Running 'anisotropyTest.R' [181s]
  Running 'block.R' [164s]
  Running 'idw.R' [167s]
  Running 'interpolate.R' [13s]
  Running 'interpolateBlock.R' [4s]
  Running 'javaR.R' [163s]
  Running 'linearVariogram.R' [3s]
  Running 'minimal.R' [3s]
  Running 'transGaussian.R' [3s]
  Running 'unbiased.R' [171s]
Running the tests in 'tests/anisotropyTest.R' failed.
Complete output:
  > library(intamap)
  Loading required package: sp
  > library(automap)
  > library(gstat)
  > set.seed(13531)
  > 
  > plotFigs = FALSE
  > npts = 1000
  > pts=SpatialPoints(cbind(runif(npts),runif(npts)))
  > d = SpatialPointsDataFrame(cbind(0,0),data.frame(z=1))
  > observations = krige(z~1,d,pts,vgm(1, "Sph", .5,anis=c(90,0.5)),nsim=1,nmax=50,beta=0)
  [using conditional Gaussian simulation]
  > 
  > #spplot(sim)
  > 
  > predictionLocations = spsample(observations, 1000, "regular")
  > 
  > # We dont know the projection of the data at this stage, assume it is
  > # somehow metric
  > 
  > 
  > 
  > formulaString = as.formula(sim1~1)
  > krigingObject = createIntamapObject(
  + 	observations = observations,
  + 	predictionLocations = predictionLocations,
  +   formulaString = formulaString
  + )
  > class(krigingObject) = c("automap")
  > 
  > checkSetup(krigingObject)
  Checking object ... OK
  > object = preProcess(krigingObject)
  > objTemp = estimateAnisotropy(object)
  > #rotate Data
  > objTemp$observations = rotateAnisotropicData(objTemp$observations, objTemp$anisPar)
  > #Estimate Variogram Model
  > vario = autofitVariogram(objTemp$formulaString, objTemp$observations, model = "Sph")$var_model
  > objTemp$anisPar
  $ratio
  [1] 1.473469
  
  $direction
  [1] 10.79148
  
  $Q
           Q11      Q22       Q12
  [1,] 52.7867 108.0044 -10.92161
  
  $doRotation
  [1] TRUE
  
  > if (plotFigs) {
  +   spplot(object$observations, "sim1", col.regions=bpy.colors())
  +   spplot(objTemp$observations, "sim1", col.regions=bpy.colors())
  +   plot(variogram(sim1~1, object$observations, alpha=c(0, 90)), vario)
  + }
  > print(vario, digits = 3)
    model   psill range
  1   Nug 0.00423 0.000
  2   Sph 0.76877 0.341
  > 
  > 
  > vmod = vgm(1, "Sph", 1, anis = c(90, 0.5))
  > krigingObject$observations = krige(z~1, d, pts, vmod, nsim = 1, nmax = 50, beta = 0)
  [using conditional Gaussian simulation]
  > object = preProcess(krigingObject)
  > objTemp = estimateAnisotropy(object)
  > objTemp$anisPar
  $ratio
  [1] 1.463776
  
  $direction
  [1] 0.4183025
  
  $Q
            Q11      Q22       Q12
  [1,] 30.88621 66.17211 -0.257632
  
  $doRotation
  [1] TRUE
  
  > objTemp$observations = rotateAnisotropicData(objTemp$observations, objTemp$anisPar)
  > vario = autofitVariogram(objTemp$formulaString, objTemp$observations, model="Sph")$var_model
  > print(vario, digits = 3)
    model psill range
  1   Nug 0.000 0.000
  2   Sph 0.943 0.671
  > vmod
    model psill range ang1 anis1
  1   Sph     1     1   90   0.5
  > 
  > vmod = vgm(1, "Sph", 2, anis=c(45, 0.2))
  > krigingObject$observations = krige(z~1, d, pts, vmod, nsim = 1, nmax = 50, beta = 0)
  [using conditional Gaussian simulation]
  > object = preProcess(krigingObject)
  > objTemp = estimateAnisotropy(object)
  > objTemp$anisPar
  $ratio
  [1] 2.932813
  
  $direction
  [1] 42.98085
  
  $Q
            Q11      Q22       Q12
  [1,] 54.28156 60.69178 -45.39902
  
  $doRotation
  [1] TRUE
  
  > objTemp$observations = rotateAnisotropicData(objTemp$observations, objTemp$anisPar)
  > vario = autofitVariogram(objTemp$formulaString, objTemp$observations, model = "Sph")$var_model
  > print(vario, digits = 3)
    model   psill range
  1   Nug 0.00102  0.00
  2   Sph 0.89779  1.24
  > vmod
    model psill range ang1 anis1
  1   Sph     1     2   45   0.2
  > 
  > 
  > vmod = vgm(1,"Sph",1,anis=c(135,0.5))
  > krigingObject$observations = krige(z~1,d,pts,vmod,nsim=1,nmax=50,beta=0)
  [using conditional Gaussian simulation]
  > object = preProcess(krigingObject)
  > objTemp=estimateAnisotropy(object)
  > objTemp$anisPar
  $ratio
  [1] 1.370417
  
  $direction
  [1] -49.22412
  
  $Q
            Q11      Q22      Q12
  [1,] 34.70327 31.72582 10.02324
  
  $doRotation
  [1] TRUE
  
  > objTemp$observations=intamap:::rotateAnisotropicData(objTemp$observations,objTemp$anisPar)
  > vario = autofitVariogram(objTemp$formulaString,objTemp$observations,model="Sph")$var_model
  > print(vario, digits = 3)
    model   psill range
  1   Nug 0.00589  0.00
  2   Sph 0.63573  0.67
  > vmod
    model psill range ang1 anis1
  1   Sph     1     1  135   0.5
  > 
  > 
  > 
  > vmod = vgm(1,"Sph",.3,anis=c(90,0.5))
  > krigingObject$observations = krige(z~1,d,pts,vmod,nsim=1,nmax=100,beta=0)
  [using conditional Gaussian simulation]
  > object = preProcess(krigingObject)
  > objTemp=estimateAnisotropy(object)
  > objTemp$anisPar
  $ratio
  [1] 1.244549
  
  $direction
  [1] -10.91575
  
  $Q
            Q11      Q22      Q12
  [1,] 92.11681 138.1476 9.220176
  
  $doRotation
  [1] TRUE
  
  > objTemp$observations=intamap:::rotateAnisotropicData(objTemp$observations,objTemp$anisPar)
  > vario = autofitVariogram(objTemp$formulaString,objTemp$observations,model="Sph")$var_model
  > print(vario, digits = 3)
    model psill range
  1   Nug 0.000 0.000
  2   Sph 0.805 0.176
  > vmod
    model psill range ang1 anis1
  1   Sph     1   0.3   90   0.5
  > if (plotFigs) {
  +   p1 = plot(variogram(sim1~1,object$observations,alpha=c(0,90)),vmod,ylim = c(0,1.2),xlim=c(0,0.6),main="orig,orig")
  +   p2 = plot(variogram(sim1~1,object$observations,alpha=c(0,90)),vario,ylim = c(0,1.2),xlim=c(0,0.6),main="orig,fitted")
  +   p3 = plot(variogram(sim1~1,objTemp$observations,alpha=c(0,90)),vmod,ylim = c(0,1.2),xlim=c(0,0.6),main="rot,orig")
  +   p4 = plot(variogram(sim1~1,objTemp$observations,alpha=c(0,90)),vario,ylim = c(0,1.2),xlim=c(0,0.6),main = "rot,fitted")
  + 
  +   print(p1,position = c(0,0.5,0.5,1),more = TRUE)
  +   print(p2,position = c(0.5,0.5,1,1),more = TRUE)
  +   print(p3,position = c(0,0,0.5,0.5),more = TRUE)
  +   print(p4,position = c(0.5,0,1,0.5))
  + 
  +   plot(variogram(sim1~1,objTemp$observations,alpha=c(seq(0,150,30))),vmod,ylim = c(0,1.2),xlim=c(0,0.6),main="rot,orig")
  + 
  +   spplot(object$observations,"sim1",col.regions=bpy.colors())
  +   spplot(objTemp$observations,"sim1",col.regions=bpy.colors())
  + }
  > 
  > 
  > 
  > 
  > data(sic2004)
  > coordinates(sic.val)=~x+y
  > sic.val$value=sic.val$dayx
  > x = sic.test$x
  > y = sic.test$y
  > 
  > coordinates(sic.test)=~x+y
  > 
  > stest = sic.test[(x > -10000 & x < 140000 & y > 100000 & y < 240000),]
  >  
  > obj<-createIntamapObject(formulaString = "joker~1",
  +    observations=sic.val,
  +    predictionLocations = stest,
  +    params = list(doAnisotropy = TRUE),
  +    class = "automap" )
  > obj = preProcess(obj)
  > obj = estimateParameters(obj)
  > obj$anisPar
  $ratio
  [1] 1.350924
  
  $direction
  [1] 25.93149
  
  $Q
                Q11         Q22           Q12
  [1,] 9.948346e-06 1.43261e-05 -2.787868e-06
  
  $doRotation
  [1] TRUE
  
  > obj = spatialPredict(obj)
  > obj = postProcess(obj)
  > summary(as.data.frame(obj$outputTable), digits = 3)
         x                y               mean        variance    
   Min.   : -8351   Min.   :103106   Min.   :121   Min.   : 4535  
   1st Qu.: 20626   1st Qu.:136147   1st Qu.:146   1st Qu.:19001  
   Median : 63885   Median :167801   Median :153   Median :19935  
   Mean   : 59128   Mean   :170171   Mean   :173   Mean   :18875  
   3rd Qu.: 94458   3rd Qu.:210897   3rd Qu.:157   3rd Qu.:20282  
   Max.   :131632   Max.   :239246   Max.   :544   Max.   :20320  
  > 
  > proc.time()
     user  system elapsed 
    11.18    0.75   11.93 
Running the tests in 'tests/block.R' failed.
Complete output:
  > library(intamap)
  Loading required package: sp
  > 
  > # observations = Something from Java...
  > # Until then we use the Meuse data:
  > data(meuse)
  > observations = data.frame(x = meuse$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$outputTable, digits = 4)
         x                y               mean          variance      
   Min.   :179530   Min.   :330383   Min.   :5.121   Min.   :0.00926  
   1st Qu.:179530   1st Qu.:331165   1st Qu.:5.847   1st Qu.:0.01225  
   Median :180051   Median :331946   Median :6.031   Median :0.06452  
   Mean   :180051   Mean   :331946   Mean   :6.048   Mean   :0.10364  
   3rd Qu.:180572   3rd Qu.:332728   3rd Qu.:6.374   3rd Qu.:0.16456  
   Max.   :180572   Max.   :333509   Max.   :6.785   Max.   :0.33726  
     excprob5.9        cumdistr5.9       quantile0.1         fat7        
   Min.   :0.000287   Min.   :0.01470   Min.   :4.783   Min.   :0.01747  
   1st Qu.:0.427082   1st Qu.:0.03642   1st Qu.:5.525   1st Qu.:0.09583  
   Median :0.698870   Median :0.30113   Median :5.716   Median :0.15141  
   Mean   :0.611131   Mean   :0.38887   Mean   :5.693   Mean   :0.16795  
   3rd Qu.:0.963581   3rd Qu.:0.57292   3rd Qu.:6.005   3rd Qu.:0.24177  
   Max.   :0.985297   Max.   :0.99971   Max.   :6.264   Max.   :0.35838  
      fatVar7             blockMax      blockMaxVar        blockMin    
   Min.   :0.0005592   Min.   :6.935   Min.   :0.1121   Min.   :3.981  
   1st Qu.:0.0032834   1st Qu.:7.544   1st Qu.:0.1568   1st Qu.:4.351  
   Median :0.0062627   Median :7.799   Median :0.1728   Median :4.459  
   Mean   :0.0130755   Mean   :7.745   Mean   :0.2076   Mean   :4.502  
   3rd Qu.:0.0239857   3rd Qu.:8.104   3rd Qu.:0.2323   3rd Qu.:4.673  
   Max.   :0.0381382   Max.   :8.265   Max.   :0.3682   Max.   :4.977  
  > 
  > proc.time()
     user  system elapsed 
     3.51    0.40    3.92 
Running the tests in 'tests/idw.R' failed.
Complete output:
  > library(intamap)
  Loading required package: sp
  > 
  > set.seed(13131)
  > 
  > # set up data:
  > data(meuse)
  > coordinates(meuse) = ~x+y
  > meuse$value = log(meuse$zinc)
  > data(meuse.grid)
  > gridded(meuse.grid) = ~x+y
  > proj4string(meuse) = CRS("+init=epsg:28992")
  Warning message:
  In CPL_crs_from_input(x) :
    GDAL Message 1: +init=epsg:XXXX syntax is deprecated. It might return a CRS with a non-EPSG compliant axis order.
  > proj4string(meuse.grid) = CRS("+init=epsg:28992")
  > 
  > # set up intamap object:
  > idwObject = createIntamapObject(
  + 	observations = meuse,
  + 	formulaString=as.formula(zinc~1),
  +   predictionLocations = meuse.grid,
  + 	targetCRS = "+init=epsg:3035",
  + 	constantBias = 0,
  + 	classes = "idw"
  + )
  [1] "rgdal has been retired. \n                           As a result of this, some of the checks on projections in the \n                           intamap package have  disappeared"
  > 
  > # run test:
  > checkSetup(idwObject)
  Checking object ... OK
  > 
  > # do interpolation steps:
  > idwObject = preProcess(idwObject)
  > idwObject = estimateParameters(idwObject, idpRange = seq(0.25,2.75,.5), nfold=3) # faster
  [1] "best idp value found is 2.25 rmse 272.204794157114"
  > idwObject = spatialPredict(idwObject)
  > idwObject = postProcess(idwObject)
  > 
  > # generate some output:
  > summary(as.data.frame(idwObject$outputTable))
         x                 y                mean       
   Min.   :4020568   Min.   :3102569   Min.   : 116.6  
   1st Qu.:4021552   1st Qu.:3103401   1st Qu.: 253.4  
   Median :4022145   Median :3104136   Median : 344.4  
   Mean   :4022165   Mean   :3104246   Mean   : 411.2  
   3rd Qu.:4022771   3rd Qu.:3104998   3rd Qu.: 511.9  
   Max.   :4023832   Max.   :3106558   Max.   :1827.1  
  > 
  > proc.time()
     user  system elapsed 
     4.56    0.50    5.06 
Running the tests in 'tests/javaR.R' failed.
Complete output:
  > 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$zinc))
  > # If you send a field just with 3 columns (x,y & z), we can let R figure
  > # out itself which names they have, for creation of a spatial object:
  > obsNames = names(observations)
  > coordinates(observations) = as.formula(paste("~",obsNames[1], "+", obsNames[2]))
  > set.seed(13531)
  > predictionLocations = spsample(observations, 50, "regular")
  > proj4string(observations) <- CRS("+proj=stere +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +units=m")
  > proj4string(predictionLocations) <- CRS("+proj=stere +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +units=m")
  > 
  > # We dont know the projection of the data at this stage, assume it is
  > # somehow metric
  > 
  > krigingObject = createIntamapObject(
  + 	observations = observations,
  + 	predictionLocations = predictionLocations,
  +   targetCRS = "+init=epsg:3035",
  + #	boundCRS = "+proj=laea +lat_0=48 +lon_0=9 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m",
  + #	boundCRS = boundCRS,
  + #	boundaries = boundaries,
  +   formulaString = as.formula(paste(obsNames[3],"~1")),
  + 	params =  list(confProj = TRUE, thresh = quantile(observations$value,0.9)),
  +   outputWhat = list(mean=TRUE, variance=TRUE, excprob = 5.9, cumdistr = 5.9, 
  + 		quantile = .1), class = "automap"
  + 
  + )
  [1] "rgdal has been retired. \n                           As a result of this, some of the checks on projections in the \n                           intamap package have  disappeared"
  > 
  > checkSetup(krigingObject)
  Checking object ... OK
  > krigingObject = preProcess(krigingObject)
  > krigingObject = estimateParameters(krigingObject)
  > krigingObject = spatialPredict(krigingObject)
  > krigingObject = postProcess(krigingObject)
  Warning message:
  In CPL_crs_from_input(x) :
    GDAL Message 1: +init=epsg:XXXX syntax is deprecated. It might return a CRS with a non-EPSG compliant axis order.
  > 
  > summary(krigingObject$outputTable)
         x                 y                mean          variance     
   Min.   :4021158   Min.   :3102915   Min.   :4.805   Min.   :0.0989  
   1st Qu.:4021733   1st Qu.:3103766   1st Qu.:5.481   1st Qu.:0.1427  
   Median :4022423   Median :3104617   Median :5.651   Median :0.3373  
   Mean   :4022423   Mean   :3104617   Mean   :5.973   Mean   :0.3976  
   3rd Qu.:4023113   3rd Qu.:3105468   3rd Qu.:6.459   3rd Qu.:0.6491  
   Max.   :4023688   Max.   :3106318   Max.   :7.524   Max.   :0.7891  
     excprob5.9        cumdistr5.9         quantile0.1   
   Min.   :0.002098   Min.   :0.0000835   Min.   :4.315  
   1st Qu.:0.137907   1st Qu.:0.2153550   1st Qu.:4.635  
   Median :0.380798   Median :0.6192015   Median :5.105  
   Mean   :0.490286   Mean   :0.5097141   Mean   :5.211  
   3rd Qu.:0.784645   3rd Qu.:0.8620925   3rd Qu.:5.700  
   Max.   :0.999917   Max.   :0.9979020   Max.   :6.971  
  > 
  > 
  > proc.time()
     user  system elapsed 
     2.51    0.42    2.92 
Running the tests in 'tests/unbiased.R' failed.
Complete output:
  > options(error = recover)
  > library(intamap)
  Loading required package: sp
  > 
  > data(meuse)
  > coordinates(meuse) = ~x+y
  > data(meuse.grid)
  > coordinates(meuse.grid) = ~x+y
  > 
  > meuse$zinc = log(meuse$zinc)
  > 
  > set.seed(112233)
  > krigingObject = createIntamapObject(
  + 	observations = meuse,
  + 	predictionLocations = spsample(meuse.grid,5,"regular"),
  + #	targetCRS = "+init=epsg:3035",
  + #	boundCRS = "+proj=laea +lat_0=48 +lon_0=9 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m",
  + #	boundCRS = boundCRS,
  + #	boundaries = boundaries,
  +   formulaString = as.formula("zinc~1"),
  + 	params =  list(debug.level = 1),
  +   outputWhat = list(mean = TRUE, variance = TRUE, MOK=7,IWQSEL = 7,excprob = 7.0)
  + )
  > class(krigingObject) = c("automap")
  > 
  > checkSetup(krigingObject)
  Checking object ... OK
  > krigingObject = preProcess(krigingObject)
  > krigingObject = estimateParameters(krigingObject)
  > krigingObject = spatialPredict(krigingObject)
  [using ordinary kriging]
  drawing 100 GLS realisations of beta...
  [using conditional Gaussian simulation]
  [1] "Finished simulations"
  > krigingObject = postProcess(krigingObject)
  > summary(krigingObject$outputTable)
         x                y               mean          variance     
   Min.   :180026   Min.   :330484   Min.   :5.991   Min.   :0.1473  
   1st Qu.:180026   1st Qu.:331281   1st Qu.:6.111   1st Qu.:0.1994  
   Median :180026   Median :332078   Median :6.230   Median :0.2515  
   Mean   :180026   Mean   :332078   Mean   :6.217   Mean   :0.3950  
   3rd Qu.:180026   3rd Qu.:332874   3rd Qu.:6.330   3rd Qu.:0.5189  
   Max.   :180026   Max.   :333671   Max.   :6.430   Max.   :0.7863  
        MOK7          IWQSEL7         excprob7       
   Min.   :6.605   Min.   :6.432   Min.   :0.004292  
   1st Qu.:6.724   1st Qu.:6.576   1st Qu.:0.033385  
   Median :6.844   Median :6.720   Median :0.062477  
   Mean   :6.831   Mean   :6.743   Mean   :0.109041  
   3rd Qu.:6.944   3rd Qu.:6.899   3rd Qu.:0.161415  
   Max.   :7.044   Max.   :7.078   Max.   :0.260354  
  > 
  > 
  > class(krigingObject) = c("yamamoto")
  > 
  > checkSetup(krigingObject)
  Checking object ... OK
  > krigingObject = preProcess(krigingObject)
  > krigingObject = estimateParameters(krigingObject)
  > krigingObject = spatialPredict(krigingObject)
  Conditional simulation  1 
  Conditional simulation  2 
  Conditional simulation  3 
  Conditional simulation  4 
  Conditional simulation  5 
  Conditional simulation  6 
  Conditional simulation  7 
  Conditional simulation  8 
  Conditional simulation  9 
  Conditional simulation  10 
  Conditional simulation  11 
  Conditional simulation  12 
  Conditional simulation  13 
  Conditional simulation  14 
  Conditional simulation  15 
  Conditional simulation  16 
  Conditional simulation  17 
  Conditional simulation  18 
  Conditional simulation  19 
  Conditional simulation  20 
  Conditional simulation  21 
  Conditional simulation  22 
  Conditional simulation  23 
  Conditional simulation  24 
  Conditional simulation  25 
  Conditional simulation  26 
  Conditional simulation  27 
  Conditional simulation  28 
  Conditional simulation  29 
  Conditional simulation  30 
  Conditional simulation  31 
  Conditional simulation  32 
  Conditional simulation  33 
  Conditional simulation  34 
  Conditional simulation  35 
  Conditional simulation  36 
  Conditional simulation  37 
  Conditional simulation  38 
  Conditional simulation  39 
  Conditional simulation  40 
  Conditional simulation  41 
  Conditional simulation  42 
  Conditional simulation  43 
  Conditional simulation  44 
  Conditional simulation  45 
  Conditional simulation  46 
  Conditional simulation  47 
  Conditional simulation  48 
  Conditional simulation  49 
  Conditional simulation  50 
  Conditional simulation  51 
  Conditional simulation  52 
  Conditional simulation  53 
  Conditional simulation  54 
  Conditional simulation  55 
  Conditional simulation  56 
  Conditional simulation  57 
  Conditional simulation  58 
  Conditional simulation  59 
  Conditional simulation  60 
  Conditional simulation  61 
  Conditional simulation  62 
  Conditional simulation  63 
  Conditional simulation  64 
  Conditional simulation  65 
  Conditional simulation  66 
  Conditional simulation  67 
  Conditional simulation  68 
  Conditional simulation  69 
  Conditional simulation  70 
  Conditional simulation  71 
  Conditional simulation  72 
  Conditional simulation  73 
  Conditional simulation  74 
  Conditional simulation  75 
  Conditional simulation  76 
  Conditional simulation  77 
  Conditional simulation  78 
  Conditional simulation  79 
  Conditional simulation  80 
  Conditional simulation  81 
  Conditional simulation  82 
  Conditional simulation  83 
  Conditional simulation  84 
  Conditional simulation  85 
  Conditional simulation  86 
  Conditional simulation  87 
  Conditional simulation  88 
  Conditional simulation  89 
  Conditional simulation  90 
  Conditional simulation  91 
  Conditional simulation  92 
  Conditional simulation  93 
  Conditional simulation  94 
  Conditional simulation  95 
  Conditional simulation  96 
  Conditional simulation  97 
  Conditional simulation  98 
  Conditional simulation  99 
  Conditional simulation  100 
  > krigingObject = postProcess(krigingObject)
  > summary(krigingObject$outputTable)
         x                y               mean          variance     
   Min.   :180026   Min.   :330484   Min.   :5.842   Min.   :0.5709  
   1st Qu.:180026   1st Qu.:331281   1st Qu.:5.902   1st Qu.:0.5751  
   Median :180026   Median :332078   Median :5.963   Median :0.5793  
   Mean   :180026   Mean   :332078   Mean   :6.024   Mean   :0.6420  
   3rd Qu.:180026   3rd Qu.:332874   3rd Qu.:6.115   3rd Qu.:0.6775  
   Max.   :180026   Max.   :333671   Max.   :6.268   Max.   :0.7757  
        MOK7          IWQSEL7         excprob7      
   Min.   :6.645   Min.   :6.806   Min.   :0.08489  
   1st Qu.:6.705   1st Qu.:6.831   1st Qu.:0.08962  
   Median :6.766   Median :6.857   Median :0.09436  
   Mean   :6.827   Mean   :6.902   Mean   :0.11574  
   3rd Qu.:6.918   3rd Qu.:6.950   3rd Qu.:0.13117  
   Max.   :7.071   Max.   :7.043   Max.   :0.16799  
  > 
  > proc.time()
     user  system elapsed 
     3.07    0.43    3.51