R Under development (unstable) (2025-07-16 r88418 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> rm(list = ls(all.names = TRUE))
> 
> # Suppress warnings and OpenMP messages
> options(warn = -1)
> options(digits=8)
> Sys.unsetenv("KMP_DEVICE_THREAD_LIMIT")
> Sys.unsetenv("KMP_ALL_THREADS")
> Sys.unsetenv("KMP_TEAMS_THREAD_LIMIT")
> Sys.unsetenv("OMP_THREAD_LIMIT")
> 
> library(automap)
> library(psgp)
Loading required package: intamap
Loading required package: sp
> 
> set.seed(13531)
> 
> data(meuse)
> observations <- data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
> coordinates(observations) = ~x+y
> 
> predictionLocations <- spsample(observations, 50, "regular")
> 
> krigingObject <- createIntamapObject(
+ 	observations = observations,
+ 	predictionLocations = predictionLocations,
+   formulaString = as.formula(value~1),
+ 	params =  list(doAnisotropy = TRUE, thresh = quantile(observations$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$outputTable)
       x                y               mean           variance       
 Min.   :179019   Min.   :330013   Min.   :4.7636   Min.   :0.035979  
 1st Qu.:179485   1st Qu.:330829   1st Qu.:5.3083   1st Qu.:0.070713  
 Median :180183   Median :331644   Median :5.8473   Median :0.254319  
 Mean   :180183   Mean   :331644   Mean   :6.2568   Mean   :0.480755  
 3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:7.4779   3rd Qu.:0.697373  
 Max.   :181348   Max.   :333275   Max.   :8.1775   Max.   :1.911630  
   excprob5.9          cumdistr5.9          quantile0.1    
 Min.   :8.3030e-06   Min.   :1.0000e-09   Min.   :3.9991  
 1st Qu.:5.4990e-02   1st Qu.:1.9720e-02   1st Qu.:4.6377  
 Median :4.6290e-01   Median :5.3710e-01   Median :5.2271  
 Mean   :5.1651e-01   Mean   :4.8349e-01   Mean   :5.4913  
 3rd Qu.:9.8028e-01   3rd Qu.:9.4501e-01   3rd Qu.:6.4796  
 Max.   :1.0000e+00   Max.   :9.9999e-01   Max.   :7.2978  
> summary(krigingObject$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.   :4.7274  
 1st Qu.:5.2883  
 Median :5.7869  
 Mean   :5.8858  
 3rd Qu.:6.5140  
 Max.   :7.5170  
> summary(autoKrige(value~1,krigingObject$observations,predictionLocations)$krige_output)
[using ordinary kriging]
Object of class SpatialPointsDataFrame
Coordinates:
         min       max
x1 179018.64 181348.14
x2 330013.38 333274.69
Is projected: NA 
proj4string : [NA]
Number of points: 48
Data attributes:
   var1.pred         var1.var         var1.stdev     
 Min.   :4.9294   Min.   :0.11529   Min.   :0.33955  
 1st Qu.:5.5178   1st Qu.:0.16147   1st Qu.:0.40183  
 Median :6.0472   Median :0.35825   Median :0.59628  
 Mean   :5.9782   Mean   :0.38267   Mean   :0.59094  
 3rd Qu.:6.3371   3rd Qu.:0.60263   3rd Qu.:0.77617  
 Max.   :7.4723   Max.   :0.67518   Max.   :0.82169  
> autofitVariogram(value~1,krigingObject$observations)$var_model
  model       psill     range
1   Nug 0.048480886   0.00000
2   Sph 0.587547410 889.90843
> 
> # Restore original settings at the end
> options(warn = 0)  # Restore warning level
> 
> proc.time()
   user  system elapsed 
   2.53    0.25    2.68