Quick plotting of empirical and theoretical logratio variograms Quick and dirty plotting of empirical logratio variograms with or without their models
# S3 method for logratioVariogram
variogramModelPlot(
vg,
model = NULL,
col = rev(rainbow(ndirections(vg))),
commonAxis = FALSE,
newfig = FALSE,
...
)empirical variogram or covariance function
optional, theoretical variogram or covariance function
colors to use for the several directional variograms
boolean, should all plots in a row share the same vertical axis?
boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed
further parameters to underlying plot or matplot functions
The function is primarily called for producing a plot. However, it
invisibly returns the graphical parameters active before the call
occurred. This is useful for constructing complex diagrams, by adding layers
of info. If you want to "freeze" your plot, embed your call in another
call to par, e.g. par(variogramModelPlot(...)).
Other variogramModelPlot:
variogramModelPlot.gstatVariogram(),
variogramModelPlot()