rRandomLocation {dbmss} R Documentation

## Simulations of a point pattern according to the null hypothesis of random location

### Description

Simulates of a point pattern according to the null hypothesis of random location.

### Usage

rRandomLocation(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

Points are redistributed randomly across the locations of the original point pattern. This randomization is equivalent to random labeling, considering the label is both point type and point weight.

### 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.

rRandomPositionK

### 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(2,2))
plot(X, main="Original pattern, Point Type", which.marks=2)
plot(X, main="Original pattern, Point Weight", which.marks=1)

# Randomize it
Y <- rRandomLabelingM(X)
Z <- Y
# Labels have been redistributed randomly across locations
plot(Y, main="Randomized pattern, Point Type", which.marks=2)
# But weights are unchanged
Y <- Z
plot(Y, main="Randomized pattern, Point Weight", which.marks=1)


[Package dbmss version 2.7-8 Index]