View source: R/gstatCompatibility.R
fit_lmc | R Documentation |
Fit a linear model of coregionalisation to an empirical variogram
fit_lmc(v, ...)
## S3 method for class 'gstatVariogram'
fit_lmc(
v,
g,
model,
fit.ranges = FALSE,
fit.lmc = !fit.ranges,
correct.diagonal = 1,
...
)
## Default S3 method:
fit_lmc(v, g, model, ...)
## S3 method for class 'logratioVariogram'
fit_lmc(v, g, model, ...)
## S3 method for class 'logratioVariogramAnisotropy'
fit_lmc(v, g, model, ...)
v |
empirical variogram |
... |
further parameters |
g |
spatial data object, containing the original data |
model |
LMC or variogram model to fit |
fit.ranges |
logical, should ranges be modified? (default=FALSE) |
fit.lmc |
logical, should the nugget and partial sill matrices be modified (default=TRUE) |
correct.diagonal |
positive value slightly larger than 1, for multiplying the direct variogram models and reduce the risk of numerically negative eigenvalues |
Method fit_lmc.gstatVariogram is a wrapper around gstat::fit.lmc()
, that calls this function
and gives the resulting model its appropriate class (c("variogramModelList", "list")).
Method fit_lmc.default returns the fitted lmc (this function currently uses gstat as a
calculation machine, but this behavior can change in the future)
gstatVariogram
: wrapper around gstat::fit.lmc method
default
: flexible wrapper method for any class for which methods
for as.gstatVariogram()
, as.gstat()
and as.variogramModel()
exist.
In the future there may be direct specialised implementations not depending on
package gstat.
logratioVariogram
: method for logratioVariogram wrapping compositions::fit.lmc.
In the future there may be direct specialised implementations,
including anisotropy (not yet possible).
logratioVariogramAnisotropy
: method for logratioVariogram with anisotropry
data("jura", package = "gstat")
X = jura.pred[,1:2]
Zc = jura.pred[,7:13]
gg = make.gmCompositionalGaussianSpatialModel(Zc, X, V="alr", formula = ~1)
vg = variogram(gg)
md = gstat::vgm(model="Sph", psill=1, nugget=1, range=1.5)
gg = fit_lmc(v=vg, g=gg, model=md)
variogramModelPlot(vg, model=gg)
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