# rThomas

From spatstat v1.10-2
by Adrian Baddeley

##### Simulate Thomas Process

Generate a random point pattern, a realisation of the Thomas cluster process.

##### Usage

`rThomas(kappa, sigma, mu, win = owin(c(0,1),c(0,1)))`

##### Arguments

- kappa
- Intensity of the Poisson process of cluster centres. A single positive number.
- sigma
- Standard deviation of displacement of a point from its cluster centre.
- mu
- Expected number of points per cluster.
- win
- Window in which to simulate the pattern.
An object of class
`"owin"`

or something acceptable to`as.owin`

.

##### Details

This algorithm generates a realisation of the
Thomas process, a special case of the Neyman-Scott process.
The algorithm
generates a uniform Poisson point process of ``parent'' points
with intensity `kappa`

. Then each parent point is
replaced by a random cluster of points, the number of points
per cluster being Poisson (`mu`

) distributed, and their
positions being isotropic Gaussian displacements from the
cluster parent location.

##### Value

- The simulated point pattern (an object of class
`"ppp"`

).Additionally, some intermediate results of the simulation are returned as attributes of this point pattern. See

`rNeymanScott`

.

##### See Also

##### Examples

`X <- rThomas(10, 0.2, 5)`

*Documentation reproduced from package spatstat, version 1.10-2, License: GPL version 2 or newer*

### Community examples

Looks like there are no examples yet.