rRandomLabelingM {dbmss}R Documentation

Simulations of a point pattern according to the null hypothesis of random labelling defined for M


Simulates of a point pattern according to the null hypothesis of random labelling defined for M


rRandomLabelingM(X, CheckArguments = TRUE)



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


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.


Point types are randomized. Locations and weights are kept unchanged. If both types and weights must be randomized together (Duranton and Overman, 2005; Marcon and Puech, 2010), use rRandomLocation.


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


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.

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

rRandomLabeling, rPopulationIndependenceM


# 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)

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)

[Package dbmss version 2.7-8 Index]