rPopulationIndependenceK {dbmss} | R Documentation |

## Simulations of a point pattern according to the null hypothesis of population independence defined for K

### Description

Simulates of a point pattern according to the null hypothesis of population independence defined for *K*.

### Usage

```
rPopulationIndependenceK(X, ReferenceType, NeighborType, CheckArguments = TRUE)
```

### Arguments

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

`ReferenceType` |
One of the point types. |

`NeighborType` |
One of the point types. |

`CheckArguments` |
Logical; if |

### Details

Reference points are kept unchanged, neighbor type point positions are shifted by `rshift`

.
Other points are lost and point weights are not kept (they are set to 1) since the K function ignores them.

### Value

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

, see `wmppp.object`

).

### References

Goreaud, F. et Pelissier, R. (2003). Avoiding misinterpretation of biotic interactions with the intertype K12 fonction: population independence vs random labelling hypotheses. *Journal of Vegetation Science* 14(5): 681-692.

### See Also

`rPopulationIndependenceM`

, `rRandomLabeling`

### Examples

```
# Simulate a point pattern with three types
X <- rpoispp(50)
PointType <- sample(c("A", "B", "C"), X$n, replace=TRUE)
PointWeight <- runif(X$n, min=1, max=10)
X$marks <- data.frame(PointType, PointWeight)
X <- as.wmppp(X)
# Plot the point pattern, using PointType as marks
autoplot(X, main="Original pattern")
# Randomize it
Y <- rPopulationIndependenceK(X, "A", "B")
# Points of type "A" are unchanged, points of type "B" have been moved altogether
# Other points are lost and point weights are set to 1
autoplot(Y, main="Randomized pattern")
```

*dbmss*version 2.9-0 Index]