CRAN Package Check Results for Package RobustGaSP

Last updated on 2019-11-17 01:48:21 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.5.7 58.77 49.21 107.98 OK
r-devel-linux-x86_64-debian-gcc 0.5.7 53.90 36.69 90.59 OK
r-devel-linux-x86_64-fedora-clang 0.5.7 150.21 NOTE
r-devel-linux-x86_64-fedora-gcc 0.5.7 154.80 OK
r-devel-windows-ix86+x86_64 0.5.7 126.00 122.00 248.00 OK
r-patched-linux-x86_64 0.5.7 55.95 46.26 102.21 OK
r-patched-solaris-x86 0.5.7 151.90 OK
r-release-linux-x86_64 0.5.7 54.25 46.64 100.89 OK
r-release-windows-ix86+x86_64 0.5.7 94.00 137.00 231.00 OK
r-release-osx-x86_64 0.5.7 NOTE
r-oldrel-windows-ix86+x86_64 0.5.7 92.00 118.00 210.00 ERROR
r-oldrel-osx-x86_64 0.5.7 ERROR

Check Details

Version: 0.5.7
Check: installed package size
Result: NOTE
     installed size is 23.4Mb
     sub-directories of 1Mb or more:
     libs 23.0Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.5.7
Check: running examples for arch ‘i386’
Result: ERROR
    Running examples in 'RobustGaSP-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: predict.ppgasp
    > ### Title: Prediction for PP GaSP model
    > ### Aliases: predict.ppgasp predict.ppgasp-class predict,ppgasp-method
    >
    > ### ** Examples
    >
    > library(RobustGaSP)
    > #----------------------------------
    > # an example of environmental model
    > #----------------------------------
    >
    > set.seed(1)
    > n=100
    > p=4
    > ##using the latin hypercube will be better
    > #library(lhs)
    > #input_samples=maximinLHS(n,p)
    > input_samples=matrix(runif(n*p),n,p)
    > input=matrix(0,n,p)
    > input[,1]=7+input_samples[,1]*6
    > input[,2]=0.02+input_samples[,2]*1
    > input[,3]=0.01+input_samples[,3]*2.99
    > input[,4]=30.01+input_samples[,4]*0.285
    >
    > k=400
    > output=matrix(0,n,k)
    > ##environ.4.data is an environmental model on a spatial-time vector
    > ##? environ.4.data
    > for(i in 1:n){
    + output[i,]=environ.4.data(input[i,],s=seq(0.15,3,0.15),t=seq(3,60,3) )
    + }
    >
    > ##samples some test inputs
    > n_star=1000
    > sample_unif=matrix(runif(n_star*p),n_star,p)
    >
    > testing_input=matrix(0,n_star,p)
    > testing_input[,1]=7+sample_unif[,1]*6
    > testing_input[,2]=0.02+sample_unif[,2]*1
    > testing_input[,3]=0.01+sample_unif[,3]*2.99
    > testing_input[,4]=30.01+sample_unif[,4]*0.285
    >
    >
    > testing_output=matrix(0,n_star,k)
    >
    > for(i in 1:n_star){
    + testing_output[i,]=environ.4.data(testing_input[i,],s=seq(0.15,3,0.15
    + ),t=seq(3,60,3) )
    + }
    >
    > ##we do a transformation of the output
    > ##one can change the number of initial values to test
    > log_output_1=log(output+1)
    > m.ppgasp=ppgasp(design=input,response=log_output_1,kernel_type
    + ='pow_exp',num_initial_values=2)
    The upper bounds of the range parameters are 931717.5 155459.4 452567 42165.19
    The initial values of range parameters are 18634.35 3109.187 9051.341 843.3038
    Start of the optimization 1 :
    The number of interation is 33
     The value of the posterior is 63671.61
     Optimized range parameters are 66.62619 0.9410182 26.11876 42165.19
     Optimized nugget parameter is 0
     Convergence: TRUE
    The initial values of range parameters are 1.050253 0.1752373 0.5101439 0.04752957
    Start of the optimization 2 :
    The number of interation is 25
     The value of the posterior is 36977.63
     Optimized range parameters are 134901.2 2173.659 225877.9 42165.19
     Optimized nugget parameter is 0
     Convergence: TRUE
    > m_pred.ppgasp=predict(m.ppgasp,testing_input)
    > ##we transform back for the prediction
    > m_pred_ppgasp_mean=exp(m_pred.ppgasp$mean)-1
    > ##mean squared error
    > mean( (m_pred_ppgasp_mean-testing_output)^2)
    [1] 0.6738959
    > ##variance of the testing outputs
    > var(as.numeric(testing_output))
    [1] 11.48627
    >
    > ##makes plots for the testing
    > par(mfrow=c(1,2))
    > t=seq(3,60,3)
    > s=seq(0.15,3,0.15)
    > testing_plot_1=matrix(testing_output[1,], length(t), length(s) )
    >
    > max_testing_plot_1=max(testing_plot_1)
    > min_testing_plot_1=min(testing_plot_1)
    >
    > image(x=t,y=s,testing_plot_1, col = hcl.colors(100, "terrain"),main='test outputs')
    Error in hcl.colors(100, "terrain") :
     could not find function "hcl.colors"
    Calls: image -> image.default
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.5.7
Check: running examples for arch ‘x64’
Result: ERROR
    Running examples in 'RobustGaSP-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: predict.ppgasp
    > ### Title: Prediction for PP GaSP model
    > ### Aliases: predict.ppgasp predict.ppgasp-class predict,ppgasp-method
    >
    > ### ** Examples
    >
    > library(RobustGaSP)
    > #----------------------------------
    > # an example of environmental model
    > #----------------------------------
    >
    > set.seed(1)
    > n=100
    > p=4
    > ##using the latin hypercube will be better
    > #library(lhs)
    > #input_samples=maximinLHS(n,p)
    > input_samples=matrix(runif(n*p),n,p)
    > input=matrix(0,n,p)
    > input[,1]=7+input_samples[,1]*6
    > input[,2]=0.02+input_samples[,2]*1
    > input[,3]=0.01+input_samples[,3]*2.99
    > input[,4]=30.01+input_samples[,4]*0.285
    >
    > k=400
    > output=matrix(0,n,k)
    > ##environ.4.data is an environmental model on a spatial-time vector
    > ##? environ.4.data
    > for(i in 1:n){
    + output[i,]=environ.4.data(input[i,],s=seq(0.15,3,0.15),t=seq(3,60,3) )
    + }
    >
    > ##samples some test inputs
    > n_star=1000
    > sample_unif=matrix(runif(n_star*p),n_star,p)
    >
    > testing_input=matrix(0,n_star,p)
    > testing_input[,1]=7+sample_unif[,1]*6
    > testing_input[,2]=0.02+sample_unif[,2]*1
    > testing_input[,3]=0.01+sample_unif[,3]*2.99
    > testing_input[,4]=30.01+sample_unif[,4]*0.285
    >
    >
    > testing_output=matrix(0,n_star,k)
    >
    > for(i in 1:n_star){
    + testing_output[i,]=environ.4.data(testing_input[i,],s=seq(0.15,3,0.15
    + ),t=seq(3,60,3) )
    + }
    >
    > ##we do a transformation of the output
    > ##one can change the number of initial values to test
    > log_output_1=log(output+1)
    > m.ppgasp=ppgasp(design=input,response=log_output_1,kernel_type
    + ='pow_exp',num_initial_values=2)
    The upper bounds of the range parameters are 936428.5 156245.4 454855.3 42378.38
    The initial values of range parameters are 18728.57 3124.908 9097.106 847.5677
    Start of the optimization 1 :
    The number of interation is 37
     The value of the posterior is 63671.61
     Optimized range parameters are 66.62619 0.9410182 26.11876 42378.38
     Optimized nugget parameter is 0
     Convergence: TRUE
    The initial values of range parameters are 1.050253 0.1752373 0.5101439 0.04752957
    Start of the optimization 2 :
    The number of interation is 23
     The value of the posterior is 36977.57
     Optimized range parameters are 135482.9 2184.202 227007.4 42378.38
     Optimized nugget parameter is 0
     Convergence: TRUE
    > m_pred.ppgasp=predict(m.ppgasp,testing_input)
    > ##we transform back for the prediction
    > m_pred_ppgasp_mean=exp(m_pred.ppgasp$mean)-1
    > ##mean squared error
    > mean( (m_pred_ppgasp_mean-testing_output)^2)
    [1] 0.6738959
    > ##variance of the testing outputs
    > var(as.numeric(testing_output))
    [1] 11.48627
    >
    > ##makes plots for the testing
    > par(mfrow=c(1,2))
    > t=seq(3,60,3)
    > s=seq(0.15,3,0.15)
    > testing_plot_1=matrix(testing_output[1,], length(t), length(s) )
    >
    > max_testing_plot_1=max(testing_plot_1)
    > min_testing_plot_1=min(testing_plot_1)
    >
    > image(x=t,y=s,testing_plot_1, col = hcl.colors(100, "terrain"),main='test outputs')
    Error in hcl.colors(100, "terrain") :
     could not find function "hcl.colors"
    Calls: image -> image.default
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.5.7
Check: examples
Result: ERROR
    Running examples in ‘RobustGaSP-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: predict.ppgasp
    > ### Title: Prediction for PP GaSP model
    > ### Aliases: predict.ppgasp predict.ppgasp-class predict,ppgasp-method
    >
    > ### ** Examples
    >
    > library(RobustGaSP)
    > #----------------------------------
    > # an example of environmental model
    > #----------------------------------
    >
    > set.seed(1)
    > n=100
    > p=4
    > ##using the latin hypercube will be better
    > #library(lhs)
    > #input_samples=maximinLHS(n,p)
    > input_samples=matrix(runif(n*p),n,p)
    > input=matrix(0,n,p)
    > input[,1]=7+input_samples[,1]*6
    > input[,2]=0.02+input_samples[,2]*1
    > input[,3]=0.01+input_samples[,3]*2.99
    > input[,4]=30.01+input_samples[,4]*0.285
    >
    > k=400
    > output=matrix(0,n,k)
    > ##environ.4.data is an environmental model on a spatial-time vector
    > ##? environ.4.data
    > for(i in 1:n){
    + output[i,]=environ.4.data(input[i,],s=seq(0.15,3,0.15),t=seq(3,60,3) )
    + }
    >
    > ##samples some test inputs
    > n_star=1000
    > sample_unif=matrix(runif(n_star*p),n_star,p)
    >
    > testing_input=matrix(0,n_star,p)
    > testing_input[,1]=7+sample_unif[,1]*6
    > testing_input[,2]=0.02+sample_unif[,2]*1
    > testing_input[,3]=0.01+sample_unif[,3]*2.99
    > testing_input[,4]=30.01+sample_unif[,4]*0.285
    >
    >
    > testing_output=matrix(0,n_star,k)
    >
    > for(i in 1:n_star){
    + testing_output[i,]=environ.4.data(testing_input[i,],s=seq(0.15,3,0.15
    + ),t=seq(3,60,3) )
    + }
    >
    > ##we do a transformation of the output
    > ##one can change the number of initial values to test
    > log_output_1=log(output+1)
    > m.ppgasp=ppgasp(design=input,response=log_output_1,kernel_type
    + ='pow_exp',num_initial_values=2)
    The upper bounds of the range parameters are 911463.7 152079.9 442729.1 41248.59
    The initial values of range parameters are 18229.27 3041.599 8854.581 824.9719
    Start of the optimization 1 :
    The number of interation is 34
     The value of the posterior is 63671.61
     Optimized range parameters are 66.62618 0.9410181 26.11876 41248.59
     Optimized nugget parameter is 0
     Convergence: TRUE
    The initial values of range parameters are 1.050253 0.1752373 0.5101439 0.04752957
    Start of the optimization 2 :
    The number of interation is 21
     The value of the posterior is 36979.08
     Optimized range parameters are 129250.4 2098.389 218146.9 40727.66
     Optimized nugget parameter is 0
     Convergence: TRUE
    > m_pred.ppgasp=predict(m.ppgasp,testing_input)
    > ##we transform back for the prediction
    > m_pred_ppgasp_mean=exp(m_pred.ppgasp$mean)-1
    > ##mean squared error
    > mean( (m_pred_ppgasp_mean-testing_output)^2)
    [1] 0.6738959
    > ##variance of the testing outputs
    > var(as.numeric(testing_output))
    [1] 11.48627
    >
    > ##makes plots for the testing
    > par(mfrow=c(1,2))
    > t=seq(3,60,3)
    > s=seq(0.15,3,0.15)
    > testing_plot_1=matrix(testing_output[1,], length(t), length(s) )
    >
    > max_testing_plot_1=max(testing_plot_1)
    > min_testing_plot_1=min(testing_plot_1)
    >
    > image(x=t,y=s,testing_plot_1, col = hcl.colors(100, "terrain"),main='test outputs')
    Error in hcl.colors(100, "terrain") :
     could not find function "hcl.colors"
    Calls: image -> image.default
    Execution halted
Flavor: r-oldrel-osx-x86_64