spatstat.explore (version 3.3-1)

plot.ssf: Plot a Spatially Sampled Function

Description

Plot a spatially sampled function object.

Usage

# S3 method for ssf
plot(x, ...,
                   how = c("smoothed", "nearest", "points"),
                   style = c("image", "contour", "imagecontour"),
                   sigma = NULL, contourargs=list())
# S3 method for ssf
image(x, ...)
# S3 method for ssf
contour(x, ..., main, sigma = NULL)

Value

NULL.

Arguments

x

Spatially sampled function (object of class "ssf").

...

Arguments passed to image.default or plot.ppp to control the plot.

how

Character string determining whether to display the function values at the data points (how="points"), a smoothed interpolation of the function (how="smoothed"), or the function value at the nearest data point (how="nearest").

style

Character string indicating whether to plot the smoothed function as a colour image, a contour map, or both.

contourargs

Arguments passed to contour.default to control the contours, if style="contour" or style="imagecontour".

sigma

Smoothing bandwidth for smooth interpolation.

main

Optional main title for the plot.

Author

Adrian Baddeley .

Details

These are methods for the generic plot, image and contour for the class "ssf".

An object of class "ssf" represents a function (real- or vector-valued) that has been sampled at a finite set of points.

For plot.ssf there are three types of display. If how="points" the exact function values will be displayed as circles centred at the locations where they were computed. If how="smoothed" (the default) these values will be kernel-smoothed using Smooth.ppp and displayed as a pixel image. If how="nearest" the values will be interpolated by nearest neighbour interpolation using nnmark and displayed as a pixel image.

For image.ssf and contour.ssf the values are kernel-smoothed before being displayed.

References

Baddeley, A. (2017) Local composite likelihood for spatial point processes. Spatial Statistics 22, 261--295.

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

See Also

ssf

Examples

  a <- ssf(cells, nndist(cells, k=1:3))
  plot(a, how="points")
  plot(a, how="smoothed")
  plot(a, how="nearest")