| miplot {spatstat.explore} | R Documentation |
Morisita Index Plot
Description
Displays the Morisita Index Plot of a spatial point pattern.
Usage
miplot(X, ...)
Arguments
X |
A point pattern (object of class |
... |
Optional arguments to control the appearance of the plot. |
Details
Morisita (1959) defined an index of spatial aggregation for a spatial
point pattern based on quadrat counts. The spatial domain of the point
pattern is first divided into Q subsets (quadrats) of equal size and
shape. The numbers of points falling in each quadrat are counted.
Then the Morisita Index is computed as
\mbox{MI} = Q \frac{\sum_{i=1}^Q n_i (n_i - 1)}{N(N-1)}
where n_i is the number of points falling in the i-th
quadrat, and N is the total number of points.
If the pattern is completely random, MI should be approximately
equal to 1. Values of MI greater than 1 suggest clustering.
The Morisita Index plot is a plot of the Morisita Index
MI against the linear dimension of the quadrats.
The point pattern dataset is divided into 2 \times 2
quadrats, then 3 \times 3 quadrats, etc, and the
Morisita Index is computed each time. This plot is an attempt to
discern different scales of dependence in the point pattern data.
Value
None.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner rolfturner@posteo.net
References
M. Morisita (1959) Measuring of the dispersion of individuals and analysis of the distributional patterns. Memoir of the Faculty of Science, Kyushu University, Series E: Biology. 2: 215–235.
See Also
Examples
miplot(longleaf)
opa <- par(mfrow=c(2,3))
plot(cells)
plot(japanesepines)
plot(redwood)
miplot(cells)
miplot(japanesepines)
miplot(redwood)
par(opa)