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 (wmppp.object). ReferenceType One of the point types. CheckArguments Logical; if TRUE, the function arguments are verified. Should be set to FALSE to save time in simulations for example, when the arguments have been checked elsewhere.

### 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.

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)

par(mfrow=c(1,2))
plot(X, main="Original pattern, Point Type", which.marks=2)

# Randomize it
Y <- rPopulationIndependenceM(X, "A")
# Points of type "A" (circles) are unchanged,
# all other points have been redistributed randomly across locations
plot(Y, main="Randomized pattern, Point Type", which.marks=2)


[Package dbmss version 2.7-8 Index]