R: Plot a Spatially Sampled Function

plot.ssf {spatstat} | R Documentation

## 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 Baddeley Adrian.Baddeley@curtin.edu.au.

### 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")

[Package spatstat version 1.63-3 Index]