rRandomLocation: Simulations of a point pattern according to the null...

In dbmss: Distance-Based Measures of Spatial Structures

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. If ReferenceType is specified, then only reference type points are kept in the orginal point pattern before randomization.

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.

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)

autoplot(X, main="Original pattern")

# Randomize it
Y <- rRandomLocation(X)
# Points have been redistributed randomly across locations
autoplot(Y, main="Randomized pattern")

dbmss documentation built on Sept. 11, 2024, 9:19 p.m.