Geostatistics for Compositional Analysis

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Documentation for package ‘gmGeostats’ version 0.11.3

• 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-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