Plot a sample variogram, and possibly a fitted model

Creates a variogram plot

Usage

# S3 method for class 'gstatVariogram'
plot(x, model = NULL, ylim, xlim, xlab = "distance", 
  ylab = attr(x, "what"), panel = vgm.panel.xyplot, multipanel = TRUE, 
  plot.numbers = FALSE, scales, ids = x$id, group.id = TRUE, skip, 
  layout, ...)
# S3 method for class 'variogramMap'
plot(x, np = FALSE, skip, threshold, ...)
# S3 method for class 'StVariogram'
plot(x, model = NULL, ..., col = bpy.colors(), xlab, ylab, 
  map = TRUE, convertMonths = FALSE, as.table = TRUE, wireframe = FALSE, 
  diff = FALSE, all = FALSE)

Arguments

x: object obtained from the method variogram, possibly containing directional or cross variograms, space-time variograms and variogram model information
model: in case of a single variogram: a variogram model, as obtained from vgm or fit.variogram, to be drawn as a line in the variogram plot; in case of a set of variograms and cross variograms: a list with variogram models; in the spatio-temporal case, a single or a list of spatio-temporal models that will be plotted next to each other for visual comparison.
ylim: numeric; vector of length 2, limits of the y-axis
xlim: numeric; vector of length 2, limits of the x-axis
xlab: character; x-axis label
ylab: character; y-axis label
panel: panel function
multipanel: logical; if TRUE, directional variograms are plotted in different panels, if FALSE, directional variograms are plotted in the same graph, using color, colored lines and symbols to distinguish them
plot.numbers: logical or numeric; if TRUE, plot number of point pairs next to each plotted semivariance symbol, if FALSE these are omitted. If numeric, TRUE is assumed and the value is passed as the relative distance to be used between symbols and numeric text values (default 0.03).
scales: optional argument that will be passed to xyplot in case of the plotting of variograms and cross variograms; use the value list(relation = "same") if y-axes need to share scales
ids: ids of the data variables and variable pairs
group.id: logical; control for directional multivariate variograms: if TRUE, panels divide direction and colors indicate variables (ids), if FALSE panels divide variables/variable pairs and colors indicate direction
skip: logical; can be used to arrange panels, see xyplot
layout: integer vector; can be used to set panel layout: c(ncol,nrow)
np: logical (only for plotting variogram maps); if TRUE, plot number of point pairs, if FALSE plot semivariances
threshold: semivariogram map values based on fewer point pairs than threshold will not be plotted
...: any arguments that will be passed to the panel plotting functions (such as auto.key in examples below)
col: colors to use
map: logical; if TRUE, plot space-time variogram map
convertMonths: logical; if TRUE, yearmon time lags will be unit converted and plotted as (integer) months, and no longer match the numeric representation of yearmon, which has years as unit
as.table: controls the plotting order for multiple panels, see xyplot for details.
wireframe: logical; if TRUE, produce a wireframe plot
diff: logical; if TRUE, plot difference between model and sample variogram; ignores all.
all: logical; if TRUE, plot sample and model variogram(s) in single wireframes.

Value

returns (or plots) the variogram plot

Details

Please note that in the spatio-temporal case the levelplot and wireframe plots use the spatial distances averaged for each time lag avgDist. For strongly varying spatial locations over time, please check the distance columns dist and avgDist of the spatio-temporal sample variogram. The lattice::cloud function is one option to plot irregular 3D data.

References

http://www.gstat.org

Author

Edzer Pebesma

Note

currently, plotting models and/or point pair numbers is not supported when a variogram is both directional and multivariable; also, three-dimensional directional variograms will probably not be displayed correctly.

Examples

library(sp)
data(meuse)
coordinates(meuse) = ~x+y
vgm1 <- variogram(log(zinc)~1, meuse)
plot(vgm1)

model.1 <- fit.variogram(vgm1,vgm(1,"Sph",300,1))
plot(vgm1, model=model.1)

plot(vgm1, plot.numbers = TRUE, pch = "+")

vgm2 <- variogram(log(zinc)~1, meuse, alpha=c(0,45,90,135))
plot(vgm2)

# the following demonstrates plotting of directional models:
model.2 <- vgm(.59,"Sph",926,.06,anis=c(0,0.3))
plot(vgm2, model=model.2)

 
g = gstat(NULL, "zinc < 200", I(zinc<200)~1, meuse)
g = gstat(g, "zinc < 400", I(zinc<400)~1, meuse)
g = gstat(g, "zinc < 800", I(zinc<800)~1, meuse)
# calculate multivariable, directional variogram:
v = variogram(g, alpha=c(0,45,90,135))
plot(v, group.id = FALSE, auto.key = TRUE) # id and id pairs panels

plot(v, group.id = TRUE, auto.key = TRUE)  # direction panels


# variogram maps:
plot(variogram(g, cutoff=1000, width=100, map=TRUE),
    main = "(cross) semivariance maps")

plot(variogram(g, cutoff=1000, width=100, map=TRUE), np=TRUE,
    main = "number of point pairs")