R Under development (unstable) (2024-09-02 r87090 ucrt) -- "Unsuffered Consequences"
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> library(automap)
> library(sp)
> # Neccessary to silence sf startup messages
> suppressMessages(library(sf))
> 
> data(meuse)
> coordinates(meuse) = ~x+y
> data(meuse.grid)
> gridded(meuse.grid) = ~x+y
> 
> kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"))
> kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, 
+        model = c("Exp"))
> kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, 
+        meuse, model = c("Exp"))
> 
> summary(kr.cv)
            [,1]     
mean_error  0.002008 
me_mean     0.0003412
MAE         0.2918   
MSE         0.1549   
MSNE        0.8555   
cor_obspred 0.8374   
cor_predres 0.04027  
RMSE        0.3936   
RMSE_sd     0.5452   
URMSE       0.3936   
iqr         0.4275   
> summary(kr_dist.cv)
            [,1]      
mean_error  -0.003065 
me_mean     -0.0005207
MAE         0.2707    
MSE         0.143     
MSNE        1.071     
cor_obspred 0.8509    
cor_predres -0.02513  
RMSE        0.3781    
RMSE_sd     0.5238    
URMSE       0.3781    
iqr         0.4035    
> summary(kr_dist_ffreq.cv)
            [,1]      
mean_error  -0.0008806
me_mean     -0.0001496
MAE         0.2345    
MSE         0.1032    
MSNE        1.083     
cor_obspred 0.8949    
cor_predres -0.02777  
RMSE        0.3213    
RMSE_sd     0.4451    
URMSE       0.3213    
iqr         0.3629    
> 
> compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv)
                kr.cv kr_dist.cv kr_dist_ffreq.cv
mean_error   0.002008  -0.003065       -0.0008806
me_mean     0.0003412 -0.0005207       -0.0001496
MAE            0.2918     0.2707           0.2345
MSE            0.1549      0.143           0.1032
MSNE           0.8555      1.071            1.083
cor_obspred    0.8374     0.8509           0.8949
cor_predres   0.04027   -0.02513         -0.02777
RMSE           0.3936     0.3781           0.3213
RMSE_sd        0.5452     0.5238           0.4451
URMSE          0.3936     0.3781           0.3213
iqr            0.4275     0.4035           0.3629
> 
> 
> meuse = as(meuse, "sf")
> meuse.grid = as(meuse.grid, "sf")
> kr.cv.sf = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"))
> kr_dist.cv.sf = autoKrige.cv(log(zinc)~sqrt(dist), meuse, 
+                           model = c("Exp"))
> kr_dist_ffreq.cv.sf = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, 
+                                 meuse, model = c("Exp"))
> 
> summary(kr.cv.sf)
            [,1]     
mean_error  0.002008 
me_mean     0.0003412
MAE         0.2918   
MSE         0.1549   
MSNE        0.8555   
cor_obspred 0.8374   
cor_predres 0.04027  
RMSE        0.3936   
RMSE_sd     0.5452   
URMSE       0.3936   
iqr         0.4275   
> summary(kr_dist.cv.sf)
            [,1]      
mean_error  -0.003065 
me_mean     -0.0005207
MAE         0.2707    
MSE         0.143     
MSNE        1.071     
cor_obspred 0.8509    
cor_predres -0.02513  
RMSE        0.3781    
RMSE_sd     0.5238    
URMSE       0.3781    
iqr         0.4035    
> summary(kr_dist_ffreq.cv.sf)
            [,1]      
mean_error  -0.0008806
me_mean     -0.0001496
MAE         0.2345    
MSE         0.1032    
MSNE        1.083     
cor_obspred 0.8949    
cor_predres -0.02777  
RMSE        0.3213    
RMSE_sd     0.4451    
URMSE       0.3213    
iqr         0.3629    
> 
> compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, kr.cv.sf, kr_dist.cv.sf, kr_dist_ffreq.cv.sf)
                kr.cv kr_dist.cv kr_dist_ffreq.cv  kr.cv.sf kr_dist.cv.sf
mean_error   0.002008  -0.003065       -0.0008806  0.002008     -0.003065
me_mean     0.0003412 -0.0005207       -0.0001496 0.0003412    -0.0005207
MAE            0.2918     0.2707           0.2345    0.2918        0.2707
MSE            0.1549      0.143           0.1032    0.1549         0.143
MSNE           0.8555      1.071            1.083    0.8555         1.071
cor_obspred    0.8374     0.8509           0.8949    0.8374        0.8509
cor_predres   0.04027   -0.02513         -0.02777   0.04027      -0.02513
RMSE           0.3936     0.3781           0.3213    0.3936        0.3781
RMSE_sd        0.5452     0.5238           0.4451    0.5452        0.5238
URMSE          0.3936     0.3781           0.3213    0.3936        0.3781
iqr            0.4275     0.4035           0.3629    0.4275        0.4035
            kr_dist_ffreq.cv.sf
mean_error           -0.0008806
me_mean              -0.0001496
MAE                      0.2345
MSE                      0.1032
MSNE                      1.083
cor_obspred              0.8949
cor_predres            -0.02777
RMSE                     0.3213
RMSE_sd                  0.4451
URMSE                    0.3213
iqr                      0.3629
> 
> proc.time()
   user  system elapsed 
  13.73    0.64   14.43