# plot.ssf

##### Plot a Spatially Sampled Function

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)

##### 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`

.

##### 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

##### Examples

```
# NOT RUN {
a <- ssf(cells, nndist(cells, k=1:3))
plot(a, how="points")
plot(a, how="smoothed")
plot(a, how="nearest")
# }
```

*Documentation reproduced from package spatstat, version 1.59-0, License: GPL (>= 2)*