R: Nearest Neighbour Clutter Removal

nnclean {spatstat} | R Documentation

## Nearest Neighbour Clutter Removal

### Description

Detect features in a 2D or 3D spatial point pattern using nearest neighbour clutter removal.

### Usage

      nnclean(X, k, ...)

      ## S3 method for class 'ppp'
    nnclean(X, k, ...,
                         edge.correct = FALSE, wrap = 0.1,
                         convergence = 0.001, plothist = FALSE,
                         verbose = TRUE, maxit = 50)

      ## S3 method for class 'pp3'
    nnclean(X, k, ...,
                         convergence = 0.001, plothist = FALSE,
                         verbose = TRUE, maxit = 50)

### Arguments

`X` | A two-dimensional spatial point pattern (object of class `"ppp"`) or a three-dimensional point pattern (object of class `"pp3"`).
`k` | Degree of neighbour: `k=1` means nearest neighbour, `k=2` means second nearest, etc.
`...` | Arguments passed to `hist.default` to control the appearance of the histogram, if `plothist=TRUE`.
`edge.correct` | Logical flag specifying whether periodic edge correction should be performed (only implemented in 2 dimensions).
`wrap` | Numeric value specifying the relative size of the margin in which data will be replicated for the periodic edge correction (if `edge.correct=TRUE`). A fraction of window width and window height.
`convergence` | Relative tolerance threshold for testing convergence of EM algorithm.
`maxit` | Maximum number of iterations for EM algorithm.
`plothist` | Logical flag specifying whether to plot a diagnostic histogram of the nearest neighbour distances and the fitted distribution.
`verbose` | Logical flag specifying whether to print progress reports.

### Details

Byers and Raftery (1998) developed a technique for recognising features in a spatial point pattern in the presence of random clutter.

For each point in the pattern, the distance to the kth nearest neighbour is computed. Then the E-M algorithm is used to fit a mixture distribution to the kth nearest neighbour distances. The mixture components represent the feature and the clutter. The mixture model can be used to classify each point as belong to one or other component.

The function `nnclean` is generic, with methods for two-dimensional point patterns (class `"ppp"`) and three-dimensional point patterns (class `"pp3"`) currently implemented.

The result is a point pattern (2D or 3D) with two additional columns of marks:

class

  A factor, with levels `"noise"` and `"feature"`, indicating the maximum likelihood classification of each point.

prob

  Numeric vector giving the estimated probabilities that each point belongs to a feature.

The object also has extra information stored in attributes: `"theta"` contains the fitted parameters of the mixture model, `"info"` contains information about the fitting procedure, and `"hist"` contains the histogram structure returned from `hist.default` if `plothist = TRUE`.

### Value

An object of the same kind as `X`, obtained by attaching marks to the points of `X`.

The object also has attributes, as described under Details.

### Author(s)

Original by Simon Byers and Adrian Raftery. Adapted for spatstat by Adrian Baddeley Adrian.Baddeley@curtin.edu.au.

### References

Byers, S. and Raftery, A.E. (1998) Nearest-neighbour clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association 93, 577–584.

### See Also

`nndist`, `split.ppp`, `cut.ppp`

### Examples

      data(shapley)
      X <- nnclean(shapley, k=17, plothist=TRUE)
      plot(X, which.marks=1, chars=c(".", "+"), cols=1:2)
      plot(X, which.marks=2, cols=function(x)hsv(0.2+0.8*(1-x),1,1))
      Y <- split(X, un=TRUE)
      plot(Y, chars="+", cex=0.5)
      marks(X) <- marks(X)$prob
      plot(cut(X, breaks=3), chars=c(".", "+", "+"), cols=1:3)

[Package spatstat version 1.54-0 Index]