| fit.variogram {sgeostat} | R Documentation |
Fit variogram models (exponential, spherical, gaussian, linear) to empirical variogram estimates.
An object of class variogram.model represents a fitted variogram model generated by fitting a function to a variogram object. A
variogram.model object is composed of a list consisting of a vector of parameters, parameters, and a semi-variogram model
function, model.
fit.variogram(model="exponential", v.object, nugget, sill,
range, slope, ...)
fit.exponential(v.object, c0, ce, ae, type='c',
iterations=10, tolerance=1e-06, echo=FALSE, plot.it=FALSE, weighted=TRUE)
fit.gaussian(v.object, c0, cg, ag, type='c',
iterations=10, tolerance=1e-06, echo=FALSE, plot.it=FALSE, weighted=TRUE)
fit.spherical(v.object, c0, cs, as, type='c', iterations=10,
tolerance=1e-06, echo=FALSE, plot.it=FALSE, weighted=TRUE,
delta=0.1, verbose=TRUE)
fit.wave(v.object, c0, cw, aw, type='c',
iterations=10, tolerance=1e-06, echo=FALSE, plot.it=FALSE, weighted=TRUE)
fit.linear(v.object, type='c', plot.it=FALSE,iterations=1, c0=0, cl=1)
model |
only available for fit.variogram, switches what kind
of model should be fitted ("exponential", "wave", "gaussian",
"spherical", "linear"). |
v.object |
a variogram object generated by est.variogram() |
nugget, sill, range, slope |
only available for fit.variogram,
initial estimates for specified variogram model (slope only for
fit.linear) |
c0 |
initial estimate for nugget effect, valid for all variogram
types, partial sill (cX) and (asymptotical) range (aX)
as follows: |
ce, ae |
initial estimates for the exponential variogram model |
cg, ag |
initial estimates for the gaussian variogram model |
cs, as |
initial estimates for the sperical variogram model |
cw, aw |
initial estimates for the periodical variogram model |
cl |
initial estimates for the linear variogram model (slope) |
type |
one of 'c' (classic), 'r' (robust), 'm' (median). Indicates to which type of empirical variogram estimate the model is
to be fit. |
iterations |
the number of iterations of the fitting procedure to execute. |
tolerance |
the tolerance used to determine if model convergence has been achieved. |
delta |
initial stepsize (relative) for pseudo Newton approximation, applies only to fit.spherical |
echo |
if TRUE, be verbose. |
verbose |
if TRUE, be verbose (show iteration for spherical model fit). |
plot.it |
if TRUE, the variogram estimate will be plotted each iteration. |
weighted |
if TRUE, the fit will be done using weighted least squares, where the weightes are given in Cressie (1991, p. 99) |
... |
only fit.variogram: additional parameters to hand
through to specific model fit functions |
A variogram.model object:
parameters |
vector of fitted model parameters |
model |
function implementing a valid variogram model |
fit.exponential, fit.gaussian and fit.wave
use an iterative, Gauss-Newton fitting algorithm to fit to an
exponential or gaussian variogram model to empirical variogram estimates.
fit.spherical uses the same algorithm but with differential
quotients in place of first derivatives. When weighted is
TRUE, the regression is weighted by n(h)/gamma(h)^2 where
the numerator is the number of pairs of points in a given lag.
Setting iterations to 0 means no fit procedure is applied. Thus
parameter values from external sources can be plugged into a variogram
model object.
http://www.gis.iastate.edu/SGeoStat/homepage.html
#
# automatic fit:
#
maas.vmod<-fit.gaussian(maas.v,c0=60000,cg=110000,ag=800,plot.it=TRUE,
iterations=30)
#
# iterations=0, means no fit, intended for "subjective" fit
#
maas.vmod.fixed<-fit.variogram("gaussian",maas.v,nugget=60000,sill=110000,range=800,plot.it=TRUE,iterations=0)