Simulates of a point pattern according to the null hypothesis of random labelling defined for M
A weighted, marked, planar point pattern (
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
A new weighted, marked, planar point pattern (an object of class
Eric Marcon <[email protected]>
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.
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# 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 <- 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)
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