# runifpoint

##### Generate N Uniform Random Points

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`

.

##### See Also

##### Examples

```
# NOT RUN {
# 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)
# }
```

*Documentation reproduced from package spatstat, version 1.52-1, License: GPL (>= 2)*