runifpoint: Generate N Uniform Random Points

View source: R/random.R

runifpointR Documentation

Generate N Uniform Random Points

Description

Generate a random point pattern containing n independent uniform random points.

Usage

 runifpoint(n, win=owin(c(0,1),c(0,1)), giveup=1000, warn=TRUE, ...,
            nsim=1, drop=TRUE, ex=NULL)

Arguments

n

Number of points.

win

Window in which to simulate the pattern. An object of class "owin" or something acceptable to as.owin. (Alternatively a tessellation; see the section on tessellations).

giveup

Number of attempts in the rejection method after which the algorithm should stop trying to generate new points.

warn

Logical. Whether to issue a warning if n is very large. See Details.

...

Ignored.

nsim

Number of simulated realisations to be generated.

drop

Logical. If nsim=1 and drop=TRUE (the default), the result will be a point pattern, rather than a list containing a point pattern.

ex

Optional. A point pattern to use as the example. If ex is given and n and win are missing, then n and win will be calculated from the point pattern ex.

Details

This function generates n independent random points, uniformly distributed in the window win. (For nonuniform distributions, see rpoint.)

The algorithm depends on the type of window, as follows:

  • If win is a rectangle then n independent random points, uniformly distributed in the rectangle, are generated by assigning uniform random values to their cartesian coordinates.

  • If win is a binary image mask, then a random sequence of pixels is selected (using sample) with equal probabilities. Then for each pixel in the sequence we generate a uniformly distributed random point in that pixel.

  • If win is a polygonal window, the algorithm uses the rejection method. It finds a rectangle enclosing the window, generates points in this rectangle, and tests whether they fall in the desired window. It gives up when giveup * n tests have been performed without yielding n successes.

The algorithm for binary image masks is faster than the rejection method but involves discretisation.

If warn=TRUE (the default), a warning will be issued if n is very large. The threshold is spatstat.options("huge.npoints"). This warning has no consequences, but it helps to trap a number of common errors.

Value

A point pattern (an object of class "ppp") or a list of point patterns.

Tessellation

The argument win may be a tessellation (object of class "tess", see tess). Then the specified number of points n will be randomly generated inside each tile of the tessellation. The argument n may be either a single integer, or an integer vector specifying the number of points to be generated in each individual tile. The result will be a point pattern in the window as.owin(win).

Author(s)

\adrian

and \rolf

See Also

ppp.object, owin.object, rpoispp, rpoint

Examples

 # 100 random points in the unit square
 pp <- runifpoint(100)
 # irregular window
 letterR
 # polygonal
 pp <- runifpoint(100, letterR)
 # binary image mask
 pp <- runifpoint(100, as.mask(letterR))

 # randomising an existing point pattern
 runifpoint(npoints(cells), win=Window(cells))
 runifpoint(ex=cells)

 # tessellation
 A <- quadrats(unit.square(), 2, 3)
 # different numbers of points in each cell
 X <- runifpoint(1:6, A)

spatstat.random documentation built on Sept. 9, 2023, 9:06 a.m.