R Under development (unstable) (2023-10-29 r85433 ucrt) -- "Unsuffered Consequences"
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> library(intamap)
Loading required package: sp
> data(meuse)
> coordinates(meuse) = ~x+y
> data(meuse.grid)
> coordinates(meuse.grid) = ~x+y
> set.seed(13531)
> 
> predictionLocations = spsample(meuse,50,"regular")
> gridded(predictionLocations) = TRUE
> cs = predictionLocations@grid@cellsize[1]/2
> meuse$value = log(meuse$zinc)
> 
> outputWhat = list(mean=TRUE,variance=TRUE,quantile=0.025,quantile=0.0975)
> res1 = interpolateBlock(meuse,predictionLocations,outputWhat,methodName = "automap")$outputTable
R 2023-10-30 11:51:29.780521 interpolating 155 observations, 48 prediction locations
Checking object ... OK
[using ordinary kriging]
[1] "performed ordinary block kriging"
Warning message:
In predictTime(nObs = nObs, nPred = nPred, formulaString = formulaString,  :
  
 using standard model for estimating time. For better 
 platform spesific predictions, please run 
 timeModels <- generateTimeModels()
  and save the workspace
> summary(t(res1))
       x                y               mean          variance       
 Min.   :179019   Min.   :330013   Min.   :4.920   Min.   :0.007617  
 1st Qu.:179485   1st Qu.:330829   1st Qu.:5.478   1st Qu.:0.022547  
 Median :180183   Median :331644   Median :5.798   Median :0.135380  
 Mean   :180183   Mean   :331644   Mean   :5.970   Mean   :0.209191  
 3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:6.477   3rd Qu.:0.395162  
 Max.   :181348   Max.   :333275   Max.   :7.207   Max.   :0.527126  
 quantile0.025   quantile0.0975 
 Min.   :4.161   Min.   :4.583  
 1st Qu.:4.720   1st Qu.:4.971  
 Median :5.133   Median :5.422  
 Mean   :5.201   Mean   :5.461  
 3rd Qu.:5.660   3rd Qu.:5.828  
 Max.   :6.767   Max.   :6.916  
> 
> 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)
> 
> res2 = interpolateBlock(meuse,predictionLocations,outputWhat,methodName="automap")$outputTable
R 2023-10-30 11:51:30.13666 interpolating 155 observations, 48 prediction locations
Checking object ... OK
[using ordinary kriging]
[1] "performed ordinary block kriging"
Warning message:
In predictTime(nObs = nObs, nPred = nPred, formulaString = formulaString,  :
  
 using standard model for estimating time. For better 
 platform spesific predictions, please run 
 timeModels <- generateTimeModels()
  and save the workspace
> summary(t(res2))
       x                y               mean          variance       
 Min.   :179019   Min.   :330013   Min.   :4.917   Min.   :0.005616  
 1st Qu.:179485   1st Qu.:330829   1st Qu.:5.479   1st Qu.:0.020985  
 Median :180183   Median :331644   Median :5.800   Median :0.136722  
 Mean   :180183   Mean   :331644   Mean   :5.970   Mean   :0.208580  
 3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:6.480   3rd Qu.:0.397064  
 Max.   :181348   Max.   :333275   Max.   :7.212   Max.   :0.528377  
 quantile0.025   quantile0.0975 
 Min.   :4.158   Min.   :4.579  
 1st Qu.:4.723   1st Qu.:4.976  
 Median :5.132   Median :5.420  
 Mean   :5.208   Mean   :5.466  
 3rd Qu.:5.665   3rd Qu.:5.840  
 Max.   :6.785   Max.   :6.930  
> 
> max((res2-res1)/res1)
[1] 0.01724326
> 
> 
> proc.time()
   user  system elapsed 
   3.51    0.42    3.90