R: Generate N Uniform Random Points

runifpoint {spatstat.core} | R 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`.
`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`, then 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"`) if `nsim=1`, or a list of point patterns if `nsim > 1`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### See Also

`ppp.object`, `owin.object`, `rpoispp`, `rpoint`

### Examples

     # 100 random points in the unit square
     pp <- runifpoint(100)
     # irregular window
     data(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)

[Package spatstat.core version 2.0-0 Index]