dbmss (version 2.3-0)

rRandomLabeling: Simulations of a point pattern according to the null hypothesis of random labeling

Description

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

Usage

rRandomLabeling(X, CheckArguments = TRUE)

Arguments

X

A weighted, marked, planar point pattern (wmppp.object).

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

Marks are redistributed randomly across the original point pattern.

References

Goreaud, F. et Pelissier, R. (2003). Avoiding misinterpretation of biotic interactions with the intertype K12 fonction: population independence vs random labelling hypotheses. Journal of Vegetation Science 14(5): 681-692.

See Also

rRandomLabelingM, rPopulationIndependenceK

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 <- rRandomLabeling(X)
Z <- Y
# Types have been redistributed randomly across locations
plot(Y, main="Randomized pattern, Point Type", which.marks=2)
# weights too
Y <- Z
plot(Y, main="Randomized pattern, Point Weight", which.marks=1)