R: Morisita Index Plot

miplot {spatstat} | 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 `"ppp"`) or something acceptable to `as.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

MI = Q * sum(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 * 2 quadrats, then 3 * 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 r.turner@auckland.ac.nz

### 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

`quadratcount`

### Examples

     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)

[Package spatstat version 1.63-3 Index]