spatstat (version 1.17-6)

nnclean: 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, ..., probs = FALSE,
                     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.

...

Ignored.

probs

Logical flag determining the type of value returned. See Details.

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

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.

Value

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

  If probs=FALSE (the default), the marks are a factor with levels "noise" and "feature" indicating the maximum likelihood classification of each point by the algorithm.

  If probs=TRUE, the marks are numeric, giving the estimated probabilities that each point belongs to a feature.

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 $k$th nearest neighbour is computed. Then the E-M algorithm is used to fit a mixture distribution to the 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.

For two-dimensional point patterns,

• in the default caseprobs=FALSE, the result is a binary classification. Each point is classified by maximum likelihood as belonging either to the feature or the clutter. The return value is a marked point pattern, where the marks are a factor with levels"noise"and"feature"indicating the classification of each point.

Ifprobs=TRUE, the result is a fuzzy or probabilistic classification. The return value is a marked point pattern with marks giving the estimated probabilities that each point belongs to a feature.

For three-dimensional point patterns, the result is a 3D point pattern with two (additional) columns of marks, one containing the probabilities, and one containing the binary classification.

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)
  plot(X, chars=c(".", "+"), cols=1:2)
  Y <- split(X)
  plot(Y, chars="+", cex=0.5)
  Z <- nnclean(shapley, k=17, probs=TRUE)
  plot(cut(Z, breaks=3), chars=c(".", "+", "+"), cols=1:3)