Last updated on 2022-09-11 12:56:18 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.2 | 67.78 | 264.72 | 332.50 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.2 | 53.02 | 190.14 | 243.16 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.2 | 412.51 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.2 | 406.33 | OK | |||
| r-devel-windows-x86_64 | 1.0.2 | 93.00 | 289.00 | 382.00 | OK | |
| r-patched-linux-x86_64 | 1.0.2 | 58.43 | 246.41 | 304.84 | NOTE | |
| r-release-linux-x86_64 | 1.0.2 | 42.06 | 248.37 | 290.43 | NOTE | |
| r-release-macos-arm64 | 1.0.1 | 172.00 | OK | |||
| r-release-macos-x86_64 | 1.0.2 | 129.00 | OK | |||
| r-release-windows-x86_64 | 1.0.2 | 98.00 | 289.00 | 387.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.1 | 176.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.0.2 | 132.00 | OK | |||
| r-oldrel-windows-ix86+x86_64 | 1.0.2 | 144.00 | 324.00 | 468.00 | ERROR |
Version: 1.0.2
Check: DESCRIPTION meta-information
Result: NOTE
Dependence on R version ‘4.0.5’ not with patchlevel 0
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.0.2
Check: running examples for arch ‘x64’
Result: ERROR
Running examples in 'GeoModels-Ex.R' failed
The error most likely occurred in:
> ### Name: GeoFit
> ### Title: Max-Likelihood-Based Fitting of Gaussian and non Gaussian RFs.
> ### Aliases: GeoFit print.GeoFit
> ### Keywords: Composite
>
> ### ** Examples
>
> library(GeoModels)
>
> ###############################################################
> ############ Examples of spatial Gaussian RFs ################
> ###############################################################
>
>
> # Define the spatial-coordinates of the points:
> set.seed(3)
> N=300 # 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))
> 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 RF:
> data <- GeoSim(coordx=coords,corrmodel=corrmodel, param=param,X=X)$data
>
>
> # setting fixed and parameters to be estimated
> fixed<-list(nugget=nugget,smooth=smooth)
> start<-list(mean=mean,mean1=mean1,scale=scale,sill=sill)
>
>
> ################################################################
> ###
> ### Example 0. Maximum independence composite likelihood fitting of
> ### a Gaussian RF
> ###
> ###############################################################
> fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
+ neighb=3,likelihood="Marginal",
+ type="Independence", start=start,fixed=fixed,X=X)
> print(fit1)
##################################################################
Maximum Composite-Likelihood Fitting of Gaussian Random Fields
Setting: Marginal Composite-Likelihood
Model: Gaussian
Type of the likelihood objects: Independence
Covariance model: Matern
Optimizer: Nelder-Mead
Number of spatial coordinates: 300
Number of dependent temporal realisations: 1
Type of the random field: univariate
Number of estimated parameters: 3
Type of convergence: Successful
Maximum log-Composite-Likelihood value: -393.74
Estimated parameters:
mean mean1 sill
0.04444 -0.44121 0.80821
##################################################################
>
>
> ################################################################
> ###
> ### Example 1. Maximum conditional pairwise likelihood fitting of
> ### a Gaussian RF using BFGS
> ###
> ###############################################################
> fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel,
+ neighb=3,likelihood="Conditional",optimizer="BFGS",
+ type="Pairwise", start=start,fixed=fixed,X=X)
> print(fit1)
##################################################################
Maximum Composite-Likelihood Fitting of Gaussian Random Fields
Setting: Conditional Composite-Likelihood
Model: Gaussian
Type of the likelihood objects: Pairwise
Covariance model: Matern
Optimizer: BFGS
Number of spatial coordinates: 300
Number of dependent temporal realisations: 1
Type of the random field: univariate
Number of estimated parameters: 4
Type of convergence: Successful
Maximum log-Composite-Likelihood value: -1017.24
Estimated parameters:
mean mean1 scale sill
0.001235 -0.391624 0.051202 0.777630
##################################################################
>
> ################################################################
> ###
> ### Example 2. Standard Maximum likelihood fitting of
> ### a Gaussian RF using nlminb
> ###
> ###############################################################
> # Define the spatial-coordinates of the points:
> set.seed(3)
> N=100 # number of location sites
> x <- runif(N, 0, 1)
> set.seed(6)
> 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)
##################################################################
Maximum Likelihood Fitting of Gaussian Random Fields
Setting: Full Likelihood
Model: Gaussian
Type of the likelihood objects: Standard
Covariance model: Matern
Optimizer: nlminb
Number of spatial coordinates: 100
Number of dependent temporal realisations: 1
Type of the random field: univariate
Number of estimated parameters: 3
Type of convergence: Successful
Maximum log-Likelihood value: -122.29
AIC : 250.6
BIC : 258.4
Estimated parameters:
mean scale sill
-0.01060 0.07271 0.93470
##################################################################
>
>
> ###############################################################
> ############ Examples of spatial non-Gaussian RFs #############
> ###############################################################
>
>
> ################################################################
> ###
> ### Example 3. Maximum pairwise likelihood fitting of a Weibull RF
> ### 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,sill=1,scale=scale,
+ shape=shape,nugget=nugget,power2=4,smooth=smooth)
> # Simulation of a non stationary weibull RF:
> data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model,X=X,
+ param=param)$data
>
> fixed<-list(nugget=nugget,power2=4,sill=1,smooth=smooth)
> start<-list(mean=mean,mean1=mean1,scale=scale,shape=shape)
>
> # Maximum conditional composite-likelihood fitting of the RF:
> fit <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
+ neighb=3,likelihood="Conditional",type="Pairwise",X=X,
+ optimizer="Nelder-Mead",
+ start=start,fixed=fixed)
> print(fit$param)
$mean
[1] 0.9991446
$mean1
[1] 1.958527
$scale
[1] 0.1591767
$shape
[1] 2.082499
>
>
>
> ################################################################
> ###
> ### Example 4. Maximum pairwise likelihood fitting of
> ### a SinhAsinh-Gaussian spatial RF with Wendland correlation
> ###
> ###############################################################
> set.seed(261)
> model="SinhAsinh"
> # Define the spatial-coordinates of the points:
> x <- runif(500, 0, 1)
> y <- runif(500, 0, 1)
> coords <- cbind(x,y)
>
> corrmodel="Wend0"
> mean=0;nugget=0
> sill=1
> skew=-0.5
> tail=1.5
> power2=4
> c_supp=0.2
>
> # model parameters
> param=list(power2=power2,skew=skew,tail=tail,
+ mean=mean,sill=sill,scale=c_supp,nugget=nugget)
> data <- GeoSim(coordx=coords, corrmodel=corrmodel,model=model, param=param)$data
>
> plot(density(data))
> fixed=list(power2=power2,nugget=nugget)
> start=list(scale=c_supp,skew=skew,tail=tail,mean=mean,sill=sill)
> # Maximum marginal pairwise likelihood:
> fit1 <- GeoFit(data=data,coordx=coords,corrmodel=corrmodel, model=model,
+ neighb=3,likelihood="Marginal",type="Pairwise",
+ start=start,fixed=fixed)
> print(fit1$param)
$mean
[1] 0.09558448
$scale
[1] 0.1878042
$sill
[1] 1.369462
$skew
[1] -0.6800365
$tail
[1] 1.774269
>
>
> ################################################################
> ###
> ### Example 5. Maximum pairwise likelihood fitting of
> ### a Binomial RF with exponential correlation
> ###
> ###############################################################
>
> set.seed(422)
> N=250
> x <- runif(N, 0, 1)
> y <- runif(N, 0, 1)
> coords <- cbind(x,y)
> mean=0.1; mean1=0.8; mean2=-0.5 # regression parameters
> X=cbind(rep(1,N),runif(N),runif(N)) # marix covariates
> corrmodel <- "Wend0"
> param=list(mean=mean,mean1=mean1,mean2=mean2,sill=1,nugget=0,scale=0.2,power2=4)
> # Simulation of the spatial Binomial-Gaussian RF:
> data <- GeoSim(coordx=coords, corrmodel=corrmodel, model="Binomial", n=10,X=X,
+ param=param)$data
>
>
> ## estimating the marginal parameters using independence cl
> fixed <- list(nugget=nugget,power2=4,sill=1)
> start <- list(mean=mean,mean1=mean1,mean2=mean2,scale=0.2)
>
> # Maximum conditional pairwise likelihood:
> fit1 <- GeoFit(data=data, coordx=coords, corrmodel=corrmodel,n=10, X=X,
+ likelihood="Conditional",type="Pairwise", neighb=3
+ ,model="Binomial", start=start, fixed=fixed, optimizer="BFGS")
>
> print(fit1)
##################################################################
Maximum Composite-Likelihood Fitting of Binomial Random Fields
Setting: Conditional Composite-Likelihood
Model: Binomial
Type of the likelihood objects: Pairwise
Covariance model: Wend0
Optimizer: BFGS
Number of spatial coordinates: 250
Number of dependent temporal realisations: 1
Type of the random field: univariate
Number of estimated parameters: 4
Type of convergence: Successful
Maximum log-Composite-Likelihood value: -1368.44
Estimated parameters:
mean mean1 mean2 scale
0.07026 0.80214 -0.46729 0.20140
##################################################################
>
>
> ###############################################################
> ######### Examples of spatio-temporal RFs ###########
> ###############################################################
> set.seed(52)
> # Define the temporal sequence:
> time <- seq(1, 9, 1)
>
> # Define the spatial-coordinates of the points:
> x <- runif(20, 0, 1)
> set.seed(42)
> y <- runif(20, 0, 1)
> coords=cbind(x,y)
>
> # Set the covariance model's parameters:
> corrmodel="Exp_Exp"
> scale_s=0.2/3
> scale_t=1
> sill=1
> nugget=0
> mean=0
>
> param<-list(mean=0,scale_s=scale_s,scale_t=scale_t,
+ sill=sill,nugget=nugget)
>
> # Simulation of the spatial-temporal Gaussian RF:
> data <- GeoSim(coordx=coords,coordt=time,corrmodel=corrmodel,
+ param=param)$data
>
> ################################################################
> ###
> ### Example 6. Maximum pairwise likelihood fitting of a
> ### space time Gaussian RF with double-exponential correlation
> ###
> ###############################################################
> # Fixed parameters
> fixed<-list(nugget=nugget)
> # 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 RF:
> fit <- GeoFit(data=data,coordx=coords,coordt=time,
+ corrmodel="Exp_Exp",maxtime=1,neighb=3,
+ likelihood="Marginal",type="Pairwise",
+ start=start,fixed=fixed)
> print(fit)
##################################################################
Maximum Composite-Likelihood Fitting of Gaussian Random Fields
Setting: Marginal Composite-Likelihood
Model: Gaussian
Type of the likelihood objects: Pairwise
Covariance model: Exp_Exp
Optimizer: Nelder-Mead
Number of spatial coordinates: 20
Number of dependent temporal realisations: 9
Type of the random field: univariate
Number of estimated parameters: 4
Type of convergence: Successful
Maximum log-Composite-Likelihood value: -4108.54
Estimated parameters:
mean scale_s scale_t sill
-0.12419 0.07261 1.16794 0.94882
##################################################################
>
>
> ###############################################################
> ######### Examples of spatial bivariate RFs ###########
> ###############################################################
>
>
> ################################################################
> ###
> ### Example 7. Maximum pairwise likelihood fitting of a
> ### bivariate Gaussian RF with separable Bivariate matern
> ### (cross) correlation model
> ###
> ###############################################################
>
> # Define the spatial-coordinates of the points:
> set.seed(8)
> x <- runif(200, 0, 1)
> y <- runif(200, 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 RF:
> data <- GeoSim(coordx=coords, corrmodel="Bi_Matern_sep",
+ param=param)$data
>
> # selecting fixed and estimated parameters
> fixed=list(nugget_1=0,nugget_2=0,smooth=0.5)
> start=list(mean_1=0,mean_2=0,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(3,3,3))
Flavor: r-oldrel-windows-ix86+x86_64