R Under development (unstable) (2025-06-23 r88347 ucrt) -- "Unsuffered Consequences"
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> rm(list = ls(all.names = TRUE))
> 
> options(warn = -1)  # Temporarily suppress warnings
> old_omp_thread_limit <- Sys.getenv("OMP_THREAD_LIMIT")
> Sys.setenv(OMP_THREAD_LIMIT = "2")
> 
> library(automap)
> library(psgp)
Loading required package: intamap
Loading required package: sp
> 
> data(meuse)
> observations <- data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
> coordinates(observations) = ~x+y
> set.seed(13531)
> predictionLocations <- spsample(observations, 50, "regular")
> 
> krigingObject <- createIntamapObject(
+ 	observations = observations,
+ 	predictionLocations = predictionLocations,
+   formulaString = as.formula(value~1),
+ 	params =  list(doAnisotropy = TRUE, thresh = quantile(observations$value,0.9)),
+   outputWhat = list(mean=TRUE, variance=TRUE, excprob = 5.9, cumdistr = 5.9, 
+ 		quantile = .1)
+ )
> class(krigingObject) <- c("psgp")
> 
> checkSetup(krigingObject)
Checking object ... OK
> krigingObject <- preProcess(krigingObject)
> krigingObject <- estimateParameters(krigingObject)
Range: 716.69
Sill: 0.77
Nugget: 0.01
Bias: 0.17
   Covariance function : Isotropic Exponential
   Range  (P0) :716.690
   Variance  (P1) :0.766
   Covariance function : Matern 5/2 covariance function
   Length scale  (P0) :716.690
   Variance  (P1) :0.766
   Covariance function : Constant
   Amplitude  (P0) :0.170
   Covariance function : White noise
   Variance  (P0) :0.007
> krigingObject <- spatialPredict(krigingObject)
   Covariance function : Isotropic Exponential
   Range  (P0) :853.589
   Variance  (P1) :1.070
   Covariance function : Matern 5/2 covariance function
   Length scale  (P0) :1307.069
   Variance  (P1) :0.881
   Covariance function : Constant
   Amplitude  (P0) :0.024
> krigingObject <- postProcess(krigingObject)
> 
> # Send predictions back to Java. 
> summary(krigingObject$outputTable)
       x                y               mean          variance      
 Min.   :179019   Min.   :330013   Min.   :4.764   Min.   :0.03598  
 1st Qu.:179485   1st Qu.:330829   1st Qu.:5.308   1st Qu.:0.07071  
 Median :180183   Median :331644   Median :5.847   Median :0.25432  
 Mean   :180183   Mean   :331644   Mean   :6.257   Mean   :0.48075  
 3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:7.478   3rd Qu.:0.69737  
 Max.   :181348   Max.   :333275   Max.   :8.178   Max.   :1.91163  
   excprob5.9         cumdistr5.9       quantile0.1   
 Min.   :0.0000083   Min.   :0.00000   Min.   :3.999  
 1st Qu.:0.0549899   1st Qu.:0.01972   1st Qu.:4.638  
 Median :0.4629025   Median :0.53710   Median :5.227  
 Mean   :0.5165062   Mean   :0.48349   Mean   :5.491  
 3rd Qu.:0.9802802   3rd Qu.:0.94501   3rd Qu.:6.480  
 Max.   :1.0000000   Max.   :0.99999   Max.   :7.298  
> summary(krigingObject$observations)
Object of class SpatialPointsDataFrame
Coordinates:
     min    max
x 178605 181390
y 329714 333611
Is projected: NA 
proj4string : [NA]
Number of points: 155
Data attributes:
     value      
 Min.   :4.727  
 1st Qu.:5.288  
 Median :5.787  
 Mean   :5.886  
 3rd Qu.:6.514  
 Max.   :7.517  
> summary(autoKrige(value~1,krigingObject$observations,predictionLocations)$krige_output)
[using ordinary kriging]
Object of class SpatialPointsDataFrame
Coordinates:
        min      max
x1 179018.6 181348.1
x2 330013.4 333274.7
Is projected: NA 
proj4string : [NA]
Number of points: 48
Data attributes:
   var1.pred        var1.var        var1.stdev    
 Min.   :4.929   Min.   :0.1153   Min.   :0.3395  
 1st Qu.:5.518   1st Qu.:0.1615   1st Qu.:0.4018  
 Median :6.047   Median :0.3582   Median :0.5963  
 Mean   :5.978   Mean   :0.3827   Mean   :0.5909  
 3rd Qu.:6.337   3rd Qu.:0.6026   3rd Qu.:0.7762  
 Max.   :7.472   Max.   :0.6752   Max.   :0.8217  
> autofitVariogram(value~1,krigingObject$observations)$var_model
  model      psill    range
1   Nug 0.04848089   0.0000
2   Sph 0.58754741 889.9084
> 
> # Restore original settings at the end
> Sys.setenv(OMP_THREAD_LIMIT = old_omp_thread_limit)
> options(warn = 0)  # Restore warning level
> 
> proc.time()
   user  system elapsed 
   2.35    0.34    2.70