variogramModelPlot.gstatVariogram {gmGeostats}

Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their models

Description

Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their models

Usage

## S3 method for class 'gstatVariogram'
variogramModelPlot(
  vg,
  model = NULL,
  col = rev(rainbow(1 + length(unique(vg$dir.hor)))),
  commonAxis = FALSE,
  newfig = TRUE,
  closeplot = TRUE,
  ...
)

Arguments

vg

empirical variogram or covariance function

model

optional, theoretical variogram or covariance function

col

colors to use for the several directional variograms

commonAxis

boolean, should all plots in a row share the same vertical axis?

newfig

boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed

closeplot

logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)? defaults to TRUE

...

further parameters to underlying plot or matplot functions

Value

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 giving argument closeplot=FALSE and then adding layers of information. If you want to "freeze" your plot, either give closeplot=TRUE or embed your call in another call to par, e.g. par(variogramModelPlot(...)).

See Also

gstat::plot.gstatVariogram()

Other variogramModelPlot: variogramModelPlot.logratioVariogram(), variogramModelPlot()

Examples

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)