rPopulationIndependenceM {dbmss} | R Documentation |

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

### Description

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

### Usage

```
rPopulationIndependenceM(X, ReferenceType, CheckArguments = TRUE)
```

### Arguments

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

`ReferenceType` |
One of the point types. |

`CheckArguments` |
Logical; if |

### Details

Reference points are kept unchanged, other points are redistributed randomly across locations.

### Value

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

, see `wmppp.object`

).

### References

Marcon, E. and Puech, F. (2010). Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods. *Journal of Economic Geography* 10(5): 745-762.

Marcon, E., F. Puech and S. Traissac (2012). Characterizing the relative spatial structure of point patterns. *International Journal of Ecology* 2012(Article ID 619281): 11.

### See Also

`rPopulationIndependenceK`

, `rRandomLabelingM`

### Examples

```
# Simulate a point pattern with five types
X <- rpoispp(50)
PointType <- sample(c("A", "B", "C", "D", "E"), 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 <- rPopulationIndependenceM(X, "A")
# Points of type "A" are unchanged,
# all other points have been redistributed randomly across locations
autoplot(Y, main="Randomized pattern")
```

*dbmss*version 2.9-0 Index]