getRtopParams: Setting parameters for the intamap package

In rtop: Interpolation of Data with Variable Spatial Support

Setting parameters for the intamap package

Description

This function sets a range of the parameters for the intamap package, to be included in the object described in rtop-package

Usage

getRtopParams(params,newPar, observations, formulaString, ...)

Arguments

params	An existing set of parameters for the interpolation process, of class intamapParams or a list of parameters for modification of the default parameters
newPar	A list of parameters for updating params or for modification of the default parameters. Possible parameters with their defaults are given below
observations	SpatialPolygonsDataFrame with observations, used for setting some of the default parameters
formulaString	formula that defines the dependent variable as a linear model of independent variables, see e.g. createRtopObject for more details.
...	Individual parameters for updating params or for modification of the default parameters. Possible parameters with their defaults are given below model = "Ex1" - variogram model type. Currently the following models are implemented: Exp - Exponential model Ex1 - Multiplication of a modified exponential and fractal model, the same model as used in Skoien et al(2006). Gau - Gaussian model Ga1 - Multiplication of gaussian and fractal model Sph - Spherical model Sp1 - Multiplication of spherical and fractal model Fra - Fractal model parInit - the initial parameters and the limits of the variogram model to be fitted, given as a matrix with three columns, where the first column is the lower limit, the second column is the upper limit and the third column are starting values. nugget = FALSE - logical; if TRUE, nugget effect should be estimated unc = TRUE - logical; if TRUE the variance of observations are in column unc rresol = 100 - minimum number of discretization points in each area hresol = 5 - number of discretization points in one direction for elements in binned variograms cloud = FALSE - logical; if TRUE use the cloud variogram for variogram fitting amul = 1 - defines the number of areal bins within one order of magnitude. Numbers between 1 and 3 are possible, as this parameter refers to the axp parameter of axTicks. dmul = 3 - defines the number of distance bins within one order of magnitude. Numbers between 1 and 3 are possible, as this parameter refers to the axp parameter of axTicks. fit.method = 9 - defines the type of Least Square method for fitting of variogram. The methods 1-7 correspond to the similar methods in fit.variogram of gstat. 1 - weighted least squares with number of pairs per bin: err = n * (yobs-ymod)^2 2 - weighted least squares difference according to Cressie (1985): err2 = abs(yobs/ymod-1) 6 - ordinary least squares difference: err = (yobs-ymod)^2 7 - similar to default of gstat, where higher weights are given to shorter distances err = n/h^2 * (yobs-mod)^2 8 - Opposite of weighted least squares difference according to Cressie (1985): err3=abs(ymod/yobs-1) 9 - neutral WLS-method - err = min(err2,err3) gDistEst = FALSE - use geostatistical distance when fitting variograms gDistPred = FALSE - use geostatistical distance for semivariogram matrices gDist - parameter to set jointly gDistEst = gDistPred = gDist nmax = 10 for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 10 observations are used. maxdist = Inf - for local kriging: only observations within a distance of maxdist from the prediction location are used for prediction or simulation; if combined with nmax, both criteria apply hstype = "regular" - sampling type for binned variograms rstype = "rtop" - sampling type for the elements, see also rtopDisc nclus = 1 - number of CPUs to use if parallel processing is wanted; nclus = 1 means no parallelization cnAreas = 100 - limit whether parallel processing should be applied; the minimum number of areas in varMat, and also controlling when to use parallel processing in rtopDisc, when nAreas*params$rresol/100 > cnAreas clusType = NULL - the cluster type to be started for parallel processing; uses the default type of the system when clusType = NULL outfile = NULL file where output can be printed during parallel execution varClean = FALSE logical; if TRUE it will remove highly correlated areas from the covariance matrix during simulation wlim = 1.5 - an upper limit for the norm of the weights in kriging, see rtopKrige wlimMethod = "all" which method to use for reducing the norm of the weights if necessary. Either "all", which modifies all weights equally or "neg" which reduces negative weights and large weights more than the smallest weights singularSolve - logical; When TRUE, the kriging function will attempt to solve singular kriging matrices by removing catchments that have the same correlations. This will usually happen when two catchments are almost overlapping, and they are discretized with the same points. See also rtopKrige. cv = FALSE - logical; for cross-validation of observations debug.level = 1 - used in some functions for giving additional output. See individual functions for more information. partialOverlap = FALSE whether to work with partially overlapping areas olim = 1e-4 smallest overlapping area to be used for partial overlap, relative to the smallest of the areas nclus = 1 option to use parallel processing, nclus > 1 defines the number of workers to be started clusType = NA which cluster type to start if nclus > 1; the default is used if nclusType = NA cnAreas = 200 The minimum number of observations or observations plus predictions allowing parallelization in the creation of the covariance matrix cDlim = 1e6 The minimum number of discretization points for allowing parallelization in the discretization process observations - used for initial values of parameters if supplied formulaString - used for initial values of parameters if supplied

Value

A list of the parameters with class rtopParams to be included in the object described in rtop-package

Note

This function will mainly be called by createRtopObject, but can also be called by the user to create a parameter set or update an existing parameter set. If none of the arguments is a list of class rtopParams, the function will assume that the argument(s) are modifications to the default set of parameters. The function can also be called by other functions in the rtop-package if the users chooses not to work with an object of class rtop.

If the function is called with two lists of parameters (but the first one is not of class rtopParams) they are both seen as modifications to the default parameter set. If they share some parameters, the parameter values from the second list will be applied.

Parallel processing has been included for some of the functions. The default is no parallel procesing, and the package also attempts to decide whether it is sensible to start a set of clusters and distribute jobs to them based on the size of the job. The default limit might not be the best for every system.

Author(s)

Jon Olav Skoien

References

Cressie, N. 1985. Fitting variogram models by weighted least squares. Mathematical Geology, 17 (5), 563-586

Skoien J. O., R. Merz, and G. Bloschl. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10:277-287, 2006

Skoien, J. O., Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., Viglione, A., 2014. Rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67.

See Also

createRtopObject and rtop-package

Examples

# Create a new set of intamapParameters, with default parameters:
params = getRtopParams()
# Make modifications to the default list of parameters
params = getRtopParams(newPar = list(gDist = TRUE, nugget = FALSE))
# Make modifications to an existing list of parameters
params = getRtopParams(params = params, newPar = list(gDist = TRUE,
         nugget = FALSE))

rtop documentation built on July 4, 2024, 9:09 a.m.