miplot
Morisita Index Plot
Displays the Morisita Index Plot of a spatial point pattern.
- Keywords
- spatial, nonparametric
Usage
miplot(X, ...)Arguments
- X
A point pattern (object of class
"ppp") or something acceptable toas.ppp.- …
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.
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
# NOT RUN {
data(longleaf)
miplot(longleaf)
opa <- par(mfrow=c(2,3))
data(cells)
data(japanesepines)
data(redwood)
plot(cells)
plot(japanesepines)
plot(redwood)
miplot(cells)
miplot(japanesepines)
miplot(redwood)
par(opa)
# }