rRandomPositionK {dbmss} | R Documentation |

## Simulations of a point pattern according to the null hypothesis of random position defined for K

### Description

Simulations of a point pattern according to the null hypothesis of random position defined for *K*.

### Usage

```
rRandomPositionK(X, Precision = 0, CheckArguments = TRUE)
```

### Arguments

`X` |
A weighted, marked, planar point pattern ( |

`Precision` |
Accuracy of point coordinates, measured as a part of distance unit. See notes. Default is 0 for no approximation. |

`CheckArguments` |
Logical; if |

### Details

Points marks are kept unchanged and their position is drawn in a binomial process by `runifpoint`

.

### Value

A new weighted, marked, planar point pattern (an object of class `wmppp`

, see `wmppp.object`

).

### Note

Simulations in a binomial process keeps the same number of points, so that marks can be redistributed. If a real CSR simulation is needed and marks are useless, use `rpoispp`

.

Actual data coordinates are often rounded. Use the `Precision`

argument to simulate point patterns with the same rounding procedure. For example, if point coordinates are in meters and rounded to the nearest half meter, use `Precision = 0.5`

so that the same approximation is applied to the simulated point patterns.

### See Also

### Examples

```
# Simulate a point pattern with two types
X <- rpoispp(5)
PointType <- sample(c("A", "B"), X$n, replace=TRUE)
PointWeight <- runif(X$n, min=1, max=10)
X$marks <- data.frame(PointType, PointWeight)
X <- as.wmppp(X)
autoplot(X, main="Original pattern")
# Randomize it
Y <- rRandomPositionK(X)
# Points are randomly distributed
autoplot(Y, main="Randomized pattern")
```

*dbmss*version 2.9-0 Index]