CRAN Package Check Results for Package CompRandFld

Last updated on 2014-09-17 18:46:47.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.3-1 4.32 42.46 46.78 OK
r-devel-linux-x86_64-debian-gcc 1.0.3-1 6.54 41.33 47.87 OK
r-devel-linux-x86_64-fedora-clang 1.0.3-1 90.99 OK
r-devel-linux-x86_64-fedora-gcc 1.0.3-1 88.78 OK
r-devel-osx-x86_64-clang 1.0.3-1 67.42 OK
r-devel-windows-ix86+x86_64 1.0.3-1 23.00 57.00 80.00 ERROR
r-patched-linux-x86_64 1.0.3-1 6.58 41.16 47.74 OK
r-patched-solaris-sparc 1.0.3-1 566.70 OK
r-patched-solaris-x86 1.0.3-1 137.10 OK
r-release-linux-ix86 1.0.3-1 8.17 29.76 37.93 ERROR
r-release-linux-x86_64 1.0.3-1 6.56 44.39 50.96 OK
r-release-osx-x86_64-mavericks 1.0.3-1 OK
r-release-osx-x86_64-snowleopard 1.0.3-1 OK
r-release-windows-ix86+x86_64 1.0.3-1 23.00 62.00 85.00 ERROR
r-oldrel-windows-ix86+x86_64 1.0.3-1 22.00 64.00 86.00 ERROR

Check Details

Version: 1.0.3-1
Check: running examples for arch 'i386'
Result: ERROR
    Running examples in 'CompRandFld-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: Covariogram
    > ### Title: Computes covariance, variogram and extremal coefficient
    > ### functions
    > ### Aliases: Covariogram
    > ### Keywords: Composite
    >
    > ### ** Examples
    >
    > library(CompRandFld)
    > library(RandomFields)
    Loading required package: sp
    > library(scatterplot3d)
    > set.seed(31231)
    >
    > # Set the coordinates of the points:
    > x <- runif(100, 0, 10)
    > y <- runif(100, 0, 10)
    > coords=cbind(x,y)
    >
    > ################################################################
    > ###
    > ### Example 1. Plot of covariance and variogram functions
    > ### estimated from a Gaussian random field with exponent
    > ### correlation. One spatial replication is simulated.
    > ###
    > ###
    > ###############################################################
    >
    > # Set the model's parameters:
    > corrmodel <- "exponential"
    > mean <- 0
    > sill <- 1
    > nugget <- 0
    > scale <- 2
    >
    > # Simulation of the Gaussian random field:
    > data <- RFsim(coordx=coords, corrmodel=corrmodel, param=list(mean=mean,
    + sill=sill, nugget=nugget, scale=scale))$data
    >
    > # Maximum composite-likelihood fitting of the Gaussian random field:
    >
    > start=list(scale=scale,sill=sill,mean=mean(data))
    > fixed=list(nugget=nugget)
    > # Maximum composite-likelihood fitting of the random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Marginal",
    + type="Pairwise",start=start,fixed=fixed,maxdist=6)
    >
    > # Results:
    > print(fit)
    
    ##################################################################
    Maximum Composite-Likelihood Fitting of Gaussian Random Fields
    
    Setting: Marginal Composite-Likelihood
    
    Model associated to the likelihood objects: Gaussian
    
    Type of the likelihood objects: Pairwise
    
    Covariance model: exponential
    Number of spatial coordinates: 100
    Number of dependent temporal realisations: 1
    Number of replicates of the random field: 1
    Number of estimated parameters: 3
    
    Maximum log-Composite-Likelihood value: -6734.94
    
    Estimated parameters:
     mean scale sill
    -0.2512 1.1151 0.5963
    
    ##################################################################
    >
    > # Empirical estimation of the variogram:
    > vario <- EVariogram(data, x, y)
    >
    > # Plot of covariance and variogram functions:
    > par(mfrow=c(1,2))
    > Covariogram(fit, show.cov=TRUE, show.range=TRUE,
    + show.vario=TRUE, vario=vario,pch=20)
    >
    >
    > ################################################################
    > ##
    > ### Example 2. Plot of covariance and extremal coefficient
    > ### functions estimated from a max-stable random field with
    > ### exponential correlation. n idd spatial replications are
    > ### simulated.
    > ###
    > ###############################################################
    >
    > set.seed(1126)
    > # Simulation of the max-stable random field:
    > data <- RFsim(coordx=coords, corrmodel=corrmodel, model="ExtGauss", replicates=20,
    + param=list(mean=mean,sill=sill,nugget=nugget,scale=scale))$data
    >
    > start=list(sill=sill,scale=scale)
    > # Maximum composite-likelihood fitting of the max-stable random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel, model='ExtGauss',
    + replicates=20, varest=TRUE, vartype='Sampling',
    + margins="Frechet",start=start)
    >
    > data <- Dist2Dist(data, to='sGumbel')
    >
    > # Empirical estimation of the madogram:
    > vario <- EVariogram(data, coordx=coords, type='madogram', replicates=20)
    >
    > # Plot of correlation and extremal coefficient functions:
    > par(mfrow=c(1,2))
    > Covariogram(fit, show.cov=TRUE, show.range=TRUE, show.extc=TRUE,
    + vario=vario, pract.range=84,pch=20)
    Error in plot.window(...) : need finite 'ylim' values
    Calls: Covariogram ... plot -> plot -> plot.default -> localWindow -> plot.window
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0.3-1
Check: examples
Result: ERROR
    Running examples in ‘CompRandFld-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: Covariogram
    > ### Title: Computes covariance, variogram and extremal coefficient
    > ### functions
    > ### Aliases: Covariogram
    > ### Keywords: Composite
    >
    > ### ** Examples
    >
    > library(CompRandFld)
    > library(RandomFields)
    Loading required package: sp
    > library(scatterplot3d)
    > set.seed(31231)
    >
    > # Set the coordinates of the points:
    > x <- runif(100, 0, 10)
    > y <- runif(100, 0, 10)
    > coords=cbind(x,y)
    >
    > ################################################################
    > ###
    > ### Example 1. Plot of covariance and variogram functions
    > ### estimated from a Gaussian random field with exponent
    > ### correlation. One spatial replication is simulated.
    > ###
    > ###
    > ###############################################################
    >
    > # Set the model's parameters:
    > corrmodel <- "exponential"
    > mean <- 0
    > sill <- 1
    > nugget <- 0
    > scale <- 2
    >
    > # Simulation of the Gaussian random field:
    > data <- RFsim(coordx=coords, corrmodel=corrmodel, param=list(mean=mean,
    + sill=sill, nugget=nugget, scale=scale))$data
    >
    > # Maximum composite-likelihood fitting of the Gaussian random field:
    >
    > start=list(scale=scale,sill=sill,mean=mean(data))
    > fixed=list(nugget=nugget)
    > # Maximum composite-likelihood fitting of the random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Marginal",
    + type="Pairwise",start=start,fixed=fixed,maxdist=6)
    >
    > # Results:
    > print(fit)
    
    ##################################################################
    Maximum Composite-Likelihood Fitting of Gaussian Random Fields
    
    Setting: Marginal Composite-Likelihood
    
    Model associated to the likelihood objects: Gaussian
    
    Type of the likelihood objects: Pairwise
    
    Covariance model: exponential
    Number of spatial coordinates: 100
    Number of dependent temporal realisations: 1
    Number of replicates of the random field: 1
    Number of estimated parameters: 3
    
    Maximum log-Composite-Likelihood value: -6734.94
    
    Estimated parameters:
     mean scale sill
    -0.2512 1.1151 0.5963
    
    ##################################################################
    >
    > # Empirical estimation of the variogram:
    > vario <- EVariogram(data, x, y)
    >
    > # Plot of covariance and variogram functions:
    > par(mfrow=c(1,2))
    > Covariogram(fit, show.cov=TRUE, show.range=TRUE,
    + show.vario=TRUE, vario=vario,pch=20)
    >
    >
    > ################################################################
    > ##
    > ### Example 2. Plot of covariance and extremal coefficient
    > ### functions estimated from a max-stable random field with
    > ### exponential correlation. n idd spatial replications are
    > ### simulated.
    > ###
    > ###############################################################
    >
    > set.seed(1126)
    > # Simulation of the max-stable random field:
    > data <- RFsim(coordx=coords, corrmodel=corrmodel, model="ExtGauss", replicates=20,
    + param=list(mean=mean,sill=sill,nugget=nugget,scale=scale))$data
    >
    > start=list(sill=sill,scale=scale)
    > # Maximum composite-likelihood fitting of the max-stable random field:
    > fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel, model='ExtGauss',
    + replicates=20, varest=TRUE, vartype='Sampling',
    + margins="Frechet",start=start)
    >
    > data <- Dist2Dist(data, to='sGumbel')
    >
    > # Empirical estimation of the madogram:
    > vario <- EVariogram(data, coordx=coords, type='madogram', replicates=20)
    >
    > # Plot of correlation and extremal coefficient functions:
    > par(mfrow=c(1,2))
    > Covariogram(fit, show.cov=TRUE, show.range=TRUE, show.extc=TRUE,
    + vario=vario, pract.range=84,pch=20)
    Error in plot.window(...) : need finite 'ylim' values
    Calls: Covariogram ... plot -> plot -> plot.default -> localWindow -> plot.window
    Execution halted
Flavor: r-release-linux-ix86