R Under development (unstable) (2023-10-29 r85433 ucrt) -- "Unsuffered Consequences"
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> library(intamap)
Loading required package: sp
> 
> # observations = Something from Java...
> # Until then we use the Meuse data:
> data(meuse)
> observations = data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
> # If you send a field just with 3 columns (x,y & z), we can let R figure
> # out itself which names they have, for creation of a spatial object:
> obsNames = names(observations)
> coordinates(observations) = as.formula(paste("~",obsNames[1], "+", obsNames[2]))
> set.seed(13531)
> predictionLocations = spsample(observations, 10, "regular")
> gridded(predictionLocations) = TRUE
> cellsize = predictionLocations@grid@cellsize
> cs = predictionLocations@grid@cellsize[1]/2
> 
> # We dont know the projection of the data at this stage, assume it is
> # somehow metric
> 
> Srl = list()
> for (i in 1:dim(coordinates(predictionLocations))[1]) {
+   pt1 = coordinates(predictionLocations)[i,]
+   x1 = pt1[1]-cs
+   x2 = pt1[1]+cs
+   y1 = pt1[2]-cs
+   y2 = pt1[2]+cs
+ 
+   boun = data.frame(x=c(x1,x2,x2,x1,x1),y=c(y1,y1,y2,y2,y1))
+   coordinates(boun) = ~x+y
+   boun = Polygon(boun)
+   Srl[[i]] = Polygons(list(boun),ID = as.character(i))
+ }
> predictionLocations = SpatialPolygons(Srl)
> 
> 
> krigingObject = createIntamapObject(
+ 	observations = observations,
+ 	predictionLocations = predictionLocations,
+ #	targetCRS = "+init=epsg:3035",
+ #	boundCRS = "+proj=laea +lat_0=48 +lon_0=9 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m",
+ #	boundCRS = boundCRS,
+ #	boundaries = boundaries,
+   formulaString = as.formula(paste(obsNames[3],"~1")),
+ 	params =  list(thresh = quantile(observations$value,0.9),block=cellsize),
+     outputWhat = list(mean=TRUE, variance=TRUE, excprob = 5.9, cumdistr = 5.9, 
+ 		quantile = .1),
+     blockWhat = list(fat=7,fatVar=7,blockMax=TRUE,blockMaxVar = TRUE,blockMin=TRUE),
+ 	class="automap"
+ )
> 
> checkSetup(krigingObject)
Checking object ... OK
> krigingObject = preProcess(krigingObject)
> krigingObject = estimateParameters(krigingObject)
> krigingObject = blockPredict(krigingObject)
[using ordinary kriging]
[1] "performed ordinary block kriging"
> krigingObject = postProcess(krigingObject)
> predictions = krigingObject$predictions
> 
> # Send predictions back to Java. Not sure how to deal with this spatial object though...?
> summary(krigingObject$outputTable, digits = 4)
       x                y               mean          variance      
 Min.   :179530   Min.   :330383   Min.   :5.121   Min.   :0.00926  
 1st Qu.:179530   1st Qu.:331165   1st Qu.:5.847   1st Qu.:0.01225  
 Median :180051   Median :331946   Median :6.031   Median :0.06452  
 Mean   :180051   Mean   :331946   Mean   :6.048   Mean   :0.10364  
 3rd Qu.:180572   3rd Qu.:332728   3rd Qu.:6.374   3rd Qu.:0.16456  
 Max.   :180572   Max.   :333509   Max.   :6.785   Max.   :0.33726  
   excprob5.9        cumdistr5.9       quantile0.1         fat7        
 Min.   :0.000287   Min.   :0.01470   Min.   :4.783   Min.   :0.01747  
 1st Qu.:0.427082   1st Qu.:0.03642   1st Qu.:5.525   1st Qu.:0.09583  
 Median :0.698870   Median :0.30113   Median :5.716   Median :0.15141  
 Mean   :0.611131   Mean   :0.38887   Mean   :5.693   Mean   :0.16795  
 3rd Qu.:0.963581   3rd Qu.:0.57292   3rd Qu.:6.005   3rd Qu.:0.24177  
 Max.   :0.985297   Max.   :0.99971   Max.   :6.264   Max.   :0.35838  
    fatVar7             blockMax      blockMaxVar        blockMin    
 Min.   :0.0005592   Min.   :6.935   Min.   :0.1121   Min.   :3.981  
 1st Qu.:0.0032834   1st Qu.:7.544   1st Qu.:0.1568   1st Qu.:4.351  
 Median :0.0062627   Median :7.799   Median :0.1728   Median :4.459  
 Mean   :0.0130755   Mean   :7.745   Mean   :0.2076   Mean   :4.502  
 3rd Qu.:0.0239857   3rd Qu.:8.104   3rd Qu.:0.2323   3rd Qu.:4.673  
 Max.   :0.0381382   Max.   :8.265   Max.   :0.3682   Max.   :4.977  
> 
> proc.time()
   user  system elapsed 
   3.40    0.46    3.85