Last updated on 2025-01-12 10:48:21 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0.9 | 54.09 | 205.23 | 259.32 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.0.9 | 36.70 | 138.86 | 175.56 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.0.9 | 439.85 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0.9 | 371.00 | ERROR | |||
| r-devel-windows-x86_64 | 2.0.9 | 61.00 | 244.00 | 305.00 | OK | |
| r-patched-linux-x86_64 | 2.0.8 | 58.56 | 196.30 | 254.86 | OK | |
| r-release-linux-x86_64 | 2.0.9 | 56.75 | 199.37 | 256.12 | OK | |
| r-release-macos-arm64 | 2.0.9 | 100.00 | NOTE | |||
| r-release-macos-x86_64 | 2.0.9 | 311.00 | NOTE | |||
| r-release-windows-x86_64 | 2.0.9 | 65.00 | 221.00 | 286.00 | OK | |
| r-oldrel-macos-arm64 | 2.0.9 | 12.00 | ERROR | |||
| r-oldrel-macos-x86_64 | 2.0.9 | 13.00 | ERROR | |||
| r-oldrel-windows-x86_64 | 2.0.9 | 32.00 | 8.00 | 40.00 | ERROR |
clang-ASAN gcc-UBSAN LTO valgrind
Version: 2.0.9
Check: examples
Result: ERROR
Running examples in ‘GeoModels-Ex.R’ failed
The error most likely occurred in:
> ### Name: GeoKrig
> ### Title: Spatial (bivariate) and spatio temporal optimal linear
> ### prediction for Gaussian and non Gaussian random fields.
> ### Aliases: GeoKrig
> ### Keywords: Composite
>
> ### ** 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
*** caught segfault ***
address 0x1, cause 'memory not mapped'
Traceback:
1: StartParam(coords[, 1], coords[, 2], coordz, coordt, coordx_dyn, corrmodel, NULL, distance, "Simulation", NULL, grid, NULL, maxdist, NULL, maxtime, model, n, param, NULL, NULL, radius, NULL, taper, tapsep, type, type, FALSE, copula, X, FALSE, FALSE)
2: GeoCovmatrix(coordx = coordx, coordy = coordy, coordz = coordz, coordt = coordt, coordx_dyn = coordx_dyn, corrmodel = corrmodel, distance = distance, grid = grid, model = "Gaussian", n = n, param = append(mc, pc), anisopars = anisopars, radius = radius, sparse = sparse, copula = NULL, X = X)
3: GeoSim(coordx = coords, coordt = times, corrmodel = corrmodel, sparse = TRUE, param = param)
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 2.0.9
Check: installed package size
Result: NOTE
installed size is 5.2Mb
sub-directories of 1Mb or more:
data 1.9Mb
libs 2.0Mb
Flavors: r-release-macos-arm64, r-release-macos-x86_64
Version: 2.0.9
Check: whether package can be installed
Result: ERROR
Installation failed.
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64