plot.ssf: Plot a Spatially Sampled Function

In spatstat.core: Core Functionality of the 'spatstat' Family

Plot a Spatially Sampled Function

Description

Plot a spatially sampled function object.

Usage

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

## S3 method for class 'ssf'
image(x, ...)

## S3 method for class 'ssf'
contour(x, ..., main, sigma = 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.

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.

Value

NULL.

Author(s)

\adrian

.

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")

spatstat.core documentation built on May 18, 2022, 9:05 a.m.