Spatial and Spatio-temporal correlation or covariance of (non) Gaussian random fields
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. UTF-8
GeoCorrFct(x,t=NULL,corrmodel, model="Gaussian", distance="Eucl", param, radius=6371,n=1, covariance=FALSE,variogram=FALSE)
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 GeoCovmatrix.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 .covariance: Logic; if TRUE then the covariance is returned. Default is FALSEvariogram: Logic; if FALSE then the covariance/correlation is returned. Otherwise the associated semivariogram is returnedReturns correlations or covariances values associated to a given parametric spatial and temporal correlation models.
Moreno Bevilacqua, moreno.bevilacqua89@gmail.com ,https://sites.google.com/view/moreno-bevilacqua/home, Víctor Morales Oñate, victor.morales@uv.cl , https://sites.google.com/site/moralesonatevictor/, Christian", Caamaño-Carrillo, chcaaman@ubiobio.cl ,https://www.researchgate.net/profile/Christian-Caamano
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")