Package: gmGeostats 0.11.3
K. Gerald van den Boogaart
gmGeostats: Geostatistics for Compositional Analysis
Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Authors:
gmGeostats_0.11.3.tar.gz
gmGeostats_0.11.3.tar.gz(r-4.5-noble)gmGeostats_0.11.3.tar.gz(r-4.4-noble)
gmGeostats_0.11.3.tgz(r-4.4-emscripten)gmGeostats_0.11.3.tgz(r-4.3-emscripten)
gmGeostats.pdf |gmGeostats.html✨
gmGeostats/json (API)
NEWS
| # Install 'gmGeostats' in R: |
| install.packages('gmGeostats', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- NGSAustralia - National Geochemical Survey of Australia: soil data
- Windarling - Ore composition of a bench at a mine in Windarling, West Australia.
- gsi.validModels - Generate D-variate variogram models
- vg.Exp - Generate D-variate variogram models
- vg.Exponential - Generate D-variate variogram models
- vg.Gau - Generate D-variate variogram models
- vg.Gauss - Generate D-variate variogram models
- vg.Sph - Generate D-variate variogram models
- vg.Spherical - Generate D-variate variogram models
- vg.exp - Generate D-variate variogram models
- vg.gauss - Generate D-variate variogram models
- vg.sph - Generate D-variate variogram models
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:63e34ff8e2. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 09 2024 |
| R-4.5-linux-x86_64 | NOTE | Sep 09 2024 |
Exports:accuracyanaanaBackwardanaForwardanis_GSLIBpar2Aanis2D_par2Aanis3D_par2AAnisotropyRangeMatrixAnisotropyScalingas.AnisotropyRangeMatrixas.AnisotropyScalingas.CompLinModCoRegas.DataFrameStackas.directorVectoras.gmCgramas.gmEVarioas.gmSpatialModelas.gstatas.gstatVariogramas.LMCAnisCompoas.logratioVariogramas.logratioVariogramAnisotropyas.variogramModelCholeskyDecompositionconstructMaskDataFrameStackDSparsfit_lmcgetGridOrdergetMaskgetStackElementgetTellusgmApplygridOrder_arraygridOrder_GSLibgridOrder_gstatgridOrder_spgsi.DSgsi.EVario2Dgsi.EVario3Dgsi.gstatCokriging2compogsi.gstatCokriging2rmultgsi.produceVgsi.TurningBandsimage_cokrigedis.anisotropySpecificationis.isotropicKrigingNeighbourhoodLeaveOneOutLMCAnisCompologratioVariogramlogratioVariogram_gmSpatialModelMafmake.gmCompositionalGaussianSpatialModelmake.gmCompositionalMPSSpatialModelmake.gmMultivariateGaussianSpatialModelndirectionsNfoldCrossValidationnoSpatCorr.testnoStackDimpairsmapprecisionpredictPredictpwlrmapRJDSequentialSimulationsetCgramsetGridOrdersetGridOrder_arraysetGridOrder_spsetMasksetStackElementsortDataInGridspatialDecorrelationspatialGridAcompspatialGridRmultspectralcolorssphTransstackDimstackDim<-swarmPlotswathTurningBandsunmaskUWEDGEvalidatevariogramvariogram_gmSpatialModelvariogramModelPlotwrite.GSLibxvErrorMeasures
Dependencies:abindbayesmbootclassclassIntcodetoolscompositionsDBIDEoptimRe1071FNNforeachgstatintervalsiteratorsKernSmoothlatticemagrittrMASSproxyRColorBrewerRcppRcppArmadillorlangrobustbases2sfsftimespspacetimestarstensorAunitswkxtszoo
How to register new layer datatypes
Rendered fromregister_new_layer_datatype.Rmdusingknitr::rmarkdownon Sep 09 2024.Last update: 2022-11-30
Started: 2021-07-15
Multivariate geostatistics with gmGeostats
Rendered fromgmGeostats.Rmdusingknitr::rmarkdownon Sep 09 2024.Last update: 2021-07-15
Started: 2020-09-16
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract rows of a DataFrameStack | [.DataFrameStack |
| Subsetting of gmCgram variogram structures | [.gmCgram |
| Subsetting of logratioVariogram objects | [.logratioVariogramAnisotropy `[.logratioVariogram` |
| Subsetting of gmCgram variogram structures | [[.gmCgram |
| Combination of gmCgram variogram structures | +.gmCgram |
| Compute accuracy and precision | accuracy accuracy.data.frame accuracy.DataFrameStack |
| Flow anamorphosis transform Compute a transformation that gaussianizes a certain data set | ana |
| Backward gaussian anamorphosis backward transformation to multivariate gaussian scores | anaBackward |
| Forward gaussian anamorphosis forward transformation to multivariate gaussian scores | anaForward |
| Produce anisotropy scaling matrix from angle and anisotropy ratios | anis2D_par2A anis3D_par2A anis_GSLIBpar2A |
| Force a matrix to be anisotropy range matrix, | AnisotropyRangeMatrix as.AnisotropyRangeMatrix.AnisotropyScaling |
| Convert to anisotropy scaling matrix | AnisotropyScaling |
| Force a matrix to be anisotropy range matrix, | as.AnisotropyRangeMatrix as.AnisotropyRangeMatrix.AnisotropyRangeMatrix as.AnisotropyRangeMatrix.default |
| Convert to anisotropy scaling matrix | as.AnisotropyScaling as.AnisotropyScaling.AnisotropyRangeMatrix as.AnisotropyScaling.AnisotropyScaling as.AnisotropyScaling.numeric |
| Convert a stacked data frame into an array | as.array.DataFrameStack |
| Recast a model to the variogram model of package "compositions" | as.CompLinModCoReg as.CompLinModCoReg.CompLinModCoReg as.CompLinModCoReg.LMCAnisCompo |
| Express a direction as a director vector | as.directorVector as.directorVector.azimuth as.directorVector.azimuthInterval as.directorVector.default |
| Convert a gmCgram object to an (evaluable) function | as.function.gmCgram predict.gmCgram |
| Convert theoretical structural functions to gmCgram format | as.gmCgram as.gmCgram.default as.gmCgram.LMCAnisCompo as.gmCgram.variogramModel as.gmCgram.variogramModelList |
| Convert empirical structural function to gmEVario format | as.gmEVario as.gmEVario.default as.gmEVario.gstatVariogram as.gmEVario.logratioVariogram as.gmEVario.logratioVariogramAnisotropy |
| Recast spatial object to gmSpatialModel format | as.gmSpatialModel as.gmSpatialModel.default as.gmSpatialModel.gstat |
| Convert a regionalized data container to gstat | as.gstat as.gstat.default |
| Represent an empirical variogram in "gstatVariogram" format | as.gstatVariogram as.gstatVariogram.default as.gstatVariogram.gmEVario as.gstatVariogram.logratioVariogram as.gstatVariogram.logratioVariogramAnisotropy |
| Convert a stacked data frame into a list of data.frames | as.list.DataFrameStack |
| Recast compositional variogram model to format LMCAnisCompo | as.LMCAnisCompo as.LMCAnisCompo.CompLinModCoReg as.LMCAnisCompo.gmCgram as.LMCAnisCompo.gstat as.LMCAnisCompo.LMCAnisCompo as.LMCAnisCompo.variogramModelList gstat2LMCAnisCompo |
| Recast empirical variogram to format logratioVariogram | as.logratioVariogram as.logratioVariogram.gmEVario as.logratioVariogram.gstatVariogram as.logratioVariogram.logratioVariogram |
| Convert empirical variogram to "logratioVariogramAnisotropy" | as.logratioVariogramAnisotropy as.logratioVariogramAnisotropy.default as.logratioVariogramAnisotropy.logratioVariogram as.logratioVariogramAnisotropy.logratioVariogramAnisotropy |
| Convert an LMC variogram model to gstat format | as.variogramModel as.variogramModel.CompLinModCoReg as.variogramModel.default as.variogramModel.gmCgram as.variogramModel.LMCAnisCompo |
| Create a parameter set specifying a LU decomposition simulation algorithm | CholeskyDecomposition |
| Colored biplot for gemeralised diagonalisations Colored biplot method for objects of class genDiag | coloredBiplot.genDiag |
| Constructs a mask for a grid | constructMask |
| Create a data frame stack | as.DataFrameStack as.DataFrameStack.array as.DataFrameStack.data.frame as.DataFrameStack.list DataFrameStack DataFrameStack.array DataFrameStack.data.frame DataFrameStack.list |
| Return the dimnames of a DataFrameStack | dimnames,Spatial-method dimnames.DataFrameStack |
| Create a parameter set specifying a direct sampling algorithm | DirectSamplingParameters DSpars |
| Empirical structural function specification | EmpiricalStructuralFunctionSpecification-class |
| Fit an LMC to an empirical variogram | fit_lmc fit_lmc.default fit_lmc.gstatVariogram fit_lmc.logratioVariogram fit_lmc.logratioVariogramAnisotropy |
| Get the mask info out of a spatial data object | getMask getMask.default getMask.SpatialPixels getMask.SpatialPixelsDataFrame getMask.SpatialPointsDataFrame |
| Set or get the i-th data frame of a data.frame stack | getStackElement getStackElement.DataFrameStack getStackElement.default getStackElement.list setStackElement setStackElement.data.frame setStackElement.DataFrameStack setStackElement.default setStackElement.list |
| Download the Tellus survey data set (NI) | getTellus |
| Apply Functions Over Array or DataFrameStack Margins | gmApply gmApply.DataFrameStack gmApply.default |
| parameters for Spatial Gaussian methods of any kind | gmGaussianMethodParameters-class |
| parameters for Gaussian Simulation methods | gmGaussianSimulationAlgorithm-class |
| parameters for Multiple-Point Statistics methods | gmMPSParameters-class |
| Neighbourhood description | gmNeighbourhoodSpecification-class |
| Parameter specification for a spatial simulation algorithm | gmSimulationAlgorithm-class |
| General description of a spatial data container | gmSpatialDataContainer-class |
| Parameter specification for any spatial method | gmSpatialMethodParameters-class |
| Conditional spatial model data container | as.gstat,gmSpatialModel-method gmSpatialModel-class logratioVariogram,gmSpatialModel-method variogram,gmSpatialModel-method |
| MPS training image class | gmTrainingImage-class |
| General description of a spatial model | gmUnconditionalSpatialModel-class |
| Validation strategy description | gmValidationStrategy-class |
| Superclass for grid or nothing | GridOrNothing-class |
| Compute covariance matrix oout of locations | gsi.calcCgram |
| Cokriging of all sorts, internal function | gsi.Cokriging |
| Internal function, conditional turning bands realisations | gsi.CondTurningBands |
| Workhorse function for direct sampling | gsi.DS |
| Empirical variogram or covariance function in 2D | gsi.EVario2D |
| Empirical variogram or covariance function in 3D | gsi.EVario3D |
| Reorganisation of cokriged compositions | gsi.gstatCokriging2compo gsi.gstatCokriging2compo.data.frame gsi.gstatCokriging2compo.default gsi.gstatCokriging2rmult gsi.gstatCokriging2rmult.data.frame gsi.gstatCokriging2rmult.default |
| extract information about the original data, if available | gsi.getV gsi.orig |
| Create a matrix of logcontrasts and name prefix | gsi.produceV |
| Internal function, unconditional turning bands realisations | gsi.TurningBands |
| Check presence of missings check presence of missings in a data.frame | has.missings.data.frame |
| Plot an image of gridded data | image_cokriged image_cokriged.default image_cokriged.spatialGridAcomp image_cokriged.spatialGridRmult |
| Plot variogram maps for anisotropic logratio variograms | image.logratioVariogramAnisotropy |
| Image method for mask objects | image.mask |
| Check for any anisotropy class | is.anisotropySpecification |
| Check for anisotropy of a theoretical variogram | is.isotropic |
| Create a parameter set of local for neighbourhood specification. | KrigingNeighbourhood |
| Specify the leave-one-out strategy for validation of a spatial model | LeaveOneOut |
| Length, and number of columns or rows | length.gmCgram ncol.gmCgram nrow.gmCgram |
| Create a anisotropic model for regionalized compositions | LMCAnisCompo |
| Empirical logratio variogram calculation | logratioVariogram |
| Variogram method for gmSpatialModel objects | logratioVariogram_gmSpatialModel variogram_gmSpatialModel |
| Logratio variogram of a compositional data | logratioVariogram,acomp-method |
| Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram | genDiag Maf Maf.acomp Maf.aplus Maf.ccomp Maf.data.frame Maf.rcomp Maf.rmult Maf.rplus RJD RJD.acomp RJD.default RJD.rcomp UWEDGE UWEDGE.acomp UWEDGE.default UWEDGE.rcomp |
| Construct a Gaussian gmSpatialModel for regionalized compositions | make.gmCompositionalGaussianSpatialModel |
| Construct a Multi-Point gmSpatialModel for regionalized compositions | make.gmCompositionalMPSSpatialModel |
| Construct a Gaussian gmSpatialModel for regionalized multivariate data | make.gmMultivariateGaussianSpatialModel |
| Mean accuracy | mean.accuracy |
| Average measures of spatial decorrelation | mean.spatialDecorrelationMeasure |
| Structural function model specification | ModelStructuralFunctionSpecification-class |
| Number of directions of an empirical variogram | ndirections ndirections.azimuth ndirections.azimuthInterval ndirections.default ndirections.gmEVario ndirections.gstatVariogram ndirections.logratioVariogram ndirections.logratioVariogramAnisotropy |
| Specify a strategy for validation of a spatial model | NfoldCrossValidation |
| National Geochemical Survey of Australia: soil data | NGSAustralia |
| Test for lack of spatial correlation | noSpatCorr.test noSpatCorr.test.data.frame noSpatCorr.test.default noSpatCorr.test.matrix |
| Multiple maps Matrix of maps showing different combinations of components of a composition, user defined | pairsmap pairsmap.default pairsmap.SpatialPointsDataFrame |
| Plot method for accuracy curves | plot.accuracy |
| Draw cuves for covariance/variogram models | plot.gmCgram |
| Plot empirical variograms | plot.gmEVario |
| Plot variogram lines of empirical directional logratio variograms | plot.logratioVariogramAnisotropy |
| Plotting method for swarmPlot objects | plot.swarmPlot |
| Precision calculations | precision precision.accuracy |
| Predict method for objects of class 'gmSpatialModel' | Predict predict Predict,gmSpatialModel,ANY,ANY-method Predict,gmSpatialModel,ANY,gmCholeskyDecomposition-method Predict,gmSpatialModel,ANY,gmDirectSamplingParameters-method Predict,gmSpatialModel,ANY,gmNeighbourhoodSpecification-method Predict,gmSpatialModel,ANY,gmSequentialSimulation-method Predict,gmSpatialModel,ANY,gmTurningBands-method predict,gmSpatialModel-method predict.gmSpatialModel predict_gmSpatialModel |
| Predict method for generalised diagonalisation objects | predict.genDiag |
| Compute model variogram values Evaluate the variogram model provided at some lag vectors | predict.LMCAnisCompo |
| Print method for mask objects | print.mask |
| Compositional maps, pairwise logratios Matrix of maps showing different combinations of components of a composition, in pairwise logratios | pwlrmap |
| Create a parameter set specifying a gaussian sequential simulation algorithm | SequentialSimulation |
| Generate D-variate variogram models | gsi.validModels setCgram vg.Exp vg.exp vg.Exponential vg.Gau vg.Gauss vg.gauss vg.Sph vg.sph vg.Spherical |
| Set or get the ordering of a grid | getGridOrder gridOrder_array gridOrder_GSLib gridOrder_gstat gridOrder_sp setGridOrder setGridOrder_array setGridOrder_sp |
| Set a mask on an object | setMask setMask.data.frame setMask.DataFrameStack setMask.default setMask.GridTopology setMask.SpatialGrid setMask.SpatialPoints |
| Reorder data in a grid | sortDataInGrid |
| Compute diagonalisation measures | spatialDecorrelation spatialDecorrelation.gmEVario spatialDecorrelation.gstatVariogram spatialDecorrelation.logratioVariogram |
| Construct a regionalized composition / reorder compositional simulations | spatialGridAcomp |
| Construct a regionalized multivariate data | spatialGridRmult |
| Spectral colors palette based on the RColorBrewer::brewer.pal(11,"Spectral") | spectralcolors |
| Spherifying transform Compute a transformation that spherifies a certain data set | sphTrans sphTrans.default |
| Get/set name/index of (non)stacking dimensions | noStackDim noStackDim.default stackDim stackDim.DataFrameStack stackDim<- stackDim<-.default |
| Get name/index of the stacking dimension of a Spatial object | stackDim,Spatial-method |
| Plot a swarm of calculated output through a DataFrameStack | swarmPlot |
| Swath plots | swath swath.acomp swath.ccomp swath.default swath.rcomp |
| Create a parameter set specifying a turning bands simulation algorithm | TurningBands |
| Unmask a masked object | unmask unmask.data.frame unmask.DataFrameStack unmask.SpatialPixels unmask.SpatialPoints |
| Validate a spatial model | validate validate.LeaveOneOut validate.NfoldCrossValidation |
| Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their models | variogramModelPlot variogramModelPlot.gmEVario |
| Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their models | variogramModelPlot.gstatVariogram |
| Quick plotting of empirical and theoretical logratio variograms Quick and dirty plotting of empirical logratio variograms with or without their models | variogramModelPlot.logratioVariogram |
| Ore composition of a bench at a mine in Windarling, West Australia. | Windarling |
| Write a regionalized data set in GSLIB format | write.GSLib |
| Cross-validation errror measures | xvErrorMeasures xvErrorMeasures.data.frame xvErrorMeasures.DataFrameStack |
| Cross-validation errror measures | xvErrorMeasures.default |