dbmss (version 2.3-0)

rRandomLocation: 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.

Value

A new weighted, marked, planar point pattern (an object of class wmppp, see wmppp.object).

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.

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.

See Also

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)