c(1,2)

plot(coords,xlim=c(0,1),ylim=c(0,1))
points(loc_to_pred,pch=20)

neigh=GeoNeighborhood(data_all, coordx=coords,  coordt=coordt,
                  loc=loc_to_pred,time=time,neighb=3,maxtime=0.5)

# first spatio-temporal neighborhoods 
# with  associated data
neigh$coordx[[1]]
neigh$coordt[[1]]
neigh$data[[1]]

###################################################
#### Example: bivariate  spatial neighborhood #####  
###################################################

set.seed(79)
coords=matrix(runif(100),50,2)

param=list(mean_1=0,mean_2=0,scale=0.12,smooth=0.5,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)

data_all = GeoSim(coordx=coords,corrmodel="Bi_matern_sep",
                 param=param)$data
##  two location to predict
loc_to_pred=matrix(runif(4),2,2)

neigh=GeoNeighborhood(data_all, coordx=coords,bivariate=TRUE,
                  loc=loc_to_pred,maxdist=0.25)

plot(coords)
points(loc_to_pred,pch=20)
points(neigh$coordx[[1]],col="red",pch=20)
points(neigh$coordx[[2]],col="red",pch=20)

library(GeoModels)
##########################################
#### Example: spatial neighborhood  ######
##########################################
set.seed(75)
coords=cbind(runif(500),runif(500))

param=list(nugget=0,mean=0,scale=0.2,sill=1,
            power2=4,smooth=1)

data_all = GeoSim(coordx=coords, corrmodel="GenWend", 
                         param=param)$data

plot(coords)
##two locations 
loc_to_pred=matrix(c(0.3,0.5,0.7,0.2),2,2)

points(loc_to_pred,pch=20)
neigh=GeoNeighborhood(data_all, coordx=coords,  
                  loc=loc_to_pred,neighb=8)

# two Neighborhoods 
neigh$coordx
points(neigh$coordx[[1]],pch=20,col="red")
points(neigh$coordx[[2]],pch=20,col="blue")
# associated data
neigh$data


###################################################
#### Example: spatio temporal spatial neighborhood#  
###################################################

set.seed(78)
coords=matrix(runif(80),40,2)
coordt=seq(0,6,0.25)

param=list(nugget=0,mean=0,scale_s=0.2/3,scale_t=0.25/3,sill=2)

data_all = GeoSim(coordx=coords, coordt=coordt,corrmodel="Exp_Exp", 
                         param=param)$data
##  two location to predict
loc_to_pred=matrix(runif(4),2,2)
## three temporal instants to predict
time=c(1,2)

plot(coords,xlim=c(0,1),ylim=c(0,1))
points(loc_to_pred,pch=20)

neigh=GeoNeighborhood(data_all, coordx=coords,  coordt=coordt,
                  loc=loc_to_pred,time=time,neighb=3,maxtime=0.5)

# first spatio-temporal neighborhoods 
# with  associated data
neigh$coordx[[1]]
neigh$coordt[[1]]
neigh$data[[1]]

###################################################
#### Example: bivariate  spatial neighborhood #####  
###################################################

set.seed(79)
coords=matrix(runif(100),50,2)

param=list(mean_1=0,mean_2=0,scale=0.12,smooth=0.5,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)

data_all = GeoSim(coordx=coords,corrmodel="Bi_matern_sep",
                 param=param)$data
##  two location to predict
loc_to_pred=matrix(runif(4),2,2)

neigh=GeoNeighborhood(data_all, coordx=coords,bivariate=TRUE,
                  loc=loc_to_pred,maxdist=0.25)

plot(coords)
points(loc_to_pred,pch=20)
points(neigh$coordx[[1]],col="red",pch=20)
points(neigh$coordx[[2]],col="red",pch=20)

A brute force algorithm for spatial or spatiotemoral optimal neighboord selection for pairwise composite likelihood estimation.

Description

The procedure performs different pairwise composite likelihood estimation using user's specified spatial or spatiotemporal neighboords in the weight function. The neighbor minimizing the sum of the squared differences between the estimated semivariogram and the empirical semivariogram is selected. The procedure needs an object obtained using the GeoVariogram function.

Usage

GeoNeighbSelect(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
    copula=NULL,corrmodel=NULL, distance="Eucl",fixed=NULL,anisopars=NULL,
    est.aniso=c(FALSE,FALSE), grid=FALSE, likelihood='Marginal',lower=NULL,
    neighb=c(1,2,3,4,5),maxtime=Inf, memdist=TRUE,model='Gaussian',
    n=1, ncores=NULL,optimizer='Nelder-Mead', parallel=FALSE, 
    bivariate=FALSE,radius=6371, start=NULL,type='Pairwise', upper=NULL, 
    weighted=FALSE,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL,vario=NULL)
GeoNeighbSelect(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
    copula=NULL,corrmodel=NULL, distance="Eucl",fixed=NULL,anisopars=NULL,
    est.aniso=c(FALSE,FALSE), grid=FALSE, likelihood='Marginal',lower=NULL,
    neighb=c(1,2,3,4,5),maxtime=Inf, memdist=TRUE,model='Gaussian',
    n=1, ncores=NULL,optimizer='Nelder-Mead', parallel=FALSE, 
    bivariate=FALSE,radius=6371, start=NULL,type='Pairwise', upper=NULL, 
    weighted=FALSE,X=NULL,nosym=FALSE,spobj=NULL,spdata=NULL,vario=NULL)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid). For the description see the Section Details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random fields is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`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 the Section Details.
`fixed`	An optional named list giving the values of the parameters that will be considered as known values. The listed parameters for a given correlation function will be not estimated.
`anisopars`	A list of two elements: "angle" and "ratio" i.e. the anisotropy angle and the anisotropy ratio, respectively.
`est.aniso`	A bivariate logical vector providing which anisotropic parameters must be estimated.
`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).
`likelihood`	String; the configuration of the composite likelihood. `Marginal` is the default (see Section Details in GeoFit).
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `bobyqa` or `optimize`. The names of the list must be the same of the names in the `start` list.
`neighb`	Numeric; a vector of positive integers indicating the order of neighborhood in the weight function of composite likelihood (see Section Details in GeoFit).
`maxtime`	Numeric; an optional positive integer indicating the order of temporal neighborhood in the composite likelihood computation.
`memdist`	Logical; if `TRUE` then all the distances useful in the composite likelihood estimation are computed before the optimization. `FALSE` is deprecated.
`model`	String; the type of random fields and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details in GeoFit.
`n`	Numeric; number of trials in a binomial random fields; number of successes in a negative binomial random fields
`ncores`	Numeric; the number of cores involved in the parallelization
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS`, `SANN`, `L-BFGS-B` and `nlminb` and `bobyqa`. In these last three cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`parallel`	Logical; if `TRUE` the procedure is parallelized using dofuture.
`bivariate`	Logical; if `TRUE` the bivariate case is considered.
`radius`	Numeric; the radius of the sphere in the case of lon-lat coordinates. The default is 6371, the radius of the earth.
`start`	An optional named list with the initial values of the parameters that are used by the numerical routines in maximization procedure. `NULL` is the default (see Section Details in GeoFit).
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods (see Section Details in GeoFit).
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `bobyqa` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted (see Section Details in GeoFit). If `FALSE` (the default) the composite likelihood is not weighted.
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`nosym`	Logical; if TRUE simmetric weights are not considered. This allows a faster but less efficient CL estimation.
`spobj`	An object of class sp or spacetime
`spdata`	Character:The name of data in the sp or spacetime object
`vario`	An object of the class GeoVariogram obtained using the GeoVariogram function

Details

Value

Returns a list with information on the best selected neighbor.

Author(s)

Examples


library(GeoModels)


######### spatial case
set.seed(32)
N=500  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
mean <- 0.2
# Set the covariance model's parameters:
corrmodel <- "Matern"
sill <- 1;nugget <- 0
scale <- 0.2/3;smooth=0.5

model="Gaussian"
param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)
# Simulation 
data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param,model=model)$data
I=Inf
fixed<-list(nugget=nugget)
start<-list(mean=mean,scale=scale,smooth=smooth,sill=sill)
lower<-list(mean=-I,scale=0,sill=0,smooth=0)
upper<-list(mean=I,scale=I,sill=I,smooth=I)

vario = GeoVariogram(coordx=coords,data=data,maxdist=0.3,numbins=15)

neighb=c(1,2,3,4) ## trying  different neighbs
selK <- GeoNeighbSelect(vario=vario,data=data,coordx=coords,corrmodel=corrmodel, 
                        model=model,neighb=neighb,
                        likelihood="Conditional",type="Pairwise",parallel=FALSE,
                        optimizer="nlminb",lower=lower,upper=upper,
                        start=start,fixed=fixed)
print(selK$best_neighb) ## selected neighbor




library(GeoModels)


######### spatial case
set.seed(32)
N=500  # number of location sites
x <- runif(N, 0, 1)
y <- runif(N, 0, 1)
coords <- cbind(x,y)
mean <- 0.2
# Set the covariance model's parameters:
corrmodel <- "Matern"
sill <- 1;nugget <- 0
scale <- 0.2/3;smooth=0.5

model="Gaussian"
param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)
# Simulation 
data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param,model=model)$data
I=Inf
fixed<-list(nugget=nugget)
start<-list(mean=mean,scale=scale,smooth=smooth,sill=sill)
lower<-list(mean=-I,scale=0,sill=0,smooth=0)
upper<-list(mean=I,scale=I,sill=I,smooth=I)

vario = GeoVariogram(coordx=coords,data=data,maxdist=0.3,numbins=15)

neighb=c(1,2,3,4) ## trying  different neighbs
selK <- GeoNeighbSelect(vario=vario,data=data,coordx=coords,corrmodel=corrmodel, 
                        model=model,neighb=neighb,
                        likelihood="Conditional",type="Pairwise",parallel=FALSE,
                        optimizer="nlminb",lower=lower,upper=upper,
                        start=start,fixed=fixed)
print(selK$best_neighb) ## selected neighbor

Spatial or spatiotemporal near neighbour indices.

Description

The function returns the indices associated with a given spatial (temporal) neighbour and/or distance

Usage

GeoNeighIndex(coordx,coordy=NULL,coordx_dyn=NULL,
coordt=NULL,distance="Eucl",neighb=4,maxdist=NULL,
maxtime=1,radius=6371,bivariate=FALSE)
GeoNeighIndex(coordx,coordy=NULL,coordx_dyn=NULL,
coordt=NULL,distance="Eucl",neighb=4,maxdist=NULL,
maxtime=1,radius=6371,bivariate=FALSE)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `GeoFit`.
`neighb`	Numeric; an optional (vector of) positive integer indicating the order of neighborhood. See the Section Details for more information.
`maxdist`	A numeric value denoting the spatial distance Details.
`maxtime`	A numeric value denoting the temporal distance Details.
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`bivariate`	Logical; if `FALSE` (the default) the data are interpreted as univariate spatial or spatial-temporal realisations. Otherwise they are intrepreted as a a realization from a bivariate field.

Details

The function returns the spatial or spatiotemporal indices of the pairs tha are neighboords of a certain order and/or with a certain fixed distance

Value

Returns a list containing the following components:

`colidx`	First vector of indices
`rowidx`	Second vector of indices
`lags`	Vector of spatial distances
`lagt`	Vector of temporal distances

Author(s)

Examples



require(GeoModels)
NN = 400
coords = cbind(runif(NN),runif(NN))
scale=0.5/3
param = list(mean=0,sill=1,nugget=0,scale=0.5/3,smooth=0.5)
corrmodel = "Matern"; 

param = list(mean=0,sill=1,nugget=0,scale=scale,smooth=0.5)
set.seed(951)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "Gaussian",param = param)$data

sel=GeoNeighIndex(coordx=coords,maxdist=0.05)        

data1=data[sel$colidx]; data2=data[sel$rowidx]
## plotting pairs  that are neighboord of order 5
plot(data1,data2,xlab="",ylab="",main="h-scatterplot, dist=0.05")

require(GeoModels)
NN = 400
coords = cbind(runif(NN),runif(NN))
scale=0.5/3
param = list(mean=0,sill=1,nugget=0,scale=0.5/3,smooth=0.5)
corrmodel = "Matern"; 

param = list(mean=0,sill=1,nugget=0,scale=scale,smooth=0.5)
set.seed(951)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "Gaussian",param = param)$data

sel=GeoNeighIndex(coordx=coords,maxdist=0.05)        

data1=data[sel$colidx]; data2=data[sel$rowidx]
## plotting pairs  that are neighboord of order 5
plot(data1,data2,xlab="",ylab="",main="h-scatterplot, dist=0.05")

GeoNosymindices.

Description

Given a matrix of indices and associated distances the function return a matrix of indices and associated distances, deleting the symmetric indices.

Usage

GeoNosymindices(X,Y)
GeoNosymindices(X,Y)

Arguments

`X`	A matrix of indices
`Y`	Associated distances

Details

The function return the matrix of indices and associated distances, deleting the symmetric indices.

Value

Returns a list containing the following components:

`xy`	Matrix of indices
`d`	Associated distance

Author(s)

Spatio (temporal) outliers detection

Description

Given a set of spatio (temporal) locations and data, the procedure select the spatial or spatiotemporal ouliers using a specific algorithm.

Usage

GeoOutlier(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, 
             distance="Eucl", grid=FALSE,  neighb=10,alpha=0.001,
             method="Z-Median", radius=6371, bivariate=FALSE,X=NULL)
GeoOutlier(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, 
             distance="Eucl", grid=FALSE,  neighb=10,alpha=0.001,
             method="Z-Median", radius=6371, bivariate=FALSE,X=NULL)

Arguments

`data`	An optional $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid).
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `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).
`neighb`	Numeric; an optional positive integer indicating the order of neighborhoodused for Z-Median algorithm.
`alpha`	Numeric; a numeric value between 0 and 1 used for Z-Median algorithm.
`method`	String; The name of the algorithm for detecting spatial ouliers. Default is Z-median proposed in Chen et al. (2008)
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`bivariate`	If TRUE then data is considered as spatial bivariate data.
`X`	Numeric; an optional Matrix of spatio (temporal) covariates.

Value

Return a matrix or a list containing the dected spatial or spatio-temporal outliers

Author(s)

References

Chen D, Lu C, Kou Y, Chen F (2008) On detecting spatial outliers. Geoinformatica 12:455–475

Examples


library(GeoModels)
set.seed(1428)
NN = 1500
coords = cbind(runif(NN),runif(NN))
###
scale=0.5/3
corrmodel = "Matern"; 

param = list(mean=0,sill=1,nugget=0,scale=scale,smooth=0.5,skew=0)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "TwoPieceGaussian",param = param)$data

K=15         #parameter for outliers detection alghoritm
alpha=0.005  #parameter for outliers detection alghoritm
outlier=GeoOutlier(data=data, coordx = coords,neighb=K,alpha=alpha)
quilt.plot(coords,data)
for (i in 1:nrow(outlier))  plotrix::draw.circle(outlier[i,1], outlier[i,2],radius=0.02,lwd=2) 
nrow(outlier) # number of outliers

param = list(mean=0,sill=1,nugget=0.4,scale=scale,smooth=0.5)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "Gaussian",param = param)$data

K=15         #parameter for outliers detection alghoritm
alpha=0.005  #parameter for outliers detection alghoritm
outlier=GeoOutlier(data=data, coordx = coords,neighb=K,alpha=alpha)
quilt.plot(coords,data)
for (i in 1:nrow(outlier))  plotrix::draw.circle(outlier[i,1], outlier[i,2],radius=0.02,lwd=2)
nrow(outlier) # number of outliers
library(GeoModels)
set.seed(1428)
NN = 1500
coords = cbind(runif(NN),runif(NN))
###
scale=0.5/3
corrmodel = "Matern"; 

param = list(mean=0,sill=1,nugget=0,scale=scale,smooth=0.5,skew=0)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "TwoPieceGaussian",param = param)$data

K=15         #parameter for outliers detection alghoritm
alpha=0.005  #parameter for outliers detection alghoritm
outlier=GeoOutlier(data=data, coordx = coords,neighb=K,alpha=alpha)
quilt.plot(coords,data)
for (i in 1:nrow(outlier))  plotrix::draw.circle(outlier[i,1], outlier[i,2],radius=0.02,lwd=2) 
nrow(outlier) # number of outliers

param = list(mean=0,sill=1,nugget=0.4,scale=scale,smooth=0.5)
data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = "Gaussian",param = param)$data

K=15         #parameter for outliers detection alghoritm
alpha=0.005  #parameter for outliers detection alghoritm
outlier=GeoOutlier(data=data, coordx = coords,neighb=K,alpha=alpha)
quilt.plot(coords,data)
for (i in 1:nrow(outlier))  plotrix::draw.circle(outlier[i,1], outlier[i,2],radius=0.02,lwd=2)
nrow(outlier) # number of outliers

Probability integral or normal score tranformation

Description

The procedure for a given GeoFit object applies the probability integral tranformation or the normal score transformation to the data

Usage

GeoPit(object,type="Uniform")GeoPit(object,type="Uniform")

Arguments

object

A GeoFit object

type

The type of transformation. If "Uniform" then the probability integral tranformation is performed. If "Gaussian" then the normal score transformation is performed.

Value

Returns an (updated) object of class GeoFit

Author(s)

Examples


library(GeoModels)

model="Beta2"
copula="Clayton"

set.seed(221)
NN=800
x <- runif(NN);y <- runif(NN)
coords=cbind(x,y)


shape=1.5
scale=0.2;power2=4
smooth=0
nugget=0
nu=8

corrmodel="GenWend"

min=-2;max=1
mean=0


param=list(smooth=smooth,power2=power2, min=min,max=max,
             mean=mean, nu=nu,
             scale=scale,nugget=nugget,shape=shape)

optimizer="nlminb"

data <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, 
model=model,param=param,copula=copula)$data

I=50
fixed<-list(nugget=nugget,sill=1,scale=scale,smooth=smooth,power2=power2,min=min,max=max,nu=nu)
start<-list(shape=shape,mean=mean)
lower<-list(shape=0,mean=-I)
upper<-list(shape=10,mean=I)

#### maximum independence likelihood
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
model=model,likelihood="Marginal",type="Independence",
                      optimizer=optimizer,lower=lower,
                      upper=upper,copula=copula,
                    start=start,fixed=fixed)

## PIT transformation
aa=GeoPit(fit1,type="Uniform")
hist(aa$data,freq=FALSE)
GeoScatterplot(aa$data,coords,neighb=c(1,2))
## Normal score transformation
bb=GeoPit(fit1,type="Gaussian")
hist(bb$data,freq=FALSE)
GeoScatterplot(bb$data,coords,neighb=c(1,2))


library(GeoModels)

model="Beta2"
copula="Clayton"

set.seed(221)
NN=800
x <- runif(NN);y <- runif(NN)
coords=cbind(x,y)


shape=1.5
scale=0.2;power2=4
smooth=0
nugget=0
nu=8

corrmodel="GenWend"

min=-2;max=1
mean=0


param=list(smooth=smooth,power2=power2, min=min,max=max,
             mean=mean, nu=nu,
             scale=scale,nugget=nugget,shape=shape)

optimizer="nlminb"

data <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, 
model=model,param=param,copula=copula)$data

I=50
fixed<-list(nugget=nugget,sill=1,scale=scale,smooth=smooth,power2=power2,min=min,max=max,nu=nu)
start<-list(shape=shape,mean=mean)
lower<-list(shape=0,mean=-I)
upper<-list(shape=10,mean=I)

#### maximum independence likelihood
fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
model=model,likelihood="Marginal",type="Independence",
                      optimizer=optimizer,lower=lower,
                      upper=upper,copula=copula,
                    start=start,fixed=fixed)

## PIT transformation
aa=GeoPit(fit1,type="Uniform")
hist(aa$data,freq=FALSE)
GeoScatterplot(aa$data,coords,neighb=c(1,2))
## Normal score transformation
bb=GeoPit(fit1,type="Gaussian")
hist(bb$data,freq=FALSE)
GeoScatterplot(bb$data,coords,neighb=c(1,2))

Quantile-quantile plot

Description

Based on a GeoFit object, the procedure plots a quantile-quantile plot or compares the fitted density with the histogram of the data. It is useful as diagnostic tool.

Usage

GeoQQ(fit,type="Q",add=FALSE,ylim=c(0,1),breaks=10,...)GeoQQ(fit,type="Q",add=FALSE,ylim=c(0,1),breaks=10,...)

Arguments

`fit`	A GeoFit object possibly obtained from `GeoResiduals`.
`type`	The type of plot. If Q then a qq-plot (default) is performed. If D then a comparison between histrogram and the estimated marginal density is performed
`add`	Logical; if TRUE the the estimated density ia added over an existing one
`ylim`	Numeric; a vector of length 2 used for the ylab parameter of the histogram plot.
`breaks`	Numeric; an integer number specifyng the number of cells ofthe histogram plot if the option type=D is chosen.
`...`	Optional parameters passed to the plot function.

Value

Produces a plot. No values are returned.

Author(s)

Examples

library(GeoModels)


##################
### Example 1
##################
set.seed(21)
model="Tukeyh";tail=0.1
N=400 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4


param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
	           scale=scale,tail=tail,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

start=list(mean=mean,mean1=mean1, scale=scale,tail=tail)
fixed=list(nugget=nugget,sill=sill,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                    fixed=fixed,neighb=4)

res=GeoResiduals(fit)
GeoQQ(res,type="Q")
GeoQQ(res,type="D",lwd=2,ylim=c(0,0.5),breaks=20)


##################
### Example 2
##################
set.seed(21)
model="Weibull";shape=1.5
N=600 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)


# regression parameters
mean = 0

# correlation parameters:
corrmodel = "Matern"
smooth=0.5
nugget = 0
scale = 0.2/3


param=list(mean=mean, sill=1, nugget=nugget, 
             scale=scale,smooth=smooth, shape=shape)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,param=param)$data

start=list(mean=mean, scale=scale,shape=shape)
I=Inf
lower=list(mean=-I, scale=0,shape=0)
upper=list(mean= I, scale=I,shape=I)
I=Inf
fixed=list(nugget=nugget,sill=1,smooth=smooth)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
                    likelihood="Conditional",type='Pairwise',start=start,
                    optimizer="nlminb",lower=lower,upper=upper,
                    fixed=fixed,neighb=3)
GeoQQ(fit,type="Q")
GeoQQ(fit,type="D",lwd=2,ylim=c(0,1),breaks=20)
library(GeoModels)


##################
### Example 1
##################
set.seed(21)
model="Tukeyh";tail=0.1
N=400 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4


param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
	           scale=scale,tail=tail,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

start=list(mean=mean,mean1=mean1, scale=scale,tail=tail)
fixed=list(nugget=nugget,sill=sill,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                    fixed=fixed,neighb=4)

res=GeoResiduals(fit)
GeoQQ(res,type="Q")
GeoQQ(res,type="D",lwd=2,ylim=c(0,0.5),breaks=20)


##################
### Example 2
##################
set.seed(21)
model="Weibull";shape=1.5
N=600 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)


# regression parameters
mean = 0

# correlation parameters:
corrmodel = "Matern"
smooth=0.5
nugget = 0
scale = 0.2/3


param=list(mean=mean, sill=1, nugget=nugget, 
             scale=scale,smooth=smooth, shape=shape)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,param=param)$data

start=list(mean=mean, scale=scale,shape=shape)
I=Inf
lower=list(mean=-I, scale=0,shape=0)
upper=list(mean= I, scale=I,shape=I)
I=Inf
fixed=list(nugget=nugget,sill=1,smooth=smooth)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,
                    likelihood="Conditional",type='Pairwise',start=start,
                    optimizer="nlminb",lower=lower,upper=upper,
                    fixed=fixed,neighb=3)
GeoQQ(fit,type="Q")
GeoQQ(fit,type="D",lwd=2,ylim=c(0,1),breaks=20)

Computes fitted covariance and/or variogram

Description

The procedure return a GeoFit object associated to the estimated residuals. For a random field Y defined on the real line (Gaussian, Skew Gaussian, Tukeyh etcc) they are computed as (Y-m)/sqrt(v) where m and v are the estimated mean and variance respectively. For a random field Y defined on the positive real line (Gamma, Weibull, Log-Gaussian) they are computed as Y/m where m is estimated mean.

Usage

GeoResiduals(fit)GeoResiduals(fit)

Arguments

fit

A fitted object obtained from the GeoFit.

Value

Returns an (updated) object of class GeoFit

Author(s)

Examples

library(GeoModels)



##############
###Example 1
##############
set.seed(211)
model="Gaussian";
N=700 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4

param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
             scale=scale,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

start=list(mean=mean,mean1=mean1, scale=scale,sill=sill)
fixed=list(nugget=nugget,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                    fixed=fixed,neighb=3)

res=GeoResiduals(fit)
mean(res$data) # should be approx 0
var(res$data) # should be approx 1
# checking goodness of fit marginal model
GeoQQ(res);GeoQQ(res,type="D",col="red",ylim=c(0,0.5),breaks=20);
# Empirical estimation of the variogram for the residuals:
vario = GeoVariogram(res$data,coordx=coords,maxdist=0.5)
# Comparison between empirical amd estimated semivariogram for the residuals
GeoCovariogram(res, show.vario=TRUE, vario=vario,pch=20)





##############
###Example 2
##############
model="Weibull";shape=4
N=700 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)


# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4

param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
	           scale=scale,shape=shape,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

I=Inf
start=list(mean=mean,mean1=mean1, scale=scale,shape=shape)
lower=list(mean=-I,mean1=-I, scale=0,shape=0)
upper=list(mean= I,mean1= I, scale=I,shape=I)
fixed=list(nugget=nugget,sill=sill,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                   optimizer="nlminb", lower=lower,upper=upper,
                    fixed=fixed,neighb=3)


res=GeoResiduals(fit)
mean(res$data) # should be approx 1
# checking goodness of fit marginal model
GeoQQ(res);GeoQQ(res,type="D",col="red",ylim=c(0,1.7),breaks=20);
# Empirical estimation of the variogram for the residuals:
vario = GeoVariogram(res$data,coordx=coords,maxdist=0.5)
# Comparison between empirical amd estimated semivariogram for the residuals
GeoCovariogram(res, show.vario=TRUE, vario=vario,pch=20)

library(GeoModels)



##############
###Example 1
##############
set.seed(211)
model="Gaussian";
N=700 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4

param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
             scale=scale,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

start=list(mean=mean,mean1=mean1, scale=scale,sill=sill)
fixed=list(nugget=nugget,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                    fixed=fixed,neighb=3)

res=GeoResiduals(fit)
mean(res$data) # should be approx 0
var(res$data) # should be approx 1
# checking goodness of fit marginal model
GeoQQ(res);GeoQQ(res,type="D",col="red",ylim=c(0,0.5),breaks=20);
# Empirical estimation of the variogram for the residuals:
vario = GeoVariogram(res$data,coordx=coords,maxdist=0.5)
# Comparison between empirical amd estimated semivariogram for the residuals
GeoCovariogram(res, show.vario=TRUE, vario=vario,pch=20)





##############
###Example 2
##############
model="Weibull";shape=4
N=700 # number of location sites
# Set the coordinates of the points:
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)


# regression parameters
mean = 5
mean1=0.8

X=cbind(rep(1,N),runif(N))
# correlation parameters:
corrmodel = "Wend0"
sill = 1
nugget = 0
scale = 0.3
power2=4

param=list(mean=mean,mean1=mean1, sill=sill, nugget=nugget, 
	           scale=scale,shape=shape,power2=power2)
# Simulation of the Gaussian RF:
data = GeoSim(coordx=coords, corrmodel=corrmodel, X=X,model=model,param=param)$data

I=Inf
start=list(mean=mean,mean1=mean1, scale=scale,shape=shape)
lower=list(mean=-I,mean1=-I, scale=0,shape=0)
upper=list(mean= I,mean1= I, scale=I,shape=I)
fixed=list(nugget=nugget,sill=sill,power2=power2)
# Maximum composite-likelihood fitting 
fit = GeoFit(data,coordx=coords, corrmodel=corrmodel,model=model,X=X,
                    likelihood="Conditional",type='Pairwise',start=start,
                   optimizer="nlminb", lower=lower,upper=upper,
                    fixed=fixed,neighb=3)


res=GeoResiduals(fit)
mean(res$data) # should be approx 1
# checking goodness of fit marginal model
GeoQQ(res);GeoQQ(res,type="D",col="red",ylim=c(0,1.7),breaks=20);
# Empirical estimation of the variogram for the residuals:
vario = GeoVariogram(res$data,coordx=coords,maxdist=0.5)
# Comparison between empirical amd estimated semivariogram for the residuals
GeoCovariogram(res, show.vario=TRUE, vario=vario,pch=20)

h-scatterplot for space and space-time data.

Description

The function produces h-scatterplots for the spatial, spatio-temporal and bivariate setting.

Usage

GeoScatterplot(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
           distance='Eucl', grid=FALSE, maxdist=NULL,neighb=NULL,
           times=NULL, numbins=4, radius=6371, bivariate=FALSE,...)
GeoScatterplot(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL,
           distance='Eucl', grid=FALSE, maxdist=NULL,neighb=NULL,
           times=NULL, numbins=4, radius=6371, bivariate=FALSE,...)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $n \times d$ )-matrix ( $n$ iid spatial realisations) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or an ( $d \times d \times n$ )-array ( $n$ iid spatial realisations on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $t \times d \times n$ )-array ( $n$ iid spatial-temporal realisations) or or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid) or an ( $d \times d \times t \times n$ )-array ( $n$ iid spatial-temporal realisations on regular grid). See `GeoFit` for details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `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.
`maxdist`	A numeric value denoting the spatial maximum distance, see the Section Details.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood. See the Section Details for more information.
`times`	A numeric vector denoting the temporal instants involved Details.
`numbins`	A numeric value denoting the numbers of bins, see the Section Details.
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`bivariate`	Logical; if `FALSE` (the default) the data are interpreted as univariate spatial or spatial-temporal realisations. Otherwise they are intrepreted as a a realization from a bivariate field.
`...`	Optional parameters passed to the plot function.

Details

h-scatterplot is the plot of the pair values that are neighborhood of a certain order or with distances belonging to a certain interval. In the first case a (vector of) neighborhood must be specified. In the second case a maximum distance (maxdist) and a number of lag-bins (numbins) must be specified. The method based on neighborhoods is recommended in particular for large datasets.

Value

Produces a plot. No values are returned.

Author(s)

Examples


library(GeoModels)
set.seed(514)

NN = 600
coords = cbind(runif(NN),runif(NN))

param = list(mean=0,sill=1,nugget=0,power2=4,scale=0.4,smooth=0)

corrmodel = "GenWend"; model = "Gaussian"

data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = model,param = param)$data

# h-scatterplots for given a vector of neighborhoods
GeoScatterplot(data,coords,neighb=c(2,4))

library(GeoModels)
set.seed(514)

NN = 600
coords = cbind(runif(NN),runif(NN))

param = list(mean=0,sill=1,nugget=0,power2=4,scale=0.4,smooth=0)

corrmodel = "GenWend"; model = "Gaussian"

data = GeoSim(coordx = coords,corrmodel = corrmodel,
                  model = model,param = param)$data

# h-scatterplots for given a vector of neighborhoods
GeoScatterplot(data,coords,neighb=c(2,4))

Computation of predictive scores

Description

The function computes some predictive scores for a spatial, spatiotemporal and bivariate Gaussian RFs

Usage

GeoScores(data_to_pred, probject=NULL,pred=NULL,mse=NULL,
   score=c("brie","crps","lscore","pit","pe"))
GeoScores(data_to_pred, probject=NULL,pred=NULL,mse=NULL,
   score=c("brie","crps","lscore","pit","pe"))

Arguments

`data_to_pred`	A numeric vector of data to predict about a response
`probject`	A Geokrig object obtained using the function Geokrig
`pred`	A numeric vector with predictions for the response.
`mse`	a numeric vector with prediction variances.
`score`	A character defining what statistic of the prediction errors should be computed. Possible values are lscore, crps, brie and pe. In the latter case scores based on prediction errors such as rmse, mae, mad are computed. Finally, the character pit allows to compute the probability integral transform for each value

Details

GeoScores computes the items required to evaluate the diagnostic criteria proposed by Gneiting et al. (2007) for assessing the calibration and the sharpness of probabilistic predictions of (cross-) validation data. To this aim, GeoScores uses the assumption that the prediction errors are Gaussian with zero mean and standard deviations equal to the Kriging standard errors. This assumption is an approximation if the errors are not Gaussian.

Value

Returns a list containing the following informations:

`LSCORE`	Logarithmic predictive score
`CRPS`	Continuous ranked probability predictive score
`RMSE`	Root mean squared error
`MAE`	Mean absolute error
`MAD`	Median absolute error
`PIT`	A vector of probability integral transformation

Author(s)

References

Gneiting T. and Raftery A. Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102

Examples


library(GeoModels)

################################################################
######### Examples of predictive score computation  ############
################################################################

library(GeoModels)
model="Gaussian"
set.seed(79)
N=1000
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data


sel=sample(1:N,N*0.8)
coords_est=coords[sel,]; coords_to_pred=coords[-sel,]
data_est=data[sel]; data_to_pred=data[-sel]

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data_est, coordx=coords_est, corrmodel=corrmodel,model=model,
                    likelihood='Marginal', type='Pairwise',neighb=3,
                    optimizer="nlminb", lower=lower,upper=upper,
                    start=start,fixed=fixed)
# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))

pr=GeoKrig(loc=coords_to_pred,coordx=coords_est,corrmodel=corrmodel,
       model=model,param= param, data=data_est,mse=TRUE)

Pr_scores =GeoScores(data_to_pred,pred=pr$pred,mse=pr$mse)
Pr_scores$rmse;Pr_scores$brie
hist(Pr_scores$pit,freq=FALSE)
library(GeoModels)

################################################################
######### Examples of predictive score computation  ############
################################################################

library(GeoModels)
model="Gaussian"
set.seed(79)
N=1000
x = runif(N, 0, 1)
y = runif(N, 0, 1)
coords=cbind(x,y)

# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=5; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel,
              param=param)$data


sel=sample(1:N,N*0.8)
coords_est=coords[sel,]; coords_to_pred=coords[-sel,]
data_est=data[sel]; data_to_pred=data[-sel]

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=0,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data_est, coordx=coords_est, corrmodel=corrmodel,model=model,
                    likelihood='Marginal', type='Pairwise',neighb=3,
                    optimizer="nlminb", lower=lower,upper=upper,
                    start=start,fixed=fixed)
# locations to predict
xx=seq(0,1,0.03)
loc_to_pred=as.matrix(expand.grid(xx,xx))

pr=GeoKrig(loc=coords_to_pred,coordx=coords_est,corrmodel=corrmodel,
       model=model,param= param, data=data_est,mse=TRUE)

Pr_scores =GeoScores(data_to_pred,pred=pr$pred,mse=pr$mse)
Pr_scores$rmse;Pr_scores$brie
hist(Pr_scores$pit,freq=FALSE)

Simulation of Gaussian and non Gaussian Random Fields.

Description

Simulation of Gaussian and some non Gaussian spatial, spatio-temporal and spatial bivariate random fields. The function return a realization of a random Field for a given covariance model and covariance parameters.Simulation is based on Cholesky decomposition.

Usage

GeoSim(coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, corrmodel, distance="Eucl",
      GPU=NULL, grid=FALSE, local=c(1,1),method="cholesky", model='Gaussian', n=1, param,
      anisopars=NULL,radius=6371, sparse=FALSE,X=NULL,spobj=NULL,nrep=1,progress=TRUE)
GeoSim(coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, corrmodel, distance="Eucl",
      GPU=NULL, grid=FALSE, local=c(1,1),method="cholesky", model='Gaussian', n=1, param,
      anisopars=NULL,radius=6371, sparse=FALSE,X=NULL,spobj=NULL,nrep=1,progress=TRUE)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial 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 the Section Details of `GeoFit`.
`GPU`	Numeric; if `NULL` (the default) no GPU computation is performed.
`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).
`local`	Numeric; number of local work-items of the GPU
`method`	String; the type of matrix decomposition used in the simulation. Default is cholesky. The other possible choices is `svd`.
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`n`	Numeric; the number of trials for binomial RFs. The number of successes in the negative Binomial RFs. Default is $1$ .
`param`	A list of parameter values required in the simulation procedure of RFs, see Examples.
`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 the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`sparse`	Logical; if `TRUE` then cholesky decomposition is performed using sparse matrices algorithms (spam packake). It should be used with compactly supported covariance models.`FALSE` is the default.
`X`	Numeric; Matrix of space-time covariates.
`spobj`	An object of class sp or spacetime
`nrep`	Numeric; Numbers of indipendent replicates.
`progress`	Logic; If TRUE then a progress bar is shown.

Value

Returns an object of class GeoSim. An object of class GeoSim is a list containing at most the following components:

`bivariate`	Logical:`TRUE` if the Gaussian RF is bivariate, 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 dynamical (in time) spatial coordinates;
`corrmodel`	The correlation model; see `GeoCovmatrix`.
`data`	The vector or matrix or array of data, see `GeoFit`;
`distance`	The type of spatial distance;
`method`	The method of simulation
`model`	The type of RF, see `GeoFit`.
`n`	The number of trial for Binomial RFs;the number of successes in a negative Binomial RFs;
`numcoord`	The number of spatial coordinates;
`numtime`	The number the temporal realisations of the RF;
`param`	The vector of parameters' estimates;
`radius`	The radius of the sphere if coordinates are passed in lon/lat format;
`spacetime`	`TRUE` if spatio-temporal and `FALSE` if spatial RF;
`nrep`	The number of indipendent replicates;

Author(s)

Examples

library(GeoModels)
library(mapproj)


################################################################
###
### Example 1. Simulation of a spatial Gaussian RF 
### with Matern and Generalized Wendland correlations
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500);y <- runif(500)
coords=cbind(x,y)
set.seed(261)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(coordx=coords, corrmodel="Matern", param=list(smooth=0.5,
             mean=0,sill=1,scale=0.4/3,nugget=0))$data

set.seed(261)
data2 <- GeoSim(coordx=coords,  corrmodel="GenWend", param=list(smooth=0,
              power2=4,mean=0,sill=1,scale=0.4,nugget=0))$data
opar=par(no.readonly = TRUE)
par(mfrow=c(1,2))
quilt.plot(coords,data1,main="Matern",xlab="",ylab="")
quilt.plot(coords,data2,main="Wendland",xlab="",ylab="")   
par(opar)
             

################################################################
###
### Example 2. Simulation of a spatial geometric RF 
### with  underlying Wend0 correlation
###
################################################################

# Define the spatial-coordinates of the points:
x <- runif(800);y <- runif(800)
coords <- cbind(x,y)
set.seed(251)
# Simulation of a spatial Binomial RF:
sim <- GeoSim(coordx=coords, corrmodel="Wend0",
             model="BinomialNeg",n=1,sparse=TRUE,
             param=list(nugget=0,mean=0,scale=.2,power2=4))

quilt.plot(coords,sim$data,nlevel=max(sim$data),col=terrain.colors(max(sim$data+1))) 


################################################################
###
### Example 3. Simulation of a spatial Weibull  RF
### with  underlying Matern correlation on a regular grid
###
###############################################################
# Define the spatial-coordinates of the points:
x <- seq(0,1,0.032)
y <- seq(0,1,0.032)
set.seed(261)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(x,y,grid=TRUE, corrmodel="Matern",model="Weibull", 
         param=list(shape=1.2,mean=0,scale=0.3/3,nugget=0,smooth=0.5))$data
image.plot(x,y,data1,main="Weibull RF",xlab="",ylab="")



################################################################
###
### Example 4. Simulation of a spatial t  RF
### with  with  underlying Generalized Wendland correlation 
###
###############################################################
# Define the spatial-coordinates of the points:
x <- seq(0,1,0.03)
y <- seq(0,1,0.03)
set.seed(268)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(x,y,grid=TRUE, corrmodel="GenWend",model="StudentT", sparse=TRUE,
         param=list(df=1/4,mean=0,sill=1,scale=0.3,nugget=0,smooth=1,power2=5))$data
image.plot(x,y,data1,col=terrain.colors(100),main="Student-t RF",xlab="",ylab="")




################################################################
###
### Example 5. Simulation of a sinhasinh RF
###   with  underlying Wend0 correlation.
###
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords <- cbind(x,y)
set.seed(261)
corrmodel="Wend0"
# Simulation of a spatial Gaussian RF:
param=list(power2=4,skew=0,tail=1,
             mean=0,sill=1,scale=0.2,nugget=0)  ## gaussian case
data0 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
plot(density(data0),xlim=c(-7,7))

param=list(power2=4,skew=0,tail=0.7,
             mean=0,sill=1,scale=0.2,nugget=0) ## heavy tails
data1 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
lines(density(data1),lty=2)

param=list(power2=4,skew=0.5,tail=1,
             mean=0,sill=1,scale=0.2,nugget=0)  ## asymmetry
data2 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
lines(density(data2),lty=3)


################################################################
###
### Example 6. Simulation of a bivariate Gaussian RF
### with  bivariate Matern correlation model
###
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a separable Bivariate Matern
set.seed(12)
param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
           scale_1=0.12,scale_2=0.1,scale_12=0.15,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)
data <- GeoSim(coordx=coords,corrmodel="Bi_matern",
              param=param)$data
opar=par(no.readonly = TRUE)
par(mfrow=c(1,2))
quilt.plot(coords,data[1,],col=terrain.colors(100),main="1",xlab="",ylab="")
quilt.plot(coords,data[2,],col=terrain.colors(100),main="2",xlab="",ylab="")
par(opar)


################################################################
###
### Example 7. Simulation of a  spatio temporal Gaussian RF.
### observed on  dynamical location sites with double exponential correlation 
###
###############################################################

# Define the dynamical spatial-coordinates of the points:

coordt=1:5
coordx_dyn=list()
maxN=30
set.seed(8)
for(k in 1:length(coordt))
{
NN=sample(1:maxN,size=1)
x <- runif(NN, 0, 1)
y <- runif(NN, 0, 1)
coordx_dyn[[k]]=cbind(x,y)
}
coordx_dyn

param<-list(nugget=0,mean=0,scale_s=0.2/3,scale_t=2/3,sill=1)
data <- GeoSim(coordx_dyn=coordx_dyn, coordt=coordt, corrmodel="Exp_Exp",
                     param=param)$data
## spatial realization at first temporal instants
data[[1]]
## spatial realization at third temporal instants
data[[3]]




################################################################
###
### Example 8. Simulation of a Gaussian RF 
###  with a Wend0 correlation in the north emisphere of the planet earth
### using geodesic distance
###############################################################
distance="Geod";radius=6371

NN=3000 ## total point on the sphere on lon/lat format
set.seed(80)
coords=cbind(runif(NN,-180,180),runif(NN,0,90))
## Set the wendland parameters
corrmodel <- "Wend0"
param<-list(mean=0,sill=1,nugget=0,scale=1000,power2=3)
# Simulation of a spatial Gaussian RF on the sphere
#set.seed(2)
data <- GeoSim(coordx=coords,corrmodel=corrmodel,sparse=TRUE,
               distance=distance,radius=radius,param=param)$data
#require(globe)
#globe::globeearth(eye=place("newyorkcity"))
#globe::globepoints(loc=coords,pch=20,col =  cm.colors(length(data),alpha=0.1)[rank(data)])




library(GeoModels)
library(mapproj)


################################################################
###
### Example 1. Simulation of a spatial Gaussian RF 
### with Matern and Generalized Wendland correlations
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500);y <- runif(500)
coords=cbind(x,y)
set.seed(261)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(coordx=coords, corrmodel="Matern", param=list(smooth=0.5,
             mean=0,sill=1,scale=0.4/3,nugget=0))$data

set.seed(261)
data2 <- GeoSim(coordx=coords,  corrmodel="GenWend", param=list(smooth=0,
              power2=4,mean=0,sill=1,scale=0.4,nugget=0))$data
opar=par(no.readonly = TRUE)
par(mfrow=c(1,2))
quilt.plot(coords,data1,main="Matern",xlab="",ylab="")
quilt.plot(coords,data2,main="Wendland",xlab="",ylab="")   
par(opar)
             

################################################################
###
### Example 2. Simulation of a spatial geometric RF 
### with  underlying Wend0 correlation
###
################################################################

# Define the spatial-coordinates of the points:
x <- runif(800);y <- runif(800)
coords <- cbind(x,y)
set.seed(251)
# Simulation of a spatial Binomial RF:
sim <- GeoSim(coordx=coords, corrmodel="Wend0",
             model="BinomialNeg",n=1,sparse=TRUE,
             param=list(nugget=0,mean=0,scale=.2,power2=4))

quilt.plot(coords,sim$data,nlevel=max(sim$data),col=terrain.colors(max(sim$data+1))) 


################################################################
###
### Example 3. Simulation of a spatial Weibull  RF
### with  underlying Matern correlation on a regular grid
###
###############################################################
# Define the spatial-coordinates of the points:
x <- seq(0,1,0.032)
y <- seq(0,1,0.032)
set.seed(261)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(x,y,grid=TRUE, corrmodel="Matern",model="Weibull", 
         param=list(shape=1.2,mean=0,scale=0.3/3,nugget=0,smooth=0.5))$data
image.plot(x,y,data1,main="Weibull RF",xlab="",ylab="")



################################################################
###
### Example 4. Simulation of a spatial t  RF
### with  with  underlying Generalized Wendland correlation 
###
###############################################################
# Define the spatial-coordinates of the points:
x <- seq(0,1,0.03)
y <- seq(0,1,0.03)
set.seed(268)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSim(x,y,grid=TRUE, corrmodel="GenWend",model="StudentT", sparse=TRUE,
         param=list(df=1/4,mean=0,sill=1,scale=0.3,nugget=0,smooth=1,power2=5))$data
image.plot(x,y,data1,col=terrain.colors(100),main="Student-t RF",xlab="",ylab="")




################################################################
###
### Example 5. Simulation of a sinhasinh RF
###   with  underlying Wend0 correlation.
###
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords <- cbind(x,y)
set.seed(261)
corrmodel="Wend0"
# Simulation of a spatial Gaussian RF:
param=list(power2=4,skew=0,tail=1,
             mean=0,sill=1,scale=0.2,nugget=0)  ## gaussian case
data0 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
plot(density(data0),xlim=c(-7,7))

param=list(power2=4,skew=0,tail=0.7,
             mean=0,sill=1,scale=0.2,nugget=0) ## heavy tails
data1 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
lines(density(data1),lty=2)

param=list(power2=4,skew=0.5,tail=1,
             mean=0,sill=1,scale=0.2,nugget=0)  ## asymmetry
data2 <- GeoSim(coordx=coords, corrmodel=corrmodel,
               model="SinhAsinh", param=param,sparse=TRUE)$data
lines(density(data2),lty=3)


################################################################
###
### Example 6. Simulation of a bivariate Gaussian RF
### with  bivariate Matern correlation model
###
###############################################################

# Define the spatial-coordinates of the points:
x <- runif(500, 0, 2)
y <- runif(500, 0, 2)
coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a separable Bivariate Matern
set.seed(12)
param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
           scale_1=0.12,scale_2=0.1,scale_12=0.15,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)
data <- GeoSim(coordx=coords,corrmodel="Bi_matern",
              param=param)$data
opar=par(no.readonly = TRUE)
par(mfrow=c(1,2))
quilt.plot(coords,data[1,],col=terrain.colors(100),main="1",xlab="",ylab="")
quilt.plot(coords,data[2,],col=terrain.colors(100),main="2",xlab="",ylab="")
par(opar)


################################################################
###
### Example 7. Simulation of a  spatio temporal Gaussian RF.
### observed on  dynamical location sites with double exponential correlation 
###
###############################################################

# Define the dynamical spatial-coordinates of the points:

coordt=1:5
coordx_dyn=list()
maxN=30
set.seed(8)
for(k in 1:length(coordt))
{
NN=sample(1:maxN,size=1)
x <- runif(NN, 0, 1)
y <- runif(NN, 0, 1)
coordx_dyn[[k]]=cbind(x,y)
}
coordx_dyn

param<-list(nugget=0,mean=0,scale_s=0.2/3,scale_t=2/3,sill=1)
data <- GeoSim(coordx_dyn=coordx_dyn, coordt=coordt, corrmodel="Exp_Exp",
                     param=param)$data
## spatial realization at first temporal instants
data[[1]]
## spatial realization at third temporal instants
data[[3]]




################################################################
###
### Example 8. Simulation of a Gaussian RF 
###  with a Wend0 correlation in the north emisphere of the planet earth
### using geodesic distance
###############################################################
distance="Geod";radius=6371

NN=3000 ## total point on the sphere on lon/lat format
set.seed(80)
coords=cbind(runif(NN,-180,180),runif(NN,0,90))
## Set the wendland parameters
corrmodel <- "Wend0"
param<-list(mean=0,sill=1,nugget=0,scale=1000,power2=3)
# Simulation of a spatial Gaussian RF on the sphere
#set.seed(2)
data <- GeoSim(coordx=coords,corrmodel=corrmodel,sparse=TRUE,
               distance=distance,radius=radius,param=param)$data
#require(globe)
#globe::globeearth(eye=place("newyorkcity"))
#globe::globepoints(loc=coords,pch=20,col =  cm.colors(length(data),alpha=0.1)[rank(data)])

Fast simulation of Gaussian and non Gaussian Random Fields.

Description

Simulation of Gaussian and some non Gaussian spatial, spatio-temporal and spatial bivariate random fields using two approximate methods of simulation: circulant embeeding and turning band. (see Examples).

Usage

GeoSimapprox(coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL,corrmodel, distance="Eucl",GPU=NULL, 
grid=FALSE,local=c(1,1),max.ext=1,
method="TB", L=1000,model='Gaussian',parallel=FALSE,ncores=NULL,
n=1,param,anisopars=NULL, radius=6371,X=NULL,spobj=NULL,
nrep=1,progress=TRUE)
GeoSimapprox(coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL,corrmodel, distance="Eucl",GPU=NULL, 
grid=FALSE,local=c(1,1),max.ext=1,
method="TB", L=1000,model='Gaussian',parallel=FALSE,ncores=NULL,
n=1,param,anisopars=NULL, radius=6371,X=NULL,spobj=NULL,
nrep=1,progress=TRUE)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	String; the name of a correlation model, for the description see the Section Details.
`parallel`	Logical; if `TRUE` then the TB method is parallelized
`ncores`	Numeric; number of cores involved in parallelization.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `GeoFit`.
`GPU`	Numeric; if `NULL` (the default) no GPU computation is performed.
`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).
`local`	Numeric; number of local work-items of the GPU
`max.ext`	Numeric; The maximum extension of the simulation window (for the spatial CE method).
`method`	String; the type of approximation method. Default is `TB` that is the turning band method. The other possible choice is and `CE` (circular embeeding).
`L`	Numeric; the number of lines in the turning band method.
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`n`	Numeric; the number of trials for binomial RFs. The number of successes in the negative Binomial RFs. Default is $1$ .
`param`	A list of parameter values required in the simulation procedure of RFs, see Examples.
`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 the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`X`	Numeric; Matrix of space-time covariates.
`spobj`	An object of class sp or spacetime
`nrep`	Numeric; Numbers of indipendent replicates.
`progress`	Logic; If TRUE then a progress bar is shown.

Value

Returns an object of class GeoSim. An object of class GeoSim is a list containing at most the following components:

`bivariate`	Logical:`TRUE` if the Gaussian RF is bivariate, 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 dynamical (in time) spatial coordinates;
`corrmodel`	The correlation model; see `GeoCovmatrix`.
`data`	The vector or matrix or array of data, see `GeoFit`;
`distance`	The type of spatial distance;
`method`	The method of simulation
`model`	The type of RF, see `GeoFit`.
`n`	The number of trial for Binomial RFs;the number of successes in a negative Binomial RFs;
`numcoord`	The number of spatial coordinates;
`numtime`	The number the temporal realisations of the RF;
`param`	The vector of parameters' estimates;
`radius`	The radius of the sphere if coordinates are passed in lon/lat format;
`spacetime`	`TRUE` if spatio-temporal and `FALSE` if spatial RF;
`nrep`	The number of indipendent replicates;

Author(s)

References

T. Gneiting, H. Sevcikova, D. B. Percival, M. Schlather and Y. Jiang (2006) Fast and Exact Simulation of Large Gaussian Lattice Systems in R2: Exploring the Limits Journal of Computational and Graphical Statistics 15 (3)

D. Arroyo, X. Emery (2020) An R Implementation of a Continuous Spectral Algorithm for Simulating Vector Gaussian Random Fields in Euclidean Spaces ACM Transactions on Mathematical Software 47(1)

Examples

library(GeoModels)


################################################################
###
### Example 1. Simulation of a large spatial Gaussian RF 
###            with  Matern  covariance model
###            using circulant embeeding method
###            It works only for regular grid
###############################################################
set.seed(68)
x = seq(0,1,0.005)
y = seq(0,1,0.005)
param=list(smooth=1.5,mean=0,sill=1,scale=0.2/3,nugget=0)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSimapprox(coordx=x,coordy=y, grid=TRUE,corrmodel="Matern", model="Gaussian",
                      method="CE",param=param)$data
fields::image.plot( matrix(data1, length(x), length(y), byrow = TRUE) )

################################################################
###
### Example 2. Simulation of a large spatial Tukey-h RF 
###            with  GenWend-Matern  covariance model
###            using Turning band method
###            It works for (ir)regular grid
###############################################################
set.seed(68)
x = runif(50000)
y = runif(50000)
coords=cbind(x,y)
param=list(smooth=0.5,mean=0,sill=1,scale=0.04,nugget=0,tail=0.15,power2=1/4)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSimapprox(coords, corrmodel="GenWend_Matern", model="Tukeyh",
                      method="TB",param=param)$data
quilt.plot(coords,data1)



################################################################
###
### Example 3. Simulation of a large spacetime Gaussian RF 
###            with separable matern  covariance model
###            using  Circular embeeding method
###            It works  for (large) regular time grid
###############################################################
set.seed(68)
coordt <- (0:100)
coords <- cbind( runif(100, 0 ,1), runif(100, 0 ,1))
param <- list(mean  = 0, sill = 1, nugget = 0.25,
              scale_s = 0.05, scale_t = 2, 
              smooth_s = 0.5, smooth_t = 0.5)
# Simulation of a spatial Gaussian RF with Matern correlation function
param<-list(nugget=0,mean=0,scale_s=0.2/3,scale_t=2/3,sill=1,smooth_s=0.5,smooth_t=0.5)

data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="Matern_Matern",
                     model="Gaussian",method="CE",param=param)$data
dim(data)

################################################################
###
### Example 4. Simulation of a large spacetime Gaussian RF 
###            with separable GenWend covariance model
###            using  Circular embeeding method in time
###############################################################
set.seed(68)
# Simulation of a spatial Gaussian RF with Matern correlation function
param<-list(nugget=0,mean=0,scale_s=0.2,scale_t=3,sill=1,
             smooth_s=0,smooth_t=0, power2_s=4,power2_t=4)

data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="GenWend_GenWend",
                     model="Gaussian",method="CE",param=param)$data
dim(data)


################################################################
###
### Example 6. Simulation of a large bivariate Gaussian RF
### with  bivariate Matern correlation model
###
###############################################################

# Define the spatial-coordinates of the points:
#x <- runif(20000, 0, 2)
#y <- runif(20000, 0, 2)
#coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a  Bivariate Matern
#set.seed(12)
#param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
#           scale_1=0.12,scale_2=0.1,scale_12=0.15,
#           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)
#data <- GeoSimapprox(coordx=coords,corrmodel="Bi_matern",
#              param=param,method="TB",L=1000)$data
#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#quilt.plot(coords,data[1,],col=terrain.colors(100),main="1",xlab="",ylab="")
#quilt.plot(coords,data[2,],col=terrain.colors(100),main="2",xlab="",ylab="")







library(GeoModels)


################################################################
###
### Example 1. Simulation of a large spatial Gaussian RF 
###            with  Matern  covariance model
###            using circulant embeeding method
###            It works only for regular grid
###############################################################
set.seed(68)
x = seq(0,1,0.005)
y = seq(0,1,0.005)
param=list(smooth=1.5,mean=0,sill=1,scale=0.2/3,nugget=0)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSimapprox(coordx=x,coordy=y, grid=TRUE,corrmodel="Matern", model="Gaussian",
                      method="CE",param=param)$data
fields::image.plot( matrix(data1, length(x), length(y), byrow = TRUE) )

################################################################
###
### Example 2. Simulation of a large spatial Tukey-h RF 
###            with  GenWend-Matern  covariance model
###            using Turning band method
###            It works for (ir)regular grid
###############################################################
set.seed(68)
x = runif(50000)
y = runif(50000)
coords=cbind(x,y)
param=list(smooth=0.5,mean=0,sill=1,scale=0.04,nugget=0,tail=0.15,power2=1/4)
# Simulation of a spatial Gaussian RF with Matern correlation function
data1 <- GeoSimapprox(coords, corrmodel="GenWend_Matern", model="Tukeyh",
                      method="TB",param=param)$data
quilt.plot(coords,data1)



################################################################
###
### Example 3. Simulation of a large spacetime Gaussian RF 
###            with separable matern  covariance model
###            using  Circular embeeding method
###            It works  for (large) regular time grid
###############################################################
set.seed(68)
coordt <- (0:100)
coords <- cbind( runif(100, 0 ,1), runif(100, 0 ,1))
param <- list(mean  = 0, sill = 1, nugget = 0.25,
              scale_s = 0.05, scale_t = 2, 
              smooth_s = 0.5, smooth_t = 0.5)
# Simulation of a spatial Gaussian RF with Matern correlation function
param<-list(nugget=0,mean=0,scale_s=0.2/3,scale_t=2/3,sill=1,smooth_s=0.5,smooth_t=0.5)

data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="Matern_Matern",
                     model="Gaussian",method="CE",param=param)$data
dim(data)

################################################################
###
### Example 4. Simulation of a large spacetime Gaussian RF 
###            with separable GenWend covariance model
###            using  Circular embeeding method in time
###############################################################
set.seed(68)
# Simulation of a spatial Gaussian RF with Matern correlation function
param<-list(nugget=0,mean=0,scale_s=0.2,scale_t=3,sill=1,
             smooth_s=0,smooth_t=0, power2_s=4,power2_t=4)

data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="GenWend_GenWend",
                     model="Gaussian",method="CE",param=param)$data
dim(data)


################################################################
###
### Example 6. Simulation of a large bivariate Gaussian RF
### with  bivariate Matern correlation model
###
###############################################################

# Define the spatial-coordinates of the points:
#x <- runif(20000, 0, 2)
#y <- runif(20000, 0, 2)
#coords <- cbind(x,y)

# Simulation of a bivariate spatial Gaussian RF:
# with a  Bivariate Matern
#set.seed(12)
#param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5,
#           scale_1=0.12,scale_2=0.1,scale_12=0.15,
#           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5)
#data <- GeoSimapprox(coordx=coords,corrmodel="Bi_matern",
#              param=param,method="TB",L=1000)$data
#opar=par(no.readonly = TRUE)
#par(mfrow=c(1,2))
#quilt.plot(coords,data[1,],col=terrain.colors(100),main="1",xlab="",ylab="")
#quilt.plot(coords,data[2,],col=terrain.colors(100),main="2",xlab="",ylab="")

Simulation of Gaussian and non Gaussian Random Fields using copula.

Description

Simulation of Gaussian and some non Gaussian spatial, spatio-temporal and spatial bivariate random fields using Gaussian or Clayton copula. The function return a realization of a Random Field for a given covariance model and covariance parameters. Simulation is based on Cholesky decomposition.

Usage

GeoSimCopula(coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL, corrmodel, distance="Eucl",
      GPU=NULL, grid=FALSE, local=c(1,1),
      method="cholesky", model='Gaussian', n=1, param,
      anisopars=NULL,radius=6371, sparse=FALSE,
      copula="Gaussian",seed=NULL, X=NULL,spobj=NULL,nrep=1)
GeoSimCopula(coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL, corrmodel, distance="Eucl",
      GPU=NULL, grid=FALSE, local=c(1,1),
      method="cholesky", model='Gaussian', n=1, param,
      anisopars=NULL,radius=6371, sparse=FALSE,
      copula="Gaussian",seed=NULL, X=NULL,spobj=NULL,nrep=1)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates. 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; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial RF is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial 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 the Section Details of `GeoFit`.
`GPU`	Numeric; if `NULL` (the default) no GPU computation is performed.
`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).
`local`	Numeric; number of local work-items of the GPU
`method`	String; the type of matrix decomposition used in the simulation. Default is cholesky. The other possible choices is `svd`.
`model`	String; the type of RF and therefore the densities associated to the likelihood objects. `Gaussian` is the default, see the Section Details.
`n`	Numeric; the number of trials for binomial RFs. The number of successes in the negative Binomial RFs. Default is $1$ .
`param`	A list of parameter values required in the simulation procedure of RFs, see Examples.
`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 the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`sparse`	Logical; if `TRUE` then cholesky decomposition is performed using sparse matrices algorithms (spam packake). It should be used with compactly supported covariance models.`FALSE` is the default.
`copula`	String; the type of copula. It can be "Clayton" or "Gaussian"
`seed`	Numeric; an integer used in set.seed function to reproduce the simulation.
`X`	Numeric; Matrix of space-time covariates.
`spobj`	An object of class sp or spacetime
`nrep`	Numeric; Numbers of indipendent replicates.

Value

Returns an object of class GeoSimCopula. An object of class GeoSimCopula is a list containing at most the following components:

`bivariate`	Logical:`TRUE` if the Gaussian RF is bivariate, 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 dynamical (in time) spatial coordinates;
`corrmodel`	The correlation model; see `GeoCovmatrix`.
`data`	The vector or matrix or array of data, see `GeoFit`;
`distance`	The type of spatial distance;
`method`	The method of simulation
`model`	The type of RF, see `GeoFit`.
`n`	The number of trial for Binomial RFs;the number of successes in a negative Binomial RFs;
`numcoord`	The number of spatial coordinates;
`numtime`	The number the temporal realisations of the RF;
`param`	A list of the parameters
`radius`	The radius of the sphere if coordinates are passed in lon/lat format;
`randseed`	The seed used for the random simulation;
`spacetime`	`TRUE` if spatio-temporal and `FALSE` if spatial RF;
`copula`	The type of copula

Author(s)

References

Bevilacqua M., Alvarado E., Caamano C. (2024) A flexible Clayton-like spatial copula with application to bounded support data. Journal of Multivariate Analysis 201

Examples

library(GeoModels)

################################################################
###
### Example q. Simulation of a reparametrized Beta RF
### for beta regression
### with Gaussian and Clayton Copula 
### with  underlying Wendland correlation.
###
###############################################################
set.seed(261)
NN=1400
x <- runif(NN);y <- runif(NN)
coords=cbind(x,y)

shape1=3
shape2=3
smooth=0


corrmodel="GenWend"
min=0;max=1


X=cbind(rep(1,NN),runif(NN))

NuisParam("Beta2",num_betas=2,copula="Gaussian")
CorrParam("GenWend")


#### Gaussian copula
param=list(smooth=smooth,power2=4, min=min,max=max,
             mean=0.1,mean1=0.1,scale=0.3,nugget=0,shape=5)

data <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, model="Beta2",param=param,
  copula="Gaussian",sparse=TRUE,X=X)$data

quilt.plot(coords,data)


#### Clayton copula
NuisParam("Beta2",num_betas=2,copula="Clayton")
CorrParam("GenWend")
param=list(smooth=smooth,power2=4, min=min,max=max,
             mean=0.2,mean1=0.1,scale=0.3,nugget=0,shape=6,nu=4)
data1 <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, model="Beta2",param=param,
  copula="Clayton",sparse=TRUE,X=X)$data

hist(data1,freq=FALSE)
quilt.plot(coords,data1)

library(GeoModels)

################################################################
###
### Example q. Simulation of a reparametrized Beta RF
### for beta regression
### with Gaussian and Clayton Copula 
### with  underlying Wendland correlation.
###
###############################################################
set.seed(261)
NN=1400
x <- runif(NN);y <- runif(NN)
coords=cbind(x,y)

shape1=3
shape2=3
smooth=0


corrmodel="GenWend"
min=0;max=1


X=cbind(rep(1,NN),runif(NN))

NuisParam("Beta2",num_betas=2,copula="Gaussian")
CorrParam("GenWend")


#### Gaussian copula
param=list(smooth=smooth,power2=4, min=min,max=max,
             mean=0.1,mean1=0.1,scale=0.3,nugget=0,shape=5)

data <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, model="Beta2",param=param,
  copula="Gaussian",sparse=TRUE,X=X)$data

quilt.plot(coords,data)


#### Clayton copula
NuisParam("Beta2",num_betas=2,copula="Clayton")
CorrParam("GenWend")
param=list(smooth=smooth,power2=4, min=min,max=max,
             mean=0.2,mean1=0.1,scale=0.3,nugget=0,shape=6,nu=4)
data1 <- GeoSimCopula(coordx=coords, corrmodel=corrmodel, model="Beta2",param=param,
  copula="Clayton",sparse=TRUE,X=X)$data

hist(data1,freq=FALSE)
quilt.plot(coords,data1)

Statistical Hypothesis Tests for Nested Models

Description

The function performs statistical hypothesis tests for nested models based on composite or standard likelihood versions of Wald-type and Wilks-type (likelihood ratio) statistics.

Usage

GeoTests(object1, object2, ..., statistic)
GeoTests(object1, object2, ..., statistic)

Arguments

`object1`	An object of class `GeoFit`.
`object2`	An object of class `GeoFit` that is a nested model within `object1`.
`...`	Further successively nested objects.
`statistic`	String; the name of the statistic used within the hypothesis test (see Details).

Details

The implemented hypothesis tests for nested models are based on the following statistics:

Wald-type (Wald);
Likelihood ratio or Wilks-type (Wilks under standard likelihood); For composite likelihood available variants of the basic version are:
- Rotnitzky and Jewell adjustment (WilksRJ);
- Satterhwaite adjustment (WilksS);
- Chandler and Bate adjustment (WilksCB);
- Pace, Salvan and Sartori adjustment (WilksPSS);

More specifically, consider an $p$ -dimensional random vector $\mathbf{Y}$ with probability density function $f(\mathbf{y};\mathbf{\theta})$ , where $\mathbf{\theta} \in \Theta$ is a $q$ -dimensional vector of parameters. Suppose that $\mathbf{\theta}=(\mathbf{\psi},\mathbf{\tau})$ can be partitioned in a $q'$ -dimensional subvector $\psi$ and $q''$ -dimensional subvector $\tau$ . Assume also to be interested in testing the specific values of the vector $\psi$ . Then, one can use some statistical hypothesis tests for testing the null hypothesis $H_0: \psi=\psi_0$ against the alternative $H_1: \psi \neq \psi_0$ . Composite likelihood versions of 'Wald' statistis have the usual asymptotic chi-square distribution with $q'$ degree of freedom. The Wald-type statistic is

$W=(\hat{\psi}-\psi_0)^T (G^{\psi \psi})^{-1}(\hat{\theta})(\hat{\psi}-\psi_0),$

where $G_{\psi \psi}$ is the $q' \times q'$ submatrix of the Godambe or Fisher information pertaining to $\psi$ and $\hat{\theta}$ is the maximum likelihood estimator from the full model. This statistic can be called from the routine GeoTests assigning at the argument statistic the value: Wald.

Alternatively to the Wald-type statistic one can use the composite version of the Wilks-type or likelihood ratio statistic, given by

$W=2[C \ell(\hat{\mathbf{\theta}};\mathbf{y}) - C \ell\{\mathbf{\psi}_0, \hat{\mathbf{\tau}}(\mathbf{\psi}_0);\mathbf{y}\}].$

In the composite likelihood case, the asymptotic distribution of the composite likelihood ratio statistic is given by

$W \dot{\sim} \sum_{i} \lambda_i \chi^2,$

for $i=1,\ldots,q'$ , where $\chi^2_i$ are $q'$ iid copies of a chi-square one random variable and $\lambda_1,\ldots,\lambda_{q'}$ are the eigenvalues of the matrix $(H^{\psi \psi})^{-1} G^{\psi \psi}$ . There exist several adjustments to the composite likelihood ratio statistic in order to get an approximated $\chi^2_{q'}$ . For example, Rotnitzky and Jewell (1990) proposed the adjustment $W'= W / \bar{\lambda}$ where $\bar{\lambda}$ is the average of the eigenvalues $\lambda_i$ . This statistic can be called within the routine by the value: WilksRJ. A better solution is proposed by Satterhwaite (1946) defining $W''=\nu W / (q' \bar{\lambda})$ , where $\nu=(\sum_i \lambda)^2 / \sum_i \lambda^2_i$ for $i=1\ldots,q'$ , is the effective number of the degree of freedom. Note that in this case the distribution of the likelihood ratio statistic is a chi-square random variable with $\nu$ degree of freedom. This statistic can be called from the routine assigning the value: WilksS. For the adjustments suggested by Chandler and Bate (2007) we refere to the article (see References). This versions can be called from the routine assigning respectively the values: WilksCB.

Value

An object of class c("data.frame"). The object contain a table with the results of the tested models. The rows represent the responses for each model and the columns the following results:

`Num.Par`	The number of the model's parameters.
`Diff.Par`	The difference between the number of parameters of the model in the previous row and those in the actual row.
`Df`	The effective number of degree of freedom of the chi-square distribution.
`Chisq`	The observed value of the statistic.
`Pr(>chisq)`	The p-value of the quantile `Chisq` computed using a chi-squared distribution with `Df` degrees of freedom.

Author(s)

References

Chandler, R. E., and Bate, S. (2007). Inference for Clustered Data Using the Independence log-likelihood. Biometrika, 94, 167–183.

Rotnitzky, A. and Jewell, N. P. (1990). Hypothesis Testing of Regression Parameters in Semiparametric Generalized Linear Models for Cluster Correlated Data. Biometrika, 77, 485–497.

Satterthwaite, F. E. (1946). An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin, 2, 110–114.

Varin, C., Reid, N. and Firth, D. (2011). An Overview of Composite Likelihood Methods. Statistica Sinica, 21, 5–42.

Examples



library(GeoModels)

################################################################
###
### Example 1. Test on the parameter
### of a regression model using conditional composite likelihood
###
###############################################################
set.seed(342)
model="Gaussian" 
# Define the spatial-coordinates of the points:
NN=1500
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=1; mean1=-1.25;  # regression parameters
nugget=0; sill=1

# matrix covariates
X=cbind(rep(1,nrow(coords)),runif(nrow(coords)))

# model correlation 
corrmodel="Wend0"
power2=4;c_supp=0.15

# simulation
param=list(power2=power2,mean=mean,mean1=mean1,
              sill=sill,scale=c_supp,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param,X=X)$data

I=Inf
##### H1: (regressian mean)
fixed=list(nugget=nugget,power2=power2)
start=list(mean=mean,mean1=mean1,scale=c_supp,sill=sill)

lower=list(mean=-I,mean1=-I,scale=0,sill=0)
upper=list(mean=I,mean1=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH1 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
              likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                   lower=lower,upper=upper,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)

unlist(fitH1$param)

##### H0: (constant mean i.e beta1=0)
fixed=list(power2=power2,nugget=nugget,mean1=0)
start=list(mean=mean,scale=c_supp,sill=sill)
lower0=list(mean=-I,scale=0,sill=0)
upper0=list(mean=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH0 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
            likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                      lower=lower0,upper=upper0,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)
unlist(fitH0$param)

# not run
##fitH1=GeoVarestbootstrap(fitH1,K=100,optimizer="nlminb",
##                     lower=lower, upper=upper)
##fitH0=GeoVarestbootstrap(fitH0,K=100,optimizer="nlminb",
##                     lower=lower0, upper=upper0)

#  Composite likelihood Wald and ratio statistic statistic tests
#  rejecting null  hypothesis beta1=0
##GeoTests(fitH1, fitH0 ,statistic='Wald')
##GeoTests(fitH1, fitH0 , statistic='WilksS')
##GeoTests(fitH1, fitH0 , statistic='WilksCB')




################################################################
###
### Example 2. Parametric test of Gaussianity
### using SinhAsinh random fields using standard likelihood
###
###############################################################
set.seed(99)
model="SinhAsinh" 
# Define the spatial-coordinates of the points:
NN=200
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=0; nugget=0; sill=1
### skew and tail parameters
skew=0;tail=1   ## H0 is Gaussianity
# underlying model correlation 
corrmodel="Wend0"
power2=4;c_supp=0.2

# simulation from Gaussian  model (H0)
param=list(power2=power2,skew=skew,tail=tail,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data


##### H1: SinhAsinh model
fixed=list(power2=power2,nugget=nugget,mean=mean)
start=list(scale=c_supp,skew=skew,tail=tail,sill=sill)

lower=list(scale=0,skew=-I, tail=0,sill=0)
upper=list(scale=I,skew= I,tail=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH1 = GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                   likelihood="Full",type="Standard",varest=TRUE,
                   lower=lower,upper=upper,
                   optimizer="nlminb",
                    start=start,fixed=fixed)

unlist(fitH1$param)

##### H0: Gaussianity (i.e tail1=1, skew=0 fixed)
fixed=list(power2=power2,nugget=nugget,mean=mean,tail=1,skew=0)
start=list(scale=c_supp,sill=sill)
lower=list(scale=0,sill=0)
upper=list(scale=2,sill=5)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH0 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    likelihood="Full",type="Standard",varest=TRUE,
                      lower=lower,upper=upper,
                   optimizer="nlminb",
                    start=start,fixed=fixed)

unlist(fitH0$param)

#  Standard likelihood Wald and ratio statistic statistic tests
#  not rejecting null  hypothesis tail=1,skew=0 (Gaussianity)
GeoTests(fitH1, fitH0,statistic='Wald')
GeoTests(fitH1, fitH0,statistic='Wilks')

library(GeoModels)

################################################################
###
### Example 1. Test on the parameter
### of a regression model using conditional composite likelihood
###
###############################################################
set.seed(342)
model="Gaussian" 
# Define the spatial-coordinates of the points:
NN=1500
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=1; mean1=-1.25;  # regression parameters
nugget=0; sill=1

# matrix covariates
X=cbind(rep(1,nrow(coords)),runif(nrow(coords)))

# model correlation 
corrmodel="Wend0"
power2=4;c_supp=0.15

# simulation
param=list(power2=power2,mean=mean,mean1=mean1,
              sill=sill,scale=c_supp,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param,X=X)$data

I=Inf
##### H1: (regressian mean)
fixed=list(nugget=nugget,power2=power2)
start=list(mean=mean,mean1=mean1,scale=c_supp,sill=sill)

lower=list(mean=-I,mean1=-I,scale=0,sill=0)
upper=list(mean=I,mean1=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH1 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
              likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                   lower=lower,upper=upper,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)

unlist(fitH1$param)

##### H0: (constant mean i.e beta1=0)
fixed=list(power2=power2,nugget=nugget,mean1=0)
start=list(mean=mean,scale=c_supp,sill=sill)
lower0=list(mean=-I,scale=0,sill=0)
upper0=list(mean=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH0 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
            likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                      lower=lower0,upper=upper0,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)
unlist(fitH0$param)

# not run
##fitH1=GeoVarestbootstrap(fitH1,K=100,optimizer="nlminb",
##                     lower=lower, upper=upper)
##fitH0=GeoVarestbootstrap(fitH0,K=100,optimizer="nlminb",
##                     lower=lower0, upper=upper0)

#  Composite likelihood Wald and ratio statistic statistic tests
#  rejecting null  hypothesis beta1=0
##GeoTests(fitH1, fitH0 ,statistic='Wald')
##GeoTests(fitH1, fitH0 , statistic='WilksS')
##GeoTests(fitH1, fitH0 , statistic='WilksCB')




################################################################
###
### Example 2. Parametric test of Gaussianity
### using SinhAsinh random fields using standard likelihood
###
###############################################################
set.seed(99)
model="SinhAsinh" 
# Define the spatial-coordinates of the points:
NN=200
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=0; nugget=0; sill=1
### skew and tail parameters
skew=0;tail=1   ## H0 is Gaussianity
# underlying model correlation 
corrmodel="Wend0"
power2=4;c_supp=0.2

# simulation from Gaussian  model (H0)
param=list(power2=power2,skew=skew,tail=tail,
             mean=mean,sill=sill,scale=c_supp,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data


##### H1: SinhAsinh model
fixed=list(power2=power2,nugget=nugget,mean=mean)
start=list(scale=c_supp,skew=skew,tail=tail,sill=sill)

lower=list(scale=0,skew=-I, tail=0,sill=0)
upper=list(scale=I,skew= I,tail=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH1 = GeoFit2(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                   likelihood="Full",type="Standard",varest=TRUE,
                   lower=lower,upper=upper,
                   optimizer="nlminb",
                    start=start,fixed=fixed)

unlist(fitH1$param)

##### H0: Gaussianity (i.e tail1=1, skew=0 fixed)
fixed=list(power2=power2,nugget=nugget,mean=mean,tail=1,skew=0)
start=list(scale=c_supp,sill=sill)
lower=list(scale=0,sill=0)
upper=list(scale=2,sill=5)
# Maximum pairwise composite-likelihood fitting of the RF:
fitH0 = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
                    likelihood="Full",type="Standard",varest=TRUE,
                      lower=lower,upper=upper,
                   optimizer="nlminb",
                    start=start,fixed=fixed)

unlist(fitH0$param)

#  Standard likelihood Wald and ratio statistic statistic tests
#  not rejecting null  hypothesis tail=1,skew=0 (Gaussianity)
GeoTests(fitH1, fitH0,statistic='Wald')
GeoTests(fitH1, fitH0,statistic='Wilks')

Update a `GeoFit` object using parametric bootstrap for std error estimation

Description

The procedure update a GeoFit object computing stderr estimation, confidence intervals and p-values using parametric bootstrap.

Usage

GeoVarestbootstrap(fit,K=100,sparse=FALSE, GPU=NULL,local=c(1,1),
  optimizer=NULL, lower=NULL, upper=NULL, 
  method="cholesky",alpha=0.95, L=1000,parallel=FALSE,ncores=NULL)GeoVarestbootstrap(fit,K=100,sparse=FALSE, GPU=NULL,local=c(1,1),
  optimizer=NULL, lower=NULL, upper=NULL, 
  method="cholesky",alpha=0.95, L=1000,parallel=FALSE,ncores=NULL)

Arguments

`fit`	A fitted object obtained from the `GeoFit`.
`K`	The number of simulations in the parametric bootstrap.
`sparse`	Logical; if `TRUE` then cholesky decomposition is performed using sparse matrices algorithms (spam packake).
`GPU`	Numeric; if `NULL` (the default) no OpenCL computation is performed. The user can choose the device to be used. Use `DeviceInfo()` function to see available devices, only double precision devices are allowed
`local`	Numeric; number of local work-items of the OpenCL setup
`optimizer`	The type of optimization algorithm (see `GeoFit` for details). If NULL then the optimization algorithm of the object fit is chosen.
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`.
`method`	String; The method of simulation. Default is `cholesky`. For large data set three options are `TB` or `CE` (see the `GeoSimapprox`) function.
`alpha`	Numeric; The level of the confidence interval.
`L`	Numeric; the number of lines in the turning band method.
`parallel`	Logical; if `TRUE` then the estimation step is parallelized
`ncores`	Numeric; number of cores involved in parallelization.

Details

The function update a GeoFit object estimating stderr estimation and confidence interval using parametric bootstrap.

Value

Returns an (updated) object of class GeoFit.

Author(s)

Examples



library(GeoModels)

################################################################
###
### Example 1. Test on the parameter
### of a regression model using conditional composite likelihood
###
###############################################################
set.seed(342)
model="Gaussian" 
# Define the spatial-coordinates of the points:
NN=3500
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=1; mean1=-1.25;  # regression parameters
 sill=1 # variance

# matrix covariates
X=cbind(rep(1,nrow(coords)),runif(nrow(coords)))

# model correlation 
corrmodel="Matern"
smooth=0.5;scale=0.1; nugget=0;

# simulation
param=list(smooth=smooth,mean=mean,mean1=mean1,
              sill=sill,scale=scale,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,
                model=model, param=param,X=X)$data

I=Inf

fixed=list(nugget=nugget,smooth=smooth)
start=list(mean=mean,mean1=mean1,scale=scale,sill=sill)

lower=list(mean=-I,mean1=-I,scale=0,sill=0)
upper=list(mean=I,mean1=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fit = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
              likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                   lower=lower,upper=upper,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)

unlist(fit$param)


#fit_update=GeoVarestbootstrap(fit,K=100,parallel=TRUE)
#fit_update$stderr
#fit_update$conf.int
#fit_update$pvalues

library(GeoModels)

################################################################
###
### Example 1. Test on the parameter
### of a regression model using conditional composite likelihood
###
###############################################################
set.seed(342)
model="Gaussian" 
# Define the spatial-coordinates of the points:
NN=3500
x = runif(NN, 0, 1)
y = runif(NN, 0, 1)
coords = cbind(x,y)
# Parameters
mean=1; mean1=-1.25;  # regression parameters
 sill=1 # variance

# matrix covariates
X=cbind(rep(1,nrow(coords)),runif(nrow(coords)))

# model correlation 
corrmodel="Matern"
smooth=0.5;scale=0.1; nugget=0;

# simulation
param=list(smooth=smooth,mean=mean,mean1=mean1,
              sill=sill,scale=scale,nugget=nugget)
data = GeoSim(coordx=coords, corrmodel=corrmodel,
                model=model, param=param,X=X)$data

I=Inf

fixed=list(nugget=nugget,smooth=smooth)
start=list(mean=mean,mean1=mean1,scale=scale,sill=sill)

lower=list(mean=-I,mean1=-I,scale=0,sill=0)
upper=list(mean=I,mean1=I,scale=I,sill=I)
# Maximum pairwise composite-likelihood fitting of the RF:
fit = GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
              likelihood="Conditional",type="Pairwise",sensitivity=TRUE,
                   lower=lower,upper=upper,neighb=3,
                   optimizer="nlminb",X=X,
                    start=start,fixed=fixed)

unlist(fit$param)


#fit_update=GeoVarestbootstrap(fit,K=100,parallel=TRUE)
#fit_update$stderr
#fit_update$conf.int
#fit_update$pvalues

Empirical semi-variogram estimation

Description

The function returns an empirical estimate of the semi-variogram for spatio (temporal) and bivariate random fields.

Usage

GeoVariogram(data, coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL,cloud=FALSE, distance="Eucl",
              grid=FALSE, maxdist=NULL,neighb=NULL,
              maxtime=NULL, numbins=NULL, 
              radius=6371, type='variogram',bivariate=FALSE)
GeoVariogram(data, coordx, coordy=NULL, coordt=NULL, 
coordx_dyn=NULL,cloud=FALSE, distance="Eucl",
              grid=FALSE, maxdist=NULL,neighb=NULL,
              maxtime=NULL, numbins=NULL, 
              radius=6371, type='variogram',bivariate=FALSE)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $n \times d$ )-matrix ( $n$ iid spatial realisations) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or an ( $d \times d \times n$ )-array ( $n$ iid spatial realisations on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $t \times d \times n$ )-array ( $n$ iid spatial-temporal realisations) or or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid) or an ( $d \times d \times t \times n$ )-array ( $n$ iid spatial-temporal realisations on regular grid). See `GeoFit` for details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates. Coordinates on a sphere for a fixed radius `radius` are passed in lon/lat format expressed in decimal degrees.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`cloud`	Logical; if `TRUE` the semivariogram cloud is computed, otherwise if `FALSE` (the default) the empirical (binned) semivariogram is returned.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `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.
`maxdist`	A numeric value denoting the spatial maximum distance, see the Section Details.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood. See the Section Details for more information.
`maxtime`	A numeric value denoting the temporal maximum distance, see the Section Details.
`numbins`	A numeric value denoting the numbers of bins, see the Section Details.
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`type`	A String denoting the type of semivariogram. The option available is : `variogram`.
`bivariate`	Logical; if `FALSE` (the default) the data are interpreted as univariate spatial or spatial-temporal realisations. Otherwise they are intrepreted as a a realization from a bivariate field.

Details

We briefly report the definitions of semi-variogram used for the spatial case. It can be easily extended to the space-time or spatial bivariate case. In the case of a spatial Gaussian random field the sample semivariogram estimator is defined by

$\hat{\gamma}(h) = 0.5 \sum_{x_i, x_j \in N(h)} (Z(x_i) - Z(x_j))^2 / |N(h)|$

where $N(h)$ is the set of all the sample pairs whose distances fall into a tolerance region with size $h$ (equispaced intervalls are considered).

The numbins parameter indicates the number of adjacent intervals to consider in order to grouped distances with which to compute the (weighted) lest squares.

The maxdist parameter indicates the maximum spatial distance below which the shorter distances will be considered in the calculation of the semivariogram.

The maxdist parameter can be coupled with the neighb parameter. This is useful when handling large dataset.

The maxtime parameter indicates the maximum temporal distance below which the shorter distances will be considered in the calculation of the spatio-temoral semivariogram.

Value

Returns an object of class Variogram. An object of class Variogram is a list containing at most the following components:

`bins`	Adjacent intervals of grouped spatial distances if `cloud=FALSE`. Otherwise if `cloud=TRUE` all the spatial pairwise distances;
`bint`	Adjacent intervals of grouped temporal distances if `cloud=FALSE`. Otherwise if `cloud=TRUE` all the temporal pairwise distances;
`cloud`	If the variogram cloud is returned (`TRUE`) or the empirical variogram (`FALSE`);
`centers`	The centers of the spatial bins;
`distance`	The type of spatial distance;
`lenbins`	The number of pairs in each spatial bin;
`lenbinst`	The number of pairs in each spatial-temporal bin;
`lenbint`	The number of pairs in each temporal bin;
`maxdist`	The maximum spatial distance used for the calculation of the variogram. If no spatial distance is specified then it is NULL;
`maxtime`	The maximum temporal distance used for the calculation of the variogram. If no temporal distance is specified then it is NULL;
`spacetime_dyn`	If the space-time variogram is obtained using dynamical coordinates then it is(`TRUE`).
`variograms`	The empirical spatial variogram;
`variogramst`	The empirical spatial-temporal variogram;
`variogramt`	The empirical temporal variogram;
`type`	The type of estimated variogram

Author(s)

References

Cressie, N. A. C. (1993) Statistics for Spatial Data. New York: Wiley.

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Spring Verlang, New York.

Examples

library(GeoModels)

################################################################
###
### Example 1. Empirical estimation of the semi-variogram from a
### spatial Gaussian random field with exponential correlation.
###
###############################################################
set.seed(514)
# Set the coordinates of the sites:
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords = cbind(x,y)
# Set the model's parameters:
corrmodel = "Exponential"
mean = 0
sill = 1
nugget = 0
scale = 0.3/3

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel, param=list(mean=mean,
              sill=sill, nugget=nugget, scale=scale))$data

# Empirical spatial semi-variogram estimation:
vario = GeoVariogram(coordx=coords,data=data,maxdist=0.6)

plot(vario,pch=20,ylim=c(0,1),ylab="Semivariogram",xlab="Distance")


################################################################
###
### Example 2. Empirical estimation of the variogram from a
### spatio-temporal Gaussian random fields with Gneiting
### correlation function.
###
###############################################################

set.seed(331)
# Define the temporal sequence:
# Set the coordinates of the sites:
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords = cbind(x,y)
times = seq(1,10,1)

# Simulation of a spatio-temporal Gaussian random field:
data = GeoSim(coordx=coords, coordt=times, corrmodel="gneiting",
              param=list(mean=0,scale_s=0.08,scale_t=0.4,sill=1,
              nugget=0,power_s=1,power_t=1,sep=0.5))$data

# Empirical spatio-temporal semi-variogram estimation:
vario_st = GeoVariogram(data=data, coordx=coords, coordt=times, maxtime=7,maxdist=0.5)

plot(vario_st)
      
################################################################
###
### Example 3. Empirical estimation of the (cross) semivariograms 
### from a bivariate Gaussian random fields with Matern
### correlation function.
###
###############################################################
# Simulation of a bivariate spatial Gaussian random field:
set.seed(293)
# Define the spatial-coordinates of the points:
x = runif(400, 0, 1)
y = runif(400, 0, 1)
coords=cbind(x,y)

# Simulation of a bivariate Gaussian Random field 
# with matern (cross)  covariance function
param=list(mean_1=0,mean_2=0,scale_1=0.1/3,scale_2=0.15/3,scale_12=0.15/3,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,
           smooth_1=0.5,smooth_12=0.5,smooth_2=0.5,pcol=0.3)  
data = GeoSim(coordx=coords, corrmodel="Bi_matern", param=param)$data

# Empirical  semi-(cross)variogram estimation:
biv_vario=GeoVariogram(data,coordx=coords, bivariate=TRUE,maxdist=0.5)  

plot(biv_vario,pch=20)
      
library(GeoModels)

################################################################
###
### Example 1. Empirical estimation of the semi-variogram from a
### spatial Gaussian random field with exponential correlation.
###
###############################################################
set.seed(514)
# Set the coordinates of the sites:
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords = cbind(x,y)
# Set the model's parameters:
corrmodel = "Exponential"
mean = 0
sill = 1
nugget = 0
scale = 0.3/3

# Simulation of the spatial Gaussian random field:
data = GeoSim(coordx=coords, corrmodel=corrmodel, param=list(mean=mean,
              sill=sill, nugget=nugget, scale=scale))$data

# Empirical spatial semi-variogram estimation:
vario = GeoVariogram(coordx=coords,data=data,maxdist=0.6)

plot(vario,pch=20,ylim=c(0,1),ylab="Semivariogram",xlab="Distance")


################################################################
###
### Example 2. Empirical estimation of the variogram from a
### spatio-temporal Gaussian random fields with Gneiting
### correlation function.
###
###############################################################

set.seed(331)
# Define the temporal sequence:
# Set the coordinates of the sites:
x = runif(200, 0, 1)
y = runif(200, 0, 1)
coords = cbind(x,y)
times = seq(1,10,1)

# Simulation of a spatio-temporal Gaussian random field:
data = GeoSim(coordx=coords, coordt=times, corrmodel="gneiting",
              param=list(mean=0,scale_s=0.08,scale_t=0.4,sill=1,
              nugget=0,power_s=1,power_t=1,sep=0.5))$data

# Empirical spatio-temporal semi-variogram estimation:
vario_st = GeoVariogram(data=data, coordx=coords, coordt=times, maxtime=7,maxdist=0.5)

plot(vario_st)
      
################################################################
###
### Example 3. Empirical estimation of the (cross) semivariograms 
### from a bivariate Gaussian random fields with Matern
### correlation function.
###
###############################################################
# Simulation of a bivariate spatial Gaussian random field:
set.seed(293)
# Define the spatial-coordinates of the points:
x = runif(400, 0, 1)
y = runif(400, 0, 1)
coords=cbind(x,y)

# Simulation of a bivariate Gaussian Random field 
# with matern (cross)  covariance function
param=list(mean_1=0,mean_2=0,scale_1=0.1/3,scale_2=0.15/3,scale_12=0.15/3,
           sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,
           smooth_1=0.5,smooth_12=0.5,smooth_2=0.5,pcol=0.3)  
data = GeoSim(coordx=coords, corrmodel="Bi_matern", param=param)$data

# Empirical  semi-(cross)variogram estimation:
biv_vario=GeoVariogram(data,coordx=coords, bivariate=TRUE,maxdist=0.5)  

plot(biv_vario,pch=20)

WLS of Random Fields

Description

the function returns the parameters' estimates of a random field obtained by the weigthed least squares estimator.

Usage

GeoWLS(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, corrmodel, 
             distance="Eucl", fixed=NULL, grid=FALSE, maxdist=NULL,neighb=NULL,
             maxtime=NULL,  model='Gaussian', optimizer='Nelder-Mead',
             numbins=NULL, radius=6371, start=NULL, weighted=FALSE,optimization=TRUE)
GeoWLS(data, coordx, coordy=NULL, coordt=NULL, coordx_dyn=NULL, corrmodel, 
             distance="Eucl", fixed=NULL, grid=FALSE, maxdist=NULL,neighb=NULL,
             maxtime=NULL,  model='Gaussian', optimizer='Nelder-Mead',
             numbins=NULL, radius=6371, start=NULL, weighted=FALSE,optimization=TRUE)

Arguments

`data`	A $d$ -dimensional vector (a single spatial realisation) or a ( $d \times d$ )-matrix (a single spatial realisation on regular grid) or an ( $d \times d \times n$ )-array ( $n$ iid spatial realisations on regular grid) or a ( $t \times d$ )-matrix (a single spatial-temporal realisation) or an ( $d \times d \times t \times n$ )-array (a single spatial-temporal realisation on regular grid). See `GeoFit` for details.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) giving 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector giving 1-dimension of spatial coordinates.
`coordy`	A numeric vector giving 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector giving 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	String; the name of a correlation model, for the description (see `GeoFit`).
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details of `GeoFit`.
`fixed`	A named list giving the values of the parameters that will be considered as known values. The listed parameters for a given correlation function will be not estimated, i.e. if `list(nugget=0)` the nugget effect is ignored.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`maxdist`	A numeric value denoting the maximum distance, see Details in `GeoFit`.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood. See Details and `GeoFit`
`maxtime`	Numeric; an optional positive value indicating the maximum temporal lag considered.See Details and `GeoFit`.
`model`	String; the type of random field. `Gaussian` is the default, see `GeoFit` for the different types.
`optimizer`	String; the optimization algorithm (see `optim` for details). 'Nelder-Mead' is the default.
`numbins`	A numeric value denoting the numbers of bins, see the Section Details
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`start`	A named list with the initial values of the parameters that are used by the numerical routines in maximization procedure. `NULL` is the default (see `GeoFit`).
`weighted`	Logical; if `TRUE` then the weighted least square estimator is considered. If `FALSE` (the default) then the classic least square is used.
`optimization`	Logical; if `TRUE` then the weighted least square minimization is performed. Otherwise the weighted least square function is evaluated at the starting value.

Details

The numbins parameter indicates the number of adjacent intervals to consider in order to grouped distances with which to compute the (weighted) lest squares.

The maxdist parameter indicates the maximum distance below which the shorter distances will be considered in the calculation of the (weigthed) least squares.

Value

Returns an object of class WLS. An object of class WLS is a list containing at most the following components:

`bins`	Adjacent intervals of grouped distances;
`bint`	Adjacent intervals of grouped temporal separations
`centers`	The centers of the bins;
`coordx`	The vector or matrix of spatial coordinates;
`coordy`	The vector of spatial coordinates;
`coordt`	The vector of temporal coordinates;
`convergence`	A string that denotes if convergence is reached;
`corrmodel`	The correlation model;
`data`	The vector or matrix of data;
`distance`	The type of spatial distance;
`fixed`	The vector of fixed parameters;
`iterations`	The number of iteration used by the numerical routine;
`maxdist`	The maximum spatial distance used for the calculation of the variogram used in least square estimation. If no spatial distance is specified then it is NULL;
`maxtime`	The maximum temporal distance used for the calculation of the variogram used in least square estimation. If no temporal distance is specified then it is NULL;
`message`	Extra message passed from the numerical routines;
`model`	The type of random fields;
`numcoord`	The number of spatial coordinates;
`numtime`	The number the temporal realisations of the random field;
`param`	The vector of parameters' estimates;
`variograms`	The empirical spatial variogram;
`variogramt`	The empirical temporal variogram;
`variogramst`	The empirical spatial-temporal variogram;
`weighted`	A logical value indicating if its the weighted method;
`wls`	The value of the least squares at the minimum.

Author(s)

References

Cressie, N. A. C. (1993) Statistics for Spatial Data. New York: Wiley.

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Spring Verlang, New York.

Examples

library(GeoModels)


# Set the coordinates of the sites:

set.seed(211)
x <- runif(200, 0, 1)
set.seed(98)
y <- runif(200, 0, 1)
coords <- cbind(x,y)

################################################################
###
### Example 1. Least square fitting of a Gaussian random field
### with exponential correlation.
###
###############################################################

# Set the model's parameters:
corrmodel <- "Exponential"
mean <- 0
sill <- 1
nugget <- 0
scale <- 0.15/3
param <- list(mean=0,sill=sill, nugget=nugget, scale=scale)
# Simulation of the Gaussian random field:
set.seed(2)
data <- GeoSim(coordx=coords, corrmodel=corrmodel, param=param)$data

fixed=list(nugget=0,mean=mean)
start=list(scale=scale,sill=sill)
# Least square fitting of the random field:
fit <- GeoWLS(data=data,coordx=coords, corrmodel=corrmodel,
         fixed=fixed,start=start,maxdist=0.5)

# Results:
print(fit)

library(GeoModels)


# Set the coordinates of the sites:

set.seed(211)
x <- runif(200, 0, 1)
set.seed(98)
y <- runif(200, 0, 1)
coords <- cbind(x,y)

################################################################
###
### Example 1. Least square fitting of a Gaussian random field
### with exponential correlation.
###
###############################################################

# Set the model's parameters:
corrmodel <- "Exponential"
mean <- 0
sill <- 1
nugget <- 0
scale <- 0.15/3
param <- list(mean=0,sill=sill, nugget=nugget, scale=scale)
# Simulation of the Gaussian random field:
set.seed(2)
data <- GeoSim(coordx=coords, corrmodel=corrmodel, param=param)$data

fixed=list(nugget=0,mean=mean)
start=list(scale=scale,sill=sill)
# Least square fitting of the random field:
fit <- GeoWLS(data=data,coordx=coords, corrmodel=corrmodel,
         fixed=fixed,start=start,maxdist=0.5)

# Results:
print(fit)

December monthly average temperature in Jamaica between 1970-2000

Description

A ( $13530x 3$ )-matrix containing spatial december average temperature (°C) observed between 1970-2000 in Jamaica with associated UTM coordinates. The data has been retrieved using the package geodata that allows to download climate data from WorldClim version 2.1. UTM coordinates has been obtained using zone 18 and datum WGS84 and rescaled by 1000 to have distances in KM.

Usage

data(Jamaicatemp)data(Jamaicatemp)

Format

A numerical matrix of dimension $13530 x 3$ .

Source

Fick, S.E., Hijmans, R.J. (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315.

Optimizes the Log Likelihood

Description

Subroutine called by GeoFit. The procedure estimates the model parameters by maximization of the log-likelihood.

Usage

Lik(copula,bivariate,coordx,coordy,coordt,
coordx_dyn,corrmodel,data,fixed,flagcor,flagnuis,
           grid,lower,mdecomp,model,namescorr,
           namesnuis,namesparam,numcoord,
           numpairs,numparamcor,numtime,optimizer,
           onlyvar,parallel,param,radius,setup,
           spacetime,sparse,varest,taper,type,
           upper,ns,X,neighb,MM,aniso)
Lik(copula,bivariate,coordx,coordy,coordt,
coordx_dyn,corrmodel,data,fixed,flagcor,flagnuis,
           grid,lower,mdecomp,model,namescorr,
           namesnuis,namesparam,numcoord,
           numpairs,numparamcor,numtime,optimizer,
           onlyvar,parallel,param,radius,setup,
           spacetime,sparse,varest,taper,type,
           upper,ns,X,neighb,MM,aniso)

Arguments

`copula`	String; the type of copula. It can be "Beta" or "Gaussian"
`bivariate`	Logical; if `TRUE` then the data come froma a bivariate random field. Otherwise from a univariate random field.
`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of spatial sites) assigning 2-dimensions of spatial coordinates or a numeric $d$ -dimensional vector assigning 1-dimension of spatial coordinates.
`coordy`	A numeric vector assigning 1-dimension of spatial coordinates; `coordy` is interpreted only if `coordx` is a numeric vector or `grid=TRUE` otherwise it will be ignored. Optional argument, the default is `NULL` then `coordx` is expected to be numeric a ( $d \times 2$ )-matrix.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates. Optional argument, the default is `NULL` then a spatial random field is expected.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	Numeric; the id of the correlation model.
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`flagcor`	A numeric vector of flags denoting which correlation parameters have to be estimated.
`flagnuis`	A numeric verctor of flags denoting which nuisance parameters have to estimated.
`fixed`	A numeric vector of parameters that will be considered as known values.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`lower`	An optional named list giving the values for the lower bound of the space parameter when the optimizer is `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`model`	Numeric; the id value of the density associated to the likelihood objects.
`namescorr`	String; the names of the correlation parameters.
`namesnuis`	String; the names of the nuisance parameters.
`namesparam`	String; the names of the parameters to be maximised.
`numcoord`	Numeric; the number of coordinates.
`numpairs`	Numeric; the number of pairs.
`numparamcor`	Numeric; the number of the correlation parameters.
`numtime`	Numeric; the number of temporal observations.
`mdecomp`	String; the type of matrix decomposition used in the simulation. Default is cholesky. The other possible choices is `svd` (Singular values decomposition).
`optimizer`	String; the optimization algorithm (see `optim` for details). `Nelder-Mead` is the default. Other possible choices are `nlm`, `BFGS` `L-BFGS-B` and `nlminb`. In these last two cases upper and lower bounds can be passed by the user. In the case of one-dimensional optimization, the function `optimize` is used.
`onlyvar`	Logical; if `TRUE` (and varest is `TRUE`) only the variance covariance matrix is computed without optimizing. `FALSE` is the default.
`parallel`	Logical; if `TRUE` optmization is performed using optimParallel using the maximum number of cores, when optimizer is `L-BFGS-B`.`FALSE` is the default.
`param`	A numeric vector of parameters.
`sparse`	Logical; if `TRUE` then maximum likelihood is computed using sparse matrices algorithms.`FALSE` is the default.
`radius`	Numeric; the radius of the sphere when considering data on a sphere.
`ns`	Numeric: vector of number of location sites for each temporal instants
`setup`	A List of useful components for the estimation based on the maximum tapered likelihood.
`spacetime`	Logical; if the random field is spatial (`FALSE`) or spatio-temporal (`TRUE`).
`varest`	Logical; if `TRUE` the estimate' variances and standard errors are returned. `FALSE` is the default.
`taper`	String; the name of the taper correlation function.
`type`	String; the type of the likelihood objects. If `Pairwise` (the default) then the marginal composite likelihood is formed by pairwise marginal likelihoods.
`upper`	An optional named list giving the values for the upper bound of the space parameter when the optimizer is or `L-BFGS-B` or `nlminb` or `optimize`. The names of the list must be the same of the names in the `start` list.
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`neighb`	Numeric;parameter for vecchia approximation using GPvecchia package
`MM`	Numeric;a non constant fixed mean
`aniso`	Logical; should anisotropy be considered?

Value

Return a list from an optim call.

Author(s)

Soil ph of Madagascar

Description

A ( $300649x 3$ )-matrix containing lon/lat coordinates and soil ph in H20 at 300649 location sites in Madagascar at one meter depth over the period 2008–2014. Data obtained using the Geodata package with the function soil_af. For more info, see https://www.isric.org/projects/soil-property-maps-africa-250-m-resolution

Usage

data(madagascarph)data(madagascarph)

Format

A numerical matrix of dimension $300649 x 3$ .

Source

Hengl T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, et al. (2015) Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814. doi:10.1371/journal.pone.0125814

Matrix decomposition

Description

Matrix decomposition.

Usage

MatDecomp(mtx, method)
MatDecomp(mtx, method)

Arguments

`mtx`	numeric; a square positive or semipositive definite matrix.
`method`	string; the type of matrix decomposition. Two possible choices: `cholesky` and `svd`.

Details

Decomposition of a square positive or positive semidefinite matrix.

Value

Return a matrix decomposition

Author(s)

Square root, inverse and log determinant of a (semi)positive definite matrix, given a matrix decomposition.

Description

Square root, inverse and log determinant of a (semi)positive definite matrix, given a matrix decomposition.

Usage

MatSqrt(mat.decomp,method) 
MatInv(mtx)
MatLogDet(mat.decomp,method)
MatSqrt(mat.decomp,method) 
MatInv(mtx)
MatLogDet(mat.decomp,method)

Arguments

`mtx`	numeric; a squared symmetric positive definite matrix.
`mat.decomp`	numeric; a matrix decomposition.
`method`	string; the type of matrix decomposition. Two possible choices: `cholesky` and `svd`.

Value

The function returna a square root or inverse or log determinant of a (semi)positive definite matrix using the function in the FastGP package.

Author(s)

Examples

 library(GeoModels)
 ################################################################
 ###
 ### Example 1. Inverse of Covariance matrix associated to
 ### a Matern correlation model
 ###
 ###############################################################
 # Define the spatial-coordinates of the points:
 x <- runif(15, 0, 1)
 y <- runif(15, 0, 1)
 coords <- cbind(x,y)
 # Matern Parameters
 param=list(smooth=0.5,sill=1,scale=0.2,nugget=0)
 a=matrix <- GeoCovmatrix(coordx=coords, corrmodel="Matern", param=param)

 ## decomposition with cholesky method  
 b=MatDecomp(a$covmat,method="cholesky")
 ## inverse of covariance matrix
 inverse=MatInv(a$covmat)
library(GeoModels)
 ################################################################
 ###
 ### Example 1. Inverse of Covariance matrix associated to
 ### a Matern correlation model
 ###
 ###############################################################
 # Define the spatial-coordinates of the points:
 x <- runif(15, 0, 1)
 y <- runif(15, 0, 1)
 coords <- cbind(x,y)
 # Matern Parameters
 param=list(smooth=0.5,sill=1,scale=0.2,nugget=0)
 a=matrix <- GeoCovmatrix(coordx=coords, corrmodel="Matern", param=param)

 ## decomposition with cholesky method  
 b=MatDecomp(a$covmat,method="cholesky")
 ## inverse of covariance matrix
 inverse=MatInv(a$covmat)

Lists the Nuisance Parameters of a Random Field

Description

The procedure returns a list with the nuisance parameters of a given random field model.

Usage

NuisParam(model, bivariate=FALSE,num_betas=c(1,1),copula=NULL)
NuisParam(model, bivariate=FALSE,num_betas=c(1,1),copula=NULL)

Arguments

`model`	String; the name of a random field.
`bivariate`	Logical; if `FALSE` (the default) the correlation model is univariate spatial or spatial-temporal. Otherwise is bivariate.
`num_betas`	Numerical; the nunber of mean parameters in the linear specification (default is 1)
`copula`	The type of copula.

Details

The function returns a list with the nuisance parameters of a given random field model.

Value

Return a vector string of nuisance parameters.

Author(s)

Examples

library(GeoModels)

NuisParam("Gaussian")

NuisParam("Binomial")

NuisParam("Weibull",num_betas=2)

NuisParam("SkewGaussian", num_betas=3)

NuisParam("SinhAsinh")

NuisParam("Beta2",copula="Clayton")

NuisParam("StudentT")
## note that in the bivariate case sill and nugget are considered as correlation parameteres
NuisParam("Gaussian", bivariate=TRUE)

library(GeoModels)

NuisParam("Gaussian")

NuisParam("Binomial")

NuisParam("Weibull",num_betas=2)

NuisParam("SkewGaussian", num_betas=3)

NuisParam("SinhAsinh")

NuisParam("Beta2",copula="Clayton")

NuisParam("StudentT")
## note that in the bivariate case sill and nugget are considered as correlation parameteres
NuisParam("Gaussian", bivariate=TRUE)

Internal function handling Nuisance Parameters of a Random Field

Description

Internal function handling Nuisance Parameters of a Random Field.

Usage

NuisParam2(model, bivariate=FALSE,num_betas=c(1,1),copula=NULL)
NuisParam2(model, bivariate=FALSE,num_betas=c(1,1),copula=NULL)

Arguments

`model`	String; the name of a random field.
`bivariate`	Logical; if `FALSE` (the default) the correlation model is univariate spatial or spatial-temporal. Otherwise is bivariate.
`num_betas`	Numerical; the nunber of mean parameters in the linear specification (default is 1)
`copula`	The type of copula.

Details

The function returns a list with the nuisance parameters of a given random field model.

Value

Return a vector string of nuisance parameters.

Author(s)

Plot Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields

Description

Plot Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields for a given set of spatial or spatiotemporal distances GeoCorrFct.

Usage

## S3 method for class 'GeoCorrFct'
plot(x,type="p", ...)
## S3 method for class 'GeoCorrFct'
plot(x,type="p", ...)

Arguments

`x`	an object of the class `"GeoCorrFct"`
`type`	The type of graphic. The possible options are "p" and "l". If "p" then a point type graphic is displayed. Otherwise a lines type graphic displayed.
`...`	Other graphical options arguments. `plot`

Details

Plot Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields

Value

Produces a plot. No values are returned.

Plot empirical spatial, spatio-temporal and spatial bivariate semi-Variogram

Description

Plot empirical spatial, spatio-temporal and spatial bivariate semi-Variogram using on object GeoVariogram.

Usage

## S3 method for class 'GeoVariogram'
plot(x, ...)
## S3 method for class 'GeoVariogram'
plot(x, ...)

Arguments

`x`	an object of the class `"GeoVariogram"`
`...`	other arguments to be passed to the function `plot`

Details

This function plots empirical semi variogram in the spatial, spatio-temporal and spatial bivariate case

Value

Produces a plot. No values are returned.

Circulant embeeding simulation

Description

Subroutine called by GeoSimapprox. The procedure return a simulation on a regular grid from a standard spatial Gaussian random field with a specified correlation model

Usage

SimCE(M,N,x,y,corrmodel,param,mean.val, max.ext)
SimCE(M,N,x,y,corrmodel,param,mean.val, max.ext)

Arguments

`M`	Numeric; the dimension of x
`N`	Numeric; the dimension of y
`x`	A numeric $M$ -dimensional vector giving 1-dimension of spatial coordinates.
`y`	A numeric $N$ -dimensional vector giving 1-dimension of spatial coordinates.
`corrmodel`	String; the name of a correlation model.
`param`	A list of parameter values required in the simulation procedure.
`mean.val`	The mean of the random field.
`max.ext`	The maximum extension of the simulation window.

Value

Return a list from an optim call.

Author(s)

Extracting information from an sp or spacetime object

Description

Extracting information from an sp or spacetime object

Usage

sp2Geo(spobj,spdata = NULL)sp2Geo(spobj,spdata = NULL)

Arguments

`spobj`	An object of class sp or spacetime
`spdata`	Character: The name of data in the sp or spacetime object

Details

The function accepts as input a sp or spacetime object and the name of the data of interest in the object and it returns a list with some useful informations for Geomodels functions.

Value

A list with spatio-temporal informations

Author(s)

Examples

# Define the spatial-coordinates of the points:
set.seed(3)
N <- 30  # number of location sites
x <- runif(N, 0, 1)
set.seed(6)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates and regression parameters
X <- cbind(rep(1,N),runif(N))
# Define spatial matrix dependent variable
Y <- rnorm(nrow(X))

obj1 <- sp::SpatialPoints(coords)
obj2 <- sp::SpatialPointsDataFrame(coords,data = data.frame(X,Y))

# sp2Geo info extraction
b <- sp2Geo(obj2,spdata  = "Y")
class(b)
b
# Define the spatial-coordinates of the points:
set.seed(3)
N <- 30  # number of location sites
x <- runif(N, 0, 1)
set.seed(6)
y <- runif(N, 0, 1)
coords <- cbind(x,y)

# Define spatial matrix covariates and regression parameters
X <- cbind(rep(1,N),runif(N))
# Define spatial matrix dependent variable
Y <- rnorm(nrow(X))

obj1 <- sp::SpatialPoints(coords)
obj2 <- sp::SpatialPointsDataFrame(coords,data = data.frame(X,Y))

# sp2Geo info extraction
b <- sp2Geo(obj2,spdata  = "Y")
class(b)
b

August monthly average wind speed in Spain between 1970-2000

Description

A ( $6000x 3$ )-matrix containing lon/lat and august monthly average wind speed (2 m above the ground, meter/second) registered at 6000 location sites in the Iberian peninsula. Data obtained from WorldClim version 2.1

Usage

data(spanish_wind)data(spanish_wind)

Format

A numerical matrix of dimension $6000 x 3$ .

Source

Fick, S.E., Hijmans, R.J. (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315.

Initializes the Parameters for Estimation Procedures

Description

Subroutine called by the fitting procedures. The procedure initializes the parameters for the fitting procedure.

Usage

StartParam(coordx, coordy, coordt,coordx_dyn, corrmodel, data, distance, fcall, 
          fixed, grid,likelihood,  maxdist, neighb, maxtime, model, n,  param, 
          parscale, paramrange, radius,  start, taper, tapsep,
          type,typereal,varest, vartype, weighted, winconst,
          winstp, winconst_t, winstp_t,copula,X,memdist,nosym)
StartParam(coordx, coordy, coordt,coordx_dyn, corrmodel, data, distance, fcall, 
          fixed, grid,likelihood,  maxdist, neighb, maxtime, model, n,  param, 
          parscale, paramrange, radius,  start, taper, tapsep,
          type,typereal,varest, vartype, weighted, winconst,
          winstp, winconst_t, winstp_t,copula,X,memdist,nosym)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	String; the name of a correlation model.
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details.
`fcall`	String; "fitting" to call the fitting procedure and "simulation" to call the simulation procedure.
`fixed`	A named list giving the values of the parameters that will be considered as known values.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`likelihood`	String; the configuration of the composite likelihood.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial distance considered in the composite-likelihood computation.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood in the composite likelihood computation. See the Section Details for more information.
`maxtime`	Numeric; an optional positive value indicating the maximum temporal lag considered in the composite-likelihood computation.
`radius`	Numeric; the radius of the sphere in the case of lon-lat coordinates. The default is 6371, the radius of the earth.
`model`	String; the density associated to the likelihood objects. `Gaussian` is the default.
`n`	Numeric; number of trials for binomial random fields.
`param`	A numeric vector of parameter values required in the simulation procedure of random fields.
`parscale`	A numeric vector of scaling factor to improve the maximizing procedure, see `optim`.
`paramrange`	A numeric vector of parameters ranges, see `optim`.
`start`	A named list with the initial values of the parameters that are used by the numerical routines in maximization procedure.
`taper`	String; the name of the type of covariance matrix. It can be `Standard` (the default value) or `Tapering` for taperd covariance matrix.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time adaptive taper (see Details).
`type`	String; the type of likelihood objects. Temporary value set to be "WLeastSquare" (weigthed least-square) in order to compute the starting values.
`typereal`	String; the real type of likelihood objects. See `GeoFit`.
`varest`	Logical; if `TRUE` the estimates' variances and standard errors are returned. `FALSE` is the default.
`vartype`	String; the type of estimation method for computing the estimate variances, see the Section Details.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted, see `GeoFit`.
`winconst`	Numeric; a positive value for computing the spatial sub-window in the sub-sampling procedure.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure.
`copula`	The type of copula.
`X`	Numeric; Matrix of space-time covariates.
`memdist`	Logical; if `TRUE` then the distances in the composite likelihood are computed before the optmization.
`nosym`	Logical; if TRUE two simmetric weights are not considered

Details

Internal function called by WlsStart.

Value

A list with a set of useful informations in the estimation procedure.

Author(s)

Irish Daily Wind Speeds

Description

A matrix containing daily wind speeds, in kilometers per hour, from 1961 to 1978 at 12 sites in Ireland

Usage

data(winds)data(winds)

Format

A ( $6574 \times 11$ )-matrix containing wind speed observations.

Source

Haslett, J. and Raftery, A. E. (1989), Space-time modelling with long-memory dependence: assessing Ireland's wind-power resource (with discussion), Applied Statistics, 38, 1–50.

Weather Stations of the Irish Daily Wind Speeds

Description

A data frame containing information about the weather stations where the data are recorded in Ireland.

Usage

data(winds.coords)data(winds.coords)

Format

A data frame containing site - the name of the city (character), abbr - the abbrevation (character), elev - the elevation (numeric), lat - latitude (numeric) and lon - longitude.

Source

Haslett, J. and Raftery, A. E. (1989), Space-time modelling with long-memory dependence: assessing Ireland's wind-power resource (with discussion), Applied Statistics, 38, 1–50.

Computes Starting Values based on Weighted Least Squares

Description

Subroutine called by GeoFit. The function returns opportune starting values for the composite-likelihood fitting procedure based on weigthed least squares.

Usage

WlsStart(coordx, coordy, coordt, coordx_dyn, corrmodel, data, distance, fcall,
        fixed, grid,likelihood, maxdist, neighb,maxtime, model, n, param, 
        parscale, paramrange, radius,start, taper, tapsep, type, varest,
        vartype, weighted, winconst,winconst_t, winstp_t, winstp,copula,X,memdist,nosym)
WlsStart(coordx, coordy, coordt, coordx_dyn, corrmodel, data, distance, fcall,
        fixed, grid,likelihood, maxdist, neighb,maxtime, model, n, param, 
        parscale, paramrange, radius,start, taper, tapsep, type, varest,
        vartype, weighted, winconst,winconst_t, winstp_t, winstp,copula,X,memdist,nosym)

Arguments

`coordx`	A numeric ( $d \times 2$ )-matrix (where `d` is the number of points) assigning 2-dimensions of coordinates or a numeric vector assigning 1-dimension of coordinates.
`coordy`	A numeric vector assigning 1-dimension of coordinates; `coordy` is interpreted only if `coordx` is a numeric vector otherwise it will be ignored.
`coordt`	A numeric vector assigning 1-dimension of temporal coordinates.
`coordx_dyn`	A list of $m$ numeric ( $d_t \times 2$ )-matrices containing dynamical (in time) spatial coordinates. Optional argument, the default is `NULL`
`corrmodel`	String; the name of a correlation model, for the description.
`data`	A numeric vector or a ( $n \times d$ )-matrix or ( $d \times d \times n$ )-matrix of observations.
`distance`	String; the name of the spatial distance. The default is `Eucl`, the euclidean distance. See the Section Details.
`fcall`	String; "fitting" to call the fitting procedure and "simulation" to call the simulation procedure.
`fixed`	A named list giving the values of the parameters that will be considered as known values.
`grid`	Logical; if `FALSE` (the default) the data are interpreted as a vector or a ( $n \times d$ )-matrix, instead if `TRUE` then ( $d \times d \times n$ )-matrix is considered.
`likelihood`	String; the configuration of the composite likelihood.
`maxdist`	Numeric; an optional positive value indicating the maximum spatial distance considered in the composite-likelihood computation.
`neighb`	Numeric; an optional positive integer indicating the order of neighborhood in the composite likelihood computation. See the Section Details for more information.
`maxtime`	Numeric; an optional positive value indicating the maximum temporal separation considered in the composite-likelihood computation.
`model`	String; the name of the model. Here the default is `NULL`.
`n`	Numeric; number of trials in a binomial random field.
`param`	A numeric vector of parameter values required in the simulation procedure of random fields.
`parscale`	A numeric vector with scaling values for improving the maximisation routine.
`paramrange`	A numeric vector with the range of the parameter space.
`radius`	Numeric; a value indicating the radius of the sphere when using the great circle distance. Default value is the radius of the earth in Km (i.e. 6371)
`start`	A numeric vector with starting values.
`taper`	String; the name of the type of covariance matrix. It can be `Standard` (the default value) or `Tapering` for taperd covariance matrix.
`tapsep`	Numeric; an optional value indicating the separabe parameter in the space time quasi taper (see Details).
`type`	String; the type of estimation method.
`varest`	Logical; if `TRUE` the estimates' variances and standard errors are returned. `FALSE` is the default.
`vartype`	String; the type of estimation method for computing the estimate variances, see the Section Details.
`weighted`	Logical; if `TRUE` the likelihood objects are weighted, see `GeoFit`.
`winconst`	Numeric; a positive value for computing the spatial sub-window in the sub-sampling procedure.
`winstp`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the spatial sub-sampling procedure.
`winconst_t`	Numeric; a positive value for computing the temporal sub-window in the sub-sampling procedure.
`winstp_t`	Numeric; a value in $(0,1]$ for defining the the proportion of overlapping in the temporal sub-sampling procedure.
`copula`	The type of copula.
`X`	Numeric; Matrix of spatio(temporal)covariates in the linear mean specification.
`memdist`	Logical; if `TRUE` then the distances in the composite likelihood are computed before the optmization.
`nosym`	Logical; if TRUE two simmetric weights are not considered

Details

Internal function called by GeoFit.

Value

A list with a set of useful informations in the estimation procedure.

Package 'GeoModels'

Help Index

Annual precipitation anomalies in U.S.

Description

Usage

Format

Source

Maximum australian temperature

Description

Usage

Format

Source

Checking Bivariate covariance models

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Checking Distance

Description

Usage

Arguments

Details

Value

Author(s)

Checking if a covariance is valid only on the sphere

Description

Usage

Arguments

Details

Value

Author(s)

Checking SpaceTime covariance models

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Checking Correlation Model

Description

Usage

Arguments

Details

Value

Author(s)

Checking Input

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Checking Composite-likelihood Type

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Checking Random Field type

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Checking Likelihood Objects

Description

Usage

Arguments

Details

Value

Author(s)