getRtopParams  R Documentation 
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 rtoppackage
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 17 correspond to the similar methods in fit.variogram of gstat .
 1
 weighted least squares with number of pairs per bin:
err = n * (yobsymod)^2
 2
 weighted least squares difference according to Cressie (1985):
err2 = abs(yobs/ymod1)
 6
 ordinary least squares difference: err = (yobsymod)^2
 7
 similar to default of gstat, where higher weights are given to shorter distances err = n/h^2 * (yobsmod)^2
 8
 Opposite of weighted least squares difference according to Cressie (1985): err3=abs(ymod/yobs1)
 9
 neutral WLSmethod  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 crossvalidation 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 = 1e4
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 rtoppackage
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 rtoppackage 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), 563586
Skoien J. O., R. Merz, and G. Bloschl. Topkriging  geostatistics on stream networks.
Hydrology and Earth System Sciences, 10:277287, 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 rtoppackage
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))