model.pgs.auto {RGeoS} | R Documentation |
Automatic PluriGaussian Model Fitting
model.pgs.auto(vario, rule = rule.input(), dbin = NA, dbout = NA, props = NA, flag.stat = TRUE, struct = melem.name(c(1,4,5,2,3)), struct2 = NA, auth.aniso = TRUE, auth.rotation = TRUE, auth.locksame = FALSE, flag.noreduce=FALSE, param = NA, lower = NA, upper = NA, draw = TRUE, wmode=2,maxiter = 100, verbose=0, tolstop = 1e-05, epsdelta = 1e-05, tolsigma = 5, delta = 1, ...)
vario |
The |
rule |
The |
dbin |
The |
dbout |
The |
props |
Vector giving the constant facies proportions in the stationary case. |
flag.stat |
When TRUE, the proportions are stationary and read from the array props. Otherwise, the proportions are read from the proportion variables of the output |
struct |
List of basic structures to be used for the first underlying Gaussian Random Functions. |
struct2 |
List of basic structures to be used for the second underlying Gaussian Random Functions. If not defined, the same functions as for the first underlying Gaussian Random Function are used. |
auth.aniso |
When TRUE, the anisotropy will be checked |
auth.rotation |
When TRUE, the fit will look for rotation in anisotropy |
auth.locksame |
When TRUE and if an anisotropy is allowed (auth.aniso), all the basic structures should share the same rotation. |
flag.noreduce |
When TRUE, the useless basic structures must not be discarded. |
param |
List of initial values for the parameters to be fitted |
lower |
List of lower bounds for the parameters to be fitted |
upper |
List of upper bounds for the parameters to be fitted |
draw |
When TRUE, both the experimental variograms and the Model are represented graphically. Otherwise, no graphic is produced. |
wmode |
Type of the weighting function used in the fitting procedure. This function is defined in the case of several directional experimental variograms, calculated in a multivariate case:
|
maxiter |
Maximum number of iterations |
verbose |
When TRUE, indications are given regarding the convergence of the algorithm used for automatic model fitting. |
tolstop |
Tolerance for the distance between the experiment and the model. |
epsdelta |
Tolerance for the direction increment used in the gradient search |
tolsigma |
Percentage of the variance below which a basic structure will be discarded |
delta |
Delta value |
... |
Arguments passed for the legend (see legend.line) |
A list containing the parameters of the model-class
of the underlying gaussian random functions.