fit.variogram {gstat} | R Documentation |
Fit ranges and/or sills from a simple or nested variogram model to a sample variogram
fit.variogram(object, model, fit.sills = TRUE, fit.ranges = TRUE, fit.method = 7, print.SSE = FALSE, debug.level = 1)
object |
sample variogram, output of variogram |
model |
variogram model, output of vgm |
fit.sills |
logical; determines whether the partial sill coefficients (including nugget variance) should be fitted; or logical vector: determines for each partial sill parameter whether it should be fitted or fixed. |
fit.ranges |
logical; determines whether the range coefficients (excluding that of the nugget component) should be fitted; or logical vector: determines for each range parameter whether it should be fitted or fixed. |
fit.method |
fitting method, used by gstat. The default method uses
weights $N_h/h^2$ with $N_h$ the number of point pairs and $h$ the
distance. This criterion is not supported by theory, but by practice.
For other values of fit.method , see table 4.2 in the gstat
manual. |
print.SSE |
logical; if TRUE, print the (weighted) sum of squared errors of the fitted model |
debug.level |
integer; set gstat internal debug level |
returns a fitted variogram model (of class variogram.model
).
This is a data.frame with a logical attribute "singular" that
indicates whether the non-linear fit converged, or ended in a
singularity.
Edzer J. Pebesma
data(meuse) vgm1 <- variogram(log(zinc)~1, ~x+y, meuse) fit.variogram(vgm1, vgm(1,"Sph",300,1))