Running 'meuse_psgp.R' [3s]
  Comparing 'meuse_psgp.Rout' to 'meuse_psgp.Rout.save' ... OK
  Running 'psgp.R' [168s]
Running the tests in 'tests/psgp.R' failed.
Complete output:
  > 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
  > 
  > proc.time()
     user  system elapsed 
     3.09    0.32    3.40