rRandomLabeling: 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 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.

Details

Marks are redistributed randomly across the original point pattern.

Value

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

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)

autoplot(X, main="Original pattern")

# Randomize it
Y <- rRandomLabeling(X)
# Types and weights have been redistributed randomly across locations
autoplot(Y, main="Randomized pattern")

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