Description Usage Arguments Details Author(s) See Also Examples
Fits a 2D or 3D variogram model based on a regression matrix and spatial domain of interest.
1 2 3 4 5 6 |
formulaString |
object of class |
rmatrix |
object of class |
predictionDomain |
object of class |
vgmFun |
character; variogram function ( |
dimensions |
character; |
anis |
vector containing 2, 5 or more anisotropy parameters; see |
subsample |
integer; size of the subset |
ivgm |
vgm; initial variogram model |
cutoff |
numeric; distance up to which point pairs are included in semivariance estimates |
width |
numeric; sample variogram bin width |
cressie |
logical; specifies whether to use cressie robust estimator |
... |
other optional arguments that can be passed to |
It will try to fit a variogram to multidimensional data. If the data set is large, this process can be time-consuming, hence one way to speed up fitting is to subset the regression matrix using the subsample
argument (i.e. randomly subset observations).
Tomislav Hengl
fit.regModel
, fit.gstatModel
, gstat::fit.variogram
1 2 3 4 5 6 7 8 9 10 11 | library(sp)
library(gstat)
## fit variogram to the Meuse data:
demo(meuse, echo=FALSE)
# produce a regression matrix:
ov <- over(meuse, meuse.grid)
ov <- cbind(data.frame(meuse["om"]), ov)
# fit a model:
v <- fit.vgmModel(om~1, rmatrix=ov, meuse.grid, dimensions="2D")
plot(variogram(om ~ 1, meuse[!is.na(meuse$om),]), v$vgm)
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GSIF version 0.5-5.1 (2019-01-04)
URL: http://gsif.r-forge.r-project.org/
Warning message:
In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
Discarded datum Amersfoort in CRS definition
Warning messages:
1: In proj4string(predictionDomain) :
CRS object has comment, which is lost in output
2: In proj4string(predictionDomain) :
CRS object has comment, which is lost in output
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