rRandomLabelingM {dbmss} | R Documentation |

## Simulations of a point pattern according to the null hypothesis of random labelling defined for M

### Description

Simulates of a point pattern according to the null hypothesis of random labelling defined for *M*

### Usage

```
rRandomLabelingM(X, CheckArguments = TRUE)
```

### Arguments

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

`CheckArguments` |
Logical; if |

### Details

Point types are randomized. Locations and weights are kept unchanged. If both types and weights must be randomized together (Duranton and Overman, 2005; Marcon and Puech, 2010), use `rRandomLocation`

.

### Value

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

, see `wmppp.object`

).

### References

Duranton, G. and Overman, H. G. (2005). Testing for Localisation Using Micro-Geographic Data. *Review of Economic Studies* 72(4): 1077-1106.

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

`rRandomLabeling`

, `rPopulationIndependenceM`

### 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 <- rRandomLabelingM(X)
# Labels have been redistributed randomly across locations
# But weights are unchanged
autoplot(Y, main="Randomized pattern")
```

*dbmss*version 2.9-0 Index]