# runifpoint: Generate N Uniform Random Points In spatstat.random: Random Generation Functionality for the 'spatstat' Family

 runifpoint 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`. (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)

and \rolf

`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)

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

# tessellation