rPopulationIndependenceM: 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 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.

See Also

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)


autoplot(X, main="Original pattern")

# Randomize it
Y <- rPopulationIndependenceM(X, "A")
# Points of type "A" are unchanged, 
# all other points have been redistributed randomly across locations
autoplot(Y, main="Randomized pattern")

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