# Geostatistics for Compositional Analysis

## Documentation for package ‘gmGeostats’ version 0.11.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D E F G H I K L M N P R S T U V W X misc

## -- A --

accuracy | Compute accuracy and precision
accuracy.data.frame | Compute accuracy and precision
accuracy.DataFrameStack | Compute accuracy and precision
ana | Flow anamorphosis transform Compute a transformation that gaussianizes a certain data set
anaBackward | Backward gaussian anamorphosis backward transformation to multivariate gaussian scores
anaForward | Forward gaussian anamorphosis forward transformation to multivariate gaussian scores
anis2D_par2A | Produce anisotropy scaling matrix from angle and anisotropy ratios
anis3D_par2A | Produce anisotropy scaling matrix from angle and anisotropy ratios
AnisotropyRangeMatrix | Force a matrix to be anisotropy range matrix,
AnisotropyScaling | Convert to anisotropy scaling matrix
anis_GSLIBpar2A | Produce anisotropy scaling matrix from angle and anisotropy ratios
as.AnisotropyRangeMatrix | Force a matrix to be anisotropy range matrix,
as.AnisotropyRangeMatrix.AnisotropyRangeMatrix | Force a matrix to be anisotropy range matrix,
as.AnisotropyRangeMatrix.AnisotropyScaling | Force a matrix to be anisotropy range matrix,
as.AnisotropyRangeMatrix.default | Force a matrix to be anisotropy range matrix,
as.AnisotropyScaling | Convert to anisotropy scaling matrix
as.AnisotropyScaling.AnisotropyRangeMatrix | Convert to anisotropy scaling matrix
as.AnisotropyScaling.AnisotropyScaling | Convert to anisotropy scaling matrix
as.AnisotropyScaling.numeric | Convert to anisotropy scaling matrix
as.array.DataFrameStack | Convert a stacked data frame into an array
as.CompLinModCoReg | Recast a model to the variogram model of package "compositions"
as.CompLinModCoReg.CompLinModCoReg | Recast a model to the variogram model of package "compositions"
as.CompLinModCoReg.LMCAnisCompo | Recast a model to the variogram model of package "compositions"
as.DataFrameStack | Create a data frame stack
as.DataFrameStack.array | Create a data frame stack
as.DataFrameStack.data.frame | Create a data frame stack
as.DataFrameStack.list | Create a data frame stack
as.directorVector | Express a direction as a director vector
as.directorVector.azimuth | Express a direction as a director vector
as.directorVector.azimuthInterval | Express a direction as a director vector
as.directorVector.default | Express a direction as a director vector
as.function.gmCgram | Convert a gmCgram object to an (evaluable) function
as.gmCgram | Convert theoretical structural functions to gmCgram format
as.gmCgram.default | Convert theoretical structural functions to gmCgram format
as.gmCgram.LMCAnisCompo | Convert theoretical structural functions to gmCgram format
as.gmCgram.variogramModel | Convert theoretical structural functions to gmCgram format
as.gmCgram.variogramModelList | Convert theoretical structural functions to gmCgram format
as.gmEVario | Convert empirical structural function to gmEVario format
as.gmEVario.default | Convert empirical structural function to gmEVario format
as.gmEVario.gstatVariogram | Convert empirical structural function to gmEVario format
as.gmEVario.logratioVariogram | Convert empirical structural function to gmEVario format
as.gmEVario.logratioVariogramAnisotropy | Convert empirical structural function to gmEVario format
as.gmSpatialModel | Recast spatial object to gmSpatialModel format
as.gmSpatialModel.default | Recast spatial object to gmSpatialModel format
as.gmSpatialModel.gstat | Recast spatial object to gmSpatialModel format
as.gstat | Convert a regionalized data container to gstat
as.gstat-method | Conditional spatial model data container
as.gstat.default | Convert a regionalized data container to gstat
as.gstatVariogram | Represent an empirical variogram in "gstatVariogram" format
as.gstatVariogram.default | Represent an empirical variogram in "gstatVariogram" format
as.gstatVariogram.gmEVario | Represent an empirical variogram in "gstatVariogram" format
as.gstatVariogram.logratioVariogram | Represent an empirical variogram in "gstatVariogram" format
as.gstatVariogram.logratioVariogramAnisotropy | Represent an empirical variogram in "gstatVariogram" format
as.list.DataFrameStack | Convert a stacked data frame into a list of data.frames
as.LMCAnisCompo | Recast compositional variogram model to format LMCAnisCompo
as.LMCAnisCompo.CompLinModCoReg | Recast compositional variogram model to format LMCAnisCompo
as.LMCAnisCompo.gmCgram | Recast compositional variogram model to format LMCAnisCompo
as.LMCAnisCompo.gstat | Recast compositional variogram model to format LMCAnisCompo
as.LMCAnisCompo.LMCAnisCompo | Recast compositional variogram model to format LMCAnisCompo
as.LMCAnisCompo.variogramModelList | Recast compositional variogram model to format LMCAnisCompo
as.logratioVariogram | Recast empirical variogram to format logratioVariogram
as.logratioVariogram.gmEVario | Recast empirical variogram to format logratioVariogram
as.logratioVariogram.gstatVariogram | Recast empirical variogram to format logratioVariogram
as.logratioVariogram.logratioVariogram | Recast empirical variogram to format logratioVariogram
as.logratioVariogramAnisotropy | Convert empirical variogram to "logratioVariogramAnisotropy"
as.logratioVariogramAnisotropy.default | Convert empirical variogram to "logratioVariogramAnisotropy"
as.logratioVariogramAnisotropy.logratioVariogram | Convert empirical variogram to "logratioVariogramAnisotropy"
as.logratioVariogramAnisotropy.logratioVariogramAnisotropy | Convert empirical variogram to "logratioVariogramAnisotropy"
as.variogramModel | Convert an LMC variogram model to gstat format
as.variogramModel.CompLinModCoReg | Convert an LMC variogram model to gstat format
as.variogramModel.default | Convert an LMC variogram model to gstat format
as.variogramModel.gmCgram | Convert an LMC variogram model to gstat format
as.variogramModel.LMCAnisCompo | Convert an LMC variogram model to gstat format

## -- C --

CholeskyDecomposition | Create a parameter set specifying a LU decomposition simulation algorithm
coloredBiplot.genDiag | Colored biplot for gemeralised diagonalisations Colored biplot method for objects of class genDiag
constructMask | Constructs a mask for a grid

## -- D --

DataFrameStack | Create a data frame stack
DataFrameStack.array | Create a data frame stack
DataFrameStack.data.frame | Create a data frame stack
DataFrameStack.list | Create a data frame stack
dimnames-method | Return the dimnames of a DataFrameStack
dimnames.DataFrameStack | Return the dimnames of a DataFrameStack
DirectSamplingParameters | Create a parameter set specifying a direct sampling algorithm
DSpars | Create a parameter set specifying a direct sampling algorithm

## -- E --

EmpiricalStructuralFunctionSpecification-class | Empirical structural function specification

## -- F --

fit_lmc | Fit an LMC to an empirical variogram
fit_lmc.default | Fit an LMC to an empirical variogram
fit_lmc.gstatVariogram | Fit an LMC to an empirical variogram
fit_lmc.logratioVariogram | Fit an LMC to an empirical variogram
fit_lmc.logratioVariogramAnisotropy | Fit an LMC to an empirical variogram

## -- G --

genDiag | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
getGridOrder | Set or get the ordering of a grid
getMask | Get the mask info out of a spatial data object
getMask.default | Get the mask info out of a spatial data object
getMask.SpatialPixels | Get the mask info out of a spatial data object
getMask.SpatialPixelsDataFrame | Get the mask info out of a spatial data object
getMask.SpatialPointsDataFrame | Get the mask info out of a spatial data object
getStackElement | Set or get the i-th data frame of a data.frame stack
getStackElement.DataFrameStack | Set or get the i-th data frame of a data.frame stack
getStackElement.default | Set or get the i-th data frame of a data.frame stack
getStackElement.list | Set or get the i-th data frame of a data.frame stack
getTellus | Download the Tellus survey data set (NI)
gmApply | Apply Functions Over Array or DataFrameStack Margins
gmApply.DataFrameStack | Apply Functions Over Array or DataFrameStack Margins
gmApply.default | Apply Functions Over Array or DataFrameStack Margins
gmGaussianMethodParameters-class | parameters for Spatial Gaussian methods of any kind
gmGaussianSimulationAlgorithm-class | parameters for Gaussian Simulation methods
gmMPSParameters-class | parameters for Multiple-Point Statistics methods
gmNeighbourhoodSpecification-class | Neighbourhood description
gmSimulationAlgorithm-class | Parameter specification for a spatial simulation algorithm
gmSpatialDataContainer-class | General description of a spatial data container
gmSpatialMethodParameters-class | Parameter specification for any spatial method
gmSpatialModel-class | Conditional spatial model data container
gmTrainingImage-class | MPS training image class
gmUnconditionalSpatialModel-class | General description of a spatial model
gmValidationStrategy-class | Validation strategy description
gridOrder_array | Set or get the ordering of a grid
gridOrder_GSLib | Set or get the ordering of a grid
gridOrder_gstat | Set or get the ordering of a grid
gridOrder_sp | Set or get the ordering of a grid
GridOrNothing-class | Superclass for grid or nothing
gsi.calcCgram | Compute covariance matrix oout of locations
gsi.Cokriging | Cokriging of all sorts, internal function
gsi.CondTurningBands | Internal function, conditional turning bands realisations
gsi.DS | Workhorse function for direct sampling
gsi.EVario2D | Empirical variogram or covariance function in 2D
gsi.EVario3D | Empirical variogram or covariance function in 3D
gsi.getV | extract information about the original data, if available
gsi.gstatCokriging2compo | Reorganisation of cokriged compositions
gsi.gstatCokriging2compo.data.frame | Reorganisation of cokriged compositions
gsi.gstatCokriging2compo.default | Reorganisation of cokriged compositions
gsi.gstatCokriging2rmult | Reorganisation of cokriged compositions
gsi.gstatCokriging2rmult.data.frame | Reorganisation of cokriged compositions
gsi.gstatCokriging2rmult.default | Reorganisation of cokriged compositions
gsi.orig | extract information about the original data, if available
gsi.produceV | Create a matrix of logcontrasts and name prefix
gsi.TurningBands | Internal function, unconditional turning bands realisations
gsi.validModels | Generate D-variate variogram models
gstat2LMCAnisCompo | Recast compositional variogram model to format LMCAnisCompo

## -- H --

has.missings.data.frame | Check presence of missings check presence of missings in a data.frame

## -- I --

image.logratioVariogramAnisotropy | Plot variogram maps for anisotropic logratio variograms
image.mask | Image method for mask objects
image_cokriged | Plot an image of gridded data
image_cokriged.default | Plot an image of gridded data
image_cokriged.spatialGridAcomp | Plot an image of gridded data
image_cokriged.spatialGridRmult | Plot an image of gridded data
is.anisotropySpecification | Check for any anisotropy class
is.isotropic | Check for anisotropy of a theoretical variogram

## -- K --

KrigingNeighbourhood | Create a parameter set of local for neighbourhood specification.

## -- L --

LeaveOneOut | Specify the leave-one-out strategy for validation of a spatial model
length.gmCgram | Length, and number of columns or rows
LMCAnisCompo | Create a anisotropic model for regionalized compositions
logratioVariogram | Empirical logratio variogram calculation
logratioVariogram-method | Conditional spatial model data container
logratioVariogram-method | Logratio variogram of a compositional data
logratioVariogram_gmSpatialModel | Variogram method for gmSpatialModel objects

## -- M --

Maf | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.acomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.aplus | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.ccomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.data.frame | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.rcomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.rmult | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
Maf.rplus | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
make.gmCompositionalGaussianSpatialModel | Construct a Gaussian gmSpatialModel for regionalized compositions
make.gmCompositionalMPSSpatialModel | Construct a Multi-Point gmSpatialModel for regionalized compositions
make.gmMultivariateGaussianSpatialModel | Construct a Gaussian gmSpatialModel for regionalized multivariate data
mean.accuracy | Mean accuracy
mean.spatialDecorrelationMeasure | Average measures of spatial decorrelation
ModelStructuralFunctionSpecification-class | Structural function model specification

## -- N --

ncol.gmCgram | Length, and number of columns or rows
ndirections | Number of directions of an empirical variogram
ndirections.azimuth | Number of directions of an empirical variogram
ndirections.azimuthInterval | Number of directions of an empirical variogram
ndirections.default | Number of directions of an empirical variogram
ndirections.gmEVario | Number of directions of an empirical variogram
ndirections.gstatVariogram | Number of directions of an empirical variogram
ndirections.logratioVariogram | Number of directions of an empirical variogram
ndirections.logratioVariogramAnisotropy | Number of directions of an empirical variogram
NfoldCrossValidation | Specify a strategy for validation of a spatial model
NGSAustralia | National Geochemical Survey of Australia: soil data
noSpatCorr.test | Test for lack of spatial correlation
noSpatCorr.test.data.frame | Test for lack of spatial correlation
noSpatCorr.test.default | Test for lack of spatial correlation
noSpatCorr.test.matrix | Test for lack of spatial correlation
noStackDim | Get/set name/index of (non)stacking dimensions
noStackDim.default | Get/set name/index of (non)stacking dimensions
nrow.gmCgram | Length, and number of columns or rows

## -- P --

pairsmap | Multiple maps Matrix of maps showing different combinations of components of a composition, user defined
pairsmap.default | Multiple maps Matrix of maps showing different combinations of components of a composition, user defined
pairsmap.SpatialPointsDataFrame | Multiple maps Matrix of maps showing different combinations of components of a composition, user defined
plot.accuracy | Plot method for accuracy curves
plot.gmCgram | Draw cuves for covariance/variogram models
plot.gmEVario | Plot empirical variograms
plot.logratioVariogramAnisotropy | Plot variogram lines of empirical directional logratio variograms
plot.swarmPlot | Plotting method for swarmPlot objects
precision | Precision calculations
precision.accuracy | Precision calculations
Predict | Predict method for objects of class 'gmSpatialModel'
predict | Predict method for objects of class 'gmSpatialModel'
Predict-method | Predict method for objects of class 'gmSpatialModel'
predict-method | Predict method for objects of class 'gmSpatialModel'
predict.genDiag | Predict method for generalised diagonalisation objects
predict.gmCgram | Convert a gmCgram object to an (evaluable) function
predict.gmSpatialModel | Predict method for objects of class 'gmSpatialModel'
predict.LMCAnisCompo | Compute model variogram values Evaluate the variogram model provided at some lag vectors
predict_gmSpatialModel | Predict method for objects of class 'gmSpatialModel'
print.mask | Print method for mask objects
pwlrmap | Compositional maps, pairwise logratios Matrix of maps showing different combinations of components of a composition, in pairwise logratios

## -- R --

RJD | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
RJD.acomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
RJD.default | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
RJD.rcomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram

## -- S --

SequentialSimulation | Create a parameter set specifying a gaussian sequential simulation algorithm
setCgram | Generate D-variate variogram models
setGridOrder | Set or get the ordering of a grid
setGridOrder_array | Set or get the ordering of a grid
setGridOrder_sp | Set or get the ordering of a grid
setMask | Set a mask on an object
setMask.data.frame | Set a mask on an object
setMask.DataFrameStack | Set a mask on an object
setMask.default | Set a mask on an object
setMask.GridTopology | Set a mask on an object
setMask.SpatialGrid | Set a mask on an object
setMask.SpatialPoints | Set a mask on an object
setStackElement | Set or get the i-th data frame of a data.frame stack
setStackElement.data.frame | Set or get the i-th data frame of a data.frame stack
setStackElement.DataFrameStack | Set or get the i-th data frame of a data.frame stack
setStackElement.default | Set or get the i-th data frame of a data.frame stack
setStackElement.list | Set or get the i-th data frame of a data.frame stack
sortDataInGrid | Reorder data in a grid
spatialDecorrelation | Compute diagonalisation measures
spatialDecorrelation.gmEVario | Compute diagonalisation measures
spatialDecorrelation.gstatVariogram | Compute diagonalisation measures
spatialDecorrelation.logratioVariogram | Compute diagonalisation measures
spatialGridAcomp | Construct a regionalized composition / reorder compositional simulations
spatialGridRmult | Construct a regionalized multivariate data
spectralcolors | Spectral colors palette based on the RColorBrewer::brewer.pal(11,"Spectral")
sphTrans | Spherifying transform Compute a transformation that spherifies a certain data set
sphTrans.default | Spherifying transform Compute a transformation that spherifies a certain data set
stackDim | Get/set name/index of (non)stacking dimensions
stackDim-method | Get name/index of the stacking dimension of a Spatial object
stackDim.DataFrameStack | Get/set name/index of (non)stacking dimensions
stackDim<- | Get/set name/index of (non)stacking dimensions
stackDim<-.default | Get/set name/index of (non)stacking dimensions
swarmPlot | Plot a swarm of calculated output through a DataFrameStack
swath | Swath plots
swath.acomp | Swath plots
swath.ccomp | Swath plots
swath.default | Swath plots
swath.rcomp | Swath plots

## -- T --

TurningBands | Create a parameter set specifying a turning bands simulation algorithm

## -- U --

unmask | Unmask a masked object
unmask.data.frame | Unmask a masked object
unmask.DataFrameStack | Unmask a masked object
unmask.SpatialPixels | Unmask a masked object
unmask.SpatialPoints | Unmask a masked object
UWEDGE | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
UWEDGE.acomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
UWEDGE.default | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram
UWEDGE.rcomp | Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram

## -- V --

validate | Validate a spatial model
validate.LeaveOneOut | Validate a spatial model
validate.NfoldCrossValidation | Validate a spatial model
variogram | Empirical variogram or covariance function
variogram-method | Conditional spatial model data container
variogramModelPlot | Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their models
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 variograms Quick and dirty plotting of empirical variograms/covariances with or without their models
variogramModelPlot.logratioVariogram | Quick plotting of empirical and theoretical logratio variograms Quick and dirty plotting of empirical logratio variograms with or without their models
variogram_gmSpatialModel | Variogram method for gmSpatialModel objects
vg.Exp | 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.gauss | Generate D-variate variogram models
vg.Sph | Generate D-variate variogram models
vg.sph | Generate D-variate variogram models
vg.Spherical | Generate D-variate variogram models

## -- W --

Windarling | Ore composition of a bench at a mine in Windarling, West Australia.
write.GSLib | Write a regionalized data set in GSLIB format

## -- X --

xvErrorMeasures | Cross-validation errror measures
xvErrorMeasures.data.frame | Cross-validation errror measures
xvErrorMeasures.DataFrameStack | Cross-validation errror measures
xvErrorMeasures.default | Cross-validation errror measures

## -- misc --

+.gmCgram | Combination of gmCgram variogram structures
[.DataFrameStack | Extract rows of a DataFrameStack
[.gmCgram | Subsetting of gmCgram variogram structures
[.logratioVariogramAnisotropy | Subsetting of logratioVariogram objects
[[.gmCgram | Subsetting of gmCgram variogram structures
`[.logratioVariogram` | Subsetting of logratioVariogram objects