Description
Usage
Arguments
Value
Note
Author(s)
References
See Also
Examples
This function sets a range of the parameters for the intamap package,
to be included in the object described in rtoppackage
 (params,newPar, observations, formulaString, )

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 = 8  defines the type of Least Square method for fitting of variogram.
The methods 17 correspond to the similar methods in 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 = 10for 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 = NULLfile where output can be printed during parallel execution
varClean = FALSElogical; 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
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 = FALSEwhether to work with partially overlapping areas
olim = 1e4smallest overlapping area to be used for partial overlap, relative to the smallest of the areas
nclus = 1option to use parallel processing, nclus > 1 defines the number of workers to be started
clusType = NAwhich cluster type to start if nclus > 1; the default is used if nclusType = NA
cnAreas = 200The minimum number of observations or observations plus predictions allowing parallelization in
the creation of the covariance matrix
cDlim = 1e6The 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

A list of the parameters with class rtopParams
to be included in the
object
described in rtoppackage
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.
Jon Olav Skoien
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.
createRtopObject
and rtoppackage
 # Create a new set of intamapParameters, with default parameters:
params = ()
# Make modifications to the default list of parameters
params = (newPar = ( = , nugget = ))
# Make modifications to an existing list of parameters
params = (params = params, newPar = ( = ,
nugget = ))
