spatstat (version 1.41-1)

distfun: Distance Map as a Function

Description

Compute the distance function of an object, and return it as a function.

Usage

distfun(X, ...)

## S3 method for class 'ppp': distfun(X, ..., k=1)

## S3 method for class 'psp': distfun(X, ...)

## S3 method for class 'owin': distfun(X, ..., invert=FALSE)

Arguments

X
Any suitable dataset representing a two-dimensional object, such as a point pattern (object of class "ppp"), a window (object of class "owin") or a line segment pattern (object of class "psp").
...
Extra arguments are ignored.
k
An integer. The distance to the kth nearest point will be computed.
invert
If TRUE, compute the distance transform of the complement of X.

Value

  • A function with arguments x,y. The function also belongs to the class "distfun" which has a method for print. It also belongs to the class "funxy" which has methods for plot, contour and persp.

Details

The distance function of a set of points $A$ is the mathematical function $f$ such that, for any two-dimensional spatial location $(x,y)$, the function value f(x,y) is the shortest distance from $(x,y)$ to $A$.

The command f <- distfun(X) returns a function in the Rlanguage, with arguments x,y, that represents the distance function of X. Evaluating the function f in the form v <- f(x,y), where x and y are any numeric vectors of equal length containing coordinates of spatial locations, yields the values of the distance function at these locations.

This should be contrasted with the related command distmap which computes the distance function of X on a grid of locations, and returns the distance values in the form of a pixel image.

The result of f <- distfun(X) also belongs to the class "funxy" and to the special class "distfun". It can be printed and plotted immediately as shown in the Examples. A distfun object can be converted to a pixel image using as.im.

See Also

distmap, plot.funxy

Examples

Run this code
data(letterR)
   f <- distfun(letterR)
   f
   plot(f)
   f(0.2, 0.3)

   plot(distfun(letterR, invert=TRUE), eps=0.1)

   d <- distfun(cells)
   d2 <- distfun(cells, k=2)
   d(0.5, 0.5)
   d2(0.5, 0.5)

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