runifpoint: Generate N Uniform Random Points In spatstat: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

Description

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

Usage

 ```1 2``` ``` 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)

and \rolf

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ``` # 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) ```

Example output

```Loading required package: nlme

spatstat 1.51-0       (nickname: 'Poetic Licence')
For an introduction to spatstat, type 'beginner'

Note: spatstat version 1.51-0 is out of date by more than 11 weeks; a newer version should be available.
Planar point pattern: 42 points
window: rectangle = [0, 1] x [0, 1] units
Planar point pattern: 42 points
window: rectangle = [0, 1] x [0, 1] units
```

spatstat documentation built on Nov. 5, 2018, 1:04 a.m.