intamapInteractive

package dependencies

Package required but not available: ‘spcosa’

See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
manual.

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tests

  Running ‘anisotropyChoice.R’ [9s/16s]
  Running ‘biasCorr.R’ [10s/24s]
  Running ‘findLocalBias.R’ [8s/20s]
  Running ‘findRegionalBias.R’ [9s/18s]
  Running ‘optimizingTest.R’ [90m/80m]
Running the tests in ‘tests/optimizingTest.R’ failed.
Complete output:
  > options(error = recover)
  > #test = TRUE
  > test = FALSE
  > mantest = FALSE
  > set.seed(1)
  > library(intamapInteractive)
  Loading required package: intamap
  Loading required package: sp
  > library(gstat)
  > #require(maptools)
  > # for SIC2004 dataset
  > data(sic2004)
  > coordinates(sic.val) = ~x+y
  > observations = sic.val["dayx"] 
  > coordinates(sic.grid)=~x+y
  > predGrid = sic.grid
  > 
  > #Finding the polygon for the candidate locations
  > bb = bbox(predGrid)
  > boun = SpatialPoints(data.frame(x=c(bb[1,1],bb[1,2],bb[1,2],bb[1,1],bb[1,1]),
  +                                 y=c(bb[2,1],bb[2,1],bb[2,2],bb[2,2],bb[2,1])))
  > Srl = Polygons(list(Polygon(boun)),ID = as.character(1))
  > candidates = SpatialPolygonsDataFrame(SpatialPolygons(list(Srl)),
  +                                       data = data.frame(ID=1))
  > 
  > # Limits the number of prediction locations to have faster UK 
  > # computations
  > nGrid = dim(coordinates(predGrid))[1]
  > predGrid = predGrid[sample(seq(1,nGrid),1000),]
  > # Fits the variogram model (using function fit.variogram from package
  > # gstat)
  > model = fit.variogram(variogram(dayx~x+y, sic.val), vgm(50, "Sph", 250000, 250))
  > #plot(variogram(dayx~x+y, sic.val), model=model)
  > # Computes the Mukv of the current network
  > initMukv <- calculateMukv(observations, predGrid, model, formulaString = dayx~x+y)
  OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
  OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).

tests

  Running ‘anisotropyChoice.R’ [10s/17s]
  Running ‘biasCorr.R’ [11s/14s]
  Running ‘findLocalBias.R’ [9s/13s]
  Running ‘findRegionalBias.R’ [10s/14s]
  Running ‘optimizingTest.R’ [90m/73m]
Running the tests in ‘tests/optimizingTest.R’ failed.
Complete output:
  > options(error = recover)
  > #test = TRUE
  > test = FALSE
  > mantest = FALSE
  > set.seed(1)
  > library(intamapInteractive)
  Loading required package: intamap
  Loading required package: sp
  > library(gstat)
  > #require(maptools)
  > # for SIC2004 dataset
  > data(sic2004)
  > coordinates(sic.val) = ~x+y
  > observations = sic.val["dayx"] 
  > coordinates(sic.grid)=~x+y
  > predGrid = sic.grid
  > 
  > #Finding the polygon for the candidate locations
  > bb = bbox(predGrid)
  > boun = SpatialPoints(data.frame(x=c(bb[1,1],bb[1,2],bb[1,2],bb[1,1],bb[1,1]),
  +                                 y=c(bb[2,1],bb[2,1],bb[2,2],bb[2,2],bb[2,1])))
  > Srl = Polygons(list(Polygon(boun)),ID = as.character(1))
  > candidates = SpatialPolygonsDataFrame(SpatialPolygons(list(Srl)),
  +                                       data = data.frame(ID=1))
  > 
  > # Limits the number of prediction locations to have faster UK 
  > # computations
  > nGrid = dim(coordinates(predGrid))[1]
  > predGrid = predGrid[sample(seq(1,nGrid),1000),]
  > # Fits the variogram model (using function fit.variogram from package
  > # gstat)
  > model = fit.variogram(variogram(dayx~x+y, sic.val), vgm(50, "Sph", 250000, 250))
  > #plot(variogram(dayx~x+y, sic.val), model=model)
  > # Computes the Mukv of the current network
  > initMukv <- calculateMukv(observations, predGrid, model, formulaString = dayx~x+y)

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