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 subsets (quadrats) of equal size and
shape. The numbers of points falling in each quadrat are counted.
Then the Morisita Index is computed as
where is the number of points falling in the
-th
quadrat, and
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
quadrats, then
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)